






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
_a   			=						"""stas/tiny-wmt19-en-ru"""





class __lowerCamelCase				(   lowerCAmelCase_  , unittest.TestCase):





	"""simple docstring"""






	UpperCamelCase__			=   FSMTTokenizer
	UpperCamelCase__			=   False

	def  UpperCamelCase					(							self					):
							"""simple docstring"""







							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(__SCREAMING_SNAKE_CASE ,					range(len(__SCREAMING_SNAKE_CASE					)					)					)					)
							_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(__SCREAMING_SNAKE_CASE					)					)
							with open(self.tgt_vocab_file ,					'w'					) as fp:
													fp.write(json.dumps(__SCREAMING_SNAKE_CASE					)					)
							with open(self.merges_file ,					'w'					) as fp:
													fp.write('\n'.join(__SCREAMING_SNAKE_CASE					)					)
							with open(__SCREAMING_SNAKE_CASE ,					'w'					) as fp:
													fp.write(json.dumps(__SCREAMING_SNAKE_CASE					)					)

	@cached_property
	def  UpperCamelCase					(							self					):
							"""simple docstring"""







							return FSMTTokenizer.from_pretrained('facebook/wmt19-ru-en'					)

	@cached_property
	def  UpperCamelCase					(							self					):
							"""simple docstring"""







							return FSMTTokenizer.from_pretrained('facebook/wmt19-en-ru'					)

	def  UpperCamelCase					(							self					):
							"""simple docstring"""







							_UpperCAmelCase             =    FSMTTokenizer.from_pretrained(__SCREAMING_SNAKE_CASE					)
							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  UpperCamelCase					(							self					):
							"""simple docstring"""







							_UpperCAmelCase             =    FSMTTokenizer(self.langs ,					self.src_vocab_file ,					self.tgt_vocab_file ,					self.merges_file					)

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

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

	@slow
	def  UpperCamelCase					(							self					):
							"""simple docstring"""







							_UpperCAmelCase             =    self.tokenizer_ru_en

							_UpperCAmelCase             =    tokenizer.encode('sequence builders' ,					add_special_tokens=__SCREAMING_SNAKE_CASE					)
							_UpperCAmelCase             =    tokenizer.encode('multi-sequence build' ,					add_special_tokens=__SCREAMING_SNAKE_CASE					)

							_UpperCAmelCase             =    tokenizer.build_inputs_with_special_tokens(__SCREAMING_SNAKE_CASE					)
							_UpperCAmelCase             =    tokenizer.build_inputs_with_special_tokens(__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE					)

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

	@slow
	def  UpperCamelCase					(							self					):
							"""simple docstring"""







							_UpperCAmelCase             =    self.tokenizer_en_ru
							_UpperCAmelCase             =    self.tokenizer_ru_en

							_UpperCAmelCase             =    [
							    [
							        'Here\'s a little song I wrote. Don\'t worry, be happy.',
							        [2470, 39, 11, 2349, 7222, 70, 5979, 7, 8450, 1050, 1_3160, 5, 26, 6445, 7, 2],
							    ],
							    ['This is it. No more. I\'m done!', [132, 21, 37, 7, 1434, 86, 7, 70, 6476, 1305, 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(__SCREAMING_SNAKE_CASE ,					return_tensors=__SCREAMING_SNAKE_CASE					)
													self.assertListEqual(__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE					)

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

	@slow
	def  UpperCamelCase					(							self					):
							"""simple docstring"""







							_UpperCAmelCase             =    FSMTTokenizer.from_pretrained('facebook/wmt19-ru-en' ,					do_lower_case=__SCREAMING_SNAKE_CASE					)
							_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(__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE					)

	@unittest.skip('FSMTConfig.__init__  requires non-optional args'					)
	def  UpperCamelCase					(							self					):
							"""simple docstring"""







							pass

	@unittest.skip('FSMTConfig.__init__  requires non-optional args'					)
	def  UpperCamelCase					(							self					):
							"""simple docstring"""







							pass








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,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


_a   			=						logging.get_logger(__name__)


if is_vision_available():
		import PIL





class __lowerCamelCase				(   snake_case__):





	"""simple docstring"""






