




lowerCamelCase				:		str     =   [
    "Audio",
    "Array2D",
    "Array3D",
    "Array4D",
    "Array5D",
    "ClassLabel",
    "Features",
    "Sequence",
    "Value",
    "Image",
    "Translation",
    "TranslationVariableLanguages",
]
from .audio import Audio
from .features import ArrayaD, ArrayaD, ArrayaD, ArrayaD, ClassLabel, Features, Sequence, Value
from .image import Image
from .translation import Translation, TranslationVariableLanguages









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   A:



 '''simple docstring'''


 UpperCamelCase						   =						BlenderbotConfig
 UpperCamelCase						   =						{}
 UpperCamelCase						   =						'''gelu'''

 def __init__(				self					:  int						,							A_					:  Optional[int]						,							A_					:  List[str]=13						,							A_					:  str=7						,							A_					:  Any=True						,							A_					:  Any=False						,							A_					:  Optional[Any]=99						,							A_					:  List[str]=32						,							A_					:  List[str]=2						,							A_					:  Dict=4						,							A_					:  List[str]=37						,							A_					:  List[str]=0.1						,							A_					:  Optional[int]=0.1						,							A_					:  str=20						,							A_					:  str=2						,							A_					:  Optional[Any]=1						,							A_					:  int=0						,							)   ->	Union[str, Any]:

       """simple docstring"""






       lowerCamelCase_		     =    parent
       lowerCamelCase_		     =    batch_size
       lowerCamelCase_		     =    seq_length
       lowerCamelCase_		     =    is_training
       lowerCamelCase_		     =    use_labels
       lowerCamelCase_		     =    vocab_size
       lowerCamelCase_		     =    hidden_size
       lowerCamelCase_		     =    num_hidden_layers
       lowerCamelCase_		     =    num_attention_heads
       lowerCamelCase_		     =    intermediate_size

       lowerCamelCase_		     =    hidden_dropout_prob
       lowerCamelCase_		     =    attention_probs_dropout_prob
       lowerCamelCase_		     =    max_position_embeddings
       lowerCamelCase_		     =    eos_token_id
       lowerCamelCase_		     =    pad_token_id
       lowerCamelCase_		     =    bos_token_id

 def 				a__ (				self					:  Optional[Any]  )   ->	Any:

       """simple docstring"""






       lowerCamelCase_		     =    ids_tensor([self.batch_size, self.seq_length - 1]						,							self.vocab_size  )
       lowerCamelCase_		     =    tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size  )						,							1  )
       lowerCamelCase_		     =    tf.concat([input_ids, eos_tensor]						,							axis=1  )

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

       lowerCamelCase_		     =    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_		     =    prepare_blenderbot_inputs_dict(A_						,							A_						,							A_  )
       return config, inputs_dict





 def 				a__ (				self					:  Tuple						,							A_					:  Union[str, Any]						,							A_					:  List[str]  )   ->	int:

       """simple docstring"""






       lowerCamelCase_		     =    TFBlenderbotModel(config=A_  ).get_decoder()
       lowerCamelCase_		     =    inputs_dict['input_ids']

       lowerCamelCase_		     =    input_ids[:1, :]
       lowerCamelCase_		     =    inputs_dict['attention_mask'][:1, :]
       lowerCamelCase_		     =    inputs_dict['head_mask']
       lowerCamelCase_		     =    1

       # first forward pass
       lowerCamelCase_		     =    model(A_						,							attention_mask=A_						,							head_mask=A_						,							use_cache=A_  )

       lowerCamelCase_     ,				lowerCamelCase_		     =    outputs.to_tuple()

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

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

       lowerCamelCase_		     =    model(A_						,							attention_mask=A_  )[0]
       lowerCamelCase_		     =    model(A_						,							attention_mask=A_						,							past_key_values=A_  )[0]

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

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

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

def 			_SCREAMING_SNAKE_CASE			(							lowercase	:       Union[str, Any]	,							lowercase	:       Any	,							lowercase	:       Tuple	,							lowercase	:       List[Any]=None	,							lowercase	:       List[str]=None	,							lowercase	:       List[Any]=None	,							lowercase	:       Tuple=None	,							lowercase	:       Union[str, Any]=None	,							):


      '''simple docstring'''



      if attention_mask is None:
            lowerCamelCase_		     =    tf.cast(tf.math.not_equal(lowercase	,							config.pad_token_id     )	,							tf.inta     )
      if decoder_attention_mask is None:
            lowerCamelCase_		     =    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_		     =    tf.ones((config.encoder_layers, config.encoder_attention_heads)     )
      if decoder_head_mask is None:
            lowerCamelCase_		     =    tf.ones((config.decoder_layers, config.decoder_attention_heads)     )
      if cross_attn_head_mask is None:
            lowerCamelCase_		     =    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'''


 UpperCamelCase						   =						(TFBlenderbotForConditionalGeneration, TFBlenderbotModel) if is_tf_available() else ()
 UpperCamelCase						   =						(TFBlenderbotForConditionalGeneration,) if is_tf_available() else ()
 UpperCamelCase						   =						(
     {
         '''conversational''': TFBlenderbotForConditionalGeneration,
         '''feature-extraction''': TFBlenderbotModel,
         '''summarization''': TFBlenderbotForConditionalGeneration,
         '''text2text-generation''': TFBlenderbotForConditionalGeneration,
         '''translation''': TFBlenderbotForConditionalGeneration,
     }
     if is_tf_available()
     else {}
 )
 UpperCamelCase						   =						True
 UpperCamelCase						   =						False
 UpperCamelCase						   =						False

 def 				a__ (				self					:  Optional[int]  )   ->	Optional[int]:

       """simple docstring"""






       lowerCamelCase_		     =    TFBlenderbotModelTester(self  )
       lowerCamelCase_		     =    ConfigTester(self						,							config_class=A_  )

 def 				a__ (				self					:  Dict  )   ->	Union[str, Any]:

       """simple docstring"""






       self.config_tester.run_common_tests()





 def 				a__ (				self					:  List[str]  )   ->	str:

       """simple docstring"""






       lowerCamelCase_		     =    self.model_tester.prepare_config_and_inputs_for_common()
       self.model_tester.check_decoder_model_past_large_inputs(*A_  )







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



 '''simple docstring'''


 UpperCamelCase						   =						['''My friends are cool but they eat too many carbs.''']
 UpperCamelCase						   =						'''facebook/blenderbot-400M-distill'''

 @cached_property
 def 				a__ (				self					:  Tuple  )   ->	Optional[int]:

       """simple docstring"""






       return BlenderbotTokenizer.from_pretrained(self.model_name  )

 @cached_property
 def 				a__ (				self					:  List[Any]  )   ->	str:

       """simple docstring"""






       lowerCamelCase_		     =    TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name  )
       return model





 @slow
 def 				a__ (				self					:  str  )   ->	str:

       """simple docstring"""






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



