



"""simple docstring"""






import re
import time
from typing import Optional

import IPython.display as disp

from ..trainer_callback import TrainerCallback
from ..trainer_utils import IntervalStrategy, has_length






def   snake_case__     (	__lowerCamelCase       :					Any			):
   """simple docstring"""
   lowerCamelCase__	:	Tuple				=int(__lowerCamelCase			)
   lowerCamelCase__	:	Dict				=t // 3600, (t // 60) % 60, t % 60
   return f'''{h}:{m:02d}:{s:02d}''' if h != 0 else f'''{m:02d}:{s:02d}'''






def   snake_case__     (	__lowerCamelCase       :					Optional[Any]							,	__lowerCamelCase       :					List[str]							,	__lowerCamelCase       :					Any							,	__lowerCamelCase       :					Tuple							,	__lowerCamelCase       :					Dict=300			):
   """simple docstring"""
   return f'''
    <div>
      {prefix}
      <progress value=\'{value}\' max=\'{total}\' style=\'width:{width}px; height:20px; vertical-align: middle;\'></progress>
      {label}
    </div>
    '''






def   snake_case__     (	__lowerCamelCase       :					Tuple			):
   """simple docstring"""
   lowerCamelCase__	:	List[str]				='''<table border="1" class="dataframe">\n'''
   html_code += """  <thead>\n <tr style="text-align: left;">\n"""
   for i in items[0]:
      html_code += f'''      <th>{i}</th>\n'''
   html_code += "    </tr>\n  </thead>\n  <tbody>\n"
   for line in items[1:]:
      html_code += "    <tr>\n"
      for elt in line:
         lowerCamelCase__	:	Any				=f'''{elt:.6f}''' if isinstance(__lowerCamelCase							,	__lowerCamelCase			) else str(__lowerCamelCase			)
         html_code += f'''      <td>{elt}</td>\n'''
      html_code += "    </tr>\n"
   html_code += "  </tbody>\n</table><p>"
   return html_code

class  __SCREAMING_SNAKE_CASE			:


     '''simple docstring'''





     _a         =							5
     _a         =							0.2



     def __init__( self							:						Any,						lowerCamelCase							:						int,						lowerCamelCase							:						Optional[str] = None,						lowerCamelCase							:						bool = True,						lowerCamelCase							:						Optional["NotebookTrainingTracker"] = None,						lowerCamelCase							:						int = 300,						)->      Tuple:
        lowerCamelCase__	:	List[Any]				=total
        lowerCamelCase__	:	int				='''''' if prefix is None else prefix
        lowerCamelCase__	:	Dict				=leave
        lowerCamelCase__	:	Any				=parent
        lowerCamelCase__	:	Dict				=width
        lowerCamelCase__	:	str				=None
        lowerCamelCase__	:	str				=None
        lowerCamelCase__	:	Tuple				=None



     def 	snake_case ( self							:						Optional[int],						lowerCamelCase							:						int,						lowerCamelCase							:						bool = False,						lowerCamelCase							:						str = None  )->      List[str]:
        lowerCamelCase__	:	Dict				=value
        if comment is not None:
           lowerCamelCase__	:	Optional[int]				=comment
        if self.last_value is None:
           lowerCamelCase__	:	str				=time.time()
           lowerCamelCase__	:	Optional[Any]				=value
           lowerCamelCase__	:	List[Any]				=None
           lowerCamelCase__	:	Dict				=self.warmup
           lowerCamelCase__	:	Tuple				=1
           self.update_bar(lowerCamelCase  )
        elif value <= self.last_value and not force_update:
           return
        elif force_update or self.first_calls > 0 or value >= min(self.last_value + self.wait_for,						self.total  ):
           if self.first_calls > 0:
              self.first_calls -= 1
           lowerCamelCase__	:	Optional[Any]				=time.time()
           lowerCamelCase__	:	Optional[Any]				=current_time - self.start_time
           # We could have value = self.start_value if the update is called twixe with the same start value.
           if value > self.start_value:
              lowerCamelCase__	:	Optional[Any]				=self.elapsed_time / (value - self.start_value)
           else:
              lowerCamelCase__	:	Tuple				=None
           if value >= self.total:
              lowerCamelCase__	:	Dict				=self.total
              lowerCamelCase__	:	Tuple				=None
              if not self.leave:
                 self.close()
           elif self.average_time_per_item is not None:
              lowerCamelCase__	:	List[Any]				=self.average_time_per_item * (self.total - value)
           self.update_bar(lowerCamelCase  )
           lowerCamelCase__	:	Any				=value
           lowerCamelCase__	:	List[str]				=current_time
           if self.average_time_per_item is None:
              lowerCamelCase__	:	str				=1
           else:
              lowerCamelCase__	:	str				=max(int(self.update_every / self.average_time_per_item  ),						1  )



