
# 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]


import argparse
from collections import defaultdict

import yaml


SCREAMING_SNAKE_CASE							:str										=    'docs/source/en/_toctree.yml'


def 						UpperCAmelCase      (     a_						)		->     int:







				"""simple docstring"""



				__A             =			defaultdict(a_						)
				for doc in model_doc:
								counts[doc["local"]] += 1
				__A             =			[key for key, value in counts.items() if value > 1]

				__A             =			[]
				for duplicate_key in duplicates:
								__A             =			list({doc["title"] for doc in model_doc if doc["local"] == duplicate_key}						)
								if len(a_						) > 1:
												raise ValueError(
												    F'''{duplicate_key} is present several times in the documentation table of content at '''
												    "`docs/source/en/_toctree.yml` with different *Title* values. Choose one of those and remove the "
												    "others."						)
								# Only add this once
								new_doc.append({"local": duplicate_key, "title": titles[0]}						)

				# Add none duplicate-keys
				new_doc.extend([doc for doc in model_doc if counts[doc["local"]] == 1]						)

				# Sort
				return sorted(a_		,       key=lambda a_						: s["title"].lower()						)


def 						UpperCAmelCase      (     a_=False						)		->     List[Any]:







				"""simple docstring"""



				with open(a_		,       encoding="utf-8"						) as f:
								__A             =			yaml.safe_load(f.read()						)

				# Get to the API doc
				__A             =			0
				while content[api_idx]["title"] != "API":
								api_idx += 1
				__A             =			content[api_idx]["sections"]

				# Then to the model doc
				__A             =			0
				while api_doc[model_idx]["title"] != "Models":
								model_idx += 1

				__A             =			api_doc[model_idx]["sections"]

				__A             =			[(idx, section) for idx, section in enumerate(a_						) if "sections" in section]
				__A             =			False
				for idx, modality_doc in modalities_docs:
								__A             =			modality_doc["sections"]
								__A             =			clean_model_doc_toc(a_						)

								if old_modality_doc != new_modality_doc:
												__A             =			True
												if overwrite:
																__A             =			new_modality_doc

				if diff:
								if overwrite:
												__A             =			model_doc
												__A             =			api_doc
												with open(a_		,       "w"		,       encoding="utf-8"						) as f:
																f.write(yaml.dump(a_		,       allow_unicode=a_						)						)
								else:
												raise ValueError(
												    "The model doc part of the table of content is not properly sorted, run `make style` to fix this."						)


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE							:Tuple										=    argparse.ArgumentParser()
					parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
					SCREAMING_SNAKE_CASE							:List[str]										=    parser.parse_args()

					check_model_doc(args.fix_and_overwrite)
