




'''simple docstring'''



from typing import Dict, List, Optional, Union

import numpy as np

from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType

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_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_valid_image,
    to_numpy_array,
    valid_images,
)
from ...utils import logging


if is_vision_available():
      import PIL

UpperCamelCase_       =							logging.get_logger(__name__)




def    lowercase__(       __UpperCamelCase: Tuple  ):

      """simple docstring"""






      if isinstance(__lowerCAmelCase	,(list, tuple)  ) and isinstance(videos[0]	,(list, tuple)  ) and is_valid_image(videos[0][0]  ):
            return videos

      elif isinstance(__lowerCAmelCase	,(list, tuple)  ) and is_valid_image(videos[0]  ):
            return [videos]

      elif is_valid_image(__lowerCAmelCase  ):
            return [[videos]]

      raise ValueError(f"Could not make batched video from {videos}"  )






class 				_a							(       UpperCAmelCase__      ):





       '''simple docstring'''






       A		:							Tuple								=		["pixel_values"]







       def __init__(	self,				A = True,				A = None,				A = PILImageResampling.BILINEAR,				A = True,				A = None,				A = True,				A = 1 / 255,				A = True,				A = True,				A = None,				A = None,				**A,				):







             '''simple docstring'''







             super().__init__(**lowerCamelCase__   )
             SCREAMING_SNAKE_CASE					:	str   =		size if size is not None else {"shortest_edge": 256}
             SCREAMING_SNAKE_CASE					:	Tuple   =		get_size_dict(lowerCamelCase__,				default_to_square=lowerCamelCase__   )
             SCREAMING_SNAKE_CASE					:	Any   =		crop_size if crop_size is not None else {"height": 224, "width": 224}
             SCREAMING_SNAKE_CASE					:	Dict   =		get_size_dict(lowerCamelCase__,				param_name='crop_size'   )

             SCREAMING_SNAKE_CASE					:	Tuple   =		do_resize
             SCREAMING_SNAKE_CASE					:	Tuple   =		size
             SCREAMING_SNAKE_CASE					:	List[Any]   =		do_center_crop
             SCREAMING_SNAKE_CASE					:	Any   =		crop_size
             SCREAMING_SNAKE_CASE					:	Dict   =		resample
             SCREAMING_SNAKE_CASE					:	List[Any]   =		do_rescale
             SCREAMING_SNAKE_CASE					:	Any   =		rescale_factor
             SCREAMING_SNAKE_CASE					:	Dict   =		offset
             SCREAMING_SNAKE_CASE					:	Any   =		do_normalize
             SCREAMING_SNAKE_CASE					:	Optional[int]   =		image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
             SCREAMING_SNAKE_CASE					:	int   =		image_std if image_std is not None else IMAGENET_STANDARD_STD







       def     UpperCamelCase_				(	self,				A,				A,				A = PILImageResampling.BILINEAR,				A = None,				**A,				):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE					:	Optional[int]   =		get_size_dict(lowerCamelCase__,				default_to_square=lowerCamelCase__   )
             if "shortest_edge" in size:
                   SCREAMING_SNAKE_CASE					:	Any   =		get_resize_output_image_size(lowerCamelCase__,				size['shortest_edge'],				default_to_square=lowerCamelCase__   )
             elif "height" in size and "width" in size:
                   SCREAMING_SNAKE_CASE					:	List[str]   =		(size["height"], size["width"])
             else:
                   raise ValueError(F"Size must have 'height' and 'width' or 'shortest_edge' as keys. Got {size.keys()}"   )
             return resize(lowerCamelCase__,				size=lowerCamelCase__,				resample=lowerCamelCase__,				data_format=lowerCamelCase__,				**lowerCamelCase__   )







       def     UpperCamelCase_				(	self,				A,				A,				A = None,				**A,				):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE					:	List[Any]   =		get_size_dict(lowerCamelCase__   )
             if "height" not in size or "width" not in size:
                   raise ValueError(F"Size must have 'height' and 'width' as keys. Got {size.keys()}"   )
             return center_crop(lowerCamelCase__,				size=(size['height'], size['width']),				data_format=lowerCamelCase__,				**lowerCamelCase__   )







       def     UpperCamelCase_				(	self,				A,				A,				A = True,				A = None,				**A,				):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE					:	List[Any]   =		image.astype(np.floataa   )
             if offset:
                   SCREAMING_SNAKE_CASE					:	Optional[int]   =		image - (scale / 2)
             return rescale(lowerCamelCase__,				scale=lowerCamelCase__,				data_format=lowerCamelCase__,				**lowerCamelCase__   )







       def     UpperCamelCase_				(	self,				A,				A,				A,				A = None,				**A,				):







             '''simple docstring'''







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







       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,				):







             '''simple docstring'''







             if do_resize and size is None or resample is None:
                   raise ValueError('Size and resample 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.'   )

             if offset and not do_rescale:
                   raise ValueError('For offset, do_rescale must also be set to True.'   )

