
'''simple docstring'''
import warnings
from typing import Dict, List, Optional, Tuple

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


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


class 			SCREAMING_SNAKE_CASE__      (     snake_case_):
				lowerCAmelCase_     			=							["""input_ids""", """attention_mask"""]







				def __init__(       self						,				A_="</s>"						,				A_="<unk>"						,				A_="<pad>"						,				A_=125						,				A_=None						,				**A_						,				)->		None:







											'''simple docstring'''






											if extra_ids > 0 and additional_special_tokens is None:
																		UpperCamelCase   			=     [F'''<extra_id_{i}>''' for i in range(A_     )]
											elif extra_ids > 0 and additional_special_tokens is not None:
																		# Check that we have the right number of extra_id special tokens
																		UpperCamelCase   			=     len(set(filter(lambda A_     : bool('extra_id' in str(A_     )     )						,				A_     )     )     )
																		if extra_tokens != extra_ids:
																									raise ValueError(
																									    F'''Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are'''
																									    ' provided to ByT5Tokenizer. In this case the additional_special_tokens must include the'
																									    ' extra_ids tokens'     )

											UpperCamelCase   			=     AddedToken(A_						,				lstrip=A_						,				rstrip=A_     ) if isinstance(A_						,				A_     ) else pad_token
											UpperCamelCase   			=     AddedToken(A_						,				lstrip=A_						,				rstrip=A_     ) if isinstance(A_						,				A_     ) else eos_token
											UpperCamelCase   			=     AddedToken(A_						,				lstrip=A_						,				rstrip=A_     ) if isinstance(A_						,				A_     ) else unk_token

											super().__init__(
											    eos_token=A_						,				unk_token=A_						,				pad_token=A_						,				extra_ids=A_						,				additional_special_tokens=A_						,				**A_						,				)

											UpperCamelCase   			=     extra_ids

											UpperCamelCase   			=     2**8  # utf is 8 bits

											# define special tokens dict
											UpperCamelCase   			=     {
											    self.pad_token: 0,
											    self.eos_token: 1,
											    self.unk_token: 2,
											}
											UpperCamelCase   			=     len(self.special_tokens_encoder     )
											UpperCamelCase   			=     len(A_     )
											for i, token in enumerate(A_     ):
																		UpperCamelCase   			=     self.vocab_size + i - n
											UpperCamelCase   			=     {v: k for k, v in self.special_tokens_encoder.items()}







				@property
				def 						UpperCAmelCase_    (       self     )->		Any:







											'''simple docstring'''






											return self._utf_vocab_size + self._num_special_tokens + self._extra_ids







				def 						UpperCAmelCase_    (       self						,				A_						,				A_ = None						,				A_ = False     )->		List[int]:







											'''simple docstring'''






											if already_has_special_tokens:
																		return super().get_special_tokens_mask(
																		    token_ids_a=A_						,				token_ids_a=A_						,				already_has_special_tokens=A_     )

											# normal case: some special tokens
											if token_ids_a is None:
																		return ([0] * len(A_     )) + [1]
											return ([0] * len(A_     )) + [1] + ([0] * len(A_     )) + [1]







				def 						UpperCAmelCase_    (       self						,				A_     )->		List[int]:







											'''simple docstring'''






											if len(A_     ) > 0 and token_ids[-1] == self.eos_token_id:
																		warnings.warn(
																		    F'''This sequence already has {self.eos_token}. In future versions this behavior may lead to duplicated'''
																		    ' eos tokens being added.'     )
																		return token_ids
											else:
																		return token_ids + [self.eos_token_id]







				def 						UpperCAmelCase_    (       self						,				A_						,				A_ = None     )->		List[int]:







											'''simple docstring'''






											UpperCamelCase   			=     [self.eos_token_id]

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







				def 						UpperCAmelCase_    (       self						,				A_						,				A_ = None     )->		List[int]:







											'''simple docstring'''






											UpperCamelCase   			=     self._add_eos_if_not_present(A_     )
											if token_ids_a is None:
																		return token_ids_a
											else:
																		UpperCamelCase   			=     self._add_eos_if_not_present(A_     )
																		return token_ids_a + token_ids_a







				def 						UpperCAmelCase_    (       self						,				A_     )->		List[str]:







											'''simple docstring'''






											UpperCamelCase   			=     [chr(A_     ) for i in text.encode('utf-8'     )]
											return tokens







				def 						UpperCAmelCase_    (       self						,				A_     )->		Any:







											'''simple docstring'''






											if token in self.special_tokens_encoder:
																		UpperCamelCase   			=     self.special_tokens_encoder[token]
											elif token in self.added_tokens_encoder:
																		UpperCamelCase   			=     self.added_tokens_encoder[token]
											elif len(A_     ) != 1:
																		UpperCamelCase   			=     self.unk_token_id
											else:
																		UpperCamelCase   			=     ord(A_     ) + self._num_special_tokens
											return token_id







				def 						UpperCAmelCase_    (       self						,				A_     )->		Optional[int]:







											'''simple docstring'''






											if index in self.special_tokens_decoder:
																		UpperCamelCase   			=     self.special_tokens_decoder[index]
											else:
																		UpperCamelCase   			=     chr(index - self._num_special_tokens     )
											return token







				def 						UpperCAmelCase_    (       self						,				A_     )->		int:







											'''simple docstring'''






											UpperCamelCase   			=     B''
											for token in tokens:
																		if token in self.special_tokens_decoder:
																									UpperCamelCase   			=     self.special_tokens_decoder[token].encode('utf-8'     )
																		elif token in self.added_tokens_decoder:
																									UpperCamelCase   			=     self.special_tokens_decoder[token].encode('utf-8'     )
																		elif token in self.special_tokens_encoder:
																									UpperCamelCase   			=     token.encode('utf-8'     )
																		elif token in self.added_tokens_encoder:
																									UpperCamelCase   			=     token.encode('utf-8'     )
																		else:
																									UpperCamelCase   			=     bytes([ord(A_     )]     )
																		bstring += tok_string
											UpperCamelCase   			=     bstring.decode('utf-8'						,				errors='ignore'     )
											return string







				def 						UpperCAmelCase_    (       self						,				A_						,				A_ = None     )->		Tuple[str]:







											'''simple docstring'''






											return ()






'''simple docstring'''
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 			SCREAMING_SNAKE_CASE__      (     unittest.TestCase):







				def __init__(       self						,				A_						,				A_=7						,				A_=3						,				A_=18						,				A_=30						,				A_=400						,				A_=True						,				A_=None						,				A_=True						,				A_=False						,				A_=True						,				A_=True						,				A_=[0.5, 0.5, 0.5]						,				A_=[0.5, 0.5, 0.5]						,				)->		Dict:







											'''simple docstring'''






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







				def 						UpperCAmelCase_    (       self     )->		List[Any]:







											'''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 			SCREAMING_SNAKE_CASE__      (     snake_case_							,       unittest.TestCase):
				lowerCAmelCase_     			=							DonutImageProcessor if is_vision_available() else None







				def 						UpperCAmelCase_    (       self     )->		str:







											'''simple docstring'''






											UpperCamelCase   			=     DonutImageProcessingTester(self     )







				@property
				def 						UpperCAmelCase_    (       self     )->		str:







											'''simple docstring'''






											return self.image_processor_tester.prepare_image_processor_dict()







				def 						UpperCAmelCase_    (       self     )->		Optional[int]:







											'''simple docstring'''






											UpperCamelCase   			=     self.image_processing_class(**self.image_processor_dict     )
											self.assertTrue(hasattr(A_						,				'do_resize'     )     )
											self.assertTrue(hasattr(A_						,				'size'     )     )
											self.assertTrue(hasattr(A_						,				'do_thumbnail'     )     )
											self.assertTrue(hasattr(A_						,				'do_align_long_axis'     )     )
											self.assertTrue(hasattr(A_						,				'do_pad'     )     )
											self.assertTrue(hasattr(A_						,				'do_normalize'     )     )
											self.assertTrue(hasattr(A_						,				'image_mean'     )     )
											self.assertTrue(hasattr(A_						,				'image_std'     )     )







				def 						UpperCAmelCase_    (       self     )->		Optional[int]:







											'''simple docstring'''






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

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







				def 						UpperCAmelCase_    (       self     )->		Tuple:







											'''simple docstring'''






											pass







				@is_flaky()
				def 						UpperCAmelCase_    (       self     )->		Any:







											'''simple docstring'''






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

											# Test not batched input
											UpperCamelCase   			=     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   			=     image_processing(A_						,				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 						UpperCAmelCase_    (       self     )->		Optional[int]:







											'''simple docstring'''






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

											# Test not batched input
											UpperCamelCase   			=     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   			=     image_processing(A_						,				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 						UpperCAmelCase_    (       self     )->		Dict:







											'''simple docstring'''






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

											# Test not batched input
											UpperCamelCase   			=     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   			=     image_processing(A_						,				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'],
											    )						,				)




