






"""simple docstring"""




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 MobileViTImageProcessor






class        UpperCamelCase			(	unittest.TestCase      ):


 def __init__(     self		:						Optional[int]							,						UpperCAmelCase__		:						Union[str, Any]							,						UpperCAmelCase__		:						Optional[Any]=7							,						UpperCAmelCase__		:						List[str]=3							,						UpperCAmelCase__		:						Optional[int]=18							,						UpperCAmelCase__		:						Dict=30							,						UpperCAmelCase__		:						int=400							,						UpperCAmelCase__		:						str=True							,						UpperCAmelCase__		:						Dict=None							,						UpperCAmelCase__		:						Optional[int]=True							,						UpperCAmelCase__		:						Tuple=None							,						UpperCAmelCase__		:						str=True							,						)					->       Dict:
   _a  :       Union[str, Any]             =	size if size is not None else {"""shortest_edge""": 20}
   _a  :       Optional[int]             =	crop_size if crop_size is not None else {"""height""": 18, """width""": 18}
   _a  :       Optional[int]             =	parent
   _a  :       str             =	batch_size
   _a  :       Optional[int]             =	num_channels
   _a  :       str             =	image_size
   _a  :       List[Any]             =	min_resolution
   _a  :       Any             =	max_resolution
   _a  :       List[str]             =	do_resize
   _a  :       str             =	size
   _a  :       Optional[Any]             =	do_center_crop
   _a  :       str             =	crop_size
   _a  :       List[Any]             =	do_flip_channel_order







 def _lowercase	(     self		:						List[Any]							)					->       List[str]:
   return {
       "do_resize": self.do_resize,
       "size": self.size,
       "do_center_crop": self.do_center_crop,
       "crop_size": self.crop_size,
       "do_flip_channel_order": self.do_flip_channel_order,
   }







@require_torch
@require_vision
class        UpperCamelCase			(	snake_case_		,     unittest.TestCase      ):
 UpperCamelCase					:       Dict			=					MobileViTImageProcessor if is_vision_available() else None


 def _lowercase	(     self		:						Any							)					->       Union[str, Any]:
   _a  :       Union[str, Any]             =	MobileViTImageProcessingTester(self							)


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


 def _lowercase	(     self		:						str							)					->       Tuple:
   _a  :       Any             =	self.image_processing_class(**self.image_processor_dict							)
   self.assertTrue(hasattr(UpperCAmelCase__							,						"""do_resize"""							)							)
   self.assertTrue(hasattr(UpperCAmelCase__							,						"""size"""							)							)
   self.assertTrue(hasattr(UpperCAmelCase__							,						"""do_center_crop"""							)							)
   self.assertTrue(hasattr(UpperCAmelCase__							,						"""center_crop"""							)							)
   self.assertTrue(hasattr(UpperCAmelCase__							,						"""do_flip_channel_order"""							)							)


 def _lowercase	(     self		:						str							)					->       str:
   _a  :       int             =	self.image_processing_class.from_dict(self.image_processor_dict							)
   self.assertEqual(image_processor.size							,						{"""shortest_edge""": 20}							)
   self.assertEqual(image_processor.crop_size							,						{"""height""": 18, """width""": 18}							)

   _a  :       List[str]             =	self.image_processing_class.from_dict(self.image_processor_dict							,						size=42							,						crop_size=84							)
   self.assertEqual(image_processor.size							,						{"""shortest_edge""": 42}							)
   self.assertEqual(image_processor.crop_size							,						{"""height""": 84, """width""": 84}							)


 def _lowercase	(     self		:						Optional[int]							)					->       List[Any]:
   pass


 def _lowercase	(     self		:						Optional[Any]							)					->       Tuple:
   # Initialize image_processing
   _a  :       str             =	self.image_processing_class(**self.image_processor_dict							)
   # create random PIL images
   _a  :       Tuple             =	prepare_image_inputs(self.image_processor_tester							,						equal_resolution=UpperCAmelCase__							)
   for image in image_inputs:
     self.assertIsInstance(UpperCAmelCase__							,						Image.Image							)

   # Test not batched input
   _a  :       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.crop_size["""height"""],
           self.image_processor_tester.crop_size["""width"""],
       )							,						)

   # Test batched
   _a  :       str             =	image_processing(UpperCAmelCase__							,						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.crop_size["""height"""],
           self.image_processor_tester.crop_size["""width"""],
       )							,						)


 def _lowercase	(     self		:						int							)					->       Tuple:
   # Initialize image_processing
   _a  :       Any             =	self.image_processing_class(**self.image_processor_dict							)
   # create random numpy tensors
   _a  :       Union[str, Any]             =	prepare_image_inputs(self.image_processor_tester							,						equal_resolution=UpperCAmelCase__							,						numpify=UpperCAmelCase__							)
   for image in image_inputs:
     self.assertIsInstance(UpperCAmelCase__							,						np.ndarray							)

   # Test not batched input
   _a  :       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.crop_size["""height"""],
           self.image_processor_tester.crop_size["""width"""],
       )							,						)

