

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


a_     =			(
    'https://raw.githubusercontent.com/huggingface/diffusers/{revision}/examples/community/{pipeline}.py'
)


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

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

def        lowerCamelCase__       (    ):
	# 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(_a)
	os.makedirs(_a							,	exist_ok=_a)
	SCREAMING_SNAKE_CASE      :					str    =				Path(_a) / "__init__.py"
	if not init_path.exists():
		init_path.touch()

def        lowerCamelCase__       (    _a):
	init_hf_modules()
	SCREAMING_SNAKE_CASE      :					Optional[int]    =				Path(_a) / 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(_a							,	exist_ok=_a)
	SCREAMING_SNAKE_CASE      :					List[Any]    =				dynamic_module_path / "__init__.py"
	if not init_path.exists():
		init_path.touch()

def        lowerCamelCase__       (    _a):
	with open(_a							,	"r"							,	encoding="utf-8") as f:
		SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				f.read()

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

def        lowerCamelCase__       (    _a):
	SCREAMING_SNAKE_CASE      :					List[str]    =				False
	SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				[module_file]
	SCREAMING_SNAKE_CASE      :					Dict    =				[]

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

		SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				Path(_a).parent
		SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				[str(module_path / m) for m in new_imports]
		SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				[f for f in new_import_files if f not in all_relative_imports]
		SCREAMING_SNAKE_CASE      :					Optional[Any]    =				[f"{f}.py" for f in new_import_files]

		SCREAMING_SNAKE_CASE      :					Dict    =				len(_a) == 0
		all_relative_imports.extend(_a)

	return all_relative_imports

def        lowerCamelCase__       (    _a):
	with open(_a							,	"r"							,	encoding="utf-8") as f:
		SCREAMING_SNAKE_CASE      :					List[str]    =				f.read()

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

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

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

	return get_relative_imports(_a)

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

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

def        lowerCamelCase__       (    _a):
	from ..pipelines import DiffusionPipeline

	SCREAMING_SNAKE_CASE      :					Optional[int]    =				dict(inspect.getmembers(_a							,	inspect.isclass))

	SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				None
	for cls_name, cls in cls_members.items():
		if (
		    cls_name != DiffusionPipeline.__name__
		    and issubclass(cls							,	_a)
		    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      :					Any    =				cls

	return pipeline_class

def        lowerCamelCase__       (    _a							,	_a							,	_a = None							,	_a = False							,	_a = False							,	_a = None							,	_a = None							,	_a = None							,	_a = False							,	):
	SCREAMING_SNAKE_CASE      :					Optional[int]    =				str(_a)

	SCREAMING_SNAKE_CASE      :					Optional[int]    =				os.path.join(_a							,	_a)

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

		# retrieve github version that matches
		if revision is None:
			SCREAMING_SNAKE_CASE      :					List[Any]    =				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      :					Optional[int]    =				f"v{revision}"
		elif revision == "main":
			SCREAMING_SNAKE_CASE      :					Dict    =				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      :					List[Any]    =				COMMUNITY_PIPELINES_URL.format(revision=_a							,	pipeline=_a)
		try:
			SCREAMING_SNAKE_CASE      :					List[str]    =				cached_download(
			    _a							,	cache_dir=_a							,	force_download=_a							,	proxies=_a							,	resume_download=_a							,	local_files_only=_a							,	use_auth_token=_a							,	)
			SCREAMING_SNAKE_CASE      :					Any    =				"git"
			SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				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      :					List[str]    =				hf_hub_download(
			    _a							,	_a							,	cache_dir=_a							,	force_download=_a							,	proxies=_a							,	resume_download=_a							,	local_files_only=_a							,	use_auth_token=_a							,	)
			SCREAMING_SNAKE_CASE      :					Any    =				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      :					int    =				check_imports(_a)

