from typing import List, Tuple

import torch
from torch.utils.data import DataLoader

from data import PigLatin
from seq2seq import EncoderWithEmbedding, DecoderWithAttention, verify_shape


def run_model(*, path: str, saved_encoder: str, saved_decoder: str, batch_size: int, device_name: str) -> None:
    from torch.nn.functional import softmax
    import numpy

    encoder: EncoderWithEmbedding = torch.load(saved_encoder)
    decoder: DecoderWithAttention = torch.load(saved_decoder)
    print(type(decoder))

    device = torch.device(device_name)

    words: PigLatin = PigLatin(path=path, vocab=decoder.vocab)
    data: DataLoader = DataLoader(dataset=words, batch_size=batch_size)

    encoder.eval()
    decoder.eval()

    with torch.no_grad():

        for batch in data:  # type: torch.Tensor

            examples: torch.Tensor = batch["data"].to(device)
            labels: torch.LongStorage = batch["labels"].to(device)
            decoder_previous_output: torch.LongTensor = batch["start-of-sequence"].squeeze(dim=1).to(device)

            # At the end of the data set, the actual batch size may be smaller than batch_size, and that's OK
            actual_batch_size: int = min(batch_size, examples.shape[0])

            decoder_hidden_state: torch.Tensor = torch.zeros(actual_batch_size, 1, decoder.hidden_size).to(device)

            verify_shape(tensor=decoder_previous_output, expected=[actual_batch_size])
            verify_shape(tensor=examples, expected=[actual_batch_size, words.max_len])
            verify_shape(tensor=labels, expected=[actual_batch_size, words.max_len])

            encoder_states: torch.Tensor = encoder(batch_size=actual_batch_size,
                                                   seq_len=words.max_len,
                                                   input_tensor=examples)

            decoder_output_list: List[torch.Tensor] = list()
            for _ in range(words.max_len):
                decoder_results: Tuple[torch.Tensor, torch.Tensor] = decoder(batch_size=actual_batch_size,
                                                                             input_seq_len=words.max_len,
                                                                             previous_decoder_output=decoder_previous_output,
                                                                             previous_decoder_hidden_state=decoder_hidden_state,
                                                                             encoder_states=encoder_states)

                decoder_raw_output: torch.Tensor = decoder_results[0]
                decoder_hidden_state: torch.Tensor = decoder_results[1]

                verify_shape(tensor=decoder_raw_output, expected=[actual_batch_size, 1, len(decoder.vocab)])
                verify_shape(tensor=decoder_hidden_state, expected=[actual_batch_size, 1, decoder.hidden_size])

                decoder_previous_output: torch.LongTensor = softmax(decoder_raw_output, dim=2).squeeze(dim=1).topk(
                    k=1).indices.squeeze(dim=1)
                verify_shape(tensor=decoder_previous_output, expected=[actual_batch_size])

                decoder_output_list.append(decoder_previous_output)

            print(len(decoder_output_list))
            predictions: torch.Tensor = torch.stack(tensors=decoder_output_list).permute(1, 0)
            verify_shape(tensor=predictions, expected=[actual_batch_size, words.max_len])

            #print(decoder_output.shape)
            #sys.exit()

            # decoder_output: torch.Tensor = decoder(batch_size=actual_batch_size,
            #                                        input_seq_len=words.max_len,
            #                                        output_seq_len=words.max_len,
            #                                        previous_decoder_output=decoder_start_of_sequence,
            #                                        previous_decoder_hidden_state=decoder_hidden_state,
            #                                        encoder_states=encoder_states)

            #verify_shape(tensor=decoder_output, expected=[actual_batch_size, words.max_len, len(decoder.vocab)])

            # for index in range(actual_batch_size):

            #   verify_shape(tensor=seq2seq_output[index], expected=[words.max_len, len(seq2seq.vocab)])

            #prediction_distributions: torch.Tensor = softmax(input=decoder_output, dim=2)
            #verify_shape(tensor=prediction_distributions,
            #             expected=[actual_batch_size, words.max_len, len(decoder.vocab)])

