






"""simple docstring"""



import argparse
import os
import sys
import urllib.request
import zipfile


_UpperCamelCase						:    int		=						['CoLA', 'SST', 'MRPC', 'QQP', 'STS', 'MNLI', 'SNLI', 'QNLI', 'RTE', 'WNLI', 'diagnostic']
_UpperCamelCase						:    List[str]		=						{
    'CoLA': 'https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FCoLA.zip?alt=media&token=46d5e637-3411-4188-bc44-5809b5bfb5f4',
    'SST': 'https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FSST-2.zip?alt=media&token=aabc5f6b-e466-44a2-b9b4-cf6337f84ac8',
    'MRPC': 'https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2Fmrpc_dev_ids.tsv?alt=media&token=ec5c0836-31d5-48f4-b431-7480817f1adc',
    'QQP': 'https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FQQP.zip?alt=media&token=700c6acf-160d-4d89-81d1-de4191d02cb5',
    'STS': 'https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FSTS-B.zip?alt=media&token=bddb94a7-8706-4e0d-a694-1109e12273b5',
    'MNLI': 'https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FMNLI.zip?alt=media&token=50329ea1-e339-40e2-809c-10c40afff3ce',
    'SNLI': 'https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FSNLI.zip?alt=media&token=4afcfbb2-ff0c-4b2d-a09a-dbf07926f4df',
    'QNLI': 'https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FQNLIv2.zip?alt=media&token=6fdcf570-0fc5-4631-8456-9505272d1601',
    'RTE': 'https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FRTE.zip?alt=media&token=5efa7e85-a0bb-4f19-8ea2-9e1840f077fb',
    'WNLI': 'https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FWNLI.zip?alt=media&token=068ad0a0-ded7-4bd7-99a5-5e00222e0faf',
    'diagnostic': 'https://storage.googleapis.com/mtl-sentence-representations.appspot.com/tsvsWithoutLabels%2FAX.tsv?GoogleAccessId=firebase-adminsdk-0khhl@mtl-sentence-representations.iam.gserviceaccount.com&Expires=2498860800&Signature=DuQ2CSPt2Yfre0C%2BiISrVYrIFaZH1Lc7hBVZDD4ZyR7fZYOMNOUGpi8QxBmTNOrNPjR3z1cggo7WXFfrgECP6FBJSsURv8Ybrue8Ypt%2FTPxbuJ0Xc2FhDi%2BarnecCBFO77RSbfuz%2Bs95hRrYhTnByqu3U%2FYZPaj3tZt5QdfpH2IUROY8LiBXoXS46LE%2FgOQc%2FKN%2BA9SoscRDYsnxHfG0IjXGwHN%2Bf88q6hOmAxeNPx6moDulUF6XMUAaXCSFU%2BnRO2RDL9CapWxj%2BDl7syNyHhB7987hZ80B%2FwFkQ3MEs8auvt5XW1%2Bd4aCU7ytgM69r8JDCwibfhZxpaa4gd50QXQ%3D%3D',
}

_UpperCamelCase						:    Tuple		=						'https://dl.fbaipublicfiles.com/senteval/senteval_data/msr_paraphrase_train.txt'
_UpperCamelCase						:    str		=						'https://dl.fbaipublicfiles.com/senteval/senteval_data/msr_paraphrase_test.txt'
def 		_SCREAMING_SNAKE_CASE					(       __snake_case     :    str						,   __snake_case     :    Tuple       ):





  '''simple docstring'''





  print(f'Downloading and extracting {task}...'       )
  lowercase				       =			f'{task}.zip'
  urllib.request.urlretrieve(TASKaPATH[task]						,   __snake_case       )
  with zipfile.ZipFile(__snake_case       ) as zip_ref:
    zip_ref.extractall(__snake_case       )
  os.remove(__snake_case       )
  print('\tCompleted!'       )
def 		_SCREAMING_SNAKE_CASE					(       __snake_case     :    Union[str, Any]						,   __snake_case     :    Optional[int]       ):