     def 	snake_case ( self							:						List[str],						lowerCamelCase							:						int,						lowerCamelCase							:						Dict=None  )->      Optional[int]:
        lowerCamelCase__	:	int				=''' ''' * (len(str(self.total  )  ) - len(str(lowerCamelCase  )  )) + str(lowerCamelCase  )
        if self.elapsed_time is None:
           lowerCamelCase__	:	Optional[int]				=F'''[{spaced_value}/{self.total} : < :'''
        elif self.predicted_remaining is None:
           lowerCamelCase__	:	Optional[Any]				=F'''[{spaced_value}/{self.total} {format_time(self.elapsed_time  )}'''
        else:
           lowerCamelCase__	:	Dict				=(
               F'''[{spaced_value}/{self.total} {format_time(self.elapsed_time  )} <'''
               F''' {format_time(self.predicted_remaining  )}'''
           )
           self.label += F''', {1/self.average_time_per_item:.2f} it/s'''
        self.label += "]" if self.comment is None or len(self.comment  ) == 0 else F''', {self.comment}]'''
        self.display()



     def 	snake_case ( self							:						List[Any]  )->      str:
        lowerCamelCase__	:	Optional[int]				=html_progress_bar(self.value,						self.total,						self.prefix,						self.label,						self.width  )
        if self.parent is not None:
           # If this is a child bar, the parent will take care of the display.
           self.parent.display()
           return
        if self.output is None:
           lowerCamelCase__	:	Tuple				=disp.display(disp.HTML(self.html_code  ),						display_id=lowerCamelCase  )
        else:
           self.output.update(disp.HTML(self.html_code  )  )




     def 	snake_case ( self							:						Union[str, Any]  )->      Any:
        if self.parent is None and self.output is not None:
           self.output.update(disp.HTML(''''''  )  )





class  __SCREAMING_SNAKE_CASE			(   lowerCAmelCase_     ):


     '''simple docstring'''





     def __init__( self							:						Any,						lowerCamelCase							:						Optional[int],						lowerCamelCase							:						Union[str, Any]=None  )->      str:
        super().__init__(lowerCamelCase  )
        lowerCamelCase__	:	List[Any]				=None if column_names is None else [column_names]
        lowerCamelCase__	:	Tuple				=None



     def 	snake_case ( self							:						int  )->      int:
        lowerCamelCase__	:	Union[str, Any]				=html_progress_bar(self.value,						self.total,						self.prefix,						self.label,						self.width  )
        if self.inner_table is not None:
           self.html_code += text_to_html_table(self.inner_table  )
        if self.child_bar is not None:
           self.html_code += self.child_bar.html_code
        if self.output is None:
           lowerCamelCase__	:	Union[str, Any]				=disp.display(disp.HTML(self.html_code  ),						display_id=lowerCamelCase  )
        else:
           self.output.update(disp.HTML(self.html_code  )  )



