
from __future__ import annotations

import copy
import os
import tempfile
from importlib import import_module
from math import isnan

from transformers import is_tf_available
from transformers.models.auto import get_values
from transformers.testing_utils import _tf_gpu_memory_limit, require_tf, slow

from ..test_modeling_tf_common import ids_tensor


if is_tf_available():
    import numpy as np
    import tensorflow as tf

    from transformers import (
        TF_MODEL_FOR_CAUSAL_LM_MAPPING,
        TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
        TF_MODEL_FOR_MASKED_LM_MAPPING,
        TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
        TF_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING,
        TF_MODEL_FOR_PRETRAINING_MAPPING,
        TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
        TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
        TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
        TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
        TFSharedEmbeddings,
    )

    if _tf_gpu_memory_limit is not None:
        __lowerCAmelCase      :					Optional[int]   	=       tf.config.list_physical_devices('GPU')
        for gpu in gpus:
            # Restrict TensorFlow to only allocate x GB of memory on the GPUs
            try:
                tf.config.set_logical_device_configuration(
                    gpu, [tf.config.LogicalDeviceConfiguration(memory_limit=_tf_gpu_memory_limit)]
                )
                __lowerCAmelCase      :					Any   	=       tf.config.list_logical_devices('GPU')
                print('Logical GPUs', logical_gpus)
            except RuntimeError as e:
                # Virtual devices must be set before GPUs have been initialized
                print(e)




@require_tf
class 	UpperCAmelCase_      :


  '''simple docstring'''







  a__   	=		None
  a__   	=		()
  a__   	=		()
  a__   	=		True
  a__   	=		True
  a__   	=		True
  a__   	=		False
  def 							_lowercase							(			self      :			Any      ,							UpperCamelCase__      :			Optional[int]      ,							UpperCamelCase__      :			Dict      ,							UpperCamelCase__      :			Any=False   )      ->     dict:







    """simple docstring"""






    __magic_name__    					=	copy.deepcopy(UpperCamelCase__   )

    if model_class in get_values(UpperCamelCase__   ):
      __magic_name__    					=	{
          k: tf.tile(tf.expand_dims(UpperCamelCase__      ,							1   )      ,							(1, self.model_tester.num_choices) + (1,) * (v.ndim - 1)   )
          if isinstance(UpperCamelCase__      ,							tf.Tensor   ) and v.ndim > 0
          else v
          for k, v in inputs_dict.items()
      }

    if return_labels:
      if model_class in get_values(UpperCamelCase__   ):
        __magic_name__    					=	tf.ones(self.model_tester.batch_size      ,							dtype=tf.intaa   )
      elif model_class in get_values(UpperCamelCase__   ):
        __magic_name__    					=	tf.zeros(self.model_tester.batch_size      ,							dtype=tf.intaa   )
        __magic_name__    					=	tf.zeros(self.model_tester.batch_size      ,							dtype=tf.intaa   )
      elif model_class in [
          *get_values(UpperCamelCase__   ),
          *get_values(UpperCamelCase__   ),
      ]:
        __magic_name__    					=	tf.zeros(self.model_tester.batch_size      ,							dtype=tf.intaa   )
      elif model_class in get_values(UpperCamelCase__   ):
        __magic_name__    					=	tf.zeros(self.model_tester.batch_size      ,							dtype=tf.intaa   )
      elif model_class in [
          *get_values(UpperCamelCase__   ),
          *get_values(UpperCamelCase__   ),
          *get_values(UpperCamelCase__   ),
          *get_values(UpperCamelCase__   ),
          *get_values(UpperCamelCase__   ),
      ]:
        __magic_name__    					=	tf.zeros(
            (self.model_tester.batch_size, self.model_tester.seq_length)      ,							dtype=tf.intaa   )
    return inputs_dict
  @slow
  def 							_lowercase							(			self      :			Dict   )      ->     Dict:







    """simple docstring"""






    __magic_name__					,   __magic_name__    					=	self.model_tester.prepare_config_and_inputs_for_common()
    for model_class in self.all_model_classes[:2]:
      __magic_name__    					=	self._prepare_for_class(UpperCamelCase__      ,							UpperCamelCase__   )
      __magic_name__    					=	model_class(UpperCamelCase__   )

