





"""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


lowerCamelCase_	:		List[Any]         =   logging.get_logger(__name__)

class 					_UpperCAmelCase    (  UpperCAmelCase__						):



      '''simple docstring'''
      lowercase_ :					str             =  ["""pixel_values"""]

      def __init__(	self   , snake_case_ = True   , snake_case_ = None   , snake_case_ = True   , snake_case_ = None   , snake_case_ = PILImageResampling.BICUBIC   , snake_case_ = True   , snake_case_ = 1 / 2_5_5   , snake_case_ = True   , snake_case_ = None   , snake_case_ = None   , **snake_case_   , ):

        """simple docstring"""

        super().__init__(**snake_case_    )
        A_    :			List[Any]									=    crop_size if crop_size is not None else {'height': 2_5_6, 'width': 2_5_6}
        A_    :			str									=    get_size_dict(snake_case_   , param_name='crop_size'    )
        A_    :			Any									=    size if size is not None else {'height': 2_2_4, 'width': 2_2_4}
        A_    :			Any									=    get_size_dict(snake_case_    )

        A_    :			List[str]									=    do_center_crop
        A_    :			Optional[int]									=    crop_size
        A_    :			str									=    do_resize
        A_    :			str									=    size
        A_    :			List[Any]									=    resample
        A_    :			Optional[int]									=    do_rescale
        A_    :			Dict									=    rescale_factor
        A_    :			Optional[int]									=    do_normalize
        A_    :			List[Any]									=    image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
        A_    :			Tuple									=    image_std if image_std is not None else IMAGENET_DEFAULT_STD

      def    lowerCamelCase_     (	self   , snake_case_   , snake_case_   , snake_case_ = None   , snake_case_ = None   , **snake_case_   , ):

        """simple docstring"""

        A_    :			Optional[int]									=    self.size if size is None else size
        A_    :			Optional[Any]									=    get_size_dict(snake_case_    )
        A_    :			Union[str, Any]									=    get_size_dict(snake_case_   , param_name='crop_size'    )

        A_		,			A_    :			Union[str, Any]									=    get_image_size(snake_case_    )
        A_    :			str									=    min(snake_case_   , snake_case_    )
        A_    :			Tuple									=    (size['height'] / crop_size['height']) * min_dim
        A_    :			Any									=    (size['width'] / crop_size['width']) * min_dim
        return center_crop(snake_case_   , size=(cropped_height, cropped_width)   , data_format=snake_case_   , **snake_case_    )

      def    lowerCamelCase_     (	self   , snake_case_   , snake_case_   , snake_case_ = PIL.Image.BICUBIC   , snake_case_ = None   , **snake_case_   , ):

        """simple docstring"""

        A_    :			int									=    get_size_dict(snake_case_    )
        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(
            snake_case_   , size=(size['height'], size['width'])   , resample=snake_case_   , data_format=snake_case_   , **snake_case_    )

      def    lowerCamelCase_     (	self   , snake_case_   , snake_case_   , snake_case_ = None   , **snake_case_   , ):

        """simple docstring"""

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

      def    lowerCamelCase_     (	self   , snake_case_   , snake_case_   , snake_case_   , snake_case_ = None   , **snake_case_   , ):

        """simple docstring"""

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

      def    lowerCamelCase_     (	self   , snake_case_   , snake_case_ = None   , snake_case_ = None   , snake_case_ = None   , snake_case_ = None   , snake_case_ = None   , snake_case_ = None   , snake_case_ = None   , snake_case_ = None   , snake_case_ = None   , snake_case_ = None   , snake_case_ = None   , snake_case_ = ChannelDimension.FIRST   , **snake_case_   , ):

        """simple docstring"""

        A_    :			List[Any]									=    do_center_crop if do_center_crop is not None else self.do_center_crop
        A_    :			List[str]									=    crop_size if crop_size is not None else self.crop_size
        A_    :			str									=    get_size_dict(snake_case_   , param_name='crop_size'    )
        A_    :			Union[str, Any]									=    do_resize if do_resize is not None else self.do_resize
        A_    :			Any									=    size if size is not None else self.size
        A_    :			Dict									=    get_size_dict(snake_case_    )
        A_    :			int									=    resample if resample is not None else self.resample
        A_    :			Any									=    do_rescale if do_rescale is not None else self.do_rescale
        A_    :			List[str]									=    rescale_factor if rescale_factor is not None else self.rescale_factor
        A_    :			List[Any]									=    do_normalize if do_normalize is not None else self.do_normalize
        A_    :			int									=    image_mean if image_mean is not None else self.image_mean
        A_    :			Tuple									=    image_std if image_std is not None else self.image_std

        A_    :			Tuple									=    make_list_of_images(snake_case_    )

        if not valid_images(snake_case_    ):
          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.
        A_    :			Dict									=    [to_numpy_array(snake_case_    ) for image in images]

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

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

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

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

        A_    :			Optional[int]									=    [to_channel_dimension_format(snake_case_   , snake_case_    ) for image in images]

