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,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


UpperCAmelCase      		=					logging.get_logger(__name__)


if is_vision_available():
 import PIL




class  __lowerCAmelCase	(							a_    ):



 '''simple docstring'''
 _lowerCamelCase    :						str					     =			['pixel_values']

 def __init__(      self							:		Any							,      lowerCamelCase							:		bool = True							,      lowerCamelCase							:		Dict[str, int] = None							,      lowerCamelCase							:		PILImageResampling = PILImageResampling.BICUBIC							,      lowerCamelCase							:		bool = True							,      lowerCamelCase							:		Dict[str, int] = None							,      lowerCamelCase							:		bool = True							,      lowerCamelCase							:		Union[int, float] = 1 / 2_5_5							,      lowerCamelCase							:		bool = True							,      lowerCamelCase							:		Optional[Union[float, List[float]]] = None							,      lowerCamelCase							:		Optional[Union[float, List[float]]] = None							,      lowerCamelCase							:		bool = True							,      **lowerCamelCase							:		Any							,      )			->				None:
        super().__init__(**lowerCamelCase	)
        _UpperCamelCase  :							Optional[int]	    =	size if size is not None else {'shortest_edge': 2_2_4}
        _UpperCamelCase  :							Any	    =	get_size_dict(lowerCamelCase							,      default_to_square=lowerCamelCase	)
        _UpperCamelCase  :							Optional[int]	    =	crop_size if crop_size is not None else {'height': 2_2_4, 'width': 2_2_4}
        _UpperCamelCase  :							int	    =	get_size_dict(lowerCamelCase	)

        _UpperCamelCase  :							Optional[int]	    =	do_resize
        _UpperCamelCase  :							Union[str, Any]	    =	size
        _UpperCamelCase  :							Tuple	    =	resample
        _UpperCamelCase  :							Any	    =	do_center_crop
        _UpperCamelCase  :							Optional[Any]	    =	crop_size
        _UpperCamelCase  :							Optional[int]	    =	do_rescale
        _UpperCamelCase  :							Optional[Any]	    =	rescale_factor
        _UpperCamelCase  :							Dict	    =	do_normalize
        _UpperCamelCase  :							Any	    =	image_mean if image_mean is not None else OPENAI_CLIP_MEAN
        _UpperCamelCase  :							Union[str, Any]	    =	image_std if image_std is not None else OPENAI_CLIP_STD
        _UpperCamelCase  :							List[str]	    =	do_convert_rgb

 def        __snake_case       (      self							:		Optional[int]							,      lowerCamelCase							:		np.ndarray							,      lowerCamelCase							:		Dict[str, int]							,      lowerCamelCase							:		PILImageResampling = PILImageResampling.BICUBIC							,      lowerCamelCase							:		Optional[Union[str, ChannelDimension]] = None							,      **lowerCamelCase							:		List[str]							,      )			->				np.ndarray:
        _UpperCamelCase  :							Dict	    =	get_size_dict(lowerCamelCase							,      default_to_square=lowerCamelCase	)
        _UpperCamelCase  :							Union[str, Any]	    =	get_resize_output_image_size(
            lowerCamelCase							,      size=(size['height'], size['width'])							,      default_to_square=lowerCamelCase	)
        return resize(lowerCamelCase							,      size=lowerCamelCase							,      resample=lowerCamelCase							,      data_format=lowerCamelCase							,      **lowerCamelCase	)

 def        __snake_case       (      self							:		Tuple							,      lowerCamelCase							:		np.ndarray							,      lowerCamelCase							:		Dict[str, int]							,      lowerCamelCase							:		Optional[Union[str, ChannelDimension]] = None							,      **lowerCamelCase							:		int							,      )			->				np.ndarray:
        _UpperCamelCase  :							Any	    =	get_size_dict(lowerCamelCase	)
        return center_crop(lowerCamelCase							,      size=(size['height'], size['width'])							,      data_format=lowerCamelCase							,      **lowerCamelCase	)

