


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


__snake_case		=       logging.get_logger(__name__)  # pylint: disable=invalid-name
def       A_   (   ):






     """simple docstring"""

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






     """simple docstring"""

     if HF_MODULES_CACHE in sys.path:
          return

     sys.path.append(_lowerCAmelCase       )
     os.makedirs(_lowerCAmelCase,     exist_ok=_lowerCAmelCase       )
     _a    	=				Path(_lowerCAmelCase       ) / '''__init__.py'''
     if not init_path.exists():
          init_path.touch()
def       A_   (   _lowerCAmelCase     :					Union[str, os.PathLike]       ):






     """simple docstring"""

     init_hf_modules()
     _a    	=				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    	=				dynamic_module_path / '''__init__.py'''
     if not init_path.exists():
          init_path.touch()
def       A_   (   _lowerCAmelCase     :					Optional[Any]       ):






     """simple docstring"""

     with open(_lowerCAmelCase,     '''r''',     encoding='''utf-8'''       ) as f:
          _a    	=				f.read()

     # Imports of the form `import .xxx`
     _a    	=				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       A_   (   _lowerCAmelCase     :					int       ):






     """simple docstring"""

     _a    	=				False
     _a    	=				[module_file]
     _a    	=				[]

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

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

          _a    	=				len(_lowerCAmelCase       ) == 0
          all_relative_imports.extend(_lowerCAmelCase       )

     return all_relative_imports
def       A_   (   _lowerCAmelCase     :					List[Any]       ):






     """simple docstring"""

     with open(_lowerCAmelCase,     '''r''',     encoding='''utf-8'''       ) as f:
          _a    	=				f.read()

     # Imports of the form `import xxx`
     _a    	=				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    	=				[imp.split('''.'''       )[0] for imp in imports if not imp.startswith('''.'''       )]

     # Unique-ify and test we got them all
     _a    	=				list(set(_lowerCAmelCase       )       )
     _a    	=				[]
     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       A_   (   _lowerCAmelCase     :					Union[str, Any],     _lowerCAmelCase     :					List[str]       ):






     """simple docstring"""

     _a    	=				module_path.replace(os.path.sep,     '''.'''       )
     _a    	=				importlib.import_module(_lowerCAmelCase       )

     if class_name is None:
          return find_pipeline_class(_lowerCAmelCase       )
     return getattr(_lowerCAmelCase,     _lowerCAmelCase       )
def       A_   (   _lowerCAmelCase     :					Any       ):






     """simple docstring"""

     from ..pipelines import DiffusionPipeline

     _a    	=				dict(inspect.getmembers(_lowerCAmelCase,     inspect.isclass       )       )

     _a    	=				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    	=				cls

     return pipeline_class
def       A_   (   _lowerCAmelCase     :					Union[str, os.PathLike],     _lowerCAmelCase     :					str,     _lowerCAmelCase     :					Optional[Union[str, os.PathLike]] = None,     _lowerCAmelCase     :					bool = False,     _lowerCAmelCase     :					bool = False,     _lowerCAmelCase     :					Optional[Dict[str, str]] = None,     _lowerCAmelCase     :					Optional[Union[bool, str]] = None,     _lowerCAmelCase     :					Optional[str] = None,     _lowerCAmelCase     :					bool = False,     ):






     """simple docstring"""

     _a    	=				str(_lowerCAmelCase       )

     _a    	=				os.path.join(_lowerCAmelCase,     _lowerCAmelCase       )

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

          # retrieve github version that matches
          if revision is None:
               _a    	=				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    	=				f'v{revision}'
          elif revision == "main":
               _a    	=				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    	=				COMMUNITY_PIPELINES_URL.format(revision=_lowerCAmelCase,     pipeline=_lowerCAmelCase       )
          try:
               _a    	=				cached_download(
                   _lowerCAmelCase,     cache_dir=_lowerCAmelCase,     force_download=_lowerCAmelCase,     proxies=_lowerCAmelCase,     resume_download=_lowerCAmelCase,     local_files_only=_lowerCAmelCase,     use_auth_token=_lowerCAmelCase,     )
               _a    	=				'''git'''
               _a    	=				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    	=				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    	=				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    	=				check_imports(_lowerCAmelCase       )

