"""simple docstring"""






import json
import os
import unittest

from transformers.models.fsmt.tokenization_fsmt import VOCAB_FILES_NAMES, FSMTTokenizer
from transformers.testing_utils import slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


# using a different tiny model than the one used for default params defined in init to ensure proper testing
lowerCAmelCase__    :    Dict			=      'stas/tiny-wmt19-en-ru'
class 							snake_case			(					__UpperCAmelCase ,       unittest.TestCase							):




					"""simple docstring"""

					snake_case__							       =					FSMTTokenizer
					snake_case__							       =					False







					def 						__lowerCAmelCase		(							self  :      int     ):
									super().setUp()

									# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
									UpperCAmelCase__			 =				[
									    'l',
									    'o',
									    'w',
									    'e',
									    'r',
									    's',
									    't',
									    'i',
									    'd',
									    'n',
									    'w</w>',
									    'r</w>',
									    't</w>',
									    'lo',
									    'low',
									    'er</w>',
									    'low</w>',
									    'lowest</w>',
									    'newer</w>',
									    'wider</w>',
									    '<unk>',
									]
									UpperCAmelCase__			 =				dict(zip(lowerCamelCase__       ,range(len(lowerCamelCase__     )     )     )     )
									UpperCAmelCase__			 =				['l o 123', 'lo w 1456', 'e r</w> 1789', '']

									UpperCAmelCase__			 =				['en', 'ru']
									UpperCAmelCase__			 =				{
									    'langs': self.langs,
									    'src_vocab_size': 10,
									    'tgt_vocab_size': 20,
									}

									UpperCAmelCase__			 =				os.path.join(self.tmpdirname       ,VOCAB_FILES_NAMES['src_vocab_file']     )
									UpperCAmelCase__			 =				os.path.join(self.tmpdirname       ,VOCAB_FILES_NAMES['tgt_vocab_file']     )
									UpperCAmelCase__			 =				os.path.join(self.tmpdirname       ,'tokenizer_config.json'     )
									UpperCAmelCase__			 =				os.path.join(self.tmpdirname       ,VOCAB_FILES_NAMES['merges_file']     )
									with open(self.src_vocab_file       ,'w'     ) as fp:
													fp.write(json.dumps(lowerCamelCase__     )     )
									with open(self.tgt_vocab_file       ,'w'     ) as fp:
													fp.write(json.dumps(lowerCamelCase__     )     )
									with open(self.merges_file       ,'w'     ) as fp:
													fp.write('\n'.join(lowerCamelCase__     )     )
									with open(lowerCamelCase__       ,'w'     ) as fp:
													fp.write(json.dumps(lowerCamelCase__     )     )







					@cached_property
					def 						__lowerCAmelCase		(							self  :      Optional[int]     ):
									return FSMTTokenizer.from_pretrained('facebook/wmt19-ru-en'     )







					@cached_property
					def 						__lowerCAmelCase		(							self  :      Union[str, Any]     ):
									return FSMTTokenizer.from_pretrained('facebook/wmt19-en-ru'     )







					def 						__lowerCAmelCase		(							self  :      List[Any]     ):
									UpperCAmelCase__			 =				FSMTTokenizer.from_pretrained(lowerCamelCase__     )
									self.assertListEqual([tokenizer.src_lang, tokenizer.tgt_lang]       ,['en', 'ru']     )
									self.assertEqual(tokenizer.src_vocab_size       ,21     )
									self.assertEqual(tokenizer.tgt_vocab_size       ,21     )







					def 						__lowerCAmelCase		(							self  :      List[str]     ):
									UpperCAmelCase__			 =				FSMTTokenizer(self.langs       ,self.src_vocab_file       ,self.tgt_vocab_file       ,self.merges_file     )

									UpperCAmelCase__			 =				'lower'
									UpperCAmelCase__			 =				['low', 'er</w>']
									UpperCAmelCase__			 =				tokenizer.tokenize(lowerCamelCase__     )
									self.assertListEqual(lowerCamelCase__       ,lowerCamelCase__     )

									UpperCAmelCase__			 =				tokens + ['<unk>']
									UpperCAmelCase__			 =				[14, 15, 20]
									self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCamelCase__     )       ,lowerCamelCase__     )







					@slow
					def 						__lowerCAmelCase		(							self  :      Optional[Any]     ):
									UpperCAmelCase__			 =				self.tokenizer_ru_en

									UpperCAmelCase__			 =				tokenizer.encode('sequence builders'       ,add_special_tokens=lowerCamelCase__     )
									UpperCAmelCase__			 =				tokenizer.encode('multi-sequence build'       ,add_special_tokens=lowerCamelCase__     )

