

'''simple docstring'''

from typing import TYPE_CHECKING

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


UpperCAmelCase				   =  {
    """auto_factory""": ["""get_values"""],
    """configuration_auto""": ["""ALL_PRETRAINED_CONFIG_ARCHIVE_MAP""", """CONFIG_MAPPING""", """MODEL_NAMES_MAPPING""", """AutoConfig"""],
    """feature_extraction_auto""": ["""FEATURE_EXTRACTOR_MAPPING""", """AutoFeatureExtractor"""],
    """image_processing_auto""": ["""IMAGE_PROCESSOR_MAPPING""", """AutoImageProcessor"""],
    """processing_auto""": ["""PROCESSOR_MAPPING""", """AutoProcessor"""],
    """tokenization_auto""": ["""TOKENIZER_MAPPING""", """AutoTokenizer"""],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCAmelCase				   =  [
          """MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING""",
          """MODEL_FOR_AUDIO_FRAME_CLASSIFICATION_MAPPING""",
          """MODEL_FOR_AUDIO_XVECTOR_MAPPING""",
          """MODEL_FOR_BACKBONE_MAPPING""",
          """MODEL_FOR_CAUSAL_IMAGE_MODELING_MAPPING""",
          """MODEL_FOR_CAUSAL_LM_MAPPING""",
          """MODEL_FOR_CTC_MAPPING""",
          """MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING""",
          """MODEL_FOR_DEPTH_ESTIMATION_MAPPING""",
          """MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING""",
          """MODEL_FOR_IMAGE_SEGMENTATION_MAPPING""",
          """MODEL_FOR_INSTANCE_SEGMENTATION_MAPPING""",
          """MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING""",
          """MODEL_FOR_MASKED_LM_MAPPING""",
          """MODEL_FOR_MASK_GENERATION_MAPPING""",
          """MODEL_FOR_MULTIPLE_CHOICE_MAPPING""",
          """MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING""",
          """MODEL_FOR_OBJECT_DETECTION_MAPPING""",
          """MODEL_FOR_PRETRAINING_MAPPING""",
          """MODEL_FOR_QUESTION_ANSWERING_MAPPING""",
          """MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING""",
          """MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING""",
          """MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING""",
          """MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING""",
          """MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING""",
          """MODEL_FOR_TEXT_ENCODING_MAPPING""",
          """MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING""",
          """MODEL_FOR_UNIVERSAL_SEGMENTATION_MAPPING""",
          """MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING""",
          """MODEL_FOR_VISION_2_SEQ_MAPPING""",
          """MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING""",
          """MODEL_MAPPING""",
          """MODEL_WITH_LM_HEAD_MAPPING""",
          """MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING""",
          """MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING""",
          """AutoModel""",
          """AutoBackbone""",
          """AutoModelForAudioClassification""",
          """AutoModelForAudioFrameClassification""",
          """AutoModelForAudioXVector""",
          """AutoModelForCausalLM""",
          """AutoModelForCTC""",
          """AutoModelForDepthEstimation""",
          """AutoModelForImageClassification""",
          """AutoModelForImageSegmentation""",
          """AutoModelForInstanceSegmentation""",
          """AutoModelForMaskGeneration""",
          """AutoModelForTextEncoding""",
          """AutoModelForMaskedImageModeling""",
          """AutoModelForMaskedLM""",
          """AutoModelForMultipleChoice""",
          """AutoModelForNextSentencePrediction""",
          """AutoModelForObjectDetection""",
          """AutoModelForPreTraining""",
          """AutoModelForQuestionAnswering""",
          """AutoModelForSemanticSegmentation""",
          """AutoModelForSeq2SeqLM""",
          """AutoModelForSequenceClassification""",
          """AutoModelForSpeechSeq2Seq""",
          """AutoModelForTableQuestionAnswering""",
          """AutoModelForTokenClassification""",
          """AutoModelForUniversalSegmentation""",
          """AutoModelForVideoClassification""",
          """AutoModelForVision2Seq""",
          """AutoModelForVisualQuestionAnswering""",
          """AutoModelForDocumentQuestionAnswering""",
          """AutoModelWithLMHead""",
          """AutoModelForZeroShotImageClassification""",
          """AutoModelForZeroShotObjectDetection""",
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCAmelCase				   =  [
          """TF_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING""",
          """TF_MODEL_FOR_CAUSAL_LM_MAPPING""",
          """TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING""",
          """TF_MODEL_FOR_MASK_GENERATION_MAPPING""",
          """TF_MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING""",
          """TF_MODEL_FOR_MASKED_LM_MAPPING""",
          """TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING""",
          """TF_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING""",
          """TF_MODEL_FOR_PRETRAINING_MAPPING""",
          """TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING""",
          """TF_MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING""",
          """TF_MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING""",
          """TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING""",
          """TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING""",
          """TF_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING""",
          """TF_MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING""",
          """TF_MODEL_FOR_TEXT_ENCODING_MAPPING""",
          """TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING""",
          """TF_MODEL_FOR_VISION_2_SEQ_MAPPING""",
          """TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING""",
          """TF_MODEL_MAPPING""",
          """TF_MODEL_WITH_LM_HEAD_MAPPING""",
          """TFAutoModel""",
          """TFAutoModelForAudioClassification""",
          """TFAutoModelForCausalLM""",
          """TFAutoModelForImageClassification""",
          """TFAutoModelForMaskedImageModeling""",
          """TFAutoModelForMaskedLM""",
          """TFAutoModelForMaskGeneration""",
          """TFAutoModelForMultipleChoice""",
          """TFAutoModelForNextSentencePrediction""",
          """TFAutoModelForPreTraining""",
          """TFAutoModelForDocumentQuestionAnswering""",
          """TFAutoModelForQuestionAnswering""",
          """TFAutoModelForSemanticSegmentation""",
          """TFAutoModelForSeq2SeqLM""",
          """TFAutoModelForSequenceClassification""",
          """TFAutoModelForSpeechSeq2Seq""",
          """TFAutoModelForTableQuestionAnswering""",
          """TFAutoModelForTextEncoding""",
          """TFAutoModelForTokenClassification""",
          """TFAutoModelForVision2Seq""",
          """TFAutoModelForZeroShotImageClassification""",
          """TFAutoModelWithLMHead""",
      ]

