





from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union

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


if TYPE_CHECKING:
						from ... import FeatureExtractionMixin, PreTrainedTokenizerBase, TensorType


_SCREAMING_SNAKE_CASE 		=						logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE 		=						{
    """microsoft/deberta-v2-xlarge""": """https://huggingface.co/microsoft/deberta-v2-xlarge/resolve/main/config.json""",
    """microsoft/deberta-v2-xxlarge""": """https://huggingface.co/microsoft/deberta-v2-xxlarge/resolve/main/config.json""",
    """microsoft/deberta-v2-xlarge-mnli""": (
        """https://huggingface.co/microsoft/deberta-v2-xlarge-mnli/resolve/main/config.json"""
    ),
    """microsoft/deberta-v2-xxlarge-mnli""": (
        """https://huggingface.co/microsoft/deberta-v2-xxlarge-mnli/resolve/main/config.json"""
    ),
}
class SCREAMING_SNAKE_CASE_  (   snake_case_   ):
						__magic_name__:     Tuple	 =    "deberta-v2"






						def __init__( self : List[Any]						,				_A : Optional[Any]=128100						,				_A : List[str]=1536						,				_A : Any=24						,				_A : List[Any]=24						,				_A : Optional[int]=6144						,				_A : int="gelu"						,				_A : int=0.1						,				_A : List[str]=0.1						,				_A : List[str]=512						,				_A : Tuple=0						,				_A : str=0.0_2						,				_A : Any=1E-7						,				_A : List[str]=False						,				_A : List[Any]=-1						,				_A : Tuple=0						,				_A : List[Any]=True						,				_A : str=None						,				_A : Optional[int]=0						,				_A : Any="gelu"						,				**_A : Union[str, Any]						,				)							->      Optional[Any]:







							"""simple docstring"""


							super().__init__(**_A		)

							snake_case_						:     Optional[int]     =		hidden_size
							snake_case_						:     Tuple     =		num_hidden_layers
							snake_case_						:     Tuple     =		num_attention_heads
							snake_case_						:     Optional[int]     =		intermediate_size
							snake_case_						:     Dict     =		hidden_act
							snake_case_						:     int     =		hidden_dropout_prob
							snake_case_						:     Dict     =		attention_probs_dropout_prob
							snake_case_						:     Optional[int]     =		max_position_embeddings
							snake_case_						:     List[Any]     =		type_vocab_size
							snake_case_						:     int     =		initializer_range
							snake_case_						:     List[Any]     =		relative_attention
							snake_case_						:     Any     =		max_relative_positions
							snake_case_						:     Optional[Any]     =		pad_token_id
							snake_case_						:     Dict     =		position_biased_input

							# Backwards compatibility
							if type(_A		) == str:
								snake_case_						:     List[str]     =		[x.strip() for x in pos_att_type.lower().split('|'		)]

							snake_case_						:     int     =		pos_att_type
							snake_case_						:     Any     =		vocab_size
							snake_case_						:     Optional[Any]     =		layer_norm_eps

							snake_case_						:     List[str]     =		kwargs.get('pooler_hidden_size'						,				_A		)
							snake_case_						:     Optional[int]     =		pooler_dropout
							snake_case_						:     List[str]     =		pooler_hidden_act
class SCREAMING_SNAKE_CASE_  (   snake_case_   ):






						@property
						def UpperCAmelCase_			( self : int		)							->      Mapping[str, Mapping[int, str]]:







							"""simple docstring"""


							if self.task == "multiple-choice":
								snake_case_						:     List[Any]     =		{0: 'batch', 1: 'choice', 2: 'sequence'}
							else:
								snake_case_						:     Any     =		{0: 'batch', 1: 'sequence'}
							if self._config.type_vocab_size > 0:
								return OrderedDict(
								    [('input_ids', dynamic_axis), ('attention_mask', dynamic_axis), ('token_type_ids', dynamic_axis)]		)
							else:
								return OrderedDict([('input_ids', dynamic_axis), ('attention_mask', dynamic_axis)]		)






