




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


if is_vision_available():
					import PIL


UpperCamelCase__    			=			logging.get_logger(__name__)







def 	_a      (						SCREAMING_SNAKE_CASE_							:				int     ):
					if isinstance(SCREAMING_SNAKE_CASE_      ,      (list, tuple)     ) and isinstance(videos[0]      ,      (list, tuple)     ) and is_valid_image(videos[0][0]     ):
										return videos

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

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

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


class 			a__    (					snake_case__      ):
						_a							:    Tuple					=    ["""pixel_values"""]






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




											"""simple docstring"""



											super().__init__(**_A			)
											__lowerCAmelCase				   =       size if size is not None else {"shortest_edge": 2_2_4}
											__lowerCAmelCase				   =       get_size_dict(_A		,			default_to_square=_A			)
											__lowerCAmelCase				   =       crop_size if crop_size is not None else {"height": 2_2_4, "width": 2_2_4}
											__lowerCAmelCase				   =       get_size_dict(_A		,			param_name="crop_size"			)

											__lowerCAmelCase				   =       do_resize
											__lowerCAmelCase				   =       size
											__lowerCAmelCase				   =       do_center_crop
											__lowerCAmelCase				   =       crop_size
											__lowerCAmelCase				   =       resample
											__lowerCAmelCase				   =       do_rescale
											__lowerCAmelCase				   =       rescale_factor
											__lowerCAmelCase				   =       do_normalize
											__lowerCAmelCase				   =       image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
											__lowerCAmelCase				   =       image_std if image_std is not None else IMAGENET_STANDARD_STD






						def 				__SCREAMING_SNAKE_CASE(   self		,			_A		,			_A		,			_A = PILImageResampling.BILINEAR		,			_A = None		,			**_A		,			):




											"""simple docstring"""



											__lowerCAmelCase				   =       get_size_dict(_A		,			default_to_square=_A			)
											if "shortest_edge" in size:
																__lowerCAmelCase				   =       get_resize_output_image_size(_A		,			size["shortest_edge"]		,			default_to_square=_A			)
											elif "height" in size and "width" in size:
																__lowerCAmelCase				   =       (size["height"], size["width"])
											else:
																raise ValueError(f"""Size must have 'height' and 'width' or 'shortest_edge' as keys. Got {size.keys()}"""			)
											return resize(_A		,			size=_A		,			resample=_A		,			data_format=_A		,			**_A			)






						def 				__SCREAMING_SNAKE_CASE(   self		,			_A		,			_A		,			_A = None		,			**_A		,			):




											"""simple docstring"""



											__lowerCAmelCase				   =       get_size_dict(_A			)
											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(_A		,			size=(size["height"], size["width"])		,			data_format=_A		,			**_A			)






						def 				__SCREAMING_SNAKE_CASE(   self		,			_A		,			_A		,			_A = None		,			**_A		,			):




											"""simple docstring"""



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






						def 				__SCREAMING_SNAKE_CASE(   self		,			_A		,			_A		,			_A		,			_A = None		,			**_A		,			):




											"""simple docstring"""



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






						def 				__SCREAMING_SNAKE_CASE(   self		,			_A		,			_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."			)

											# All transformations expect numpy arrays.
											__lowerCAmelCase				   =       to_numpy_array(_A			)

											if do_resize:
																__lowerCAmelCase				   =       self.resize(image=_A		,			size=_A		,			resample=_A			)

											if do_center_crop:
																__lowerCAmelCase				   =       self.center_crop(_A		,			size=_A			)

											if do_rescale:
																__lowerCAmelCase				   =       self.rescale(image=_A		,			scale=_A			)

											if do_normalize:
																__lowerCAmelCase				   =       self.normalize(image=_A		,			mean=_A		,			std=_A			)

											__lowerCAmelCase				   =       to_channel_dimension_format(_A		,			_A			)
											return image






						def 				__SCREAMING_SNAKE_CASE(   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		,			):




											"""simple docstring"""



											__lowerCAmelCase				   =       do_resize if do_resize is not None else self.do_resize
											__lowerCAmelCase				   =       resample if resample is not None else self.resample
											__lowerCAmelCase				   =       do_center_crop if do_center_crop is not None else self.do_center_crop
											__lowerCAmelCase				   =       do_rescale if do_rescale is not None else self.do_rescale
											__lowerCAmelCase				   =       rescale_factor if rescale_factor is not None else self.rescale_factor
											__lowerCAmelCase				   =       do_normalize if do_normalize is not None else self.do_normalize
											__lowerCAmelCase				   =       image_mean if image_mean is not None else self.image_mean
											__lowerCAmelCase				   =       image_std if image_std is not None else self.image_std

											__lowerCAmelCase				   =       size if size is not None else self.size
											__lowerCAmelCase				   =       get_size_dict(_A		,			default_to_square=_A			)
											__lowerCAmelCase				   =       crop_size if crop_size is not None else self.crop_size
											__lowerCAmelCase				   =       get_size_dict(_A		,			param_name="crop_size"			)

											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."			)

											__lowerCAmelCase				   =       make_batched(_A			)

											__lowerCAmelCase				   =       [
											    [
											        self._preprocess_image(
											            image=_A		,			do_resize=_A		,			size=_A		,			resample=_A		,			do_center_crop=_A		,			crop_size=_A		,			do_rescale=_A		,			rescale_factor=_A		,			do_normalize=_A		,			image_mean=_A		,			image_std=_A		,			data_format=_A		,			)
											        for img in video
											    ]
											    for video in videos
											]

											__lowerCAmelCase				   =       {"pixel_values": videos}
											return BatchFeature(data=_A		,			tensor_type=_A			)








'''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"



