

"""simple docstring"""




import argparse
from collections import defaultdict

import yaml


A_				:      Optional[Any]           =			"docs/source/en/_toctree.yml"

def 						A							(  snake_case__     ):




			'''simple docstring'''


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

			SCREAMING_SNAKE_CASE__										=   []
			for duplicate_key in duplicates:
						SCREAMING_SNAKE_CASE__										=   list({doc["""title"""] for doc in model_doc if doc["""local"""] == duplicate_key}     )
						if len(snake_case__     ) > 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(snake_case__		,					key=lambda snake_case__     : s["title"].lower()     )

def 						A							(  snake_case__=False     ):




			'''simple docstring'''


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

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

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

			SCREAMING_SNAKE_CASE__										=   api_doc[model_idx]["""sections"""]

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

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

			if diff:
						if overwrite:
									SCREAMING_SNAKE_CASE__										=   model_doc
									SCREAMING_SNAKE_CASE__										=   api_doc
									with open(snake_case__		,					"""w"""		,					encoding="""utf-8"""     ) as f:
												f.write(yaml.dump(snake_case__		,					allow_unicode=snake_case__     )     )
						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__":
						A_				:      Dict           =			argparse.ArgumentParser()
						parser.add_argument("--fix_and_overwrite", action="store_true", help="Whether to fix inconsistencies.")
						A_				:      Optional[int]           =			parser.parse_args()

						check_model_doc(args.fix_and_overwrite)





"""simple docstring"""




import logging
from dataclasses import dataclass, field
from typing import Optional

from seqaseq_trainer import arg_to_scheduler

from transformers import TrainingArguments


A_				:      Optional[Any]           =			logging.getLogger(__name__)





@dataclass
class  lowerCamelCase    (A__      ):
			lowerCamelCase__ :		Optional[float]       		=				field(
			    default=0.0      ,metadata={'help': 'The label smoothing epsilon to apply (if not zero).'}      )
			lowerCamelCase__ :		bool       		=				field(default=A__      ,metadata={'help': 'Whether to SortishSamler or not.'}      )
			lowerCamelCase__ :		bool       		=				field(
			    default=A__      ,metadata={'help': 'Whether to use generate to calculate generative metrics (ROUGE, BLEU).'}      )
			lowerCamelCase__ :		bool       		=				field(default=A__      ,metadata={'help': 'whether to use adafactor'}      )
			lowerCamelCase__ :		Optional[float]       		=				field(
			    default=A__      ,metadata={'help': 'Encoder layer dropout probability. Goes into model.config.'}      )
			lowerCamelCase__ :		Optional[float]       		=				field(
			    default=A__      ,metadata={'help': 'Decoder layer dropout probability. Goes into model.config.'}      )
			lowerCamelCase__ :		Optional[float]       		=				field(default=A__      ,metadata={'help': 'Dropout probability. Goes into model.config.'}      )
			lowerCamelCase__ :		Optional[float]       		=				field(
			    default=A__      ,metadata={'help': 'Attention dropout probability. Goes into model.config.'}      )
			lowerCamelCase__ :		Optional[str]       		=				field(
			    default='linear'      ,metadata={'help': f"Which lr scheduler to use. Selected in {sorted(arg_to_scheduler.keys()      )}"}      ,)


