
import importlib
import json
import os
from collections import OrderedDict
from typing import Dict, Optional, Union

# Build the list of all feature extractors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_trust_remote_code
from ...feature_extraction_utils import FeatureExtractionMixin
from ...utils import CONFIG_NAME, FEATURE_EXTRACTOR_NAME, get_file_from_repo, logging
from .auto_factory import _LazyAutoMapping
from .configuration_auto import (
    CONFIG_MAPPING_NAMES,
    AutoConfig,
    model_type_to_module_name,
    replace_list_option_in_docstrings,
)


SCREAMING_SNAKE_CASE							:Optional[Any]										=    logging.get_logger(__name__)

SCREAMING_SNAKE_CASE							:Optional[Any]										=    OrderedDict(
    [
        ('audio-spectrogram-transformer', 'ASTFeatureExtractor'),
        ('beit', 'BeitFeatureExtractor'),
        ('chinese_clip', 'ChineseCLIPFeatureExtractor'),
        ('clap', 'ClapFeatureExtractor'),
        ('clip', 'CLIPFeatureExtractor'),
        ('clipseg', 'ViTFeatureExtractor'),
        ('conditional_detr', 'ConditionalDetrFeatureExtractor'),
        ('convnext', 'ConvNextFeatureExtractor'),
        ('cvt', 'ConvNextFeatureExtractor'),
        ('data2vec-audio', 'Wav2Vec2FeatureExtractor'),
        ('data2vec-vision', 'BeitFeatureExtractor'),
        ('deformable_detr', 'DeformableDetrFeatureExtractor'),
        ('deit', 'DeiTFeatureExtractor'),
        ('detr', 'DetrFeatureExtractor'),
        ('dinat', 'ViTFeatureExtractor'),
        ('donut-swin', 'DonutFeatureExtractor'),
        ('dpt', 'DPTFeatureExtractor'),
        ('encodec', 'EncodecFeatureExtractor'),
        ('flava', 'FlavaFeatureExtractor'),
        ('glpn', 'GLPNFeatureExtractor'),
        ('groupvit', 'CLIPFeatureExtractor'),
        ('hubert', 'Wav2Vec2FeatureExtractor'),
        ('imagegpt', 'ImageGPTFeatureExtractor'),
        ('layoutlmv2', 'LayoutLMv2FeatureExtractor'),
        ('layoutlmv3', 'LayoutLMv3FeatureExtractor'),
        ('levit', 'LevitFeatureExtractor'),
        ('maskformer', 'MaskFormerFeatureExtractor'),
        ('mctct', 'MCTCTFeatureExtractor'),
        ('mobilenet_v1', 'MobileNetV1FeatureExtractor'),
        ('mobilenet_v2', 'MobileNetV2FeatureExtractor'),
        ('mobilevit', 'MobileViTFeatureExtractor'),
        ('nat', 'ViTFeatureExtractor'),
        ('owlvit', 'OwlViTFeatureExtractor'),
        ('perceiver', 'PerceiverFeatureExtractor'),
        ('poolformer', 'PoolFormerFeatureExtractor'),
        ('regnet', 'ConvNextFeatureExtractor'),
        ('resnet', 'ConvNextFeatureExtractor'),
        ('segformer', 'SegformerFeatureExtractor'),
        ('sew', 'Wav2Vec2FeatureExtractor'),
        ('sew-d', 'Wav2Vec2FeatureExtractor'),
        ('speech_to_text', 'Speech2TextFeatureExtractor'),
        ('speecht5', 'SpeechT5FeatureExtractor'),
        ('swiftformer', 'ViTFeatureExtractor'),
        ('swin', 'ViTFeatureExtractor'),
        ('swinv2', 'ViTFeatureExtractor'),
        ('table-transformer', 'DetrFeatureExtractor'),
        ('timesformer', 'VideoMAEFeatureExtractor'),
        ('tvlt', 'TvltFeatureExtractor'),
        ('unispeech', 'Wav2Vec2FeatureExtractor'),
        ('unispeech-sat', 'Wav2Vec2FeatureExtractor'),
        ('van', 'ConvNextFeatureExtractor'),
        ('videomae', 'VideoMAEFeatureExtractor'),
        ('vilt', 'ViltFeatureExtractor'),
        ('vit', 'ViTFeatureExtractor'),
        ('vit_mae', 'ViTFeatureExtractor'),
        ('vit_msn', 'ViTFeatureExtractor'),
        ('wav2vec2', 'Wav2Vec2FeatureExtractor'),
        ('wav2vec2-conformer', 'Wav2Vec2FeatureExtractor'),
        ('wavlm', 'Wav2Vec2FeatureExtractor'),
        ('whisper', 'WhisperFeatureExtractor'),
        ('xclip', 'CLIPFeatureExtractor'),
        ('yolos', 'YolosFeatureExtractor'),
    ]
)

