





"""simple docstring"""





from __future__ import annotations

import unittest

from transformers import BlenderbotConfig, BlenderbotTokenizer, 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, TFBlenderbotForConditionalGeneration, TFBlenderbotModel






@require_tf
class    _lowerCAmelCase  :
       """simple docstring"""




       __magic_name__	:int						    =   BlenderbotConfig
       __magic_name__	:Union[str, Any]						    =   {}
       __magic_name__	:Any						    =   """gelu"""






       def __init__(       self       ,					__UpperCAmelCase       ,					__UpperCAmelCase=1_3       ,					__UpperCAmelCase=7       ,					__UpperCAmelCase=True       ,					__UpperCAmelCase=False       ,					__UpperCAmelCase=9_9       ,					__UpperCAmelCase=3_2       ,					__UpperCAmelCase=2       ,					__UpperCAmelCase=4       ,					__UpperCAmelCase=3_7       ,					__UpperCAmelCase=0.1       ,					__UpperCAmelCase=0.1       ,					__UpperCAmelCase=2_0       ,					__UpperCAmelCase=2       ,					__UpperCAmelCase=1       ,					__UpperCAmelCase=0       ,					):



         '''simple docstring'''

         lowerCAmelCase__     :Tuple											=				parent
         lowerCAmelCase__     :Union[str, Any]											=				batch_size
         lowerCAmelCase__     :List[Any]											=				seq_length
         lowerCAmelCase__     :Union[str, Any]											=				is_training
         lowerCAmelCase__     :int											=				use_labels
         lowerCAmelCase__     :List[Any]											=				vocab_size
         lowerCAmelCase__     :Optional[int]											=				hidden_size
         lowerCAmelCase__     :List[str]											=				num_hidden_layers
         lowerCAmelCase__     :Dict											=				num_attention_heads
         lowerCAmelCase__     :int											=				intermediate_size

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






       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :Optional[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__     :Dict											=				tf.concat([input_ids, eos_tensor]       ,					axis=1 )

         lowerCAmelCase__     :str											=				ids_tensor([self.batch_size, self.seq_length]       ,					self.vocab_size )

         lowerCAmelCase__     :Optional[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__     :List[str]											=				prepare_blenderbot_inputs_dict(__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase )
         return config, inputs_dict






       def 	snake_case		(       self       ,					__UpperCAmelCase       ,					__UpperCAmelCase ):



         '''simple docstring'''

         lowerCAmelCase__     :Optional[int]											=				TFBlenderbotModel(config=__UpperCAmelCase ).get_decoder()
         lowerCAmelCase__     :Tuple											=				inputs_dict['input_ids']

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

         # first forward pass
         lowerCAmelCase__     :Optional[Any]											=				model(__UpperCAmelCase       ,					attention_mask=__UpperCAmelCase       ,					head_mask=__UpperCAmelCase       ,					use_cache=__UpperCAmelCase )

         lowerCAmelCase__				,							lowerCAmelCase__     :str											=				outputs.to_tuple()

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

         # append to next input_ids and
         lowerCAmelCase__     :Tuple											=				tf.concat([input_ids, next_tokens]       ,					axis=-1 )
         lowerCAmelCase__     :Union[str, Any]											=				tf.concat([attention_mask, next_attn_mask]       ,					axis=-1 )

         lowerCAmelCase__     :List[str]											=				model(__UpperCAmelCase       ,					attention_mask=__UpperCAmelCase )[0]
         lowerCAmelCase__     :List[str]											=				model(__UpperCAmelCase       ,					attention_mask=__UpperCAmelCase       ,					past_key_values=__UpperCAmelCase )[0]

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

         # select random slice
         lowerCAmelCase__     :Optional[int]											=				int(ids_tensor((1,)       ,					output_from_past.shape[-1] ) )
         lowerCAmelCase__     :Optional[int]											=				output_from_no_past[:, -3:, random_slice_idx]
         lowerCAmelCase__     :List[Any]											=				output_from_past[:, :, random_slice_idx]

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






def 					__A  (_SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE=None	,  _SCREAMING_SNAKE_CASE=None	,  _SCREAMING_SNAKE_CASE=None	,  _SCREAMING_SNAKE_CASE=None	,  _SCREAMING_SNAKE_CASE=None	,  )   ->Union[str, Any]:





  """simple docstring"""


  if attention_mask is None:
    lowerCAmelCase__     :Tuple											=				tf.cast(tf.math.not_equal(_SCREAMING_SNAKE_CASE	,  config.pad_token_id     )	,  tf.inta     )
  if decoder_attention_mask is None:
    lowerCAmelCase__     :int											=				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__     :Union[str, Any]											=				tf.ones((config.encoder_layers, config.encoder_attention_heads)     )
  if decoder_head_mask is None:
    lowerCAmelCase__     :Any											=				tf.ones((config.decoder_layers, config.decoder_attention_heads)     )
  if cross_attn_head_mask is None:
    lowerCAmelCase__     :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    _lowerCAmelCase  (	a    ,  a    ,  unittest.TestCase			):
       """simple docstring"""




       __magic_name__	:Dict						    =   (TFBlenderbotForConditionalGeneration, TFBlenderbotModel) if is_tf_available() else ()
       __magic_name__	:str						    =   (TFBlenderbotForConditionalGeneration,) if is_tf_available() else ()
       __magic_name__	:Dict						    =   (
           {
               """conversational""": TFBlenderbotForConditionalGeneration,
               """feature-extraction""": TFBlenderbotModel,
               """summarization""": TFBlenderbotForConditionalGeneration,
               """text2text-generation""": TFBlenderbotForConditionalGeneration,
               """translation""": TFBlenderbotForConditionalGeneration,
           }
           if is_tf_available()
           else {}
       )
       __magic_name__	:str						    =   True
       __magic_name__	:str						    =   False
       __magic_name__	:Dict						    =   False






       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :Any											=				TFBlenderbotModelTester(self )
         lowerCAmelCase__     :int											=				ConfigTester(self       ,					config_class=__UpperCAmelCase )






       def 	snake_case		(       self ):



         '''simple docstring'''

         self.config_tester.run_common_tests()






       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :Optional[int]											=				self.model_tester.prepare_config_and_inputs_for_common()
         self.model_tester.check_decoder_model_past_large_inputs(*__UpperCAmelCase )






@require_tokenizers
@require_tf
class    _lowerCAmelCase  (	unittest.TestCase			):
       """simple docstring"""




       __magic_name__	:Optional[Any]						    =   ["""My friends are cool but they eat too many carbs."""]
       __magic_name__	:int						    =   """facebook/blenderbot-400M-distill"""






       @cached_property
       def 	snake_case		(       self ):



         '''simple docstring'''

         return BlenderbotTokenizer.from_pretrained(self.model_name )






       @cached_property
       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :int											=				TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name )
         return model






       @slow
       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :Optional[Any]											=				self.tokenizer(self.src_text       ,					return_tensors='tf' )
         lowerCAmelCase__     :Optional[int]											=				self.model.generate(
             model_inputs.input_ids       ,					)
         lowerCAmelCase__     :Optional[Any]											=				self.tokenizer.batch_decode(generated_ids.numpy()       ,					skip_special_tokens=__UpperCAmelCase )[0]
         assert (
             generated_words
             == " That's unfortunate. Are they trying to lose weight or are they just trying to be healthier?"
         )







"""simple docstring"""





def 					__A  (_SCREAMING_SNAKE_CASE     )   ->str:





  """simple docstring"""


  lowerCAmelCase__     :List[Any]											=				int(_SCREAMING_SNAKE_CASE     )
  if decimal in (0, 1):  # Exit cases for the recursion
    return str(_SCREAMING_SNAKE_CASE     )
  lowerCAmelCase__				,							lowerCAmelCase__     :int											=				divmod(_SCREAMING_SNAKE_CASE	,  2     )
  return binary_recursive(_SCREAMING_SNAKE_CASE     ) + str(_SCREAMING_SNAKE_CASE     )
def 					__A  (_SCREAMING_SNAKE_CASE     )   ->str:





  """simple docstring"""


  lowerCAmelCase__     :str											=				str(_SCREAMING_SNAKE_CASE     ).strip()
  if not number:
    raise ValueError('No input value was provided'     )
  lowerCAmelCase__     :Dict											=				'-' if number.startswith('-'     ) else ''
  lowerCAmelCase__     :int											=				number.lstrip('-'     )
  if not number.isnumeric():
    raise ValueError('Input value is not an integer'     )
  return F"{negative}0b{binary_recursive(int(_SCREAMING_SNAKE_CASE     )     )}"


if __name__ == "__main__":
  from doctest import testmod

  testmod()
