


'''simple docstring'''






import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
							import torch

if is_vision_available():
							from PIL import Image

							from transformers import ViltImageProcessor
class   __UpperCamelCase		(  unittest.TestCase							):





							def __init__(     self						:Any     ,_UpperCamelCase						:List[str]     ,_UpperCamelCase						:List[Any]=7     ,_UpperCamelCase						:List[Any]=3     ,_UpperCamelCase						:Dict=1_8     ,_UpperCamelCase						:Optional[int]=3_0     ,_UpperCamelCase						:Optional[int]=4_0_0     ,_UpperCamelCase						:List[str]=True     ,_UpperCamelCase						:Optional[Any]=None     ,_UpperCamelCase						:Any=2     ,_UpperCamelCase						:List[Any]=True     ,_UpperCamelCase						:Union[str, Any]=[0.5, 0.5, 0.5]     ,_UpperCamelCase						:Any=[0.5, 0.5, 0.5]     ,):
									snake_case_						:							int					=  size if size is not None else {'shortest_edge': 3_0}
									snake_case_						:							int					=  parent
									snake_case_						:							List[str]					=  batch_size
									snake_case_						:							Dict					=  num_channels
									snake_case_						:							Dict					=  image_size
									snake_case_						:							List[Any]					=  min_resolution
									snake_case_						:							Optional[int]					=  max_resolution
									snake_case_						:							Union[str, Any]					=  do_resize
									snake_case_						:							int					=  size
									snake_case_						:							int					=  size_divisor
									snake_case_						:							Optional[Any]					=  do_normalize
									snake_case_						:							List[str]					=  image_mean
									snake_case_						:							Tuple					=  image_std





							def 						a__      (     self						:str	):
									return {
									    "image_mean": self.image_mean,
									    "image_std": self.image_std,
									    "do_normalize": self.do_normalize,
									    "do_resize": self.do_resize,
									    "size": self.size,
									    "size_divisor": self.size_divisor,
									}





							def 						a__      (     self						:Optional[Any]     ,_UpperCamelCase						:Dict     ,_UpperCamelCase						:Any=False	):
									if not batched:
											snake_case_						:							List[str]					=  self.size['shortest_edge']
											snake_case_						:							int					=  image_inputs[0]
											if isinstance(_UpperCamelCase     ,Image.Image	):
													snake_case_						:							int					=  image.size
											else:
													snake_case_						:							Tuple					=  image.shape[1], image.shape[2]
											snake_case_						:							Union[str, Any]					=  size / min(_UpperCamelCase     ,_UpperCamelCase	)
											if h < w:
													snake_case_						:							Any					=  size, scale * w
											else:
													snake_case_						:							int					=  scale * h, size

											snake_case_						:							str					=  int((1_3_3_3 / 8_0_0) * size	)
											if max(_UpperCamelCase     ,_UpperCamelCase	) > max_size:
													snake_case_						:							List[Any]					=  max_size / max(_UpperCamelCase     ,_UpperCamelCase	)
													snake_case_						:							List[str]					=  newh * scale
													snake_case_						:							Tuple					=  neww * scale

											snake_case_						:							Optional[Any]					=  int(newh + 0.5	), int(neww + 0.5	)
											snake_case_						:							Dict					=  (
											    newh // self.size_divisor * self.size_divisor,
											    neww // self.size_divisor * self.size_divisor,
											)

									else:
											snake_case_						:							Tuple					=  []
											for image in image_inputs:
													snake_case_						:							List[str]					=  self.get_expected_values([image]	)
													expected_values.append((expected_height, expected_width)	)
											snake_case_						:							str					=  max(_UpperCamelCase     ,key=lambda _UpperCamelCase	: item[0]	)[0]
											snake_case_						:							int					=  max(_UpperCamelCase     ,key=lambda _UpperCamelCase	: item[1]	)[1]

									return expected_height, expected_width





@require_torch
@require_vision
class   __UpperCamelCase		(  a_  ,     unittest.TestCase							):
							lowercase	:						Tuple      						=     ViltImageProcessor if is_vision_available() else None





							def 						a__      (     self						:Optional[int]	):
									snake_case_						:							List[Any]					=  ViltImageProcessingTester(self	)





							@property
							def 						a__      (     self						:List[Any]	):
									return self.image_processor_tester.prepare_image_processor_dict()





							def 						a__      (     self						:Optional[int]	):
									snake_case_						:							str					=  self.image_processing_class(**self.image_processor_dict	)
									self.assertTrue(hasattr(_UpperCamelCase     ,"""image_mean"""	)	)
									self.assertTrue(hasattr(_UpperCamelCase     ,"""image_std"""	)	)
									self.assertTrue(hasattr(_UpperCamelCase     ,"""do_normalize"""	)	)
									self.assertTrue(hasattr(_UpperCamelCase     ,"""do_resize"""	)	)
									self.assertTrue(hasattr(_UpperCamelCase     ,"""size"""	)	)
									self.assertTrue(hasattr(_UpperCamelCase     ,"""size_divisor"""	)	)