SCREAMING_SNAKE_CASE							:Union[str, Any]										=    _LazyAutoMapping(CONFIG_MAPPING_NAMES, FEATURE_EXTRACTOR_MAPPING_NAMES)


def 						UpperCAmelCase      (     a_						)		->     Optional[int]:







				"""simple docstring"""



				for module_name, extractors in FEATURE_EXTRACTOR_MAPPING_NAMES.items():
								if class_name in extractors:
												__A             =			model_type_to_module_name(a_						)

												__A             =			importlib.import_module(F'''.{module_name}'''		,       "transformers.models"						)
												try:
																return getattr(a_		,       a_						)
												except AttributeError:
																continue

				for _, extractor in FEATURE_EXTRACTOR_MAPPING._extra_content.items():
								if getattr(a_		,       "__name__"		,       a_						) == class_name:
												return extractor

    # We did not fine the class, but maybe it's because a dep is missing. In that case, the class will be in the main
    # init and we return the proper dummy to get an appropriate error message.
				__A             =			importlib.import_module("transformers"						)
				if hasattr(a_		,       a_						):
								return getattr(a_		,       a_						)

				return None


def 						UpperCAmelCase      (     a_		,       a_ = None		,       a_ = False		,       a_ = False		,       a_ = None		,       a_ = None		,       a_ = None		,       a_ = False		,       **a_		,       )		->     str:







				"""simple docstring"""



				__A             =			get_file_from_repo(
				    a_		,       a_		,       cache_dir=a_		,       force_download=a_		,       resume_download=a_		,       proxies=a_		,       use_auth_token=a_		,       revision=a_		,       local_files_only=a_		,       )
				if resolved_config_file is None:
								logger.info(
								    "Could not locate the feature extractor configuration file, will try to use the model config instead."						)
								return {}

				with open(a_		,       encoding="utf-8"						) as reader:
								return json.load(a_						)





class 							UpperCAmelCase				:




	'''simple docstring'''





	def __init__(	self :			Optional[Any]			):
					raise EnvironmentError(
					    "AutoFeatureExtractor is designed to be instantiated "
					    "using the `AutoFeatureExtractor.from_pretrained(pretrained_model_name_or_path)` method."			)






	@classmethod
	@replace_list_option_in_docstrings(A			)
	def     UpperCamelCase_  (	cls :			str							,A :			Optional[int]							,**A :			Optional[int]			):
					__A             =			kwargs.pop("config"							,A			)
					__A             =			kwargs.pop("trust_remote_code"							,A			)
					__A             =			True

					__A      ,							__A             =			FeatureExtractionMixin.get_feature_extractor_dict(A							,**A			)
					__A             =			config_dict.get("feature_extractor_type"							,A			)
					__A             =			None
					if "AutoFeatureExtractor" in config_dict.get("auto_map"							,{}			):
									__A             =			config_dict["auto_map"]["AutoFeatureExtractor"]

					# If we don't find the feature extractor class in the feature extractor config, let's try the model config.
					if feature_extractor_class is None and feature_extractor_auto_map is None:
									if not isinstance(A							,A			):
													__A             =			AutoConfig.from_pretrained(A							,**A			)
									# It could be in `config.feature_extractor_type``
									__A             =			getattr(A							,"feature_extractor_type"							,A			)
									if hasattr(A							,"auto_map"			) and "AutoFeatureExtractor" in config.auto_map:
													__A             =			config.auto_map["AutoFeatureExtractor"]

					if feature_extractor_class is not None:
									__A             =			feature_extractor_class_from_name(A			)

