


import argparse
import os
import re

import packaging.version


snake_case__			 =						'''examples/'''
snake_case__			 =						{
    '''examples''': (re.compile(r'''^check_min_version\("[^"]+"\)\s*$''', re.MULTILINE), '''check_min_version("VERSION")\n'''),
    '''init''': (re.compile(r'''^__version__\s+=\s+"([^"]+)"\s*$''', re.MULTILINE), '''__version__ = "VERSION"\n'''),
    '''setup''': (re.compile(r'''^(\s*)version\s*=\s*"[^"]+",''', re.MULTILINE), r'''\1version="VERSION",'''),
    '''doc''': (re.compile(r'''^(\s*)release\s*=\s*"[^"]+"$''', re.MULTILINE), '''release = "VERSION"\n'''),
}
snake_case__			 =						{
    '''init''': '''src/diffusers/__init__.py''',
    '''setup''': '''setup.py''',
}
snake_case__			 =						'''README.md'''


def 				lowerCamelCase__	(      a	:   List[str]	,  a	:   Dict	,  a	:   Optional[Any]				)    ->	Dict:





			"""simple docstring"""
			with open(a	,  "r"	,  encoding="utf-8"	,  newline="\n"				) as f:
						a__ :Union[str, Any]						  =    f.read()
			a__  ,  a__ :Dict						  =    REPLACE_PATTERNS[pattern]
			a__ :List[str]						  =    replace.replace("VERSION"	,  a				)
			a__ :Any						  =    re_pattern.sub(a	,  a				)
			with open(a	,  "w"	,  encoding="utf-8"	,  newline="\n"				) as f:
						f.write(a				)


def 				lowerCamelCase__	(      a	:   Optional[int]				)    ->	List[str]:





			"""simple docstring"""
			for folder, directories, fnames in os.walk(a				):
						# Removing some of the folders with non-actively maintained examples from the walk
						if "research_projects" in directories:
									directories.remove("research_projects"				)
						if "legacy" in directories:
									directories.remove("legacy"				)
						for fname in fnames:
									if fname.endswith(".py"				):
												update_version_in_file(os.path.join(a	,  a				)	,  a	,  pattern="examples"				)


def 				lowerCamelCase__	(      a	:   List[str]	,  a	:   Any=False				)    ->	int:





			"""simple docstring"""
			for pattern, fname in REPLACE_FILES.items():
						update_version_in_file(a	,  a	,  a				)
			if not patch:
						update_version_in_examples(a				)


def 				lowerCamelCase__	(      )    ->	Optional[Any]:





			"""simple docstring"""
			a__ :Any						  =    "🤗 Transformers currently provides the following architectures"
			a__ :Optional[int]						  =    "1. Want to contribute a new model?"
			with open(a	,  "r"	,  encoding="utf-8"	,  newline="\n"				) as f:
						a__ :Dict						  =    f.readlines()

			# Find the start of the list.
			a__ :Any						  =    0
			while not lines[start_index].startswith(_start_prompt				):
						start_index += 1
			start_index += 1

			a__ :List[str]						  =    start_index
			# Update the lines in the model list.
			while not lines[index].startswith(_end_prompt				):
						if lines[index].startswith("1."				):
									a__ :Any						  =    lines[index].replace(
									    "https://huggingface.co/docs/diffusers/main/model_doc"	,  "https://huggingface.co/docs/diffusers/model_doc"	,  )
						index += 1

			with open(a	,  "w"	,  encoding="utf-8"	,  newline="\n"				) as f:
						f.writelines(a				)


def 				lowerCamelCase__	(      )    ->	Union[str, Any]:





			"""simple docstring"""
			with open(REPLACE_FILES["init"]	,  "r"				) as f:
						a__ :Tuple						  =    f.read()
			a__ :Tuple						  =    REPLACE_PATTERNS["init"][0].search(a				).groups()[0]
			return packaging.version.parse(a				)


def 				lowerCamelCase__	(      a	:   List[str]=False				)    ->	Tuple:





			"""simple docstring"""
			a__ :int						  =    get_version()
			if patch and default_version.is_devrelease:
						raise ValueError("Can't create a patch version from the dev branch, checkout a released version!"				)
			if default_version.is_devrelease:
						a__ :Any						  =    default_version.base_version
			elif patch:
						a__ :Dict						  =    F'''{default_version.major}.{default_version.minor}.{default_version.micro + 1}'''
			else:
						a__ :Union[str, Any]						  =    F'''{default_version.major}.{default_version.minor + 1}.0'''

			# Now let's ask nicely if that's the right one.
			a__ :Dict						  =    input(F'''Which version are you releasing? [{default_version}]'''				)
			if len(a				) == 0:
						a__ :List[Any]						  =    default_version

			print(F'''Updating version to {version}.'''				)
			global_version_update(a	,  patch=a				)


def 				lowerCamelCase__	(      )    ->	int:





			"""simple docstring"""
			a__ :List[str]						  =    get_version()
			a__ :Union[str, Any]						  =    F'''{current_version.major}.{current_version.minor + 1}.0.dev0'''
			a__ :Tuple						  =    current_version.base_version