      @tf.function
      def run_in_graph_mode():
        return model(UpperCamelCase__   )

      __magic_name__    					=	run_in_graph_mode()
      self.assertIsNotNone(UpperCamelCase__   )
  @slow
  def 							_lowercase							(			self      :			Union[str, Any]   )      ->     Any:







    """simple docstring"""






    __magic_name__					,   __magic_name__    					=	self.model_tester.prepare_config_and_inputs_for_common()
    for model_class in self.all_model_classes[:2]:
      __magic_name__    					=	self._prepare_for_class(UpperCamelCase__      ,							UpperCamelCase__   )
      __magic_name__    					=	model_class(UpperCamelCase__   )

      @tf.function(experimental_compile=UpperCamelCase__   )
      def run_in_graph_mode():
        return model(UpperCamelCase__   )

      __magic_name__    					=	run_in_graph_mode()
      self.assertIsNotNone(UpperCamelCase__   )
  @slow
  def 							_lowercase							(			self      :			List[Any]   )      ->     str:







    """simple docstring"""






    __magic_name__					,   __magic_name__    					=	self.model_tester.prepare_config_and_inputs_for_common()
    for model_class in self.all_model_classes[:2]:
      __magic_name__    					=	model_class(UpperCamelCase__   )
      if getattr(UpperCamelCase__      ,							"""hf_compute_loss"""      ,							UpperCamelCase__   ):
        # Test that model correctly compute the loss with kwargs
        __magic_name__    					=	self._prepare_for_class(inputs_dict.copy()      ,							UpperCamelCase__      ,							return_labels=UpperCamelCase__   )
        # Is there a better way to remove these decoder inputs?
        __magic_name__    					=	{
            key: val
            for key, val in prepared_for_class.items()
            if key not in ("""head_mask""", """decoder_head_mask""", """cross_attn_head_mask""", """decoder_input_ids""")
        }

        __magic_name__    					=	{
            """labels""",
            """label""",
            """label_ids""",
            """start_positions""",
            """start_position""",
            """end_positions""",
            """end_position""",
            """next_sentence_label""",
        }
        __magic_name__    					=	possible_label_cols.intersection(set(UpperCamelCase__   )   )
        self.assertGreater(len(UpperCamelCase__   )      ,							0      ,							msg="""No matching label names found!"""   )
        __magic_name__    					=	{key: val for key, val in prepared_for_class.items() if key in label_names}
        __magic_name__    					=	{key: val for key, val in prepared_for_class.items() if key not in label_names}
        self.assertGreater(len(UpperCamelCase__   )      ,							0   )

        # Make sure it works with XLA!
        model.compile(optimizer=tf.keras.optimizers.SGD(0.0   )      ,							jit_compile=UpperCamelCase__   )
        # Make sure the model fits without crashing regardless of where we pass the labels
        __magic_name__    					=	model.fit(
            UpperCamelCase__      ,							validation_data=UpperCamelCase__      ,							steps_per_epoch=1      ,							validation_steps=1      ,							shuffle=UpperCamelCase__      ,							verbose=0      ,							)
        __magic_name__    					=	history.history["""loss"""][0]
        self.assertTrue(not isnan(UpperCamelCase__   )   )
        __magic_name__    					=	history.history["""val_loss"""][0]
        self.assertTrue(not isnan(UpperCamelCase__   )   )

        # Now test it with separate labels, to make sure that path works in XLA too.
        __magic_name__    					=	model_class(UpperCamelCase__   )
        model.compile(optimizer=tf.keras.optimizers.SGD(0.0   )      ,							jit_compile=UpperCamelCase__   )
        __magic_name__    					=	model.fit(
            UpperCamelCase__      ,							UpperCamelCase__      ,							validation_data=(inputs_minus_labels, labels)      ,							steps_per_epoch=1      ,							validation_steps=1      ,							shuffle=UpperCamelCase__      ,							verbose=0      ,							)

        __magic_name__    					=	history.history["""loss"""][0]
        self.assertTrue(not isnan(UpperCamelCase__   )   )
        __magic_name__    					=	history.history["""val_loss"""][0]
        self.assertTrue(not isnan(UpperCamelCase__   )   )
  @slow
  def 							_lowercase							(			self      :			Optional[int]   )      ->     Optional[int]:







