



from collections import Counter

import sacrebleu
import sacremoses
from packaging import version

import datasets


A_		:  str										=  '''\
@inproceedings{xu-etal-2016-optimizing,
title = {Optimizing Statistical Machine Translation for Text Simplification},
authors={Xu, Wei and Napoles, Courtney and Pavlick, Ellie and Chen, Quanze and Callison-Burch, Chris},
journal = {Transactions of the Association for Computational Linguistics},
volume = {4},
year={2016},
url = {https://www.aclweb.org/anthology/Q16-1029},
pages = {401--415},
}
'''

A_		:  Optional[int]										=  '''\
SARI is a metric used for evaluating automatic text simplification systems.
The metric compares the predicted simplified sentences against the reference
and the source sentences. It explicitly measures the goodness of words that are
added, deleted and kept by the system.
Sari = (F1_add + F1_keep + P_del) / 3
where
F1_add: n-gram F1 score for add operation
F1_keep: n-gram F1 score for keep operation
P_del: n-gram precision score for delete operation
n = 4, as in the original paper.

This implementation is adapted from Tensorflow\'s tensor2tensor implementation [3].
It has two differences with the original GitHub [1] implementation:
  (1) Defines 0/0=1 instead of 0 to give higher scores for predictions that match
      a target exactly.
  (2) Fixes an alleged bug [2] in the keep score computation.
[1] https://github.com/cocoxu/simplification/blob/master/SARI.py
    (commit 0210f15)
[2] https://github.com/cocoxu/simplification/issues/6
[3] https://github.com/tensorflow/tensor2tensor/blob/master/tensor2tensor/utils/sari_hook.py
'''


A_		:  int										=  '''
Calculates sari score (between 0 and 100) given a list of source and predicted
sentences, and a list of lists of reference sentences.
Args:
    sources: list of source sentences where each sentence should be a string.
    predictions: list of predicted sentences where each sentence should be a string.
    references: list of lists of reference sentences where each sentence should be a string.
Returns:
    sari: sari score
Examples:
    >>> sources=["About 95 species are currently accepted ."]
    >>> predictions=["About 95 you now get in ."]
    >>> references=[["About 95 species are currently known .","About 95 species are now accepted .","95 species are now accepted ."]]
    >>> sari = datasets.load_metric("sari")
    >>> results = sari.compute(sources=sources, predictions=predictions, references=references)
    >>> print(results)
    {\'sari\': 26.953601953601954}
'''


def 						UpperCAmelCase__   (   UpperCAmelCase__			:Tuple			,       UpperCAmelCase__			:Union[str, Any]			,       UpperCAmelCase__			:str			,       UpperCAmelCase__			:str       ):






 '''simple docstring'''

 a							=		[rgram for rgrams in rgramslist for rgram in rgrams]
 a							=		Counter(UpperCAmelCase__       )

 a							=		Counter(UpperCAmelCase__       )
 a							=		Counter()
 for sgram, scount in sgramcounter.items():
  a							=		scount * numref

 a							=		Counter(UpperCAmelCase__       )
 a							=		Counter()
 for cgram, ccount in cgramcounter.items():
  a							=		ccount * numref

 # KEEP
 a							=		sgramcounter_rep & cgramcounter_rep
 a							=		keepgramcounter_rep & rgramcounter
 a							=		sgramcounter_rep & rgramcounter

 a							=		0
 a							=		0
 for keepgram in keepgramcountergood_rep:
  keeptmpscorea += keepgramcountergood_rep[keepgram] / keepgramcounter_rep[keepgram]
  # Fix an alleged bug [2] in the keep score computation.
  # keeptmpscore2 += keepgramcountergood_rep[keepgram] / keepgramcounterall_rep[keepgram]
  keeptmpscorea += keepgramcountergood_rep[keepgram]
 # Define 0/0=1 instead of 0 to give higher scores for predictions that match
 #      a target exactly.
 a							=		1
 a							=		1
 if len(UpperCAmelCase__       ) > 0:
  a							=		keeptmpscorea / len(UpperCAmelCase__       )
 if len(UpperCAmelCase__       ) > 0:
  # Fix an alleged bug [2] in the keep score computation.
  # keepscore_recall = keeptmpscore2 / len(keepgramcounterall_rep)
  a							=		keeptmpscorea / sum(keepgramcounterall_rep.values()       )
 a							=		0
 if keepscore_precision > 0 or keepscore_recall > 0:
  a							=		2 * keepscore_precision * keepscore_recall / (keepscore_precision + keepscore_recall)

