



import logging
import os
from dataclasses import dataclass, field
from importlib import import_module
from typing import Dict, List, Optional, Tuple

import numpy as np
from seqeval.metrics import classification_report, fa_score, precision_score, recall_score
from utils_ner import Split, TFTokenClassificationDataset, TokenClassificationTask

from transformers import (
    AutoConfig,
    AutoTokenizer,
    EvalPrediction,
    HfArgumentParser,
    TFAutoModelForTokenClassification,
    TFTrainer,
    TFTrainingArguments,
)
from transformers.utils import logging as hf_logging


hf_logging.set_verbosity_info()
hf_logging.enable_default_handler()
hf_logging.enable_explicit_format()


A_		:  int										=  logging.getLogger(__name__)



@dataclass
class 		_lowercase		:
   _UpperCAmelCase  						=      field(
       metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''}						)
   _UpperCAmelCase  						=      field(
       default=UpperCAmelCase__, metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''}						)
   _UpperCAmelCase  						=      field(
       default='''NER''', metadata={'''help''': '''Task type to fine tune in training (e.g. NER, POS, etc)'''}						)
   _UpperCAmelCase  						=      field(
       default=UpperCAmelCase__, metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''}						)
   _UpperCAmelCase  						=      field(default=UpperCAmelCase__, metadata={'''help''': '''Set this flag to use fast tokenization.'''}						)
   # If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
   # or just modify its tokenizer_config.json.
   _UpperCAmelCase  						=      field(
       default=UpperCAmelCase__, metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''}, )



@dataclass
class 		_lowercase		:
   _UpperCAmelCase  						=      field(
       metadata={'''help''': '''The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task.'''}						)
   _UpperCAmelCase  						=      field(
       metadata={'''help''': '''Path to a file containing all labels. If not specified, CoNLL-2003 labels are used.'''}						)
   _UpperCAmelCase  						=      field(
       default=128, metadata={
           '''help''': (
               '''The maximum total input sequence length after tokenization. Sequences longer '''
               '''than this will be truncated, sequences shorter will be padded.'''
           )
       }, )
   _UpperCAmelCase  						=      field(
       default=UpperCAmelCase__, metadata={'''help''': '''Overwrite the cached training and evaluation sets'''}						)


def 						UpperCAmelCase__   (   ):






 '''simple docstring'''

 a							=		HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)       )
 a		,	a		,	a							=		parser.parse_args_into_dataclasses()

 if (
     os.path.exists(training_args.output_dir       )
     and os.listdir(training_args.output_dir       )
     and training_args.do_train
     and not training_args.overwrite_output_dir
 ):
  raise ValueError(
      F"""Output directory ({training_args.output_dir}) already exists and is not empty. Use"""
      " --overwrite_output_dir to overcome."       )

 a							=		import_module("tasks"       )

 try:
  a							=		getattr(UpperCAmelCase__			,       model_args.task_type       )
  a							=		token_classification_task_clazz()
 except AttributeError:
  raise ValueError(
      F"""Task {model_args.task_type} needs to be defined as a TokenClassificationTask subclass in {module}. """
      F"""Available tasks classes are: {TokenClassificationTask.__subclasses__()}"""       )

 # Setup logging
 logging.basicConfig(
     format="%(asctime)s - %(levelname)s - %(name)s - %(message)s"			,       datefmt="%m/%d/%Y %H:%M:%S"			,       level=logging.INFO			,       )
 logger.info(
     "n_replicas: %s, distributed training: %s, 16-bits training: %s"			,       training_args.n_replicas			,       bool(training_args.n_replicas > 1       )			,       training_args.fpaa			,       )
 logger.info("Training/evaluation parameters %s"			,       UpperCAmelCase__       )

 # Prepare Token Classification task
 a							=		token_classification_task.get_labels(data_args.labels       )
 a							=		dict(enumerate(UpperCAmelCase__       )       )
 a							=		len(UpperCAmelCase__       )

 # Load pretrained model and tokenizer
 #
 # Distributed training:
 # The .from_pretrained methods guarantee that only one local process can concurrently
 # download model & vocab.

