





'''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   _lowercase						:
   _UpperCamelCase       		=   BlenderbotSmallConfig
   _UpperCamelCase       		=   {}
   _UpperCamelCase       		=   """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    ,   ):
       A       :       Union[str, Any]							=						parent
       A       :       List[Any]							=						batch_size
       A       :       Optional[int]							=						seq_length
       A       :       Optional[int]							=						is_training
       A       :       Optional[int]							=						use_labels
       A       :       Optional[int]							=						vocab_size
       A       :       Optional[int]							=						hidden_size
       A       :       List[str]							=						num_hidden_layers
       A       :       Optional[Any]							=						num_attention_heads
       A       :       Dict							=						intermediate_size

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


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

       A       :       Union[str, Any]							=						ids_tensor([self.batch_size, self.seq_length]    ,   self.vocab_size					)

       A       :       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    ,   )
       A       :       Union[str, Any]							=						prepare_blenderbot_small_inputs_dict(snake_case__    ,   snake_case__    ,   snake_case__					)
       return config, inputs_dict


   def 					snake_case				( self    ,   _UpperCAmelCase    ,   _UpperCAmelCase					):
       A       :       Optional[Any]							=						TFBlenderbotSmallModel(config=snake_case__					).get_decoder()
       A       :       List[str]							=						inputs_dict["input_ids"]

       A       :       Optional[int]							=						input_ids[:1, :]
       A       :       Tuple							=						inputs_dict["attention_mask"][:1, :]
       A       :       Optional[int]							=						inputs_dict["head_mask"]
       A       :       int							=						1

       # first forward pass
       A       :       Union[str, Any]							=						model(snake_case__    ,   attention_mask=snake_case__    ,   head_mask=snake_case__    ,   use_cache=snake_case__					)

       A       :       Union[str, Any]							=						outputs.to_tuple()

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

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

       A       :       Optional[int]							=						model(snake_case__    ,   attention_mask=snake_case__					)[0]
       A       :       Optional[Any]							=						model(snake_case__    ,   attention_mask=snake_case__    ,   past_key_values=snake_case__					)[0]

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

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

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







def 							_lowerCamelCase(  UpperCamelCase__    : Union[str, Any]    ,       UpperCamelCase__    : Dict    ,       UpperCamelCase__    : List[str]    ,       UpperCamelCase__    : Optional[Any]=None    ,       UpperCamelCase__    : Tuple=None    ,       UpperCamelCase__    : str=None    ,       UpperCamelCase__    : str=None    ,       UpperCamelCase__    : Any=None    ,       )		->  Union[str, Any]:



    if attention_mask is None:
        A       :       Optional[int]							=						tf.cast(tf.math.not_equal(UpperCamelCase__    ,       config.pad_token_id		)    ,       tf.inta		)
    if decoder_attention_mask is None:
        A       :       List[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:
        A       :       Tuple							=						tf.ones((config.encoder_layers, config.encoder_attention_heads)		)
    if decoder_head_mask is None:
        A       :       List[str]							=						tf.ones((config.decoder_layers, config.decoder_attention_heads)		)
    if cross_attn_head_mask is None:
        A       :       List[Any]							=						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   _lowercase						(							SCREAMING_SNAKE_CASE__		,					SCREAMING_SNAKE_CASE__		,					unittest.TestCase				):
   _UpperCamelCase       		=   (
       (TFBlenderbotSmallForConditionalGeneration, TFBlenderbotSmallModel) if is_tf_available() else ()
   )
   _UpperCamelCase       		=   (TFBlenderbotSmallForConditionalGeneration,) if is_tf_available() else ()
   _UpperCamelCase       		=   (
       {
           """conversational""": TFBlenderbotSmallForConditionalGeneration,
           """feature-extraction""": TFBlenderbotSmallModel,
           """summarization""": TFBlenderbotSmallForConditionalGeneration,
           """text2text-generation""": TFBlenderbotSmallForConditionalGeneration,
           """translation""": TFBlenderbotSmallForConditionalGeneration,
       }
       if is_tf_available()
       else {}
   )
   _UpperCamelCase       		=   True
   _UpperCamelCase       		=   False
   _UpperCamelCase       		=   False


   def 					snake_case				( self					):
       A       :       List[str]							=						TFBlenderbotSmallModelTester(self					)
       A       :       Optional[Any]							=						ConfigTester(self    ,   config_class=snake_case__					)


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


   def 					snake_case				( self					):
       A       :       Optional[int]							=						self.model_tester.prepare_config_and_inputs_for_common()
       self.model_tester.check_decoder_model_past_large_inputs(*snake_case__					)







@require_tokenizers
@require_tf
class   _lowercase						(							unittest.TestCase				):
   _UpperCamelCase       		=   [
       """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?"""
   ]
   _UpperCamelCase       		=   """facebook/blenderbot_small-90M"""


   @cached_property
   def 					snake_case				( self					):
       return BlenderbotSmallTokenizer.from_pretrained('''facebook/blenderbot-90M'''					)


   @cached_property
   def 					snake_case				( self					):
       A       :       Tuple							=						TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name					)
       return model


   @slow
   def 					snake_case				( self					):
       A       :       Any							=						self.tokenizer(self.src_text    ,   return_tensors='''tf'''					)
       A       :       Dict							=						self.model.generate(
           model_inputs.input_ids    ,   attention_mask=model_inputs.attention_mask    ,   num_beams=2    ,   use_cache=snake_case__    ,   )
       A       :       Optional[Any]							=						self.tokenizer.batch_decode(generated_ids.numpy()    ,   skip_special_tokens=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.",
       )







'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_torch_available,
)


snake_case_     =	{
    """configuration_speecht5""": [
        """SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP""",
        """SpeechT5Config""",
        """SpeechT5HifiGanConfig""",
    ],
    """feature_extraction_speecht5""": ["""SpeechT5FeatureExtractor"""],
    """processing_speecht5""": ["""SpeechT5Processor"""],
}

try:
     if not is_sentencepiece_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     snake_case_     =	["""SpeechT5Tokenizer"""]

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     snake_case_     =	[
         """SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """SpeechT5ForSpeechToText""",
         """SpeechT5ForSpeechToSpeech""",
         """SpeechT5ForTextToSpeech""",
         """SpeechT5Model""",
         """SpeechT5PreTrainedModel""",
         """SpeechT5HifiGan""",
     ]

if TYPE_CHECKING:
     from .configuration_speechta import (
         SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP,
         SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP,
         SpeechTaConfig,
         SpeechTaHifiGanConfig,
     )
     from .feature_extraction_speechta import SpeechTaFeatureExtractor
     from .processing_speechta import SpeechTaProcessor

     try:
          if not is_sentencepiece_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_speechta import SpeechTaTokenizer

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_speechta import (
              SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST,
              SpeechTaForSpeechToSpeech,
              SpeechTaForSpeechToText,
              SpeechTaForTextToSpeech,
              SpeechTaHifiGan,
              SpeechTaModel,
              SpeechTaPreTrainedModel,
          )

else:
     import sys

     snake_case_     =	_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