	UpperCamelCase__			=   ["pixel_values"]

	def __init__(							self ,					UpperCAmelCase = True ,					UpperCAmelCase = None ,					UpperCAmelCase = PILImageResampling.BICUBIC ,					UpperCAmelCase = True ,					UpperCAmelCase = None ,					UpperCAmelCase = True ,					UpperCAmelCase = 1 / 255 ,					UpperCAmelCase = True ,					UpperCAmelCase = None ,					UpperCAmelCase = None ,					UpperCAmelCase = True ,					**UpperCAmelCase ,					):
							"""simple docstring"""







							super().__init__(**UpperCAmelCase					)
							_UpperCAmelCase             =    size if size is not None else {'shortest_edge': 224}
							_UpperCAmelCase             =    get_size_dict(UpperCAmelCase ,					default_to_square=UpperCAmelCase					)
							_UpperCAmelCase             =    crop_size if crop_size is not None else {'height': 224, 'width': 224}
							_UpperCAmelCase             =    get_size_dict(UpperCAmelCase					)

							_UpperCAmelCase             =    do_resize
							_UpperCAmelCase             =    size
							_UpperCAmelCase             =    resample
							_UpperCAmelCase             =    do_center_crop
							_UpperCAmelCase             =    crop_size
							_UpperCAmelCase             =    do_rescale
							_UpperCAmelCase             =    rescale_factor
							_UpperCAmelCase             =    do_normalize
							_UpperCAmelCase             =    image_mean if image_mean is not None else OPENAI_CLIP_MEAN
							_UpperCAmelCase             =    image_std if image_std is not None else OPENAI_CLIP_STD
							_UpperCAmelCase             =    do_convert_rgb

	def  UpperCamelCase					(							self ,					UpperCAmelCase ,					UpperCAmelCase ,					UpperCAmelCase = PILImageResampling.BICUBIC ,					UpperCAmelCase = None ,					**UpperCAmelCase ,					):
							"""simple docstring"""







							_UpperCAmelCase             =    get_size_dict(UpperCAmelCase ,					default_to_square=UpperCAmelCase					)
							_UpperCAmelCase             =    get_resize_output_image_size(
							    UpperCAmelCase ,					size=(size['height'], size['width']) ,					default_to_square=UpperCAmelCase					)
							return resize(UpperCAmelCase ,					size=UpperCAmelCase ,					resample=UpperCAmelCase ,					data_format=UpperCAmelCase ,					**UpperCAmelCase					)

	def  UpperCamelCase					(							self ,					UpperCAmelCase ,					UpperCAmelCase ,					UpperCAmelCase = None ,					**UpperCAmelCase ,					):
							"""simple docstring"""







							_UpperCAmelCase             =    get_size_dict(UpperCAmelCase					)
							return center_crop(UpperCAmelCase ,					size=(size['height'], size['width']) ,					data_format=UpperCAmelCase ,					**UpperCAmelCase					)

	def  UpperCamelCase					(							self ,					UpperCAmelCase ,					UpperCAmelCase ,					UpperCAmelCase = None ,					**UpperCAmelCase ,					):
							"""simple docstring"""







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

	def  UpperCamelCase					(							self ,					UpperCAmelCase ,					UpperCAmelCase ,					UpperCAmelCase ,					UpperCAmelCase = None ,					**UpperCAmelCase ,					):
							"""simple docstring"""







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

	def  UpperCamelCase					(							self ,					UpperCAmelCase ,					UpperCAmelCase = None ,					UpperCAmelCase = None ,					UpperCAmelCase = None ,					UpperCAmelCase = None ,					UpperCAmelCase = None ,					UpperCAmelCase = None ,					UpperCAmelCase = None ,					UpperCAmelCase = None ,					UpperCAmelCase = None ,					UpperCAmelCase = None ,					UpperCAmelCase = None ,					UpperCAmelCase = None ,					UpperCAmelCase = ChannelDimension.FIRST ,					**UpperCAmelCase ,					):
							"""simple docstring"""







							_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(UpperCAmelCase ,					default_to_square=UpperCAmelCase					)
							_UpperCAmelCase             =    resample if resample is not None else self.resample
							_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(UpperCAmelCase					)
							_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             =    do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

							_UpperCAmelCase             =    make_list_of_images(UpperCAmelCase					)

							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.'					)

							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.'					)

							# PIL RGBA images are converted to RGB
							if do_convert_rgb:
													_UpperCAmelCase             =    [convert_to_rgb(UpperCAmelCase					) for image in images]

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

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

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

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

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

							_UpperCAmelCase             =    [to_channel_dimension_format(UpperCAmelCase ,					UpperCAmelCase					) for image in images]

							_UpperCAmelCase             =    {'pixel_values': images}
							return BatchFeature(data=UpperCAmelCase ,					tensor_type=UpperCAmelCase					)
