
def 						UpperCAmelCase      (     )		->     list[list[int]]:







				"""simple docstring"""



				return [list(range(1_0_0_0 - i		,       -1_0_0_0 - i		,       -1						)						) for i in range(1_0_0_0						)]


SCREAMING_SNAKE_CASE							:List[str]										=    generate_large_matrix()
SCREAMING_SNAKE_CASE							:str										=    (
    [[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 						UpperCAmelCase      (     a_						)		->     None:







				"""simple docstring"""



				assert all(row == sorted(a_		,       reverse=a_						) for row in grid						)
				assert all(list(a_						) == sorted(a_		,       reverse=a_						) for col in zip(*a_						)						)


def 						UpperCAmelCase      (     a_						)		->     int:







				"""simple docstring"""



				__A             =			0
				__A             =			len(a_						) - 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:
								__A             =			(left + right) // 2
								__A             =			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:
												__A             =			mid + 1
								else:
												__A             =			mid - 1
    # No negative numbers so return the last index of the array + 1 which is the length.
				return len(a_						)


def 						UpperCAmelCase      (     a_						)		->     int:







				"""simple docstring"""



				__A             =			0
				__A             =			len(grid[0]						)

				for i in range(len(a_						)						):
								__A             =			find_negative_index(grid[i][:bound]						)
								total += bound
				return (len(a_						) * len(grid[0]						)) - total


def 						UpperCAmelCase      (     a_						)		->     int:







				"""simple docstring"""



				return len([number for row in grid for number in row if number < 0]						)


def 						UpperCAmelCase      (     a_						)		->     int:







				"""simple docstring"""



				__A             =			0
				for row in grid:
								for i, number in enumerate(a_						):
												if number < 0:
																total += len(a_						) - i
																break
				return total


def 						UpperCAmelCase      (     )		->     None:







				"""simple docstring"""



				from timeit import timeit

				print("Running benchmarks"						)
				__A             =			(
				    "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
				):
								__A             =			timeit(F'''{func}(grid=grid)'''		,       setup=a_		,       number=5_0_0						)
								print(F'''{func}() took {time:0.4f} seconds'''						)


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	benchmark()


# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import torch

from ..models.auto import AutoModelForSequenceClassification, AutoTokenizer
from .base import PipelineTool





class 							UpperCAmelCase				(  __SCREAMING_SNAKE_CASE  ):




	'''simple docstring'''





	snake_case_    =					"facebook/bart-large-mnli"
	snake_case_    =					(
	    "This is a tool that classifies an English text using provided labels. It takes two inputs: `text`, which "
	    "should be the text to classify, and `labels`, which should be the list of labels to use for classification. "
	    "It returns the most likely label in the list of provided `labels` for the input text."
	)
	snake_case_    =					"text_classifier"
	snake_case_    =					AutoTokenizer
	snake_case_    =					AutoModelForSequenceClassification

	snake_case_    =					["text", ["text"]]
	snake_case_    =					["text"]






	def     UpperCamelCase_  (	self :			str			):
					super().setup()
					__A             =			self.model.config
					__A             =			-1
					for idx, label in config.idalabel.items():
									if label.lower().startswith("entail"			):
													__A             =			int(A			)
					if self.entailment_id == -1:
									raise ValueError("Could not determine the entailment ID from the model config, please pass it at init."			)






	def     UpperCamelCase_  (	self :			int							,A :			Optional[Any]							,A :			Dict			):
					__A             =			labels
					return self.pre_processor(
					    [text] * len(A			)							,[f'''This example is {label}''' for label in labels]							,return_tensors="pt"							,padding="max_length"							,)







	def     UpperCamelCase_  (	self :			Union[str, Any]							,A :			Tuple			):
					__A             =			outputs.logits
					__A             =			torch.argmax(logits[:, 2]			).item()
					return self._labels[label_id]
