



"""simple docstring"""




import json
import os
import re
import warnings
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union

import sentencepiece

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


_UpperCamelCase:					str     				=	logging.get_logger(__name__)

_UpperCamelCase:					int     				=	{
    'source_spm': 'source.spm',
    'target_spm': 'target.spm',
    'vocab': 'vocab.json',
    'target_vocab_file': 'target_vocab.json',
    'tokenizer_config_file': 'tokenizer_config.json',
}

_UpperCamelCase:					Tuple     				=	{
    'source_spm': {
        'Helsinki-NLP/opus-mt-en-de': 'https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/source.spm'
    },
    'target_spm': {
        'Helsinki-NLP/opus-mt-en-de': 'https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/target.spm'
    },
    'vocab': {
        'Helsinki-NLP/opus-mt-en-de': 'https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/vocab.json'
    },
    'tokenizer_config_file': {
        'Helsinki-NLP/opus-mt-en-de': (
            'https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/tokenizer_config.json'
        )
    },
}

_UpperCamelCase:					Optional[int]     				=	{'Helsinki-NLP/opus-mt-en-de': 5_1_2}
_UpperCamelCase:					Tuple     				=	{}


class 	a__		(    SCREAMING_SNAKE_CASE__  ):
   _lowerCamelCase       				=					VOCAB_FILES_NAMES
   _lowerCamelCase       				=					PRETRAINED_VOCAB_FILES_MAP
   _lowerCamelCase       				=					PRETRAINED_INIT_CONFIGURATION
   _lowerCamelCase       				=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   _lowerCamelCase       				=					['input_ids', 'attention_mask']
   _lowerCamelCase       				=					re.compile('>>.+<<'  )  # type: re.Pattern


   def __init__(				self   :				Optional[Any],  lowerCAmelCase   :				Tuple,  lowerCAmelCase   :				Tuple,  lowerCAmelCase   :				Any,  lowerCAmelCase   :				Optional[Any]=None,  lowerCAmelCase   :				str=None,  lowerCAmelCase   :				Dict=None,  lowerCAmelCase   :				Union[str, Any]="<unk>",  lowerCAmelCase   :				Optional[Any]="</s>",  lowerCAmelCase   :				Dict="<pad>",  lowerCAmelCase   :				Dict=512,  lowerCAmelCase   :				Optional[Dict[str, Any]] = None,  lowerCAmelCase   :				Any=False,  **lowerCAmelCase   :				int,  )     ->							None:
         lowercase	:   List[Any]											= {} if sp_model_kwargs is None else sp_model_kwargs

         super().__init__(
             # bos_token=bos_token,  unused. Start decoding with config.decoder_start_token_id
             source_lang=lowerCAmelCase,  target_lang=lowerCAmelCase,  unk_token=lowerCAmelCase,  eos_token=lowerCAmelCase,  pad_token=lowerCAmelCase,  model_max_length=lowerCAmelCase,  sp_model_kwargs=self.sp_model_kwargs,  target_vocab_file=lowerCAmelCase,  separate_vocabs=lowerCAmelCase,  **lowerCAmelCase,  )
         assert Path(lowerCAmelCase      ).exists(), f'''cannot find spm source {source_spm}'''

         lowercase	:   List[str]											= separate_vocabs
         lowercase	:   Optional[Any]											= load_json(lowerCAmelCase      )
         if self.unk_token not in self.encoder:
               raise KeyError('<unk> token must be in vocab'      )
         assert self.pad_token in self.encoder

         if separate_vocabs:
               lowercase	:   Optional[Any]											= load_json(lowerCAmelCase      )
               lowercase	:   Optional[Any]											= {v: k for k, v in self.target_encoder.items()}
               lowercase	:   List[str]											= []
         else:
               lowercase	:   Optional[int]											= {v: k for k, v in self.encoder.items()}
               lowercase	:   list											= [k for k in self.encoder if k.startswith('>>'      ) and k.endswith('<<'      )]

         lowercase	:   List[str]											= source_lang
         lowercase	:   Optional[Any]											= target_lang
         lowercase	:   Any											= [source_spm, target_spm]