        A_    :			str									=    {'pixel_values': images}
        return BatchFeature(data=snake_case_   , tensor_type=snake_case_    )





'''simple docstring'''




from typing import TYPE_CHECKING

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


snake_case__ 							=    {
    """configuration_utils""": ["""GenerationConfig"""],
    """streamers""": ["""TextIteratorStreamer""", """TextStreamer"""],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							snake_case__ 							=    [
							    """Constraint""",
							    """ConstraintListState""",
							    """DisjunctiveConstraint""",
							    """PhrasalConstraint""",
							]
							snake_case__ 							=    [
							    """BeamHypotheses""",
							    """BeamScorer""",
							    """BeamSearchScorer""",
							    """ConstrainedBeamSearchScorer""",
							]
							snake_case__ 							=    [
							    """EpsilonLogitsWarper""",
							    """EtaLogitsWarper""",
							    """ForcedBOSTokenLogitsProcessor""",
							    """ForcedEOSTokenLogitsProcessor""",
							    """HammingDiversityLogitsProcessor""",
							    """InfNanRemoveLogitsProcessor""",
							    """LogitsProcessor""",
							    """LogitsProcessorList""",
							    """LogitsWarper""",
							    """MinLengthLogitsProcessor""",
							    """MinNewTokensLengthLogitsProcessor""",
							    """NoBadWordsLogitsProcessor""",
							    """NoRepeatNGramLogitsProcessor""",
							    """PrefixConstrainedLogitsProcessor""",
							    """RepetitionPenaltyLogitsProcessor""",
							    """SequenceBiasLogitsProcessor""",
							    """EncoderRepetitionPenaltyLogitsProcessor""",
							    """TemperatureLogitsWarper""",
							    """TopKLogitsWarper""",
							    """TopPLogitsWarper""",
							    """TypicalLogitsWarper""",
							    """EncoderNoRepeatNGramLogitsProcessor""",
							    """ExponentialDecayLengthPenalty""",
							    """LogitNormalization""",
							]
							snake_case__ 							=    [
							    """MaxNewTokensCriteria""",
							    """MaxLengthCriteria""",
							    """MaxTimeCriteria""",
							    """StoppingCriteria""",
							    """StoppingCriteriaList""",
							    """validate_stopping_criteria""",
							]
							snake_case__ 							=    [
							    """GenerationMixin""",
							    """top_k_top_p_filtering""",
							    """GreedySearchEncoderDecoderOutput""",
							    """GreedySearchDecoderOnlyOutput""",
							    """SampleEncoderDecoderOutput""",
							    """SampleDecoderOnlyOutput""",
							    """BeamSearchEncoderDecoderOutput""",
							    """BeamSearchDecoderOnlyOutput""",
							    """BeamSampleEncoderDecoderOutput""",
							    """BeamSampleDecoderOnlyOutput""",
							    """ContrastiveSearchEncoderDecoderOutput""",
							    """ContrastiveSearchDecoderOnlyOutput""",
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							snake_case__ 							=    [
							    """TFForcedBOSTokenLogitsProcessor""",
							    """TFForcedEOSTokenLogitsProcessor""",
							    """TFLogitsProcessor""",
							    """TFLogitsProcessorList""",
							    """TFLogitsWarper""",
							    """TFMinLengthLogitsProcessor""",
							    """TFNoBadWordsLogitsProcessor""",
							    """TFNoRepeatNGramLogitsProcessor""",
							    """TFRepetitionPenaltyLogitsProcessor""",
							    """TFTemperatureLogitsWarper""",
							    """TFTopKLogitsWarper""",
							    """TFTopPLogitsWarper""",
							    """TFForceTokensLogitsProcessor""",
							    """TFSuppressTokensAtBeginLogitsProcessor""",
							    """TFSuppressTokensLogitsProcessor""",
							]
							snake_case__ 							=    [
							    """TFGenerationMixin""",
							    """tf_top_k_top_p_filtering""",
							    """TFGreedySearchDecoderOnlyOutput""",
							    """TFGreedySearchEncoderDecoderOutput""",
							    """TFSampleEncoderDecoderOutput""",
							    """TFSampleDecoderOnlyOutput""",
							    """TFBeamSearchEncoderDecoderOutput""",
							    """TFBeamSearchDecoderOnlyOutput""",
							    """TFBeamSampleEncoderDecoderOutput""",
							    """TFBeamSampleDecoderOnlyOutput""",
							    """TFContrastiveSearchEncoderDecoderOutput""",
							    """TFContrastiveSearchDecoderOnlyOutput""",
							]

try:
							if not is_flax_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							snake_case__ 							=    [
							    """FlaxForcedBOSTokenLogitsProcessor""",
							    """FlaxForcedEOSTokenLogitsProcessor""",
							    """FlaxLogitsProcessor""",
							    """FlaxLogitsProcessorList""",
							    """FlaxLogitsWarper""",
							    """FlaxMinLengthLogitsProcessor""",
							    """FlaxTemperatureLogitsWarper""",
							    """FlaxTopKLogitsWarper""",
							    """FlaxTopPLogitsWarper""",
							]
							snake_case__ 							=    [
							    """FlaxGenerationMixin""",
							    """FlaxGreedySearchOutput""",
							    """FlaxSampleOutput""",
							    """FlaxBeamSearchOutput""",
							]

if TYPE_CHECKING:
							from .configuration_utils import GenerationConfig
							from .streamers import TextIteratorStreamer, TextStreamer

