'''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 		__A       :
       snake_case       :int				    =					BlenderbotConfig
       snake_case       :int				    =					{}
       snake_case       :Dict				    =					"gelu"






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

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






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

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

              __UpperCAmelCase  :				Union[str, Any]       				=		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  :				Union[str, Any]       				=		prepare_blenderbot_inputs_dict(UpperCamelCase_				,     UpperCamelCase_				,     UpperCamelCase_      )
              return config, inputs_dict







       def 			_snake_case						(      self				,     UpperCamelCase_				,     UpperCamelCase_      ):
              __UpperCAmelCase  :				str       				=		TFBlenderbotModel(config=UpperCamelCase_      ).get_decoder()
              __UpperCAmelCase  :				Union[str, Any]       				=		inputs_dict["input_ids"]

              __UpperCAmelCase  :				Dict       				=		input_ids[:1, :]
              __UpperCAmelCase  :				int       				=		inputs_dict["attention_mask"][:1, :]
              __UpperCAmelCase  :				Union[str, Any]       				=		inputs_dict["head_mask"]
              __UpperCAmelCase  :				Union[str, Any]       				=		1

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

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

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

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

              __UpperCAmelCase  :				Tuple       				=		model(UpperCamelCase_				,     attention_mask=UpperCamelCase_      )[0]
              __UpperCAmelCase  :				Union[str, Any]       				=		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
              __UpperCAmelCase  :				List[Any]       				=		int(ids_tensor((1,)				,     output_from_past.shape[-1]      )      )
              __UpperCAmelCase  :				Tuple       				=		output_from_no_past[:, -3:, random_slice_idx]
              __UpperCAmelCase  :				Dict       				=		output_from_past[:, :, random_slice_idx]

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





def _lowercase	(      lowerCamelCase__							,    lowerCamelCase__							,    lowerCamelCase__							,    lowerCamelCase__=None							,    lowerCamelCase__=None							,    lowerCamelCase__=None							,    lowerCamelCase__=None							,    lowerCamelCase__=None							,    )					->       Union[str, Any]:





       """simple docstring"""


       if attention_mask is None:
              __UpperCAmelCase  :				Union[str, Any]       				=		tf.cast(tf.math.not_equal(lowerCamelCase__							,    config.pad_token_id						)							,    tf.inta						)
       if decoder_attention_mask is None:
              __UpperCAmelCase  :				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:
              __UpperCAmelCase  :				Dict       				=		tf.ones((config.encoder_layers, config.encoder_attention_heads)						)
       if decoder_head_mask is None:
              __UpperCAmelCase  :				Optional[int]       				=		tf.ones((config.decoder_layers, config.decoder_attention_heads)						)
       if cross_attn_head_mask is None:
              __UpperCAmelCase  :				Optional[int]       				=		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       (__magic_name__ ,					__magic_name__ ,					unittest.TestCase   ):
       snake_case       :Union[str, Any]				    =					(TFBlenderbotForConditionalGeneration, TFBlenderbotModel) if is_tf_available() else ()
       snake_case       :str				    =					(TFBlenderbotForConditionalGeneration,) if is_tf_available() else ()
       snake_case       :Optional[int]				    =					(
           {
               "conversational": TFBlenderbotForConditionalGeneration,
               "feature-extraction": TFBlenderbotModel,
               "summarization": TFBlenderbotForConditionalGeneration,
               "text2text-generation": TFBlenderbotForConditionalGeneration,
               "translation": TFBlenderbotForConditionalGeneration,
           }
           if is_tf_available()
           else {}
       )
       snake_case       :Tuple				    =					True
       snake_case       :Any				    =					False
       snake_case       :Any				    =					False






       def 			_snake_case						(      self      ):
              __UpperCAmelCase  :				Optional[int]       				=		TFBlenderbotModelTester(self      )
              __UpperCAmelCase  :				str       				=		ConfigTester(self				,     config_class=UpperCamelCase_      )






       def 			_snake_case						(      self      ):
              self.config_tester.run_common_tests()







       def 			_snake_case						(      self      ):
              __UpperCAmelCase  :				Tuple       				=		self.model_tester.prepare_config_and_inputs_for_common()
              self.model_tester.check_decoder_model_past_large_inputs(*UpperCamelCase_      )






