






import json
import os
import tempfile

from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available


if is_torch_available():
							import numpy as np
							import torch

if is_vision_available():
							from PIL import Image






def     lowerCamelCase__				(   __lowerCamelCase      :					int  ,		__lowerCamelCase      :					Optional[int]=False  ,		__lowerCamelCase      :					List[str]=False  ,		__lowerCamelCase      :					str=False		):
		assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

		__UpperCAmelCase							:     Tuple									=  []
		for i in range(image_processor_tester.batch_size		):
				if equal_resolution:
						__UpperCAmelCase							:     Dict									=  image_processor_tester.max_resolution
				else:
						# To avoid getting image width/height 0
						__UpperCAmelCase							:     Union[str, Any]									=  image_processor_tester.min_resolution
						if getattr(__lowerCamelCase  ,		"""size_divisor"""  ,		__lowerCamelCase		):
								# If `size_divisor` is defined, the image needs to have width/size >= `size_divisor`
								__UpperCAmelCase							:     str									=  max(image_processor_tester.size_divisor  ,		__lowerCamelCase		)
						__UpperCAmelCase							:     List[Any]									=  np.random.choice(np.arange(__lowerCamelCase  ,		image_processor_tester.max_resolution		)  ,		2		)
				image_inputs.append(
				    np.random.randint(255  ,		size=(image_processor_tester.num_channels, width, height)  ,		dtype=np.uinta		)		)

		if not numpify and not torchify:
				# PIL expects the channel dimension as last dimension
				__UpperCAmelCase							:     List[str]									=  [Image.fromarray(np.moveaxis(__lowerCamelCase  ,		0  ,		-1		)		) for image in image_inputs]

		if torchify:
				__UpperCAmelCase							:     int									=  [torch.from_numpy(__lowerCamelCase		) for image in image_inputs]

		return image_inputs






def     lowerCamelCase__				(   __lowerCamelCase      :					int  ,		__lowerCamelCase      :					str=10  ,		__lowerCamelCase      :					Any=10  ,		__lowerCamelCase      :					List[Any]=False  ,		__lowerCamelCase      :					Optional[int]=False		):
		__UpperCAmelCase							:     List[str]									=  []
		for i in range(image_processor_tester.num_frames		):
				video.append(np.random.randint(255  ,		size=(image_processor_tester.num_channels, width, height)  ,		dtype=np.uinta		)		)

		if not numpify and not torchify:
				# PIL expects the channel dimension as last dimension
				__UpperCAmelCase							:     List[str]									=  [Image.fromarray(np.moveaxis(__lowerCamelCase  ,		0  ,		-1		)		) for frame in video]

		if torchify:
				__UpperCAmelCase							:     int									=  [torch.from_numpy(__lowerCamelCase		) for frame in video]

		return video






def     lowerCamelCase__				(   __lowerCamelCase      :					Optional[Any]  ,		__lowerCamelCase      :					Any=False  ,		__lowerCamelCase      :					str=False  ,		__lowerCamelCase      :					Optional[Any]=False		):
		assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

		__UpperCAmelCase							:     Any									=  []
		for i in range(image_processor_tester.batch_size		):
				if equal_resolution:
						__UpperCAmelCase							:     Optional[int]									=  image_processor_tester.max_resolution
				else:
						__UpperCAmelCase							:     str									=  np.random.choice(
						    np.arange(image_processor_tester.min_resolution  ,		image_processor_tester.max_resolution		)  ,		2		)
						__UpperCAmelCase							:     Any									=  prepare_video(
						    image_processor_tester=__lowerCamelCase  ,		width=__lowerCamelCase  ,		height=__lowerCamelCase  ,		numpify=__lowerCamelCase  ,		torchify=__lowerCamelCase  ,		)
				video_inputs.append(__lowerCamelCase		)

		return video_inputs







class    a   :



						"""simple docstring"""


						a  :			Any								=     None
						def      UpperCAmelCase				(				self :      Union[str, Any] )							->				Union[str, Any]:
								__UpperCAmelCase							:     Dict									=  self.image_processing_class(**self.image_processor_dict )
								__UpperCAmelCase							:     List[str]									=  json.loads(image_processor.to_json_string() )
								for key, value in self.image_processor_dict.items():
										self.assertEqual(obj[key]		,				__lowercase )
						def      UpperCAmelCase				(				self :      List[Any] )							->				Optional[int]:
								__UpperCAmelCase							:     Dict									=  self.image_processing_class(**self.image_processor_dict )

								with tempfile.TemporaryDirectory() as tmpdirname:
										__UpperCAmelCase							:     List[Any]									=  os.path.join(__lowercase		,				"""image_processor.json""" )
										image_processor_first.to_json_file(__lowercase )
										__UpperCAmelCase							:     Dict									=  self.image_processing_class.from_json_file(__lowercase )

								self.assertEqual(image_processor_second.to_dict()		,				image_processor_first.to_dict() )
						def      UpperCAmelCase				(				self :      Union[str, Any] )							->				Dict:
								__UpperCAmelCase							:     int									=  self.image_processing_class(**self.image_processor_dict )

								with tempfile.TemporaryDirectory() as tmpdirname:
										__UpperCAmelCase							:     Optional[Any]									=  image_processor_first.save_pretrained(__lowercase )[0]
										check_json_file_has_correct_format(__lowercase )
										__UpperCAmelCase							:     Union[str, Any]									=  self.image_processing_class.from_pretrained(__lowercase )

								self.assertEqual(image_processor_second.to_dict()		,				image_processor_first.to_dict() )
						def      UpperCAmelCase				(				self :      Dict )							->				List[Any]:
								__UpperCAmelCase							:     Optional[Any]									=  self.image_processing_class()
								self.assertIsNotNone(__lowercase )




