

import argparse
from collections import defaultdict

import yaml


UpperCAmelCase_							:					List[str]		 =   '''docs/source/en/_toctree.yml'''
def 							__SCREAMING_SNAKE_CASE     (   a__    :   int       )					->				Union[str, Any]:
	__A :			Union[str, Any]		    =			defaultdict(a__       )
	__A :			Dict		    =			[]
	__A :			Dict		    =			[]
	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__       )

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

	__A :			Tuple		    =			[]
	for duplicate_key in duplicates:
		__A :			Union[str, 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]       )
	__A :			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__    :   Tuple=False       )					->				List[str]:
	with open(a__			,encoding="""utf-8"""       ) as f:
		__A :			Optional[Any]		    =			yaml.safe_load(f.read()       )

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

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

	__A :			List[Any]		    =			api_doc[scheduler_idx]["""sections"""]
	__A :			Tuple		    =			clean_doc_toc(a__       )

	__A :			Tuple		    =			False
	if new_scheduler_doc != scheduler_doc:
		__A :			List[str]		    =			True
		if overwrite:
			__A :			List[str]		    =			new_scheduler_doc

	if diff:
		if overwrite:
			__A :			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__    :   Any=False       )					->				Any:
	with open(a__			,encoding="""utf-8"""       ) as f:
		__A :			Union[str, Any]		    =			yaml.safe_load(f.read()       )

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

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

	__A :			Optional[Any]		    =			False
	__A :			List[str]		    =			api_doc[pipeline_idx]["""sections"""]
	__A :			Optional[int]		    =			[]

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

	# sort overall pipeline doc
	__A :			Tuple		    =			clean_doc_toc(a__       )

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

	if diff:
		if overwrite:
			__A :			Optional[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__":
			UpperCAmelCase_							:					Tuple		 =   argparse.ArgumentParser()
			parser.add_argument('''--fix_and_overwrite''', action='''store_true''', help='''Whether to fix inconsistencies.''')
			UpperCAmelCase_							:					Tuple		 =   parser.parse_args()

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








import functools
import operator

from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase_							:					List[str]		 =   logging.get_logger(__name__)

UpperCAmelCase_							:					str		 =   {
    '''microsoft/unispeech-large-1500h-cv''': (
        '''https://huggingface.co/microsoft/unispeech-large-1500h-cv/resolve/main/config.json'''
    ),
    # See all UniSpeech models at https://huggingface.co/models?filter=unispeech
}






class      lowerCamelCase_      (	_lowercase	):
	_lowercase		:   Dict     					=				'''unispeech'''




