
"""simple docstring"""




import argparse
import os
import re

import packaging.version


lowercase__		:Union[str, Any]			  =   "examples/"
lowercase__		: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"),
}
lowercase__		:Tuple			  =   {
    "init": "src/diffusers/__init__.py",
    "setup": "setup.py",
}
lowercase__		:int			  =   "README.md"



def 	UpperCamelCase   (							lowerCAmelCase__			,      lowerCAmelCase__			,      lowerCAmelCase__		):
					'''simple docstring'''
					with open(__lowerCamelCase			,      '''r'''			,      encoding='''utf-8'''			,      newline='''\n'''		) as f:
										lowercase						     =						f.read()
					lowercase     ,						lowercase						     =						REPLACE_PATTERNS[pattern]
					lowercase						     =						replace.replace('''VERSION'''			,      __lowerCamelCase		)
					lowercase						     =						re_pattern.sub(__lowerCamelCase			,      __lowerCamelCase		)
					with open(__lowerCamelCase			,      '''w'''			,      encoding='''utf-8'''			,      newline='''\n'''		) as f:
										f.write(__lowerCamelCase		)



def 	UpperCamelCase   (							lowerCAmelCase__		):
					'''simple docstring'''
					for folder, directories, fnames in os.walk(__lowerCamelCase		):
										# 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(__lowerCamelCase			,      __lowerCamelCase		)			,      __lowerCamelCase			,      pattern='''examples'''		)



def 	UpperCamelCase   (							lowerCAmelCase__			,      lowerCAmelCase__=False		):
					'''simple docstring'''
					for pattern, fname in REPLACE_FILES.items():
										update_version_in_file(__lowerCamelCase			,      __lowerCamelCase			,      __lowerCamelCase		)
					if not patch:
										update_version_in_examples(__lowerCamelCase		)



def 	UpperCamelCase   (							):
					'''simple docstring'''
					lowercase						     =						'''🤗 Transformers currently provides the following architectures'''
					lowercase						     =						'''1. Want to contribute a new model?'''
					with open(__lowerCamelCase			,      '''r'''			,      encoding='''utf-8'''			,      newline='''\n'''		) as f:
										lowercase						     =						f.readlines()

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

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

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



def 	UpperCamelCase   (							):
					'''simple docstring'''
					with open(REPLACE_FILES['''init''']			,      '''r'''		) as f:
										lowercase						     =						f.read()
					lowercase						     =						REPLACE_PATTERNS['''init'''][0].search(__lowerCamelCase		).groups()[0]
					return packaging.version.parse(__lowerCamelCase		)



def 	UpperCamelCase   (							lowerCAmelCase__=False		):
					'''simple docstring'''
					lowercase						     =						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:
										lowercase						     =						default_version.base_version
					elif patch:
										lowercase						     =						f'{default_version.major}.{default_version.minor}.{default_version.micro + 1}'
					else:
										lowercase						     =						f'{default_version.major}.{default_version.minor + 1}.0'

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

					print(f'Updating version to {version}.'		)
					global_version_update(__lowerCamelCase			,      patch=__lowerCamelCase		)



def 	UpperCamelCase   (							):
					'''simple docstring'''
					lowercase						     =						get_version()
					lowercase						     =						f'{current_version.major}.{current_version.minor + 1}.0.dev0'
					lowercase						     =						current_version.base_version

					# Check with the user we got that right.
					lowercase						     =						input(f'Which version are we developing now? [{dev_version}]'		)
					if len(__lowerCamelCase		) == 0:
										lowercase						     =						dev_version

					print(f'Updating version to {version}.'		)
					global_version_update(__lowerCamelCase		)


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


if __name__ == "__main__":
							lowercase__		:Dict			  =   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.")
							lowercase__		:Optional[int]			  =   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 inspect
import os
import re

from transformers.configuration_utils import PretrainedConfig
from transformers.utils import direct_transformers_import


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
lowercase__		:Optional[Any]			  =   "src/transformers"


# This is to make sure the transformers module imported is the one in the repo.
lowercase__		:int			  =   direct_transformers_import(PATH_TO_TRANSFORMERS)

lowercase__		:List[Any]			  =   transformers.models.auto.configuration_auto.CONFIG_MAPPING

