
"""simple docstring"""




import collections
import os
from typing import List, Optional, Tuple

from transformers.utils import is_jieba_available, requires_backends


if is_jieba_available():
  import jieba

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


A__ :       Any				= logging.get_logger(__name__)

A__ :       Optional[Any]				= {'vocab_file': 'vocab.txt'}

A__ :       Optional[Any]				= {
    'vocab_file': {
        'openbmb/cpm-ant-10b': 'https://huggingface.co/openbmb/cpm-ant-10b/blob/main/vocab.txt',
    },
}

A__ :       List[Any]				= {
    'openbmb/cpm-ant-10b': 1_0_2_4,
}




def 			_lowerCAmelCase		(		_UpperCamelCase    ):






  """simple docstring"""



  _lowercase:      Union[str, Any]   			=  collections.OrderedDict()
  with open(_UpperCamelCase			,					'''r'''			,					encoding='''utf-8'''    ) as reader:
    _lowercase:      List[Any]   			=  reader.readlines()
  for index, token in enumerate(_UpperCamelCase    ):
    _lowercase:      Tuple   			=  token.rstrip('''\n'''    )
    _lowercase:      Union[str, Any]   			=  index
  return vocab


class       __magic_name__							(							SCREAMING_SNAKE_CASE__			):
       def __init__(  self			,					A_			,					A_="<unk>"			,					A_=200							)  ->						Dict:





         """simple docstring"""




         _lowercase:      Tuple   			=  vocab
         _lowercase:      Union[str, Any]   			=  unk_token
         _lowercase:      Optional[int]   			=  max_input_chars_per_word



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





         """simple docstring"""




         _lowercase:      str   			=  list(A_							)
         if len(A_							) > self.max_input_chars_per_word:
           return [self.unk_token]

         _lowercase:      Union[str, Any]   			=  0
         _lowercase:      Any   			=  []
         while start < len(A_							):
           _lowercase:      str   			=  len(A_							)
           _lowercase:      Any   			=  None
           while start < end:
             _lowercase:      int   			=  ''''''.join(chars[start:end]							)
             if substr in self.vocab:
               _lowercase:      List[str]   			=  substr
               break
             end -= 1
           if cur_substr is None:
             sub_tokens.append(self.unk_token							)
             start += 1
           else:
             sub_tokens.append(A_							)
             _lowercase:      Optional[int]   			=  end

         return sub_tokens


class       __magic_name__							(							SCREAMING_SNAKE_CASE__			):
       UpperCamelCase_ =		VOCAB_FILES_NAMES
       UpperCamelCase_ =		PRETRAINED_VOCAB_FILES_MAP
       UpperCamelCase_ =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       UpperCamelCase_ =		['''input_ids''', '''attention_mask''']
       UpperCamelCase_ =		False
       def __init__(  self			,					A_			,					A_="<d>"			,					A_="</d>"			,					A_="<s>"			,					A_="</s>"			,					A_="<pad>"			,					A_="<unk>"			,					A_="</n>"			,					A_="</_>"			,					A_="left"			,					**A_			,					)  ->						Optional[int]:





         """simple docstring"""




         requires_backends(self			,					['''jieba''']							)
         super().__init__(
             bod_token=A_			,					eod_token=A_			,					bos_token=A_			,					eos_token=A_			,					pad_token=A_			,					unk_token=A_			,					line_token=A_			,					space_token=A_			,					padding_side=A_			,					**A_			,					)
         _lowercase:      Optional[int]   			=  bod_token
         _lowercase:      Optional[Any]   			=  eod_token
         _lowercase:      Optional[Any]   			=  load_vocab(A_							)
         _lowercase:      Union[str, Any]   			=  self.encoder[space_token]
         _lowercase:      List[Any]   			=  self.encoder[line_token]

         del self.encoder[space_token]
         del self.encoder[line_token]

         _lowercase:      Any   			=  collections.OrderedDict(sorted(self.encoder.items()			,					key=lambda A_							: x[1]							)							)
         _lowercase:      Any   			=  {v: k for k, v in self.encoder.items()}

         _lowercase:      Union[str, Any]   			=  WordpieceTokenizer(vocab=self.encoder			,					unk_token=self.unk_token							)
       @property
       def 				lowercase_				(  self							)  ->						Any:





         """simple docstring"""




         return self.encoder[self.bod_token]
       @property
       def 				lowercase_				(  self							)  ->						Optional[Any]:





         """simple docstring"""




         return self.encoder[self.eod_token]
       @property
       def 				lowercase_				(  self							)  ->						Any:





         """simple docstring"""




         return self.encoder["\n"]
       @property
       def 				lowercase_				(  self							)  ->						int:





         """simple docstring"""




         return len(self.encoder							)
       def 				lowercase_				(  self							)  ->						Any:





         """simple docstring"""




         return dict(self.encoder			,					**self.added_tokens_encoder							)
       def 				lowercase_				(  self			,					A_							)  ->						Union[str, Any]:





         """simple docstring"""




         _lowercase:      Tuple   			=  []
         for x in jieba.cut(A_			,					cut_all=A_							):
           output_tokens.extend(self.wordpiece_tokenizer.tokenize(A_							)							)
         return output_tokens
       def 				lowercase_				(  self			,					A_			,					**A_							)  ->						Tuple:





         """simple docstring"""




         _lowercase:      Optional[int]   			=  [i for i in token_ids if i >= 0]
         _lowercase:      Any   			=  [
             x for x in token_ids if x != self.pad_token_id and x != self.eos_token_id and x != self.bos_token_id
         ]
         return super()._decode(A_			,					**A_							)
       def 				lowercase_				(  self			,					A_							)  ->						Union[str, Any]:





         """simple docstring"""




         return token in self.encoder
       def 				lowercase_				(  self			,					A_							)  ->						str:





         """simple docstring"""




         return "".join(A_							)
       def 				lowercase_				(  self			,					A_							)  ->						Any:





         """simple docstring"""




         return self.encoder.get(A_			,					self.encoder.get(self.unk_token							)							)
       def 				lowercase_				(  self			,					A_							)  ->						List[Any]:





         """simple docstring"""




         return self.decoder.get(A_			,					self.unk_token							)
       def 				lowercase_				(  self			,					A_			,					A_ = None							)  ->						Tuple[str]:





         """simple docstring"""




         if os.path.isdir(A_							):
           _lowercase:      Optional[int]   			=  os.path.join(
               A_			,					(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']							)
         else:
           _lowercase:      str   			=  (filename_prefix + '''-''' if filename_prefix else '''''') + save_directory
         _lowercase:      int   			=  0
         if " " in self.encoder:
           _lowercase:      str   			=  self.encoder[''' ''']
           del self.encoder[" "]
         if "\n" in self.encoder:
           _lowercase:      Any   			=  self.encoder['''\n''']
           del self.encoder["\n"]
         _lowercase:      Union[str, Any]   			=  collections.OrderedDict(sorted(self.encoder.items()			,					key=lambda A_							: x[1]							)							)
         with open(A_			,					'''w'''			,					encoding='''utf-8'''							) as writer:
           for token, token_index in self.encoder.items():
             if index != token_index:
               logger.warning(
                   f'''Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive.'''
                   ''' Please check that the vocabulary is not corrupted!'''							)
               _lowercase:      Union[str, Any]   			=  token_index
             writer.write(token + '''\n'''							)
             index += 1
         return (vocab_file,)
       def 				lowercase_				(  self			,					A_			,					A_ = None							)  ->						List[int]:





         """simple docstring"""




         if token_ids_a is None:
           return [self.bos_token_id] + token_ids_a
         return [self.bos_token_id] + token_ids_a + [self.bos_token_id] + token_ids_a



       def 				lowercase_				(  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_							)

         if token_ids_a is not None:
           return [1] + ([0] * len(A_							)) + [1] + ([0] * len(A_							))
         return [1] + ([0] * len(A_							))


"""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       __magic_name__							(							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]			,					)  ->						Optional[int]:





         """simple docstring"""




         _lowercase:      Optional[int]   			=  parent
         _lowercase:      str   			=  batch_size
         _lowercase:      Any   			=  num_channels
         _lowercase:      Any   			=  image_size
         _lowercase:      Any   			=  min_resolution
         _lowercase:      int   			=  max_resolution
         _lowercase:      Union[str, Any]   			=  do_resize
         _lowercase:      List[Any]   			=  size if size is not None else {'''height''': 18, '''width''': 20}
         _lowercase:      Optional[int]   			=  do_thumbnail
         _lowercase:      List[str]   			=  do_align_axis
         _lowercase:      int   			=  do_pad
         _lowercase:      Optional[Any]   			=  do_normalize
         _lowercase:      List[Any]   			=  image_mean
         _lowercase:      Union[str, Any]   			=  image_std



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





         """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       __magic_name__							(							SCREAMING_SNAKE_CASE__ ,       unittest.TestCase			):
       UpperCamelCase_ =		DonutImageProcessor if is_vision_available() else None
       def 				lowercase_				(  self							)  ->						Optional[int]:





         """simple docstring"""




         _lowercase:      str   			=  DonutImageProcessingTester(self							)
       @property
       def 				lowercase_				(  self							)  ->						int:





         """simple docstring"""




         return self.image_processor_tester.prepare_image_processor_dict()
       def 				lowercase_				(  self							)  ->						Dict:





         """simple docstring"""




         _lowercase:      Optional[int]   			=  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 				lowercase_				(  self							)  ->						List[Any]:





         """simple docstring"""




         _lowercase:      Optional[Any]   			=  self.image_processing_class.from_dict(self.image_processor_dict							)
         self.assertEqual(image_processor.size			,					{'''height''': 18, '''width''': 20}							)

         _lowercase:      List[str]   			=  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
         _lowercase:      Any   			=  self.image_processing_class.from_dict(self.image_processor_dict			,					size=(42, 84)							)
         self.assertEqual(image_processor.size			,					{'''height''': 84, '''width''': 42}							)
       def 				lowercase_				(  self							)  ->						Union[str, Any]:





         """simple docstring"""




         pass
       @is_flaky()
       def 				lowercase_				(  self							)  ->						str:





         """simple docstring"""




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

         # Test not batched input
         _lowercase:      Tuple   			=  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
         _lowercase:      List[Any]   			=  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 				lowercase_				(  self							)  ->						Any:





         """simple docstring"""




         _lowercase:      str   			=  self.image_processing_class(**self.image_processor_dict							)
         # create random numpy tensors
         _lowercase:      str   			=  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
         _lowercase:      Union[str, 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
         _lowercase:      Dict   			=  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 				lowercase_				(  self							)  ->						Tuple:





         """simple docstring"""




         _lowercase:      List[Any]   			=  self.image_processing_class(**self.image_processor_dict							)
         # create random PyTorch tensors
         _lowercase:      List[Any]   			=  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
         _lowercase:      Union[str, 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
         _lowercase:      Dict   			=  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'''],
             )			,					)
