
'''simple docstring'''







import numpy as np
import torch
from torch.utils.data import Dataset, IterableDataset

from ..utils.generic import ModelOutput


class 			lowerCAmelCase (				__UpperCAmelCase			):




		def __init__(				self					,						UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase      ):
								_SCREAMING_SNAKE_CASE				= dataset
								_SCREAMING_SNAKE_CASE				= process
								_SCREAMING_SNAKE_CASE				= params




		def __len__(				self      ):
								return len(self.dataset      )




		def __getitem__(				self					,						UpperCamelCase      ):
								_SCREAMING_SNAKE_CASE				= self.dataset[i]
								_SCREAMING_SNAKE_CASE				= self.process(UpperCamelCase					,						**self.params      )
								return processed


class 			lowerCAmelCase (				__UpperCAmelCase			):




		def __init__(				self					,						UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase=None      ):
								_SCREAMING_SNAKE_CASE				= loader
								_SCREAMING_SNAKE_CASE				= infer
								_SCREAMING_SNAKE_CASE				= params
								if loader_batch_size == 1:
														# Let's spare some time by deactivating altogether
														_SCREAMING_SNAKE_CASE				= None
								_SCREAMING_SNAKE_CASE				= loader_batch_size

								# Internal bookkeeping
								_SCREAMING_SNAKE_CASE				= None
								_SCREAMING_SNAKE_CASE				= None




		def __len__(				self      ):
								return len(self.loader      )




		def __iter__(				self      ):
								_SCREAMING_SNAKE_CASE				= iter(self.loader      )
								return self




		def     lowercase       (				self      ):
								if isinstance(self._loader_batch_data					,						torch.Tensor      ):
														# Batch data is simple tensor, just fetch the slice
														_SCREAMING_SNAKE_CASE				= self._loader_batch_data[self._loader_batch_index]
								else:
														# Batch data is assumed to be BaseModelOutput (or dict)
														_SCREAMING_SNAKE_CASE				= {}
														for k, element in self._loader_batch_data.items():
																				if isinstance(UpperCamelCase					,						UpperCamelCase      ):
																										# Convert ModelOutput to tuple first
																										_SCREAMING_SNAKE_CASE				= element.to_tuple()
																										if isinstance(element[0]					,						torch.Tensor      ):
																																_SCREAMING_SNAKE_CASE				= tuple(el[self._loader_batch_index].unsqueeze(0      ) for el in element      )
																										elif isinstance(element[0]					,						np.ndarray      ):
																																_SCREAMING_SNAKE_CASE				= tuple(np.expand_dims(el[self._loader_batch_index]					,						0      ) for el in element      )
																										continue
																				if k in {"hidden_states", "past_key_values", "attentions"} and isinstance(UpperCamelCase					,						UpperCamelCase      ):
																										# Those are stored as lists of tensors so need specific unbatching.
																										if isinstance(element[0]					,						torch.Tensor      ):
																																_SCREAMING_SNAKE_CASE				= tuple(el[self._loader_batch_index].unsqueeze(0      ) for el in element      )
																										elif isinstance(element[0]					,						np.ndarray      ):
																																_SCREAMING_SNAKE_CASE				= tuple(np.expand_dims(el[self._loader_batch_index]					,						0      ) for el in element      )
																										continue
																				if element is None:
																										# This can happen for optional data that get passed around
																										_SCREAMING_SNAKE_CASE				= None
																				elif isinstance(element[self._loader_batch_index]					,						torch.Tensor      ):
																										# Take correct batch data, but make it looked like batch_size=1
																										# For compatibility with other methods within transformers

