



import unittest

import numpy as np

from transformers.testing_utils import is_flaky, 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 DonutImageProcessor





class 					__lowerCAmelCase (     unittest.TestCase       ):




				def __init__(							self						,				_snake_case						,				_snake_case=7						,				_snake_case=3						,				_snake_case=18						,				_snake_case=30						,				_snake_case=400						,				_snake_case=True						,				_snake_case=None						,				_snake_case=True						,				_snake_case=False						,				_snake_case=True						,				_snake_case=True						,				_snake_case=[0.5, 0.5, 0.5]						,				_snake_case=[0.5, 0.5, 0.5]						,				):



									"""simple docstring"""





									_lowerCAmelCase						=    parent
									_lowerCAmelCase						=    batch_size
									_lowerCAmelCase						=    num_channels
									_lowerCAmelCase						=    image_size
									_lowerCAmelCase						=    min_resolution
									_lowerCAmelCase						=    max_resolution
									_lowerCAmelCase						=    do_resize
									_lowerCAmelCase						=    size if size is not None else {"""height""": 18, """width""": 20}
									_lowerCAmelCase						=    do_thumbnail
									_lowerCAmelCase						=    do_align_axis
									_lowerCAmelCase						=    do_pad
									_lowerCAmelCase						=    do_normalize
									_lowerCAmelCase						=    image_mean
									_lowerCAmelCase						=    image_std




				def 							snake_case   (							self		):



									"""simple docstring"""





									return {
									    "do_resize": self.do_resize,
									    "size": self.size,
									    "do_thumbnail": self.do_thumbnail,
									    "do_align_long_axis": self.do_align_axis,
									    "do_pad": self.do_pad,
									    "do_normalize": self.do_normalize,
									    "image_mean": self.image_mean,
									    "image_std": self.image_std,
									}





@require_torch
@require_vision
class 					__lowerCAmelCase (     lowerCamelCase__       ,						unittest.TestCase       ):
				__lowerCamelCase =				DonutImageProcessor if is_vision_available() else None




				def 							snake_case   (							self		):



									"""simple docstring"""





									_lowerCAmelCase						=    DonutImageProcessingTester(self		)




				@property
				def 							snake_case   (							self		):



									"""simple docstring"""





									return self.image_processor_tester.prepare_image_processor_dict()




				def 							snake_case   (							self		):



									"""simple docstring"""





									_lowerCAmelCase						=    self.image_processing_class(**self.image_processor_dict		)
									self.assertTrue(hasattr(_snake_case						,				"""do_resize"""		)		)
									self.assertTrue(hasattr(_snake_case						,				"""size"""		)		)
									self.assertTrue(hasattr(_snake_case						,				"""do_thumbnail"""		)		)
									self.assertTrue(hasattr(_snake_case						,				"""do_align_long_axis"""		)		)
									self.assertTrue(hasattr(_snake_case						,				"""do_pad"""		)		)
									self.assertTrue(hasattr(_snake_case						,				"""do_normalize"""		)		)
									self.assertTrue(hasattr(_snake_case						,				"""image_mean"""		)		)
									self.assertTrue(hasattr(_snake_case						,				"""image_std"""		)		)




				def 							snake_case   (							self		):



									"""simple docstring"""





									_lowerCAmelCase						=    self.image_processing_class.from_dict(self.image_processor_dict		)
									self.assertEqual(image_processor.size						,				{"""height""": 18, """width""": 20}		)

									_lowerCAmelCase						=    self.image_processing_class.from_dict(self.image_processor_dict						,				size=42		)
									self.assertEqual(image_processor.size						,				{"""height""": 42, """width""": 42}		)

									# Previous config had dimensions in (width, height) order
									_lowerCAmelCase						=    self.image_processing_class.from_dict(self.image_processor_dict						,				size=(42, 84)		)
									self.assertEqual(image_processor.size						,				{"""height""": 84, """width""": 42}		)




				def 							snake_case   (							self		):



									"""simple docstring"""





									pass




				@is_flaky()
				def 							snake_case   (							self		):



									"""simple docstring"""





									_lowerCAmelCase						=    self.image_processing_class(**self.image_processor_dict		)
									# create random PIL images
									_lowerCAmelCase						=    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
									_lowerCAmelCase						=    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
									_lowerCAmelCase						=    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"""],
									    )						,				)




				@is_flaky()
				def 							snake_case   (							self		):



									"""simple docstring"""