         # load SentencePiece model for pre-processing
         lowercase	:   Union[str, Any]											= load_spm(lowerCAmelCase,  self.sp_model_kwargs      )
         lowercase	:   int											= load_spm(lowerCAmelCase,  self.sp_model_kwargs      )
         lowercase	:   Any											= self.spm_source
         lowercase	:   str											= self.encoder

         # Multilingual target side: default to using first supported language code.

         self._setup_normalizer()


   def 							lowercase					(				self   :				List[Any]      )     ->							int:
         try:
               from sacremoses import MosesPunctNormalizer

               lowercase	:   Any											= MosesPunctNormalizer(self.source_lang      ).normalize
         except (ImportError, FileNotFoundError):
               warnings.warn('Recommended: pip install sacremoses.'      )
               lowercase	:   Optional[Any]											= lambda lowerCAmelCase      : x


   def 							lowercase					(				self   :				List[Any],  lowerCAmelCase   :				str      )     ->							str:
         return self.punc_normalizer(lowerCAmelCase      ) if x else ""


   def 							lowercase					(				self   :				Dict,  lowerCAmelCase   :				Tuple      )     ->							List[Any]:
         return self.current_encoder.get(lowerCAmelCase,  self.current_encoder[self.unk_token]      )


   def 							lowercase					(				self   :				Optional[int],  lowerCAmelCase   :				str      )     ->							List[Any]:
         lowercase	:   Any											= self.language_code_re.match(lowerCAmelCase      )
         lowercase	:   list											= [match.group(0      )] if match else []
         return code, self.language_code_re.sub('',  lowerCAmelCase      )


   def 							lowercase					(				self   :				Optional[Any],  lowerCAmelCase   :				str      )     ->							List[str]:
         lowercase						,     lowercase	:   Any											= self.remove_language_code(lowerCAmelCase      )
         lowercase	:   Any											= self.current_spm.encode(lowerCAmelCase,  out_type=lowerCAmelCase      )
         return code + pieces


   def 							lowercase					(				self   :				List[Any],  lowerCAmelCase   :				int      )     ->							str:
         return self.decoder.get(lowerCAmelCase,  self.unk_token      )


   def 							lowercase					(				self   :				List[Any],  lowerCAmelCase   :				str,  **lowerCAmelCase   :				Dict      )     ->							List[Any]:
         return super().batch_decode(lowerCAmelCase,  **lowerCAmelCase      )


   def 							lowercase					(				self   :				str,  lowerCAmelCase   :				Any,  **lowerCAmelCase   :				Optional[Any]      )     ->							str:
         return super().decode(lowerCAmelCase,  **lowerCAmelCase      )


   def 							lowercase					(				self   :				Optional[int],  lowerCAmelCase   :				List[str]      )     ->							str:
         lowercase	:   Any											= self.spm_source if self._decode_use_source_tokenizer else self.spm_target
         lowercase	:   str											= []
         lowercase	:   Optional[Any]											= ''
         for token in tokens:
               # make sure that special tokens are not decoded using sentencepiece model
               if token in self.all_special_tokens:
                     out_string += sp_model.decode_pieces(lowerCAmelCase      ) + token + " "
                     lowercase	:   int											= []
               else:
                     current_sub_tokens.append(lowerCAmelCase      )
         out_string += sp_model.decode_pieces(lowerCAmelCase      )
         return out_string.strip()


   def 							lowercase					(				self   :				Optional[Any],  lowerCAmelCase   :				str,  lowerCAmelCase   :				Any=None      )     ->							List[int]:
         if token_ids_a is None:
               return token_ids_a + [self.eos_token_id]
         # We don't expect to process pairs, but leave the pair logic for API consistency
         return token_ids_a + token_ids_a + [self.eos_token_id]


   def 							lowercase					(				self   :				Optional[int]      )     ->							int:
         lowercase	:   List[str]											= self.spm_source
         lowercase	:   Optional[Any]											= self.encoder


   def 							lowercase					(				self   :				Dict      )     ->							Tuple:
         lowercase	:   Dict											= self.spm_target
         if self.separate_vocabs:
               lowercase	:   Optional[Any]											= self.target_encoder