																										_SCREAMING_SNAKE_CASE				= element[self._loader_batch_index].unsqueeze(0      )
																				elif isinstance(element[self._loader_batch_index]					,						np.ndarray      ):
																										# Take correct batch data, but make it looked like batch_size=1
																										# For compatibility with other methods within transformers
																										_SCREAMING_SNAKE_CASE				= np.expand_dims(element[self._loader_batch_index]					,						0      )
																				else:
																										# This is typically a list, so no need to `unsqueeze`.
																										_SCREAMING_SNAKE_CASE				= element[self._loader_batch_index]
            # Recreate the element by reusing the original class to make it look
            # batch_size=1
														_SCREAMING_SNAKE_CASE				= self._loader_batch_data.__class__(UpperCamelCase      )
								self._loader_batch_index += 1
								return result




		def     lowercase       (				self      ):
								if self._loader_batch_index is not None and self._loader_batch_index < self.loader_batch_size:
														# We are currently unrolling a batch so we just need to return
														# the current item within a batch
														return self.loader_batch_item()

								# We're out of items within a batch
								_SCREAMING_SNAKE_CASE				= next(self.iterator      )
								_SCREAMING_SNAKE_CASE				= self.infer(UpperCamelCase					,						**self.params      )
								# We now have a batch of "inferred things".
								if self.loader_batch_size is not None:
														# Try to infer the size of the batch
														if isinstance(UpperCamelCase					,						torch.Tensor      ):
																				_SCREAMING_SNAKE_CASE				= processed
														else:
																				_SCREAMING_SNAKE_CASE				= list(processed.keys()      )[0]
																				_SCREAMING_SNAKE_CASE				= processed[key]
														if isinstance(UpperCamelCase					,						UpperCamelCase      ):
																				_SCREAMING_SNAKE_CASE				= len(UpperCamelCase      )
														else:
																				_SCREAMING_SNAKE_CASE				= first_tensor.shape[0]
														if 0 < observed_batch_size < self.loader_batch_size:
																				# could be last batch so we can't unroll as many
																				# elements.
																				_SCREAMING_SNAKE_CASE				= observed_batch_size
														# Setting internal index to unwrap the batch
														_SCREAMING_SNAKE_CASE				= processed
														_SCREAMING_SNAKE_CASE				= 0
														return self.loader_batch_item()
								else:
														# We're not unrolling batches
														return processed


class 			lowerCAmelCase (				__UpperCAmelCase			):




		def __init__(				self					,						UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase=None      ):
								super().__init__(UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase      )




		def __iter__(				self      ):
								_SCREAMING_SNAKE_CASE				= iter(self.loader      )
								_SCREAMING_SNAKE_CASE				= None
								return self




		def     lowercase       (				self      ):
								if self.subiterator is None:
														_SCREAMING_SNAKE_CASE				= self.infer(next(self.iterator      )					,						**self.params      )
								try:
														# Try to return next item
														_SCREAMING_SNAKE_CASE				= next(self.subiterator      )
								except StopIteration:
														# When a preprocess iterator ends, we can start lookig at the next item
														# ChunkIterator will keep feeding until ALL elements of iterator
														# all have created their subiterator and have been iterating against.
														#
														# Another way to look at it, is we're basically flattening lists of lists
														# into a single list, but with generators
														_SCREAMING_SNAKE_CASE				= self.infer(next(self.iterator      )					,						**self.params      )
														_SCREAMING_SNAKE_CASE				= next(self.subiterator      )
								return processed


class 			lowerCAmelCase (				__UpperCAmelCase			):




		def __iter__(				self      ):
								_SCREAMING_SNAKE_CASE				= iter(self.loader      )
								return self




		def     lowercase       (				self      ):
								# Extremely similar to PipelineIterator in its unpacking mechanism
								# BUT, we have an extra required item which is the presence of `is_last`
								# That is because everything is flattened by `PipelineChunkIterator` we
								# need to keep track of how to regroup here in the original `process`
								# boundaries so that `process` and `postprocess` see the same data.