									_lowerCAmelCase						=    self.image_processing_class(**self.image_processor_dict		)
									# create random numpy tensors
									_lowerCAmelCase						=    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
									_lowerCAmelCase						=    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
									_lowerCAmelCase						=    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"""],
									    )						,				)




				@is_flaky()
				def 							snake_case   (							self		):



									"""simple docstring"""





									_lowerCAmelCase						=    self.image_processing_class(**self.image_processor_dict		)
									# create random PyTorch tensors
									_lowerCAmelCase						=    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
									_lowerCAmelCase						=    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
									_lowerCAmelCase						=    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"""],
									    )						,				)







"""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_barthez import BarthezTokenizer
else:
							UpperCAmelCase    :     Union[str, Any]   						=		None

UpperCAmelCase    :     Union[str, Any]   						=		logging.get_logger(__name__)

UpperCAmelCase    :     Union[str, Any]   						=		{'vocab_file': 'sentencepiece.bpe.model', 'tokenizer_file': 'tokenizer.json'}

UpperCAmelCase    :     List[str]   						=		{
    'vocab_file': {
        'moussaKam/mbarthez': 'https://huggingface.co/moussaKam/mbarthez/resolve/main/sentencepiece.bpe.model',
        'moussaKam/barthez': 'https://huggingface.co/moussaKam/barthez/resolve/main/sentencepiece.bpe.model',
        'moussaKam/barthez-orangesum-title': (
            'https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/sentencepiece.bpe.model'
        ),
    },
    'tokenizer_file': {
        'moussaKam/mbarthez': 'https://huggingface.co/moussaKam/mbarthez/resolve/main/tokenizer.json',
        'moussaKam/barthez': 'https://huggingface.co/moussaKam/barthez/resolve/main/tokenizer.json',
        'moussaKam/barthez-orangesum-title': (
            'https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/tokenizer.json'
        ),
    },
}

UpperCAmelCase    :     Tuple   						=		{
    'moussaKam/mbarthez': 1024,
    'moussaKam/barthez': 1024,
    'moussaKam/barthez-orangesum-title': 1024,
}

UpperCAmelCase    :     str   						=		'▁'





class      lowerCamelCase__   (     A				):







					"""simple docstring"""


					__a	       =							VOCAB_FILES_NAMES
					__a	       =							PRETRAINED_VOCAB_FILES_MAP
					__a	       =							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					__a	       =							["""input_ids""", """attention_mask"""]
					__a	       =							BarthezTokenizer






					def __init__(       self    : Optional[int] ,       UpperCamelCase    : List[str]=None ,       UpperCamelCase    : List[str]=None ,       UpperCamelCase    : Union[str, Any]="<s>" ,       UpperCamelCase    : Any="</s>" ,       UpperCamelCase    : Tuple="</s>" ,       UpperCamelCase    : Tuple="<s>" ,       UpperCamelCase    : int="<unk>" ,       UpperCamelCase    : List[str]="<pad>" ,       UpperCamelCase    : int="<mask>" ,       **UpperCamelCase    : Any ,       ):
												'''simple docstring'''






												__UpperCAmelCase   :						Tuple      							=					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 ,       unk_token=UpperCamelCase ,       sep_token=UpperCamelCase ,       cls_token=UpperCamelCase ,       pad_token=UpperCamelCase ,       mask_token=UpperCamelCase ,       **UpperCamelCase ,       )

												__UpperCAmelCase   :						List[Any]      							=					vocab_file
												__UpperCAmelCase   :						Tuple      							=					False if not self.vocab_file else True






					def lowerCamelCase__							(       self    : Any ,       UpperCamelCase    : List[int] ,       UpperCamelCase    : Optional[List[int]] = None  ):
												'''simple docstring'''






												if token_ids_a is None:
																			return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
												__UpperCAmelCase   :						List[Any]      							=					[self.cls_token_id]
												__UpperCAmelCase   :						Tuple      							=					[self.sep_token_id]
												return cls + token_ids_a + sep + sep + token_ids_a + sep






					def lowerCamelCase__							(       self    : Union[str, Any] ,       UpperCamelCase    : List[int] ,       UpperCamelCase    : Optional[List[int]] = None  ):
												'''simple docstring'''






												__UpperCAmelCase   :						List[Any]      							=					[self.sep_token_id]
												__UpperCAmelCase   :						Union[str, 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 lowerCamelCase__							(       self    : Tuple ,       UpperCamelCase    : str ,       UpperCamelCase    : Optional[str] = None  ):
												'''simple docstring'''






												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
												__UpperCAmelCase   :						List[Any]      							=					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,)
