
'''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:
						UpperCAmelCase            = None


UpperCAmelCase            = logging.get_logger(__name__)

UpperCAmelCase            = {"vocab_file": "sentencepiece.bpe.model", "tokenizer_file": "tokenizer.json"}

UpperCAmelCase            = {
    "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"
        ),
    },
}

UpperCAmelCase            = {
    "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     a (					__magic_name__     ):
				_snake_case		     =			VOCAB_FILES_NAMES
				_snake_case		     =			PRETRAINED_VOCAB_FILES_MAP
				_snake_case		     =			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				_snake_case		     =			['''input_ids''', '''attention_mask''']
				_snake_case		     =			XLMRobertaTokenizer




				def __init__(				self						:					List[str],     SCREAMING_SNAKE_CASE_						:					Any=None,     SCREAMING_SNAKE_CASE_						:					int=None,     SCREAMING_SNAKE_CASE_						:					str="<s>",     SCREAMING_SNAKE_CASE_						:					List[Any]="</s>",     SCREAMING_SNAKE_CASE_						:					Union[str, Any]="</s>",     SCREAMING_SNAKE_CASE_						:					Any="<s>",     SCREAMING_SNAKE_CASE_						:					str="<unk>",     SCREAMING_SNAKE_CASE_						:					List[str]="<pad>",     SCREAMING_SNAKE_CASE_						:					Dict="<mask>",     **SCREAMING_SNAKE_CASE_						:					Optional[Any],     ):
										# Mask token behave like a normal word, i.e. include the space before it
										snake_case							:  List[Any]			=     AddedToken(SCREAMING_SNAKE_CASE_,     lstrip=SCREAMING_SNAKE_CASE_,     rstrip=SCREAMING_SNAKE_CASE_   ) if isinstance(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_   ) else mask_token

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

										snake_case							:  Dict			=     vocab_file
										snake_case							:  str			=     False if not self.vocab_file else True




				def 						__snake_case				(				self						:					Dict,     SCREAMING_SNAKE_CASE_						:					List[int],     SCREAMING_SNAKE_CASE_						:					Optional[List[int]] = None   ):

										if token_ids_a is None:
																return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
										snake_case							:  Optional[int]			=     [self.cls_token_id]
										snake_case							:  Any			=     [self.sep_token_id]
										return cls + token_ids_a + sep + sep + token_ids_a + sep




				def 						__snake_case				(				self						:					Optional[int],     SCREAMING_SNAKE_CASE_						:					List[int],     SCREAMING_SNAKE_CASE_						:					Optional[List[int]] = None   ):
										snake_case							:  Tuple			=     [self.sep_token_id]
										snake_case							:  Optional[Any]			=     [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 						__snake_case				(				self						:					Tuple,     SCREAMING_SNAKE_CASE_						:					str,     SCREAMING_SNAKE_CASE_						:					Optional[str] = None   ):
										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(SCREAMING_SNAKE_CASE_   ):
																logger.error(F"""Vocabulary path ({save_directory}) should be a directory."""   )
																return
										snake_case							:  str			=     os.path.join(
										    SCREAMING_SNAKE_CASE_,     (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']   )

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

										return (out_vocab_file,)


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 EfficientNetImageProcessor







class       __snake_case (  unittest.TestCase ):
 def __init__(	self		, snake_case__		, snake_case__=13		, snake_case__=3		, snake_case__=18		, snake_case__=30		, snake_case__=400		, snake_case__=True		, snake_case__=None		, snake_case__=True		, snake_case__=[0.5, 0.5, 0.5]		, snake_case__=[0.5, 0.5, 0.5]		, )						->				Any:
       '''simple docstring'''





       UpperCAmelCase     :			int     	=size if size is not None else {'''height''': 18, '''width''': 18}
       UpperCAmelCase     :			Tuple     	=parent
       UpperCAmelCase     :			List[str]     	=batch_size
       UpperCAmelCase     :			int     	=num_channels
       UpperCAmelCase     :			List[str]     	=image_size
       UpperCAmelCase     :			str     	=min_resolution
       UpperCAmelCase     :			Optional[int]     	=max_resolution
       UpperCAmelCase     :			Optional[int]     	=do_resize
       UpperCAmelCase     :			int     	=size
       UpperCAmelCase     :			Union[str, Any]     	=do_normalize
       UpperCAmelCase     :			Dict     	=image_mean
       UpperCAmelCase     :			List[Any]     	=image_std






 def 						UpperCAmelCase__							(	self    )						->				int:
       '''simple docstring'''





       return {
           "image_mean": self.image_mean,
           "image_std": self.image_std,
           "do_normalize": self.do_normalize,
           "do_resize": self.do_resize,
           "size": self.size,
       }