   # Test batched
   _a  :       Any             =	image_processing(UpperCAmelCase__							,						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.crop_size["""height"""],
           self.image_processor_tester.crop_size["""width"""],
       )							,						)







 def _lowercase	(     self		:						List[str]							)					->       Dict:
   # Initialize image_processing
   _a  :       str             =	self.image_processing_class(**self.image_processor_dict							)
   # create random PyTorch tensors
   _a  :       Any             =	prepare_image_inputs(self.image_processor_tester							,						equal_resolution=UpperCAmelCase__							,						torchify=UpperCAmelCase__							)
   for image in image_inputs:
     self.assertIsInstance(UpperCAmelCase__							,						torch.Tensor							)

   # Test not batched input
   _a  :       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.crop_size["""height"""],
           self.image_processor_tester.crop_size["""width"""],
       )							,						)

   # Test batched
   _a  :       str             =	image_processing(UpperCAmelCase__							,						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.crop_size["""height"""],
           self.image_processor_tester.crop_size["""width"""],
       )							,						)









"""simple docstring"""




import os
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

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


_snake_case          =   logging.get_logger(__name__)

_snake_case          =   {'vocab_file': 'spiece.model'}

_snake_case          =   {
    'vocab_file': {
        'TsinghuaAI/CPM-Generate': 'https://huggingface.co/TsinghuaAI/CPM-Generate/resolve/main/spiece.model',
    }
}






class        UpperCamelCase			(	snake_case_      ):


 def __init__(     self		:						Tuple							,						UpperCAmelCase__		:						Optional[Any]							,						UpperCAmelCase__		:						int=False							,						UpperCAmelCase__		:						Dict=True							,						UpperCAmelCase__		:						Union[str, Any]=False							,						UpperCAmelCase__		:						Dict="<s>"							,						UpperCAmelCase__		:						Any="</s>"							,						UpperCAmelCase__		:						Any="<unk>"							,						UpperCAmelCase__		:						int="<sep>"							,						UpperCAmelCase__		:						Tuple="<pad>"							,						UpperCAmelCase__		:						Any="<cls>"							,						UpperCAmelCase__		:						Optional[Any]="<mask>"							,						UpperCAmelCase__		:						int=["<eop>", "<eod>"]							,						UpperCAmelCase__		:						Optional[Dict[str, Any]] = None							,						**UpperCAmelCase__		:						List[str]							,						)					->       None:
   _a  :       Optional[int]             =	AddedToken(UpperCAmelCase__							,						lstrip=UpperCAmelCase__							,						rstrip=UpperCAmelCase__							) if isinstance(UpperCAmelCase__							,						UpperCAmelCase__							) else mask_token

   _a  :       Union[str, Any]             =	{} if sp_model_kwargs is None else sp_model_kwargs

   super().__init__(
       do_lower_case=UpperCAmelCase__							,						remove_space=UpperCAmelCase__							,						keep_accents=UpperCAmelCase__							,						bos_token=UpperCAmelCase__							,						eos_token=UpperCAmelCase__							,						unk_token=UpperCAmelCase__							,						sep_token=UpperCAmelCase__							,						pad_token=UpperCAmelCase__							,						cls_token=UpperCAmelCase__							,						mask_token=UpperCAmelCase__							,						additional_special_tokens=UpperCAmelCase__							,						sp_model_kwargs=self.sp_model_kwargs							,						**UpperCAmelCase__							,						)

   _a  :       Optional[Any]             =	3

   _a  :       Tuple             =	do_lower_case
   _a  :       Tuple             =	remove_space
   _a  :       Tuple             =	keep_accents
   _a  :       Tuple             =	vocab_file

   _a  :       Any             =	spm.SentencePieceProcessor(**self.sp_model_kwargs							)
   self.sp_model.Load(UpperCAmelCase__							)

   try:
     import jieba
   except ModuleNotFoundError as error:
     raise error.__class__(
         """You need to install jieba to use CpmTokenizer or CpmTokenizerFast. """
         """See https://pypi.org/project/jieba/ for installation."""							)
   _a  :       int             =	jieba
   _a  :       Tuple             =	str.maketrans(""" \n"""							,						"""\u2582\u2583"""							)


 @property
 # Copied from transformers.models.xlnet.tokenization_xlnet.XLNetTokenizer.vocab_size
 def _lowercase	(     self		:						Optional[Any]							)					->       Any:
   return len(self.sp_model							)


 def _lowercase	(     self		:						str							)					->       Union[str, Any]:
   _a  :       int             =	{self.convert_ids_to_tokens(UpperCAmelCase__							): i for i in range(self.vocab_size							)}
   vocab.update(self.added_tokens_encoder							)
   return vocab


 def __getstate__(     self		:						Tuple							)					->       List[str]:
   _a  :       Tuple             =	self.__dict__.copy()
   _a  :       Tuple             =	None
   return state


 def __setstate__(     self		:						Any							,						UpperCAmelCase__		:						Dict							)					->       Dict:
   _a  :       Tuple             =	d

   # for backward compatibility
   if not hasattr(self							,						"""sp_model_kwargs"""							):
     _a  :       Tuple             =	{}