@require_tokenizers
@require_tf
class 		__A       (unittest.TestCase   ):
       snake_case       :Union[str, Any]				    =					["My friends are cool but they eat too many carbs."]
       snake_case       :Tuple				    =					"facebook/blenderbot-400M-distill"






       @cached_property
       def 			_snake_case						(      self      ):
              return BlenderbotTokenizer.from_pretrained(self.model_name      )






       @cached_property
       def 			_snake_case						(      self      ):
              __UpperCAmelCase  :				Union[str, Any]       				=		TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name      )
              return model







       @slow
       def 			_snake_case						(      self      ):
              __UpperCAmelCase  :				List[str]       				=		self.tokenizer(self.src_text				,     return_tensors="tf"      )
              __UpperCAmelCase  :				List[str]       				=		self.model.generate(
                  model_inputs.input_ids				,     )
              __UpperCAmelCase  :				int       				=		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'''




from ...configuration_utils import PretrainedConfig
from ...utils import logging


_a   :	Any 							=   logging.get_logger(__name__)

_a   :	int 							=   {
    "facebook/s2t-wav2vec2-large-en-de": (
        "https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/config.json"
    ),
    # See all Speech2Text models at https://huggingface.co/models?filter=speech2text2
}






class 		__A       (__magic_name__   ):
       snake_case       :Optional[int]				    =					"speech_to_text_2"
       snake_case       :List[Any]				    =					["past_key_values"]
       snake_case       :str				    =					{"num_attention_heads": "decoder_attention_heads", "hidden_size": "d_model"}






       def __init__(      self				,     UpperCamelCase_=1_00_00				,     UpperCamelCase_=6				,     UpperCamelCase_=20_48				,     UpperCamelCase_=4				,     UpperCamelCase_=0.0				,     UpperCamelCase_=True				,     UpperCamelCase_="relu"				,     UpperCamelCase_=2_56				,     UpperCamelCase_=0.1				,     UpperCamelCase_=0.0				,     UpperCamelCase_=0.0				,     UpperCamelCase_=0.0_2				,     UpperCamelCase_=2				,     UpperCamelCase_=True				,     UpperCamelCase_=1				,     UpperCamelCase_=0				,     UpperCamelCase_=2				,     UpperCamelCase_=10_24				,     **UpperCamelCase_				,     ):
              __UpperCAmelCase  :				Any       				=		vocab_size
              __UpperCAmelCase  :				Optional[int]       				=		d_model
              __UpperCAmelCase  :				Tuple       				=		decoder_ffn_dim
              __UpperCAmelCase  :				List[str]       				=		decoder_layers
              __UpperCAmelCase  :				str       				=		decoder_attention_heads
              __UpperCAmelCase  :				Dict       				=		dropout
              __UpperCAmelCase  :				Optional[Any]       				=		attention_dropout
              __UpperCAmelCase  :				int       				=		activation_dropout
              __UpperCAmelCase  :				Dict       				=		activation_function
              __UpperCAmelCase  :				Tuple       				=		init_std
              __UpperCAmelCase  :				Any       				=		decoder_layerdrop
              __UpperCAmelCase  :				str       				=		use_cache
              __UpperCAmelCase  :				int       				=		decoder_layers
              __UpperCAmelCase  :				Any       				=		scale_embedding  # scale factor will be sqrt(d_model) if True
              __UpperCAmelCase  :				Union[str, Any]       				=		max_target_positions

              super().__init__(
                  pad_token_id=UpperCamelCase_				,     bos_token_id=UpperCamelCase_				,     eos_token_id=UpperCamelCase_				,     decoder_start_token_id=UpperCamelCase_				,     **UpperCamelCase_				,     )