 # DELETION
 a							=		sgramcounter_rep - cgramcounter_rep
 a							=		delgramcounter_rep - rgramcounter
 a							=		sgramcounter_rep - rgramcounter
 a							=		0
 a							=		0
 for delgram in delgramcountergood_rep:
  deltmpscorea += delgramcountergood_rep[delgram] / delgramcounter_rep[delgram]
  deltmpscorea += delgramcountergood_rep[delgram] / delgramcounterall_rep[delgram]
 # Define 0/0=1 instead of 0 to give higher scores for predictions that match
 # a target exactly.
 a							=		1
 if len(UpperCAmelCase__       ) > 0:
  a							=		deltmpscorea / len(UpperCAmelCase__       )

 # ADDITION
 a							=		set(UpperCAmelCase__       ) - set(UpperCAmelCase__       )
 a							=		set(UpperCAmelCase__       ) & set(UpperCAmelCase__       )
 a							=		set(UpperCAmelCase__       ) - set(UpperCAmelCase__       )

 a							=		0
 for addgram in addgramcountergood:
  addtmpscore += 1

 # Define 0/0=1 instead of 0 to give higher scores for predictions that match
 # a target exactly.
 a							=		1
 a							=		1
 if len(UpperCAmelCase__       ) > 0:
  a							=		addtmpscore / len(UpperCAmelCase__       )
 if len(UpperCAmelCase__       ) > 0:
  a							=		addtmpscore / len(UpperCAmelCase__       )
 a							=		0
 if addscore_precision > 0 or addscore_recall > 0:
  a							=		2 * addscore_precision * addscore_recall / (addscore_precision + addscore_recall)

 return (keepscore, delscore_precision, addscore)


def 						UpperCAmelCase__   (   UpperCAmelCase__			:Union[str, Any]			,       UpperCAmelCase__			:Any			,       UpperCAmelCase__			:Tuple       ):






 '''simple docstring'''

 a							=		len(UpperCAmelCase__       )

 a							=		ssent.split(" "       )
 a							=		csent.split(" "       )
 a							=		[]
 a							=		[]
 a							=		[]
 a							=		[]
 a							=		[]
 a							=		[]

 a							=		[]
 a							=		[]
 a							=		[]
 a							=		[]
 for rsent in rsents:
  a							=		rsent.split(" "       )
  a							=		[]
  a							=		[]
  a							=		[]
  ragramslist.append(UpperCAmelCase__       )
  for i in range(0			,       len(UpperCAmelCase__       ) - 1       ):
   if i < len(UpperCAmelCase__       ) - 1:
    a							=		ragrams[i] + " " + ragrams[i + 1]
    ragrams.append(UpperCAmelCase__       )
   if i < len(UpperCAmelCase__       ) - 2:
    a							=		ragrams[i] + " " + ragrams[i + 1] + " " + ragrams[i + 2]
    ragrams.append(UpperCAmelCase__       )
   if i < len(UpperCAmelCase__       ) - 3:
    a							=		ragrams[i] + " " + ragrams[i + 1] + " " + ragrams[i + 2] + " " + ragrams[i + 3]
    ragrams.append(UpperCAmelCase__       )
  ragramslist.append(UpperCAmelCase__       )
  ragramslist.append(UpperCAmelCase__       )
  ragramslist.append(UpperCAmelCase__       )

 for i in range(0			,       len(UpperCAmelCase__       ) - 1       ):
  if i < len(UpperCAmelCase__       ) - 1:
   a							=		sagrams[i] + " " + sagrams[i + 1]
   sagrams.append(UpperCAmelCase__       )
  if i < len(UpperCAmelCase__       ) - 2:
   a							=		sagrams[i] + " " + sagrams[i + 1] + " " + sagrams[i + 2]
   sagrams.append(UpperCAmelCase__       )
  if i < len(UpperCAmelCase__       ) - 3:
   a							=		sagrams[i] + " " + sagrams[i + 1] + " " + sagrams[i + 2] + " " + sagrams[i + 3]
   sagrams.append(UpperCAmelCase__       )

 for i in range(0			,       len(UpperCAmelCase__       ) - 1       ):
  if i < len(UpperCAmelCase__       ) - 1:
   a							=		cagrams[i] + " " + cagrams[i + 1]
   cagrams.append(UpperCAmelCase__       )
  if i < len(UpperCAmelCase__       ) - 2:
   a							=		cagrams[i] + " " + cagrams[i + 1] + " " + cagrams[i + 2]
   cagrams.append(UpperCAmelCase__       )
  if i < len(UpperCAmelCase__       ) - 3:
   a							=		cagrams[i] + " " + cagrams[i + 1] + " " + cagrams[i + 2] + " " + cagrams[i + 3]
   cagrams.append(UpperCAmelCase__       )