    """simple docstring"""






    __magic_name__					,   __magic_name__    					=	self.model_tester.prepare_config_and_inputs_for_common()
    __magic_name__    					=	True
    __magic_name__    					=	True

    if hasattr(UpperCamelCase__      ,							"""use_cache"""   ):
      __magic_name__    					=	True

    __magic_name__    					=	getattr(self.model_tester      ,							"""encoder_seq_length"""      ,							self.model_tester.seq_length   )
    __magic_name__    					=	getattr(self.model_tester      ,							"""key_length"""      ,							UpperCamelCase__   )

    for model_class in self.all_model_classes[:2]:
      __magic_name__    					=	self._prepare_for_class(UpperCamelCase__      ,							UpperCamelCase__   )
      __magic_name__    					=	model_class(UpperCamelCase__   )
      model.build()
      __magic_name__    					=	len(model(UpperCamelCase__   )   )

      for key in list(class_inputs_dict.keys()   ):
        # Remove keys not in the serving signature, as the SavedModel will not be compiled to deal with them
        if key not in model.input_signature:
          del class_inputs_dict[key]
        # Check it's a tensor, in case the inputs dict has some bools in it too
        elif isinstance(class_inputs_dict[key]      ,							tf.Tensor   ) and class_inputs_dict[key].dtype.is_integer:
          __magic_name__    					=	tf.cast(class_inputs_dict[key]      ,							tf.intaa   )

      if set(class_inputs_dict.keys()   ) != set(model.input_signature.keys()   ):
        continue  # Some models have inputs that the preparation functions don't create, we skip those

      with tempfile.TemporaryDirectory() as tmpdirname:
        model.save_pretrained(UpperCamelCase__      ,							saved_model=UpperCamelCase__   )
        __magic_name__    					=	os.path.join(UpperCamelCase__      ,							"""saved_model"""      ,							"""1"""   )
        __magic_name__    					=	tf.keras.models.load_model(UpperCamelCase__   )
        __magic_name__    					=	model(UpperCamelCase__   )

        if self.is_encoder_decoder:
          __magic_name__    					=	outputs["""encoder_hidden_states"""]
          __magic_name__    					=	outputs["""encoder_attentions"""]
        else:
          __magic_name__    					=	outputs["""hidden_states"""]
          __magic_name__    					=	outputs["""attentions"""]

        self.assertEqual(len(UpperCamelCase__   )      ,							UpperCamelCase__   )

        __magic_name__    					=	getattr(
            self.model_tester      ,							"""expected_num_hidden_layers"""      ,							self.model_tester.num_hidden_layers + 1   )

        self.assertEqual(len(UpperCamelCase__   )      ,							UpperCamelCase__   )
        self.assertListEqual(
            list(output_hidden_states[0].shape[-2:]   )      ,							[self.model_tester.seq_length, self.model_tester.hidden_size]      ,							)

        self.assertEqual(len(UpperCamelCase__   )      ,							self.model_tester.num_hidden_layers   )
        self.assertListEqual(
            list(output_attentions[0].shape[-3:]   )      ,							[self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length]      ,							)
  @slow
  def 							_lowercase							(			self      :			List[Any]   )      ->     Tuple:







    """simple docstring"""






    tf.keras.mixed_precision.set_global_policy("""mixed_float16"""   )

    # try/finally block to ensure subsequent tests run in float32
    try:
      __magic_name__					,   __magic_name__    					=	self.model_tester.prepare_config_and_inputs_for_common()
      for model_class in self.all_model_classes[:2]:
        __magic_name__    					=	self._prepare_for_class(UpperCamelCase__      ,							UpperCamelCase__   )
        __magic_name__    					=	model_class(UpperCamelCase__   )
        __magic_name__    					=	model(UpperCamelCase__   )

        self.assertIsNotNone(UpperCamelCase__   )
    finally:
      tf.keras.mixed_precision.set_global_policy("""float32"""   )
  @slow
  def 							_lowercase							(			self      :			List[str]   )      ->     Any:







    """simple docstring"""






