





import warnings

from .generation import FlaxGenerationMixin




class   _UpperCAmelCase	(   _lowercase					):
					# warning at import time
					warnings.warn(
					    "Importing `FlaxGenerationMixin` from `src/transformers/generation_flax_utils.py` is deprecated and will "
					    "be removed in Transformers v5. Import as `from transformers import FlaxGenerationMixin` instead." ,       _lowercase ,       )












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 (
    get_resize_output_image_size,
    normalize,
    pad,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging
from ...utils.import_utils import is_vision_available


__lowerCAmelCase     =			logging.get_logger(__name__)


if is_vision_available():
				import PIL




class   _UpperCAmelCase	(   _lowercase					):
					__A          =   ["pixel_values"]




					def __init__(						self    ,      __lowerCAmelCase = True    ,      __lowerCAmelCase = None    ,      __lowerCAmelCase = PILImageResampling.BILINEAR    ,      __lowerCAmelCase = True    ,      __lowerCAmelCase = False    ,      __lowerCAmelCase = True    ,      __lowerCAmelCase = True    ,      __lowerCAmelCase = 1 / 255    ,      __lowerCAmelCase = True    ,      __lowerCAmelCase = None    ,      __lowerCAmelCase = None    ,      **__lowerCAmelCase    ,      )						->  None:
							super().__init__(**__lowerCAmelCase       )

							UpperCamelCase__         =	size if size is not None else {"""height""": 2_560, """width""": 1_920}
							if isinstance(__lowerCAmelCase    ,      (tuple, list)       ):
									# The previous feature extractor size parameter was in (width, height) format
									UpperCamelCase__         =	size[::-1]
							UpperCamelCase__         =	get_size_dict(__lowerCAmelCase       )

							UpperCamelCase__         =	do_resize
							UpperCamelCase__         =	size
							UpperCamelCase__         =	resample
							UpperCamelCase__         =	do_thumbnail
							UpperCamelCase__         =	do_align_long_axis
							UpperCamelCase__         =	do_pad
							UpperCamelCase__         =	do_rescale
							UpperCamelCase__         =	rescale_factor
							UpperCamelCase__         =	do_normalize
							UpperCamelCase__         =	image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
							UpperCamelCase__         =	image_std if image_std is not None else IMAGENET_STANDARD_STD




					def 	snake_case_		(						self    ,      __lowerCAmelCase    ,      __lowerCAmelCase    ,      __lowerCAmelCase = None       )						->  np.ndarray:
							UpperCamelCase__  ,			UpperCamelCase__         =	get_image_size(__lowerCAmelCase       )
							UpperCamelCase__  ,			UpperCamelCase__         =	size["""height"""], size["""width"""]

							if (output_width < output_height and input_width > input_height) or (
							    output_width > output_height and input_width < input_height
							):
									UpperCamelCase__         =	np.rotaa(__lowerCAmelCase    ,      3       )

							if data_format is not None:
									UpperCamelCase__         =	to_channel_dimension_format(__lowerCAmelCase    ,      __lowerCAmelCase       )

							return image




					def 	snake_case_		(						self    ,      __lowerCAmelCase    ,      __lowerCAmelCase    ,      __lowerCAmelCase = False    ,      __lowerCAmelCase = None    ,      )						->  np.ndarray:
							UpperCamelCase__  ,			UpperCamelCase__         =	size["""height"""], size["""width"""]
							UpperCamelCase__  ,			UpperCamelCase__         =	get_image_size(__lowerCAmelCase       )

							UpperCamelCase__         =	output_width - input_width
							UpperCamelCase__         =	output_height - input_height

							if random_padding:
									UpperCamelCase__         =	np.random.randint(low=0    ,      high=delta_height + 1       )
									UpperCamelCase__         =	np.random.randint(low=0    ,      high=delta_width + 1       )
							else:
									UpperCamelCase__         =	delta_height // 2
									UpperCamelCase__         =	delta_width // 2

							UpperCamelCase__         =	delta_height - pad_top
							UpperCamelCase__         =	delta_width - pad_left

							UpperCamelCase__         =	((pad_top, pad_bottom), (pad_left, pad_right))
							return pad(__lowerCAmelCase    ,      __lowerCAmelCase    ,      data_format=__lowerCAmelCase       )




					def 	snake_case_		(						self    ,      *__lowerCAmelCase    ,      **__lowerCAmelCase       )						->  List[Any]:
							logger.info("""pad is deprecated and will be removed in version 4.27. Please use pad_image instead."""       )
							return self.pad_image(*__lowerCAmelCase    ,      **__lowerCAmelCase       )




					def 	snake_case_		(						self    ,      __lowerCAmelCase    ,      __lowerCAmelCase    ,      __lowerCAmelCase = PILImageResampling.BICUBIC    ,      __lowerCAmelCase = None    ,      **__lowerCAmelCase    ,      )						->  np.ndarray:
							UpperCamelCase__  ,			UpperCamelCase__         =	get_image_size(__lowerCAmelCase       )
							UpperCamelCase__  ,			UpperCamelCase__         =	size["""height"""], size["""width"""]

