






"""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,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_batched,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


_UpperCamelCase					      =  logging.get_logger(__name__)







class  lowerCamelCase__ (	snake_case			):
				SCREAMING_SNAKE_CASE		=      ['''pixel_values''']






				def __init__(			self     ,A = True     ,A = None     ,A = PILImageResampling.BICUBIC     ,A = True     ,A = True     ,A = 1 / 255     ,A = None     ,A = True     ,A = None     ,A = None     ,**A     ,):
										super().__init__(**A )
										UpperCAmelCase          =	size if size is not None else {"""height""": 224, """width""": 224}
										UpperCAmelCase          =	get_size_dict(A )
										UpperCAmelCase          =	crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
										UpperCAmelCase          =	get_size_dict(A     ,default_to_square=A     ,param_name="""crop_size""" )

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






				def     _UpperCamelCase							(			self     ,A     ,A     ,A = PILImageResampling.BILINEAR     ,A = None     ,**A     ,):
										UpperCAmelCase          =	get_size_dict(A )

										if "shortest_edge" in size:
																UpperCAmelCase          =	get_resize_output_image_size(A     ,size=size["""shortest_edge"""]     ,default_to_square=A )
																# size = get_resize_output_image_size(image, size["shortest_edge"], size["longest_edge"])
										elif "height" in size and "width" in size:
																UpperCAmelCase          =	(size["""height"""], size["""width"""])
										else:
																raise ValueError(F'''Size must contain \'height\' and \'width\' keys or \'shortest_edge\' key. Got {size.keys()}''' )
										return resize(A     ,size=A     ,resample=A     ,data_format=A     ,**A )






				def     _UpperCamelCase							(			self     ,A     ,A     ,A = None     ,**A     ,):
										UpperCAmelCase          =	get_size_dict(A )
										if "height" not in size or "width" not in size:
																raise ValueError(F'''The `size` parameter must contain the keys (height, width). Got {size.keys()}''' )
										return center_crop(A     ,size=(size["""height"""], size["""width"""])     ,data_format=A     ,**A )






				def     _UpperCamelCase							(			self     ,A     ,A     ,A = None     ,**A ):
										return rescale(A     ,scale=A     ,data_format=A     ,**A )






				def     _UpperCamelCase							(			self     ,A     ,A     ,A     ,A = None     ,**A     ,):
										return normalize(A     ,mean=A     ,std=A     ,data_format=A     ,**A )






				def     _UpperCamelCase							(			self     ,A     ,A = None     ,A = None     ,A = None     ,A = None     ,A = None     ,A = None     ,A = None     ,A = None     ,A = None     ,A = None     ,A = None     ,A = ChannelDimension.FIRST     ,**A     ,):
										UpperCAmelCase          =	do_resize if do_resize is not None else self.do_resize
										UpperCAmelCase          =	do_rescale if do_rescale is not None else self.do_rescale
										UpperCAmelCase          =	do_normalize if do_normalize is not None else self.do_normalize
										UpperCAmelCase          =	do_center_crop if do_center_crop is not None else self.do_center_crop
										UpperCAmelCase          =	crop_size if crop_size is not None else self.crop_size
										UpperCAmelCase          =	get_size_dict(A     ,param_name="""crop_size"""     ,default_to_square=A )
										UpperCAmelCase          =	resample if resample is not None else self.resample
										UpperCAmelCase          =	rescale_factor if rescale_factor is not None else self.rescale_factor
										UpperCAmelCase          =	image_mean if image_mean is not None else self.image_mean
										UpperCAmelCase          =	image_std if image_std is not None else self.image_std

										UpperCAmelCase          =	size if size is not None else self.size
										UpperCAmelCase          =	get_size_dict(A )

										if not is_batched(A ):
																UpperCAmelCase          =	[images]

										if not valid_images(A ):
																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.""" )

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

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

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

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

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

										UpperCAmelCase          =	[to_channel_dimension_format(A     ,A ) for image in images]

										UpperCAmelCase          =	{"""pixel_values""": images}
										return BatchFeature(data=A     ,tensor_type=A )











"""simple docstring"""







from typing import TYPE_CHECKING

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


_UpperCamelCase					      =  {"""configuration_mbart""": ["""MBART_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MBartConfig""", """MBartOnnxConfig"""]}

try:
  if not is_sentencepiece_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _UpperCamelCase					      =  ["""MBartTokenizer"""]

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _UpperCamelCase					      =  ["""MBartTokenizerFast"""]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _UpperCamelCase					      =  [
      """MBART_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """MBartForCausalLM""",
      """MBartForConditionalGeneration""",
      """MBartForQuestionAnswering""",
      """MBartForSequenceClassification""",
      """MBartModel""",
      """MBartPreTrainedModel""",
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _UpperCamelCase					      =  [
      """TFMBartForConditionalGeneration""",
      """TFMBartModel""",
      """TFMBartPreTrainedModel""",
  ]

try:
  if not is_flax_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _UpperCamelCase					      =  [
      """FlaxMBartForConditionalGeneration""",
      """FlaxMBartForQuestionAnswering""",
      """FlaxMBartForSequenceClassification""",
      """FlaxMBartModel""",
      """FlaxMBartPreTrainedModel""",
  ]


if TYPE_CHECKING:
  from .configuration_mbart import MBART_PRETRAINED_CONFIG_ARCHIVE_MAP, MBartConfig, MBartOnnxConfig

  try:
    if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_mbart import MBartTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_mbart_fast import MBartTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_mbart import (
        MBART_PRETRAINED_MODEL_ARCHIVE_LIST,
        MBartForCausalLM,
        MBartForConditionalGeneration,
        MBartForQuestionAnswering,
        MBartForSequenceClassification,
        MBartModel,
        MBartPreTrainedModel,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_mbart import TFMBartForConditionalGeneration, TFMBartModel, TFMBartPreTrainedModel

  try:
    if not is_flax_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_flax_mbart import (
        FlaxMBartForConditionalGeneration,
        FlaxMBartForQuestionAnswering,
        FlaxMBartForSequenceClassification,
        FlaxMBartModel,
        FlaxMBartPreTrainedModel,
    )

else:
  import sys

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