@require_torch
@require_vision
class       __snake_case (  lowerCamelCase__       , unittest.TestCase ):
 __lowerCamelCase     : Any 	=   EfficientNetImageProcessor if is_vision_available() else None
 def 						UpperCAmelCase__							(	self    )						->				Tuple:
       '''simple docstring'''





       UpperCAmelCase     :			Dict     	=EfficientNetImageProcessorTester(self    )
 @property
 def 						UpperCAmelCase__							(	self    )						->				int:
       '''simple docstring'''





       return self.image_processor_tester.prepare_image_processor_dict()
 def 						UpperCAmelCase__							(	self    )						->				List[str]:
       '''simple docstring'''





       UpperCAmelCase     :			Tuple     	=self.image_processing_class(**self.image_processor_dict    )
       self.assertTrue(hasattr(snake_case__		, '''image_mean'''    )    )
       self.assertTrue(hasattr(snake_case__		, '''image_std'''    )    )
       self.assertTrue(hasattr(snake_case__		, '''do_normalize'''    )    )
       self.assertTrue(hasattr(snake_case__		, '''do_resize'''    )    )
       self.assertTrue(hasattr(snake_case__		, '''size'''    )    )
 def 						UpperCAmelCase__							(	self    )						->				Tuple:
       '''simple docstring'''





       UpperCAmelCase     :			str     	=self.image_processing_class.from_dict(self.image_processor_dict    )
       self.assertEqual(image_processor.size		, {'''height''': 18, '''width''': 18}    )

       UpperCAmelCase     :			List[str]     	=self.image_processing_class.from_dict(self.image_processor_dict		, size=42    )
       self.assertEqual(image_processor.size		, {'''height''': 42, '''width''': 42}    )
 def 						UpperCAmelCase__							(	self    )						->				Dict:
       '''simple docstring'''





       UpperCAmelCase     :			List[Any]     	=self.image_processing_class(**self.image_processor_dict    )
       # create random PIL images
       UpperCAmelCase     :			Optional[Any]     	=prepare_image_inputs(self.image_processor_tester		, equal_resolution=snake_case__    )
       for image in image_inputs:
             self.assertIsInstance(snake_case__		, Image.Image    )

       # Test not batched input
       UpperCAmelCase     :			int     	=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.size['''height'''],
               self.image_processor_tester.size['''width'''],
           )		, )

       # Test batched
       UpperCAmelCase     :			int     	=image_processing(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.size['''height'''],
               self.image_processor_tester.size['''width'''],
           )		, )
 def 						UpperCAmelCase__							(	self    )						->				Dict:
       '''simple docstring'''





       UpperCAmelCase     :			Tuple     	=self.image_processing_class(**self.image_processor_dict    )
       # create random numpy tensors
       UpperCAmelCase     :			List[str]     	=prepare_image_inputs(self.image_processor_tester		, equal_resolution=snake_case__		, numpify=snake_case__    )
       for image in image_inputs:
             self.assertIsInstance(snake_case__		, np.ndarray    )

       # Test not batched input
       UpperCAmelCase     :			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.size['''height'''],
               self.image_processor_tester.size['''width'''],
           )		, )

       # Test batched
       UpperCAmelCase     :			Union[str, Any]     	=image_processing(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.size['''height'''],
               self.image_processor_tester.size['''width'''],
           )		, )






 def 						UpperCAmelCase__							(	self    )						->				str:
       '''simple docstring'''





       UpperCAmelCase     :			int     	=self.image_processing_class(**self.image_processor_dict    )
       # create random PyTorch tensors
       UpperCAmelCase     :			int     	=prepare_image_inputs(self.image_processor_tester		, equal_resolution=snake_case__		, torchify=snake_case__    )
       for image in image_inputs:
             self.assertIsInstance(snake_case__		, torch.Tensor    )

       # Test not batched input
       UpperCAmelCase     :			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.size['''height'''],
               self.image_processor_tester.size['''width'''],
           )		, )

       # Test batched
       UpperCAmelCase     :			List[str]     	=image_processing(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.size['''height'''],
               self.image_processor_tester.size['''width'''],
           )		, )