try:
      if not is_flax_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCAmelCase				   =  [
          """FLAX_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING""",
          """FLAX_MODEL_FOR_CAUSAL_LM_MAPPING""",
          """FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING""",
          """FLAX_MODEL_FOR_MASKED_LM_MAPPING""",
          """FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING""",
          """FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING""",
          """FLAX_MODEL_FOR_PRETRAINING_MAPPING""",
          """FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING""",
          """FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING""",
          """FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING""",
          """FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING""",
          """FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING""",
          """FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING""",
          """FLAX_MODEL_MAPPING""",
          """FlaxAutoModel""",
          """FlaxAutoModelForCausalLM""",
          """FlaxAutoModelForImageClassification""",
          """FlaxAutoModelForMaskedLM""",
          """FlaxAutoModelForMultipleChoice""",
          """FlaxAutoModelForNextSentencePrediction""",
          """FlaxAutoModelForPreTraining""",
          """FlaxAutoModelForQuestionAnswering""",
          """FlaxAutoModelForSeq2SeqLM""",
          """FlaxAutoModelForSequenceClassification""",
          """FlaxAutoModelForSpeechSeq2Seq""",
          """FlaxAutoModelForTokenClassification""",
          """FlaxAutoModelForVision2Seq""",
      ]


if TYPE_CHECKING:
      from .auto_factory import get_values
      from .configuration_auto import ALL_PRETRAINED_CONFIG_ARCHIVE_MAP, CONFIG_MAPPING, MODEL_NAMES_MAPPING, AutoConfig
      from .feature_extraction_auto import FEATURE_EXTRACTOR_MAPPING, AutoFeatureExtractor
      from .image_processing_auto import IMAGE_PROCESSOR_MAPPING, AutoImageProcessor
      from .processing_auto import PROCESSOR_MAPPING, AutoProcessor
      from .tokenization_auto import TOKENIZER_MAPPING, AutoTokenizer