  '''simple docstring'''





  print('Processing MRPC...'       )
  lowercase				       =			os.path.join(__snake_case						,   'MRPC'       )
  if not os.path.isdir(__snake_case       ):
    os.mkdir(__snake_case       )
  if path_to_data:
    lowercase				       =			os.path.join(__snake_case						,   'msr_paraphrase_train.txt'       )
    lowercase				       =			os.path.join(__snake_case						,   'msr_paraphrase_test.txt'       )
  else:
    print('Local MRPC data not specified, downloading data from %s' % MRPC_TRAIN       )
    lowercase				       =			os.path.join(__snake_case						,   'msr_paraphrase_train.txt'       )
    lowercase				       =			os.path.join(__snake_case						,   'msr_paraphrase_test.txt'       )
    urllib.request.urlretrieve(__snake_case						,   __snake_case       )
    urllib.request.urlretrieve(__snake_case						,   __snake_case       )
  if not os.path.isfile(__snake_case       ):
    raise ValueError(f'Train data not found at {mrpc_train_file}'       )
  if not os.path.isfile(__snake_case       ):
    raise ValueError(f'Test data not found at {mrpc_test_file}'       )
  urllib.request.urlretrieve(TASKaPATH['MRPC']						,   os.path.join(__snake_case						,   'dev_ids.tsv'       )       )

  lowercase				       =			[]
  with open(os.path.join(__snake_case						,   'dev_ids.tsv'       )						,   encoding='utf8'       ) as ids_fh:
    for row in ids_fh:
      dev_ids.append(row.strip().split('\t'       )       )

  with open(__snake_case						,   encoding='utf8'       ) as data_fh, open(
      os.path.join(__snake_case						,   'train.tsv'       )						,   'w'						,   encoding='utf8'       ) as train_fh, open(os.path.join(__snake_case						,   'dev.tsv'       )						,   'w'						,   encoding='utf8'       ) as dev_fh:
    lowercase				       =			data_fh.readline()
    train_fh.write(__snake_case       )
    dev_fh.write(__snake_case       )
    for row in data_fh:
      lowercase       ,	lowercase       ,	lowercase       ,	lowercase       ,	lowercase				       =			row.strip().split('\t'       )
      if [ida, ida] in dev_ids:
        dev_fh.write('%s\t%s\t%s\t%s\t%s\n' % (label, ida, ida, sa, sa)       )
      else:
        train_fh.write('%s\t%s\t%s\t%s\t%s\n' % (label, ida, ida, sa, sa)       )

  with open(__snake_case						,   encoding='utf8'       ) as data_fh, open(
      os.path.join(__snake_case						,   'test.tsv'       )						,   'w'						,   encoding='utf8'       ) as test_fh:
    lowercase				       =			data_fh.readline()
    test_fh.write('index\t#1 ID\t#2 ID\t#1 String\t#2 String\n'       )
    for idx, row in enumerate(__snake_case       ):
      lowercase       ,	lowercase       ,	lowercase       ,	lowercase       ,	lowercase				       =			row.strip().split('\t'       )
      test_fh.write('%d\t%s\t%s\t%s\t%s\n' % (idx, ida, ida, sa, sa)       )
  print('\tCompleted!'       )
def 		_SCREAMING_SNAKE_CASE					(       __snake_case     :    Optional[Any]       ):





  '''simple docstring'''





  print('Downloading and extracting diagnostic...'       )
  if not os.path.isdir(os.path.join(__snake_case						,   'diagnostic'       )       ):
    os.mkdir(os.path.join(__snake_case						,   'diagnostic'       )       )
  lowercase				       =			os.path.join(__snake_case						,   'diagnostic'						,   'diagnostic.tsv'       )
  urllib.request.urlretrieve(TASKaPATH['diagnostic']						,   __snake_case       )
  print('\tCompleted!'       )
  return
def 		_SCREAMING_SNAKE_CASE					(       __snake_case     :    List[Any]       ):





