






def 		SCREAMING_SNAKE_CASE							(					__lowerCAmelCase = "The quick brown fox jumps over the lazy dog"					,    ) ->	bool:
     UpperCamelCase__			:   Optional[int]       				=  set()

     # Replace all the whitespace in our sentence
     UpperCamelCase__			:   Dict       				=  input_str.replace(" "					,    ""			)
     for alpha in input_str:
          if "a" <= alpha.lower() <= "z":
               frequency.add(alpha.lower()			)
     return len(__UpperCAmelCase			) == 26







def 		SCREAMING_SNAKE_CASE							(					__lowerCAmelCase = "The quick brown fox jumps over the lazy dog"					,    ) ->	bool:
     UpperCamelCase__			:   List[str]       				=  [False] * 26
     for char in input_str:
          if char.islower():
               UpperCamelCase__			:   List[Any]       				=  True
          elif char.isupper():
               UpperCamelCase__			:   Dict       				=  True
     return all(__UpperCAmelCase			)







def 		SCREAMING_SNAKE_CASE							(					__lowerCAmelCase = "The quick brown fox jumps over the lazy dog"					,    ) ->	bool:
     return len({char for char in input_str.lower() if char.isalpha()}			) == 26







def 		SCREAMING_SNAKE_CASE							(					) ->	None:
     from timeit import timeit

     UpperCamelCase__			:   List[Any]       				=  "from __main__ import is_pangram, is_pangram_faster, is_pangram_fastest"
     print(timeit("is_pangram()"					,    setup=__UpperCAmelCase			)			)
     print(timeit("is_pangram_faster()"					,    setup=__UpperCAmelCase			)			)
     print(timeit("is_pangram_fastest()"					,    setup=__UpperCAmelCase			)			)
     # 5.348480500048026, 2.6477354579837993, 1.8470395830227062
     # 5.036091582966037, 2.644472333951853,  1.8869528750656173


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 benchmark()





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


lowerCamelCase__ :   List[str]					=							logging.get_logger(__name__)

lowerCamelCase__ :   List[str]					=							{
    '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        lowerCamelCase_							(      _SCREAMING_SNAKE_CASE					):


 '''simple docstring'''







 lowercase_				    =    "speech_to_text"
 lowercase_				    =    ["past_key_values"]
 lowercase_				    =    {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}



 def __init__(							self  :		Tuple				,				_lowerCAmelCase  :		List[Any]=10_000				,				_lowerCAmelCase  :		List[Any]=12				,				_lowerCAmelCase  :		Union[str, Any]=2_048				,				_lowerCAmelCase  :		Optional[int]=4				,				_lowerCAmelCase  :		Union[str, Any]=6				,				_lowerCAmelCase  :		Optional[int]=2_048				,				_lowerCAmelCase  :		Optional[Any]=4				,				_lowerCAmelCase  :		Any=0.0				,				_lowerCAmelCase  :		Optional[Any]=0.0				,				_lowerCAmelCase  :		List[str]=True				,				_lowerCAmelCase  :		List[Any]=True				,				_lowerCAmelCase  :		int="relu"				,				_lowerCAmelCase  :		Union[str, Any]=256				,				_lowerCAmelCase  :		List[str]=0.1				,				_lowerCAmelCase  :		str=0.0				,				_lowerCAmelCase  :		Tuple=0.0				,				_lowerCAmelCase  :		List[str]=0.02				,				_lowerCAmelCase  :		List[str]=2				,				_lowerCAmelCase  :		Tuple=True				,				_lowerCAmelCase  :		List[str]=1				,				_lowerCAmelCase  :		str=0				,				_lowerCAmelCase  :		str=2				,				_lowerCAmelCase  :		Union[str, Any]=6_000				,				_lowerCAmelCase  :		List[str]=1_024				,				_lowerCAmelCase  :		str=2				,				_lowerCAmelCase  :		Optional[Any]=(5, 5)				,				_lowerCAmelCase  :		str=1_024				,				_lowerCAmelCase  :		str=80				,				_lowerCAmelCase  :		Tuple=1				,				**_lowerCAmelCase  :		Any				,				):
       SCREAMING_SNAKE_CASE_							=			vocab_size
       SCREAMING_SNAKE_CASE_							=			d_model
       SCREAMING_SNAKE_CASE_							=			encoder_ffn_dim
       SCREAMING_SNAKE_CASE_							=			encoder_layers
       SCREAMING_SNAKE_CASE_							=			encoder_attention_heads
       SCREAMING_SNAKE_CASE_							=			decoder_ffn_dim
       SCREAMING_SNAKE_CASE_							=			decoder_layers
       SCREAMING_SNAKE_CASE_							=			decoder_attention_heads
       SCREAMING_SNAKE_CASE_							=			dropout
       SCREAMING_SNAKE_CASE_							=			attention_dropout
       SCREAMING_SNAKE_CASE_							=			activation_dropout
       SCREAMING_SNAKE_CASE_							=			activation_function
       SCREAMING_SNAKE_CASE_							=			init_std
       SCREAMING_SNAKE_CASE_							=			encoder_layerdrop
       SCREAMING_SNAKE_CASE_							=			decoder_layerdrop
       SCREAMING_SNAKE_CASE_							=			use_cache
       SCREAMING_SNAKE_CASE_							=			encoder_layers
       SCREAMING_SNAKE_CASE_							=			scale_embedding  # scale factor will be sqrt(d_model) if True
       SCREAMING_SNAKE_CASE_							=			max_source_positions
       SCREAMING_SNAKE_CASE_							=			max_target_positions
       SCREAMING_SNAKE_CASE_							=			num_conv_layers
       SCREAMING_SNAKE_CASE_							=			list(_lowerCAmelCase						)
       SCREAMING_SNAKE_CASE_							=			conv_channels
       SCREAMING_SNAKE_CASE_							=			input_feat_per_channel
       SCREAMING_SNAKE_CASE_							=			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=_lowerCAmelCase				,				bos_token_id=_lowerCAmelCase				,				eos_token_id=_lowerCAmelCase				,				is_encoder_decoder=_lowerCAmelCase				,				decoder_start_token_id=_lowerCAmelCase				,				**_lowerCAmelCase				,				)