

from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


A_       : str         =			logging.get_logger(__name__)

A_       : Optional[Any]         =			{
    'distilbert-base-uncased': 'https://huggingface.co/distilbert-base-uncased/resolve/main/config.json',
    'distilbert-base-uncased-distilled-squad': (
        'https://huggingface.co/distilbert-base-uncased-distilled-squad/resolve/main/config.json'
    ),
    'distilbert-base-cased': 'https://huggingface.co/distilbert-base-cased/resolve/main/config.json',
    'distilbert-base-cased-distilled-squad': (
        'https://huggingface.co/distilbert-base-cased-distilled-squad/resolve/main/config.json'
    ),
    'distilbert-base-german-cased': 'https://huggingface.co/distilbert-base-german-cased/resolve/main/config.json',
    'distilbert-base-multilingual-cased': (
        'https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/config.json'
    ),
    'distilbert-base-uncased-finetuned-sst-2-english': (
        'https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english/resolve/main/config.json'
    ),
}



class     _a						(__magic_name__				):


					'''simple docstring'''






					UpperCAmelCase__:   Any  							=       '''distilbert'''
					UpperCAmelCase__:   List[str]  							=       {
					    '''hidden_size''': '''dim''',
					    '''num_attention_heads''': '''n_heads''',
					    '''num_hidden_layers''': '''n_layers''',
					}






					def __init__( self    ,       A__=3_0522    ,       A__=512    ,       A__=False    ,       A__=6    ,       A__=12    ,       A__=768    ,       A__=4 * 768    ,       A__=0.1    ,       A__=0.1    ,       A__="gelu"    ,       A__=0.0_2    ,       A__=0.1    ,       A__=0.2    ,       A__=0    ,       **A__    ,       ):
						A__							:    Optional[int]        =		vocab_size
						A__							:    List[str]        =		max_position_embeddings
						A__							:    List[str]        =		sinusoidal_pos_embds
						A__							:    List[Any]        =		n_layers
						A__							:    List[Any]        =		n_heads
						A__							:    List[str]        =		dim
						A__							:    Optional[Any]        =		hidden_dim
						A__							:    List[Any]        =		dropout
						A__							:    Optional[int]        =		attention_dropout
						A__							:    int        =		activation
						A__							:    Union[str, Any]        =		initializer_range
						A__							:    Dict        =		qa_dropout
						A__							:    Tuple        =		seq_classif_dropout
						super().__init__(**A__    ,       pad_token_id=A__			)



class     _a						(__magic_name__				):


					'''simple docstring'''






					@property
					def   __A			( self			):
						if self.task == "multiple-choice":
							A__							:    Union[str, Any]        =		{0: """batch""", 1: """choice""", 2: """sequence"""}
						else:
							A__							:    Tuple        =		{0: """batch""", 1: """sequence"""}
						return OrderedDict(
						    [
						        ("""input_ids""", dynamic_axis),
						        ("""attention_mask""", dynamic_axis),
						    ]			)






import argparse
from collections import defaultdict

import yaml


A_       : List[str]         =			'docs/source/en/_toctree.yml'

def 						UpperCamelCase					(lowercase_: Optional[int]					)     ->      List[str]:
	A__							:    Dict        =		defaultdict(lowercase_					)
	A__							:    Optional[int]        =		[]
	A__							:    Union[str, Any]        =		[]
	for doc in doc_list:
		if "local" in doc:
			counts[doc["local"]] += 1

		if doc["title"].lower() == "overview":
			overview_doc.append({"""local""": doc["""local"""], """title""": doc["""title"""]}					)
		else:
			new_doc_list.append(lowercase_					)

	A__							:    Optional[int]        =		new_doc_list
	A__							:    Optional[int]        =		[key for key, value in counts.items() if value > 1]

	A__							:    Optional[Any]        =		[]
	for duplicate_key in duplicates:
		A__							:    List[Any]        =		list({doc["""title"""] for doc in doc_list if doc["""local"""] == duplicate_key}					)
		if len(lowercase_					) > 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 doc_list if """local""" not in counts or counts[doc["""local"""]] == 1]					)
	A__							:    Dict        =		sorted(lowercase_    ,      key=lambda lowercase_					: s["title"].lower()					)

	# "overview" gets special treatment and is always first
	if len(lowercase_					) > 1:
		raise ValueError("""{doc_list} has two 'overview' docs which is not allowed."""					)

	overview_doc.extend(lowercase_					)

	# Sort
	return overview_doc

def 						UpperCamelCase					(lowercase_: Tuple=False					)     ->      List[Any]:
	with open(lowercase_    ,      encoding="""utf-8"""					) as f:
		A__							:    Dict        =		yaml.safe_load(f.read()					)

	# Get to the API doc
	A__							:    List[Any]        =		0
	while content[api_idx]["title"] != "API":
		api_idx += 1
	A__							:    Union[str, Any]        =		content[api_idx]["""sections"""]

	# Then to the model doc
	A__							:    Dict        =		0
	while api_doc[scheduler_idx]["title"] != "Schedulers":
		scheduler_idx += 1

	A__							:    List[Any]        =		api_doc[scheduler_idx]["""sections"""]
	A__							:    Union[str, Any]        =		clean_doc_toc(lowercase_					)

	A__							:    Optional[int]        =		False
	if new_scheduler_doc != scheduler_doc:
		A__							:    List[Any]        =		True
		if overwrite:
			A__							:    Optional[int]        =		new_scheduler_doc

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

def 						UpperCamelCase					(lowercase_: Dict=False					)     ->      Optional[Any]:
	with open(lowercase_    ,      encoding="""utf-8"""					) as f:
		A__							:    int        =		yaml.safe_load(f.read()					)

	# Get to the API doc
	A__							:    Optional[Any]        =		0
	while content[api_idx]["title"] != "API":
		api_idx += 1
	A__							:    List[str]        =		content[api_idx]["""sections"""]

	# Then to the model doc
	A__							:    List[Any]        =		0
	while api_doc[pipeline_idx]["title"] != "Pipelines":
		pipeline_idx += 1

	A__							:    Dict        =		False
	A__							:    Tuple        =		api_doc[pipeline_idx]["""sections"""]
	A__							:    Tuple        =		[]

	# sort sub pipeline docs
	for pipeline_doc in pipeline_docs:
		if "section" in pipeline_doc:
			A__							:    List[Any]        =		pipeline_doc["""section"""]
			A__							:    Dict        =		clean_doc_toc(lowercase_					)
			if overwrite:
				A__							:    Optional[Any]        =		new_sub_pipeline_doc
		new_pipeline_docs.append(lowercase_					)

	# sort overall pipeline doc
	A__							:    Optional[int]        =		clean_doc_toc(lowercase_					)

	if new_pipeline_docs != pipeline_docs:
		A__							:    int        =		True
		if overwrite:
			A__							:    List[Any]        =		new_pipeline_docs

	if diff:
		if overwrite:
			A__							:    Union[str, Any]        =		api_doc
			with open(lowercase_    ,      """w"""    ,      encoding="""utf-8"""					) as f:
				f.write(yaml.dump(lowercase_    ,      allow_unicode=lowercase_					)					)
		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_       : str         =			argparse.ArgumentParser()
	parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
	A_       : str         =			parser.parse_args()

	check_scheduler_doc(args.fix_and_overwrite)
	check_pipeline_doc(args.fix_and_overwrite)