    __magic_name__					,   __magic_name__    					=	self.model_tester.prepare_config_and_inputs_for_common()
    # head_mask and decoder_head_mask has different shapes than other input args
    if "head_mask" in inputs_dict:
      del inputs_dict["head_mask"]
    if "decoder_head_mask" in inputs_dict:
      del inputs_dict["decoder_head_mask"]
    if "cross_attn_head_mask" in inputs_dict:
      del inputs_dict["cross_attn_head_mask"]
    __magic_name__    					=	{
        module_member
        for model_class in self.all_model_classes
        for module in (import_module(model_class.__module__   ),)
        for module_member_name in dir(UpperCamelCase__   )
        if module_member_name.endswith("""MainLayer"""   )
        for module_member in (getattr(UpperCamelCase__      ,							UpperCamelCase__   ),)
        if isinstance(UpperCamelCase__      ,							UpperCamelCase__   )
        and tf.keras.layers.Layer in module_member.__bases__
        and getattr(UpperCamelCase__      ,							"""_keras_serializable"""      ,							UpperCamelCase__   )
    }

    for main_layer_class in tf_main_layer_classes:
      # T5MainLayer needs an embed_tokens parameter when called without the inputs_embeds parameter
      if "T5" in main_layer_class.__name__:
        # Take the same values than in TFT5ModelTester for this shared layer
        __magic_name__    					=	TFSharedEmbeddings(self.model_tester.vocab_size      ,							self.model_tester.hidden_size      ,							name="""shared"""   )
        __magic_name__    					=	False
        __magic_name__    					=	main_layer_class(UpperCamelCase__      ,							embed_tokens=UpperCamelCase__   )
      else:
        __magic_name__    					=	main_layer_class(UpperCamelCase__   )

      __magic_name__    					=	{
          name: tf.keras.Input(tensor.shape[1:]      ,							dtype=tensor.dtype   ) for name, tensor in inputs_dict.items()
      }

      if hasattr(self.model_tester      ,							"""num_labels"""   ):
        __magic_name__    					=	self.model_tester.num_labels
      else:
        __magic_name__    					=	2

      __magic_name__    					=	tf.data.Dataset.from_tensor_slices(
          (inputs_dict, np.ones((self.model_tester.batch_size, self.model_tester.seq_length, num_labels, 1)   ))   ).batch(1   )

      __magic_name__    					=	main_layer(UpperCamelCase__   )[0]
      __magic_name__    					=	tf.keras.layers.Dense(UpperCamelCase__      ,							activation="""softmax"""      ,							name="""outputs"""   )(UpperCamelCase__   )
      __magic_name__    					=	tf.keras.models.Model(inputs=UpperCamelCase__      ,							outputs=[outputs]   )

      model.compile(loss="""binary_crossentropy"""      ,							optimizer="""adam"""      ,							metrics=["""binary_accuracy"""]   )
      model.fit(UpperCamelCase__      ,							epochs=1   )

      with tempfile.TemporaryDirectory() as tmpdirname:
        __magic_name__    					=	os.path.join(UpperCamelCase__      ,							"""keras_model.h5"""   )
        model.save(UpperCamelCase__   )
        if "T5" in main_layer_class.__name__:
          __magic_name__    					=	tf.keras.models.load_model(
              UpperCamelCase__      ,							custom_objects={
                  main_layer_class.__name__: main_layer_class,
                  """TFSharedEmbeddings""": TFSharedEmbeddings,
              }      ,							)
        else:
          __magic_name__    					=	tf.keras.models.load_model(
              UpperCamelCase__      ,							custom_objects={main_layer_class.__name__: main_layer_class}   )
        assert isinstance(UpperCamelCase__      ,							tf.keras.Model   )
        model(UpperCamelCase__   )
  @slow
  def 							_lowercase							(			self      :			Dict   )      ->     Any:







    """simple docstring"""






    __magic_name__					,   __magic_name__    					=	self.model_tester.prepare_config_and_inputs_for_common()

    for model_class in self.all_model_classes[:2]:
      __magic_name__    					=	model_class(UpperCamelCase__   )