     def 	snake_case ( self							:						List[Any],						lowerCamelCase							:						Any  )->      Optional[int]:
        if self.inner_table is None:
           lowerCamelCase__	:	List[str]				=[list(values.keys()  ), list(values.values()  )]
        else:
           lowerCamelCase__	:	Tuple				=self.inner_table[0]
           if len(self.inner_table  ) == 1:
              # We give a chance to update the column names at the first iteration
              for key in values.keys():
                 if key not in columns:
                    columns.append(lowerCamelCase  )
              lowerCamelCase__	:	List[str]				=columns
           self.inner_table.append([values[c] for c in columns]  )



     def 	snake_case ( self							:						str,						lowerCamelCase							:						Optional[Any],						lowerCamelCase							:						Optional[int]=None,						lowerCamelCase							:						Tuple=300  )->      List[Any]:
        lowerCamelCase__	:	Optional[Any]				=NotebookProgressBar(lowerCamelCase,						prefix=lowerCamelCase,						parent=self,						width=lowerCamelCase  )
        return self.child_bar




     def 	snake_case ( self							:						Union[str, Any]  )->      List[str]:
        lowerCamelCase__	:	List[str]				=None
        self.display()





class  __SCREAMING_SNAKE_CASE			(   lowerCAmelCase_     ):


     '''simple docstring'''





     def __init__( self							:						Optional[int]  )->      Dict:
        lowerCamelCase__	:	Optional[int]				=None
        lowerCamelCase__	:	List[Any]				=None
        lowerCamelCase__	:	Dict				=False



     def 	snake_case ( self							:						int,						lowerCamelCase							:						Optional[int],						lowerCamelCase							:						int,						lowerCamelCase							:						Tuple,						**lowerCamelCase							:						List[Any]  )->      List[str]:
        lowerCamelCase__	:	Optional[int]				='''Epoch''' if args.evaluation_strategy == IntervalStrategy.EPOCH else '''Step'''
        lowerCamelCase__	:	Union[str, Any]				=0
        lowerCamelCase__	:	Tuple				=0
        lowerCamelCase__	:	List[Any]				=[self.first_column] + ['''Training Loss''']
        if args.evaluation_strategy != IntervalStrategy.NO:
           column_names.append('''Validation Loss'''  )
        lowerCamelCase__	:	List[str]				=NotebookTrainingTracker(state.max_steps,						lowerCamelCase  )



     def 	snake_case ( self							:						str,						lowerCamelCase							:						Any,						lowerCamelCase							:						Dict,						lowerCamelCase							:						Optional[Any],						**lowerCamelCase							:						Union[str, Any]  )->      List[str]:
        lowerCamelCase__	:	Optional[int]				=int(state.epoch  ) if int(state.epoch  ) == state.epoch else F'''{state.epoch:.2f}'''
        self.training_tracker.update(
            state.global_step + 1,						comment=F'''Epoch {epoch}/{state.num_train_epochs}''',						force_update=self._force_next_update,						)
        lowerCamelCase__	:	Optional[Any]				=False



     def 	snake_case ( self							:						Any,						lowerCamelCase							:						Dict,						lowerCamelCase							:						Dict,						lowerCamelCase							:						int,						lowerCamelCase							:						Union[str, Any]=None,						**lowerCamelCase							:						List[Any]  )->      Optional[int]:
        if not has_length(lowerCamelCase  ):
           return
        if self.prediction_bar is None:
           if self.training_tracker is not None:
              lowerCamelCase__	:	Any				=self.training_tracker.add_child(len(lowerCamelCase  )  )
           else:
              lowerCamelCase__	:	str				=NotebookProgressBar(len(lowerCamelCase  )  )
           self.prediction_bar.update(1  )
        else:
           self.prediction_bar.update(self.prediction_bar.value + 1  )



