




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 DeiTImageProcessor





class 		A__				(   unittest.TestCase ):






	def __init__(	self				,     lowerCamelCase				,     lowerCamelCase=7				,     lowerCamelCase=3				,     lowerCamelCase=18				,     lowerCamelCase=30				,     lowerCamelCase=400				,     lowerCamelCase=True				,     lowerCamelCase=None				,     lowerCamelCase=True				,     lowerCamelCase=None				,     lowerCamelCase=True				,     lowerCamelCase=[0.5, 0.5, 0.5]				,     lowerCamelCase=[0.5, 0.5, 0.5]				,     )      ->   List[Any]:




						"""simple docstring"""






						__magic_name__					:							Any		   =							size if size is not None else {'''height''': 20, '''width''': 20}
						__magic_name__					:							List[str]		   =							crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}

						__magic_name__					:							Union[str, Any]		   =							parent
						__magic_name__					:							int		   =							batch_size
						__magic_name__					:							Optional[Any]		   =							num_channels
						__magic_name__					:							Any		   =							image_size
						__magic_name__					:							Optional[Any]		   =							min_resolution
						__magic_name__					:							Any		   =							max_resolution
						__magic_name__					:							int		   =							do_resize
						__magic_name__					:							Optional[Any]		   =							size
						__magic_name__					:							Optional[Any]		   =							do_center_crop
						__magic_name__					:							Dict		   =							crop_size
						__magic_name__					:							int		   =							do_normalize
						__magic_name__					:							Tuple		   =							image_mean
						__magic_name__					:							Tuple		   =							image_std






	def  lowercase      (	self						)      ->   Any:




						"""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,
						}





@require_torch
@require_vision
class 		A__				(   __SCREAMING_SNAKE_CASE		,     unittest.TestCase ):
	lowerCamelCase__		: Optional[int]				    =DeiTImageProcessor if is_vision_available() else None
	lowerCamelCase__		: List[Any]				    =True






	def  lowercase      (	self						)      ->   str:




						"""simple docstring"""






						__magic_name__					:							Optional[int]		   =							DeiTImageProcessingTester(self						)






	@property
	def  lowercase      (	self						)      ->   List[Any]:




						"""simple docstring"""






						return self.image_processor_tester.prepare_image_processor_dict()






	def  lowercase      (	self						)      ->   List[str]:




						"""simple docstring"""






						__magic_name__					:							List[Any]		   =							self.image_processing_class(**self.image_processor_dict						)
						self.assertTrue(hasattr(lowerCamelCase				,     '''do_resize'''						)						)
						self.assertTrue(hasattr(lowerCamelCase				,     '''size'''						)						)
						self.assertTrue(hasattr(lowerCamelCase				,     '''do_center_crop'''						)						)
						self.assertTrue(hasattr(lowerCamelCase				,     '''center_crop'''						)						)
						self.assertTrue(hasattr(lowerCamelCase				,     '''do_normalize'''						)						)
						self.assertTrue(hasattr(lowerCamelCase				,     '''image_mean'''						)						)
						self.assertTrue(hasattr(lowerCamelCase				,     '''image_std'''						)						)






	def  lowercase      (	self						)      ->   Any:




						"""simple docstring"""






						__magic_name__					:							int		   =							self.image_processing_class.from_dict(self.image_processor_dict						)
						self.assertEqual(image_processor.size				,     {'''height''': 20, '''width''': 20}						)
						self.assertEqual(image_processor.crop_size				,     {'''height''': 18, '''width''': 18}						)

						__magic_name__					:							str		   =							self.image_processing_class.from_dict(self.image_processor_dict				,     size=42				,     crop_size=84						)
						self.assertEqual(image_processor.size				,     {'''height''': 42, '''width''': 42}						)
						self.assertEqual(image_processor.crop_size				,     {'''height''': 84, '''width''': 84}						)






	def  lowercase      (	self						)      ->   List[str]:




						"""simple docstring"""






						pass






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




						"""simple docstring"""






						__magic_name__					:							Dict		   =							self.image_processing_class(**self.image_processor_dict						)
						# create random PIL images
						__magic_name__					:							Any		   =							prepare_image_inputs(self.image_processor_tester				,     equal_resolution=lowerCamelCase						)
						for image in image_inputs:
											self.assertIsInstance(lowerCamelCase				,     Image.Image						)

						# Test not batched input
						__magic_name__					:							List[Any]		   =							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
						__magic_name__					:							Optional[int]		   =							image_processing(lowerCamelCase				,     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  lowercase      (	self						)      ->   Dict:




						"""simple docstring"""






						__magic_name__					:							int		   =							self.image_processing_class(**self.image_processor_dict						)
						# create random numpy tensors
						__magic_name__					:							Tuple		   =							prepare_image_inputs(self.image_processor_tester				,     equal_resolution=lowerCamelCase				,     numpify=lowerCamelCase						)
						for image in image_inputs:
											self.assertIsInstance(lowerCamelCase				,     np.ndarray						)

						# Test not batched input
						__magic_name__					:							List[str]		   =							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
						__magic_name__					:							Any		   =							image_processing(lowerCamelCase				,     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  lowercase      (	self						)      ->   Optional[int]:




						"""simple docstring"""






						__magic_name__					:							Optional[Any]		   =							self.image_processing_class(**self.image_processor_dict						)
						# create random PyTorch tensors
						__magic_name__					:							int		   =							prepare_image_inputs(self.image_processor_tester				,     equal_resolution=lowerCamelCase				,     torchify=lowerCamelCase						)
						for image in image_inputs:
											self.assertIsInstance(lowerCamelCase				,     torch.Tensor						)

						# Test not batched input
						__magic_name__					:							Optional[Any]		   =							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
						__magic_name__					:							Union[str, Any]		   =							image_processing(lowerCamelCase				,     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'''],
						    )				,     )





