






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:
					snake_case_   :	Optional[int]							      =None


snake_case_   :	List[str]							      =logging.get_logger(__name__)

snake_case_   :	Optional[int]							      ={'''vocab_file''': '''sentencepiece.bpe.model''', '''tokenizer_file''': '''tokenizer.json'''}

snake_case_   :	Union[str, Any]							      ={
    '''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'''
        ),
    },
}

snake_case_   :	int							      ={
    '''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__      (	lowerCAmelCase__		):
		UpperCAmelCase_							:       int 					= VOCAB_FILES_NAMES
		UpperCAmelCase_							:       str 					= PRETRAINED_VOCAB_FILES_MAP
		UpperCAmelCase_							:       Union[str, Any] 					= PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		UpperCAmelCase_							:       int 					= ['input_ids', 'attention_mask']
		UpperCAmelCase_							:       Optional[int] 					= XLMRobertaTokenizer






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

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

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






		def 					_lowerCamelCase					(      self							, lowercase__							, lowercase__ = None						)				->				List[int]:

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






		def 					_lowerCamelCase					(      self							, lowercase__							, lowercase__ = None						)				->				List[int]:
				__A    =							[self.sep_token_id]
				__A    =							[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							, lowercase__							, lowercase__ = 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(lowercase__						):
						logger.error(F"""Vocabulary path ({save_directory}) should be a directory."""						)
						return
				__A    =							os.path.join(
				    lowercase__							, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]						)

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

				return (out_vocab_file,)









import unittest

import numpy as np
from datasets import load_dataset

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 SegformerImageProcessor





class        a__      (	unittest.TestCase		):






		def __init__(      self							, lowercase__							, lowercase__=7							, lowercase__=3							, lowercase__=30							, lowercase__=400							, lowercase__=True							, lowercase__=None							, lowercase__=True							, lowercase__=[0.5, 0.5, 0.5]							, lowercase__=[0.5, 0.5, 0.5]							, lowercase__=False							, )				->				Tuple:
				__A    =							size if size is not None else {"height": 30, "width": 30}
				__A    =							parent
				__A    =							batch_size
				__A    =							num_channels
				__A    =							min_resolution
				__A    =							max_resolution
				__A    =							do_resize
				__A    =							size
				__A    =							do_normalize
				__A    =							image_mean
				__A    =							image_std
				__A    =							do_reduce_labels






		def 					_lowerCamelCase					(      self						)				->				Union[str, Any]:
				return {
				    "do_resize": self.do_resize,
				    "size": self.size,
				    "do_normalize": self.do_normalize,
				    "image_mean": self.image_mean,
				    "image_std": self.image_std,
				    "do_reduce_labels": self.do_reduce_labels,
				}



def      UpperCAmelCase						( ):


		'''simple docstring'''

		__A    =							load_dataset("hf-internal-testing/fixtures_ade20k"					,     split="test"	)

		__A    =							Image.open(dataset[0]["file"]	)
		__A    =							Image.open(dataset[1]["file"]	)

		return image, map



def      UpperCAmelCase						( ):


		'''simple docstring'''

		__A    =							load_dataset("hf-internal-testing/fixtures_ade20k"					,     split="test"	)

		__A    =							Image.open(dataset[0]["file"]	)
		__A    =							Image.open(dataset[1]["file"]	)
		__A    =							Image.open(dataset[2]["file"]	)
		__A    =							Image.open(dataset[3]["file"]	)

		return [imagea, imagea], [mapa, mapa]





@require_torch
@require_vision
class        a__      (	lowerCAmelCase__	,       unittest.TestCase		):
		UpperCAmelCase_							:       Dict 					= SegformerImageProcessor if is_vision_available() else None






		def 					_lowerCamelCase					(      self						)				->				List[str]:
				__A    =							SegformerImageProcessingTester(self						)






		@property
		def 					_lowerCamelCase					(      self						)				->				str:
				return self.image_processor_tester.prepare_image_processor_dict()






		def 					_lowerCamelCase					(      self						)				->				Union[str, Any]:
				__A    =							self.image_processing_class(**self.image_processor_dict						)
				self.assertTrue(hasattr(lowercase__							, "do_resize"						)						)
				self.assertTrue(hasattr(lowercase__							, "size"						)						)
				self.assertTrue(hasattr(lowercase__							, "do_normalize"						)						)
				self.assertTrue(hasattr(lowercase__							, "image_mean"						)						)
				self.assertTrue(hasattr(lowercase__							, "image_std"						)						)
				self.assertTrue(hasattr(lowercase__							, "do_reduce_labels"						)						)






		def 					_lowerCamelCase					(      self						)				->				Union[str, Any]:
				__A    =							self.image_processing_class.from_dict(self.image_processor_dict						)
				self.assertEqual(image_processor.size							, {"height": 30, "width": 30}						)
				self.assertEqual(image_processor.do_reduce_labels							, lowercase__						)

				__A    =							self.image_processing_class.from_dict(self.image_processor_dict							, size=42							, reduce_labels=lowercase__						)
				self.assertEqual(image_processor.size							, {"height": 42, "width": 42}						)
				self.assertEqual(image_processor.do_reduce_labels							, lowercase__						)






		def 					_lowerCamelCase					(      self						)				->				Optional[int]:
				pass






		def 					_lowerCamelCase					(      self						)				->				Union[str, Any]:
				# Initialize image_processing
				__A    =							self.image_processing_class(**self.image_processor_dict						)
				# create random PIL images
				__A    =							prepare_image_inputs(self.image_processor_tester							, equal_resolution=lowercase__						)
				for image in image_inputs:
						self.assertIsInstance(lowercase__							, 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.size["height"],
				        self.image_processor_tester.size["width"],
				    )							, )

