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


_lowerCamelCase					:   List[str]							=		logging.get_logger(__name__)


class 						lowercase    (     a     ):
							lowercase__						:       Optional[int]								=							["""pixel_values"""]




							def __init__(						self   :					List[str]	,							_UpperCamelCase   :					bool = True	,							_UpperCamelCase   :					Optional[Dict[str, int]] = None	,							_UpperCamelCase   :					PILImageResampling = PILImageResampling.BICUBIC	,							_UpperCamelCase   :					bool = True	,							_UpperCamelCase   :					bool = True	,							_UpperCamelCase   :					Union[int, float] = 1 / 255	,							_UpperCamelCase   :					Dict[str, int] = None	,							_UpperCamelCase   :					bool = True	,							_UpperCamelCase   :					Optional[Union[float, List[float]]] = None	,							_UpperCamelCase   :					Optional[Union[float, List[float]]] = None	,							**_UpperCamelCase   :					Dict	,							)					->				None:



												'''simple docstring'''

												super().__init__(**_UpperCamelCase      )
												SCREAMING_SNAKE_CASE    			=       size if size is not None else {"height": 224, "width": 224}
												SCREAMING_SNAKE_CASE    			=       get_size_dict(_UpperCamelCase      )
												SCREAMING_SNAKE_CASE    			=       crop_size if crop_size is not None else {"height": 224, "width": 224}
												SCREAMING_SNAKE_CASE    			=       get_size_dict(_UpperCamelCase	,							default_to_square=_UpperCamelCase	,							param_name="crop_size"      )

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




							def 	__snake_case(						self   :					Any	,							_UpperCamelCase   :					np.ndarray	,							_UpperCamelCase   :					Dict[str, int]	,							_UpperCamelCase   :					PILImageResampling = PILImageResampling.BILINEAR	,							_UpperCamelCase   :					Optional[Union[str, ChannelDimension]] = None	,							**_UpperCamelCase   :					str	,							)					->				np.ndarray:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       get_size_dict(_UpperCamelCase      )

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




							def 	__snake_case(						self   :					Optional[int]	,							_UpperCamelCase   :					np.ndarray	,							_UpperCamelCase   :					Dict[str, int]	,							_UpperCamelCase   :					Optional[Union[str, ChannelDimension]] = None	,							**_UpperCamelCase   :					str	,							)					->				np.ndarray:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       get_size_dict(_UpperCamelCase      )
												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(_UpperCamelCase	,							size=(size["height"], size["width"])	,							data_format=_UpperCamelCase	,							**_UpperCamelCase      )




							def 	__snake_case(						self   :					Dict	,							_UpperCamelCase   :					np.ndarray	,							_UpperCamelCase   :					float	,							_UpperCamelCase   :					Optional[Union[str, ChannelDimension]] = None	,							**_UpperCamelCase   :					Dict      )					->				np.ndarray:



												'''simple docstring'''

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




							def 	__snake_case(						self   :					List[Any]	,							_UpperCamelCase   :					np.ndarray	,							_UpperCamelCase   :					Union[float, List[float]]	,							_UpperCamelCase   :					Union[float, List[float]]	,							_UpperCamelCase   :					Optional[Union[str, ChannelDimension]] = None	,							**_UpperCamelCase   :					Optional[Any]	,							)					->				np.ndarray:



												'''simple docstring'''

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





							def 	__snake_case(						self   :					Optional[Any]	,							_UpperCamelCase   :					ImageInput	,							_UpperCamelCase   :					Optional[bool] = None	,							_UpperCamelCase   :					Dict[str, int] = None	,							_UpperCamelCase   :					PILImageResampling = None	,							_UpperCamelCase   :					bool = None	,							_UpperCamelCase   :					int = None	,							_UpperCamelCase   :					Optional[bool] = None	,							_UpperCamelCase   :					Optional[float] = None	,							_UpperCamelCase   :					Optional[bool] = None	,							_UpperCamelCase   :					Optional[Union[float, List[float]]] = None	,							_UpperCamelCase   :					Optional[Union[float, List[float]]] = None	,							_UpperCamelCase   :					Optional[Union[str, TensorType]] = None	,							_UpperCamelCase   :					Union[str, ChannelDimension] = ChannelDimension.FIRST	,							**_UpperCamelCase   :					Any	,							)					->				BatchFeature:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       do_resize if do_resize is not None else self.do_resize
												SCREAMING_SNAKE_CASE    			=       do_rescale if do_rescale is not None else self.do_rescale
												SCREAMING_SNAKE_CASE    			=       do_normalize if do_normalize is not None else self.do_normalize
												SCREAMING_SNAKE_CASE    			=       do_center_crop if do_center_crop is not None else self.do_center_crop
												SCREAMING_SNAKE_CASE    			=       crop_size if crop_size is not None else self.crop_size
												SCREAMING_SNAKE_CASE    			=       get_size_dict(_UpperCamelCase	,							param_name="crop_size"	,							default_to_square=_UpperCamelCase      )
												SCREAMING_SNAKE_CASE    			=       resample if resample is not None else self.resample
												SCREAMING_SNAKE_CASE    			=       rescale_factor if rescale_factor is not None else self.rescale_factor
												SCREAMING_SNAKE_CASE    			=       image_mean if image_mean is not None else self.image_mean
												SCREAMING_SNAKE_CASE    			=       image_std if image_std is not None else self.image_std

												SCREAMING_SNAKE_CASE    			=       size if size is not None else self.size
												SCREAMING_SNAKE_CASE    			=       get_size_dict(_UpperCamelCase      )

												if not is_batched(_UpperCamelCase      ):
																	SCREAMING_SNAKE_CASE    			=       [images]

												if not valid_images(_UpperCamelCase      ):
																	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.
												SCREAMING_SNAKE_CASE    			=       [to_numpy_array(_UpperCamelCase      ) for image in images]

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

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

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

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

												SCREAMING_SNAKE_CASE    			=       [to_channel_dimension_format(_UpperCamelCase	,							_UpperCamelCase      ) for image in images]

												SCREAMING_SNAKE_CASE    			=       {"pixel_values": images}
												return BatchFeature(data=_UpperCamelCase	,							tensor_type=_UpperCamelCase      )





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(a     )				,       """Tatoeba directory does not exist."""     )
class 						lowercase    (     unittest.TestCase     ):




							@cached_property
							def 	__snake_case(						self   :					int      )					->				List[Any]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       tempfile.mkdtemp()
												return TatoebaConverter(save_dir=_UpperCamelCase      )




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



												'''simple docstring'''

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





							@slow
							def 	__snake_case(						self   :					Any      )					->				Optional[int]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE			,   SCREAMING_SNAKE_CASE    			=       self.resolver.write_model_card("opus-mt-he-en"	,							dry_run=_UpperCamelCase      )
												assert mmeta["long_pair"] == "heb-eng"




