






'''simple docstring'''


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


SCREAMING_SNAKE_CASE_					   =				logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_					   =				{
    'microsoft/trocr-base-handwritten': (
        'https://huggingface.co/microsoft/trocr-base-handwritten/resolve/main/config.json'
    ),
    # See all TrOCR models at https://huggingface.co/models?filter=trocr
}




class 				lowerCAmelCase       (				_lowerCamelCase ):







   """simple docstring"""







   _A							= '''trocr'''
   _A							= ['''past_key_values''']
   _A							= {
       '''num_attention_heads''': '''decoder_attention_heads''',
       '''hidden_size''': '''d_model''',
       '''num_hidden_layers''': '''decoder_layers''',
   }



   def __init__(			self , _A=50265 , _A=1024 , _A=12 , _A=16 , _A=4096 , _A="gelu" , _A=512 , _A=0.1 , _A=0.0 , _A=0.0 , _A=2 , _A=0.02 , _A=0.0 , _A=True , _A=False , _A=True , _A=True , _A=1 , _A=0 , _A=2 , **_A , )     -> Optional[Any]:
    __a     :						str          =    vocab_size
    __a     :						str          =    d_model
    __a     :						Tuple          =    decoder_layers
    __a     :						Optional[Any]          =    decoder_attention_heads
    __a     :						str          =    decoder_ffn_dim
    __a     :						str          =    activation_function
    __a     :						Optional[Any]          =    max_position_embeddings
    __a     :						List[Any]          =    dropout
    __a     :						List[Any]          =    attention_dropout
    __a     :						Any          =    activation_dropout
    __a     :						int          =    init_std
    __a     :						Tuple          =    decoder_layerdrop
    __a     :						List[Any]          =    use_cache
    __a     :						Union[str, Any]          =    scale_embedding
    __a     :						Union[str, Any]          =    use_learned_position_embeddings
    __a     :						List[str]          =    layernorm_embedding

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









"""simple docstring"""

from __future__ import annotations
def  __snake_case							(	__A      :						list[int]     )     ->       list[int]:






				'''simple docstring'''
				if len(__A     ) == 0:
								return array

				SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						:							Optional[int]							=     min(__A     ), max(__A     )

				# Compute the variables
				SCREAMING_SNAKE_CASE						:							Optional[int]							=     _max - _min + 1
				SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						:							List[str]							=     [0] * holes_range, [0] * holes_range

				# Make the sorting.
				for i in array:
								SCREAMING_SNAKE_CASE						:							List[Any]							=     i - _min
								SCREAMING_SNAKE_CASE						:							Dict							=     i
								holes_repeat[index] += 1

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

    # Returns the sorted array.
				return array


if __name__ == "__main__":
							import doctest

							doctest.testmod()
							A_						:    Optional[Any]				=      input('Enter numbers separated by comma:\n')
							A_						:    Union[str, Any]				=      [int(x) for x in user_input.split(',')]
							print(pigeon_sort(unsorted))