				# Test batched
				__A    =							image_processing(lowercase__							, 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 					_lowerCamelCase					(      self						)				->				List[str]:
				# Initialize image_processing
				__A    =							self.image_processing_class(**self.image_processor_dict						)
				# create random numpy tensors
				__A    =							prepare_image_inputs(self.image_processor_tester							, equal_resolution=lowercase__							, numpify=lowercase__						)
				for image in image_inputs:
						self.assertIsInstance(lowercase__							, 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.size["height"],
				        self.image_processor_tester.size["width"],
				    )							, )

				# Test batched
				__A    =							image_processing(lowercase__							, 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 					_lowerCamelCase					(      self						)				->				List[str]:
				# Initialize image_processing
				__A    =							self.image_processing_class(**self.image_processor_dict						)
				# create random PyTorch tensors
				__A    =							prepare_image_inputs(self.image_processor_tester							, equal_resolution=lowercase__							, torchify=lowercase__						)
				for image in image_inputs:
						self.assertIsInstance(lowercase__							, 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.size["height"],
				        self.image_processor_tester.size["width"],
				    )							, )

				# Test batched
				__A    =							image_processing(lowercase__							, 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 					_lowerCamelCase					(      self						)				->				Dict:
				# Initialize image_processing
				__A    =							self.image_processing_class(**self.image_processor_dict						)
				# create random PyTorch tensors
				__A    =							prepare_image_inputs(self.image_processor_tester							, equal_resolution=lowercase__							, torchify=lowercase__						)
				__A    =							[]
				for image in image_inputs:
						self.assertIsInstance(lowercase__							, torch.Tensor						)
						maps.append(torch.zeros(image.shape[-2:]						).long()						)

				# Test not batched input
				__A    =							image_processing(image_inputs[0]							, maps[0]							, return_tensors="pt"						)
				self.assertEqual(
				    encoding["pixel_values"].shape							, (
				        1,
				        self.image_processor_tester.num_channels,
				        self.image_processor_tester.size["height"],
				        self.image_processor_tester.size["width"],
				    )							, )
				self.assertEqual(
				    encoding["labels"].shape							, (
				        1,
				        self.image_processor_tester.size["height"],
				        self.image_processor_tester.size["width"],
				    )							, )
				self.assertEqual(encoding["labels"].dtype							, torch.long						)
				self.assertTrue(encoding["labels"].min().item() >= 0						)
				self.assertTrue(encoding["labels"].max().item() <= 255						)

				# Test batched
				__A    =							image_processing(lowercase__							, lowercase__							, return_tensors="pt"						)
				self.assertEqual(
				    encoding["pixel_values"].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"],
				    )							, )
				self.assertEqual(
				    encoding["labels"].shape							, (
				        self.image_processor_tester.batch_size,
				        self.image_processor_tester.size["height"],
				        self.image_processor_tester.size["width"],
				    )							, )
				self.assertEqual(encoding["labels"].dtype							, torch.long						)
				self.assertTrue(encoding["labels"].min().item() >= 0						)
				self.assertTrue(encoding["labels"].max().item() <= 255						)

				# Test not batched input (PIL images)
				__A ,   __A    =							prepare_semantic_single_inputs()

				__A    =							image_processing(lowercase__							, lowercase__							, return_tensors="pt"						)
				self.assertEqual(
				    encoding["pixel_values"].shape							, (
				        1,
				        self.image_processor_tester.num_channels,
				        self.image_processor_tester.size["height"],
				        self.image_processor_tester.size["width"],
				    )							, )
				self.assertEqual(
				    encoding["labels"].shape							, (
				        1,
				        self.image_processor_tester.size["height"],
				        self.image_processor_tester.size["width"],
				    )							, )
				self.assertEqual(encoding["labels"].dtype							, torch.long						)
				self.assertTrue(encoding["labels"].min().item() >= 0						)
				self.assertTrue(encoding["labels"].max().item() <= 255						)

				# Test batched input (PIL images)
				__A ,   __A    =							prepare_semantic_batch_inputs()

				__A    =							image_processing(lowercase__							, lowercase__							, return_tensors="pt"						)
				self.assertEqual(
				    encoding["pixel_values"].shape							, (
				        2,
				        self.image_processor_tester.num_channels,
				        self.image_processor_tester.size["height"],
				        self.image_processor_tester.size["width"],
				    )							, )
				self.assertEqual(
				    encoding["labels"].shape							, (
				        2,
				        self.image_processor_tester.size["height"],
				        self.image_processor_tester.size["width"],
				    )							, )
				self.assertEqual(encoding["labels"].dtype							, torch.long						)
				self.assertTrue(encoding["labels"].min().item() >= 0						)
				self.assertTrue(encoding["labels"].max().item() <= 255						)






		def 					_lowerCamelCase					(      self						)				->				Tuple:
				# Initialize image_processing
				__A    =							self.image_processing_class(**self.image_processor_dict						)

				# ADE20k has 150 classes, and the background is included, so labels should be between 0 and 150
				__A ,   __A    =							prepare_semantic_single_inputs()
				__A    =							image_processing(lowercase__							, lowercase__							, return_tensors="pt"						)
				self.assertTrue(encoding["labels"].min().item() >= 0						)
				self.assertTrue(encoding["labels"].max().item() <= 150						)

				__A    =							True
				__A    =							image_processing(lowercase__							, lowercase__							, return_tensors="pt"						)
				self.assertTrue(encoding["labels"].min().item() >= 0						)
				self.assertTrue(encoding["labels"].max().item() <= 255						)