								# This iterator accumulates items (possibly while unbatching) until it
								# its a `is_last` and then just passes it on to the caller.
								_SCREAMING_SNAKE_CASE				= False
								_SCREAMING_SNAKE_CASE				= []
								if self._loader_batch_index is not None and self._loader_batch_index < self.loader_batch_size:
														while self._loader_batch_index < self.loader_batch_size:
																				_SCREAMING_SNAKE_CASE				= self.loader_batch_item()
																				_SCREAMING_SNAKE_CASE				= item.pop("is_last"      )
																				accumulator.append(UpperCamelCase      )
																				if is_last:
																										return accumulator

								while not is_last:
														_SCREAMING_SNAKE_CASE				= self.infer(next(self.iterator      )					,						**self.params      )
														if self.loader_batch_size is not None:
																				if isinstance(UpperCamelCase					,						torch.Tensor      ):
																										_SCREAMING_SNAKE_CASE				= processed
																				else:
																										_SCREAMING_SNAKE_CASE				= list(processed.keys()      )[0]
																										_SCREAMING_SNAKE_CASE				= processed[key]
																				if isinstance(UpperCamelCase					,						UpperCamelCase      ):
																										_SCREAMING_SNAKE_CASE				= len(UpperCamelCase      )
																				else:
																										_SCREAMING_SNAKE_CASE				= first_tensor.shape[0]
																				if 0 < observed_batch_size < self.loader_batch_size:
																										# could be last batch so we can't unroll as many
																										# elements.
																										_SCREAMING_SNAKE_CASE				= observed_batch_size
																				_SCREAMING_SNAKE_CASE				= processed
																				_SCREAMING_SNAKE_CASE				= 0
																				while self._loader_batch_index < self.loader_batch_size:
																										_SCREAMING_SNAKE_CASE				= self.loader_batch_item()
																										_SCREAMING_SNAKE_CASE				= item.pop("is_last"      )
																										accumulator.append(UpperCamelCase      )
																										if is_last:
																																return accumulator
														else:
																				_SCREAMING_SNAKE_CASE				= processed
																				_SCREAMING_SNAKE_CASE				= item.pop("is_last"      )
																				accumulator.append(UpperCamelCase      )
								return accumulator


class 			lowerCAmelCase (				__UpperCAmelCase			):




		def __init__(				self					,						UpperCamelCase					,						UpperCamelCase      ):
								_SCREAMING_SNAKE_CASE				= dataset
								_SCREAMING_SNAKE_CASE				= key




		def __len__(				self      ):
								return len(self.dataset      )




		def __getitem__(				self					,						UpperCamelCase      ):
								return self.dataset[i][self.key]


class 			lowerCAmelCase (				__UpperCAmelCase			):




		def __init__(				self					,						UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase      ):
								_SCREAMING_SNAKE_CASE				= dataset
								_SCREAMING_SNAKE_CASE				= keya
								_SCREAMING_SNAKE_CASE				= keya




		def __len__(				self      ):
								return len(self.dataset      )




		def __getitem__(				self					,						UpperCamelCase      ):
								return {"text": self.dataset[i][self.keya], "text_pair": self.dataset[i][self.keya]}

'''simple docstring'''







import importlib
import inspect
import json
import os
import re
import shutil
import sys
from pathlib import Path
from typing import Dict, Optional, Union
from urllib import request

from huggingface_hub import HfFolder, cached_download, hf_hub_download, model_info
from packaging import version

from .. import __version__
from . import DIFFUSERS_DYNAMIC_MODULE_NAME, HF_MODULES_CACHE, logging


_snake_case				:   Optional[int]     =				(
    """https://raw.githubusercontent.com/huggingface/diffusers/{revision}/examples/community/{pipeline}.py"""
)