							# We always resize to the smallest of either the input or output size.
							UpperCamelCase__         =	min(__lowerCAmelCase    ,      __lowerCAmelCase       )
							UpperCamelCase__         =	min(__lowerCAmelCase    ,      __lowerCAmelCase       )

							if height == input_height and width == input_width:
									return image

							if input_height > input_width:
									UpperCamelCase__         =	int(input_width * height / input_height       )
							elif input_width > input_height:
									UpperCamelCase__         =	int(input_height * width / input_width       )

							return resize(
							    __lowerCAmelCase    ,      size=(height, width)    ,      resample=__lowerCAmelCase    ,      reducing_gap=2.0    ,      data_format=__lowerCAmelCase    ,      **__lowerCAmelCase       )




					def 	snake_case_		(						self    ,      __lowerCAmelCase    ,      __lowerCAmelCase    ,      __lowerCAmelCase = PILImageResampling.BICUBIC    ,      __lowerCAmelCase = None    ,      **__lowerCAmelCase    ,      )						->  np.ndarray:
							UpperCamelCase__         =	get_size_dict(__lowerCAmelCase       )
							UpperCamelCase__         =	min(size["""height"""]    ,      size["""width"""]       )
							UpperCamelCase__         =	get_resize_output_image_size(__lowerCAmelCase    ,      size=__lowerCAmelCase    ,      default_to_square=__lowerCAmelCase       )
							UpperCamelCase__         =	resize(__lowerCAmelCase    ,      size=__lowerCAmelCase    ,      resample=__lowerCAmelCase    ,      data_format=__lowerCAmelCase    ,      **__lowerCAmelCase       )
							return resized_image




					def 	snake_case_		(						self    ,      __lowerCAmelCase    ,      __lowerCAmelCase    ,      __lowerCAmelCase = None    ,      **__lowerCAmelCase    ,      )						->  List[str]:
							return rescale(__lowerCAmelCase    ,      scale=__lowerCAmelCase    ,      data_format=__lowerCAmelCase    ,      **__lowerCAmelCase       )




					def 	snake_case_		(						self    ,      __lowerCAmelCase    ,      __lowerCAmelCase    ,      __lowerCAmelCase    ,      __lowerCAmelCase = None    ,      **__lowerCAmelCase    ,      )						->  np.ndarray:
							return normalize(__lowerCAmelCase    ,      mean=__lowerCAmelCase    ,      std=__lowerCAmelCase    ,      data_format=__lowerCAmelCase    ,      **__lowerCAmelCase       )




					def 	snake_case_		(						self    ,      __lowerCAmelCase    ,      __lowerCAmelCase = None    ,      __lowerCAmelCase = None    ,      __lowerCAmelCase = None    ,      __lowerCAmelCase = None    ,      __lowerCAmelCase = None    ,      __lowerCAmelCase = None    ,      __lowerCAmelCase = False    ,      __lowerCAmelCase = None    ,      __lowerCAmelCase = None    ,      __lowerCAmelCase = None    ,      __lowerCAmelCase = None    ,      __lowerCAmelCase = None    ,      __lowerCAmelCase = None    ,      __lowerCAmelCase = ChannelDimension.FIRST    ,      **__lowerCAmelCase    ,      )						->  PIL.Image.Image:
							UpperCamelCase__         =	do_resize if do_resize is not None else self.do_resize
							UpperCamelCase__         =	size if size is not None else self.size
							if isinstance(__lowerCAmelCase    ,      (tuple, list)       ):
									# Previous feature extractor had size in (width, height) format
									UpperCamelCase__         =	size[::-1]
							UpperCamelCase__         =	get_size_dict(__lowerCAmelCase       )
							UpperCamelCase__         =	resample if resample is not None else self.resample
							UpperCamelCase__         =	do_thumbnail if do_thumbnail is not None else self.do_thumbnail
							UpperCamelCase__         =	do_align_long_axis if do_align_long_axis is not None else self.do_align_long_axis
							UpperCamelCase__         =	do_pad if do_pad is not None else self.do_pad
							UpperCamelCase__         =	do_rescale if do_rescale is not None else self.do_rescale
							UpperCamelCase__         =	rescale_factor if rescale_factor is not None else self.rescale_factor
							UpperCamelCase__         =	do_normalize if do_normalize is not None else self.do_normalize
							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__         =	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_rescale and rescale_factor is None:
									raise ValueError("""Rescale factor must be specified if do_rescale is True."""       )

							if do_pad and size is None:
									raise ValueError("""Size must be specified if do_pad 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."""       )

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

							if do_align_long_axis:
									UpperCamelCase__         =	[self.align_long_axis(__lowerCAmelCase    ,      size=__lowerCAmelCase       ) for image in images]

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

							if do_thumbnail:
									UpperCamelCase__         =	[self.thumbnail(image=__lowerCAmelCase    ,      size=__lowerCAmelCase       ) for image in images]

							if do_pad:
									UpperCamelCase__         =	[self.pad_image(image=__lowerCAmelCase    ,      size=__lowerCAmelCase    ,      random_padding=__lowerCAmelCase       ) for image in images]

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

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

							UpperCamelCase__         =	[to_channel_dimension_format(__lowerCAmelCase    ,      __lowerCAmelCase       ) for image in images]

							UpperCamelCase__         =	{"""pixel_values""": images}
							return BatchFeature(data=__lowerCAmelCase    ,      tensor_type=__lowerCAmelCase       )





