



from typing import TYPE_CHECKING

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


__SCREAMING_SNAKE_CASE           =  {
    """configuration_bert""": ["""BERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """BertConfig""", """BertOnnxConfig"""],
    """tokenization_bert""": ["""BasicTokenizer""", """BertTokenizer""", """WordpieceTokenizer"""],
}

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__SCREAMING_SNAKE_CASE           =  ["""BertTokenizerFast"""]

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__SCREAMING_SNAKE_CASE           =  [
							    """BERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """BertForMaskedLM""",
							    """BertForMultipleChoice""",
							    """BertForNextSentencePrediction""",
							    """BertForPreTraining""",
							    """BertForQuestionAnswering""",
							    """BertForSequenceClassification""",
							    """BertForTokenClassification""",
							    """BertLayer""",
							    """BertLMHeadModel""",
							    """BertModel""",
							    """BertPreTrainedModel""",
							    """load_tf_weights_in_bert""",
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__SCREAMING_SNAKE_CASE           =  [
							    """TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """TFBertEmbeddings""",
							    """TFBertForMaskedLM""",
							    """TFBertForMultipleChoice""",
							    """TFBertForNextSentencePrediction""",
							    """TFBertForPreTraining""",
							    """TFBertForQuestionAnswering""",
							    """TFBertForSequenceClassification""",
							    """TFBertForTokenClassification""",
							    """TFBertLMHeadModel""",
							    """TFBertMainLayer""",
							    """TFBertModel""",
							    """TFBertPreTrainedModel""",
							]
try:
							if not is_tensorflow_text_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__SCREAMING_SNAKE_CASE           =  ["""TFBertTokenizer"""]

try:
							if not is_flax_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__SCREAMING_SNAKE_CASE           =  [
							    """FlaxBertForCausalLM""",
							    """FlaxBertForMaskedLM""",
							    """FlaxBertForMultipleChoice""",
							    """FlaxBertForNextSentencePrediction""",
							    """FlaxBertForPreTraining""",
							    """FlaxBertForQuestionAnswering""",
							    """FlaxBertForSequenceClassification""",
							    """FlaxBertForTokenClassification""",
							    """FlaxBertModel""",
							    """FlaxBertPreTrainedModel""",
							]

if TYPE_CHECKING:
							from .configuration_bert import BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, BertConfig, BertOnnxConfig
							from .tokenization_bert import BasicTokenizer, BertTokenizer, WordpieceTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_bert_fast import BertTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_bert import (
														    BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
														    BertForMaskedLM,
														    BertForMultipleChoice,
														    BertForNextSentencePrediction,
														    BertForPreTraining,
														    BertForQuestionAnswering,
														    BertForSequenceClassification,
														    BertForTokenClassification,
														    BertLayer,
														    BertLMHeadModel,
														    BertModel,
														    BertPreTrainedModel,
														    load_tf_weights_in_bert,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_bert import (
														    TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFBertEmbeddings,
														    TFBertForMaskedLM,
														    TFBertForMultipleChoice,
														    TFBertForNextSentencePrediction,
														    TFBertForPreTraining,
														    TFBertForQuestionAnswering,
														    TFBertForSequenceClassification,
														    TFBertForTokenClassification,
														    TFBertLMHeadModel,
														    TFBertMainLayer,
														    TFBertModel,
														    TFBertPreTrainedModel,
														)

							try:
														if not is_tensorflow_text_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_bert_tf import TFBertTokenizer

							try:
														if not is_flax_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_flax_bert import (
														    FlaxBertForCausalLM,
														    FlaxBertForMaskedLM,
														    FlaxBertForMultipleChoice,
														    FlaxBertForNextSentencePrediction,
														    FlaxBertForPreTraining,
														    FlaxBertForQuestionAnswering,
														    FlaxBertForSequenceClassification,
														    FlaxBertForTokenClassification,
														    FlaxBertModel,
														    FlaxBertPreTrainedModel,
														)

else:
							import sys

							__SCREAMING_SNAKE_CASE           =  _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




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


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


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




def 	UpperCAmelCase   (						):
			A							:					Union[str, Any]              =						"https://pypi.org/pypi/diffusers/json"
			A							:					List[Any]              =						json.loads(request.urlopen(_lowerCamelCase    ).read()    )["releases"].keys()
			return sorted(_lowerCamelCase ,    key=lambda _lowerCamelCase    : version.Version(_lowerCamelCase    )    )