            #predictions: torch.LongTensor = torch.topk(input=prediction_distributions, k=1).indices.squeeze(dim=2)
            #verify_shape(tensor=predictions, expected=[actual_batch_size, words.max_len])

            for b in range(actual_batch_size):

                int_tensor: torch.LongTensor = predictions[b]
                verify_shape(tensor=int_tensor, expected=[words.max_len])

                word: str = "".join([decoder.vocab.i2s[i] for i in int_tensor.tolist()])
                label: str = "".join([decoder.vocab.i2s[i] for i in labels[b].tolist()])
                print(f"{b}\t{words.max_len}\t{word}\t{label}\t{labels[b]}")


if __name__ == "__main__":

    import sys

    if len(sys.argv) == 4:

        run_model(path=sys.argv[1],
                  saved_encoder=sys.argv[2],
                  saved_decoder=sys.argv[3],
                  batch_size=10,
                  device_name="cuda:0" if torch.cuda.is_available() else "cpu")

d93b089cdb366b97b6e9780bb893d5dff97c7b9e
<mask token>

<mask token>


def run_model(*, path: str, saved_encoder: str, saved_decoder: str,
    batch_size: int, device_name: str) ->None:
    from torch.nn.functional import softmax
    import numpy
    encoder: EncoderWithEmbedding = torch.load(saved_encoder)
    decoder: DecoderWithAttention = torch.load(saved_decoder)
    print(type(decoder))
    device = torch.device(device_name)
    words: PigLatin = PigLatin(path=path, vocab=decoder.vocab)
    data: DataLoader = DataLoader(dataset=words, batch_size=batch_size)
    encoder.eval()
    decoder.eval()
    with torch.no_grad():
        for batch in data:
            examples: torch.Tensor = batch['data'].to(device)
            labels: torch.LongStorage = batch['labels'].to(device)
            decoder_previous_output: torch.LongTensor = batch[
                'start-of-sequence'].squeeze(dim=1).to(device)
            actual_batch_size: int = min(batch_size, examples.shape[0])
            decoder_hidden_state: torch.Tensor = torch.zeros(actual_batch_size,
                1, decoder.hidden_size).to(device)
            verify_shape(tensor=decoder_previous_output, expected=[
                actual_batch_size])
            verify_shape(tensor=examples, expected=[actual_batch_size,
                words.max_len])
            verify_shape(tensor=labels, expected=[actual_batch_size, words.
                max_len])
            encoder_states: torch.Tensor = encoder(batch_size=
                actual_batch_size, seq_len=words.max_len, input_tensor=examples
                )
            decoder_output_list: List[torch.Tensor] = list()
            for _ in range(words.max_len):
                decoder_results: Tuple[torch.Tensor, torch.Tensor] = decoder(
                    batch_size=actual_batch_size, input_seq_len=words.
                    max_len, previous_decoder_output=
                    decoder_previous_output, previous_decoder_hidden_state=
                    decoder_hidden_state, encoder_states=encoder_states)
                decoder_raw_output: torch.Tensor = decoder_results[0]
                decoder_hidden_state: torch.Tensor = decoder_results[1]
                verify_shape(tensor=decoder_raw_output, expected=[
                    actual_batch_size, 1, len(decoder.vocab)])
                verify_shape(tensor=decoder_hidden_state, expected=[
                    actual_batch_size, 1, decoder.hidden_size])
                decoder_previous_output: torch.LongTensor = softmax(
                    decoder_raw_output, dim=2).squeeze(dim=1).topk(k=1
                    ).indices.squeeze(dim=1)
                verify_shape(tensor=decoder_previous_output, expected=[
                    actual_batch_size])
                decoder_output_list.append(decoder_previous_output)
            print(len(decoder_output_list))
            predictions: torch.Tensor = torch.stack(tensors=decoder_output_list
                ).permute(1, 0)
            verify_shape(tensor=predictions, expected=[actual_batch_size,
                words.max_len])
            for b in range(actual_batch_size):
                int_tensor: torch.LongTensor = predictions[b]
                verify_shape(tensor=int_tensor, expected=[words.max_len])
                word: str = ''.join([decoder.vocab.i2s[i] for i in
                    int_tensor.tolist()])
                label: str = ''.join([decoder.vocab.i2s[i] for i in labels[
                    b].tolist()])
                print(f'{b}\t{words.max_len}\t{word}\t{label}\t{labels[b]}')