 a							=		AutoConfig.from_pretrained(
     model_args.config_name if model_args.config_name else model_args.model_name_or_path			,       num_labels=UpperCAmelCase__			,       idalabel=UpperCAmelCase__			,       labelaid={label: i for i, label in enumerate(UpperCAmelCase__       )}			,       cache_dir=model_args.cache_dir			,       )
 a							=		AutoTokenizer.from_pretrained(
     model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path			,       cache_dir=model_args.cache_dir			,       use_fast=model_args.use_fast			,       )

 with training_args.strategy.scope():
  a							=		TFAutoModelForTokenClassification.from_pretrained(
      model_args.model_name_or_path			,       from_pt=bool(".bin" in model_args.model_name_or_path       )			,       config=UpperCAmelCase__			,       cache_dir=model_args.cache_dir			,       )

 # Get datasets
 a							=		(
     TFTokenClassificationDataset(
         token_classification_task=UpperCAmelCase__			,       data_dir=data_args.data_dir			,       tokenizer=UpperCAmelCase__			,       labels=UpperCAmelCase__			,       model_type=config.model_type			,       max_seq_length=data_args.max_seq_length			,       overwrite_cache=data_args.overwrite_cache			,       mode=Split.train			,       )
     if training_args.do_train
     else None
 )
 a							=		(
     TFTokenClassificationDataset(
         token_classification_task=UpperCAmelCase__			,       data_dir=data_args.data_dir			,       tokenizer=UpperCAmelCase__			,       labels=UpperCAmelCase__			,       model_type=config.model_type			,       max_seq_length=data_args.max_seq_length			,       overwrite_cache=data_args.overwrite_cache			,       mode=Split.dev			,       )
     if training_args.do_eval
     else None
 )

 def align_predictions(UpperCAmelCase__			:np.ndarray			,       UpperCAmelCase__			:np.ndarray       ) -> Tuple[List[int], List[int]]:
  a							=		np.argmax(UpperCAmelCase__			,       axis=2       )
  a		,	a							=		preds.shape
  a							=		[[] for _ in range(UpperCAmelCase__       )]
  a							=		[[] for _ in range(UpperCAmelCase__       )]

  for i in range(UpperCAmelCase__       ):
   for j in range(UpperCAmelCase__       ):
    if label_ids[i, j] != -1_00:
     out_label_list[i].append(label_map[label_ids[i][j]]       )
     preds_list[i].append(label_map[preds[i][j]]       )

  return preds_list, out_label_list

 def compute_metrics(UpperCAmelCase__			:EvalPrediction       ) -> Dict:
  a		,	a							=		align_predictions(p.predictions			,       p.label_ids       )

  return {
      "precision": precision_score(UpperCAmelCase__			,       UpperCAmelCase__       ),
      "recall": recall_score(UpperCAmelCase__			,       UpperCAmelCase__       ),
      "f1": fa_score(UpperCAmelCase__			,       UpperCAmelCase__       ),
  }

 # Initialize our Trainer
 a							=		TFTrainer(
     model=UpperCAmelCase__			,       args=UpperCAmelCase__			,       train_dataset=train_dataset.get_dataset() if train_dataset else None			,       eval_dataset=eval_dataset.get_dataset() if eval_dataset else None			,       compute_metrics=UpperCAmelCase__			,       )

 # Training
 if training_args.do_train:
  trainer.train()
  trainer.save_model()
  tokenizer.save_pretrained(training_args.output_dir       )

 # Evaluation
 a							=		{}
 if training_args.do_eval:
  logger.info("*** Evaluate ***"       )

  a							=		trainer.evaluate()
  a							=		os.path.join(training_args.output_dir			,       "eval_results.txt"       )

  with open(UpperCAmelCase__			,       "w"       ) as writer:
   logger.info("***** Eval results *****"       )

   for key, value in result.items():
    logger.info("  %s = %s"			,       UpperCAmelCase__			,       UpperCAmelCase__       )
    writer.write("%s = %s\n" % (key, value)       )

   results.update(UpperCAmelCase__       )

    # Predict
 if training_args.do_predict:
  a							=		TFTokenClassificationDataset(
      token_classification_task=UpperCAmelCase__			,       data_dir=data_args.data_dir			,       tokenizer=UpperCAmelCase__			,       labels=UpperCAmelCase__			,       model_type=config.model_type			,       max_seq_length=data_args.max_seq_length			,       overwrite_cache=data_args.overwrite_cache			,       mode=Split.test			,       )

  a		,	a		,	a							=		trainer.predict(test_dataset.get_dataset()       )
  a		,	a							=		align_predictions(UpperCAmelCase__			,       UpperCAmelCase__       )
  a							=		classification_report(UpperCAmelCase__			,       UpperCAmelCase__       )

  logger.info("\n%s"			,       UpperCAmelCase__       )

  a							=		os.path.join(training_args.output_dir			,       "test_results.txt"       )

  with open(UpperCAmelCase__			,       "w"       ) as writer:
   writer.write("%s\n" % report       )

