






from typing import List, Optional, Union

import numpy as np
import PIL.Image

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


SCREAMING_SNAKE_CASE__		:							Optional[Any] 				=				logging.get_logger(__name__)







class 				UpperCAmelCase_  (		_lowercase		):
     __lowerCamelCase      				=  ['pixel_values']




     def __init__(				self	,						_lowerCAmelCase = True	,						_lowerCAmelCase = 32	,						_lowerCAmelCase=PILImageResampling.BILINEAR	,						_lowerCAmelCase = True	,						**_lowerCAmelCase	,						):
          UpperCAmelCase__      :  Any				      =			do_resize
          UpperCAmelCase__      :  Tuple				      =			do_rescale
          UpperCAmelCase__      :  Optional[int]				      =			size_divisor
          UpperCAmelCase__      :  str				      =			resample
          super().__init__(**__SCREAMING_SNAKE_CASE		)




     def   __UpperCAmelCase  (				self	,						_lowerCAmelCase	,						_lowerCAmelCase	,						_lowerCAmelCase	,						_lowerCAmelCase = None	,						**_lowerCAmelCase		):
          UpperCAmelCase__      ,		UpperCAmelCase__      :  Optional[Any]				      =			get_image_size(__SCREAMING_SNAKE_CASE		)
          # Rounds the height and width down to the closest multiple of size_divisor
          UpperCAmelCase__      :  List[Any]				      =			height // size_divisor * size_divisor
          UpperCAmelCase__      :  int				      =			width // size_divisor * size_divisor
          UpperCAmelCase__      :  Union[str, Any]				      =			resize(__SCREAMING_SNAKE_CASE	,						(new_h, new_w)	,						resample=__SCREAMING_SNAKE_CASE	,						data_format=__SCREAMING_SNAKE_CASE	,						**__SCREAMING_SNAKE_CASE		)
          return image




     def   __UpperCAmelCase  (				self	,						_lowerCAmelCase	,						_lowerCAmelCase	,						_lowerCAmelCase = None	,						**_lowerCAmelCase		):
          return rescale(image=__SCREAMING_SNAKE_CASE	,						scale=__SCREAMING_SNAKE_CASE	,						data_format=__SCREAMING_SNAKE_CASE	,						**__SCREAMING_SNAKE_CASE		)




     def   __UpperCAmelCase  (				self	,						_lowerCAmelCase	,						_lowerCAmelCase = None	,						_lowerCAmelCase = None	,						_lowerCAmelCase=None	,						_lowerCAmelCase = None	,						_lowerCAmelCase = None	,						_lowerCAmelCase = ChannelDimension.FIRST	,						**_lowerCAmelCase	,						):
          UpperCAmelCase__      :  Any				      =			do_resize if do_resize is not None else self.do_resize
          UpperCAmelCase__      :  int				      =			do_rescale if do_rescale is not None else self.do_rescale
          UpperCAmelCase__      :  Union[str, Any]				      =			size_divisor if size_divisor is not None else self.size_divisor
          UpperCAmelCase__      :  Tuple				      =			resample if resample is not None else self.resample

          if do_resize and size_divisor is None:
               raise ValueError("""size_divisor is required for resizing"""		)

          UpperCAmelCase__      :  int				      =			make_list_of_images(__SCREAMING_SNAKE_CASE		)

          if not valid_images(__SCREAMING_SNAKE_CASE		):
               raise ValueError("""Invalid image(s)"""		)

          # All transformations expect numpy arrays.
          UpperCAmelCase__      :  Optional[int]				      =			[to_numpy_array(__SCREAMING_SNAKE_CASE		) for img in images]

          if do_resize:
               UpperCAmelCase__      :  int				      =			[self.resize(__SCREAMING_SNAKE_CASE	,						size_divisor=__SCREAMING_SNAKE_CASE	,						resample=__SCREAMING_SNAKE_CASE		) for image in images]

          if do_rescale:
               UpperCAmelCase__      :  str				      =			[self.rescale(__SCREAMING_SNAKE_CASE	,						scale=1 / 255		) for image in images]

          UpperCAmelCase__      :  Any				      =			[to_channel_dimension_format(__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE		) for image in images]

          UpperCAmelCase__      :  str				      =			{"""pixel_values""": images}
          return BatchFeature(data=__SCREAMING_SNAKE_CASE	,						tensor_type=__SCREAMING_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


a		:       Any     						=       get_tests_dir() + '/test_data/fsmt/fsmt_val_data.json'
with io.open(filename, 'r', encoding='utf-8') as f:
    a		:       Tuple     						=       json.load(f)







@require_torch
class     lowercase(unittest.TestCase       ):



     def 			lowercase__  (							self     ,				__SCREAMING_SNAKE_CASE   )		->     Optional[Any]:







         """simple docstring"""



         return FSMTTokenizer.from_pretrained(__SCREAMING_SNAKE_CASE   )



     def 			lowercase__  (							self     ,				__SCREAMING_SNAKE_CASE   )		->     int:







         """simple docstring"""



         a__		=			FSMTForConditionalGeneration.from_pretrained(__SCREAMING_SNAKE_CASE   ).to(__SCREAMING_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 			lowercase__  (							self     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE   )		->     Tuple:







         """simple docstring"""



         a__		=			f'facebook/wmt19-{pair}'
         a__		=			self.get_tokenizer(__SCREAMING_SNAKE_CASE   )
         a__		=			self.get_model(__SCREAMING_SNAKE_CASE   )

         a__		=			bleu_data[pair]['src']
         a__		=			bleu_data[pair]['tgt']

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


