from typing import List, Optional, Union

import numpy as np

from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging


lowerCamelCase__  =					logging.get_logger(__name__)




class 					_UpperCAmelCase			(     lowerCAmelCase  ):



 '''simple docstring'''




 __A			       =					['''input_values''', '''padding_mask''']







 def __init__(		self				:   Union[str, Any]							, lowercase_				:   List[str] = 1							, lowercase_				:   Optional[int] = 24000							, lowercase_				:   int = 0.0							, lowercase_				:   Optional[int] = None							, lowercase_				:   List[Any] = None							, **lowercase_				:   List[Any]							, )						->	int:


   """simple docstring"""







   super().__init__(feature_size=A_							, sampling_rate=A_							, padding_value=A_							, **A_)
   _UpperCamelCase           =    chunk_length_s
   _UpperCamelCase           =    overlap







 @property
 def 		__UpperCAmelCase				(		self				:   List[str])						->	Dict:


   """simple docstring"""







   if self.chunk_length_s is None:
     return None
   else:
     return int(self.chunk_length_s * self.sampling_rate)







 @property
 def 		__UpperCAmelCase				(		self				:   str)						->	Any:


   """simple docstring"""







   if self.chunk_length_s is None or self.overlap is None:
     return None
   else:
     return max(1							, int((1.0 - self.overlap) * self.chunk_length))







 def __call__(		self				:   Union[str, Any]							, lowercase_				:   List[str]							, lowercase_				:   List[str] = None							, lowercase_				:   Any = False							, lowercase_				:   Dict = None							, lowercase_				:   int = None							, lowercase_				:   Tuple = None							, )						->	Tuple:


   """simple docstring"""







   if sampling_rate is not None:
     if sampling_rate != self.sampling_rate:
       raise ValueError(
           f'The model corresponding to this feature extractor: {self} was trained using a sampling rate of'
           f' {self.sampling_rate}. Please make sure that the provided audio input was sampled with'
           f' {self.sampling_rate} and not {sampling_rate}.')
   else:
     logger.warning(
         "It is strongly recommended to pass the `sampling_rate` argument to this function. "
         "Failing to do so can result in silent errors that might be hard to debug.")

   if padding and truncation:
     raise ValueError("Both padding and truncation were set. Make sure you only set one.")
   elif padding is None:
     # by default let's pad the inputs
     _UpperCamelCase           =    True

   _UpperCamelCase           =    bool(
       isinstance(A_							, (list, tuple)) and (isinstance(raw_audio[0]							, (np.ndarray, tuple, list))))

   if is_batched:
     _UpperCamelCase           =    [np.asarray(A_							, dtype=np.floataa).T for audio in raw_audio]
   elif not is_batched and not isinstance(A_							, np.ndarray):
     _UpperCamelCase           =    np.asarray(A_							, dtype=np.floataa)
   elif isinstance(A_							, np.ndarray) and raw_audio.dtype is np.dtype(np.floataa):
     _UpperCamelCase           =    raw_audio.astype(np.floataa)

   # always return batch
   if not is_batched:
     _UpperCamelCase           =    [np.asarray(A_).T]

   # verify inputs are valid
   for idx, example in enumerate(A_):
     if example.ndim > 2:
       raise ValueError(f'Expected input shape (channels, length) but got shape {example.shape}')
     if self.feature_size == 1 and example.ndim != 1:
       raise ValueError(f'Expected mono audio but example has {example.shape[-1]} channels')
     if self.feature_size == 2 and example.shape[-1] != 2:
       raise ValueError(f'Expected stereo audio but example has {example.shape[-1]} channels')

   _UpperCamelCase           =    None
   _UpperCamelCase           =    BatchFeature({"input_values": raw_audio})
   if self.chunk_stride is not None and self.chunk_length is not None and max_length is None:
     if truncation:
       _UpperCamelCase           =    min(array.shape[0] for array in raw_audio)
       _UpperCamelCase           =    int(np.floor(max_length / self.chunk_stride))
       _UpperCamelCase           =    (nb_step - 1) * self.chunk_stride + self.chunk_length
     elif padding:
       _UpperCamelCase           =    max(array.shape[0] for array in raw_audio)
       _UpperCamelCase           =    int(np.ceil(max_length / self.chunk_stride))
       _UpperCamelCase           =    (nb_step - 1) * self.chunk_stride + self.chunk_length
       _UpperCamelCase           =    """max_length"""
     else:
       _UpperCamelCase           =    input_values

        # normal padding on batch
   if padded_inputs is None:
     _UpperCamelCase           =    self.pad(
         A_							, max_length=A_							, truncation=A_							, padding=A_							, return_attention_mask=A_							, )
     if padding:
       _UpperCamelCase           =    padded_inputs.pop("attention_mask")

   _UpperCamelCase           =    []
   for example in padded_inputs.pop("input_values"):
     if self.feature_size == 1:
       _UpperCamelCase           =    example[..., None]
     input_values.append(example.T)