							def 						a__      (     self						:List[Any]	):
									snake_case_						:							Tuple					=  self.image_processing_class.from_dict(self.image_processor_dict	)
									self.assertEqual(image_processor.size     ,{"""shortest_edge""": 3_0}	)

									snake_case_						:							Union[str, Any]					=  self.image_processing_class.from_dict(self.image_processor_dict     ,size=4_2	)
									self.assertEqual(image_processor.size     ,{"""shortest_edge""": 4_2}	)





							def 						a__      (     self						:Tuple	):
									pass





							def 						a__      (     self						:List[Any]	):
									# Initialize image_processing
									snake_case_						:							Union[str, Any]					=  self.image_processing_class(**self.image_processor_dict	)
									# create random PIL images
									snake_case_						:							str					=  prepare_image_inputs(self.image_processor_tester     ,equal_resolution=_UpperCamelCase	)
									for image in image_inputs:
											self.assertIsInstance(_UpperCamelCase     ,Image.Image	)

									# Test not batched input
									snake_case_						:							Tuple					=  image_processing(image_inputs[0]     ,return_tensors="""pt"""	).pixel_values

									snake_case_						:							str					=  self.image_processor_tester.get_expected_values(_UpperCamelCase	)
									self.assertEqual(
									    encoded_images.shape     ,(1, self.image_processor_tester.num_channels, expected_height, expected_width)     ,)

									# Test batched
									snake_case_						:							Optional[int]					=  image_processing(_UpperCamelCase     ,return_tensors="""pt"""	).pixel_values

									snake_case_						:							str					=  self.image_processor_tester.get_expected_values(_UpperCamelCase     ,batched=_UpperCamelCase	)
									self.assertEqual(
									    encoded_images.shape     ,(
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_channels,
									        expected_height,
									        expected_width,
									    )     ,)





							def 						a__      (     self						:Tuple	):
									# Initialize image_processing
									snake_case_						:							Any					=  self.image_processing_class(**self.image_processor_dict	)
									# create random numpy tensors
									snake_case_						:							Optional[Any]					=  prepare_image_inputs(self.image_processor_tester     ,equal_resolution=_UpperCamelCase     ,numpify=_UpperCamelCase	)
									for image in image_inputs:
											self.assertIsInstance(_UpperCamelCase     ,np.ndarray	)

									# Test not batched input
									snake_case_						:							Union[str, Any]					=  image_processing(image_inputs[0]     ,return_tensors="""pt"""	).pixel_values

									snake_case_						:							Tuple					=  self.image_processor_tester.get_expected_values(_UpperCamelCase	)
									self.assertEqual(
									    encoded_images.shape     ,(1, self.image_processor_tester.num_channels, expected_height, expected_width)     ,)

									# Test batched
									snake_case_						:							Tuple					=  image_processing(_UpperCamelCase     ,return_tensors="""pt"""	).pixel_values

									snake_case_						:							Tuple					=  self.image_processor_tester.get_expected_values(_UpperCamelCase     ,batched=_UpperCamelCase	)
									self.assertEqual(
									    encoded_images.shape     ,(
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_channels,
									        expected_height,
									        expected_width,
									    )     ,)





							def 						a__      (     self						:Any	):
									# Initialize image_processing
									snake_case_						:							Union[str, Any]					=  self.image_processing_class(**self.image_processor_dict	)
									# create random PyTorch tensors
									snake_case_						:							List[Any]					=  prepare_image_inputs(self.image_processor_tester     ,equal_resolution=_UpperCamelCase     ,torchify=_UpperCamelCase	)
									for image in image_inputs:
											self.assertIsInstance(_UpperCamelCase     ,torch.Tensor	)

									# Test not batched input
									snake_case_						:							str					=  image_processing(image_inputs[0]     ,return_tensors="""pt"""	).pixel_values

									snake_case_						:							Dict					=  self.image_processor_tester.get_expected_values(_UpperCamelCase	)
									self.assertEqual(
									    encoded_images.shape     ,(1, self.image_processor_tester.num_channels, expected_height, expected_width)     ,)

									# Test batched
									snake_case_						:							List[str]					=  image_processing(_UpperCamelCase     ,return_tensors="""pt"""	).pixel_values

									snake_case_						:							Tuple					=  self.image_processor_tester.get_expected_values(_UpperCamelCase     ,batched=_UpperCamelCase	)
									self.assertEqual(
									    encoded_images.shape     ,(
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_channels,
									        expected_height,
									        expected_width,
									    )     ,)
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
UpperCAmelCase							= '''stas/tiny-wmt19-en-ru'''




class       __lowerCAmelCase     (				a_,		unittest.TestCase  ):



 '''simple docstring'''
 _lowerCamelCase  :		str 			=       FSMTTokenizer
 _lowerCamelCase  :		Tuple 			=       False

 def     __snake_case    (	self				:						Dict     )       ->							Union[str, Any]:
        super().setUp()

        # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
        _UpperCamelCase   :				Dict       =     [
            '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   :				List[str]       =     dict(zip(lowerCamelCase				,	range(len(lowerCamelCase     )     )     )     )
        _UpperCamelCase   :				List[str]       =     ['l o 123', 'lo w 1456', 'e r</w> 1789', '']

        _UpperCamelCase   :				List[str]       =     ['en', 'ru']
        _UpperCamelCase   :				int       =     {
            'langs': self.langs,
            'src_vocab_size': 1_0,
            'tgt_vocab_size': 2_0,
        }

        _UpperCamelCase   :				Tuple       =     os.path.join(self.tmpdirname				,	VOCAB_FILES_NAMES['src_vocab_file']     )
        _UpperCamelCase   :				Optional[int]       =     os.path.join(self.tmpdirname				,	VOCAB_FILES_NAMES['tgt_vocab_file']     )
        _UpperCamelCase   :				Any       =     os.path.join(self.tmpdirname				,	'tokenizer_config.json'     )
        _UpperCamelCase   :				int       =     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     __snake_case    (	self				:						str     )       ->							Any:
        return FSMTTokenizer.from_pretrained('facebook/wmt19-ru-en'     )

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

 def     __snake_case    (	self				:						Optional[Any]     )       ->							Any:
        _UpperCamelCase   :				Optional[int]       =     FSMTTokenizer.from_pretrained(lowerCamelCase     )
        self.assertListEqual([tokenizer.src_lang, tokenizer.tgt_lang]				,	['en', 'ru']     )
        self.assertEqual(tokenizer.src_vocab_size				,	2_1     )
        self.assertEqual(tokenizer.tgt_vocab_size				,	2_1     )

 def     __snake_case    (	self				:						List[str]     )       ->							List[Any]:
        _UpperCamelCase   :				Union[str, Any]       =     FSMTTokenizer(self.langs				,	self.src_vocab_file				,	self.tgt_vocab_file				,	self.merges_file     )

        _UpperCamelCase   :				Optional[int]       =     'lower'
        _UpperCamelCase   :				str       =     ['low', 'er</w>']
        _UpperCamelCase   :				Optional[Any]       =     tokenizer.tokenize(lowerCamelCase     )
        self.assertListEqual(lowerCamelCase				,	lowerCamelCase     )

        _UpperCamelCase   :				Tuple       =     tokens + ['<unk>']
        _UpperCamelCase   :				int       =     [1_4, 1_5, 2_0]
        self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCamelCase     )				,	lowerCamelCase     )

 @slow
 def     __snake_case    (	self				:						List[Any]     )       ->							Union[str, Any]:
        _UpperCamelCase   :				Dict       =     self.tokenizer_ru_en

        _UpperCamelCase   :				List[str]       =     tokenizer.encode('sequence builders'				,	add_special_tokens=lowerCamelCase     )
        _UpperCamelCase   :				Any       =     tokenizer.encode('multi-sequence build'				,	add_special_tokens=lowerCamelCase     )

        _UpperCamelCase   :				List[Any]       =     tokenizer.build_inputs_with_special_tokens(lowerCamelCase     )
        _UpperCamelCase   :				Tuple       =     tokenizer.build_inputs_with_special_tokens(lowerCamelCase				,	lowerCamelCase     )

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

 @slow
 def     __snake_case    (	self				:						List[str]     )       ->							Optional[int]:
        _UpperCamelCase   :				Optional[Any]       =     self.tokenizer_en_ru
        _UpperCamelCase   :				Union[str, Any]       =     self.tokenizer_ru_en

        _UpperCamelCase   :				List[str]       =     [
            [
                'Here\'s a little song I wrote. Don\'t worry, be happy.',
                [2_4_7_0, 3_9, 1_1, 2_3_4_9, 7_2_2_2, 7_0, 5_9_7_9, 7, 8_4_5_0, 1_0_5_0, 1_3_1_6_0, 5, 2_6, 6_4_4_5, 7, 2],
            ],
            ['This is it. No more. I\'m done!', [1_3_2, 2_1, 3_7, 7, 1_4_3_4, 8_6, 7, 7_0, 6_4_7_6, 1_3_0_5, 4_2_7, 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   :				Union[str, Any]       =     tokenizer_enc.encode(lowerCamelCase				,	return_tensors=lowerCamelCase     )
               self.assertListEqual(lowerCamelCase				,	lowerCamelCase     )

               # and decode backward, using the reversed languages model
               _UpperCamelCase   :				Optional[int]       =     tokenizer_dec.decode(lowerCamelCase				,	skip_special_tokens=lowerCamelCase     )
               self.assertEqual(lowerCamelCase				,	lowerCamelCase     )

 @slow
 def     __snake_case    (	self				:						Any     )       ->							Tuple:
        _UpperCamelCase   :				Optional[int]       =     FSMTTokenizer.from_pretrained('facebook/wmt19-ru-en'				,	do_lower_case=lowerCamelCase     )
        _UpperCamelCase   :				str       =     tokenizer.tokenize('USA is United States of America'     )
        _UpperCamelCase   :				Optional[int]       =     ['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     __snake_case    (	self				:						Dict     )       ->							List[str]:
        pass

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