 def        __snake_case       (      self							:		Any							,      lowerCamelCase							:		np.ndarray							,      lowerCamelCase							:		Union[int, float]							,      lowerCamelCase							:		Optional[Union[str, ChannelDimension]] = None							,      **lowerCamelCase							:		Optional[int]							,      )			->				int:
        return rescale(lowerCamelCase							,      scale=lowerCamelCase							,      data_format=lowerCamelCase							,      **lowerCamelCase	)

 def        __snake_case       (      self							:		str							,      lowerCamelCase							:		np.ndarray							,      lowerCamelCase							:		Union[float, List[float]]							,      lowerCamelCase							:		Union[float, List[float]]							,      lowerCamelCase							:		Optional[Union[str, ChannelDimension]] = None							,      **lowerCamelCase							:		Optional[int]							,      )			->				np.ndarray:
        return normalize(lowerCamelCase							,      mean=lowerCamelCase							,      std=lowerCamelCase							,      data_format=lowerCamelCase							,      **lowerCamelCase	)

 def        __snake_case       (      self							:		str							,      lowerCamelCase							:		ImageInput							,      lowerCamelCase							:		bool = None							,      lowerCamelCase							:		Dict[str, int] = None							,      lowerCamelCase							:		PILImageResampling = None							,      lowerCamelCase							:		bool = None							,      lowerCamelCase							:		int = None							,      lowerCamelCase							:		bool = None							,      lowerCamelCase							:		float = None							,      lowerCamelCase							:		bool = None							,      lowerCamelCase							:		Optional[Union[float, List[float]]] = None							,      lowerCamelCase							:		Optional[Union[float, List[float]]] = None							,      lowerCamelCase							:		bool = None							,      lowerCamelCase							:		Optional[Union[str, TensorType]] = None							,      lowerCamelCase							:		Optional[ChannelDimension] = ChannelDimension.FIRST							,      **lowerCamelCase							:		int							,      )			->				PIL.Image.Image:
        _UpperCamelCase  :							List[Any]	    =	do_resize if do_resize is not None else self.do_resize
        _UpperCamelCase  :							str	    =	size if size is not None else self.size
        _UpperCamelCase  :							str	    =	get_size_dict(lowerCamelCase							,      default_to_square=lowerCamelCase	)
        _UpperCamelCase  :							Tuple	    =	resample if resample is not None else self.resample
        _UpperCamelCase  :							str	    =	do_center_crop if do_center_crop is not None else self.do_center_crop
        _UpperCamelCase  :							str	    =	crop_size if crop_size is not None else self.crop_size
        _UpperCamelCase  :							Optional[Any]	    =	get_size_dict(lowerCamelCase	)
        _UpperCamelCase  :							List[Any]	    =	do_rescale if do_rescale is not None else self.do_rescale
        _UpperCamelCase  :							Union[str, Any]	    =	rescale_factor if rescale_factor is not None else self.rescale_factor
        _UpperCamelCase  :							List[str]	    =	do_normalize if do_normalize is not None else self.do_normalize
        _UpperCamelCase  :							str	    =	image_mean if image_mean is not None else self.image_mean
        _UpperCamelCase  :							Any	    =	image_std if image_std is not None else self.image_std
        _UpperCamelCase  :							Union[str, Any]	    =	do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

        _UpperCamelCase  :							Optional[Any]	    =	make_list_of_images(lowerCamelCase	)

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

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

        if do_center_crop and crop_size is None:
               raise ValueError('Crop size must be specified if do_center_crop 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 std must be specified if do_normalize is True.'	)

        # PIL RGBA images are converted to RGB
        if do_convert_rgb:
               _UpperCamelCase  :							List[Any]	    =	[convert_to_rgb(lowerCamelCase	) for image in images]

        # All transformations expect numpy arrays.
        _UpperCamelCase  :							Optional[int]	    =	[to_numpy_array(lowerCamelCase	) for image in images]

        if do_resize:
               _UpperCamelCase  :							List[str]	    =	[self.resize(image=lowerCamelCase							,      size=lowerCamelCase							,      resample=lowerCamelCase	) for image in images]

        if do_center_crop:
               _UpperCamelCase  :							Optional[int]	    =	[self.center_crop(image=lowerCamelCase							,      size=lowerCamelCase	) for image in images]

        if do_rescale:
               _UpperCamelCase  :							Optional[Any]	    =	[self.rescale(image=lowerCamelCase							,      scale=lowerCamelCase	) for image in images]

