import numpy as np
try:
    import tensorflow.compat.v1 as tf
    tf.disable_v2_behavior()
except ImportError:
    import tensorflow as tf
from .bedm import BEDM

class TF(BEDM):
    float32 = tf.float32
    name = "TensorFlow"
    class Session():
        def __init__(self, *arg, **kwarg):
            self.sess = tf.Session(*arg, **kwarg)

        def run(self, tensor_list, feed_dict=None):
            if type(tensor_list) is not list:
                tensor_list = [tensor_list]
            return self.sess.run(tensor_list, feed_dict=feed_dict)

    class Model():
        def __init__(self, *arg, **kwarg):
            pass

        def run(self, sess, *arg, **kwarg):
            inputs = {}
            # check arg
            if len(self.arg) != len(arg):
                raise RuntimeError("the positional argument does not match the graph")
            # check kwarg
            for kk in self.kwarg:
                if kk not in kwarg:
                    raise RuntimeError("the keyword argument dose not match the graph")
            for kk in kwarg:
                if kk not in self.kwarg:
                    raise RuntimeError("the keyword argument dose not match the graph")
            for ii in range(len(self.arg)):
                inputs[self.arg[ii]] = arg[ii]
            for ii in self.kwarg.keys():
                inputs[self.kwarg[ii]] = kwarg[ii]
            return sess.run(self.graph, feed_dict=inputs)

        def store_graph(func):
            def wrapper(self, *arg, **kwarg):
                self.arg = arg
                self.kwarg = kwarg
                self.graph = func(self, *arg, **kwarg)
                return self.graph
            return wrapper

    @staticmethod
    def placeholder(
            dtype,
            shape = None,
            name = None):
        return tf.placeholder(dtype, shape=shape, name=name)

    @staticmethod
    def constant(
            value,
            dtype = None,
            shape = None,
            name = None):
        return tf.constant(value, dtype=dtype, shape=shape, name=name)

    @staticmethod
    def constant_initializer(
            value
    ):
        return tf.constant_initializer(value)


    @staticmethod
    def global_initialize(        
            sess
    ):
        init_op = tf.global_variables_initializer()
        sess.run(init_op)

    @staticmethod
    def get_variable(        
            model,
            name,
            shape = None,
            dtype = None,
            initializer = None
    ):
        return tf.get_variable(
            name, 
            shape = shape, 
            dtype = dtype,
            initializer = initializer)

    @staticmethod
    def matmul(
            aa,
            bb, 
            transpose_a = False, 
            transpose_b = False, 
            name = None
    ):
        return tf.matmul(aa, bb, transpose_a=transpose_a, transpose_b=transpose_b, name=name)

    @staticmethod
    def tanh(
            xx,
            name = None
    ):
        return tf.nn.tanh(xx, name=name)

592f72751d593086d1e3394ca602335d4abd33e5
<mask token>


class TF(BEDM):
    <mask token>
    <mask token>


    class Session:

        def __init__(self, *arg, **kwarg):
            self.sess = tf.Session(*arg, **kwarg)

        def run(self, tensor_list, feed_dict=None):
            if type(tensor_list) is not list:
                tensor_list = [tensor_list]
            return self.sess.run(tensor_list, feed_dict=feed_dict)


    class Model:

        def __init__(self, *arg, **kwarg):
            pass

        def run(self, sess, *arg, **kwarg):
            inputs = {}
            if len(self.arg) != len(arg):
                raise RuntimeError(
                    'the positional argument does not match the graph')
            for kk in self.kwarg:
                if kk not in kwarg:
                    raise RuntimeError(
                        'the keyword argument dose not match the graph')
            for kk in kwarg:
                if kk not in self.kwarg:
                    raise RuntimeError(
                        'the keyword argument dose not match the graph')
            for ii in range(len(self.arg)):
                inputs[self.arg[ii]] = arg[ii]
            for ii in self.kwarg.keys():
                inputs[self.kwarg[ii]] = kwarg[ii]
            return sess.run(self.graph, feed_dict=inputs)

        def store_graph(func):

            def wrapper(self, *arg, **kwarg):
                self.arg = arg
                self.kwarg = kwarg
                self.graph = func(self, *arg, **kwarg)
                return self.graph
            return wrapper
    <mask token>

    @staticmethod
    def constant(value, dtype=None, shape=None, name=None):
        return tf.constant(value, dtype=dtype, shape=shape, name=name)

    @staticmethod
    def constant_initializer(value):
        return tf.constant_initializer(value)

    @staticmethod
    def global_initialize(sess):
        init_op = tf.global_variables_initializer()
        sess.run(init_op)

    @staticmethod
    def get_variable(model, name, shape=None, dtype=None, initializer=None):
        return tf.get_variable(name, shape=shape, dtype=dtype, initializer=
            initializer)

    @staticmethod
    def matmul(aa, bb, transpose_a=False, transpose_b=False, name=None):
        return tf.matmul(aa, bb, transpose_a=transpose_a, transpose_b=
            transpose_b, name=name)
    <mask token>