   @property
   def 							lowercase					(				self   :				Optional[int]      )     ->							int:
         return len(self.encoder      )


   def 							lowercase					(				self   :				Tuple,  lowerCAmelCase   :				str,  lowerCAmelCase   :				Optional[str] = None      )     ->							Tuple[str]:
         if not os.path.isdir(lowerCAmelCase      ):
               logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''      )
               return
         lowercase	:   List[str]											= []

         if self.separate_vocabs:
               lowercase	:   str											= os.path.join(
                   lowerCAmelCase,  (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab'],  )
               lowercase	:   str											= os.path.join(
                   lowerCAmelCase,  (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['target_vocab_file'],  )
               save_json(self.encoder,  lowerCAmelCase      )
               save_json(self.target_encoder,  lowerCAmelCase      )
               saved_files.append(lowerCAmelCase      )
               saved_files.append(lowerCAmelCase      )
         else:
               lowercase	:   str											= os.path.join(
                   lowerCAmelCase,  (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab']      )
               save_json(self.encoder,  lowerCAmelCase      )
               saved_files.append(lowerCAmelCase      )

         for spm_save_filename, spm_orig_path, spm_model in zip(
             [VOCAB_FILES_NAMES['source_spm'], VOCAB_FILES_NAMES['target_spm']],  self.spm_files,  [self.spm_source, self.spm_target],  ):
               lowercase	:   Optional[Any]											= os.path.join(
                   lowerCAmelCase,  (filename_prefix + '-' if filename_prefix else '') + spm_save_filename      )
               if os.path.abspath(lowerCAmelCase      ) != os.path.abspath(lowerCAmelCase      ) and os.path.isfile(lowerCAmelCase      ):
                     copyfile(lowerCAmelCase,  lowerCAmelCase      )
                     saved_files.append(lowerCAmelCase      )
               elif not os.path.isfile(lowerCAmelCase      ):
                     with open(lowerCAmelCase,  'wb'      ) as fi:
                           lowercase	:   List[Any]											= spm_model.serialized_model_proto()
                           fi.write(lowerCAmelCase      )
                     saved_files.append(lowerCAmelCase      )

         return tuple(lowerCAmelCase      )


   def 							lowercase					(				self   :				Any      )     ->							Dict:
         return self.get_src_vocab()


   def 							lowercase					(				self   :				Tuple      )     ->							Optional[int]:
         return dict(self.encoder,  **self.added_tokens_encoder      )


   def 							lowercase					(				self   :				List[str]      )     ->							Optional[int]:
         return dict(self.target_encoder,  **self.added_tokens_decoder      )


   def __getstate__(				self   :				Dict      )     ->							Dict:
         lowercase	:   List[Any]											= self.__dict__.copy()
         state.update(
             {k: None for k in ['spm_source', 'spm_target', 'current_spm', 'punc_normalizer', 'target_vocab_file']}      )
         return state


   def __setstate__(				self   :				Union[str, Any],  lowerCAmelCase   :				Dict      )     ->							None:
         lowercase	:   int											= d

         # for backward compatibility
         if not hasattr(self,  'sp_model_kwargs'      ):
               lowercase	:   str											= {}

         lowercase						,     lowercase	:   List[Any]											= (load_spm(lowerCAmelCase,  self.sp_model_kwargs      ) for f in self.spm_files)
         lowercase	:   Tuple											= self.spm_source
         self._setup_normalizer()


   def 							lowercase					(				self   :				Any,  *lowerCAmelCase   :				Union[str, Any],  **lowerCAmelCase   :				Tuple      )     ->							Any:
         return 1


   def 							lowercase					(				self   :				Optional[Any],  lowerCAmelCase   :				Tuple      )     ->							Optional[Any]:
         lowercase	:   Union[str, Any]											= set(self.all_special_ids      )  # call it once instead of inside list comp
         all_special_ids.remove(self.unk_token_id      )  # <unk> is only sometimes special
         return [1 if x in all_special_ids else 0 for x in seq]






   def 							lowercase					(				self   :				Any,  lowerCAmelCase   :				List,  lowerCAmelCase   :				Optional[List] = None,  lowerCAmelCase   :				bool = False      )     ->							List[int]:
         if already_has_special_tokens:
               return self._special_token_mask(lowerCAmelCase      )
         elif token_ids_a is None:
               return self._special_token_mask(lowerCAmelCase      ) + [1]
         else:
               return self._special_token_mask(token_ids_a + token_ids_a      ) + [1]




def 	lowercase__  (      _UpperCAmelCase						,   _UpperCAmelCase					)  ->      sentencepiece.SentencePieceProcessor:






      '''simple docstring'''


      lowercase	:   Dict											= sentencepiece.SentencePieceProcessor(**_UpperCAmelCase					)
      spm.Load(_UpperCAmelCase					)
      return spm


def 	lowercase__  (      _UpperCAmelCase						,   _UpperCAmelCase					)  ->      None:






      '''simple docstring'''


      with open(_UpperCAmelCase						,   'w'					) as f:
            json.dump(_UpperCAmelCase						,   _UpperCAmelCase						,   indent=2					)





def 	lowercase__  (      _UpperCAmelCase					)  ->      Union[Dict, List]:






      '''simple docstring'''


      with open(_UpperCAmelCase						,   'r'					) as f:
            return json.load(_UpperCAmelCase					)





"""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:					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)]
                    )
                    _UpperCamelCase:					List[str]     				=	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__		:
   _lowerCamelCase       				=					None
   _lowerCamelCase       				=					()
   _lowerCamelCase       				=					()
   _lowerCamelCase       				=					True
   _lowerCamelCase       				=					True
   _lowerCamelCase       				=					True
   _lowerCamelCase       				=					False


   def 							lowercase					(				self   :				Dict,  lowerCAmelCase   :				Dict,  lowerCAmelCase   :				Optional[Any],  lowerCAmelCase   :				Optional[int]=False      )     ->							dict:
         lowercase	:   Tuple											= copy.deepcopy(lowerCAmelCase      )

         if model_class in get_values(lowerCAmelCase      ):
               lowercase	:   Union[str, Any]											= {
                   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	:   List[str]											= tf.ones(self.model_tester.batch_size,  dtype=tf.intaa      )
               elif model_class in get_values(lowerCAmelCase      ):
                     lowercase	:   Optional[Any]											= tf.zeros(self.model_tester.batch_size,  dtype=tf.intaa      )
                     lowercase	:   Union[str, Any]											= tf.zeros(self.model_tester.batch_size,  dtype=tf.intaa      )
               elif model_class in [
                   *get_values(lowerCAmelCase      ),
                   *get_values(lowerCAmelCase      ),
               ]:
                     lowercase	:   str											= tf.zeros(self.model_tester.batch_size,  dtype=tf.intaa      )
               elif model_class in get_values(lowerCAmelCase      ):
                     lowercase	:   str											= 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	:   Tuple											= tf.zeros(
                         (self.model_tester.batch_size, self.model_tester.seq_length),  dtype=tf.intaa      )
         return inputs_dict


   @slow
   def 							lowercase					(				self   :				int      )     ->							Union[str, Any]:
         lowercase						,     lowercase	:   Optional[int]											= self.model_tester.prepare_config_and_inputs_for_common()
         for model_class in self.all_model_classes[:2]:
               lowercase	:   List[str]											= self._prepare_for_class(lowerCAmelCase,  lowerCAmelCase      )
               lowercase	:   List[Any]											= model_class(lowerCAmelCase      )

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

               lowercase	:   Optional[Any]											= run_in_graph_mode()
               self.assertIsNotNone(lowerCAmelCase      )


   @slow
   def 							lowercase					(				self   :				List[str]      )     ->							Optional[int]:
         lowercase						,     lowercase	:   Tuple											= self.model_tester.prepare_config_and_inputs_for_common()
         for model_class in self.all_model_classes[:2]:
               lowercase	:   Optional[Any]											= self._prepare_for_class(lowerCAmelCase,  lowerCAmelCase      )
               lowercase	:   int											= model_class(lowerCAmelCase      )