     def 	snake_case ( self							:						List[Any],						lowerCamelCase							:						Any,						lowerCamelCase							:						Any,						lowerCamelCase							:						str,						**lowerCamelCase							:						List[Any]  )->      Union[str, Any]:
        if self.prediction_bar is not None:
           self.prediction_bar.close()
        lowerCamelCase__	:	List[Any]				=None



     def 	snake_case ( self							:						List[Any],						lowerCamelCase							:						Optional[Any],						lowerCamelCase							:						Dict,						lowerCamelCase							:						Optional[int],						lowerCamelCase							:						List[Any]=None,						**lowerCamelCase							:						Dict  )->      List[Any]:
        # Only for when there is no evaluation
        if args.evaluation_strategy == IntervalStrategy.NO and "loss" in logs:
           lowerCamelCase__	:	Dict				={'''Training Loss''': logs['''loss''']}
           # First column is necessarily Step sine we're not in epoch eval strategy
           lowerCamelCase__	:	Tuple				=state.global_step
           self.training_tracker.write_line(lowerCamelCase  )



     def 	snake_case ( self							:						Dict,						lowerCamelCase							:						int,						lowerCamelCase							:						Optional[Any],						lowerCamelCase							:						Union[str, Any],						lowerCamelCase							:						Union[str, Any]=None,						**lowerCamelCase							:						Optional[int]  )->      Optional[int]:
        if self.training_tracker is not None:
           lowerCamelCase__	:	str				={'''Training Loss''': '''No log''', '''Validation Loss''': '''No log'''}
           for log in reversed(state.log_history  ):
              if "loss" in log:
                 lowerCamelCase__	:	Any				=log['''loss''']
                 break

           if self.first_column == "Epoch":
              lowerCamelCase__	:	str				=int(state.epoch  )
           else:
              lowerCamelCase__	:	List[Any]				=state.global_step
           lowerCamelCase__	:	List[Any]				='''eval'''
           for k in metrics:
              if k.endswith('''_loss'''  ):
                 lowerCamelCase__	:	str				=re.sub(r'''\_loss$''',						'''''',						lowerCamelCase  )
           lowerCamelCase__	:	Dict				=metrics.pop('''total_flos''',						lowerCamelCase  )
           lowerCamelCase__	:	Union[str, Any]				=metrics.pop('''epoch''',						lowerCamelCase  )
           lowerCamelCase__	:	str				=metrics.pop(F'''{metric_key_prefix}_runtime''',						lowerCamelCase  )
           lowerCamelCase__	:	Tuple				=metrics.pop(F'''{metric_key_prefix}_samples_per_second''',						lowerCamelCase  )
           lowerCamelCase__	:	Optional[Any]				=metrics.pop(F'''{metric_key_prefix}_steps_per_second''',						lowerCamelCase  )
           lowerCamelCase__	:	Any				=metrics.pop(F'''{metric_key_prefix}_jit_compilation_time''',						lowerCamelCase  )
           for k, v in metrics.items():
              if k == F'''{metric_key_prefix}_loss''':
                 lowerCamelCase__	:	Any				=v
              else:
                 lowerCamelCase__	:	List[Any]				=k.split('''_'''  )
                 lowerCamelCase__	:	Tuple				=''' '''.join([part.capitalize() for part in splits[1:]]  )
                 lowerCamelCase__	:	List[str]				=v
           self.training_tracker.write_line(lowerCamelCase  )
           self.training_tracker.remove_child()
           lowerCamelCase__	:	List[Any]				=None
           # Evaluation takes a long time so we should force the next update.
           lowerCamelCase__	:	Union[str, Any]				=True




     def 	snake_case ( self							:						List[str],						lowerCamelCase							:						List[str],						lowerCamelCase							:						Optional[int],						lowerCamelCase							:						str,						**lowerCamelCase							:						str  )->      Union[str, Any]:
        self.training_tracker.update(
            state.global_step,						comment=F'''Epoch {int(state.epoch  )}/{state.num_train_epochs}''',						force_update=lowerCamelCase  )
        lowerCamelCase__	:	int				=None