lowercase__		:List[str]			  =   {
    # used to compute the property `self.chunk_length`
    "EncodecConfig": ["overlap"],
    # used as `self.bert_model = BertModel(config, ...)`
    "DPRConfig": True,
    # not used in modeling files, but it's an important information
    "FSMTConfig": ["langs"],
    # used internally in the configuration class file
    "GPTNeoConfig": ["attention_types"],
    # used internally in the configuration class file
    "EsmConfig": ["is_folding_model"],
    # used during training (despite we don't have training script for these models yet)
    "Mask2FormerConfig": ["ignore_value"],
    # `ignore_value` used during training (despite we don't have training script for these models yet)
    # `norm` used in conversion script (despite not using in the modeling file)
    "OneFormerConfig": ["ignore_value", "norm"],
    # used during preprocessing and collation, see `collating_graphormer.py`
    "GraphormerConfig": ["spatial_pos_max"],
    # used internally in the configuration class file
    "T5Config": ["feed_forward_proj"],
    # used internally in the configuration class file
    # `tokenizer_class` get default value `T5Tokenizer` intentionally
    "MT5Config": ["feed_forward_proj", "tokenizer_class"],
    "UMT5Config": ["feed_forward_proj", "tokenizer_class"],
    # used internally in the configuration class file
    "LongT5Config": ["feed_forward_proj"],
    # used internally in the configuration class file
    "SwitchTransformersConfig": ["feed_forward_proj"],
    # having default values other than `1e-5` - we can't fix them without breaking
    "BioGptConfig": ["layer_norm_eps"],
    # having default values other than `1e-5` - we can't fix them without breaking
    "GLPNConfig": ["layer_norm_eps"],
    # having default values other than `1e-5` - we can't fix them without breaking
    "SegformerConfig": ["layer_norm_eps"],
    # having default values other than `1e-5` - we can't fix them without breaking
    "CvtConfig": ["layer_norm_eps"],
    # having default values other than `1e-5` - we can't fix them without breaking
    "PerceiverConfig": ["layer_norm_eps"],
    # used internally to calculate the feature size
    "InformerConfig": ["num_static_real_features", "num_time_features"],
    # used internally to calculate the feature size
    "TimeSeriesTransformerConfig": ["num_static_real_features", "num_time_features"],
    # used internally to calculate the feature size
    "AutoformerConfig": ["num_static_real_features", "num_time_features"],
    # used internally to calculate `mlp_dim`
    "SamVisionConfig": ["mlp_ratio"],
    # For (head) training, but so far not implemented
    "ClapAudioConfig": ["num_classes"],
    # Not used, but providing useful information to users
    "SpeechT5HifiGanConfig": ["sampling_rate"],
}


# TODO (ydshieh): Check the failing cases, try to fix them or move some cases to the above block once we are sure
SPECIAL_CASES_TO_ALLOW.update(
    {
        "CLIPSegConfig": True,
        "DeformableDetrConfig": True,
        "DetaConfig": True,
        "DinatConfig": True,
        "DonutSwinConfig": True,
        "EfficientFormerConfig": True,
        "FSMTConfig": True,
        "JukeboxConfig": True,
        "LayoutLMv2Config": True,
        "MaskFormerSwinConfig": True,
        "MT5Config": True,
        "NatConfig": True,
        "OneFormerConfig": True,
        "PerceiverConfig": True,
        "RagConfig": True,
        "SpeechT5Config": True,
        "SwinConfig": True,
        "Swin2SRConfig": True,
        "Swinv2Config": True,
        "SwitchTransformersConfig": True,
        "TableTransformerConfig": True,
        "TapasConfig": True,
        "TransfoXLConfig": True,
        "UniSpeechConfig": True,
        "UniSpeechSatConfig": True,
        "WavLMConfig": True,
        "WhisperConfig": True,
        # TODO: @Arthur (for `alignment_head` and `alignment_layer`)
        "JukeboxPriorConfig": True,
        # TODO: @Younes (for `is_decoder`)
        "Pix2StructTextConfig": True,
    }
)



def 	UpperCamelCase   (							lowerCAmelCase__			,      lowerCAmelCase__			,      lowerCAmelCase__			,      lowerCAmelCase__		):
					'''simple docstring'''
					lowercase						     =						False
					for attribute in attributes:
										for modeling_source in source_strings:
															# check if we can find `config.xxx`, `getattr(config, "xxx", ...)` or `getattr(self.config, "xxx", ...)`
															if (
															    f'config.{attribute}' in modeling_source
															    or f'getattr(config, "{attribute}"' in modeling_source
															    or f'getattr(self.config, "{attribute}"' in modeling_source
															):
																				lowercase						     =						True
															# Deal with multi-line cases
															elif (
															    re.search(
															        Rf'getattr[ \t\v\n\r\f]*\([ \t\v\n\r\f]*(self\.)?config,[ \t\v\n\r\f]*"{attribute}"'			,      lowerCAmelCase__			,      )
															    is not None
															):
																				lowercase						     =						True
															# `SequenceSummary` is called with `SequenceSummary(config)`
															elif attribute in [
															    "summary_type",
															    "summary_use_proj",
															    "summary_activation",
															    "summary_last_dropout",
															    "summary_proj_to_labels",
															    "summary_first_dropout",
															]:
																				if "SequenceSummary" in modeling_source:
																									lowercase						     =						True
															if attribute_used:
																				break
										if attribute_used:
															break