  '''simple docstring'''





  lowercase				       =			task_names.split(','       )
  if "all" in task_names:
    lowercase				       =			TASKS
  else:
    lowercase				       =			[]
    for task_name in task_names:
      if task_name not in TASKS:
        raise ValueError(f'Task {task_name} not found!'       )
      tasks.append(__snake_case       )
  return tasks
def 		_SCREAMING_SNAKE_CASE					(       __snake_case     :    Tuple       ):





  '''simple docstring'''





  lowercase				       =			argparse.ArgumentParser()
  parser.add_argument('--data_dir'						,   help='directory to save data to'						,   type=__snake_case						,   default='glue_data'       )
  parser.add_argument(
      '--tasks'						,   help='tasks to download data for as a comma separated string'						,   type=__snake_case						,   default='all'       )
  parser.add_argument(
      '--path_to_mrpc'						,   help='path to directory containing extracted MRPC data, msr_paraphrase_train.txt and msr_paraphrase_text.txt'						,   type=__snake_case						,   default=''						,   )
  lowercase				       =			parser.parse_args(__snake_case       )

  if not os.path.isdir(args.data_dir       ):
    os.mkdir(args.data_dir       )
  lowercase				       =			get_tasks(args.tasks       )

  for task in tasks:
    if task == "MRPC":
      format_mrpc(args.data_dir						,   args.path_to_mrpc       )
    elif task == "diagnostic":
      download_diagnostic(args.data_dir       )
    else:
      download_and_extract(__snake_case						,   args.data_dir       )


if __name__ == "__main__":
    sys.exit(main(sys.argv[1:]))










"""simple docstring"""



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:
        _UpperCamelCase						:    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)]
                )
                _UpperCamelCase						:    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 			a	:
    UpperCAmelCase_					:	Union[str, Any]      	=None
    UpperCAmelCase_					:	Any      	=()
    UpperCAmelCase_					:	Optional[int]      	=()
    UpperCAmelCase_					:	Any      	=True
    UpperCAmelCase_					:	int      	=True
    UpperCAmelCase_					:	Dict      	=True
    UpperCAmelCase_					:	Any      	=False







    def      UpperCamelCase_ (     self				,						_lowerCamelCase				,						_lowerCamelCase				,						_lowerCamelCase=False			):
      lowercase				       =			copy.deepcopy(_lowerCamelCase			)

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

      if return_labels:
        if model_class in get_values(_lowerCamelCase			):
          lowercase				       =			tf.ones(self.model_tester.batch_size				,						dtype=tf.intaa			)
        elif model_class in get_values(_lowerCamelCase			):
          lowercase				       =			tf.zeros(self.model_tester.batch_size				,						dtype=tf.intaa			)
          lowercase				       =			tf.zeros(self.model_tester.batch_size				,						dtype=tf.intaa			)
        elif model_class in [
            *get_values(_lowerCamelCase			),
            *get_values(_lowerCamelCase			),
        ]:
          lowercase				       =			tf.zeros(self.model_tester.batch_size				,						dtype=tf.intaa			)
        elif model_class in get_values(_lowerCamelCase			):
          lowercase				       =			tf.zeros(self.model_tester.batch_size				,						dtype=tf.intaa			)
        elif model_class in [
            *get_values(_lowerCamelCase			),
            *get_values(_lowerCamelCase			),
            *get_values(_lowerCamelCase			),
            *get_values(_lowerCamelCase			),
            *get_values(_lowerCamelCase			),
        ]:
          lowercase				       =			tf.zeros(
              (self.model_tester.batch_size, self.model_tester.seq_length)				,						dtype=tf.intaa			)
      return inputs_dict