<mask token>

<mask token>


def run_model(*, path: str, saved_encoder: str, saved_decoder: str,
    batch_size: int, device_name: str) ->None:
    from torch.nn.functional import softmax
    import numpy
    encoder: EncoderWithEmbedding = torch.load(saved_encoder)
    decoder: DecoderWithAttention = torch.load(saved_decoder)
    print(type(decoder))
    device = torch.device(device_name)
    words: PigLatin = PigLatin(path=path, vocab=decoder.vocab)
    data: DataLoader = DataLoader(dataset=words, batch_size=batch_size)
    encoder.eval()
    decoder.eval()
    with torch.no_grad():
        for batch in data:
            examples: torch.Tensor = batch['data'].to(device)
            labels: torch.LongStorage = batch['labels'].to(device)
            decoder_previous_output: torch.LongTensor = batch[
                'start-of-sequence'].squeeze(dim=1).to(device)
            actual_batch_size: int = min(batch_size, examples.shape[0])
            decoder_hidden_state: torch.Tensor = torch.zeros(actual_batch_size,
                1, decoder.hidden_size).to(device)
            verify_shape(tensor=decoder_previous_output, expected=[
                actual_batch_size])
            verify_shape(tensor=examples, expected=[actual_batch_size,
                words.max_len])
            verify_shape(tensor=labels, expected=[actual_batch_size, words.
                max_len])
            encoder_states: torch.Tensor = encoder(batch_size=
                actual_batch_size, seq_len=words.max_len, input_tensor=examples
                )
            decoder_output_list: List[torch.Tensor] = list()
            for _ in range(words.max_len):
                decoder_results: Tuple[torch.Tensor, torch.Tensor] = decoder(
                    batch_size=actual_batch_size, input_seq_len=words.
                    max_len, previous_decoder_output=
                    decoder_previous_output, previous_decoder_hidden_state=
                    decoder_hidden_state, encoder_states=encoder_states)
                decoder_raw_output: torch.Tensor = decoder_results[0]
                decoder_hidden_state: torch.Tensor = decoder_results[1]
                verify_shape(tensor=decoder_raw_output, expected=[
                    actual_batch_size, 1, len(decoder.vocab)])
                verify_shape(tensor=decoder_hidden_state, expected=[
                    actual_batch_size, 1, decoder.hidden_size])
                decoder_previous_output: torch.LongTensor = softmax(
                    decoder_raw_output, dim=2).squeeze(dim=1).topk(k=1
                    ).indices.squeeze(dim=1)
                verify_shape(tensor=decoder_previous_output, expected=[
                    actual_batch_size])
                decoder_output_list.append(decoder_previous_output)
            print(len(decoder_output_list))
            predictions: torch.Tensor = torch.stack(tensors=decoder_output_list
                ).permute(1, 0)
            verify_shape(tensor=predictions, expected=[actual_batch_size,
                words.max_len])
            for b in range(actual_batch_size):
                int_tensor: torch.LongTensor = predictions[b]
                verify_shape(tensor=int_tensor, expected=[words.max_len])
                word: str = ''.join([decoder.vocab.i2s[i] for i in
                    int_tensor.tolist()])
                label: str = ''.join([decoder.vocab.i2s[i] for i in labels[
                    b].tolist()])
                print(f'{b}\t{words.max_len}\t{word}\t{label}\t{labels[b]}')


if __name__ == '__main__':
    import sys
    if len(sys.argv) == 4:
        run_model(path=sys.argv[1], saved_encoder=sys.argv[2],
            saved_decoder=sys.argv[3], batch_size=10, device_name='cuda:0' if
            torch.cuda.is_available() else 'cpu')

from typing import List, Tuple
import torch
from torch.utils.data import DataLoader
from data import PigLatin
from seq2seq import EncoderWithEmbedding, DecoderWithAttention, verify_shape


