"""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
lowercase__   			=      'stas/tiny-wmt19-en-ru'




class   __snake_case			(	snake_case__			, unittest.TestCase							):
      a__								=						FSMTTokenizer
      a__								=						False



      def     lowerCamelCase_  (		self)      ->     Optional[Any]:





        '''simple docstring'''





        super().setUp()

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

        a__:	Optional[int]     				=      ['en', 'ru']
        a__:	List[Any]     				=      {
            'langs': self.langs,
            'src_vocab_size': 10,
            'tgt_vocab_size': 20,
        }

        a__:	Tuple     				=      os.path.join(self.tmpdirname       ,     VOCAB_FILES_NAMES['src_vocab_file'])
        a__:	str     				=      os.path.join(self.tmpdirname       ,     VOCAB_FILES_NAMES['tgt_vocab_file'])
        a__:	int     				=      os.path.join(self.tmpdirname       ,     'tokenizer_config.json')
        a__:	List[Any]     				=      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     lowerCamelCase_  (		self)      ->     Dict:





        '''simple docstring'''





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



      @cached_property
      def     lowerCamelCase_  (		self)      ->     int:





        '''simple docstring'''





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



      def     lowerCamelCase_  (		self)      ->     int:





        '''simple docstring'''





        a__:	Optional[int]     				=      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     lowerCamelCase_  (		self)      ->     List[str]:





        '''simple docstring'''





        a__:	Optional[int]     				=      FSMTTokenizer(self.langs       ,     self.src_vocab_file       ,     self.tgt_vocab_file       ,     self.merges_file)

        a__:	str     				=      'lower'
        a__:	Optional[int]     				=      ['low', 'er</w>']
        a__:	List[str]     				=      tokenizer.tokenize(_SCREAMING_SNAKE_CASE)
        self.assertListEqual(_SCREAMING_SNAKE_CASE       ,     _SCREAMING_SNAKE_CASE)

        a__:	Union[str, Any]     				=      tokens + ['<unk>']
        a__:	Optional[int]     				=      [14, 15, 20]
        self.assertListEqual(tokenizer.convert_tokens_to_ids(_SCREAMING_SNAKE_CASE)       ,     _SCREAMING_SNAKE_CASE)



      @slow
      def     lowerCamelCase_  (		self)      ->     str:





        '''simple docstring'''





        a__:	Dict     				=      self.tokenizer_ru_en

        a__:	Dict     				=      tokenizer.encode('sequence builders'       ,     add_special_tokens=_SCREAMING_SNAKE_CASE)
        a__:	List[Any]     				=      tokenizer.encode('multi-sequence build'       ,     add_special_tokens=_SCREAMING_SNAKE_CASE)

        a__:	List[Any]     				=      tokenizer.build_inputs_with_special_tokens(_SCREAMING_SNAKE_CASE)
        a__:	Optional[Any]     				=      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     lowerCamelCase_  (		self)      ->     Dict:





        '''simple docstring'''





        a__:	str     				=      self.tokenizer_en_ru
        a__:	List[Any]     				=      self.tokenizer_ru_en

        a__:	Tuple     				=      [
            [
                'Here\'s a little song I wrote. Don\'t worry, be happy.',
                [24_70, 39, 11, 23_49, 72_22, 70, 59_79, 7, 84_50, 10_50, 1_31_60, 5, 26, 64_45, 7, 2],
            ],
            ['This is it. No more. I\'m done!', [1_32, 21, 37, 7, 14_34, 86, 7, 70, 64_76, 13_05, 4_27, 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:
          a__:	str     				=      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
          a__:	Any     				=      tokenizer_dec.decode(_SCREAMING_SNAKE_CASE       ,     skip_special_tokens=_SCREAMING_SNAKE_CASE)
          self.assertEqual(_SCREAMING_SNAKE_CASE       ,     _SCREAMING_SNAKE_CASE)



      @slow
      def     lowerCamelCase_  (		self)      ->     Optional[int]:





        '''simple docstring'''





        a__:	List[Any]     				=      FSMTTokenizer.from_pretrained('facebook/wmt19-ru-en'       ,     do_lower_case=_SCREAMING_SNAKE_CASE)
        a__:	Dict     				=      tokenizer.tokenize('USA is United States of America')
        a__:	Optional[Any]     				=      ['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     lowerCamelCase_  (		self)      ->     List[str]:





        '''simple docstring'''





        pass



      @unittest.skip('FSMTConfig.__init__  requires non-optional args')
      def     lowerCamelCase_  (		self)      ->     str:





        '''simple docstring'''





        pass










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


if is_torch_available():
					import torch

if is_vision_available():
					from PIL import Image

					from transformers import CLIPImageProcessor
class   UpperCamelCase			(					unittest.TestCase			):







		"""simple docstring"""



		def __init__(	self		:							int    ,_SCREAMING_SNAKE_CASE		:							Any    ,_SCREAMING_SNAKE_CASE		:							Optional[int]=7    ,_SCREAMING_SNAKE_CASE		:							List[Any]=3    ,_SCREAMING_SNAKE_CASE		:							int=1_8    ,_SCREAMING_SNAKE_CASE		:							Tuple=3_0    ,_SCREAMING_SNAKE_CASE		:							Optional[Any]=4_0_0    ,_SCREAMING_SNAKE_CASE		:							Dict=True    ,_SCREAMING_SNAKE_CASE		:							int=None    ,_SCREAMING_SNAKE_CASE		:							Tuple=True    ,_SCREAMING_SNAKE_CASE		:							Any=None    ,_SCREAMING_SNAKE_CASE		:							Dict=True    ,_SCREAMING_SNAKE_CASE		:							List[str]=[0.48145466, 0.4578275, 0.40821073]    ,_SCREAMING_SNAKE_CASE		:							int=[0.26862954, 0.26130258, 0.27577711]    ,_SCREAMING_SNAKE_CASE		:							Union[str, Any]=True    ,)     -> List[str]:


						'''simple docstring'''
						A									=   size if size is not None else {'shortest_edge': 2_0}
						A									=   crop_size if crop_size is not None else {'height': 1_8, 'width': 1_8}
						A									=   parent
						A									=   batch_size
						A									=   num_channels
						A									=   image_size
						A									=   min_resolution
						A									=   max_resolution
						A									=   do_resize
						A									=   size
						A									=   do_center_crop
						A									=   crop_size
						A									=   do_normalize
						A									=   image_mean
						A									=   image_std
						A									=   do_convert_rgb
		def    A(	self		:							List[str]		)     -> Optional[int]:


						'''simple docstring'''
						return {
						    "do_resize": self.do_resize,
						    "size": self.size,
						    "do_center_crop": self.do_center_crop,
						    "crop_size": self.crop_size,
						    "do_normalize": self.do_normalize,
						    "image_mean": self.image_mean,
						    "image_std": self.image_std,
						    "do_convert_rgb": self.do_convert_rgb,
						}







		def    A(	self		:							Optional[int]    ,_SCREAMING_SNAKE_CASE		:							str=False    ,_SCREAMING_SNAKE_CASE		:							List[str]=False    ,_SCREAMING_SNAKE_CASE		:							Optional[Any]=False		)     -> str:


						'''simple docstring'''
						assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

						if equal_resolution:
										A									=   []
										for i in range(self.batch_size		):
														image_inputs.append(
														    np.random.randint(
														        2_5_5    ,size=(self.num_channels, self.max_resolution, self.max_resolution)    ,dtype=np.uinta		)		)
						else:
										A									=   []
										for i in range(self.batch_size		):
														A			,		A									=   np.random.choice(np.arange(self.min_resolution    ,self.max_resolution		)    ,2		)
														image_inputs.append(np.random.randint(2_5_5    ,size=(self.num_channels, width, height)    ,dtype=np.uinta		)		)

						if not numpify and not torchify:
										# PIL expects the channel dimension as last dimension
										A									=   [Image.fromarray(np.moveaxis(_SCREAMING_SNAKE_CASE    ,0    ,-1		)		) for x in image_inputs]

						if torchify:
										A									=   [torch.from_numpy(_SCREAMING_SNAKE_CASE		) for x in image_inputs]

						return image_inputs


@require_torch
@require_vision
class   UpperCamelCase			(					snake_case__      ,							unittest.TestCase			):