_snake_case				:   Dict     =				logging.get_logger(__name__)  # pylint: disable=invalid-name



def 			_a		(	):
						_SCREAMING_SNAKE_CASE				= "https://pypi.org/pypi/diffusers/json"
						_SCREAMING_SNAKE_CASE				= json.loads(request.urlopen(_SCREAMING_SNAKE_CASE  ).read()  )["releases"].keys()
						return sorted(_SCREAMING_SNAKE_CASE	,			key=lambda _SCREAMING_SNAKE_CASE  : version.Version(_SCREAMING_SNAKE_CASE  )  )



def 			_a		(	):
						# This function has already been executed if HF_MODULES_CACHE already is in the Python path.
						if HF_MODULES_CACHE in sys.path:
												return

						sys.path.append(_SCREAMING_SNAKE_CASE  )
						os.makedirs(_SCREAMING_SNAKE_CASE	,			exist_ok=_SCREAMING_SNAKE_CASE  )
						_SCREAMING_SNAKE_CASE				= Path(_SCREAMING_SNAKE_CASE  ) / "__init__.py"
						if not init_path.exists():
												init_path.touch()



def 			_a		(	_SCREAMING_SNAKE_CASE		:   Union[str, os.PathLike]  ):
						init_hf_modules()
						_SCREAMING_SNAKE_CASE				= Path(_SCREAMING_SNAKE_CASE  ) / name
						# If the parent module does not exist yet, recursively create it.
						if not dynamic_module_path.parent.exists():
												create_dynamic_module(dynamic_module_path.parent  )
						os.makedirs(_SCREAMING_SNAKE_CASE	,			exist_ok=_SCREAMING_SNAKE_CASE  )
						_SCREAMING_SNAKE_CASE				= dynamic_module_path / "__init__.py"
						if not init_path.exists():
												init_path.touch()



def 			_a		(	_SCREAMING_SNAKE_CASE		:   Optional[Any]  ):
						with open(_SCREAMING_SNAKE_CASE	,			"r"	,			encoding="utf-8"  ) as f:
												_SCREAMING_SNAKE_CASE				= f.read()

						# Imports of the form `import .xxx`
						_SCREAMING_SNAKE_CASE				= re.findall("^\s*import\s+\.(\S+)\s*$"	,			_SCREAMING_SNAKE_CASE	,			flags=re.MULTILINE  )
						# Imports of the form `from .xxx import yyy`
						relative_imports += re.findall("^\s*from\s+\.(\S+)\s+import"	,			_SCREAMING_SNAKE_CASE	,			flags=re.MULTILINE  )
						# Unique-ify
						return list(set(_SCREAMING_SNAKE_CASE  )  )



def 			_a		(	_SCREAMING_SNAKE_CASE		:   List[str]  ):
						_SCREAMING_SNAKE_CASE				= False
						_SCREAMING_SNAKE_CASE				= [module_file]
						_SCREAMING_SNAKE_CASE				= []

						# Let's recurse through all relative imports
						while not no_change:
												_SCREAMING_SNAKE_CASE				= []
												for f in files_to_check:
																		new_imports.extend(get_relative_imports(_SCREAMING_SNAKE_CASE  )  )

												_SCREAMING_SNAKE_CASE				= Path(_SCREAMING_SNAKE_CASE  ).parent
												_SCREAMING_SNAKE_CASE				= [str(module_path / m  ) for m in new_imports]
												_SCREAMING_SNAKE_CASE				= [f for f in new_import_files if f not in all_relative_imports]
												_SCREAMING_SNAKE_CASE				= [F'{f}.py' for f in new_import_files]

												_SCREAMING_SNAKE_CASE				= len(_SCREAMING_SNAKE_CASE  ) == 0
												all_relative_imports.extend(_SCREAMING_SNAKE_CASE  )

						return all_relative_imports



def 			_a		(	_SCREAMING_SNAKE_CASE		:   str  ):
						with open(_SCREAMING_SNAKE_CASE	,			"r"	,			encoding="utf-8"  ) as f:
												_SCREAMING_SNAKE_CASE				= f.read()