def 	UpperCAmelCase   (						):
			# 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(_lowerCamelCase    )
			os.makedirs(_lowerCamelCase ,    exist_ok=_lowerCamelCase    )
			A							:					List[Any]              =						Path(_lowerCamelCase    ) / "__init__.py"
			if not init_path.exists():
						init_path.touch()




def 	UpperCAmelCase   (						_lowerCamelCase    ):
			init_hf_modules()
			A							:					Tuple              =						Path(_lowerCamelCase    ) / 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(_lowerCamelCase ,    exist_ok=_lowerCamelCase    )
			A							:					Optional[int]              =						dynamic_module_path / "__init__.py"
			if not init_path.exists():
						init_path.touch()




def 	UpperCAmelCase   (						_lowerCamelCase    ):
			with open(_lowerCamelCase ,    "r" ,    encoding="utf-8"    ) as f:
						A							:					Union[str, Any]              =						f.read()

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




def 	UpperCAmelCase   (						_lowerCamelCase    ):
			A							:					Optional[int]              =						False
			A							:					Tuple              =						[module_file]
			A							:					Optional[int]              =						[]

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

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

						A							:					Tuple              =						len(_lowerCamelCase    ) == 0
						all_relative_imports.extend(_lowerCamelCase    )

			return all_relative_imports




def 	UpperCAmelCase   (						_lowerCamelCase    ):
			with open(_lowerCamelCase ,    "r" ,    encoding="utf-8"    ) as f:
						A							:					Dict              =						f.read()

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

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

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

			return get_relative_imports(_lowerCamelCase    )




def 	UpperCAmelCase   (						_lowerCamelCase ,    _lowerCamelCase    ):
			A							:					int              =						module_path.replace(os.path.sep ,    "."    )
			A							:					Optional[Any]              =						importlib.import_module(_lowerCamelCase    )

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




def 	UpperCAmelCase   (						_lowerCamelCase    ):
			from ..pipelines import DiffusionPipeline

			A							:					int              =						dict(inspect.getmembers(_lowerCamelCase ,    inspect.isclass    )    )

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

			return pipeline_class




def 	UpperCAmelCase   (						_lowerCamelCase ,    _lowerCamelCase ,    _lowerCamelCase = None ,    _lowerCamelCase = False ,    _lowerCamelCase = False ,    _lowerCamelCase = None ,    _lowerCamelCase = None ,    _lowerCamelCase = None ,    _lowerCamelCase = False ,    ):
			A							:					List[Any]              =						str(_lowerCamelCase    )

			A							:					Any              =						os.path.join(_lowerCamelCase ,    _lowerCamelCase    )

			if os.path.isfile(_lowerCamelCase    ):
						A							:					Union[str, Any]              =						module_file_or_url
						A							:					Any              =						"local"
			elif pretrained_model_name_or_path.count("/"    ) == 0:
						A							:					Optional[Any]              =						get_diffusers_versions()
						# cut ".dev0"
						A							:					Union[str, Any]              =						"v" + ".".join(__version__.split("."    )[:3]    )

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

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

						A							:					List[str]              =						model_info(_lowerCamelCase ,    revision=_lowerCamelCase ,    token=_lowerCamelCase    ).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.
						A							:					str              =						submodule_path / commit_hash
						A							:					List[str]              =						full_submodule + os.path.sep + commit_hash
						create_dynamic_module(_lowerCamelCase    )

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






def 	UpperCAmelCase   (						_lowerCamelCase ,    _lowerCamelCase ,    _lowerCamelCase = None ,    _lowerCamelCase = None ,    _lowerCamelCase = False ,    _lowerCamelCase = False ,    _lowerCamelCase = None ,    _lowerCamelCase = None ,    _lowerCamelCase = None ,    _lowerCamelCase = False ,    **_lowerCamelCase ,    ):
			A							:					int              =						get_cached_module_file(
			    _lowerCamelCase ,    _lowerCamelCase ,    cache_dir=_lowerCamelCase ,    force_download=_lowerCamelCase ,    resume_download=_lowerCamelCase ,    proxies=_lowerCamelCase ,    use_auth_token=_lowerCamelCase ,    revision=_lowerCamelCase ,    local_files_only=_lowerCamelCase ,    )
			return get_class_in_module(_lowerCamelCase ,    final_module.replace(".py" ,    ""    )    )