					__A             =			feature_extractor_auto_map is not None
					__A             =			feature_extractor_class is not None or type(A			) in FEATURE_EXTRACTOR_MAPPING
					__A             =			resolve_trust_remote_code(
					    A							,A							,A							,A			)

					if has_remote_code and trust_remote_code:
									__A             =			get_class_from_dynamic_module(
									    A							,A							,**A			)
									__A             =			kwargs.pop("code_revision"							,A			)
									if os.path.isdir(A			):
													feature_extractor_class.register_for_auto_class()
									return feature_extractor_class.from_dict(A							,**A			)
					elif feature_extractor_class is not None:
									return feature_extractor_class.from_dict(A							,**A			)
					# Last try: we use the FEATURE_EXTRACTOR_MAPPING.
					elif type(A			) in FEATURE_EXTRACTOR_MAPPING:
									__A             =			FEATURE_EXTRACTOR_MAPPING[type(A			)]
									return feature_extractor_class.from_dict(A							,**A			)

					raise ValueError(
					    f'''Unrecognized feature extractor in {pretrained_model_name_or_path}. Should have a '''
					    f'''`feature_extractor_type` key in its {FEATURE_EXTRACTOR_NAME} of {CONFIG_NAME}, or one of the following '''
					    f'''`model_type` keys in its {CONFIG_NAME}: {', '.join(c for c in FEATURE_EXTRACTOR_MAPPING_NAMES.keys()			)}'''			)







	@staticmethod
	def     UpperCamelCase_  (	A :			List[Any]							,A :			List[Any]			):
					FEATURE_EXTRACTOR_MAPPING.register(A							,A			)


import argparse
import os
import re

import packaging.version


SCREAMING_SNAKE_CASE							:int										=    'examples/'
SCREAMING_SNAKE_CASE							:str										=    {
    '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'),
}
SCREAMING_SNAKE_CASE							:int										=    {
    'init': 'src/diffusers/__init__.py',
    'setup': 'setup.py',
}
SCREAMING_SNAKE_CASE							:List[str]										=    'README.md'


def 						UpperCAmelCase      (     a_		,       a_		,       a_						)		->     Tuple:







				"""simple docstring"""



				with open(a_		,       "r"		,       encoding="utf-8"		,       newline="\n"						) as f:
								__A             =			f.read()
				__A      ,							__A             =			REPLACE_PATTERNS[pattern]
				__A             =			replace.replace("VERSION"		,       a_						)
				__A             =			re_pattern.sub(a_		,       a_						)
				with open(a_		,       "w"		,       encoding="utf-8"		,       newline="\n"						) as f:
								f.write(a_						)


def 						UpperCAmelCase      (     a_						)		->     Optional[Any]:







				"""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 						UpperCAmelCase      (     a_		,       a_=False						)		->     str:







				"""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 						UpperCAmelCase      (     )		->     Optional[int]:







				"""simple docstring"""



				__A             =			"🤗 Transformers currently provides the following architectures"
				__A             =			"1. Want to contribute a new model?"
				with open(a_		,       "r"		,       encoding="utf-8"		,       newline="\n"						) as f:
								__A             =			f.readlines()

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

				__A             =			start_index
				# Update the lines in the model list.
				while not lines[index].startswith(_end_prompt						):
								if lines[index].startswith("1."						):
												__A             =			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 						UpperCAmelCase      (     )		->     Dict:







				"""simple docstring"""



				with open(REPLACE_FILES["init"]		,       "r"						) as f:
								__A             =			f.read()
				__A             =			REPLACE_PATTERNS["init"][0].search(a_						).groups()[0]
				return packaging.version.parse(a_						)


def 						UpperCAmelCase      (     a_=False						)		->     str:







				"""simple docstring"""



				__A             =			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             =			default_version.base_version
				elif patch:
								__A             =			F'''{default_version.major}.{default_version.minor}.{default_version.micro + 1}'''
				else:
								__A             =			F'''{default_version.major}.{default_version.minor + 1}.0'''

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

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


def 						UpperCAmelCase      (     )		->     Optional[int]:







				"""simple docstring"""



				__A             =			get_version()
				__A             =			F'''{current_version.major}.{current_version.minor + 1}.0.dev0'''
				__A             =			current_version.base_version

				# Check with the user we got that right.
				__A             =			input(F'''Which version are we developing now? [{dev_version}]'''						)
				if len(a_						) == 0:
								__A             =			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__":
					SCREAMING_SNAKE_CASE							:Union[str, Any]										=    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.')
					SCREAMING_SNAKE_CASE							:List[str]										=    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()