"""simple docstring"""






from __future__ import annotations

import unittest

from transformers import BlenderbotSmallConfig, BlenderbotSmallTokenizer, is_tf_available
from transformers.testing_utils import require_tf, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
		import tensorflow as tf

		from transformers import TFAutoModelForSeqaSeqLM, TFBlenderbotSmallForConditionalGeneration, TFBlenderbotSmallModel

@require_tf
class  __SCREAMING_SNAKE_CASE			:


					'''simple docstring'''





					_a         =							BlenderbotSmallConfig
					_a         =							{}
					_a         =							'gelu'



					def __init__( self							:						Union[str, Any],						lowerCamelCase							:						List[str],						lowerCamelCase							:						Dict=13,						lowerCamelCase							:						Optional[Any]=7,						lowerCamelCase							:						Optional[int]=True,						lowerCamelCase							:						int=False,						lowerCamelCase							:						Union[str, Any]=99,						lowerCamelCase							:						str=32,						lowerCamelCase							:						List[Any]=2,						lowerCamelCase							:						Optional[int]=4,						lowerCamelCase							:						Union[str, Any]=37,						lowerCamelCase							:						str=0.1,						lowerCamelCase							:						Optional[int]=0.1,						lowerCamelCase							:						Optional[Any]=20,						lowerCamelCase							:						int=2,						lowerCamelCase							:						Any=1,						lowerCamelCase							:						Optional[Any]=0,						)->      List[str]:
								lowerCamelCase__	:	Any				=parent
								lowerCamelCase__	:	Dict				=batch_size
								lowerCamelCase__	:	Optional[int]				=seq_length
								lowerCamelCase__	:	Tuple				=is_training
								lowerCamelCase__	:	Dict				=use_labels
								lowerCamelCase__	:	List[Any]				=vocab_size
								lowerCamelCase__	:	str				=hidden_size
								lowerCamelCase__	:	str				=num_hidden_layers
								lowerCamelCase__	:	Union[str, Any]				=num_attention_heads
								lowerCamelCase__	:	Any				=intermediate_size

								lowerCamelCase__	:	Dict				=hidden_dropout_prob
								lowerCamelCase__	:	List[Any]				=attention_probs_dropout_prob
								lowerCamelCase__	:	str				=max_position_embeddings
								lowerCamelCase__	:	Optional[int]				=eos_token_id
								lowerCamelCase__	:	str				=pad_token_id
								lowerCamelCase__	:	Union[str, Any]				=bos_token_id



					def 	snake_case ( self							:						Any  )->      Any:
								lowerCamelCase__	:	Any				=ids_tensor([self.batch_size, self.seq_length - 1],						self.vocab_size  )
								lowerCamelCase__	:	Tuple				=tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size  ),						1  )
								lowerCamelCase__	:	Any				=tf.concat([input_ids, eos_tensor],						axis=1  )

								lowerCamelCase__	:	Optional[int]				=ids_tensor([self.batch_size, self.seq_length],						self.vocab_size  )

								lowerCamelCase__	:	int				=self.config_cls(
								    vocab_size=self.vocab_size,						d_model=self.hidden_size,						encoder_layers=self.num_hidden_layers,						decoder_layers=self.num_hidden_layers,						encoder_attention_heads=self.num_attention_heads,						decoder_attention_heads=self.num_attention_heads,						encoder_ffn_dim=self.intermediate_size,						decoder_ffn_dim=self.intermediate_size,						dropout=self.hidden_dropout_prob,						attention_dropout=self.attention_probs_dropout_prob,						max_position_embeddings=self.max_position_embeddings,						eos_token_ids=[2],						bos_token_id=self.bos_token_id,						pad_token_id=self.pad_token_id,						decoder_start_token_id=self.pad_token_id,						**self.config_updates,						)
								lowerCamelCase__	:	Optional[int]				=prepare_blenderbot_small_inputs_dict(lowerCamelCase,						lowerCamelCase,						lowerCamelCase  )
								return config, inputs_dict