   _a  :       int             =	spm.SentencePieceProcessor(**self.sp_model_kwargs							)
   self.sp_model.Load(self.vocab_file							)


 def _lowercase	(     self		:						List[str]							,						UpperCAmelCase__		:						Union[str, Any]							)					->       Dict:
   if self.remove_space:
     _a  :       Optional[int]             =	""" """.join(inputs.strip().split()							)
   else:
     _a  :       List[Any]             =	inputs
   _a  :       int             =	outputs.replace("""``"""							,						"""\""""							).replace("""''"""							,						"""\""""							)

   if not self.keep_accents:
     _a  :       Optional[Any]             =	unicodedata.normalize("""NFKD"""							,						UpperCAmelCase__							)
     _a  :       Dict             =	"""""".join([c for c in outputs if not unicodedata.combining(UpperCAmelCase__							)]							)
   if self.do_lower_case:
     _a  :       Union[str, Any]             =	outputs.lower()

   return outputs


 def _lowercase	(     self		:						List[str]							,						UpperCAmelCase__		:						str							)					->       List[str]:
   _a  :       str             =	self.preprocess_text(UpperCAmelCase__							)
   _a  :       Dict             =	self.sp_model.encode(UpperCAmelCase__							,						out_type=UpperCAmelCase__							)
   _a  :       Union[str, Any]             =	[]
   for piece in pieces:
     if len(UpperCAmelCase__							) > 1 and piece[-1] == str(""","""							) and piece[-2].isdigit():
       _a  :       Dict             =	self.sp_model.EncodeAsPieces(piece[:-1].replace(UpperCAmelCase__							,						""""""							)							)
       if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
         if len(cur_pieces[0]							) == 1:
           _a  :       Dict             =	cur_pieces[1:]
         else:
           _a  :       Any             =	cur_pieces[0][1:]
       cur_pieces.append(piece[-1]							)
       new_pieces.extend(UpperCAmelCase__							)
     else:
       new_pieces.append(UpperCAmelCase__							)

   return new_pieces


 def _lowercase	(     self		:						Union[str, Any]							,						UpperCAmelCase__		:						int							)					->       int:
   return self.sp_model.PieceToId(UpperCAmelCase__							)


 def _lowercase	(     self		:						List[Any]							,						UpperCAmelCase__		:						Optional[Any]							)					->       Any:
   return self.sp_model.IdToPiece(UpperCAmelCase__							)


 def _lowercase	(     self		:						Any							,						UpperCAmelCase__		:						Any							)					->       Dict:
   _a  :       Dict             =	"""""".join(UpperCAmelCase__							).replace(UpperCAmelCase__							,						""" """							).strip()
   return out_string


 def _lowercase	(     self		:						Any							,						UpperCAmelCase__		:						List[int]							,						UpperCAmelCase__		:						Optional[List[int]] = None							)					->       List[int]:
   _a  :       Optional[Any]             =	[self.sep_token_id]
   _a  :       Dict             =	[self.cls_token_id]
   if token_ids_a is None:
     return token_ids_a + sep + cls
   return token_ids_a + sep + token_ids_a + sep + cls


 def _lowercase	(     self		:						Tuple							,						UpperCAmelCase__		:						List[int]							,						UpperCAmelCase__		:						Optional[List[int]] = None							,						UpperCAmelCase__		:						bool = False							)					->       List[int]:

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

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


 def _lowercase	(     self		:						str							,						UpperCAmelCase__		:						List[int]							,						UpperCAmelCase__		:						Optional[List[int]] = None							)					->       List[int]:
   _a  :       Any             =	[self.sep_token_id]
   _a  :       Optional[Any]             =	[2]

   if token_ids_a is None:
     return len(token_ids_a + sep							) * [0] + cls_segment_id
   return len(token_ids_a + sep							) * [0] + len(token_ids_a + sep							) * [1] + cls_segment_id


 def _lowercase	(     self		:						Optional[int]							,						UpperCAmelCase__		:						str							,						UpperCAmelCase__		:						Optional[str] = None							)					->       Tuple[str]:
   if not os.path.isdir(UpperCAmelCase__							):
     logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""							)
     return
   _a  :       Union[str, 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__							) and os.path.isfile(self.vocab_file							):
     copyfile(self.vocab_file							,						UpperCAmelCase__							)
   elif not os.path.isfile(self.vocab_file							):
     with open(UpperCAmelCase__							,						"""wb"""							) as fi:
       _a  :       Optional[Any]             =	self.sp_model.serialized_model_proto()
       fi.write(UpperCAmelCase__							)

   return (out_vocab_file,)







 def _lowercase	(     self		:						Any							,						*UpperCAmelCase__		:						List[str]							,						**UpperCAmelCase__		:						List[str]							)					->       List[str]:
   _a  :       Tuple             =	super()._decode(*UpperCAmelCase__							,						**UpperCAmelCase__							)
   _a  :       Optional[Any]             =	text.replace(""" """							,						""""""							).replace("""\u2582"""							,						""" """							).replace("""\u2583"""							,						"""\n"""							)
   return text