									UpperCAmelCase__			 =				tokenizer.build_inputs_with_special_tokens(lowerCamelCase__     )
									UpperCAmelCase__			 =				tokenizer.build_inputs_with_special_tokens(lowerCamelCase__       ,lowerCamelCase__     )

									assert encoded_sentence == text + [2]
									assert encoded_pair == text + [2] + text_a + [2]







					@slow
					def 						__lowerCAmelCase		(							self  :      List[Any]     ):
									UpperCAmelCase__			 =				self.tokenizer_en_ru
									UpperCAmelCase__			 =				self.tokenizer_ru_en

									UpperCAmelCase__			 =				[
									    [
									        'Here\'s a little song I wrote. Don\'t worry, be happy.',
									        [2_470, 39, 11, 2_349, 7_222, 70, 5_979, 7, 8_450, 1_050, 13_160, 5, 26, 6_445, 7, 2],
									    ],
									    ['This is it. No more. I\'m done!', [132, 21, 37, 7, 1_434, 86, 7, 70, 6_476, 1_305, 427, 2]],
									]

									# if data needs to be recreated or added, run:
									# import torch
									# model = torch.hub.load("pytorch/fairseq", "transformer.wmt19.en-ru", checkpoint_file="model4.pt", tokenizer="moses", bpe="fastbpe")
									# for src_text, _ in targets: print(f"""[\n"{src_text}",\n {model.encode(src_text).tolist()}\n],""")

									for src_text, tgt_input_ids in targets:
													UpperCAmelCase__			 =				tokenizer_enc.encode(lowerCamelCase__       ,return_tensors=lowerCamelCase__     )
													self.assertListEqual(lowerCamelCase__       ,lowerCamelCase__     )

													# and decode backward, using the reversed languages model
													UpperCAmelCase__			 =				tokenizer_dec.decode(lowerCamelCase__       ,skip_special_tokens=lowerCamelCase__     )
													self.assertEqual(lowerCamelCase__       ,lowerCamelCase__     )







					@slow
					def 						__lowerCAmelCase		(							self  :      Union[str, Any]     ):
									UpperCAmelCase__			 =				FSMTTokenizer.from_pretrained('facebook/wmt19-ru-en'       ,do_lower_case=lowerCamelCase__     )
									UpperCAmelCase__			 =				tokenizer.tokenize('USA is United States of America'     )
									UpperCAmelCase__			 =				['us', 'a</w>', 'is</w>', 'un', 'i', 'ted</w>', 'st', 'ates</w>', 'of</w>', 'am', 'er', 'ica</w>']
									self.assertListEqual(lowerCamelCase__       ,lowerCamelCase__     )







					@unittest.skip('FSMTConfig.__init__  requires non-optional args'     )
					def 						__lowerCAmelCase		(							self  :      int     ):
									pass







					@unittest.skip('FSMTConfig.__init__  requires non-optional args'     )
					def 						__lowerCAmelCase		(							self  :      List[str]     ):
									pass




"""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, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
						import PIL


lowerCAmelCase__    :    Tuple			=      logging.get_logger(__name__)
class 							snake_case			(					__UpperCAmelCase							):




					"""simple docstring"""

					snake_case__							       =					["pixel_values"]







					def __init__(							self  :      str       ,lowerCamelCase__  :      bool = True       ,lowerCamelCase__  :      Dict[str, int] = None       ,lowerCamelCase__  :      bool = True       ,lowerCamelCase__  :      Dict[str, int] = None       ,lowerCamelCase__  :      PILImageResampling = PILImageResampling.BICUBIC       ,lowerCamelCase__  :      bool = True       ,lowerCamelCase__  :      Union[int, float] = 1 / 255       ,lowerCamelCase__  :      bool = True       ,lowerCamelCase__  :      Optional[Union[float, List[float]]] = None       ,lowerCamelCase__  :      Optional[Union[float, List[float]]] = None       ,**lowerCamelCase__  :      List[Any]       ,):
									super().__init__(**lowerCamelCase__     )
									UpperCAmelCase__			 =				crop_size if crop_size is not None else {'height': 256, 'width': 256}
									UpperCAmelCase__			 =				get_size_dict(lowerCamelCase__       ,param_name='crop_size'     )
									UpperCAmelCase__			 =				size if size is not None else {'height': 224, 'width': 224}
									UpperCAmelCase__			 =				get_size_dict(lowerCamelCase__     )

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







					def 						__lowerCAmelCase		(							self  :      Optional[int]       ,lowerCamelCase__  :      np.ndarray       ,lowerCamelCase__  :      Dict[str, int]       ,lowerCamelCase__  :      Optional[int] = None       ,lowerCamelCase__  :      Optional[Union[str, ChannelDimension]] = None       ,**lowerCamelCase__  :      List[str]       ,):
									UpperCAmelCase__			 =				self.size if size is None else size
									UpperCAmelCase__			 =				get_size_dict(lowerCamelCase__     )
									UpperCAmelCase__			 =				get_size_dict(lowerCamelCase__       ,param_name='crop_size'     )