     # Now we move the module inside our cached dynamic modules.
     _a    	=				DIFFUSERS_DYNAMIC_MODULE_NAME + os.path.sep + submodule
     create_dynamic_module(_lowerCAmelCase       )
     _a    	=				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    	=				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    	=				use_auth_token
          elif use_auth_token is True:
               _a    	=				HfFolder.get_token()
          else:
               _a    	=				None

          _a    	=				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    	=				submodule_path / commit_hash
          _a    	=				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       A_   (   _lowerCAmelCase     :					Union[str, os.PathLike],     _lowerCAmelCase     :					str,     _lowerCAmelCase     :					Optional[str] = None,     _lowerCAmelCase     :					Optional[Union[str, os.PathLike]] = None,     _lowerCAmelCase     :					bool = False,     _lowerCAmelCase     :					bool = False,     _lowerCAmelCase     :					Optional[Dict[str, str]] = None,     _lowerCAmelCase     :					Optional[Union[bool, str]] = None,     _lowerCAmelCase     :					Optional[str] = None,     _lowerCAmelCase     :					bool = False,     **_lowerCAmelCase     :					Any,     ):






     """simple docstring"""

     _a    	=				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''',     ''''''       )       )



"""simple docstring"""






import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding


class     __lowerCamelCase  (					a__				):

     '''simple docstring'''
     A_       :	Optional[int]    =		['image_processor', 'tokenizer']
     A_       :	Union[str, Any]    =		'ChineseCLIPImageProcessor'
     A_       :	Union[str, Any]    =		('BertTokenizer', 'BertTokenizerFast')


     def __init__(      self    ,				__UpperCAmelCase=None    ,				__UpperCAmelCase=None    ,				**__UpperCAmelCase							)       ->							List[str]:
          _a    	=				None
          if "feature_extractor" in kwargs:
               warnings.warn(
                   '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
                   ''' instead.'''    ,				__UpperCAmelCase    ,				)
               _a    	=				kwargs.pop('''feature_extractor'''							)

          _a    	=				image_processor if image_processor is not None else feature_extractor
          if image_processor is None:
               raise ValueError('''You need to specify an `image_processor`.'''							)
          if tokenizer is None:
               raise ValueError('''You need to specify a `tokenizer`.'''							)

          super().__init__(__UpperCAmelCase    ,				__UpperCAmelCase							)
          _a    	=				self.image_processor


     def __call__(      self    ,				__UpperCAmelCase=None    ,				__UpperCAmelCase=None    ,				__UpperCAmelCase=None    ,				**__UpperCAmelCase							)       ->							List[str]:

          if text is None and images is None:
               raise ValueError('''You have to specify either text or images. Both cannot be none.'''							)

          if text is not None:
               _a    	=				self.tokenizer(__UpperCAmelCase    ,				return_tensors=__UpperCAmelCase    ,				**__UpperCAmelCase							)

          if images is not None:
               _a    	=				self.image_processor(__UpperCAmelCase    ,				return_tensors=__UpperCAmelCase    ,				**__UpperCAmelCase							)

          if text is not None and images is not None:
               _a    	=				image_features.pixel_values
               return encoding
          elif text is not None:
               return encoding
          else:
               return BatchEncoding(data=dict(**__UpperCAmelCase							)    ,				tensor_type=__UpperCAmelCase							)


     def 							_UpperCAmelCase      (      self    ,				*__UpperCAmelCase    ,				**__UpperCAmelCase							)       ->							List[str]:
          return self.tokenizer.batch_decode(*__UpperCAmelCase    ,				**__UpperCAmelCase							)


     def 							_UpperCAmelCase      (      self    ,				*__UpperCAmelCase    ,				**__UpperCAmelCase							)       ->							Optional[int]:
          return self.tokenizer.decode(*__UpperCAmelCase    ,				**__UpperCAmelCase							)


     @property
     def 							_UpperCAmelCase      (      self							)       ->							List[str]:
          _a    	=				self.tokenizer.model_input_names
          _a    	=				self.image_processor.model_input_names
          return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names							)							)





     @property
     def 							_UpperCAmelCase      (      self							)       ->							Tuple:
          warnings.warn(
              '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''    ,				__UpperCAmelCase    ,				)
          return self.image_processor_class