  # Save predictions
  a							=		os.path.join(training_args.output_dir			,       "test_predictions.txt"       )

  with open(UpperCAmelCase__			,       "w"       ) as writer:
   with open(os.path.join(data_args.data_dir			,       "test.txt"       )			,       "r"       ) as f:
    a							=		0

    for line in f:
     if line.startswith("-DOCSTART-"       ) or line == "" or line == "\n":
      writer.write(UpperCAmelCase__       )

      if not preds_list[example_id]:
       example_id += 1
     elif preds_list[example_id]:
      a							=		line.split()[0] + " " + preds_list[example_id].pop(0       ) + "\n"

      writer.write(UpperCAmelCase__       )
     else:
      logger.warning("Maximum sequence length exceeded: No prediction for '%s'."			,       line.split()[0]       )

 return results


if __name__ == "__main__":
     main()






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:
          A_		:  List[Any]										=  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)]
                    )
                    A_		:  List[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 		_lowercase		:
   _UpperCAmelCase  						=      None
   _UpperCAmelCase  						=      ()
   _UpperCAmelCase  						=      ()
   _UpperCAmelCase  						=      True
   _UpperCAmelCase  						=      True
   _UpperCAmelCase  						=      True
   _UpperCAmelCase  						=      False

   def 		A				(   self      :							Dict				, __lowerCAmelCase      :							List[str]				, __lowerCAmelCase      :							List[Any]				, __lowerCAmelCase      :							Union[str, Any]=False				)							->					dict:


    """simple docstring"""






    a							=		copy.deepcopy(__lowerCAmelCase				)

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

   @slow
   def 		A				(   self      :							List[str]				)							->					str:


    """simple docstring"""






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

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

     a							=		run_in_graph_mode()
     self.assertIsNotNone(__lowerCAmelCase				)

   @slow
   def 		A				(   self      :							Optional[int]				)							->					Union[str, Any]:


    """simple docstring"""






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

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

     a							=		run_in_graph_mode()
     self.assertIsNotNone(__lowerCAmelCase				)

   @slow
   def 		A				(   self      :							str				)							->					List[Any]:


    """simple docstring"""






    a		,	a							=		self.model_tester.prepare_config_and_inputs_for_common()
    for model_class in self.all_model_classes[:2]:
     a							=		model_class(__lowerCAmelCase				)
     if getattr(__lowerCAmelCase				, "hf_compute_loss"				, __lowerCAmelCase				):
      # Test that model correctly compute the loss with kwargs
      a							=		self._prepare_for_class(inputs_dict.copy()				, __lowerCAmelCase				, return_labels=__lowerCAmelCase				)
      # Is there a better way to remove these decoder inputs?
      a							=		{
          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")
      }

      a							=		{
          "labels",
          "label",
          "label_ids",
          "start_positions",
          "start_position",
          "end_positions",
          "end_position",
          "next_sentence_label",
      }
      a							=		possible_label_cols.intersection(set(__lowerCAmelCase				)				)
      self.assertGreater(len(__lowerCAmelCase				)				, 0				, msg="No matching label names found!"				)
      a							=		{key: val for key, val in prepared_for_class.items() if key in label_names}
      a							=		{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
      a							=		model.fit(
          __lowerCAmelCase				, validation_data=__lowerCAmelCase				, steps_per_epoch=1				, validation_steps=1				, shuffle=__lowerCAmelCase				, verbose=0				, )
      a							=		history.history["loss"][0]
      self.assertTrue(not isnan(__lowerCAmelCase				)				)
      a							=		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.
      a							=		model_class(__lowerCAmelCase				)
      model.compile(optimizer=tf.keras.optimizers.SGD(0.0				)				, jit_compile=__lowerCAmelCase				)
      a							=		model.fit(
          __lowerCAmelCase				, __lowerCAmelCase				, validation_data=(inputs_minus_labels, labels)				, steps_per_epoch=1				, validation_steps=1				, shuffle=__lowerCAmelCase				, verbose=0				, )

      a							=		history.history["loss"][0]
      self.assertTrue(not isnan(__lowerCAmelCase				)				)
      a							=		history.history["val_loss"][0]
      self.assertTrue(not isnan(__lowerCAmelCase				)				)