						# Imports of the form `import xxx`
						_SCREAMING_SNAKE_CASE				= re.findall("^\s*import\s+(\S+)\s*$"	,			_SCREAMING_SNAKE_CASE	,			flags=re.MULTILINE  )
						# Imports of the form `from xxx import yyy`
						imports += re.findall("^\s*from\s+(\S+)\s+import"	,			_SCREAMING_SNAKE_CASE	,			flags=re.MULTILINE  )
						# Only keep the top-level module
						_SCREAMING_SNAKE_CASE				= [imp.split("."  )[0] for imp in imports if not imp.startswith("."  )]

						# Unique-ify and test we got them all
						_SCREAMING_SNAKE_CASE				= list(set(_SCREAMING_SNAKE_CASE  )  )
						_SCREAMING_SNAKE_CASE				= []
						for imp in imports:
												try:
																		importlib.import_module(_SCREAMING_SNAKE_CASE  )
												except ImportError:
																		missing_packages.append(_SCREAMING_SNAKE_CASE  )

						if len(_SCREAMING_SNAKE_CASE  ) > 0:
												raise ImportError(
												    "This modeling file requires the following packages that were not found in your environment: "
												    F'{", ".join(_SCREAMING_SNAKE_CASE  )}. Run `pip install {" ".join(_SCREAMING_SNAKE_CASE  )}`'  )

						return get_relative_imports(_SCREAMING_SNAKE_CASE  )



def 			_a		(	_SCREAMING_SNAKE_CASE		:   Union[str, Any]	,			_SCREAMING_SNAKE_CASE		:   int  ):
						_SCREAMING_SNAKE_CASE				= module_path.replace(os.path.sep	,			"."  )
						_SCREAMING_SNAKE_CASE				= importlib.import_module(_SCREAMING_SNAKE_CASE  )

						if class_name is None:
												return find_pipeline_class(_SCREAMING_SNAKE_CASE  )
						return getattr(_SCREAMING_SNAKE_CASE	,			_SCREAMING_SNAKE_CASE  )



def 			_a		(	_SCREAMING_SNAKE_CASE		:   List[Any]  ):
						from ..pipelines import DiffusionPipeline

						_SCREAMING_SNAKE_CASE				= dict(inspect.getmembers(_SCREAMING_SNAKE_CASE	,			inspect.isclass  )  )

						_SCREAMING_SNAKE_CASE				= None
						for cls_name, cls in cls_members.items():
												if (
												    cls_name != DiffusionPipeline.__name__
												    and issubclass(cls	,			_SCREAMING_SNAKE_CASE  )
												    and cls.__module__.split("."  )[0] != "diffusers"
												):
																		if pipeline_class is not None:
																								raise ValueError(
																								    F'Multiple classes that inherit from {DiffusionPipeline.__name__} have been found:'
																								    F' {pipeline_class.__name__}, and {cls_name}. Please make sure to define only one in'
																								    F' {loaded_module}.'  )
																		_SCREAMING_SNAKE_CASE				= cls

						return pipeline_class



def 			_a		(	_SCREAMING_SNAKE_CASE		:   Union[str, os.PathLike]	,			_SCREAMING_SNAKE_CASE		:   str	,			_SCREAMING_SNAKE_CASE		:   Optional[Union[str, os.PathLike]] = None	,			_SCREAMING_SNAKE_CASE		:   bool = False	,			_SCREAMING_SNAKE_CASE		:   bool = False	,			_SCREAMING_SNAKE_CASE		:   Optional[Dict[str, str]] = None	,			_SCREAMING_SNAKE_CASE		:   Optional[Union[bool, str]] = None	,			_SCREAMING_SNAKE_CASE		:   Optional[str] = None	,			_SCREAMING_SNAKE_CASE		:   bool = False	,			):
						_SCREAMING_SNAKE_CASE				= str(_SCREAMING_SNAKE_CASE  )