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_auto import (
                MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING,
                MODEL_FOR_AUDIO_FRAME_CLASSIFICATION_MAPPING,
                MODEL_FOR_AUDIO_XVECTOR_MAPPING,
                MODEL_FOR_BACKBONE_MAPPING,
                MODEL_FOR_CAUSAL_IMAGE_MODELING_MAPPING,
                MODEL_FOR_CAUSAL_LM_MAPPING,
                MODEL_FOR_CTC_MAPPING,
                MODEL_FOR_DEPTH_ESTIMATION_MAPPING,
                MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING,
                MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
                MODEL_FOR_IMAGE_SEGMENTATION_MAPPING,
                MODEL_FOR_INSTANCE_SEGMENTATION_MAPPING,
                MODEL_FOR_MASK_GENERATION_MAPPING,
                MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING,
                MODEL_FOR_MASKED_LM_MAPPING,
                MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
                MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING,
                MODEL_FOR_OBJECT_DETECTION_MAPPING,
                MODEL_FOR_PRETRAINING_MAPPING,
                MODEL_FOR_QUESTION_ANSWERING_MAPPING,
                MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING,
                MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
                MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
                MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING,
                MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING,
                MODEL_FOR_TEXT_ENCODING_MAPPING,
                MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
                MODEL_FOR_UNIVERSAL_SEGMENTATION_MAPPING,
                MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING,
                MODEL_FOR_VISION_2_SEQ_MAPPING,
                MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING,
                MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING,
                MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING,
                MODEL_MAPPING,
                MODEL_WITH_LM_HEAD_MAPPING,
                AutoBackbone,
                AutoModel,
                AutoModelForAudioClassification,
                AutoModelForAudioFrameClassification,
                AutoModelForAudioXVector,
                AutoModelForCausalLM,
                AutoModelForCTC,
                AutoModelForDepthEstimation,
                AutoModelForDocumentQuestionAnswering,
                AutoModelForImageClassification,
                AutoModelForImageSegmentation,
                AutoModelForInstanceSegmentation,
                AutoModelForMaskedImageModeling,
                AutoModelForMaskedLM,
                AutoModelForMaskGeneration,
                AutoModelForMultipleChoice,
                AutoModelForNextSentencePrediction,
                AutoModelForObjectDetection,
                AutoModelForPreTraining,
                AutoModelForQuestionAnswering,
                AutoModelForSemanticSegmentation,
                AutoModelForSeqaSeqLM,
                AutoModelForSequenceClassification,
                AutoModelForSpeechSeqaSeq,
                AutoModelForTableQuestionAnswering,
                AutoModelForTextEncoding,
                AutoModelForTokenClassification,
                AutoModelForUniversalSegmentation,
                AutoModelForVideoClassification,
                AutoModelForVisionaSeq,
                AutoModelForVisualQuestionAnswering,
                AutoModelForZeroShotImageClassification,
                AutoModelForZeroShotObjectDetection,
                AutoModelWithLMHead,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_auto import (
                TF_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING,
                TF_MODEL_FOR_CAUSAL_LM_MAPPING,
                TF_MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING,
                TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
                TF_MODEL_FOR_MASK_GENERATION_MAPPING,
                TF_MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING,
                TF_MODEL_FOR_MASKED_LM_MAPPING,
                TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
                TF_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING,
                TF_MODEL_FOR_PRETRAINING_MAPPING,
                TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
                TF_MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING,
                TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
                TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
                TF_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING,
                TF_MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING,
                TF_MODEL_FOR_TEXT_ENCODING_MAPPING,
                TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
                TF_MODEL_FOR_VISION_2_SEQ_MAPPING,
                TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING,
                TF_MODEL_MAPPING,
                TF_MODEL_WITH_LM_HEAD_MAPPING,
                TFAutoModel,
                TFAutoModelForAudioClassification,
                TFAutoModelForCausalLM,
                TFAutoModelForDocumentQuestionAnswering,
                TFAutoModelForImageClassification,
                TFAutoModelForMaskedImageModeling,
                TFAutoModelForMaskedLM,
                TFAutoModelForMaskGeneration,
                TFAutoModelForMultipleChoice,
                TFAutoModelForNextSentencePrediction,
                TFAutoModelForPreTraining,
                TFAutoModelForQuestionAnswering,
                TFAutoModelForSemanticSegmentation,
                TFAutoModelForSeqaSeqLM,
                TFAutoModelForSequenceClassification,
                TFAutoModelForSpeechSeqaSeq,
                TFAutoModelForTableQuestionAnswering,
                TFAutoModelForTextEncoding,
                TFAutoModelForTokenClassification,
                TFAutoModelForVisionaSeq,
                TFAutoModelForZeroShotImageClassification,
                TFAutoModelWithLMHead,
            )