		"""simple docstring"""



		snake_case			 = CLIPImageProcessor if is_vision_available() else None
		def    A(	self		:							Optional[Any]		)     -> Union[str, Any]:


						'''simple docstring'''
						A									=   CLIPImageProcessingTester(self		)
		@property
		def    A(	self		:							Optional[int]		)     -> Any:


						'''simple docstring'''
						return self.image_processor_tester.prepare_image_processor_dict()
		def    A(	self		:							Optional[int]		)     -> Tuple:


						'''simple docstring'''
						A									=   self.image_processing_class(**self.image_processor_dict		)
						self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE    ,'do_resize'		)		)
						self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE    ,'size'		)		)
						self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE    ,'do_center_crop'		)		)
						self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE    ,'center_crop'		)		)
						self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE    ,'do_normalize'		)		)
						self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE    ,'image_mean'		)		)
						self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE    ,'image_std'		)		)
						self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE    ,'do_convert_rgb'		)		)
		def    A(	self		:							Optional[Any]		)     -> int:


						'''simple docstring'''
						A									=   self.image_processing_class.from_dict(self.image_processor_dict		)
						self.assertEqual(image_processor.size    ,{'shortest_edge': 2_0}		)
						self.assertEqual(image_processor.crop_size    ,{'height': 1_8, 'width': 1_8}		)

						A									=   self.image_processing_class.from_dict(self.image_processor_dict    ,size=4_2    ,crop_size=8_4		)
						self.assertEqual(image_processor.size    ,{'shortest_edge': 4_2}		)
						self.assertEqual(image_processor.crop_size    ,{'height': 8_4, 'width': 8_4}		)
		def    A(	self		:							Any		)     -> Union[str, Any]:


						'''simple docstring'''
						pass
		def    A(	self		:							Tuple		)     -> Optional[int]:


						'''simple docstring'''
						# Initialize image_processing
						A									=   self.image_processing_class(**self.image_processor_dict		)
						# create random PIL images
						A									=   self.image_processor_tester.prepare_inputs(equal_resolution=_SCREAMING_SNAKE_CASE		)
						for image in image_inputs:
										self.assertIsInstance(_SCREAMING_SNAKE_CASE    ,Image.Image		)

						# Test not batched input
						A									=   image_processing(image_inputs[0]    ,return_tensors='pt'		).pixel_values
						self.assertEqual(
						    encoded_images.shape    ,(
						        1,
						        self.image_processor_tester.num_channels,
						        self.image_processor_tester.crop_size['height'],
						        self.image_processor_tester.crop_size['width'],
						    )    ,)

						# Test batched
						A									=   image_processing(_SCREAMING_SNAKE_CASE    ,return_tensors='pt'		).pixel_values
						self.assertEqual(
						    encoded_images.shape    ,(
						        self.image_processor_tester.batch_size,
						        self.image_processor_tester.num_channels,
						        self.image_processor_tester.crop_size['height'],
						        self.image_processor_tester.crop_size['width'],
						    )    ,)
		def    A(	self		:							int		)     -> Tuple:


						'''simple docstring'''
						# Initialize image_processing
						A									=   self.image_processing_class(**self.image_processor_dict		)
						# create random numpy tensors
						A									=   self.image_processor_tester.prepare_inputs(equal_resolution=_SCREAMING_SNAKE_CASE    ,numpify=_SCREAMING_SNAKE_CASE		)
						for image in image_inputs:
										self.assertIsInstance(_SCREAMING_SNAKE_CASE    ,np.ndarray		)

						# Test not batched input
						A									=   image_processing(image_inputs[0]    ,return_tensors='pt'		).pixel_values
						self.assertEqual(
						    encoded_images.shape    ,(
						        1,
						        self.image_processor_tester.num_channels,
						        self.image_processor_tester.crop_size['height'],
						        self.image_processor_tester.crop_size['width'],
						    )    ,)