	def __init__(     self						:     str					,      __A						:     Tuple=32					,      __A						:     List[str]=768					,      __A						:     Dict=12					,      __A						:     Union[str, Any]=12					,      __A						:     Tuple=3072					,      __A						:     Any="gelu"					,      __A						:     int=0.1					,      __A						:     Optional[int]=0.1					,      __A						:     List[Any]=0.1					,      __A						:     Any=0.0					,      __A						:     List[str]=0.0					,      __A						:     int=0.1					,      __A						:     List[Any]=0.1					,      __A						:     List[str]=0.0_2					,      __A						:     List[str]=1e-5					,      __A						:     List[Any]="group"					,      __A						:     int="gelu"					,      __A						:     Any=(512, 512, 512, 512, 512, 512, 512)					,      __A						:     Union[str, Any]=(5, 2, 2, 2, 2, 2, 2)					,      __A						:     Tuple=(10, 3, 3, 3, 3, 2, 2)					,      __A						:     Optional[int]=False					,      __A						:     Any=128					,      __A						:     Union[str, Any]=16					,      __A						:     Optional[Any]=False					,      __A						:     str=True					,      __A						:     Dict=0.0_5					,      __A						:     Optional[Any]=10					,      __A						:     Dict=2					,      __A						:     int=0.0					,      __A						:     List[str]=10					,      __A						:     str=0					,      __A						:     List[str]=320					,      __A						:     List[Any]=2					,      __A						:     Tuple=0.1					,      __A						:     Optional[int]=100					,      __A						:     Any=256					,      __A						:     Dict=256					,      __A						:     Tuple=0.1					,      __A						:     List[str]="mean"					,      __A						:     int=False					,      __A						:     List[str]=False					,      __A						:     List[Any]=256					,      __A						:     str=80					,      __A						:     Tuple=0					,      __A						:     Tuple=1					,      __A						:     int=2					,      __A						:     Dict=0.5					,      **__A						:     List[Any]					,      ):
		super().__init__(**__A					,      pad_token_id=__A					,      bos_token_id=__A					,      eos_token_id=__A							)
		__A :			Dict		    =			hidden_size
		__A :			Optional[Any]		    =			feat_extract_norm
		__A :			List[Any]		    =			feat_extract_activation
		__A :			str		    =			list(__A							)
		__A :			Optional[Any]		    =			list(__A							)
		__A :			Optional[int]		    =			list(__A							)
		__A :			List[Any]		    =			conv_bias
		__A :			Optional[int]		    =			num_conv_pos_embeddings
		__A :			List[Any]		    =			num_conv_pos_embedding_groups
		__A :			int		    =			len(self.conv_dim							)
		__A :			Optional[Any]		    =			num_hidden_layers
		__A :			List[str]		    =			intermediate_size
		__A :			Union[str, Any]		    =			hidden_act
		__A :			Optional[int]		    =			num_attention_heads
		__A :			Tuple		    =			hidden_dropout
		__A :			Optional[Any]		    =			attention_dropout
		__A :			Union[str, Any]		    =			activation_dropout
		__A :			Dict		    =			feat_proj_dropout
		__A :			Optional[int]		    =			final_dropout
		__A :			Dict		    =			layerdrop
		__A :			Optional[int]		    =			layer_norm_eps
		__A :			Optional[Any]		    =			initializer_range
		__A :			Optional[int]		    =			num_ctc_classes
		__A :			Dict		    =			vocab_size
		__A :			List[str]		    =			do_stable_layer_norm
		__A :			Tuple		    =			use_weighted_layer_sum
		__A :			Any		    =			classifier_proj_size

		if (
		    (len(self.conv_stride							) != self.num_feat_extract_layers)
		    or (len(self.conv_kernel							) != self.num_feat_extract_layers)
		    or (len(self.conv_dim							) != self.num_feat_extract_layers)
		):
			raise ValueError(
			    """Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =="""
			    """ `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ="""
			    F""" {len(self.conv_dim							)}`, `len(config.conv_stride) = {len(self.conv_stride							)}`,"""
			    F""" `len(config.conv_kernel) = {len(self.conv_kernel							)}`."""							)

		# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
		__A :			Tuple		    =			apply_spec_augment
		__A :			Union[str, Any]		    =			mask_time_prob
		__A :			Optional[Any]		    =			mask_time_length
		__A :			List[Any]		    =			mask_time_min_masks
		__A :			List[Any]		    =			mask_feature_prob
		__A :			Any		    =			mask_feature_length
		__A :			List[Any]		    =			mask_feature_min_masks

		# parameters for pretraining with codevector quantized representations
		__A :			Any		    =			num_codevectors_per_group
		__A :			Tuple		    =			num_codevector_groups
		__A :			List[str]		    =			contrastive_logits_temperature
		__A :			Optional[int]		    =			feat_quantizer_dropout
		__A :			int		    =			num_negatives
		__A :			List[str]		    =			codevector_dim
		__A :			int		    =			proj_codevector_dim
		__A :			Union[str, Any]		    =			diversity_loss_weight

		# ctc loss
		__A :			List[str]		    =			ctc_loss_reduction
		__A :			Any		    =			ctc_zero_infinity

		# pretraining loss
		__A :			Union[str, Any]		    =			replace_prob




	@property
	def        lowerCAmelCase_       (     self						:     int							):
		return functools.reduce(operator.mul					,      self.conv_stride					,      1							)





