






'''simple docstring'''

import warnings

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






class 							__magic_name__     (	UpperCAmelCase_				):







	"""simple docstring"""



	SCREAMING_SNAKE_CASE_					:      Tuple        =  ['image_processor', 'tokenizer']
	SCREAMING_SNAKE_CASE_					:      Tuple        =  'ViTImageProcessor'
	SCREAMING_SNAKE_CASE_					:      str        =  ('CLIPTokenizer', 'CLIPTokenizerFast')
	def __init__(							self  ,      _lowercase=None  ,      _lowercase=None  ,      **_lowercase       )						->  Optional[int]:
							lowercase_      :   Dict        =			None
							if "feature_extractor" in kwargs:
													warnings.warn(
													    'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
													    ' instead.'  ,      _lowercase  ,      )
													lowercase_      :   str        =			kwargs.pop('feature_extractor'       )

							lowercase_      :   Any        =			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__(_lowercase  ,      _lowercase       )
	def __call__(							self  ,      _lowercase=None  ,      _lowercase=None  ,      _lowercase=None  ,      _lowercase=None  ,      **_lowercase       )						->  Union[str, Any]:
							if text is None and visual_prompt is None and images is None:
													raise ValueError('You have to specify either text, visual prompt or images.'       )

							if text is not None and visual_prompt is not None:
													raise ValueError('You have to specify exactly one type of prompt. Either text or visual prompt.'       )

							if text is not None:
													lowercase_      :   str        =			self.tokenizer(_lowercase  ,      return_tensors=_lowercase  ,      **_lowercase       )

							if visual_prompt is not None:
													lowercase_      :   Any        =			self.image_processor(_lowercase  ,      return_tensors=_lowercase  ,      **_lowercase       )

							if images is not None:
													lowercase_      :   Optional[Any]        =			self.image_processor(_lowercase  ,      return_tensors=_lowercase  ,      **_lowercase       )

							if visual_prompt is not None and images is not None:
													lowercase_      :   List[Any]        =			{
													    'pixel_values': image_features.pixel_values,
													    'conditional_pixel_values': prompt_features.pixel_values,
													}
													return encoding
							elif text is not None and images is not None:
													lowercase_      :   Optional[Any]        =			image_features.pixel_values
													return encoding
							elif text is not None:
													return encoding
							elif visual_prompt is not None:
													lowercase_      :   Tuple        =			{
													    'conditional_pixel_values': prompt_features.pixel_values,
													}
													return encoding
							else:
													return BatchEncoding(data=dict(**_lowercase       )  ,      tensor_type=_lowercase       )
	def        lowerCamelCase__		(							self  ,      *_lowercase  ,      **_lowercase       )						->  Optional[int]:
							return self.tokenizer.batch_decode(*_lowercase  ,      **_lowercase       )
	def        lowerCamelCase__		(							self  ,      *_lowercase  ,      **_lowercase       )						->  Union[str, Any]:
							return self.tokenizer.decode(*_lowercase  ,      **_lowercase       )
	@property
	def        lowerCamelCase__		(							self       )						->  int:
							warnings.warn(
							    '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'  ,      _lowercase  ,      )
							return self.image_processor_class







	@property
	def        lowerCamelCase__		(							self       )						->  Optional[int]:
							warnings.warn(
							    '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'  ,      _lowercase  ,      )
							return self.image_processor








'''simple docstring'''

from typing import TYPE_CHECKING

from ..utils import _LazyModule


A:      int  				=				{
    "config": [
        "EXTERNAL_DATA_FORMAT_SIZE_LIMIT",
        "OnnxConfig",
        "OnnxConfigWithPast",
        "OnnxSeq2SeqConfigWithPast",
        "PatchingSpec",
    ],
    "convert": ["export", "validate_model_outputs"],
    "features": ["FeaturesManager"],
    "utils": ["ParameterFormat", "compute_serialized_parameters_size"],
}


if TYPE_CHECKING:
						from .config import (
						    EXTERNAL_DATA_FORMAT_SIZE_LIMIT,
						    OnnxConfig,
						    OnnxConfigWithPast,
						    OnnxSeqaSeqConfigWithPast,
						    PatchingSpec,
						)
						from .convert import export, validate_model_outputs
						from .features import FeaturesManager
						from .utils import ParameterFormat, compute_serialized_parameters_size

else:
						import sys

						A:      Dict  				=				_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