    @slow
    def      UpperCamelCase_ (     self			):
      lowercase       ,	lowercase				       =			self.model_tester.prepare_config_and_inputs_for_common()
      for model_class in self.all_model_classes[:2]:
        lowercase				       =			self._prepare_for_class(_lowerCamelCase				,						_lowerCamelCase			)
        lowercase				       =			model_class(_lowerCamelCase			)

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

        lowercase				       =			run_in_graph_mode()
        self.assertIsNotNone(_lowerCamelCase			)







    @slow
    def      UpperCamelCase_ (     self			):
      lowercase       ,	lowercase				       =			self.model_tester.prepare_config_and_inputs_for_common()
      for model_class in self.all_model_classes[:2]:
        lowercase				       =			self._prepare_for_class(_lowerCamelCase				,						_lowerCamelCase			)
        lowercase				       =			model_class(_lowerCamelCase			)

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

        lowercase				       =			run_in_graph_mode()
        self.assertIsNotNone(_lowerCamelCase			)







    @slow
    def      UpperCamelCase_ (     self			):
      # This is a copy of the test_keras_fit method, but we use XLA compilation instead of eager
      lowercase       ,	lowercase				       =			self.model_tester.prepare_config_and_inputs_for_common()
      for model_class in self.all_model_classes[:2]:
        lowercase				       =			model_class(_lowerCamelCase			)
        if getattr(_lowerCamelCase				,						'hf_compute_loss'				,						_lowerCamelCase			):
          # Test that model correctly compute the loss with kwargs
          lowercase				       =			self._prepare_for_class(inputs_dict.copy()				,						_lowerCamelCase				,						return_labels=_lowerCamelCase			)
          # Is there a better way to remove these decoder inputs?
          lowercase				       =			{
              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')
          }

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

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

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

          lowercase				       =			history.history['loss'][0]
          self.assertTrue(not isnan(_lowerCamelCase			)			)
          lowercase				       =			history.history['val_loss'][0]
          self.assertTrue(not isnan(_lowerCamelCase			)			)







    @slow
    def      UpperCamelCase_ (     self			):
      lowercase       ,	lowercase				       =			self.model_tester.prepare_config_and_inputs_for_common()
      lowercase				       =			True
      lowercase				       =			True

      if hasattr(_lowerCamelCase				,						'use_cache'			):
        lowercase				       =			True

      lowercase				       =			getattr(self.model_tester				,						'encoder_seq_length'				,						self.model_tester.seq_length			)
      lowercase				       =			getattr(self.model_tester				,						'key_length'				,						_lowerCamelCase			)

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

        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:
            lowercase				       =			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(_lowerCamelCase				,						saved_model=_lowerCamelCase			)
          lowercase				       =			os.path.join(_lowerCamelCase				,						'saved_model'				,						'1'			)
          lowercase				       =			tf.keras.models.load_model(_lowerCamelCase			)
          lowercase				       =			model(_lowerCamelCase			)

          if self.is_encoder_decoder:
            lowercase				       =			outputs['encoder_hidden_states']
            lowercase				       =			outputs['encoder_attentions']
          else:
            lowercase				       =			outputs['hidden_states']
            lowercase				       =			outputs['attentions']

          self.assertEqual(len(_lowerCamelCase			)				,						_lowerCamelCase			)

          lowercase				       =			getattr(
              self.model_tester				,						'expected_num_hidden_layers'				,						self.model_tester.num_hidden_layers + 1			)

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

          self.assertEqual(len(_lowerCamelCase			)				,						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      UpperCamelCase_ (     self			):
      tf.keras.mixed_precision.set_global_policy('mixed_float16'			)

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

          self.assertIsNotNone(_lowerCamelCase			)
      finally:
        tf.keras.mixed_precision.set_global_policy('float32'			)







