"""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_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


__lowerCAmelCase    :							Union[str, Any]													=						logging.get_logger(__name__)







def 							_UpperCAmelCase		(       lowerCamelCase__     ):





		"""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			):
					UpperCamelCase_ :	int			=    ["pixel_values"]




					def __init__(	self						:     Optional[Any]  ,						snake_case__						:     bool = True  ,						snake_case__						:     Dict[str, int] = None  ,						snake_case__						:     PILImageResampling = PILImageResampling.BILINEAR  ,						snake_case__						:     bool = True  ,						snake_case__						:     Dict[str, int] = None  ,						snake_case__						:     bool = True  ,						snake_case__						:     Union[int, float] = 1 / 255  ,						snake_case__						:     bool = True  ,						snake_case__						:     Optional[Union[float, List[float]]] = None  ,						snake_case__						:     Optional[Union[float, List[float]]] = None  ,						**snake_case__						:     Optional[Any]  ,						):
							super().__init__(**snake_case__     )
							lowerCAmelCase__        =   size if size is not None else {"""shortest_edge""": 224}
							lowerCAmelCase__        =   get_size_dict(snake_case__  ,						default_to_square=snake_case__     )
							lowerCAmelCase__        =   crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
							lowerCAmelCase__        =   get_size_dict(snake_case__  ,						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						:     Optional[Any]  ,						snake_case__						:     np.ndarray  ,						snake_case__						:     Dict[str, int]  ,						snake_case__						:     PILImageResampling = PILImageResampling.BILINEAR  ,						snake_case__						:     Optional[Union[str, ChannelDimension]] = None  ,						**snake_case__						:     Tuple  ,						):
							lowerCAmelCase__        =   get_size_dict(snake_case__  ,						default_to_square=snake_case__     )
							if "shortest_edge" in size:
									lowerCAmelCase__        =   get_resize_output_image_size(snake_case__  ,						size["""shortest_edge"""]  ,						default_to_square=snake_case__     )
							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(snake_case__  ,						size=snake_case__  ,						resample=snake_case__  ,						data_format=snake_case__  ,						**snake_case__     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Optional[Any]  ,						snake_case__						:     np.ndarray  ,						snake_case__						:     Dict[str, int]  ,						snake_case__						:     Optional[Union[str, ChannelDimension]] = None  ,						**snake_case__						:     List[Any]  ,						):
							lowerCAmelCase__        =   get_size_dict(snake_case__     )
							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(snake_case__  ,						size=(size["""height"""], size["""width"""])  ,						data_format=snake_case__  ,						**snake_case__     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Dict  ,						snake_case__						:     np.ndarray  ,						snake_case__						:     Union[int, float]  ,						snake_case__						:     Optional[Union[str, ChannelDimension]] = None  ,						**snake_case__						:     Tuple  ,						):
							return rescale(snake_case__  ,						scale=snake_case__  ,						data_format=snake_case__  ,						**snake_case__     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Tuple  ,						snake_case__						:     np.ndarray  ,						snake_case__						:     Union[float, List[float]]  ,						snake_case__						:     Union[float, List[float]]  ,						snake_case__						:     Optional[Union[str, ChannelDimension]] = None  ,						**snake_case__						:     List[Any]  ,						):
							return normalize(snake_case__  ,						mean=snake_case__  ,						std=snake_case__  ,						data_format=snake_case__  ,						**snake_case__     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Any  ,						snake_case__						:     ImageInput  ,						snake_case__						:     bool = None  ,						snake_case__						:     Dict[str, int] = None  ,						snake_case__						:     PILImageResampling = None  ,						snake_case__						:     bool = None  ,						snake_case__						:     Dict[str, int] = None  ,						snake_case__						:     bool = None  ,						snake_case__						:     float = None  ,						snake_case__						:     bool = None  ,						snake_case__						:     Optional[Union[float, List[float]]] = None  ,						snake_case__						:     Optional[Union[float, List[float]]] = None  ,						snake_case__						:     Optional[ChannelDimension] = ChannelDimension.FIRST  ,						):
							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(snake_case__     )

							if do_resize:
									lowerCAmelCase__        =   self.resize(image=snake_case__  ,						size=snake_case__  ,						resample=snake_case__     )

							if do_center_crop:
									lowerCAmelCase__        =   self.center_crop(snake_case__  ,						size=snake_case__     )

							if do_rescale:
									lowerCAmelCase__        =   self.rescale(image=snake_case__  ,						scale=snake_case__     )

							if do_normalize:
									lowerCAmelCase__        =   self.normalize(image=snake_case__  ,						mean=snake_case__  ,						std=snake_case__     )

							lowerCAmelCase__        =   to_channel_dimension_format(snake_case__  ,						snake_case__     )
							return image