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

               lowercase	:   int											= run_in_graph_mode()
               self.assertIsNotNone(lowerCAmelCase      )


   @slow
   def 							lowercase					(				self   :				List[Any]      )     ->							Any:
         # This is a copy of the test_keras_fit method, but we use XLA compilation instead of eager
         lowercase						,     lowercase	:   Tuple											= self.model_tester.prepare_config_and_inputs_for_common()
         for model_class in self.all_model_classes[:2]:
               lowercase	:   Optional[Any]											= model_class(lowerCAmelCase      )
               if getattr(lowerCAmelCase,  'hf_compute_loss',  lowerCAmelCase      ):
                     # Test that model correctly compute the loss with kwargs
                     lowercase	:   int											= self._prepare_for_class(inputs_dict.copy(),  lowerCAmelCase,  return_labels=lowerCAmelCase      )
                     # Is there a better way to remove these decoder inputs?
                     lowercase	:   Dict											= {
                         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	:   int											= {
                         'labels',
                         'label',
                         'label_ids',
                         'start_positions',
                         'start_position',
                         'end_positions',
                         'end_position',
                         'next_sentence_label',
                     }
                     lowercase	:   int											= possible_label_cols.intersection(set(lowerCAmelCase      )      )
                     self.assertGreater(len(lowerCAmelCase      ),  0,  msg='No matching label names found!'      )
                     lowercase	:   Union[str, Any]											= {key: val for key, val in prepared_for_class.items() if key in label_names}
                     lowercase	:   Tuple											= {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	:   str											= model.fit(
                         lowerCAmelCase,  validation_data=lowerCAmelCase,  steps_per_epoch=1,  validation_steps=1,  shuffle=lowerCAmelCase,  verbose=0,  )
                     lowercase	:   Dict											= history.history['loss'][0]
                     self.assertTrue(not isnan(lowerCAmelCase      )      )
                     lowercase	:   List[str]											= 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	:   int											= model_class(lowerCAmelCase      )
                     model.compile(optimizer=tf.keras.optimizers.SGD(0.0      ),  jit_compile=lowerCAmelCase      )
                     lowercase	:   Optional[Any]											= model.fit(
                         lowerCAmelCase,  lowerCAmelCase,  validation_data=(inputs_minus_labels, labels),  steps_per_epoch=1,  validation_steps=1,  shuffle=lowerCAmelCase,  verbose=0,  )

                     lowercase	:   str											= history.history['loss'][0]
                     self.assertTrue(not isnan(lowerCAmelCase      )      )
                     lowercase	:   Optional[int]											= history.history['val_loss'][0]
                     self.assertTrue(not isnan(lowerCAmelCase      )      )


   @slow
   def 							lowercase					(				self   :				Optional[Any]      )     ->							Tuple:
         lowercase						,     lowercase	:   int											= self.model_tester.prepare_config_and_inputs_for_common()
         lowercase	:   List[str]											= True
         lowercase	:   Dict											= True

         if hasattr(lowerCAmelCase,  'use_cache'      ):
               lowercase	:   Optional[int]											= True

         lowercase	:   Tuple											= getattr(self.model_tester,  'encoder_seq_length',  self.model_tester.seq_length      )
         lowercase	:   Tuple											= getattr(self.model_tester,  'key_length',  lowerCAmelCase      )

         for model_class in self.all_model_classes[:2]:
               lowercase	:   Any											= self._prepare_for_class(lowerCAmelCase,  lowerCAmelCase      )
               lowercase	:   Optional[int]											= model_class(lowerCAmelCase      )
               model.build()
               lowercase	:   Dict											= 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	:   Tuple											= 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	:   Any											= os.path.join(lowerCAmelCase,  'saved_model',  '1'      )
                     lowercase	:   List[Any]											= tf.keras.models.load_model(lowerCAmelCase      )
                     lowercase	:   Dict											= model(lowerCAmelCase      )

                     if self.is_encoder_decoder:
                           lowercase	:   List[str]											= outputs['encoder_hidden_states']
                           lowercase	:   Union[str, Any]											= outputs['encoder_attentions']
                     else:
                           lowercase	:   Any											= outputs['hidden_states']
                           lowercase	:   Optional[Any]											= outputs['attentions']

                     self.assertEqual(len(lowerCAmelCase      ),  lowerCAmelCase      )

                     lowercase	:   Optional[Any]											= 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 							lowercase					(				self   :				Dict      )     ->							Union[str, Any]:
         tf.keras.mixed_precision.set_global_policy('mixed_float16'      )