      try:
            if not is_flax_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_flax_auto import (
                FLAX_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING,
                FLAX_MODEL_FOR_CAUSAL_LM_MAPPING,
                FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
                FLAX_MODEL_FOR_MASKED_LM_MAPPING,
                FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
                FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING,
                FLAX_MODEL_FOR_PRETRAINING_MAPPING,
                FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
                FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
                FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
                FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING,
                FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
                FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING,
                FLAX_MODEL_MAPPING,
                FlaxAutoModel,
                FlaxAutoModelForCausalLM,
                FlaxAutoModelForImageClassification,
                FlaxAutoModelForMaskedLM,
                FlaxAutoModelForMultipleChoice,
                FlaxAutoModelForNextSentencePrediction,
                FlaxAutoModelForPreTraining,
                FlaxAutoModelForQuestionAnswering,
                FlaxAutoModelForSeqaSeqLM,
                FlaxAutoModelForSequenceClassification,
                FlaxAutoModelForSpeechSeqaSeq,
                FlaxAutoModelForTokenClassification,
                FlaxAutoModelForVisionaSeq,
            )

else:
      import sys

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





'''simple docstring'''

from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import center_crop, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
       import PIL


UpperCAmelCase				   =  logging.get_logger(__name__)


class   lowerCAmelCase     (      A   ):
       lowerCAmelCase_		= ["pixel_values"]







       def __init__(							self			: Dict						,      __lowercase			: bool = True						,      __lowercase			: Dict[str, int] = None						,      __lowercase			: bool = True						,      __lowercase			: Dict[str, int] = None						,      __lowercase			: PILImageResampling = PILImageResampling.BICUBIC						,      __lowercase			: bool = True						,      __lowercase			: Union[int, float] = 1 / 255						,      __lowercase			: bool = True						,      __lowercase			: Optional[Union[float, List[float]]] = None						,      __lowercase			: Optional[Union[float, List[float]]] = None						,      **__lowercase			: Any						,      ):







            """simple docstring"""







            super().__init__(**__lowercase      )
            __lowercase					=crop_size if crop_size is not None else {'height': 256, 'width': 256}
            __lowercase					=get_size_dict(__lowercase						,      param_name='crop_size'      )
            __lowercase					=size if size is not None else {'height': 224, 'width': 224}
            __lowercase					=get_size_dict(__lowercase      )

            __lowercase					=do_center_crop
            __lowercase					=crop_size
            __lowercase					=do_resize
            __lowercase					=size
            __lowercase					=resample
            __lowercase					=do_rescale
            __lowercase					=rescale_factor
            __lowercase					=do_normalize
            __lowercase					=image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
            __lowercase					=image_std if image_std is not None else IMAGENET_DEFAULT_STD







       def 			snake_case   (							self			: int						,      __lowercase			: np.ndarray						,      __lowercase			: Dict[str, int]						,      __lowercase			: Optional[int] = None						,      __lowercase			: Optional[Union[str, ChannelDimension]] = None						,      **__lowercase			: Optional[Any]						,      ):







            """simple docstring"""







            __lowercase					=self.size if size is None else size
            __lowercase					=get_size_dict(__lowercase      )
            __lowercase					=get_size_dict(__lowercase						,      param_name='crop_size'      )

            __lowercase		,     __lowercase					=get_image_size(__lowercase      )
            __lowercase					=min(__lowercase						,      __lowercase      )
            __lowercase					=(size['height'] / crop_size['height']) * min_dim
            __lowercase					=(size['width'] / crop_size['width']) * min_dim
            return center_crop(__lowercase						,      size=(cropped_height, cropped_width)						,      data_format=__lowercase						,      **__lowercase      )







       def 			snake_case   (							self			: str						,      __lowercase			: np.ndarray						,      __lowercase			: Dict[str, int]						,      __lowercase			: PILImageResampling = PIL.Image.BICUBIC						,      __lowercase			: Optional[Union[str, ChannelDimension]] = None						,      **__lowercase			: Optional[int]						,      ):







            """simple docstring"""







