def 					_snake_case	(  ):
					return [list(range(1000 - i  ,     -1000 - i  ,     -1						)						) for i in range(1000						)]


_lowerCAmelCase       =		generate_large_matrix()
_lowerCAmelCase       =		(
    [[4, 3, 2, -1], [3, 2, 1, -1], [1, 1, -1, -2], [-1, -1, -2, -3]],
    [[3, 2], [1, 0]],
    [[7, 7, 6]],
    [[7, 7, 6], [-1, -2, -3]],
    grid,
)
def 					_snake_case	(  __snake_case						):
					assert all(row == sorted(__snake_case  ,     reverse=__snake_case						) for row in grid						)
					assert all(list(__snake_case						) == sorted(__snake_case  ,     reverse=__snake_case						) for col in zip(*__snake_case						)						)
def 					_snake_case	(  __snake_case						):
					_UpperCamelCase			     =     0
					_UpperCamelCase			     =     len(__snake_case						) - 1

					# Edge cases such as no values or all numbers are negative.
					if not array or array[0] < 0:
										return 0

					while right + 1 > left:
										_UpperCamelCase			     =     (left + right) // 2
										_UpperCamelCase			     =     array[mid]

										# Num must be negative and the index must be greater than or equal to 0.
										if num < 0 and array[mid - 1] >= 0:
															return mid

										if num >= 0:
															_UpperCamelCase			     =     mid + 1
										else:
															_UpperCamelCase			     =     mid - 1
    # No negative numbers so return the last index of the array + 1 which is the length.
					return len(__snake_case						)
def 					_snake_case	(  __snake_case						):
					_UpperCamelCase			     =     0
					_UpperCamelCase			     =     len(grid[0]						)

					for i in range(len(__snake_case						)						):
										_UpperCamelCase			     =     find_negative_index(grid[i][:bound]						)
										total += bound
					return (len(__snake_case						) * len(grid[0]						)) - total
def 					_snake_case	(  __snake_case						):
					return len([number for row in grid for number in row if number < 0]						)
def 					_snake_case	(  __snake_case						):
					_UpperCamelCase			     =     0
					for row in grid:
										for i, number in enumerate(__snake_case						):
															if number < 0:
																				total += len(__snake_case						) - i
																				break
					return total
def 					_snake_case	(  ):
					from timeit import timeit

					print('''Running benchmarks'''						)
					_UpperCamelCase			     =     (
					    '''from __main__ import count_negatives_binary_search, '''
					    '''count_negatives_brute_force, count_negatives_brute_force_with_break, grid'''
					)
					for func in (
					    "count_negatives_binary_search",  # took 0.7727 seconds
					    "count_negatives_brute_force_with_break",  # took 4.6505 seconds
					    "count_negatives_brute_force",  # took 12.8160 seconds
					):
										_UpperCamelCase			     =     timeit(f"""{func}(grid=grid)"""  ,     setup=__snake_case  ,     number=500						)
										print(f"""{func}() took {time:0.4f} seconds"""						)


if __name__ == "__main__":
					import doctest

					doctest.testmod()
					benchmark()





"""simple docstring"""







from collections import OrderedDict
from typing import Any, Mapping, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...feature_extraction_utils import FeatureExtractionMixin
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import TensorType, logging


UpperCamelCase     =							logging.get_logger(__name__)

UpperCamelCase     =							{
    """deepmind/language-perceiver""": """https://huggingface.co/deepmind/language-perceiver/resolve/main/config.json""",
    # See all Perceiver models at https://huggingface.co/models?filter=perceiver
}






class       UpperCamelCase__			(   _lowerCAmelCase      ):

			"""simple docstring"""


			A__	:					Optional[int]							  =     "perceiver"


			def __init__(      self						,	SCREAMING_SNAKE_CASE__=256						,	SCREAMING_SNAKE_CASE__=1280						,	SCREAMING_SNAKE_CASE__=768						,	SCREAMING_SNAKE_CASE__=1						,	SCREAMING_SNAKE_CASE__=26						,	SCREAMING_SNAKE_CASE__=8						,	SCREAMING_SNAKE_CASE__=8						,	SCREAMING_SNAKE_CASE__=None						,	SCREAMING_SNAKE_CASE__=None						,	SCREAMING_SNAKE_CASE__="kv"						,	SCREAMING_SNAKE_CASE__=1						,	SCREAMING_SNAKE_CASE__=1						,	SCREAMING_SNAKE_CASE__="gelu"						,	SCREAMING_SNAKE_CASE__=0.1						,	SCREAMING_SNAKE_CASE__=0.0_2						,	SCREAMING_SNAKE_CASE__=1e-12						,	SCREAMING_SNAKE_CASE__=True						,	SCREAMING_SNAKE_CASE__=262						,	SCREAMING_SNAKE_CASE__=2048						,	SCREAMING_SNAKE_CASE__=56						,	SCREAMING_SNAKE_CASE__=[368, 496]						,	SCREAMING_SNAKE_CASE__=16						,	SCREAMING_SNAKE_CASE__=1920						,	SCREAMING_SNAKE_CASE__=16						,	SCREAMING_SNAKE_CASE__=[1, 16, 224, 224]						,	**SCREAMING_SNAKE_CASE__						,	)      ->	Optional[int]:
				super().__init__(**SCREAMING_SNAKE_CASE__		)

