



import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils_fast import AddedToken, PreTrainedTokenizerFast
from ...utils import logging


a			:     Dict     = logging.get_logger(__name__)

a			:     Dict     = {'vocab_file': 'spiece.model', 'tokenizer_file': 'tokenizer.json'}

a			:     List[str]     = {
    'vocab_file': {
        'TsinghuaAI/CPM-Generate': 'https://huggingface.co/TsinghuaAI/CPM-Generate/resolve/main/spiece.model',
    },
    'tokenizer_file': {
        'TsinghuaAI/CPM-Generate': 'https://huggingface.co/TsinghuaAI/CPM-Generate/resolve/main/tokenizer.json',
    },
}







class 							_a							(      a_				):

			def __init__(self,      SCREAMING_SNAKE_CASE_=None,      SCREAMING_SNAKE_CASE_=None,      SCREAMING_SNAKE_CASE_=False,      SCREAMING_SNAKE_CASE_=True,      SCREAMING_SNAKE_CASE_=False,      SCREAMING_SNAKE_CASE_="<s>",      SCREAMING_SNAKE_CASE_="</s>",      SCREAMING_SNAKE_CASE_="<unk>",      SCREAMING_SNAKE_CASE_="<sep>",      SCREAMING_SNAKE_CASE_="<pad>",      SCREAMING_SNAKE_CASE_="<cls>",      SCREAMING_SNAKE_CASE_="<mask>",      SCREAMING_SNAKE_CASE_=["<eop>", "<eod>"],      **SCREAMING_SNAKE_CASE_,      )					->      Any:
						UpperCAmelCase_:		str       			=    AddedToken(SCREAMING_SNAKE_CASE_,      lstrip=SCREAMING_SNAKE_CASE_,      rstrip=SCREAMING_SNAKE_CASE_				) if isinstance(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_				) else mask_token

						super().__init__(
						    vocab_file=SCREAMING_SNAKE_CASE_,      tokenizer_file=SCREAMING_SNAKE_CASE_,      do_lower_case=SCREAMING_SNAKE_CASE_,      remove_space=SCREAMING_SNAKE_CASE_,      keep_accents=SCREAMING_SNAKE_CASE_,      bos_token=SCREAMING_SNAKE_CASE_,      eos_token=SCREAMING_SNAKE_CASE_,      unk_token=SCREAMING_SNAKE_CASE_,      sep_token=SCREAMING_SNAKE_CASE_,      pad_token=SCREAMING_SNAKE_CASE_,      cls_token=SCREAMING_SNAKE_CASE_,      mask_token=SCREAMING_SNAKE_CASE_,      additional_special_tokens=SCREAMING_SNAKE_CASE_,      **SCREAMING_SNAKE_CASE_,      )

						UpperCAmelCase_:		str       			=    3
						UpperCAmelCase_:		List[str]       			=    do_lower_case
						UpperCAmelCase_:		int       			=    remove_space
						UpperCAmelCase_:		str       			=    keep_accents
						UpperCAmelCase_:		str       			=    vocab_file
						UpperCAmelCase_:		Any       			=    False if not self.vocab_file else True

						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."""				)
						UpperCAmelCase_:		Union[str, Any]       			=    jieba
						UpperCAmelCase_:		int       			=    str.maketrans(""" \n""",      """\u2582\u2583"""				)

			def     __snake_case					(self,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_ = None				)					->      List[int]:
						UpperCAmelCase_:		Tuple       			=    [self.sep_token_id]
						UpperCAmelCase_:		Tuple       			=    [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     __snake_case					(self,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_ = None				)					->      List[int]:
						UpperCAmelCase_:		Optional[Any]       			=    [self.sep_token_id]
						UpperCAmelCase_:		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     __snake_case					(self,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_ = 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(SCREAMING_SNAKE_CASE_				):
									logger.error(f'Vocabulary path ({save_directory}) should be a directory'				)
									return
						UpperCAmelCase_:		Any       			=    os.path.join(
						    SCREAMING_SNAKE_CASE_,      (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]				)

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

						return (out_vocab_file,)

			def     __snake_case					(self,      SCREAMING_SNAKE_CASE_,      *SCREAMING_SNAKE_CASE_,      **SCREAMING_SNAKE_CASE_				)					->      List[Any]:
						UpperCAmelCase_:		List[str]       			=    [
						    ' '.join([x.translate(self.translator				) for x in self.jieba.cut(SCREAMING_SNAKE_CASE_,      cut_all=SCREAMING_SNAKE_CASE_				)]				)
						    for text in batch_text_or_text_pairs
						]
						return super()._batch_encode_plus(SCREAMING_SNAKE_CASE_,      *SCREAMING_SNAKE_CASE_,      **SCREAMING_SNAKE_CASE_				)

			def     __snake_case					(self,      *SCREAMING_SNAKE_CASE_,      **SCREAMING_SNAKE_CASE_				)					->      Union[str, Any]:
						UpperCAmelCase_:		Optional[int]       			=    super()._decode(*SCREAMING_SNAKE_CASE_,      **SCREAMING_SNAKE_CASE_				)
						UpperCAmelCase_:		Optional[int]       			=    text.replace(""" """,      """"""				).replace("""\u2582""",      """ """				).replace("""\u2583""",      """\n"""				)
						return text











