



"""simple docstring"""







# 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__			(   _lowerCAmelCase      ):

			"""simple docstring"""


			A__	:					List[Any]							  =     "facebook/bart-large-mnli"
			A__	:					int							  =     (
			    "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."
			)
			A__	:					Union[str, Any]							  =     "text_classifier"
			A__	:					int							  =     AutoTokenizer
			A__	:					str							  =     AutoModelForSequenceClassification

			A__	:					Union[str, Any]							  =     ["text", ["text"]]
			A__	:					int							  =     ["text"]


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


			def   snake_case__	(      self						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__		)      ->	int:
				A__											=					labels
				return self.pre_processor(
				    [text] * len(SCREAMING_SNAKE_CASE__		)						,	[f"""This example is {label}""" for label in labels]						,	return_tensors="pt"						,	padding="max_length"						,	)







			def   snake_case__	(      self						,	SCREAMING_SNAKE_CASE__		)      ->	Dict:
				A__											=					outputs.logits
				A__											=					torch.argmax(logits[:, 2]		).item()
				return self._labels[label_id]








def     __SCREAMING_SNAKE_CASE							(       )		->     Optional[int]:




						'''simple docstring'''







						__UpperCAmelCase			:							str	    =	[31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31]

						__UpperCAmelCase			:							Union[str, Any]	    =	6
						__UpperCAmelCase			:							Optional[Any]	    =	1
						__UpperCAmelCase			:							Tuple	    =	1901

						__UpperCAmelCase			:							Any	    =	0

						while year < 2001:
												day += 7

												if (year % 4 == 0 and year % 100 != 0) or (year % 400 == 0):
																		if day > days_per_month[month - 1] and month != 2:
																								month += 1
																								__UpperCAmelCase			:							Optional[int]	    =	day - days_per_month[month - 2]
																		elif day > 29 and month == 2:
																								month += 1
																								__UpperCAmelCase			:							Optional[Any]	    =	day - 29
												else:
																		if day > days_per_month[month - 1]:
																								month += 1
																								__UpperCAmelCase			:							Optional[Any]	    =	day - days_per_month[month - 2]

												if month > 12:
																		year += 1
																		__UpperCAmelCase			:							int	    =	1

												if year < 2001 and day == 1:
																		sundays += 1
						return sundays


if __name__ == "__main__":
				print(solution())