	# Now we move the module inside our cached dynamic modules.
	SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				DIFFUSERS_DYNAMIC_MODULE_NAME + os.path.sep + submodule
	create_dynamic_module(_a)
	SCREAMING_SNAKE_CASE      :					str    =				Path(_a) / 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(_a							,	submodule_path / module_file)
		for module_needed in modules_needed:
			SCREAMING_SNAKE_CASE      :					str    =				f"{module_needed}.py"
			shutil.copy(os.path.join(_a							,	_a)							,	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(_a							,	_a):
			SCREAMING_SNAKE_CASE      :					List[Any]    =				use_auth_token
		elif use_auth_token is True:
			SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				HfFolder.get_token()
		else:
			SCREAMING_SNAKE_CASE      :					int    =				None

		SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				model_info(_a							,	revision=_a							,	token=_a).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      :					str    =				submodule_path / commit_hash
		SCREAMING_SNAKE_CASE      :					Optional[Any]    =				full_submodule + os.path.sep + commit_hash
		create_dynamic_module(_a)

		if not (submodule_path / module_file).exists():
			shutil.copy(_a							,	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(
				    _a							,	f"{module_needed}.py"							,	cache_dir=_a							,	force_download=_a							,	resume_download=_a							,	proxies=_a							,	use_auth_token=_a							,	revision=_a							,	local_files_only=_a							,	)
	return os.path.join(_a							,	_a)


def        lowerCamelCase__       (    _a							,	_a							,	_a = None							,	_a = None							,	_a = False							,	_a = False							,	_a = None							,	_a = None							,	_a = None							,	_a = False							,	**_a							,	):
	SCREAMING_SNAKE_CASE      :					str    =				get_cached_module_file(
	    _a							,	_a							,	cache_dir=_a							,	force_download=_a							,	resume_download=_a							,	proxies=_a							,	use_auth_token=_a							,	revision=_a							,	local_files_only=_a							,	)
	return get_class_in_module(_a							,	final_module.replace(".py"							,	""))

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__a  			=					{
    'configuration_rembert': ['REMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'RemBertConfig', 'RemBertOnnxConfig']
}

try:
						if not is_sentencepiece_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a  			=					['RemBertTokenizer']

try:
						if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a  			=					['RemBertTokenizerFast']

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a  			=					[
						    'REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'RemBertForCausalLM',
						    'RemBertForMaskedLM',
						    'RemBertForMultipleChoice',
						    'RemBertForQuestionAnswering',
						    'RemBertForSequenceClassification',
						    'RemBertForTokenClassification',
						    'RemBertLayer',
						    'RemBertModel',
						    'RemBertPreTrainedModel',
						    'load_tf_weights_in_rembert',
						]


try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a  			=					[
						    'TF_REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'TFRemBertForCausalLM',
						    'TFRemBertForMaskedLM',
						    'TFRemBertForMultipleChoice',
						    'TFRemBertForQuestionAnswering',
						    'TFRemBertForSequenceClassification',
						    'TFRemBertForTokenClassification',
						    'TFRemBertLayer',
						    'TFRemBertModel',
						    'TFRemBertPreTrainedModel',
						]


if TYPE_CHECKING:
						from .configuration_rembert import REMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RemBertConfig, RemBertOnnxConfig

						try:
												if not is_sentencepiece_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_rembert import RemBertTokenizer

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_rembert_fast import RemBertTokenizerFast

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_rembert import (
												    REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    RemBertForCausalLM,
												    RemBertForMaskedLM,
												    RemBertForMultipleChoice,
												    RemBertForQuestionAnswering,
												    RemBertForSequenceClassification,
												    RemBertForTokenClassification,
												    RemBertLayer,
												    RemBertModel,
												    RemBertPreTrainedModel,
												    load_tf_weights_in_rembert,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_rembert import (
												    TF_REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TFRemBertForCausalLM,
												    TFRemBertForMaskedLM,
												    TFRemBertForMultipleChoice,
												    TFRemBertForQuestionAnswering,
												    TFRemBertForSequenceClassification,
												    TFRemBertForTokenClassification,
												    TFRemBertLayer,
												    TFRemBertModel,
												    TFRemBertPreTrainedModel,
												)


else:
						import sys

						__a  			=					_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




