






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 				_A			:
    """simple docstring"""

    lowerCamelCase	:							Any				= BlenderbotSmallConfig
    lowerCamelCase	:							List[Any]				= {}
    lowerCamelCase	:							Optional[int]				= 'gelu'



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

           __UpperCAmelCase     				=hidden_dropout_prob
           __UpperCAmelCase     				=attention_probs_dropout_prob
           __UpperCAmelCase     				=max_position_embeddings
           __UpperCAmelCase     				=eos_token_id
           __UpperCAmelCase     				=pad_token_id
           __UpperCAmelCase     				=bos_token_id



    def _a							(				self      :     List[Any]      )      ->							Union[str, Any]:
           __UpperCAmelCase     				=ids_tensor([self.batch_size, self.seq_length - 1] ,     self.vocab_size      )
           __UpperCAmelCase     				=tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size      ) ,     1      )
           __UpperCAmelCase     				=tf.concat([input_ids, eos_tensor] ,     axis=1      )

           __UpperCAmelCase     				=ids_tensor([self.batch_size, self.seq_length] ,     self.vocab_size      )

           __UpperCAmelCase     				=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 ,     )
           __UpperCAmelCase     				=prepare_blenderbot_small_inputs_dict(__SCREAMING_SNAKE_CASE ,     __SCREAMING_SNAKE_CASE ,     __SCREAMING_SNAKE_CASE      )
           return config, inputs_dict



    def _a							(				self      :     str ,     __SCREAMING_SNAKE_CASE      :     Union[str, Any] ,     __SCREAMING_SNAKE_CASE      :     Tuple      )      ->							Tuple:
           __UpperCAmelCase     				=TFBlenderbotSmallModel(config=__SCREAMING_SNAKE_CASE      ).get_decoder()
           __UpperCAmelCase     				=inputs_dict["""input_ids"""]

           __UpperCAmelCase     				=input_ids[:1, :]
           __UpperCAmelCase     				=inputs_dict["""attention_mask"""][:1, :]
           __UpperCAmelCase     				=inputs_dict["""head_mask"""]
           __UpperCAmelCase     				=1

           # first forward pass
           __UpperCAmelCase     				=model(__SCREAMING_SNAKE_CASE ,     attention_mask=__SCREAMING_SNAKE_CASE ,     head_mask=__SCREAMING_SNAKE_CASE ,     use_cache=__SCREAMING_SNAKE_CASE      )

           __UpperCAmelCase				, __UpperCAmelCase     				=outputs.to_tuple()

           # create hypothetical next token and extent to next_input_ids
           __UpperCAmelCase     				=ids_tensor((self.batch_size, 3) ,     config.vocab_size      )
           __UpperCAmelCase     				=tf.cast(ids_tensor((self.batch_size, 3) ,     2      ) ,     tf.inta      )

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

           __UpperCAmelCase     				=model(__SCREAMING_SNAKE_CASE ,     attention_mask=__SCREAMING_SNAKE_CASE      )[0]
           __UpperCAmelCase     				=model(__SCREAMING_SNAKE_CASE ,     attention_mask=__SCREAMING_SNAKE_CASE ,     past_key_values=__SCREAMING_SNAKE_CASE      )[0]

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

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

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







def       lowercase__	(					A_:						str  ,      A_:						int  ,      A_:						Tuple  ,      A_:						Any=None  ,      A_:						Tuple=None  ,      A_:						str=None  ,      A_:						int=None  ,      A_:						List[str]=None  ,      )	->				Tuple:







       """simple docstring"""






       if attention_mask is None:
              __UpperCAmelCase     				=tf.cast(tf.math.not_equal(A_  ,      config.pad_token_id		)  ,      tf.inta		)
       if decoder_attention_mask is None:
              __UpperCAmelCase     				=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:
              __UpperCAmelCase     				=tf.ones((config.encoder_layers, config.encoder_attention_heads)		)
       if decoder_head_mask is None:
              __UpperCAmelCase     				=tf.ones((config.decoder_layers, config.decoder_attention_heads)		)
       if cross_attn_head_mask is None:
              __UpperCAmelCase     				=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 				_A			(     UpperCamelCase					,					UpperCamelCase					,					unittest.TestCase				):
    """simple docstring"""

    lowerCamelCase	:							Tuple				= (
        (TFBlenderbotSmallForConditionalGeneration, TFBlenderbotSmallModel) if is_tf_available() else ()
    )
    lowerCamelCase	:							Optional[int]				= (TFBlenderbotSmallForConditionalGeneration,) if is_tf_available() else ()
    lowerCamelCase	:							Any				= (
        {
            'conversational': TFBlenderbotSmallForConditionalGeneration,
            'feature-extraction': TFBlenderbotSmallModel,
            'summarization': TFBlenderbotSmallForConditionalGeneration,
            'text2text-generation': TFBlenderbotSmallForConditionalGeneration,
            'translation': TFBlenderbotSmallForConditionalGeneration,
        }
        if is_tf_available()
        else {}
    )
    lowerCamelCase	:							str				= True
    lowerCamelCase	:							str				= False
    lowerCamelCase	:							Union[str, Any]				= False



    def _a							(				self      :     Tuple      )      ->							List[Any]:
           __UpperCAmelCase     				=TFBlenderbotSmallModelTester(self      )
           __UpperCAmelCase     				=ConfigTester(self ,     config_class=__SCREAMING_SNAKE_CASE      )



    def _a							(				self      :     int      )      ->							str:
           self.config_tester.run_common_tests()



    def _a							(				self      :     Optional[Any]      )      ->							Tuple:
           __UpperCAmelCase     				=self.model_tester.prepare_config_and_inputs_for_common()
           self.model_tester.check_decoder_model_past_large_inputs(*__SCREAMING_SNAKE_CASE      )




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

    lowerCamelCase	:							List[str]				= [
        '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?'
    ]
    lowerCamelCase	:							str				= 'facebook/blenderbot_small-90M'



    @cached_property
    def _a							(				self      :     int      )      ->							str:
           # use "old" tokenizer here because of bug when downloading new tokenizer
           return BlenderbotSmallTokenizer.from_pretrained("""facebook/blenderbot-90M"""      )



    @cached_property
    def _a							(				self      :     Optional[Any]      )      ->							str:
           __UpperCAmelCase     				=TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name      )
           return model



    @slow
    def _a							(				self      :     Dict      )      ->							int:
           __UpperCAmelCase     				=self.tokenizer(self.src_text ,     return_tensors="""tf"""      )
           __UpperCAmelCase     				=self.model.generate(
               model_inputs.input_ids ,     attention_mask=model_inputs.attention_mask ,     num_beams=2 ,     use_cache=__SCREAMING_SNAKE_CASE ,     )
           __UpperCAmelCase     				=self.tokenizer.batch_decode(generated_ids.numpy() ,     skip_special_tokens=__SCREAMING_SNAKE_CASE      )[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.",
           )









import math





def 		a__      (		_UpperCamelCase      :			float			,_UpperCamelCase      :			float				):
   if (
       not isinstance(_UpperCamelCase			,(int, float)				)
       or power_factor < -1
       or power_factor > 1
   ):
      raise ValueError('''power_factor must be a valid float value between -1 and 1.'''				)
   return apparent_power * power_factor





def 		a__      (		_UpperCamelCase      :			float			,_UpperCamelCase      :			float				):
   if (
       not isinstance(_UpperCamelCase			,(int, float)				)
       or power_factor < -1
       or power_factor > 1
   ):
      raise ValueError('''power_factor must be a valid float value between -1 and 1.'''				)
   return apparent_power * math.sqrt(1 - power_factor**2				)


if __name__ == "__main__":
  import doctest

  doctest.testmod()