def run_model(*, path: str, saved_encoder: str, saved_decoder: str,
    batch_size: int, device_name: str) ->None:
    from torch.nn.functional import softmax
    import numpy
    encoder: EncoderWithEmbedding = torch.load(saved_encoder)
    decoder: DecoderWithAttention = torch.load(saved_decoder)
    print(type(decoder))
    device = torch.device(device_name)
    words: PigLatin = PigLatin(path=path, vocab=decoder.vocab)
    data: DataLoader = DataLoader(dataset=words, batch_size=batch_size)
    encoder.eval()
    decoder.eval()
    with torch.no_grad():
        for batch in data:
            examples: torch.Tensor = batch['data'].to(device)
            labels: torch.LongStorage = batch['labels'].to(device)
            decoder_previous_output: torch.LongTensor = batch[
                'start-of-sequence'].squeeze(dim=1).to(device)
            actual_batch_size: int = min(batch_size, examples.shape[0])
            decoder_hidden_state: torch.Tensor = torch.zeros(actual_batch_size,
                1, decoder.hidden_size).to(device)
            verify_shape(tensor=decoder_previous_output, expected=[
                actual_batch_size])
            verify_shape(tensor=examples, expected=[actual_batch_size,
                words.max_len])
            verify_shape(tensor=labels, expected=[actual_batch_size, words.
                max_len])
            encoder_states: torch.Tensor = encoder(batch_size=
                actual_batch_size, seq_len=words.max_len, input_tensor=examples
                )
            decoder_output_list: List[torch.Tensor] = list()
            for _ in range(words.max_len):
                decoder_results: Tuple[torch.Tensor, torch.Tensor] = decoder(
                    batch_size=actual_batch_size, input_seq_len=words.
                    max_len, previous_decoder_output=
                    decoder_previous_output, previous_decoder_hidden_state=
                    decoder_hidden_state, encoder_states=encoder_states)
                decoder_raw_output: torch.Tensor = decoder_results[0]
                decoder_hidden_state: torch.Tensor = decoder_results[1]
                verify_shape(tensor=decoder_raw_output, expected=[
                    actual_batch_size, 1, len(decoder.vocab)])
                verify_shape(tensor=decoder_hidden_state, expected=[
                    actual_batch_size, 1, decoder.hidden_size])
                decoder_previous_output: torch.LongTensor = softmax(
                    decoder_raw_output, dim=2).squeeze(dim=1).topk(k=1
                    ).indices.squeeze(dim=1)
                verify_shape(tensor=decoder_previous_output, expected=[
                    actual_batch_size])
                decoder_output_list.append(decoder_previous_output)
            print(len(decoder_output_list))
            predictions: torch.Tensor = torch.stack(tensors=decoder_output_list
                ).permute(1, 0)
            verify_shape(tensor=predictions, expected=[actual_batch_size,
                words.max_len])
            for b in range(actual_batch_size):
                int_tensor: torch.LongTensor = predictions[b]
                verify_shape(tensor=int_tensor, expected=[words.max_len])
                word: str = ''.join([decoder.vocab.i2s[i] for i in
                    int_tensor.tolist()])
                label: str = ''.join([decoder.vocab.i2s[i] for i in labels[
                    b].tolist()])
                print(f'{b}\t{words.max_len}\t{word}\t{label}\t{labels[b]}')


if __name__ == '__main__':
    import sys
    if len(sys.argv) == 4:
        run_model(path=sys.argv[1], saved_encoder=sys.argv[2],
            saved_decoder=sys.argv[3], batch_size=10, device_name='cuda:0' if
            torch.cuda.is_available() else 'cpu')

from typing import List, Tuple

import torch
from torch.utils.data import DataLoader

from data import PigLatin
from seq2seq import EncoderWithEmbedding, DecoderWithAttention, verify_shape


def run_model(*, path: str, saved_encoder: str, saved_decoder: str, batch_size: int, device_name: str) -> None:
    from torch.nn.functional import softmax
    import numpy

    encoder: EncoderWithEmbedding = torch.load(saved_encoder)
    decoder: DecoderWithAttention = torch.load(saved_decoder)
    print(type(decoder))

    device = torch.device(device_name)

    words: PigLatin = PigLatin(path=path, vocab=decoder.vocab)
    data: DataLoader = DataLoader(dataset=words, batch_size=batch_size)

    encoder.eval()
    decoder.eval()

    with torch.no_grad():

        for batch in data:  # type: torch.Tensor

            examples: torch.Tensor = batch["data"].to(device)
            labels: torch.LongStorage = batch["labels"].to(device)
            decoder_previous_output: torch.LongTensor = batch["start-of-sequence"].squeeze(dim=1).to(device)