						@require_torch
						@require_vision
						def      UpperCAmelCase				(				self :      List[Any] )							->				Optional[Any]:
								if self.test_cast_dtype is not None:
										# Initialize image_processor
										__UpperCAmelCase							:     str									=  self.image_processing_class(**self.image_processor_dict )

										# create random PyTorch tensors
										__UpperCAmelCase							:     Tuple									=  prepare_image_inputs(self.image_processor_tester		,				equal_resolution=__lowercase		,				torchify=__lowercase )

										__UpperCAmelCase							:     Dict									=  image_processor(__lowercase		,				return_tensors="""pt""" )
										# for layoutLM compatiblity
										self.assertEqual(encoding.pixel_values.device		,				torch.device("""cpu""" ) )
										self.assertEqual(encoding.pixel_values.dtype		,				torch.floataa )

										__UpperCAmelCase							:     int									=  image_processor(__lowercase		,				return_tensors="""pt""" ).to(torch.floataa )
										self.assertEqual(encoding.pixel_values.device		,				torch.device("""cpu""" ) )
										self.assertEqual(encoding.pixel_values.dtype		,				torch.floataa )

										__UpperCAmelCase							:     Optional[Any]									=  image_processor(__lowercase		,				return_tensors="""pt""" ).to("""cpu"""		,				torch.bfloataa )
										self.assertEqual(encoding.pixel_values.device		,				torch.device("""cpu""" ) )
										self.assertEqual(encoding.pixel_values.dtype		,				torch.bfloataa )

										with self.assertRaises(__lowercase ):
												__UpperCAmelCase							:     Tuple									=  image_processor(__lowercase		,				return_tensors="""pt""" ).to(torch.bfloataa		,				"""cpu""" )

										# Try with text + image feature
										__UpperCAmelCase							:     Optional[int]									=  image_processor(__lowercase		,				return_tensors="""pt""" )
										encoding.update({"""input_ids""": torch.LongTensor([[1, 2, 3], [4, 5, 6]] )} )
										__UpperCAmelCase							:     List[str]									=  encoding.to(torch.floataa )

										self.assertEqual(encoding.pixel_values.device		,				torch.device("""cpu""" ) )
										self.assertEqual(encoding.pixel_values.dtype		,				torch.floataa )
										self.assertEqual(encoding.input_ids.dtype		,				torch.long )










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

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


a		:	Any      					=			logging.get_logger(__name__)

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

a		:	Optional[int]      					=			{
    "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   (			lowercase__       ):



      """simple docstring"""


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

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

        __UpperCAmelCase							:     Optional[int]									=  3
        __UpperCAmelCase							:     Dict									=  do_lower_case
        __UpperCAmelCase							:     Union[str, Any]									=  remove_space
        __UpperCAmelCase							:     Tuple									=  keep_accents
        __UpperCAmelCase							:     int									=  vocab_file
        __UpperCAmelCase							:     Dict									=  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							:     Dict									=  jieba
        __UpperCAmelCase							:     str									=  str.maketrans(""" \n"""		,				"""\u2582\u2583""" )
      def      UpperCAmelCase				(				self :      Tuple		,				__lowercase :      List[int]		,				__lowercase :      Optional[List[int]] = None )							->				List[int]:
        __UpperCAmelCase							:     List[str]									=  [self.sep_token_id]
        __UpperCAmelCase							:     List[str]									=  [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      UpperCAmelCase				(				self :      Union[str, Any]		,				__lowercase :      List[int]		,				__lowercase :      Optional[List[int]] = None )							->				List[int]:
        __UpperCAmelCase							:     Dict									=  [self.sep_token_id]
        __UpperCAmelCase							:     List[str]									=  [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      UpperCAmelCase				(				self :      Union[str, Any]		,				__lowercase :      str		,				__lowercase :      Optional[str] = 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(__lowercase ):
          logger.error(f"""Vocabulary path ({save_directory}) should be a directory""" )
          return
        __UpperCAmelCase							:     List[str]									=  os.path.join(
            __lowercase		,				(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )

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

        return (out_vocab_file,)
      def      UpperCAmelCase				(				self :      Optional[Any]		,				__lowercase :      Any		,				*__lowercase :      Dict		,				**__lowercase :      int )							->				Optional[Any]:
        __UpperCAmelCase							:     List[str]									=  [
            """ """.join([x.translate(self.translator ) for x in self.jieba.cut(__lowercase		,				cut_all=__lowercase )] )
            for text in batch_text_or_text_pairs
        ]
        return super()._batch_encode_plus(__lowercase		,				*__lowercase		,				**__lowercase )




      def      UpperCAmelCase				(				self :      int		,				*__lowercase :      List[Any]		,				**__lowercase :      Tuple )							->				List[str]:
        __UpperCAmelCase							:     List[str]									=  super()._decode(*__lowercase		,				**__lowercase )
        __UpperCAmelCase							:     List[Any]									=  text.replace(""" """		,				"""""" ).replace("""\u2582"""		,				""" """ ).replace("""\u2583"""		,				"""\n""" )
        return text