						@property
						def UpperCAmelCase_			( self : Any		)							->      int:







							"""simple docstring"""


							return 12






						def UpperCAmelCase_			( self : Union[str, Any]						,				_A : Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"]						,				_A : int = -1						,				_A : int = -1						,				_A : int = -1						,				_A : bool = False						,				_A : Optional["TensorType"] = None						,				_A : int = 3						,				_A : int = 40						,				_A : int = 40						,				_A : "PreTrainedTokenizerBase" = None						,				)							->      Mapping[str, Any]:







							"""simple docstring"""


							snake_case_						:     List[str]     =		super().generate_dummy_inputs(preprocessor=_A						,				framework=_A		)
							if self._config.type_vocab_size == 0 and "token_type_ids" in dummy_inputs:
								del dummy_inputs["token_type_ids"]
							return dummy_inputs








import argparse
from collections import defaultdict

import yaml


_SCREAMING_SNAKE_CASE 		=						"""docs/source/en/_toctree.yml"""


def 							SCREAMING_SNAKE_CASE__  (      __a				):
	snake_case_						:     List[Any]     =		defaultdict(__a				)
	snake_case_						:     Optional[Any]     =		[]
	snake_case_						:     Optional[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(__a				)

	snake_case_						:     Any     =		new_doc_list
	snake_case_						:     str     =		[key for key, value in counts.items() if value > 1]

	snake_case_						:     Any     =		[]
	for duplicate_key in duplicates:
		snake_case_						:     Any     =		list({doc['title'] for doc in doc_list 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 doc_list if 'local' not in counts or counts[doc['local']] == 1]				)
	snake_case_						:     str     =		sorted(__a				,	key=lambda __a				: s["title"].lower()				)

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

	overview_doc.extend(__a				)

	# Sort
	return overview_doc


def 							SCREAMING_SNAKE_CASE__  (      __a=False				):
	with open(__a				,	encoding='utf-8'				) as f:
		snake_case_						:     int     =		yaml.safe_load(f.read()				)

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

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

	snake_case_						:     Union[str, Any]     =		api_doc[scheduler_idx]['sections']
	snake_case_						:     Optional[Any]     =		clean_doc_toc(__a				)

	snake_case_						:     int     =		False
	if new_scheduler_doc != scheduler_doc:
		snake_case_						:     int     =		True
		if overwrite:
			snake_case_						:     Union[str, Any]     =		new_scheduler_doc

	if diff:
		if overwrite:
			snake_case_						:     Optional[int]     =		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.'				)


def 							SCREAMING_SNAKE_CASE__  (      __a=False				):
	with open(__a				,	encoding='utf-8'				) as f:
		snake_case_						:     Dict     =		yaml.safe_load(f.read()				)

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

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

	snake_case_						:     Dict     =		False
	snake_case_						:     Union[str, Any]     =		api_doc[pipeline_idx]['sections']
	snake_case_						:     Union[str, Any]     =		[]

	# sort sub pipeline docs
	for pipeline_doc in pipeline_docs:
		if "section" in pipeline_doc:
			snake_case_						:     Optional[Any]     =		pipeline_doc['section']
			snake_case_						:     Optional[int]     =		clean_doc_toc(__a				)
			if overwrite:
				snake_case_						:     Tuple     =		new_sub_pipeline_doc
		new_pipeline_docs.append(__a				)

	# sort overall pipeline doc
	snake_case_						:     Optional[Any]     =		clean_doc_toc(__a				)

	if new_pipeline_docs != pipeline_docs:
		snake_case_						:     List[str]     =		True
		if overwrite:
			snake_case_						:     List[str]     =		new_pipeline_docs

	if diff:
		if overwrite:
			snake_case_						:     List[Any]     =		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 		=						argparse.ArgumentParser()
						parser.add_argument("""--fix_and_overwrite""", action="""store_true""", help="""Whether to fix inconsistencies.""")
						_SCREAMING_SNAKE_CASE 		=						parser.parse_args()

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