						# Test batched
						A									=   image_processing(_SCREAMING_SNAKE_CASE    ,return_tensors='pt'		).pixel_values
						self.assertEqual(
						    encoded_images.shape    ,(
						        self.image_processor_tester.batch_size,
						        self.image_processor_tester.num_channels,
						        self.image_processor_tester.crop_size['height'],
						        self.image_processor_tester.crop_size['width'],
						    )    ,)







		def    A(	self		:							str		)     -> str:


						'''simple docstring'''
						# Initialize image_processing
						A									=   self.image_processing_class(**self.image_processor_dict		)
						# create random PyTorch tensors
						A									=   self.image_processor_tester.prepare_inputs(equal_resolution=_SCREAMING_SNAKE_CASE    ,torchify=_SCREAMING_SNAKE_CASE		)
						for image in image_inputs:
										self.assertIsInstance(_SCREAMING_SNAKE_CASE    ,torch.Tensor		)

						# Test not batched input
						A									=   image_processing(image_inputs[0]    ,return_tensors='pt'		).pixel_values
						self.assertEqual(
						    encoded_images.shape    ,(
						        1,
						        self.image_processor_tester.num_channels,
						        self.image_processor_tester.crop_size['height'],
						        self.image_processor_tester.crop_size['width'],
						    )    ,)

						# Test batched
						A									=   image_processing(_SCREAMING_SNAKE_CASE    ,return_tensors='pt'		).pixel_values
						self.assertEqual(
						    encoded_images.shape    ,(
						        self.image_processor_tester.batch_size,
						        self.image_processor_tester.num_channels,
						        self.image_processor_tester.crop_size['height'],
						        self.image_processor_tester.crop_size['width'],
						    )    ,)


@require_torch
@require_vision
class   UpperCamelCase			(					snake_case__      ,							unittest.TestCase			):







		"""simple docstring"""



		snake_case			 = CLIPImageProcessor if is_vision_available() else None
		def    A(	self		:							Tuple		)     -> Union[str, Any]:


						'''simple docstring'''
						A									=   CLIPImageProcessingTester(self    ,num_channels=4		)
						A									=   3
		@property
		def    A(	self		:							str		)     -> Dict:


						'''simple docstring'''
						return self.image_processor_tester.prepare_image_processor_dict()
		def    A(	self		:							Any		)     -> int:


						'''simple docstring'''
						A									=   self.image_processing_class(**self.image_processor_dict		)
						self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE    ,'do_resize'		)		)
						self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE    ,'size'		)		)
						self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE    ,'do_center_crop'		)		)
						self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE    ,'center_crop'		)		)
						self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE    ,'do_normalize'		)		)
						self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE    ,'image_mean'		)		)
						self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE    ,'image_std'		)		)
						self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE    ,'do_convert_rgb'		)		)
		def    A(	self		:							Any		)     -> str:


						'''simple docstring'''
						pass







		def    A(	self		:							Union[str, Any]		)     -> Dict:


						'''simple docstring'''
						# Initialize image_processing
						A									=   self.image_processing_class(**self.image_processor_dict		)
						# create random PIL images
						A									=   self.image_processor_tester.prepare_inputs(equal_resolution=_SCREAMING_SNAKE_CASE		)
						for image in image_inputs:
										self.assertIsInstance(_SCREAMING_SNAKE_CASE    ,Image.Image		)

						# Test not batched input
						A									=   image_processing(image_inputs[0]    ,return_tensors='pt'		).pixel_values
						self.assertEqual(
						    encoded_images.shape    ,(
						        1,
						        self.expected_encoded_image_num_channels,
						        self.image_processor_tester.crop_size['height'],
						        self.image_processor_tester.crop_size['width'],
						    )    ,)

						# Test batched
						A									=   image_processing(_SCREAMING_SNAKE_CASE    ,return_tensors='pt'		).pixel_values
						self.assertEqual(
						    encoded_images.shape    ,(
						        self.image_processor_tester.batch_size,
						        self.expected_encoded_image_num_channels,
						        self.image_processor_tester.crop_size['height'],
						        self.image_processor_tester.crop_size['width'],
						    )    ,)