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

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


   @slow
   def 							lowercase					(				self   :				Dict      )     ->							List[str]:
         lowercase						,     lowercase	:   int											= 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	:   Dict											= {
             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	:   List[Any]											= TFSharedEmbeddings(self.model_tester.vocab_size,  self.model_tester.hidden_size,  name='shared'      )
                     lowercase	:   str											= False
                     lowercase	:   Any											= main_layer_class(lowerCAmelCase,  embed_tokens=lowerCAmelCase      )
               else:
                     lowercase	:   Tuple											= main_layer_class(lowerCAmelCase      )

               lowercase	:   Optional[int]											= {
                   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	:   int											= self.model_tester.num_labels
               else:
                     lowercase	:   Optional[int]											= 2

               lowercase	:   Any											= 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	:   str											= main_layer(lowerCAmelCase      )[0]
               lowercase	:   List[Any]											= tf.keras.layers.Dense(lowerCAmelCase,  activation='softmax',  name='outputs'      )(lowerCAmelCase      )
               lowercase	:   Optional[Any]											= 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	:   Dict											= os.path.join(lowerCAmelCase,  'keras_model.h5'      )
                     model.save(lowerCAmelCase      )
                     if "T5" in main_layer_class.__name__:
                           lowercase	:   Tuple											= tf.keras.models.load_model(
                               lowerCAmelCase,  custom_objects={
                                   main_layer_class.__name__: main_layer_class,
                                   'TFSharedEmbeddings': TFSharedEmbeddings,
                               },  )
                     else:
                           lowercase	:   str											= 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 							lowercase					(				self   :				Tuple      )     ->							List[str]:
         lowercase						,     lowercase	:   Any											= self.model_tester.prepare_config_and_inputs_for_common()

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

               lowercase	:   List[str]											= copy.deepcopy(lowerCAmelCase      )

               if not self.is_encoder_decoder:
                     lowercase	:   List[Any]											= inputs['input_ids']
                     del inputs["input_ids"]
               else:
                     lowercase	:   Optional[Any]											= inputs['input_ids']
                     lowercase	:   Union[str, Any]											= inputs.get('decoder_input_ids',  lowerCAmelCase      )
                     del inputs["input_ids"]
                     inputs.pop('decoder_input_ids',  lowerCAmelCase      )

               if not self.is_encoder_decoder:
                     lowercase	:   Union[str, Any]											= model.get_input_embeddings()(lowerCAmelCase      )
               else:
                     lowercase	:   Dict											= model.get_input_embeddings()(lowerCAmelCase      )
                     lowercase	:   Any											= model.get_input_embeddings()(lowerCAmelCase      )

               lowercase	:   Union[str, Any]											= self._prepare_for_class(lowerCAmelCase,  lowerCAmelCase      )

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

               lowercase	:   int											= run_in_graph_mode()
               self.assertIsNotNone(lowerCAmelCase      )


   def 							lowercase					(				self   :				str,  lowerCAmelCase   :				List[Any],  lowerCAmelCase   :				int      )     ->							Dict:
         # special tokens cannot be bad tokens
         lowercase	:   List[Any]											= []
         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	:   List[Any]											= []
         while len(lowerCAmelCase      ) < num_bad_tokens:
               lowercase	:   str											= 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 							lowercase					(				self   :				List[str],  lowerCAmelCase   :				Any      )     ->							str:
         for token_id in output_ids[0].numpy().tolist():
               self.assertGreaterEqual(lowerCAmelCase,  0      )
               self.assertLess(lowerCAmelCase,  self.model_tester.vocab_size      )






   def 							lowercase					(				self   :				str,  lowerCAmelCase   :				Any,  lowerCAmelCase   :				str      )     ->							Any:
         # 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