             # All transformations expect numpy arrays.
             SCREAMING_SNAKE_CASE					:	Any   =		to_numpy_array(lowerCamelCase__   )

             if do_resize:
                   SCREAMING_SNAKE_CASE					:	List[Any]   =		self.resize(image=lowerCamelCase__,				size=lowerCamelCase__,				resample=lowerCamelCase__   )

             if do_center_crop:
                   SCREAMING_SNAKE_CASE					:	Union[str, Any]   =		self.center_crop(lowerCamelCase__,				size=lowerCamelCase__   )

             if do_rescale:
                   SCREAMING_SNAKE_CASE					:	Union[str, Any]   =		self.rescale(image=lowerCamelCase__,				scale=lowerCamelCase__,				offset=lowerCamelCase__   )

             if do_normalize:
                   SCREAMING_SNAKE_CASE					:	List[Any]   =		self.normalize(image=lowerCamelCase__,				mean=lowerCamelCase__,				std=lowerCamelCase__   )

             SCREAMING_SNAKE_CASE					:	Optional[int]   =		to_channel_dimension_format(lowerCamelCase__,				lowerCamelCase__   )
             return image







       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 = None,				A = ChannelDimension.FIRST,				**A,				):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE					:	Optional[Any]   =		do_resize if do_resize is not None else self.do_resize
             SCREAMING_SNAKE_CASE					:	Optional[Any]   =		resample if resample is not None else self.resample
             SCREAMING_SNAKE_CASE					:	int   =		do_center_crop if do_center_crop is not None else self.do_center_crop
             SCREAMING_SNAKE_CASE					:	Dict   =		do_rescale if do_rescale is not None else self.do_rescale
             SCREAMING_SNAKE_CASE					:	List[Any]   =		rescale_factor if rescale_factor is not None else self.rescale_factor
             SCREAMING_SNAKE_CASE					:	Optional[Any]   =		offset if offset is not None else self.offset
             SCREAMING_SNAKE_CASE					:	Any   =		do_normalize if do_normalize is not None else self.do_normalize
             SCREAMING_SNAKE_CASE					:	str   =		image_mean if image_mean is not None else self.image_mean
             SCREAMING_SNAKE_CASE					:	Union[str, Any]   =		image_std if image_std is not None else self.image_std

             SCREAMING_SNAKE_CASE					:	List[Any]   =		size if size is not None else self.size
             SCREAMING_SNAKE_CASE					:	List[Any]   =		get_size_dict(lowerCamelCase__,				default_to_square=lowerCamelCase__   )
             SCREAMING_SNAKE_CASE					:	Optional[Any]   =		crop_size if crop_size is not None else self.crop_size
             SCREAMING_SNAKE_CASE					:	List[str]   =		get_size_dict(lowerCamelCase__,				param_name='crop_size'   )

             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.'   )

             SCREAMING_SNAKE_CASE					:	str   =		make_batched(lowerCamelCase__   )

             SCREAMING_SNAKE_CASE					:	Union[str, Any]   =		[
                 [
                     self._preprocess_image(
                         image=lowerCamelCase__,				do_resize=lowerCamelCase__,				size=lowerCamelCase__,				resample=lowerCamelCase__,				do_center_crop=lowerCamelCase__,				crop_size=lowerCamelCase__,				do_rescale=lowerCamelCase__,				rescale_factor=lowerCamelCase__,				offset=lowerCamelCase__,				do_normalize=lowerCamelCase__,				image_mean=lowerCamelCase__,				image_std=lowerCamelCase__,				data_format=lowerCamelCase__,				)
                     for img in video
                 ]
                 for video in videos
             ]

             SCREAMING_SNAKE_CASE					:	Tuple   =		{"pixel_values": videos}
             return BatchFeature(data=lowerCamelCase__,				tensor_type=lowerCamelCase__   )






'''simple docstring'''


import os
import tempfile
import unittest

from transformers.models.marian.convert_marian_tatoeba_to_pytorch import DEFAULT_REPO, TatoebaConverter
from transformers.testing_utils import slow
from transformers.utils import cached_property






@unittest.skipUnless(os.path.exists(UpperCAmelCase__		)			,				"Tatoeba directory does not exist."		)
class 		lowerCAmelCase__	(	unittest.TestCase		):




			@cached_property
			def 							lowerCAmelCase__			(						self     :   Optional[Any]       )      ->List[Any]:



					'''simple docstring'''







					_UpperCAmelCase						:				List[str]	    =					tempfile.mkdtemp()
					return TatoebaConverter(save_dir=lowerCamelCase__       )




			@slow
			def 							lowerCAmelCase__			(						self     :   Optional[int]       )      ->List[str]:



					'''simple docstring'''







					self.resolver.convert_models(["heb-eng"]       )






			@slow
			def 							lowerCAmelCase__			(						self     :   List[Any]       )      ->Any:



					'''simple docstring'''







					_UpperCAmelCase    ,			_UpperCAmelCase						:				Tuple	    =					self.resolver.write_model_card("opus-mt-he-en"		,     dry_run=lowerCamelCase__       )
					assert mmeta["long_pair"] == "heb-eng"



