






'''simple docstring'''






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


__A    	=   logging.get_logger(__name__)

__A    	=   {
    '''facebook/s2t-small-librispeech-asr''': (
        '''https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/config.json'''
    ),
    # See all Speech2Text models at https://huggingface.co/models?filter=speech_to_text
}




class a_    (    UpperCamelCase_							):
   _snake_case        =							"""speech_to_text"""
   _snake_case        =							["""past_key_values"""]
   _snake_case        =							{"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}







   def __init__(self					,   __a=1_0_0_0_0					,   __a=1_2					,   __a=2_0_4_8					,   __a=4					,   __a=6					,   __a=2_0_4_8					,   __a=4					,   __a=0.0					,   __a=0.0					,   __a=True					,   __a=True					,   __a="relu"					,   __a=2_5_6					,   __a=0.1					,   __a=0.0					,   __a=0.0					,   __a=0.02					,   __a=2					,   __a=True					,   __a=1					,   __a=0					,   __a=2					,   __a=6_0_0_0					,   __a=1_0_2_4					,   __a=2					,   __a=(5, 5)					,   __a=1_0_2_4					,   __a=8_0					,   __a=1					,   **__a					,   )			->      str:
      """simple docstring"""






      __snake_case				:      List[str]     							= vocab_size
      __snake_case				:      Tuple     							= d_model
      __snake_case				:      str     							= encoder_ffn_dim
      __snake_case				:      Optional[int]     							= encoder_layers
      __snake_case				:      Tuple     							= encoder_attention_heads
      __snake_case				:      Dict     							= decoder_ffn_dim
      __snake_case				:      Optional[Any]     							= decoder_layers
      __snake_case				:      str     							= decoder_attention_heads
      __snake_case				:      Optional[int]     							= dropout
      __snake_case				:      str     							= attention_dropout
      __snake_case				:      Tuple     							= activation_dropout
      __snake_case				:      Tuple     							= activation_function
      __snake_case				:      Dict     							= init_std
      __snake_case				:      Any     							= encoder_layerdrop
      __snake_case				:      Union[str, Any]     							= decoder_layerdrop
      __snake_case				:      List[str]     							= use_cache
      __snake_case				:      List[Any]     							= encoder_layers
      __snake_case				:      int     							= scale_embedding  # scale factor will be sqrt(d_model) if True
      __snake_case				:      Union[str, Any]     							= max_source_positions
      __snake_case				:      Union[str, Any]     							= max_target_positions
      __snake_case				:      Any     							= num_conv_layers
      __snake_case				:      Optional[int]     							= list(__a)
      __snake_case				:      Dict     							= conv_channels
      __snake_case				:      Union[str, Any]     							= input_feat_per_channel
      __snake_case				:      Optional[int]     							= input_channels

      if len(self.conv_kernel_sizes) != self.num_conv_layers:
         raise ValueError(
             'Configuration for convolutional module is incorrect. '
             'It is required that `len(config.conv_kernel_sizes)` == `config.num_conv_layers` '
             F"""but is `len(config.conv_kernel_sizes) = {len(self.conv_kernel_sizes)}`, """
             F"""`config.num_conv_layers = {self.num_conv_layers}`.""")

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







'''simple docstring'''






import contextlib
import importlib
import io
import unittest

import transformers

# Try to import everything from transformers to ensure every object can be loaded.
from transformers import *  # noqa F406
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, require_flax, require_tf, require_torch
from transformers.utils import ContextManagers, find_labels, is_flax_available, is_tf_available, is_torch_available


if is_torch_available():
      from transformers import BertForPreTraining, BertForQuestionAnswering, BertForSequenceClassification

if is_tf_available():
      from transformers import TFBertForPreTraining, TFBertForQuestionAnswering, TFBertForSequenceClassification

if is_flax_available():
      from transformers import FlaxBertForPreTraining, FlaxBertForQuestionAnswering, FlaxBertForSequenceClassification


__A    	=   DUMMY_UNKNOWN_IDENTIFIER
# An actual model hosted on huggingface.co

__A    	=   '''main'''
# Default branch name
__A    	=   '''f2c752cfc5c0ab6f4bdec59acea69eefbee381c2'''
# One particular commit (not the top of `main`)
__A    	=   '''aaaaaaa'''
# This commit does not exist, so we should 404.