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 ViTImageProcessor







class 							_a							(      unittest.TestCase				):

			def __init__(self,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_=7,      SCREAMING_SNAKE_CASE_=3,      SCREAMING_SNAKE_CASE_=18,      SCREAMING_SNAKE_CASE_=30,      SCREAMING_SNAKE_CASE_=400,      SCREAMING_SNAKE_CASE_=True,      SCREAMING_SNAKE_CASE_=None,      SCREAMING_SNAKE_CASE_=True,      SCREAMING_SNAKE_CASE_=[0.5, 0.5, 0.5],      SCREAMING_SNAKE_CASE_=[0.5, 0.5, 0.5],      )					->      Union[str, Any]:
						UpperCAmelCase_:		Any       			=    size if size is not None else {"""height""": 18, """width""": 18}
						UpperCAmelCase_:		str       			=    parent
						UpperCAmelCase_:		int       			=    batch_size
						UpperCAmelCase_:		Tuple       			=    num_channels
						UpperCAmelCase_:		Optional[int]       			=    image_size
						UpperCAmelCase_:		Optional[int]       			=    min_resolution
						UpperCAmelCase_:		Optional[int]       			=    max_resolution
						UpperCAmelCase_:		Optional[Any]       			=    do_resize
						UpperCAmelCase_:		Optional[int]       			=    size
						UpperCAmelCase_:		int       			=    do_normalize
						UpperCAmelCase_:		List[str]       			=    image_mean
						UpperCAmelCase_:		Tuple       			=    image_std

			def     __snake_case					(self				)					->      Tuple:
						return {
						    "image_mean": self.image_mean,
						    "image_std": self.image_std,
						    "do_normalize": self.do_normalize,
						    "do_resize": self.do_resize,
						    "size": self.size,
						}







@require_torch
@require_vision
class 							_a							(      _lowerCAmelCase   ,		unittest.TestCase				):
			A					=  ViTImageProcessor if is_vision_available() else None

			def     __snake_case					(self				)					->      Optional[int]:
						UpperCAmelCase_:		str       			=    ViTImageProcessingTester(self				)

			@property
			def     __snake_case					(self				)					->      List[str]:
						return self.image_processor_tester.prepare_image_processor_dict()

			def     __snake_case					(self				)					->      int:
						UpperCAmelCase_:		Any       			=    self.image_processing_class(**self.image_processor_dict				)
						self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_,      """image_mean"""				)				)
						self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_,      """image_std"""				)				)
						self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_,      """do_normalize"""				)				)
						self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_,      """do_resize"""				)				)
						self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_,      """size"""				)				)

			def     __snake_case					(self				)					->      Union[str, Any]:
						UpperCAmelCase_:		List[str]       			=    self.image_processing_class.from_dict(self.image_processor_dict				)
						self.assertEqual(image_processor.size,      {"""height""": 18, """width""": 18}				)

						UpperCAmelCase_:		Union[str, Any]       			=    self.image_processing_class.from_dict(self.image_processor_dict,      size=42				)
						self.assertEqual(image_processor.size,      {"""height""": 42, """width""": 42}				)

			def     __snake_case					(self				)					->      Optional[Any]:
						pass

			def     __snake_case					(self				)					->      List[str]:
						# Initialize image_processing
						UpperCAmelCase_:		Any       			=    self.image_processing_class(**self.image_processor_dict				)
						# create random PIL images
						UpperCAmelCase_:		List[Any]       			=    prepare_image_inputs(self.image_processor_tester,      equal_resolution=SCREAMING_SNAKE_CASE_				)
						for image in image_inputs:
									self.assertIsInstance(SCREAMING_SNAKE_CASE_,      Image.Image				)

						# Test not batched input
						UpperCAmelCase_:		List[str]       			=    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_:		str       			=    image_processing(SCREAMING_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"""],
						    ),      )

			def     __snake_case					(self				)					->      Dict:
						# Initialize image_processing
						UpperCAmelCase_:		List[Any]       			=    self.image_processing_class(**self.image_processor_dict				)
						# create random numpy tensors
						UpperCAmelCase_:		int       			=    prepare_image_inputs(self.image_processor_tester,      equal_resolution=SCREAMING_SNAKE_CASE_,      numpify=SCREAMING_SNAKE_CASE_				)
						for image in image_inputs:
									self.assertIsInstance(SCREAMING_SNAKE_CASE_,      np.ndarray				)

						# Test not batched input
						UpperCAmelCase_:		Dict       			=    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_:		Optional[Any]       			=    image_processing(SCREAMING_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"""],
						    ),      )

			def     __snake_case					(self				)					->      Tuple:
						# Initialize image_processing
						UpperCAmelCase_:		List[Any]       			=    self.image_processing_class(**self.image_processor_dict				)
						# create random PyTorch tensors
						UpperCAmelCase_:		Any       			=    prepare_image_inputs(self.image_processor_tester,      equal_resolution=SCREAMING_SNAKE_CASE_,      torchify=SCREAMING_SNAKE_CASE_				)
						for image in image_inputs:
									self.assertIsInstance(SCREAMING_SNAKE_CASE_,      torch.Tensor				)

						# Test not batched input
						UpperCAmelCase_:		Dict       			=    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_:		str       			=    image_processing(SCREAMING_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"""],
						    ),      )