    @slow
    def      UpperCamelCase_ (     self			):
      lowercase       ,	lowercase				       =			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"]
      lowercase				       =			{
          module_member
          for model_class in self.all_model_classes
          for module in (import_module(model_class.__module__			),)
          for module_member_name in dir(_lowerCamelCase			)
          if module_member_name.endswith('MainLayer'			)
          for module_member in (getattr(_lowerCamelCase				,						_lowerCamelCase			),)
          if isinstance(_lowerCamelCase				,						_lowerCamelCase			)
          and tf.keras.layers.Layer in module_member.__bases__
          and getattr(_lowerCamelCase				,						'_keras_serializable'				,						_lowerCamelCase			)
      }

      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
          lowercase				       =			TFSharedEmbeddings(self.model_tester.vocab_size				,						self.model_tester.hidden_size				,						name='shared'			)
          lowercase				       =			False
          lowercase				       =			main_layer_class(_lowerCamelCase				,						embed_tokens=_lowerCamelCase			)
        else:
          lowercase				       =			main_layer_class(_lowerCamelCase			)

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

        if hasattr(self.model_tester				,						'num_labels'			):
          lowercase				       =			self.model_tester.num_labels
        else:
          lowercase				       =			2

        lowercase				       =			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			)

        lowercase				       =			main_layer(_lowerCamelCase			)[0]
        lowercase				       =			tf.keras.layers.Dense(_lowerCamelCase				,						activation='softmax'				,						name='outputs'			)(_lowerCamelCase			)
        lowercase				       =			tf.keras.models.Model(inputs=_lowerCamelCase				,						outputs=[outputs]			)

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

        with tempfile.TemporaryDirectory() as tmpdirname:
          lowercase				       =			os.path.join(_lowerCamelCase				,						'keras_model.h5'			)
          model.save(_lowerCamelCase			)
          if "T5" in main_layer_class.__name__:
            lowercase				       =			tf.keras.models.load_model(
                _lowerCamelCase				,						custom_objects={
                    main_layer_class.__name__: main_layer_class,
                    'TFSharedEmbeddings': TFSharedEmbeddings,
                }				,						)
          else:
            lowercase				       =			tf.keras.models.load_model(
                _lowerCamelCase				,						custom_objects={main_layer_class.__name__: main_layer_class}			)
          assert isinstance(_lowerCamelCase				,						tf.keras.Model			)
          model(_lowerCamelCase			)







    @slow
    def      UpperCamelCase_ (     self			):
      lowercase       ,	lowercase				       =			self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes[:2]:
        lowercase				       =			model_class(_lowerCamelCase			)

        lowercase				       =			copy.deepcopy(_lowerCamelCase			)

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

        if not self.is_encoder_decoder:
          lowercase				       =			model.get_input_embeddings()(_lowerCamelCase			)
        else:
          lowercase				       =			model.get_input_embeddings()(_lowerCamelCase			)
          lowercase				       =			model.get_input_embeddings()(_lowerCamelCase			)

        lowercase				       =			self._prepare_for_class(_lowerCamelCase				,						_lowerCamelCase			)

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

        lowercase				       =			run_in_graph_mode()
        self.assertIsNotNone(_lowerCamelCase			)







    def      UpperCamelCase_ (     self				,						_lowerCamelCase				,						_lowerCamelCase			):
      # special tokens cannot be bad tokens
      lowercase				       =			[]
      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
      lowercase				       =			[]
      while len(_lowerCamelCase			) < num_bad_tokens:
        lowercase				       =			tf.squeeze(ids_tensor((1, 1)				,						self.model_tester.vocab_size			)				,						0			).numpy()[0]
        if token not in special_tokens:
          bad_tokens.append(_lowerCamelCase			)
      return bad_tokens







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







    def      UpperCamelCase_ (     self				,						_lowerCamelCase				,						_lowerCamelCase			):
      # for all bad word tokens
      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(_lowerCamelCase			)				,						len(_lowerCamelCase			)			):
            # if tokens match
            if generated_ids_slice[i - len(_lowerCamelCase			) : i] == bad_word_ids:
              return True
      return False


