



"""simple docstring"""

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


a_			   =					logging.get_logger(__name__)

a_			   =					{
    '''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    __lowercase     (					_UpperCAmelCase):







						"""simple docstring"""







						_A		:							Optional[Any]							       =		"""trocr"""
						_A		:							Union[str, Any]							       =		["""past_key_values"""]
						_A		:							str							       =		{
						    """num_attention_heads""": """decoder_attention_heads""",
						    """hidden_size""": """d_model""",
						    """num_hidden_layers""": """decoder_layers""",
						}







						def __init__(self   ,		lowercase__=5_02_65   ,		lowercase__=10_24   ,		lowercase__=12   ,		lowercase__=16   ,		lowercase__=40_96   ,		lowercase__="gelu"   ,		lowercase__=5_12   ,		lowercase__=0.1   ,		lowercase__=0.0   ,		lowercase__=0.0   ,		lowercase__=2   ,		lowercase__=0.02   ,		lowercase__=0.0   ,		lowercase__=True   ,		lowercase__=False   ,		lowercase__=True   ,		lowercase__=True   ,		lowercase__=1   ,		lowercase__=0   ,		lowercase__=2   ,		**lowercase__   ,		):
											snake_case_					:		Union[str, Any] 					=					vocab_size
											snake_case_					:		int 					=					d_model
											snake_case_					:		Any 					=					decoder_layers
											snake_case_					:		str 					=					decoder_attention_heads
											snake_case_					:		Optional[Any] 					=					decoder_ffn_dim
											snake_case_					:		str 					=					activation_function
											snake_case_					:		Dict 					=					max_position_embeddings
											snake_case_					:		Dict 					=					dropout
											snake_case_					:		Optional[int] 					=					attention_dropout
											snake_case_					:		Dict 					=					activation_dropout
											snake_case_					:		Tuple 					=					init_std
											snake_case_					:		Optional[int] 					=					decoder_layerdrop
											snake_case_					:		Optional[int] 					=					use_cache
											snake_case_					:		Union[str, Any] 					=					scale_embedding
											snake_case_					:		Optional[Any] 					=					use_learned_position_embeddings
											snake_case_					:		Dict 					=					layernorm_embedding

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









"""simple docstring"""

from random import randint
from tempfile import TemporaryFile

import numpy as np







def 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  List[Any]				,    SCREAMING_SNAKE_CASE__ :  Dict				,    SCREAMING_SNAKE_CASE__ :  Optional[int]			):



					"""simple docstring"""





					snake_case_					:		Union[str, Any] 					=					0
					if start < end:
										snake_case_					:		Union[str, Any] 					=					randint(SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__			)
										snake_case_					:		List[Any] 					=					a[end]
										snake_case_					:		Dict 					=					a[pivot]
										snake_case_					:		Any 					=					temp

										snake_case_					,			snake_case_					:		Dict 					=					_in_place_partition(SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__			)
										count += _in_place_quick_sort(SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__				,    p - 1			)
										count += _in_place_quick_sort(SCREAMING_SNAKE_CASE__				,    p + 1				,    SCREAMING_SNAKE_CASE__			)
					return count







def 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  int				,    SCREAMING_SNAKE_CASE__ :  List[Any]				,    SCREAMING_SNAKE_CASE__ :  Dict			):



					"""simple docstring"""





					snake_case_					:		Tuple 					=					0
					snake_case_					:		List[Any] 					=					randint(SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__			)
					snake_case_					:		Dict 					=					a[end]
					snake_case_					:		List[Any] 					=					a[pivot]
					snake_case_					:		Optional[Any] 					=					temp
					snake_case_					:		List[str] 					=					start - 1
					for index in range(SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__			):
										count += 1
										if a[index] < a[end]:  # check if current val is less than pivot value
															snake_case_					:		Any 					=					new_pivot_index + 1
															snake_case_					:		Tuple 					=					a[new_pivot_index]
															snake_case_					:		Optional[int] 					=					a[index]
															snake_case_					:		Tuple 					=					temp

					snake_case_					:		Union[str, Any] 					=					a[new_pivot_index + 1]
					snake_case_					:		Union[str, Any] 					=					a[end]
					snake_case_					:		Union[str, Any] 					=					temp
					return new_pivot_index + 1, count


a_			   =					TemporaryFile()
a_			   =					100  # 1000 elements are to be sorted


a_   ,    a_			   =					0, 1  # mean and standard deviation
a_			   =					np.random.normal(mu, sigma, p)
np.save(outfile, X)
print('''The array is''')
print(X)


outfile.seek(0)  # using the same array
a_			   =					np.load(outfile)
a_			   =					len(M) - 1
a_			   =					_in_place_quick_sort(M, 0, r)

print(
    '''No of Comparisons for 100 elements selected from a standard normal distribution'''
    '''is :'''
)


print(z)