						_SCREAMING_SNAKE_CASE				= os.path.join(_SCREAMING_SNAKE_CASE	,			_SCREAMING_SNAKE_CASE  )

						if os.path.isfile(_SCREAMING_SNAKE_CASE  ):
												_SCREAMING_SNAKE_CASE				= module_file_or_url
												_SCREAMING_SNAKE_CASE				= "local"
						elif pretrained_model_name_or_path.count("/"  ) == 0:
												_SCREAMING_SNAKE_CASE				= get_diffusers_versions()
												# cut ".dev0"
												_SCREAMING_SNAKE_CASE				= "v" + ".".join(__version__.split("."  )[:3]  )

												# retrieve github version that matches
												if revision is None:
																		_SCREAMING_SNAKE_CASE				= latest_version if latest_version[1:] in available_versions else "main"
																		logger.info(F'Defaulting to latest_version: {revision}.'  )
												elif revision in available_versions:
																		_SCREAMING_SNAKE_CASE				= F'v{revision}'
												elif revision == "main":
																		_SCREAMING_SNAKE_CASE				= revision
												else:
																		raise ValueError(
																		    F'`custom_revision`: {revision} does not exist. Please make sure to choose one of'
																		    F' {", ".join(available_versions + ["main"]  )}.'  )

												# community pipeline on GitHub
												_SCREAMING_SNAKE_CASE				= COMMUNITY_PIPELINES_URL.format(revision=_SCREAMING_SNAKE_CASE	,			pipeline=_SCREAMING_SNAKE_CASE  )
												try:
																		_SCREAMING_SNAKE_CASE				= cached_download(
																		    _SCREAMING_SNAKE_CASE	,			cache_dir=_SCREAMING_SNAKE_CASE	,			force_download=_SCREAMING_SNAKE_CASE	,			proxies=_SCREAMING_SNAKE_CASE	,			resume_download=_SCREAMING_SNAKE_CASE	,			local_files_only=_SCREAMING_SNAKE_CASE	,			use_auth_token=_SCREAMING_SNAKE_CASE	,			)
																		_SCREAMING_SNAKE_CASE				= "git"
																		_SCREAMING_SNAKE_CASE				= pretrained_model_name_or_path + ".py"
												except EnvironmentError:
																		logger.error(F'Could not locate the {module_file} inside {pretrained_model_name_or_path}.'  )
																		raise
						else:
												try:
																		# Load from URL or cache if already cached
																		_SCREAMING_SNAKE_CASE				= hf_hub_download(
																		    _SCREAMING_SNAKE_CASE	,			_SCREAMING_SNAKE_CASE	,			cache_dir=_SCREAMING_SNAKE_CASE	,			force_download=_SCREAMING_SNAKE_CASE	,			proxies=_SCREAMING_SNAKE_CASE	,			resume_download=_SCREAMING_SNAKE_CASE	,			local_files_only=_SCREAMING_SNAKE_CASE	,			use_auth_token=_SCREAMING_SNAKE_CASE	,			)
																		_SCREAMING_SNAKE_CASE				= os.path.join("local"	,			"--".join(pretrained_model_name_or_path.split("/"  )  )  )
												except EnvironmentError:
																		logger.error(F'Could not locate the {module_file} inside {pretrained_model_name_or_path}.'  )
																		raise

    # Check we have all the requirements in our environment
						_SCREAMING_SNAKE_CASE				= check_imports(_SCREAMING_SNAKE_CASE  )

						# Now we move the module inside our cached dynamic modules.
						_SCREAMING_SNAKE_CASE				= DIFFUSERS_DYNAMIC_MODULE_NAME + os.path.sep + submodule
						create_dynamic_module(_SCREAMING_SNAKE_CASE  )
						_SCREAMING_SNAKE_CASE				= Path(_SCREAMING_SNAKE_CASE  ) / full_submodule
						if submodule == "local" or submodule == "git":
												# We always copy local files (we could hash the file to see if there was a change, and give them the name of
												# that hash, to only copy when there is a modification but it seems overkill for now).
												# The only reason we do the copy is to avoid putting too many folders in sys.path.
												shutil.copy(_SCREAMING_SNAKE_CASE	,			submodule_path / module_file  )
												for module_needed in modules_needed:
																		_SCREAMING_SNAKE_CASE				= F'{module_needed}.py'
																		shutil.copy(os.path.join(_SCREAMING_SNAKE_CASE	,			_SCREAMING_SNAKE_CASE  )	,			submodule_path / module_needed  )
						else:
												# Get the commit hash
												# TODO: we will get this info in the etag soon, so retrieve it from there and not here.
												if isinstance(_SCREAMING_SNAKE_CASE	,			_SCREAMING_SNAKE_CASE  ):
																		_SCREAMING_SNAKE_CASE				= use_auth_token
												elif use_auth_token is True:
																		_SCREAMING_SNAKE_CASE				= HfFolder.get_token()
												else:
																		_SCREAMING_SNAKE_CASE				= None