   @slow
   def 		A				(   self      :							int				)							->					List[str]:


    """simple docstring"""






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

    if hasattr(__lowerCAmelCase				, "use_cache"				):
     a							=		True

    a							=		getattr(self.model_tester				, "encoder_seq_length"				, self.model_tester.seq_length				)
    a							=		getattr(self.model_tester				, "key_length"				, __lowerCAmelCase				)

    for model_class in self.all_model_classes[:2]:
     a							=		self._prepare_for_class(__lowerCAmelCase				, __lowerCAmelCase				)
     a							=		model_class(__lowerCAmelCase				)
     model.build()
     a							=		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:
       a							=		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				)
      a							=		os.path.join(__lowerCAmelCase				, "saved_model"				, "1"				)
      a							=		tf.keras.models.load_model(__lowerCAmelCase				)
      a							=		model(__lowerCAmelCase				)

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

      self.assertEqual(len(__lowerCAmelCase				)				, __lowerCAmelCase				)

      a							=		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 		A				(   self      :							Optional[int]				)							->					Tuple:


    """simple docstring"""






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

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

      self.assertIsNotNone(__lowerCAmelCase				)
    finally:
     tf.keras.mixed_precision.set_global_policy("float32"				)

   @slow
   def 		A				(   self      :							Optional[int]				)							->					Dict:


    """simple docstring"""






    a		,	a							=		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"]
    a							=		{
        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
      a							=		TFSharedEmbeddings(self.model_tester.vocab_size				, self.model_tester.hidden_size				, name="shared"				)
      a							=		False
      a							=		main_layer_class(__lowerCAmelCase				, embed_tokens=__lowerCAmelCase				)
     else:
      a							=		main_layer_class(__lowerCAmelCase				)

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

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

     a							=		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				)

     a							=		main_layer(__lowerCAmelCase				)[0]
     a							=		tf.keras.layers.Dense(__lowerCAmelCase				, activation="softmax"				, name="outputs"				)(__lowerCAmelCase				)
     a							=		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:
      a							=		os.path.join(__lowerCAmelCase				, "keras_model.h5"				)
      model.save(__lowerCAmelCase				)
      if "T5" in main_layer_class.__name__:
       a							=		tf.keras.models.load_model(
           __lowerCAmelCase				, custom_objects={
               main_layer_class.__name__: main_layer_class,
               "TFSharedEmbeddings": TFSharedEmbeddings,
           }				, )
      else:
       a							=		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 		A				(   self      :							Any				)							->					Union[str, Any]:


    """simple docstring"""






    a		,	a							=		self.model_tester.prepare_config_and_inputs_for_common()

    for model_class in self.all_model_classes[:2]:
     a							=		model_class(__lowerCAmelCase				)

     a							=		copy.deepcopy(__lowerCAmelCase				)

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

     if not self.is_encoder_decoder:
      a							=		model.get_input_embeddings()(__lowerCAmelCase				)
     else:
      a							=		model.get_input_embeddings()(__lowerCAmelCase				)
      a							=		model.get_input_embeddings()(__lowerCAmelCase				)

     a							=		self._prepare_for_class(__lowerCAmelCase				, __lowerCAmelCase				)

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

     a							=		run_in_graph_mode()
     self.assertIsNotNone(__lowerCAmelCase				)

   def 		A				(   self      :							Union[str, Any]				, __lowerCAmelCase      :							Dict				, __lowerCAmelCase      :							List[str]				)							->					List[str]:


    """simple docstring"""






    a							=		[]
    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
    a							=		[]
    while len(__lowerCAmelCase				) < num_bad_tokens:
     a							=		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 		A				(   self      :							int				, __lowerCAmelCase      :							int				)							->					int:


    """simple docstring"""






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




   def 		A				(   self      :							Optional[Any]				, __lowerCAmelCase      :							List[str]				, __lowerCAmelCase      :							Any				)							->					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(__lowerCAmelCase				)				, len(__lowerCAmelCase				)				):
       # if tokens match
       if generated_ids_slice[i - len(__lowerCAmelCase				) : i] == bad_word_ids:
        return True
    return False

