



'''simple docstring'''

def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						list[list[int | float]]							)      ->						int:







	"""simple docstring"""
	lowerCAmelCase   =     len(_SCREAMING_SNAKE_CASE							)
	lowerCAmelCase   =     len(matrix[0]							)
	lowerCAmelCase   =     min(_SCREAMING_SNAKE_CASE							,		_SCREAMING_SNAKE_CASE							)

	for row in range(_SCREAMING_SNAKE_CASE							):
		# Check if diagonal element is not zero
		if matrix[row][row] != 0:
			# Eliminate all the elements below the diagonal
			for col in range(row + 1							,		_SCREAMING_SNAKE_CASE							):
				lowerCAmelCase   =     matrix[col][row] / matrix[row][row]
				for i in range(_SCREAMING_SNAKE_CASE							,		_SCREAMING_SNAKE_CASE							):
					matrix[col][i] -= multiplier * matrix[row][i]
		else:
			# Find a non-zero diagonal element to swap rows
			lowerCAmelCase   =     True
			for i in range(row + 1							,		_SCREAMING_SNAKE_CASE							):
				if matrix[i][row] != 0:
					lowerCAmelCase,       lowerCAmelCase   =     matrix[i], matrix[row]
					lowerCAmelCase   =     False
					break
			if reduce:
				rank -= 1
				for i in range(_SCREAMING_SNAKE_CASE							):
					lowerCAmelCase   =     matrix[i][rank]

            # Reduce the row pointer by one to stay on the same row
			row -= 1

	return rank


if __name__ == "__main__":
				import doctest

				doctest.testmod()




'''simple docstring'''

from typing import List, Optional, Union

from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType

class __snake_case(				_lowerCAmelCase ):


						'''simple docstring'''



						UpperCAmelCase							:   Union[str, Any]									=		["image_processor", "tokenizer"]
						UpperCAmelCase							:   Tuple									=		"BlipImageProcessor"
						UpperCAmelCase							:   Union[str, Any]									=		("BertTokenizer", "BertTokenizerFast")


						def __init__( self     ,	A_     ,	A_				)     ->     Dict:
							lowerCAmelCase   =     False
							super().__init__(A_     ,	A_				)
							lowerCAmelCase   =     self.image_processor


						def __call__( self     ,	A_ = None     ,	A_ = None     ,	A_ = True     ,	A_ = False     ,	A_ = None     ,	A_ = None     ,	A_ = 0     ,	A_ = None     ,	A_ = None     ,	A_ = False     ,	A_ = False     ,	A_ = False     ,	A_ = False     ,	A_ = False     ,	A_ = True     ,	A_ = None     ,	**A_     ,	)     ->     BatchEncoding:
							if images is None and text is None:
								raise ValueError("""You have to specify either images or text."""				)

							# Get only text
							if images is None:
								lowerCAmelCase   =     self.tokenizer
								lowerCAmelCase   =     self.tokenizer(
								    text=A_     ,	add_special_tokens=A_     ,	padding=A_     ,	truncation=A_     ,	max_length=A_     ,	stride=A_     ,	pad_to_multiple_of=A_     ,	return_attention_mask=A_     ,	return_overflowing_tokens=A_     ,	return_special_tokens_mask=A_     ,	return_offsets_mapping=A_     ,	return_token_type_ids=A_     ,	return_length=A_     ,	verbose=A_     ,	return_tensors=A_     ,	**A_     ,	)
								return text_encoding

							# add pixel_values
							lowerCAmelCase   =     self.image_processor(A_     ,	return_tensors=A_				)

							if text is not None:
								lowerCAmelCase   =     self.tokenizer(
								    text=A_     ,	add_special_tokens=A_     ,	padding=A_     ,	truncation=A_     ,	max_length=A_     ,	stride=A_     ,	pad_to_multiple_of=A_     ,	return_attention_mask=A_     ,	return_overflowing_tokens=A_     ,	return_special_tokens_mask=A_     ,	return_offsets_mapping=A_     ,	return_token_type_ids=A_     ,	return_length=A_     ,	verbose=A_     ,	return_tensors=A_     ,	**A_     ,	)
							else:
								lowerCAmelCase   =     None

							if text_encoding is not None:
								encoding_image_processor.update(A_				)

							return encoding_image_processor


						def     __snake_case   ( self     ,	*A_     ,	**A_				)     ->     Union[str, Any]:
							return self.tokenizer.batch_decode(*A_     ,	**A_				)


						def     __snake_case   ( self     ,	*A_     ,	**A_				)     ->     Tuple:
							return self.tokenizer.decode(*A_     ,	**A_				)




						@property
						def     __snake_case   ( self				)     ->     str:
							lowerCAmelCase   =     self.tokenizer.model_input_names
							lowerCAmelCase   =     self.image_processor.model_input_names
							return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names				)				)