





from __future__ import annotations





def       _UpperCamelCase       (a__				:list[int]    ) ->    Optional[Any]:






							"""simple docstring"""





							if len(a__    ) == 0:
														return array

							UpperCamelCase__       ,       UpperCamelCase__			       =     min(a__    ), max(a__    )

							# Compute the variables
							UpperCamelCase__			       =     _max - _min + 1
							UpperCamelCase__       ,       UpperCamelCase__			       =     [0] * holes_range, [0] * holes_range

							# Make the sorting.
							for i in array:
														UpperCamelCase__			       =     i - _min
														UpperCamelCase__			       =     i
														holes_repeat[index] += 1

							# Makes the array back by replacing the numbers.
							UpperCamelCase__			       =     0
							for i in range(a__    ):
														while holes_repeat[i] > 0:
																					UpperCamelCase__			       =     holes[i]
																					index += 1
																					holes_repeat[i] -= 1

    # Returns the sorted array.
							return array


if __name__ == "__main__":
					import doctest

					doctest.testmod()
					UpperCamelCase__     =	input("Enter numbers separated by comma:\n")
					UpperCamelCase__     =	[int(x) for x in user_input.split(",")]
					print(pigeon_sort(unsorted))











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


UpperCamelCase__     =	logging.get_logger(__name__)

UpperCamelCase__     =	{
    "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     __SCREAMING_SNAKE_CASE   (       _a    ):
							snake_case       : Dict         =    """speech_to_text_2"""
							snake_case       : List[Any]         =    ["""past_key_values"""]
							snake_case       : List[str]         =    {"""num_attention_heads""": """decoder_attention_heads""", """hidden_size""": """d_model"""}






							def __init__(  self					,	__lowerCAmelCase=10000					,	__lowerCAmelCase=6					,	__lowerCAmelCase=2048					,	__lowerCAmelCase=4					,	__lowerCAmelCase=0.0					,	__lowerCAmelCase=True					,	__lowerCAmelCase="relu"					,	__lowerCAmelCase=256					,	__lowerCAmelCase=0.1					,	__lowerCAmelCase=0.0					,	__lowerCAmelCase=0.0					,	__lowerCAmelCase=0.02					,	__lowerCAmelCase=2					,	__lowerCAmelCase=True					,	__lowerCAmelCase=1					,	__lowerCAmelCase=0					,	__lowerCAmelCase=2					,	__lowerCAmelCase=1024					,	**__lowerCAmelCase					,	):
														UpperCamelCase__			       =     vocab_size
														UpperCamelCase__			       =     d_model
														UpperCamelCase__			       =     decoder_ffn_dim
														UpperCamelCase__			       =     decoder_layers
														UpperCamelCase__			       =     decoder_attention_heads
														UpperCamelCase__			       =     dropout
														UpperCamelCase__			       =     attention_dropout
														UpperCamelCase__			       =     activation_dropout
														UpperCamelCase__			       =     activation_function
														UpperCamelCase__			       =     init_std
														UpperCamelCase__			       =     decoder_layerdrop
														UpperCamelCase__			       =     use_cache
														UpperCamelCase__			       =     decoder_layers
														UpperCamelCase__			       =     scale_embedding  # scale factor will be sqrt(d_model) if True
														UpperCamelCase__			       =     max_target_positions

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




