





import inspect
import warnings
from typing import Any, Dict, Optional, Union

from packaging import version







def       UpperCAmelCase							(     *_lowerCamelCase      :  Optional[Any]			,   _lowerCamelCase      :  Optional[Union[Dict, Any]] = None			,   _lowerCamelCase      :  Dict=True			,   _lowerCamelCase      :  Dict=2     ):





						'''simple docstring'''


						from .. import __version__

						SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					take_from
						SCREAMING_SNAKE_CASE__     :				str   =					()
						if not isinstance(args[0]			,   _lowerCamelCase     ):
												SCREAMING_SNAKE_CASE__     :				Dict   =					(args,)

						for attribute, version_name, message in args:
												if version.parse(version.parse(_lowerCamelCase     ).base_version     ) >= version.parse(_lowerCamelCase     ):
																		raise ValueError(
																		    f"""The deprecation tuple {(attribute, version_name, message)} should be removed since diffusers'"""
																		    f""" version {__version__} is >= {version_name}"""     )

												SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					None
												if isinstance(_lowerCamelCase			,   _lowerCamelCase     ) and attribute in deprecated_kwargs:
																		values += (deprecated_kwargs.pop(_lowerCamelCase     ),)
																		SCREAMING_SNAKE_CASE__     :				Optional[int]   =					f"""The `{attribute}` argument is deprecated and will be removed in version {version_name}."""
												elif hasattr(_lowerCamelCase			,   _lowerCamelCase     ):
																		values += (getattr(_lowerCamelCase			,   _lowerCamelCase     ),)
																		SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					f"""The `{attribute}` attribute is deprecated and will be removed in version {version_name}."""
												elif deprecated_kwargs is None:
																		SCREAMING_SNAKE_CASE__     :				List[Any]   =					f"""`{attribute}` is deprecated and will be removed in version {version_name}."""

												if warning is not None:
																		SCREAMING_SNAKE_CASE__     :				List[str]   =					warning + " " if standard_warn else ""
																		warnings.warn(warning + message			,   _lowerCamelCase			,   stacklevel=_lowerCamelCase     )

						if isinstance(_lowerCamelCase			,   _lowerCamelCase     ) and len(_lowerCamelCase     ) > 0:
												SCREAMING_SNAKE_CASE__     :				List[str]   =					inspect.getouterframes(inspect.currentframe()     )[1]
												SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					call_frame.filename
												SCREAMING_SNAKE_CASE__     :				Tuple   =					call_frame.lineno
												SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					call_frame.function
												SCREAMING_SNAKE_CASE__,							SCREAMING_SNAKE_CASE__     :				List[str]   =					next(iter(deprecated_kwargs.items()     )     )
												raise TypeError(f"""{function} in {filename} line {line_number-1} got an unexpected keyword argument `{key}`"""     )

						if len(_lowerCamelCase     ) == 0:
												return
						elif len(_lowerCamelCase     ) == 1:
												return values[0]
						return values






import warnings

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



class    _a     (				lowercase__							):






							"""simple docstring"""

							snake_case_							 =						["image_processor", "tokenizer"]
							snake_case_							 =						"CLIPImageProcessor"
							snake_case_							 =						("CLIPTokenizer", "CLIPTokenizerFast")

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

													SCREAMING_SNAKE_CASE__     :				int   =					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__(a							,	a    )

							def __call__(      self				:     Tuple							,	a				:     Tuple=None							,	a				:     Union[str, Any]=None							,	a				:     List[str]=None							,	**a				:     Optional[Any]    )	->Optional[Any]:

													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:
																			SCREAMING_SNAKE_CASE__     :				str   =					self.tokenizer(a							,	return_tensors=a							,	**a    )

													if images is not None:
																			SCREAMING_SNAKE_CASE__     :				int   =					self.image_processor(a							,	return_tensors=a							,	**a    )

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

							def      A_  (      self				:     Optional[int]							,	*a				:     Any							,	**a				:     List[str]    )	->Any:
													return self.tokenizer.batch_decode(*a							,	**a    )

							def      A_  (      self				:     Any							,	*a				:     Optional[int]							,	**a				:     Dict    )	->Any:
													return self.tokenizer.decode(*a							,	**a    )

							@property
							def      A_  (      self				:     List[str]    )	->Union[str, Any]:
													SCREAMING_SNAKE_CASE__     :				Dict   =					self.tokenizer.model_input_names
													SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					self.image_processor.model_input_names
													return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names    )    )

							@property
							def      A_  (      self				:     Optional[int]    )	->List[Any]:
													warnings.warn(
													    "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."							,	a							,	)
													return self.image_processor_class






							@property
							def      A_  (      self				:     Dict    )	->str:
													warnings.warn(
													    "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."							,	a							,	)
													return self.image_processor