            # At the end of the data set, the actual batch size may be smaller than batch_size, and that's OK
            actual_batch_size: int = min(batch_size, examples.shape[0])

            decoder_hidden_state: torch.Tensor = torch.zeros(actual_batch_size, 1, decoder.hidden_size).to(device)

            verify_shape(tensor=decoder_previous_output, expected=[actual_batch_size])
            verify_shape(tensor=examples, expected=[actual_batch_size, words.max_len])
            verify_shape(tensor=labels, expected=[actual_batch_size, words.max_len])

            encoder_states: torch.Tensor = encoder(batch_size=actual_batch_size,
                                                   seq_len=words.max_len,
                                                   input_tensor=examples)

            decoder_output_list: List[torch.Tensor] = list()
            for _ in range(words.max_len):
                decoder_results: Tuple[torch.Tensor, torch.Tensor] = decoder(batch_size=actual_batch_size,
                                                                             input_seq_len=words.max_len,
                                                                             previous_decoder_output=decoder_previous_output,
                                                                             previous_decoder_hidden_state=decoder_hidden_state,
                                                                             encoder_states=encoder_states)

                decoder_raw_output: torch.Tensor = decoder_results[0]
                decoder_hidden_state: torch.Tensor = decoder_results[1]

                verify_shape(tensor=decoder_raw_output, expected=[actual_batch_size, 1, len(decoder.vocab)])
                verify_shape(tensor=decoder_hidden_state, expected=[actual_batch_size, 1, decoder.hidden_size])

                decoder_previous_output: torch.LongTensor = softmax(decoder_raw_output, dim=2).squeeze(dim=1).topk(
                    k=1).indices.squeeze(dim=1)
                verify_shape(tensor=decoder_previous_output, expected=[actual_batch_size])

                decoder_output_list.append(decoder_previous_output)

            print(len(decoder_output_list))
            predictions: torch.Tensor = torch.stack(tensors=decoder_output_list).permute(1, 0)
            verify_shape(tensor=predictions, expected=[actual_batch_size, words.max_len])

            #print(decoder_output.shape)
            #sys.exit()

            # decoder_output: torch.Tensor = decoder(batch_size=actual_batch_size,
            #                                        input_seq_len=words.max_len,
            #                                        output_seq_len=words.max_len,
            #                                        previous_decoder_output=decoder_start_of_sequence,
            #                                        previous_decoder_hidden_state=decoder_hidden_state,
            #                                        encoder_states=encoder_states)

            #verify_shape(tensor=decoder_output, expected=[actual_batch_size, words.max_len, len(decoder.vocab)])

            # for index in range(actual_batch_size):

            #   verify_shape(tensor=seq2seq_output[index], expected=[words.max_len, len(seq2seq.vocab)])

            #prediction_distributions: torch.Tensor = softmax(input=decoder_output, dim=2)
            #verify_shape(tensor=prediction_distributions,
            #             expected=[actual_batch_size, words.max_len, len(decoder.vocab)])

            #predictions: torch.LongTensor = torch.topk(input=prediction_distributions, k=1).indices.squeeze(dim=2)
            #verify_shape(tensor=predictions, expected=[actual_batch_size, words.max_len])

            for b in range(actual_batch_size):

                int_tensor: torch.LongTensor = predictions[b]
                verify_shape(tensor=int_tensor, expected=[words.max_len])

                word: str = "".join([decoder.vocab.i2s[i] for i in int_tensor.tolist()])
                label: str = "".join([decoder.vocab.i2s[i] for i in labels[b].tolist()])
                print(f"{b}\t{words.max_len}\t{word}\t{label}\t{labels[b]}")


if __name__ == "__main__":

    import sys

    if len(sys.argv) == 4:

        run_model(path=sys.argv[1],
                  saved_encoder=sys.argv[2],
                  saved_decoder=sys.argv[3],
                  batch_size=10,
                  device_name="cuda:0" if torch.cuda.is_available() else "cpu")