      __magic_name__    					=	copy.deepcopy(UpperCamelCase__   )

      if not self.is_encoder_decoder:
        __magic_name__    					=	inputs["""input_ids"""]
        del inputs["input_ids"]
      else:
        __magic_name__    					=	inputs["""input_ids"""]
        __magic_name__    					=	inputs.get("""decoder_input_ids"""      ,							UpperCamelCase__   )
        del inputs["input_ids"]
        inputs.pop("""decoder_input_ids"""      ,							UpperCamelCase__   )

      if not self.is_encoder_decoder:
        __magic_name__    					=	model.get_input_embeddings()(UpperCamelCase__   )
      else:
        __magic_name__    					=	model.get_input_embeddings()(UpperCamelCase__   )
        __magic_name__    					=	model.get_input_embeddings()(UpperCamelCase__   )

      __magic_name__    					=	self._prepare_for_class(UpperCamelCase__      ,							UpperCamelCase__   )

      @tf.function
      def run_in_graph_mode():
        return model(UpperCamelCase__   )

      __magic_name__    					=	run_in_graph_mode()
      self.assertIsNotNone(UpperCamelCase__   )
  def 							_lowercase							(			self      :			Union[str, Any]      ,							UpperCamelCase__      :			int      ,							UpperCamelCase__      :			List[str]   )      ->     Any:







    """simple docstring"""






    __magic_name__    					=	[]
    if model.config.bos_token_id is not None:
      special_tokens.append(model.config.bos_token_id   )
    if model.config.pad_token_id is not None:
      special_tokens.append(model.config.pad_token_id   )
    if model.config.eos_token_id is not None:
      special_tokens.append(model.config.eos_token_id   )

    # create random bad tokens that are not special tokens
    __magic_name__    					=	[]
    while len(UpperCamelCase__   ) < num_bad_tokens:
      __magic_name__    					=	tf.squeeze(ids_tensor((1, 1)      ,							self.model_tester.vocab_size   )      ,							0   ).numpy()[0]
      if token not in special_tokens:
        bad_tokens.append(UpperCamelCase__   )
    return bad_tokens
  def 							_lowercase							(			self      :			Dict      ,							UpperCamelCase__      :			Tuple   )      ->     str:







    """simple docstring"""






    for token_id in output_ids[0].numpy().tolist():
      self.assertGreaterEqual(UpperCamelCase__      ,							0   )
      self.assertLess(UpperCamelCase__      ,							self.model_tester.vocab_size   )





  def 							_lowercase							(			self      :			List[Any]      ,							UpperCamelCase__      :			List[Any]      ,							UpperCamelCase__      :			Dict   )      ->     Optional[int]:







    """simple docstring"""






    for bad_word_ids in bad_words_ids:
      # for all slices in batch
      for generated_ids_slice in generated_ids:
        # for all word idx
        for i in range(len(UpperCamelCase__   )      ,							len(UpperCamelCase__   )   ):
          # if tokens match
          if generated_ids_slice[i - len(UpperCamelCase__   ) : i] == bad_word_ids:
            return True
    return False



class 	UpperCAmelCase_      :


  '''simple docstring'''







  def __init__(			self      :			Tuple      ,							UpperCamelCase__      :			Dict=None   )      ->     List[str]:







    """simple docstring"""






    __magic_name__    					=	data
    __magic_name__    					=	None





  def __repr__(			self      :			Optional[int]   )      ->     Optional[int]:







    """simple docstring"""






    __magic_name__    					=	""""""
    __magic_name__    					=	self
    while temp:
      string_rep += F'''<{temp.data}> ---> '''
      __magic_name__    					=	temp.next
    string_rep += "<END>"
    return string_rep







def a__   (	A_ ):






  '''simple docstring'''





  if not elements_list:
    raise Exception("""The Elements List is empty""" )

  # Set first element as Head
  __magic_name__    					=	Node(elements_list[0] )
  __magic_name__    					=	head
  # Loop through elements from position 1
  for data in elements_list[1:]:
    __magic_name__    					=	Node(A_ )
    __magic_name__    					=	current.next
  return head


__lowerCAmelCase      :					Union[str, Any]   	=       [1, 3, 5, 32, 44, 12, 43]
print(F'''List: {list_data}''')
print('Creating Linked List from List.')
__lowerCAmelCase      :					Optional[Any]   	=       make_linked_list(list_data)
print('Linked List:')





print(linked_list)