					def 	snake_case ( self							:						Any,						lowerCamelCase							:						str,						lowerCamelCase							:						Any  )->      Optional[Any]:
								lowerCamelCase__	:	Union[str, Any]				=TFBlenderbotSmallModel(config=lowerCamelCase  ).get_decoder()
								lowerCamelCase__	:	List[Any]				=inputs_dict['''input_ids''']

								lowerCamelCase__	:	Optional[int]				=input_ids[:1, :]
								lowerCamelCase__	:	str				=inputs_dict['''attention_mask'''][:1, :]
								lowerCamelCase__	:	Union[str, Any]				=inputs_dict['''head_mask''']
								lowerCamelCase__	:	Optional[Any]				=1

								# first forward pass
								lowerCamelCase__	:	Dict				=model(lowerCamelCase,						attention_mask=lowerCamelCase,						head_mask=lowerCamelCase,						use_cache=lowerCamelCase  )

								lowerCamelCase__			,     lowerCamelCase__	:	List[str]				=outputs.to_tuple()

								# create hypothetical next token and extent to next_input_ids
								lowerCamelCase__	:	Union[str, Any]				=ids_tensor((self.batch_size, 3),						config.vocab_size  )
								lowerCamelCase__	:	Tuple				=tf.cast(ids_tensor((self.batch_size, 3),						2  ),						tf.inta  )

								# append to next input_ids and
								lowerCamelCase__	:	List[str]				=tf.concat([input_ids, next_tokens],						axis=-1  )
								lowerCamelCase__	:	str				=tf.concat([attention_mask, next_attn_mask],						axis=-1  )

								lowerCamelCase__	:	Optional[int]				=model(lowerCamelCase,						attention_mask=lowerCamelCase  )[0]
								lowerCamelCase__	:	Union[str, Any]				=model(lowerCamelCase,						attention_mask=lowerCamelCase,						past_key_values=lowerCamelCase  )[0]

								self.parent.assertEqual(next_tokens.shape[1],						output_from_past.shape[1]  )

								# select random slice
								lowerCamelCase__	:	Tuple				=int(ids_tensor((1,),						output_from_past.shape[-1]  )  )
								lowerCamelCase__	:	int				=output_from_no_past[:, -3:, random_slice_idx]
								lowerCamelCase__	:	List[str]				=output_from_past[:, :, random_slice_idx]

								# test that outputs are equal for slice
								tf.debugging.assert_near(lowerCamelCase,						lowerCamelCase,						rtol=1E-3  )






def   snake_case__     (	__lowerCamelCase       :					Tuple							,	__lowerCamelCase       :					Union[str, Any]							,	__lowerCamelCase       :					Union[str, Any]							,	__lowerCamelCase       :					Any=None							,	__lowerCamelCase       :					List[str]=None							,	__lowerCamelCase       :					Tuple=None							,	__lowerCamelCase       :					Dict=None							,	__lowerCamelCase       :					Optional[int]=None							,	):
			"""simple docstring"""
			if attention_mask is None:
						lowerCamelCase__	:	List[str]				=tf.cast(tf.math.not_equal(__lowerCamelCase							,	config.pad_token_id			)							,	tf.inta			)
			if decoder_attention_mask is None:
						lowerCamelCase__	:	str				=tf.concat(
						    [
						        tf.ones(decoder_input_ids[:, :1].shape							,	dtype=tf.inta			),
						        tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:]							,	config.pad_token_id			)							,	tf.inta			),
						    ]							,	axis=-1							,	)
			if head_mask is None:
						lowerCamelCase__	:	int				=tf.ones((config.encoder_layers, config.encoder_attention_heads)			)
			if decoder_head_mask is None:
						lowerCamelCase__	:	int				=tf.ones((config.decoder_layers, config.decoder_attention_heads)			)
			if cross_attn_head_mask is None:
						lowerCamelCase__	:	List[str]				=tf.ones((config.decoder_layers, config.decoder_attention_heads)			)
			return {
			    "input_ids": input_ids,
			    "decoder_input_ids": decoder_input_ids,
			    "attention_mask": attention_mask,
			    "decoder_attention_mask": decoder_attention_mask,
			    "head_mask": head_mask,
			    "decoder_head_mask": decoder_head_mask,
			    "cross_attn_head_mask": cross_attn_head_mask,
			}