									UpperCAmelCase__ ,      UpperCAmelCase__			 =				get_image_size(lowerCamelCase__     )
									UpperCAmelCase__			 =				min(lowerCamelCase__       ,lowerCamelCase__     )
									UpperCAmelCase__			 =				(size['height'] / crop_size['height']) * min_dim
									UpperCAmelCase__			 =				(size['width'] / crop_size['width']) * min_dim
									return center_crop(lowerCamelCase__       ,size=(cropped_height, cropped_width)       ,data_format=lowerCamelCase__       ,**lowerCamelCase__     )







					def 						__lowerCAmelCase		(							self  :      Any       ,lowerCamelCase__  :      np.ndarray       ,lowerCamelCase__  :      Dict[str, int]       ,lowerCamelCase__  :      PILImageResampling = PIL.Image.BICUBIC       ,lowerCamelCase__  :      Optional[Union[str, ChannelDimension]] = None       ,**lowerCamelCase__  :      Any       ,):
									UpperCAmelCase__			 =				get_size_dict(lowerCamelCase__     )
									if "height" not in size or "width" not in size:
													raise ValueError(f'''The size dictionary must contain the keys \'height\' and \'width\'. Got {size.keys()}'''     )
									return resize(
									    lowerCamelCase__       ,size=(size['height'], size['width'])       ,resample=lowerCamelCase__       ,data_format=lowerCamelCase__       ,**lowerCamelCase__     )







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







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







					def 						__lowerCAmelCase		(							self  :      Optional[Any]       ,lowerCamelCase__  :      ImageInput       ,lowerCamelCase__  :      Optional[bool] = None       ,lowerCamelCase__  :      Optional[Dict[str, int]] = None       ,lowerCamelCase__  :      Optional[bool] = None       ,lowerCamelCase__  :      Optional[Dict[str, int]] = None       ,lowerCamelCase__  :      PILImageResampling = None       ,lowerCamelCase__  :      Optional[bool] = None       ,lowerCamelCase__  :      Optional[float] = None       ,lowerCamelCase__  :      Optional[bool] = None       ,lowerCamelCase__  :      Optional[Union[float, List[float]]] = None       ,lowerCamelCase__  :      Optional[Union[float, List[float]]] = None       ,lowerCamelCase__  :      Optional[Union[str, TensorType]] = None       ,lowerCamelCase__  :      ChannelDimension = ChannelDimension.FIRST       ,**lowerCamelCase__  :      Optional[int]       ,):
									UpperCAmelCase__			 =				do_center_crop if do_center_crop is not None else self.do_center_crop
									UpperCAmelCase__			 =				crop_size if crop_size is not None else self.crop_size
									UpperCAmelCase__			 =				get_size_dict(lowerCamelCase__       ,param_name='crop_size'     )
									UpperCAmelCase__			 =				do_resize if do_resize is not None else self.do_resize
									UpperCAmelCase__			 =				size if size is not None else self.size
									UpperCAmelCase__			 =				get_size_dict(lowerCamelCase__     )
									UpperCAmelCase__			 =				resample if resample is not None else self.resample
									UpperCAmelCase__			 =				do_rescale if do_rescale is not None else self.do_rescale
									UpperCAmelCase__			 =				rescale_factor if rescale_factor is not None else self.rescale_factor
									UpperCAmelCase__			 =				do_normalize if do_normalize is not None else self.do_normalize
									UpperCAmelCase__			 =				image_mean if image_mean is not None else self.image_mean
									UpperCAmelCase__			 =				image_std if image_std is not None else self.image_std

									UpperCAmelCase__			 =				make_list_of_images(lowerCamelCase__     )

									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.'     )

									if do_center_crop and crop_size is None:
													raise ValueError('If `do_center_crop` is set to `True`, `crop_size` must be provided.'     )

									if do_resize and size is None:
													raise ValueError('Size must be specified if do_resize 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 image standard deviation must be specified if do_normalize is True.'     )

									# All transformations expect numpy arrays.
									UpperCAmelCase__			 =				[to_numpy_array(lowerCamelCase__     ) for image in images]

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

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

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

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

									UpperCAmelCase__			 =				[to_channel_dimension_format(lowerCamelCase__       ,lowerCamelCase__     ) for image in images]

									UpperCAmelCase__			 =				{'pixel_values': images}
									return BatchFeature(data=lowerCamelCase__       ,tensor_type=lowerCamelCase__     )