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Optional[Any]  ,						snake_case__						:     ImageInput  ,						snake_case__						:     bool = None  ,						snake_case__						:     Dict[str, int] = None  ,						snake_case__						:     PILImageResampling = None  ,						snake_case__						:     bool = None  ,						snake_case__						:     Dict[str, int] = None  ,						snake_case__						:     bool = None  ,						snake_case__						:     float = None  ,						snake_case__						:     bool = None  ,						snake_case__						:     Optional[Union[float, List[float]]] = None  ,						snake_case__						:     Optional[Union[float, List[float]]] = None  ,						snake_case__						:     Optional[Union[str, TensorType]] = None  ,						snake_case__						:     ChannelDimension = ChannelDimension.FIRST  ,						**snake_case__						:     Any  ,						):
							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(snake_case__  ,						default_to_square=snake_case__     )
							lowerCAmelCase__        =   crop_size if crop_size is not None else self.crop_size
							lowerCAmelCase__        =   get_size_dict(snake_case__  ,						param_name="""crop_size"""     )

							if not valid_images(snake_case__     ):
									raise ValueError(
									    """Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
									    """torch.Tensor, tf.Tensor or jax.ndarray."""     )

							lowerCAmelCase__        =   make_batched(snake_case__     )

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

							lowerCAmelCase__        =   {"""pixel_values""": videos}
							return BatchFeature(data=snake_case__  ,						tensor_type=snake_case__     )





"""simple docstring"""




import io
import json
import unittest

from parameterized import parameterized

from transformers import FSMTForConditionalGeneration, FSMTTokenizer
from transformers.testing_utils import get_tests_dir, require_torch, slow, torch_device
from utils import calculate_bleu


__lowerCAmelCase    :							Union[str, Any]													=						get_tests_dir() + "/test_data/fsmt/fsmt_val_data.json"
with io.open(filename, "r", encoding="utf-8") as f:
			__lowerCAmelCase    :							Optional[int]													=						json.load(f)

@require_torch
class       a_      (     unittest.TestCase			):




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Optional[Any]  ,						snake_case__						:     Dict     ):
							return FSMTTokenizer.from_pretrained(snake_case__     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     List[str]  ,						snake_case__						:     Any     ):
							lowerCAmelCase__        =   FSMTForConditionalGeneration.from_pretrained(snake_case__     ).to(snake_case__     )
							if torch_device == "cuda":
									model.half()
							return model




					@parameterized.expand(
					    [
					        ["""en-ru""", 26.0],
					        ["""ru-en""", 22.0],
					        ["""en-de""", 22.0],
					        ["""de-en""", 29.0],
					    ]     )
					@slow
					def 						_SCREAMING_SNAKE_CASE						(	self						:     Dict  ,						snake_case__						:     Any  ,						snake_case__						:     int     ):
							# note: this test is not testing the best performance since it only evals a small batch
							# but it should be enough to detect a regression in the output quality
							lowerCAmelCase__        =   F"""facebook/wmt19-{pair}"""
							lowerCAmelCase__        =   self.get_tokenizer(snake_case__     )
							lowerCAmelCase__        =   self.get_model(snake_case__     )

							lowerCAmelCase__        =   bleu_data[pair]["""src"""]
							lowerCAmelCase__        =   bleu_data[pair]["""tgt"""]

							lowerCAmelCase__        =   tokenizer(snake_case__  ,						return_tensors="""pt"""  ,						truncation=snake_case__  ,						padding="""longest"""     ).to(snake_case__     )
							lowerCAmelCase__        =   model.generate(
							    input_ids=batch.input_ids  ,						num_beams=8  ,						)
							lowerCAmelCase__        =   tokenizer.batch_decode(
							    snake_case__  ,						skip_special_tokens=snake_case__  ,						clean_up_tokenization_spaces=snake_case__     )
							lowerCAmelCase__        =   calculate_bleu(snake_case__  ,						snake_case__     )
							print(snake_case__     )
							self.assertGreaterEqual(scores["""bleu"""]  ,						snake_case__     )