__A    	=   '''d9e9f15bc825e4b2c9249e9578f884bbcb5e3684'''
# Sha-1 of config.json on the top of `main`, for checking purposes
__A    	=   '''4b243c475af8d0a7754e87d7d096c92e5199ec2fe168a2ee7998e3b8e9bcb1d3'''




@contextlib.contextmanager
def        _SCREAMING_SNAKE_CASE  (				)    -> List[Any]:







   """simple docstring"""
   print('Welcome!'  )
   yield
   print('Bye!'  )




@contextlib.contextmanager
def        _SCREAMING_SNAKE_CASE  (				)    -> Optional[Any]:







   """simple docstring"""
   print('Bonjour!'  )
   yield
   print('Au revoir!'  )




class a_    (    unittest.TestCase							):







   def     SCREAMING_SNAKE_CASE__					(self)			->      int:
      """simple docstring"""






      assert transformers.__spec__ is not None
      assert importlib.util.find_spec('transformers') is not None




class a_    (    unittest.TestCase							):







   @unittest.mock.patch('sys.stdout'					,   new_callable=io.StringIO)
   def     SCREAMING_SNAKE_CASE__					(self					,   __a)			->      int:
      """simple docstring"""






      with ContextManagers([]):
         print('Transformers are awesome!')
      # The print statement adds a new line at the end of the output
      self.assertEqual(mock_stdout.getvalue()					,   'Transformers are awesome!\n')







   @unittest.mock.patch('sys.stdout'					,   new_callable=io.StringIO)
   def     SCREAMING_SNAKE_CASE__					(self					,   __a)			->      List[str]:
      """simple docstring"""






      with ContextManagers([context_en()]):
         print('Transformers are awesome!')
      # The output should be wrapped with an English welcome and goodbye
      self.assertEqual(mock_stdout.getvalue()					,   'Welcome!\nTransformers are awesome!\nBye!\n')







   @unittest.mock.patch('sys.stdout'					,   new_callable=io.StringIO)
   def     SCREAMING_SNAKE_CASE__					(self					,   __a)			->      Tuple:
      """simple docstring"""






      with ContextManagers([context_fr(), context_en()]):
         print('Transformers are awesome!')
      # The output should be wrapped with an English and French welcome and goodbye
      self.assertEqual(mock_stdout.getvalue()					,   'Bonjour!\nWelcome!\nTransformers are awesome!\nBye!\nAu revoir!\n')







   @require_torch
   def     SCREAMING_SNAKE_CASE__					(self)			->      Union[str, Any]:
      """simple docstring"""






      self.assertEqual(find_labels(__a)					,   ['labels'])
      self.assertEqual(find_labels(__a)					,   ['labels', 'next_sentence_label'])
      self.assertEqual(find_labels(__a)					,   ['start_positions', 'end_positions'])




      class a_    (    UpperCamelCase_							):
         pass


      self.assertEqual(find_labels(__a)					,   ['labels'])







   @require_tf
   def     SCREAMING_SNAKE_CASE__					(self)			->      List[Any]:
      """simple docstring"""






      self.assertEqual(find_labels(__a)					,   ['labels'])
      self.assertEqual(find_labels(__a)					,   ['labels', 'next_sentence_label'])
      self.assertEqual(find_labels(__a)					,   ['start_positions', 'end_positions'])




      class a_    (    UpperCamelCase_							):
         pass


      self.assertEqual(find_labels(__a)					,   ['labels'])







   @require_flax
   def     SCREAMING_SNAKE_CASE__					(self)			->      int:
      """simple docstring"""






      self.assertEqual(find_labels(__a)					,   [])
      self.assertEqual(find_labels(__a)					,   [])
      self.assertEqual(find_labels(__a)					,   [])




      class a_    (    UpperCamelCase_							):
         pass


      self.assertEqual(find_labels(__a)					,   [])