            __lowercase					=get_size_dict(__lowercase      )
            if "height" not in size or "width" not in size:
                 raise ValueError(f'''The size dictionary must contain the keys \'height\' and \'width\'. Got {size.keys()}'''      )
            return resize(
                __lowercase						,      size=(size['height'], size['width'])						,      resample=__lowercase						,      data_format=__lowercase						,      **__lowercase      )







       def 			snake_case   (							self			: Dict						,      __lowercase			: np.ndarray						,      __lowercase			: Union[int, float]						,      __lowercase			: Optional[Union[str, ChannelDimension]] = None						,      **__lowercase			: Dict						,      ):







            """simple docstring"""







            return rescale(__lowercase						,      scale=__lowercase						,      data_format=__lowercase						,      **__lowercase      )







       def 			snake_case   (							self			: Union[str, Any]						,      __lowercase			: np.ndarray						,      __lowercase			: Union[float, List[float]]						,      __lowercase			: Union[float, List[float]]						,      __lowercase			: Optional[Union[str, ChannelDimension]] = None						,      **__lowercase			: int						,      ):







            """simple docstring"""







            return normalize(__lowercase						,      mean=__lowercase						,      std=__lowercase						,      data_format=__lowercase						,      **__lowercase      )







       def 			snake_case   (							self			: Dict						,      __lowercase			: ImageInput						,      __lowercase			: Optional[bool] = None						,      __lowercase			: Optional[Dict[str, int]] = None						,      __lowercase			: Optional[bool] = None						,      __lowercase			: Optional[Dict[str, int]] = None						,      __lowercase			: PILImageResampling = None						,      __lowercase			: Optional[bool] = None						,      __lowercase			: Optional[float] = None						,      __lowercase			: Optional[bool] = None						,      __lowercase			: Optional[Union[float, List[float]]] = None						,      __lowercase			: Optional[Union[float, List[float]]] = None						,      __lowercase			: Optional[Union[str, TensorType]] = None						,      __lowercase			: ChannelDimension = ChannelDimension.FIRST						,      **__lowercase			: Optional[int]						,      ):







            """simple docstring"""







            __lowercase					=do_center_crop if do_center_crop is not None else self.do_center_crop
            __lowercase					=crop_size if crop_size is not None else self.crop_size
            __lowercase					=get_size_dict(__lowercase						,      param_name='crop_size'      )
            __lowercase					=do_resize if do_resize is not None else self.do_resize
            __lowercase					=size if size is not None else self.size
            __lowercase					=get_size_dict(__lowercase      )
            __lowercase					=resample if resample is not None else self.resample
            __lowercase					=do_rescale if do_rescale is not None else self.do_rescale
            __lowercase					=rescale_factor if rescale_factor is not None else self.rescale_factor
            __lowercase					=do_normalize if do_normalize is not None else self.do_normalize
            __lowercase					=image_mean if image_mean is not None else self.image_mean
            __lowercase					=image_std if image_std is not None else self.image_std

            __lowercase					=make_list_of_images(__lowercase      )

            if not valid_images(__lowercase      ):
                 raise ValueError(
                     'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
                     'torch.Tensor, tf.Tensor or jax.ndarray.'      )

            if do_center_crop and crop_size is None:
                 raise ValueError('If `do_center_crop` is set to `True`, `crop_size` must be provided.'      )

            if do_resize and size is None:
                 raise ValueError('Size must be specified if do_resize is True.'      )

            if do_rescale and rescale_factor is None:
                 raise ValueError('Rescale factor must be specified if do_rescale is True.'      )

            if do_normalize and (image_mean is None or image_std is None):
                 raise ValueError('Image mean and image standard deviation must be specified if do_normalize is True.'      )

            # All transformations expect numpy arrays.
            __lowercase					=[to_numpy_array(__lowercase      ) for image in images]

            if do_center_crop:
                 __lowercase					=[self.center_crop(__lowercase						,      __lowercase						,      size=__lowercase      ) for image in images]

            if do_resize:
                 __lowercase					=[self.resize(image=__lowercase						,      size=__lowercase						,      resample=__lowercase      ) for image in images]

            if do_rescale:
                 __lowercase					=[self.rescale(image=__lowercase						,      scale=__lowercase      ) for image in images]

            if do_normalize:
                 __lowercase					=[self.normalize(image=__lowercase						,      mean=__lowercase						,      std=__lowercase      ) for image in images]

            __lowercase					=[to_channel_dimension_format(__lowercase						,      __lowercase      ) for image in images]

            __lowercase					={'pixel_values': images}
            return BatchFeature(data=__lowercase						,      tensor_type=__lowercase      )