			# Check with the user we got that right.
			a__ :Any						  =    input(F'''Which version are we developing now? [{dev_version}]'''				)
			if len(a				) == 0:
						a__ :str						  =    dev_version

			print(F'''Updating version to {version}.'''				)
			global_version_update(a				)


#    print("Cleaning main README, don't forget to run `make fix-copies`.")
#    clean_main_ref_in_model_list()


if __name__ == "__main__":
		snake_case__			 =						argparse.ArgumentParser()
		parser.add_argument('''--post_release''', action='''store_true''', help='''Whether this is pre or post release.''')
		parser.add_argument('''--patch''', action='''store_true''', help='''Whether or not this is a patch release.''')
		snake_case__			 =						parser.parse_args()
		if not args.post_release:
				pre_release_work(patch=args.patch)
		elif args.patch:
				print('''Nothing to do after a patch :-)''')
		else:
				post_release_work()






import functools
import operator

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


snake_case__			 =						logging.get_logger(__name__)

snake_case__			 =						{
    '''asapp/sew-tiny-100k''': '''https://huggingface.co/asapp/sew-tiny-100k/resolve/main/config.json''',
    # See all SEW models at https://huggingface.co/models?filter=sew
}


class 					lowerCAmelCase_ (       _a):
						lowerCamelCase_         =     'sew'




						def __init__(	self						:   Any   ,     __A						:   str=32   ,     __A						:   Dict=768   ,     __A						:   int=12   ,     __A						:   Dict=12   ,     __A						:   Dict=3072   ,     __A						:   int=2   ,     __A						:   Union[str, Any]="gelu"   ,     __A						:   Union[str, Any]=0.1   ,     __A						:   Optional[Any]=0.1   ,     __A						:   Union[str, Any]=0.1   ,     __A						:   str=0.0   ,     __A						:   Union[str, Any]=0.1   ,     __A						:   Optional[Any]=0.1   ,     __A						:   Tuple=0.02   ,     __A						:   Any=1E-5   ,     __A						:   Optional[Any]="group"   ,     __A						:   str="gelu"   ,     __A						:   Dict=(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512)   ,     __A						:   Tuple=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1)   ,     __A						:   int=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1)   ,     __A						:   List[str]=False   ,     __A						:   Tuple=128   ,     __A						:   Tuple=16   ,     __A						:   Optional[int]=True   ,     __A						:   Union[str, Any]=0.05   ,     __A						:   List[str]=10   ,     __A						:   Optional[int]=2   ,     __A						:   List[Any]=0.0   ,     __A						:   Optional[Any]=10   ,     __A						:   Tuple=0   ,     __A						:   Tuple="mean"   ,     __A						:   Any=False   ,     __A						:   str=False   ,     __A						:   Dict=256   ,     __A						:   Union[str, Any]=0   ,     __A						:   Optional[int]=1   ,     __A						:   Optional[Any]=2   ,     **__A						:   Tuple   ,     )      ->Tuple:






									"""simple docstring"""






									super().__init__(**__A   ,     pad_token_id=__A   ,     bos_token_id=__A   ,     eos_token_id=__A						)
									a__ :List[Any]						  =    hidden_size
									a__ :List[Any]						  =    feat_extract_norm
									a__ :List[str]						  =    feat_extract_activation
									a__ :Any						  =    list(__A						)
									a__ :Dict						  =    list(__A						)
									a__ :Optional[int]						  =    list(__A						)
									a__ :Any						  =    conv_bias
									a__ :List[str]						  =    num_conv_pos_embeddings
									a__ :str						  =    num_conv_pos_embedding_groups
									a__ :Optional[int]						  =    len(self.conv_dim						)
									a__ :List[Any]						  =    num_hidden_layers
									a__ :str						  =    intermediate_size
									a__ :Dict						  =    squeeze_factor
									a__ :List[Any]						  =    hidden_act
									a__ :Optional[Any]						  =    num_attention_heads
									a__ :Tuple						  =    hidden_dropout
									a__ :Tuple						  =    attention_dropout
									a__ :List[Any]						  =    activation_dropout
									a__ :str						  =    feat_proj_dropout
									a__ :Any						  =    final_dropout
									a__ :Dict						  =    layerdrop
									a__ :List[str]						  =    layer_norm_eps
									a__ :Tuple						  =    initializer_range
									a__ :Dict						  =    vocab_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)`,"
												    F'''but is `len(config.conv_dim) = {len(self.conv_dim						)}`, `len(config.conv_stride)'''
												    F'''= {len(self.conv_stride						)}`, `len(config.conv_kernel) = {len(self.conv_kernel						)}`.'''						)

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

									# ctc loss
									a__ :Dict						  =    ctc_loss_reduction
									a__ :Any						  =    ctc_zero_infinity

									# sequence classification
									a__ :Optional[int]						  =    use_weighted_layer_sum
									a__ :Tuple						  =    classifier_proj_size






						@property
						def  _snake_case					(	self						:   Dict						)      ->Union[str, Any]:






									"""simple docstring"""






									return functools.reduce(operator.mul   ,     self.conv_stride   ,     1						)


