






'''simple docstring'''







def 				SCREAMING_SNAKE_CASE_ (UpperCamelCase   )   -> Any:
							lowerCamelCase__      :			List[Any] 						= current_set.copy()
							for row_index, row in enumerate(snake_case_   ):
														lowerCamelCase__      :			str 						= row[0]
														for column_index, column in enumerate(snake_case_   ):
																					if magnitude == 0:
																												lowerCamelCase__      :			Any 						= column
																												continue
																					lowerCamelCase__      :			Dict 						= column / magnitude
																					 # Subtract to cancel term
							lowerCamelCase__      :			Optional[Any] 						= current_set[0]
							lowerCamelCase__      :			Any 						= [first_row]
							lowerCamelCase__      :			str 						= current_set[1::]
							for row in current_set:
														lowerCamelCase__      :			Dict 						= []
														# If first term is 0, it is already in form we want, so we preserve it
														if row[0] == 0:
																					final_set.append(snake_case_   )
																					continue
														for column_index in range(len(snake_case_   )   ):
																					temp_row.append(first_row[column_index] - row[column_index]   )
														final_set.append(snake_case_   )
							# Create next recursion iteration set
							if len(final_set[0]   ) != 3:
														lowerCamelCase__      :			Tuple 						= final_set[0]
														lowerCamelCase__      :			Dict 						= []
														lowerCamelCase__      :			List[str] 						= []
														for row in final_set[1::]:
																					current_first_column.append(row[0]   )
																					next_iteration.append(row[1::]   )
														lowerCamelCase__      :			List[Any] 						= simplify(snake_case_   )
														for i in range(len(snake_case_   )   ):
																					resultant[i].insert(0       ,    current_first_column[i]   )
														resultant.insert(0       ,    snake_case_   )
														lowerCamelCase__      :			Optional[Any] 						= resultant
							return final_set





def 				SCREAMING_SNAKE_CASE_ (UpperCamelCase   )   -> Optional[int]:
							if len(snake_case_   ) == 0:
														raise IndexError("""solve_simultaneous() requires n lists of length n+1"""   )
							lowerCamelCase__      :			Dict 						= len(snake_case_   ) + 1
							if any(len(snake_case_   ) != _length for item in equations   ):
														raise IndexError("""solve_simultaneous() requires n lists of length n+1"""   )
							for row in equations:
														if any(not isinstance(snake_case_       ,    (int, float)   ) for column in row   ):
																					raise ValueError("""solve_simultaneous() requires lists of integers"""   )
							if len(snake_case_   ) == 1:
														return [equations[0][-1] / equations[0][0]]
							lowerCamelCase__      :			str 						= equations.copy()
							if any(0 in row for row in data_set   ):
														lowerCamelCase__      :			Dict 						= data_set.copy()
														lowerCamelCase__      :			Union[str, Any] 						= []
														for row_index, row in enumerate(snake_case_   ):
																					if 0 not in row:
																												lowerCamelCase__      :			List[Any] 						= data_set.pop(snake_case_   )
																												break
														if not full_row:
																					raise ValueError("""solve_simultaneous() requires at least 1 full equation"""   )
														data_set.insert(0       ,    snake_case_   )
							lowerCamelCase__      :			int 						= data_set.copy()
							lowerCamelCase__      :			int 						= simplify(snake_case_   )
							lowerCamelCase__      :			List[Any] 						= simplified[::-1]
							lowerCamelCase__      :			list 						= []
							for row in simplified:
														lowerCamelCase__      :			Optional[int] 						= row[-1]
														if not solutions:
																					if row[-2] == 0:
																												solutions.append(0   )
																												continue
																					solutions.append(current_solution / row[-2]   )
																					continue
														lowerCamelCase__      :			Tuple 						= row.copy()[: len(snake_case_   ) - 1 :]
														while temp_row[0] == 0:
																					temp_row.pop(0   )
														if len(snake_case_   ) == 0:
																					solutions.append(0   )
																					continue
														lowerCamelCase__      :			List[Any] 						= temp_row[1::]
														lowerCamelCase__      :			List[Any] 						= temp_row[::-1]
														for column_index, column in enumerate(snake_case_   ):
																					current_solution -= column * solutions[column_index]
														solutions.append(snake_case_   )
							lowerCamelCase__      :			List[Any] 						= []
							for item in solutions:
														final.append(float(round(snake_case_       ,    5   )   )   )
							return final[::-1]


if __name__ == "__main__":
			import doctest

			doctest.testmod()
			_A			:    Tuple					  =[
			    [2, 1, 1, 1, 1, 4],
			    [1, 2, 1, 1, 1, 5],
			    [1, 1, 2, 1, 1, 6],
			    [1, 1, 1, 2, 1, 7],
			    [1, 1, 1, 1, 2, 8],
			]
			print(solve_simultaneous(eq))
			print(solve_simultaneous([[4, 2]]))









'''simple docstring'''







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


_A			:    Any					  =logging.get_logger(__name__)

_A			:    Dict					  ={
    '''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			(       _lowercase	):
							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:       Optional[Any]    ,							UpperCamelCase__:       int=50_265    ,							UpperCamelCase__:       int=1_024    ,							UpperCamelCase__:       Optional[Any]=12    ,							UpperCamelCase__:       Dict=16    ,							UpperCamelCase__:       int=4_096    ,							UpperCamelCase__:       Tuple="gelu"    ,							UpperCamelCase__:       int=512    ,							UpperCamelCase__:       Dict=0.1    ,							UpperCamelCase__:       Tuple=0.0    ,							UpperCamelCase__:       str=0.0    ,							UpperCamelCase__:       Any=2    ,							UpperCamelCase__:       Dict=0.02    ,							UpperCamelCase__:       Optional[Any]=0.0    ,							UpperCamelCase__:       str=True    ,							UpperCamelCase__:       Tuple=False    ,							UpperCamelCase__:       Union[str, Any]=True    ,							UpperCamelCase__:       Tuple=True    ,							UpperCamelCase__:       Dict=1    ,							UpperCamelCase__:       List[str]=0    ,							UpperCamelCase__:       Union[str, Any]=2    ,							**UpperCamelCase__:       str    ,							):
														lowerCamelCase__      :			Any 						= vocab_size
														lowerCamelCase__      :			Tuple 						= d_model
														lowerCamelCase__      :			Any 						= decoder_layers
														lowerCamelCase__      :			Dict 						= decoder_attention_heads
														lowerCamelCase__      :			str 						= decoder_ffn_dim
														lowerCamelCase__      :			Tuple 						= activation_function
														lowerCamelCase__      :			Dict 						= max_position_embeddings
														lowerCamelCase__      :			int 						= dropout
														lowerCamelCase__      :			int 						= attention_dropout
														lowerCamelCase__      :			List[Any] 						= activation_dropout
														lowerCamelCase__      :			Union[str, Any] 						= init_std
														lowerCamelCase__      :			Optional[int] 						= decoder_layerdrop
														lowerCamelCase__      :			Dict 						= use_cache
														lowerCamelCase__      :			Any 						= scale_embedding
														lowerCamelCase__      :			Optional[int] 						= use_learned_position_embeddings
														lowerCamelCase__      :			List[str] 						= layernorm_embedding

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