<mask token>


class TF(BEDM):
    <mask token>
    <mask token>


    class Session:

        def __init__(self, *arg, **kwarg):
            self.sess = tf.Session(*arg, **kwarg)

        def run(self, tensor_list, feed_dict=None):
            if type(tensor_list) is not list:
                tensor_list = [tensor_list]
            return self.sess.run(tensor_list, feed_dict=feed_dict)


    class Model:

        def __init__(self, *arg, **kwarg):
            pass

        def run(self, sess, *arg, **kwarg):
            inputs = {}
            if len(self.arg) != len(arg):
                raise RuntimeError(
                    'the positional argument does not match the graph')
            for kk in self.kwarg:
                if kk not in kwarg:
                    raise RuntimeError(
                        'the keyword argument dose not match the graph')
            for kk in kwarg:
                if kk not in self.kwarg:
                    raise RuntimeError(
                        'the keyword argument dose not match the graph')
            for ii in range(len(self.arg)):
                inputs[self.arg[ii]] = arg[ii]
            for ii in self.kwarg.keys():
                inputs[self.kwarg[ii]] = kwarg[ii]
            return sess.run(self.graph, feed_dict=inputs)

        def store_graph(func):

            def wrapper(self, *arg, **kwarg):
                self.arg = arg
                self.kwarg = kwarg
                self.graph = func(self, *arg, **kwarg)
                return self.graph
            return wrapper
    <mask token>

    @staticmethod
    def constant(value, dtype=None, shape=None, name=None):
        return tf.constant(value, dtype=dtype, shape=shape, name=name)

    @staticmethod
    def constant_initializer(value):
        return tf.constant_initializer(value)

    @staticmethod
    def global_initialize(sess):
        init_op = tf.global_variables_initializer()
        sess.run(init_op)

    @staticmethod
    def get_variable(model, name, shape=None, dtype=None, initializer=None):
        return tf.get_variable(name, shape=shape, dtype=dtype, initializer=
            initializer)

    @staticmethod
    def matmul(aa, bb, transpose_a=False, transpose_b=False, name=None):
        return tf.matmul(aa, bb, transpose_a=transpose_a, transpose_b=
            transpose_b, name=name)

    @staticmethod
    def tanh(xx, name=None):
        return tf.nn.tanh(xx, name=name)

<mask token>


class TF(BEDM):
    <mask token>
    <mask token>


    class Session:

        def __init__(self, *arg, **kwarg):
            self.sess = tf.Session(*arg, **kwarg)

        def run(self, tensor_list, feed_dict=None):
            if type(tensor_list) is not list:
                tensor_list = [tensor_list]
            return self.sess.run(tensor_list, feed_dict=feed_dict)


    class Model:

        def __init__(self, *arg, **kwarg):
            pass

        def run(self, sess, *arg, **kwarg):
            inputs = {}
            if len(self.arg) != len(arg):
                raise RuntimeError(
                    'the positional argument does not match the graph')
            for kk in self.kwarg:
                if kk not in kwarg:
                    raise RuntimeError(
                        'the keyword argument dose not match the graph')
            for kk in kwarg:
                if kk not in self.kwarg:
                    raise RuntimeError(
                        'the keyword argument dose not match the graph')
            for ii in range(len(self.arg)):
                inputs[self.arg[ii]] = arg[ii]
            for ii in self.kwarg.keys():
                inputs[self.kwarg[ii]] = kwarg[ii]
            return sess.run(self.graph, feed_dict=inputs)

        def store_graph(func):

            def wrapper(self, *arg, **kwarg):
                self.arg = arg
                self.kwarg = kwarg
                self.graph = func(self, *arg, **kwarg)
                return self.graph
            return wrapper

    @staticmethod
    def placeholder(dtype, shape=None, name=None):
        return tf.placeholder(dtype, shape=shape, name=name)

    @staticmethod
    def constant(value, dtype=None, shape=None, name=None):
        return tf.constant(value, dtype=dtype, shape=shape, name=name)

    @staticmethod
    def constant_initializer(value):
        return tf.constant_initializer(value)

    @staticmethod
    def global_initialize(sess):
        init_op = tf.global_variables_initializer()
        sess.run(init_op)

    @staticmethod
    def get_variable(model, name, shape=None, dtype=None, initializer=None):
        return tf.get_variable(name, shape=shape, dtype=dtype, initializer=
            initializer)

    @staticmethod
    def matmul(aa, bb, transpose_a=False, transpose_b=False, name=None):
        return tf.matmul(aa, bb, transpose_a=transpose_a, transpose_b=
            transpose_b, name=name)

    @staticmethod
    def tanh(xx, name=None):
        return tf.nn.tanh(xx, name=name)