@require_tf
class  __SCREAMING_SNAKE_CASE			(   lowerCAmelCase_	,     lowerCAmelCase_	,     unittest.TestCase     ):


					'''simple docstring'''





					_a         =							(
					    (TFBlenderbotSmallForConditionalGeneration, TFBlenderbotSmallModel) if is_tf_available() else ()
					)
					_a         =							(TFBlenderbotSmallForConditionalGeneration,) if is_tf_available() else ()
					_a         =							(
					    {
					        'conversational': TFBlenderbotSmallForConditionalGeneration,
					        'feature-extraction': TFBlenderbotSmallModel,
					        'summarization': TFBlenderbotSmallForConditionalGeneration,
					        'text2text-generation': TFBlenderbotSmallForConditionalGeneration,
					        'translation': TFBlenderbotSmallForConditionalGeneration,
					    }
					    if is_tf_available()
					    else {}
					)
					_a         =							True
					_a         =							False
					_a         =							False



					def 	snake_case ( self							:						Any  )->      str:
								lowerCamelCase__	:	Tuple				=TFBlenderbotSmallModelTester(self  )
								lowerCamelCase__	:	Union[str, Any]				=ConfigTester(self,						config_class=lowerCamelCase  )



					def 	snake_case ( self							:						Any  )->      Optional[int]:
								self.config_tester.run_common_tests()




					def 	snake_case ( self							:						int  )->      str:
								lowerCamelCase__	:	List[Any]				=self.model_tester.prepare_config_and_inputs_for_common()
								self.model_tester.check_decoder_model_past_large_inputs(*lowerCamelCase  )





@require_tokenizers
@require_tf
class  __SCREAMING_SNAKE_CASE			(   unittest.TestCase     ):


					'''simple docstring'''





					_a         =							[
					    'Social anxiety\nWow, I am never shy. Do you have anxiety?\nYes. I end up sweating and blushing and feel like '
					    '  i\'m going to throw up.\nand why is that?'
					]
					_a         =							'facebook/blenderbot_small-90M'



					@cached_property
					def 	snake_case ( self							:						Any  )->      List[Any]:
								# use "old" tokenizer here because of bug when downloading new tokenizer
								return BlenderbotSmallTokenizer.from_pretrained('''facebook/blenderbot-90M'''  )



					@cached_property
					def 	snake_case ( self							:						int  )->      List[Any]:
								lowerCamelCase__	:	str				=TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name  )
								return model




					@slow
					def 	snake_case ( self							:						Tuple  )->      int:
								lowerCamelCase__	:	Dict				=self.tokenizer(self.src_text,						return_tensors='''tf'''  )
								lowerCamelCase__	:	Any				=self.model.generate(
								    model_inputs.input_ids,						attention_mask=model_inputs.attention_mask,						num_beams=2,						use_cache=lowerCamelCase,						)
								lowerCamelCase__	:	Any				=self.tokenizer.batch_decode(generated_ids.numpy(),						skip_special_tokens=lowerCamelCase  )[0]
								assert generated_words in (
								    "i don't know. i just feel like i'm going to throw up. it's not fun.",
								    "i'm not sure. i just feel like i've been feeling like i have to be in a certain place",
								    "i'm not sure. i just feel like i've been in a bad situation.",
								)