 ((a)		,	(a)		,	(a))							=		SARIngram(UpperCAmelCase__			,       UpperCAmelCase__			,       UpperCAmelCase__			,       UpperCAmelCase__       )
 ((a)		,	(a)		,	(a))							=		SARIngram(UpperCAmelCase__			,       UpperCAmelCase__			,       UpperCAmelCase__			,       UpperCAmelCase__       )
 ((a)		,	(a)		,	(a))							=		SARIngram(UpperCAmelCase__			,       UpperCAmelCase__			,       UpperCAmelCase__			,       UpperCAmelCase__       )
 ((a)		,	(a)		,	(a))							=		SARIngram(UpperCAmelCase__			,       UpperCAmelCase__			,       UpperCAmelCase__			,       UpperCAmelCase__       )
 a							=		sum([keepascore, keepascore, keepascore, keepascore]       ) / 4
 a							=		sum([delascore, delascore, delascore, delascore]       ) / 4
 a							=		sum([addascore, addascore, addascore, addascore]       ) / 4
 a							=		(avgkeepscore + avgdelscore + avgaddscore) / 3
 return finalscore


def 						UpperCAmelCase__   (   UpperCAmelCase__			:Optional[Any]			,       UpperCAmelCase__			:bool = True			,       UpperCAmelCase__			:str = "13a"			,       UpperCAmelCase__			:bool = True       ):






 '''simple docstring'''

 if lowercase:
  a							=		sentence.lower()

 if tokenizer in ["13a", "intl"]:
  if version.parse(sacrebleu.__version__       ).major >= 2:
   a							=		sacrebleu.metrics.bleu._get_tokenizer(UpperCAmelCase__       )()(UpperCAmelCase__       )
  else:
   a							=		sacrebleu.TOKENIZERS[tokenizer]()(UpperCAmelCase__       )
 elif tokenizer == "moses":
  a							=		sacremoses.MosesTokenizer().tokenize(UpperCAmelCase__			,       return_str=UpperCAmelCase__			,       escape=UpperCAmelCase__       )
 elif tokenizer == "penn":
  a							=		sacremoses.MosesTokenizer().penn_tokenize(UpperCAmelCase__			,       return_str=UpperCAmelCase__       )
 else:
  a							=		sentence

 if not return_str:
  a							=		normalized_sent.split()

 return normalized_sent




@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION, _KWARGS_DESCRIPTION						)
class 		_lowercase		(							datasets.Metric						):

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


    """simple docstring"""






    return datasets.MetricInfo(
        description=_DESCRIPTION				, citation=_CITATION				, inputs_description=_KWARGS_DESCRIPTION				, features=datasets.Features(
            {
                "sources": datasets.Value("string"				, id="sequence"				),
                "predictions": datasets.Value("string"				, id="sequence"				),
                "references": datasets.Sequence(datasets.Value("string"				, id="sequence"				)				, id="references"				),
            }				)				, codebase_urls=[
            "https://github.com/cocoxu/simplification/blob/master/SARI.py",
            "https://github.com/tensorflow/tensor2tensor/blob/master/tensor2tensor/utils/sari_hook.py",
        ]				, reference_urls=["https://www.aclweb.org/anthology/Q16-1029.pdf"]				, )




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


    """simple docstring"""






    if not (len(__lowerCAmelCase				) == len(__lowerCAmelCase				) == len(__lowerCAmelCase				)):
     raise ValueError("Sources length must match predictions and references lengths."				)
    a							=		0
    for src, pred, refs in zip(__lowerCAmelCase				, __lowerCAmelCase				, __lowerCAmelCase				):
     sari_score += SARIsent(normalize(__lowerCAmelCase				)				, normalize(__lowerCAmelCase				)				, [normalize(__lowerCAmelCase				) for sent in refs]				)
    a							=		sari_score / len(__lowerCAmelCase				)
    return {"sari": 100 * sari_score}






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