<mask token>
try:
    import tensorflow.compat.v1 as tf
    tf.disable_v2_behavior()
except ImportError:
    import tensorflow as tf
<mask token>


class TF(BEDM):
    float32 = tf.float32
    name = 'TensorFlow'


    class Session:

        def __init__(self, *arg, **kwarg):
            self.sess = tf.Session(*arg, **kwarg)

        def run(self, tensor_list, feed_dict=None):
            if type(tensor_list) is not list:
                tensor_list = [tensor_list]
            return self.sess.run(tensor_list, feed_dict=feed_dict)


    class Model:

        def __init__(self, *arg, **kwarg):
            pass

        def run(self, sess, *arg, **kwarg):
            inputs = {}
            if len(self.arg) != len(arg):
                raise RuntimeError(
                    'the positional argument does not match the graph')
            for kk in self.kwarg:
                if kk not in kwarg:
                    raise RuntimeError(
                        'the keyword argument dose not match the graph')
            for kk in kwarg:
                if kk not in self.kwarg:
                    raise RuntimeError(
                        'the keyword argument dose not match the graph')
            for ii in range(len(self.arg)):
                inputs[self.arg[ii]] = arg[ii]
            for ii in self.kwarg.keys():
                inputs[self.kwarg[ii]] = kwarg[ii]
            return sess.run(self.graph, feed_dict=inputs)

        def store_graph(func):

            def wrapper(self, *arg, **kwarg):
                self.arg = arg
                self.kwarg = kwarg
                self.graph = func(self, *arg, **kwarg)
                return self.graph
            return wrapper

    @staticmethod
    def placeholder(dtype, shape=None, name=None):
        return tf.placeholder(dtype, shape=shape, name=name)

    @staticmethod
    def constant(value, dtype=None, shape=None, name=None):
        return tf.constant(value, dtype=dtype, shape=shape, name=name)

    @staticmethod
    def constant_initializer(value):
        return tf.constant_initializer(value)

    @staticmethod
    def global_initialize(sess):
        init_op = tf.global_variables_initializer()
        sess.run(init_op)

    @staticmethod
    def get_variable(model, name, shape=None, dtype=None, initializer=None):
        return tf.get_variable(name, shape=shape, dtype=dtype, initializer=
            initializer)

    @staticmethod
    def matmul(aa, bb, transpose_a=False, transpose_b=False, name=None):
        return tf.matmul(aa, bb, transpose_a=transpose_a, transpose_b=
            transpose_b, name=name)

    @staticmethod
    def tanh(xx, name=None):
        return tf.nn.tanh(xx, name=name)

import numpy as np
try:
    import tensorflow.compat.v1 as tf
    tf.disable_v2_behavior()
except ImportError:
    import tensorflow as tf
from .bedm import BEDM

class TF(BEDM):
    float32 = tf.float32
    name = "TensorFlow"
    class Session():
        def __init__(self, *arg, **kwarg):
            self.sess = tf.Session(*arg, **kwarg)

        def run(self, tensor_list, feed_dict=None):
            if type(tensor_list) is not list:
                tensor_list = [tensor_list]
            return self.sess.run(tensor_list, feed_dict=feed_dict)

    class Model():
        def __init__(self, *arg, **kwarg):
            pass

        def run(self, sess, *arg, **kwarg):
            inputs = {}
            # check arg
            if len(self.arg) != len(arg):
                raise RuntimeError("the positional argument does not match the graph")
            # check kwarg
            for kk in self.kwarg:
                if kk not in kwarg:
                    raise RuntimeError("the keyword argument dose not match the graph")
            for kk in kwarg:
                if kk not in self.kwarg:
                    raise RuntimeError("the keyword argument dose not match the graph")
            for ii in range(len(self.arg)):
                inputs[self.arg[ii]] = arg[ii]
            for ii in self.kwarg.keys():
                inputs[self.kwarg[ii]] = kwarg[ii]
            return sess.run(self.graph, feed_dict=inputs)

        def store_graph(func):
            def wrapper(self, *arg, **kwarg):
                self.arg = arg
                self.kwarg = kwarg
                self.graph = func(self, *arg, **kwarg)
                return self.graph
            return wrapper

    @staticmethod
    def placeholder(
            dtype,
            shape = None,
            name = None):
        return tf.placeholder(dtype, shape=shape, name=name)

    @staticmethod
    def constant(
            value,
            dtype = None,
            shape = None,
            name = None):
        return tf.constant(value, dtype=dtype, shape=shape, name=name)

    @staticmethod
    def constant_initializer(
            value
    ):
        return tf.constant_initializer(value)


    @staticmethod
    def global_initialize(        
            sess
    ):
        init_op = tf.global_variables_initializer()
        sess.run(init_op)

    @staticmethod
    def get_variable(        
            model,
            name,
            shape = None,
            dtype = None,
            initializer = None
    ):
        return tf.get_variable(
            name, 
            shape = shape, 
            dtype = dtype,
            initializer = initializer)

    @staticmethod
    def matmul(
            aa,
            bb, 
            transpose_a = False, 
            transpose_b = False, 
            name = None
    ):
        return tf.matmul(aa, bb, transpose_a=transpose_a, transpose_b=transpose_b, name=name)

    @staticmethod
    def tanh(
            xx,
            name = None
    ):
        return tf.nn.tanh(xx, name=name)