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

        _UpperCamelCase  :							Any	    =	[to_channel_dimension_format(lowerCamelCase							,      lowerCamelCase	) for image in images]

        _UpperCamelCase  :							Tuple	    =	{'pixel_values': images}
        return BatchFeature(data=lowerCamelCase							,      tensor_type=lowerCamelCase	)




from typing import TYPE_CHECKING

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


UpperCAmelCase      		=					{
    '''configuration_utils''': ['''GenerationConfig'''],
    '''streamers''': ['''TextIteratorStreamer''', '''TextStreamer'''],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 UpperCAmelCase      		=					[
     '''Constraint''',
     '''ConstraintListState''',
     '''DisjunctiveConstraint''',
     '''PhrasalConstraint''',
 ]
 UpperCAmelCase      		=					[
     '''BeamHypotheses''',
     '''BeamScorer''',
     '''BeamSearchScorer''',
     '''ConstrainedBeamSearchScorer''',
 ]
 UpperCAmelCase      		=					[
     '''EpsilonLogitsWarper''',
     '''EtaLogitsWarper''',
     '''ForcedBOSTokenLogitsProcessor''',
     '''ForcedEOSTokenLogitsProcessor''',
     '''HammingDiversityLogitsProcessor''',
     '''InfNanRemoveLogitsProcessor''',
     '''LogitsProcessor''',
     '''LogitsProcessorList''',
     '''LogitsWarper''',
     '''MinLengthLogitsProcessor''',
     '''MinNewTokensLengthLogitsProcessor''',
     '''NoBadWordsLogitsProcessor''',
     '''NoRepeatNGramLogitsProcessor''',
     '''PrefixConstrainedLogitsProcessor''',
     '''RepetitionPenaltyLogitsProcessor''',
     '''SequenceBiasLogitsProcessor''',
     '''EncoderRepetitionPenaltyLogitsProcessor''',
     '''TemperatureLogitsWarper''',
     '''TopKLogitsWarper''',
     '''TopPLogitsWarper''',
     '''TypicalLogitsWarper''',
     '''EncoderNoRepeatNGramLogitsProcessor''',
     '''ExponentialDecayLengthPenalty''',
     '''LogitNormalization''',
 ]
 UpperCAmelCase      		=					[
     '''MaxNewTokensCriteria''',
     '''MaxLengthCriteria''',
     '''MaxTimeCriteria''',
     '''StoppingCriteria''',
     '''StoppingCriteriaList''',
     '''validate_stopping_criteria''',
 ]
 UpperCAmelCase      		=					[
     '''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:
 UpperCAmelCase      		=					[
     '''TFForcedBOSTokenLogitsProcessor''',
     '''TFForcedEOSTokenLogitsProcessor''',
     '''TFLogitsProcessor''',
     '''TFLogitsProcessorList''',
     '''TFLogitsWarper''',
     '''TFMinLengthLogitsProcessor''',
     '''TFNoBadWordsLogitsProcessor''',
     '''TFNoRepeatNGramLogitsProcessor''',
     '''TFRepetitionPenaltyLogitsProcessor''',
     '''TFTemperatureLogitsWarper''',
     '''TFTopKLogitsWarper''',
     '''TFTopPLogitsWarper''',
     '''TFForceTokensLogitsProcessor''',
     '''TFSuppressTokensAtBeginLogitsProcessor''',
     '''TFSuppressTokensLogitsProcessor''',
 ]
 UpperCAmelCase      		=					[
     '''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:
 UpperCAmelCase      		=					[
     '''FlaxForcedBOSTokenLogitsProcessor''',
     '''FlaxForcedEOSTokenLogitsProcessor''',
     '''FlaxLogitsProcessor''',
     '''FlaxLogitsProcessorList''',
     '''FlaxLogitsWarper''',
     '''FlaxMinLengthLogitsProcessor''',
     '''FlaxTemperatureLogitsWarper''',
     '''FlaxTopKLogitsWarper''',
     '''FlaxTopPLogitsWarper''',
 ]
 UpperCAmelCase      		=					[
     '''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

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