"""simple docstring"""


import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
			from .tokenization_xlm_roberta import XLMRobertaTokenizer
else:
			__A       						=       None


__A       						=       logging.get_logger(__name__)

__A       						=       {'vocab_file': 'sentencepiece.bpe.model', 'tokenizer_file': 'tokenizer.json'}

__A       						=       {
    'vocab_file': {
        'xlm-roberta-base': 'https://huggingface.co/xlm-roberta-base/resolve/main/sentencepiece.bpe.model',
        'xlm-roberta-large': 'https://huggingface.co/xlm-roberta-large/resolve/main/sentencepiece.bpe.model',
        'xlm-roberta-large-finetuned-conll02-dutch': (
            'https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/sentencepiece.bpe.model'
        ),
        'xlm-roberta-large-finetuned-conll02-spanish': (
            'https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/sentencepiece.bpe.model'
        ),
        'xlm-roberta-large-finetuned-conll03-english': (
            'https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/sentencepiece.bpe.model'
        ),
        'xlm-roberta-large-finetuned-conll03-german': (
            'https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/sentencepiece.bpe.model'
        ),
    },
    'tokenizer_file': {
        'xlm-roberta-base': 'https://huggingface.co/xlm-roberta-base/resolve/main/tokenizer.json',
        'xlm-roberta-large': 'https://huggingface.co/xlm-roberta-large/resolve/main/tokenizer.json',
        'xlm-roberta-large-finetuned-conll02-dutch': (
            'https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/tokenizer.json'
        ),
        'xlm-roberta-large-finetuned-conll02-spanish': (
            'https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/tokenizer.json'
        ),
        'xlm-roberta-large-finetuned-conll03-english': (
            'https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/tokenizer.json'
        ),
        'xlm-roberta-large-finetuned-conll03-german': (
            'https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/tokenizer.json'
        ),
    },
}

__A       						=       {
    'xlm-roberta-base': 512,
    'xlm-roberta-large': 512,
    'xlm-roberta-large-finetuned-conll02-dutch': 512,
    'xlm-roberta-large-finetuned-conll02-spanish': 512,
    'xlm-roberta-large-finetuned-conll03-english': 512,
    'xlm-roberta-large-finetuned-conll03-german': 512,
}







class 		lowerCamelCase__					(					__magic_name__				):





			'''simple docstring'''
			lowerCamelCase  					=		VOCAB_FILES_NAMES
			lowerCamelCase  					=		PRETRAINED_VOCAB_FILES_MAP
			lowerCamelCase  					=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			lowerCamelCase  					=		['''input_ids''', '''attention_mask''']
			lowerCamelCase  					=		XLMRobertaTokenizer



			def __init__(							self     ,     __UpperCAmelCase=None     ,     __UpperCAmelCase=None     ,     __UpperCAmelCase="<s>"     ,     __UpperCAmelCase="</s>"     ,     __UpperCAmelCase="</s>"     ,     __UpperCAmelCase="<s>"     ,     __UpperCAmelCase="<unk>"     ,     __UpperCAmelCase="<pad>"     ,     __UpperCAmelCase="<mask>"     ,     **__UpperCAmelCase     ,     ) ->     str:
				# Mask token behave like a normal word, i.e. include the space before it
				_lowerCAmelCase								=AddedToken(__UpperCAmelCase     ,     lstrip=__UpperCAmelCase     ,     rstrip=__UpperCAmelCase  ) if isinstance(__UpperCAmelCase     ,     __UpperCAmelCase  ) else mask_token

				super().__init__(
				    __UpperCAmelCase     ,     tokenizer_file=__UpperCAmelCase     ,     bos_token=__UpperCAmelCase     ,     eos_token=__UpperCAmelCase     ,     sep_token=__UpperCAmelCase     ,     cls_token=__UpperCAmelCase     ,     unk_token=__UpperCAmelCase     ,     pad_token=__UpperCAmelCase     ,     mask_token=__UpperCAmelCase     ,     **__UpperCAmelCase     ,     )

				_lowerCAmelCase								=vocab_file
				_lowerCAmelCase								=False if not self.vocab_file else True



			def 					_lowerCAmelCase		(							self     ,     __UpperCAmelCase     ,     __UpperCAmelCase = None  ) ->     List[int]:

				if token_ids_a is None:
					return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
				_lowerCAmelCase								=[self.cls_token_id]
				_lowerCAmelCase								=[self.sep_token_id]
				return cls + token_ids_a + sep + sep + token_ids_a + sep



			def 					_lowerCAmelCase		(							self     ,     __UpperCAmelCase     ,     __UpperCAmelCase = None  ) ->     List[int]:
				_lowerCAmelCase								=[self.sep_token_id]
				_lowerCAmelCase								=[self.cls_token_id]

				if token_ids_a is None:
					return len(cls + token_ids_a + sep  ) * [0]
				return len(cls + token_ids_a + sep + sep + token_ids_a + sep  ) * [0]



			def 					_lowerCAmelCase		(							self     ,     __UpperCAmelCase     ,     __UpperCAmelCase = None  ) ->     Tuple[str]:
				if not self.can_save_slow_tokenizer:
					raise ValueError(
					    """Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
					    """tokenizer."""  )

				if not os.path.isdir(__UpperCAmelCase  ):
					logger.error(f'''Vocabulary path ({save_directory}) should be a directory.'''  )
					return
				_lowerCAmelCase								=os.path.join(
				    __UpperCAmelCase     ,     (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]  )

				if os.path.abspath(self.vocab_file  ) != os.path.abspath(__UpperCAmelCase  ):
					copyfile(self.vocab_file     ,     __UpperCAmelCase  )

				return (out_vocab_file,)