												_SCREAMING_SNAKE_CASE				= model_info(_SCREAMING_SNAKE_CASE	,			revision=_SCREAMING_SNAKE_CASE	,			token=_SCREAMING_SNAKE_CASE  ).sha

												# The module file will end up being placed in a subfolder with the git hash of the repo. This way we get the
												# benefit of versioning.
												_SCREAMING_SNAKE_CASE				= submodule_path / commit_hash
												_SCREAMING_SNAKE_CASE				= full_submodule + os.path.sep + commit_hash
												create_dynamic_module(_SCREAMING_SNAKE_CASE  )

												if not (submodule_path / module_file).exists():
																		shutil.copy(_SCREAMING_SNAKE_CASE	,			submodule_path / module_file  )
												# Make sure we also have every file with relative
												for module_needed in modules_needed:
																		if not (submodule_path / module_needed).exists():
																								get_cached_module_file(
																								    _SCREAMING_SNAKE_CASE	,			F'{module_needed}.py'	,			cache_dir=_SCREAMING_SNAKE_CASE	,			force_download=_SCREAMING_SNAKE_CASE	,			resume_download=_SCREAMING_SNAKE_CASE	,			proxies=_SCREAMING_SNAKE_CASE	,			use_auth_token=_SCREAMING_SNAKE_CASE	,			revision=_SCREAMING_SNAKE_CASE	,			local_files_only=_SCREAMING_SNAKE_CASE	,			)
						return os.path.join(_SCREAMING_SNAKE_CASE	,			_SCREAMING_SNAKE_CASE  )




def 			_a		(	_SCREAMING_SNAKE_CASE		:   Union[str, os.PathLike]	,			_SCREAMING_SNAKE_CASE		:   str	,			_SCREAMING_SNAKE_CASE		:   Optional[str] = None	,			_SCREAMING_SNAKE_CASE		:   Optional[Union[str, os.PathLike]] = None	,			_SCREAMING_SNAKE_CASE		:   bool = False	,			_SCREAMING_SNAKE_CASE		:   bool = False	,			_SCREAMING_SNAKE_CASE		:   Optional[Dict[str, str]] = None	,			_SCREAMING_SNAKE_CASE		:   Optional[Union[bool, str]] = None	,			_SCREAMING_SNAKE_CASE		:   Optional[str] = None	,			_SCREAMING_SNAKE_CASE		:   bool = False	,			**_SCREAMING_SNAKE_CASE		:   Tuple	,			):
						_SCREAMING_SNAKE_CASE				= get_cached_module_file(
						    _SCREAMING_SNAKE_CASE	,			_SCREAMING_SNAKE_CASE	,			cache_dir=_SCREAMING_SNAKE_CASE	,			force_download=_SCREAMING_SNAKE_CASE	,			resume_download=_SCREAMING_SNAKE_CASE	,			proxies=_SCREAMING_SNAKE_CASE	,			use_auth_token=_SCREAMING_SNAKE_CASE	,			revision=_SCREAMING_SNAKE_CASE	,			local_files_only=_SCREAMING_SNAKE_CASE	,			)
						return get_class_in_module(_SCREAMING_SNAKE_CASE	,			final_module.replace(".py"	,			""  )  )