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .beam_constraints import Constraint, ConstraintListState, DisjunctiveConstraint, PhrasalConstraint
														from .beam_search import BeamHypotheses, BeamScorer, BeamSearchScorer, ConstrainedBeamSearchScorer
														from .logits_process import (
														    EncoderNoRepeatNGramLogitsProcessor,
														    EncoderRepetitionPenaltyLogitsProcessor,
														    EpsilonLogitsWarper,
														    EtaLogitsWarper,
														    ExponentialDecayLengthPenalty,
														    ForcedBOSTokenLogitsProcessor,
														    ForcedEOSTokenLogitsProcessor,
														    HammingDiversityLogitsProcessor,
														    InfNanRemoveLogitsProcessor,
														    LogitNormalization,
														    LogitsProcessor,
														    LogitsProcessorList,
														    LogitsWarper,
														    MinLengthLogitsProcessor,
														    MinNewTokensLengthLogitsProcessor,
														    NoBadWordsLogitsProcessor,
														    NoRepeatNGramLogitsProcessor,
														    PrefixConstrainedLogitsProcessor,
														    RepetitionPenaltyLogitsProcessor,
														    SequenceBiasLogitsProcessor,
														    TemperatureLogitsWarper,
														    TopKLogitsWarper,
														    TopPLogitsWarper,
														    TypicalLogitsWarper,
														)
														from .stopping_criteria import (
														    MaxLengthCriteria,
														    MaxNewTokensCriteria,
														    MaxTimeCriteria,
														    StoppingCriteria,
														    StoppingCriteriaList,
														    validate_stopping_criteria,
														)
														from .utils import (
														    BeamSampleDecoderOnlyOutput,
														    BeamSampleEncoderDecoderOutput,
														    BeamSearchDecoderOnlyOutput,
														    BeamSearchEncoderDecoderOutput,
														    ContrastiveSearchDecoderOnlyOutput,
														    ContrastiveSearchEncoderDecoderOutput,
														    GenerationMixin,
														    GreedySearchDecoderOnlyOutput,
														    GreedySearchEncoderDecoderOutput,
														    SampleDecoderOnlyOutput,
														    SampleEncoderDecoderOutput,
														    top_k_top_p_filtering,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tf_logits_process import (
														    TFForcedBOSTokenLogitsProcessor,
														    TFForcedEOSTokenLogitsProcessor,
														    TFForceTokensLogitsProcessor,
														    TFLogitsProcessor,
														    TFLogitsProcessorList,
														    TFLogitsWarper,
														    TFMinLengthLogitsProcessor,
														    TFNoBadWordsLogitsProcessor,
														    TFNoRepeatNGramLogitsProcessor,
														    TFRepetitionPenaltyLogitsProcessor,
														    TFSuppressTokensAtBeginLogitsProcessor,
														    TFSuppressTokensLogitsProcessor,
														    TFTemperatureLogitsWarper,
														    TFTopKLogitsWarper,
														    TFTopPLogitsWarper,
														)
														from .tf_utils import (
														    TFBeamSampleDecoderOnlyOutput,
														    TFBeamSampleEncoderDecoderOutput,
														    TFBeamSearchDecoderOnlyOutput,
														    TFBeamSearchEncoderDecoderOutput,
														    TFContrastiveSearchDecoderOnlyOutput,
														    TFContrastiveSearchEncoderDecoderOutput,
														    TFGenerationMixin,
														    TFGreedySearchDecoderOnlyOutput,
														    TFGreedySearchEncoderDecoderOutput,
														    TFSampleDecoderOnlyOutput,
														    TFSampleEncoderDecoderOutput,
														    tf_top_k_top_p_filtering,
														)

							try:
														if not is_flax_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .flax_logits_process import (
														    FlaxForcedBOSTokenLogitsProcessor,
														    FlaxForcedEOSTokenLogitsProcessor,
														    FlaxLogitsProcessor,
														    FlaxLogitsProcessorList,
														    FlaxLogitsWarper,
														    FlaxMinLengthLogitsProcessor,
														    FlaxTemperatureLogitsWarper,
														    FlaxTopKLogitsWarper,
														    FlaxTopPLogitsWarper,
														)
														from .flax_utils import FlaxBeamSearchOutput, FlaxGenerationMixin, FlaxGreedySearchOutput, FlaxSampleOutput
else:
							import sys

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