    # common and important attributes, even if they do not always appear in the modeling files
					lowercase						     =						[
					    '''bos_index''',
					    '''eos_index''',
					    '''pad_index''',
					    '''unk_index''',
					    '''mask_index''',
					    '''image_size''',
					    '''use_cache''',
					    '''out_features''',
					    '''out_indices''',
					]
					lowercase						     =						['''encoder_no_repeat_ngram_size''']

					# Special cases to be allowed
					lowercase						     =						True
					if not attribute_used:
										lowercase						     =						False
										for attribute in attributes:
															# Allow if the default value in the configuration class is different from the one in `PretrainedConfig`
															if attribute in ["is_encoder_decoder"] and default_value is True:
																				lowercase						     =						True
															elif attribute in ["tie_word_embeddings"] and default_value is False:
																				lowercase						     =						True

															# Allow cases without checking the default value in the configuration class
															elif attribute in attributes_to_allow + attributes_used_in_generation:
																				lowercase						     =						True
															elif attribute.endswith('''_token_id'''		):
																				lowercase						     =						True

															# configuration class specific cases
															if not case_allowed:
																				lowercase						     =						SPECIAL_CASES_TO_ALLOW.get(config_class.__name__			,      []		)
																				lowercase						     =						allowed_cases is True or attribute in allowed_cases

					return attribute_used or case_allowed



def 	UpperCamelCase   (							lowerCAmelCase__		):
					'''simple docstring'''
					lowercase						     =						dict(inspect.signature(config_class.__init__		).parameters		)
					lowercase						     =						[x for x in list(signature.keys()		) if x not in ['''self''', '''kwargs''']]
					lowercase						     =						[signature[param].default for param in parameter_names]

					# If `attribute_map` exists, an attribute can have different names to be used in the modeling files, and as long
					# as one variant is used, the test should pass
					lowercase						     =						{}
					if len(config_class.attribute_map		) > 0:
										lowercase						     =						{v: k for k, v in config_class.attribute_map.items()}

					# Get the path to modeling source files
					lowercase						     =						inspect.getsourcefile(lowerCAmelCase__		)
					lowercase						     =						os.path.dirname(lowerCAmelCase__		)
					# Let's check against all frameworks: as long as one framework uses an attribute, we are good.
					lowercase						     =						[os.path.join(lowerCAmelCase__			,      lowerCAmelCase__		) for fn in os.listdir(lowerCAmelCase__		) if fn.startswith('''modeling_'''		)]

					# Get the source code strings
					lowercase						     =						[]
					for path in modeling_paths:
										if os.path.isfile(lowerCAmelCase__		):
															with open(lowerCAmelCase__		) as fp:
																				modeling_sources.append(fp.read()		)

					lowercase						     =						[]
					for config_param, default_value in zip(lowerCAmelCase__			,      lowerCAmelCase__		):
										# `attributes` here is all the variant names for `config_param`
										lowercase						     =						[config_param]
										# some configuration classes have non-empty `attribute_map`, and both names could be used in the
										# corresponding modeling files. As long as one of them appears, it is fine.
										if config_param in reversed_attribute_map:
															attributes.append(reversed_attribute_map[config_param]		)

										if not check_attribute_being_used(lowerCAmelCase__			,      lowerCAmelCase__			,      lowerCAmelCase__			,      lowerCAmelCase__		):
															unused_attributes.append(attributes[0]		)

					return sorted(lowerCAmelCase__		)



def 	UpperCamelCase   (							):
					'''simple docstring'''
					lowercase						     =						{}
					for _config_class in list(CONFIG_MAPPING.values()		):
										# Skip deprecated models
										if "models.deprecated" in _config_class.__module__:
															continue
										# Some config classes are not in `CONFIG_MAPPING` (e.g. `CLIPVisionConfig`, `Blip2VisionConfig`, etc.)
										lowercase						     =						[
										    cls
										    for name, cls in inspect.getmembers(
										        inspect.getmodule(_config_class		)			,      lambda lowerCAmelCase__		: inspect.isclass(lowerCAmelCase__		)
										        and issubclass(lowerCAmelCase__			,      lowerCAmelCase__		)
										        and inspect.getmodule(lowerCAmelCase__		) == inspect.getmodule(_config_class		)			,      )
										]
										for config_class in config_classes_in_module:
															lowercase						     =						check_config_attributes_being_used(lowerCAmelCase__		)
															if len(lowerCAmelCase__		) > 0:
																				lowercase						     =						unused_attributes

					if len(lowerCAmelCase__		) > 0:
										lowercase						     =						'''The following configuration classes contain unused attributes in the corresponding modeling files:\n'''
										for name, attributes in configs_with_unused_attributes.items():
															error += f'{name}: {attributes}\n'

										raise ValueError(lowerCAmelCase__		)


if __name__ == "__main__":
							check_config_attributes()