				A__											=					num_latents
				A__											=					d_latents
				A__											=					d_model
				A__											=					num_blocks
				A__											=					num_self_attends_per_block
				A__											=					num_self_attention_heads
				A__											=					num_cross_attention_heads
				A__											=					qk_channels
				A__											=					v_channels
				A__											=					cross_attention_shape_for_attention
				A__											=					self_attention_widening_factor
				A__											=					cross_attention_widening_factor
				A__											=					hidden_act
				A__											=					attention_probs_dropout_prob
				A__											=					initializer_range
				A__											=					layer_norm_eps
				A__											=					use_query_residual
				# masked language modeling attributes
				A__											=					vocab_size
				A__											=					max_position_embeddings
				# image classification attributes
				A__											=					image_size
				# flow attributes
				A__											=					train_size
				# multimodal autoencoding attributes
				A__											=					num_frames
				A__											=					audio_samples_per_frame
				A__											=					samples_per_patch
				A__											=					output_shape






class       UpperCamelCase__			(   _lowerCAmelCase      ):

			"""simple docstring"""


			@property
			def   snake_case__	(      self		)      ->	Mapping[str, Mapping[int, str]]:
				if self.task == "multiple-choice":
					A__											=					{0: "batch", 1: "choice", 2: "sequence"}
				else:
					A__											=					{0: "batch", 1: "sequence"}
				return OrderedDict(
				    [
				        ("inputs", dynamic_axis),
				        ("attention_mask", dynamic_axis),
				    ]		)


			@property
			def   snake_case__	(      self		)      ->	float:
				return 1e-4







			def   snake_case__	(      self						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__ = -1						,	SCREAMING_SNAKE_CASE__ = -1						,	SCREAMING_SNAKE_CASE__ = -1						,	SCREAMING_SNAKE_CASE__ = False						,	SCREAMING_SNAKE_CASE__ = None						,	SCREAMING_SNAKE_CASE__ = 3						,	SCREAMING_SNAKE_CASE__ = 40						,	SCREAMING_SNAKE_CASE__ = 40						,	)      ->	Mapping[str, Any]:
				# copied from `transformers.onnx.config.OnnxConfig` and slightly altered/simplified

				if isinstance(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__		):
					# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
					A__											=					compute_effective_axis_dimension(
					    SCREAMING_SNAKE_CASE__						,	fixed_dimension=OnnxConfig.default_fixed_batch						,	num_token_to_add=0		)
					# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
					A__											=					preprocessor.num_special_tokens_to_add(SCREAMING_SNAKE_CASE__		)
					A__											=					compute_effective_axis_dimension(
					    SCREAMING_SNAKE_CASE__						,	fixed_dimension=OnnxConfig.default_fixed_sequence						,	num_token_to_add=SCREAMING_SNAKE_CASE__		)
					# Generate dummy inputs according to compute batch and sequence
					A__											=					[" ".join(["a"]		) * seq_length] * batch_size
					A__											=					dict(preprocessor(SCREAMING_SNAKE_CASE__						,	return_tensors=SCREAMING_SNAKE_CASE__		)		)
					A__											=					inputs.pop("input_ids"		)
					return inputs
				elif isinstance(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__		) and preprocessor.model_input_names[0] == "pixel_values":
					# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
					A__											=					compute_effective_axis_dimension(SCREAMING_SNAKE_CASE__						,	fixed_dimension=OnnxConfig.default_fixed_batch		)
					A__											=					self._generate_dummy_images(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__		)
					A__											=					dict(preprocessor(images=SCREAMING_SNAKE_CASE__						,	return_tensors=SCREAMING_SNAKE_CASE__		)		)
					A__											=					inputs.pop("pixel_values"		)
					return inputs
				else:
					raise ValueError(
					    "Unable to generate dummy inputs for the model. Please provide a tokenizer or a preprocessor."		)