   _UpperCamelCase           =    input_values
   if return_tensors is not None:
     _UpperCamelCase           =    padded_inputs.convert_to_tensors(A_)

   return padded_inputs








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 		_SCREAMING_SNAKE_CASE     :
     __SCREAMING_SNAKE_CASE     					=    BlenderbotConfig
     __SCREAMING_SNAKE_CASE     					=    {}
     __SCREAMING_SNAKE_CASE     					=    '''gelu'''


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

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


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

       _UpperCAmelCase		:							int							=     ids_tensor([self.batch_size, self.seq_length]							,					self.vocab_size    )

       _UpperCAmelCase		:							List[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		:							Dict							=     prepare_blenderbot_inputs_dict(A_							,					A_							,					A_    )
       return config, inputs_dict




     def        __snake_case(  self							,					A_							,					A_    ):
       _UpperCAmelCase		:							Union[str, Any]							=     TFBlenderbotModel(config=A_    ).get_decoder()
       _UpperCAmelCase		:							List[Any]							=     inputs_dict["""input_ids"""]

       _UpperCAmelCase		:							Any							=     input_ids[:1, :]
       _UpperCAmelCase		:							List[str]							=     inputs_dict["""attention_mask"""][:1, :]
       _UpperCAmelCase		:							Tuple							=     inputs_dict["""head_mask"""]
       _UpperCAmelCase		:							Union[str, Any]							=     1

       # first forward pass
       _UpperCAmelCase		:							List[str]							=     model(A_							,					attention_mask=A_							,					head_mask=A_							,					use_cache=A_    )

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

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

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

       _UpperCAmelCase		:							Tuple							=     model(A_							,					attention_mask=A_    )[0]
       _UpperCAmelCase		:							int							=     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
       _UpperCAmelCase		:							Tuple							=     int(ids_tensor((1,)							,					output_from_past.shape[-1]    )    )
       _UpperCAmelCase		:							str							=     output_from_no_past[:, -3:, random_slice_idx]
       _UpperCAmelCase		:							str							=     output_from_past[:, :, random_slice_idx]

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




def    a__    (		snake_case__					:							Optional[Any]						,   snake_case__					:							str						,   snake_case__					:							Dict						,   snake_case__					:							str=None						,   snake_case__					:							Optional[int]=None						,   snake_case__					:							str=None						,   snake_case__					:							List[str]=None						,   snake_case__					:							List[str]=None						,   ):
  if attention_mask is None:
    _UpperCAmelCase		:							List[Any]							=     tf.cast(tf.math.not_equal(snake_case__						,   config.pad_token_id     )						,   tf.inta     )
  if decoder_attention_mask is None:
    _UpperCAmelCase		:							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:
    _UpperCAmelCase		:							List[str]							=     tf.ones((config.encoder_layers, config.encoder_attention_heads)     )
  if decoder_head_mask is None:
    _UpperCAmelCase		:							Tuple							=     tf.ones((config.decoder_layers, config.decoder_attention_heads)     )
  if cross_attn_head_mask is None:
    _UpperCAmelCase		:							Dict							=     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 		_SCREAMING_SNAKE_CASE     (						A							,	A							,	unittest.TestCase     ):
     __SCREAMING_SNAKE_CASE     					=    (TFBlenderbotForConditionalGeneration, TFBlenderbotModel) if is_tf_available() else ()
     __SCREAMING_SNAKE_CASE     					=    (TFBlenderbotForConditionalGeneration,) if is_tf_available() else ()
     __SCREAMING_SNAKE_CASE     					=    (
         {
             '''conversational''': TFBlenderbotForConditionalGeneration,
             '''feature-extraction''': TFBlenderbotModel,
             '''summarization''': TFBlenderbotForConditionalGeneration,
             '''text2text-generation''': TFBlenderbotForConditionalGeneration,
             '''translation''': TFBlenderbotForConditionalGeneration,
         }
         if is_tf_available()
         else {}
     )
     __SCREAMING_SNAKE_CASE     					=    True
     __SCREAMING_SNAKE_CASE     					=    False
     __SCREAMING_SNAKE_CASE     					=    False


     def        __snake_case(  self    ):
       _UpperCAmelCase		:							Union[str, Any]							=     TFBlenderbotModelTester(self    )
       _UpperCAmelCase		:							Any							=     ConfigTester(self							,					config_class=A_    )


     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(*A_    )


@require_tokenizers
@require_tf
class 		_SCREAMING_SNAKE_CASE     (						unittest.TestCase     ):
     __SCREAMING_SNAKE_CASE     					=    ['''My friends are cool but they eat too many carbs.''']
     __SCREAMING_SNAKE_CASE     					=    '''facebook/blenderbot-400M-distill'''


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


     @cached_property
     def        __snake_case(  self    ):
       _UpperCAmelCase		:							List[Any]							=     TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name    )
       return model




     @slow
     def        __snake_case(  self    ):
       _UpperCAmelCase		:							Any							=     self.tokenizer(self.src_text							,					return_tensors="""tf"""    )
       _UpperCAmelCase		:							List[Any]							=     self.model.generate(
           model_inputs.input_ids							,					)
       _UpperCAmelCase		:							List[str]							=     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?"
       )





