






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_video_inputs


if is_torch_available():
       import torch

if is_vision_available():
       from PIL import Image

       from transformers import VideoMAEImageProcessor
class    a   (			unittest.TestCase       ):



      """simple docstring"""


      def __init__(				self :      List[Any]		,				__lowercase :      List[Any]		,				__lowercase :      Optional[Any]=7		,				__lowercase :      Optional[Any]=3		,				__lowercase :      List[Any]=10		,				__lowercase :      Optional[int]=18		,				__lowercase :      List[str]=30		,				__lowercase :      Any=400		,				__lowercase :      Optional[Any]=True		,				__lowercase :      Tuple=None		,				__lowercase :      str=True		,				__lowercase :      Union[str, Any]=[0.5, 0.5, 0.5]		,				__lowercase :      int=[0.5, 0.5, 0.5]		,				__lowercase :      Dict=None		,				)							->				Any:
        __UpperCAmelCase							:     int									=  size if size is not None else {"""shortest_edge""": 18}
        __UpperCAmelCase							:     Any									=  crop_size if crop_size is not None else {"""height""": 18, """width""": 18}

        __UpperCAmelCase							:     Tuple									=  parent
        __UpperCAmelCase							:     Optional[int]									=  batch_size
        __UpperCAmelCase							:     Union[str, Any]									=  num_channels
        __UpperCAmelCase							:     Optional[Any]									=  num_frames
        __UpperCAmelCase							:     Tuple									=  image_size
        __UpperCAmelCase							:     Dict									=  min_resolution
        __UpperCAmelCase							:     Any									=  max_resolution
        __UpperCAmelCase							:     List[Any]									=  do_resize
        __UpperCAmelCase							:     str									=  size
        __UpperCAmelCase							:     Any									=  do_normalize
        __UpperCAmelCase							:     Optional[Any]									=  image_mean
        __UpperCAmelCase							:     Dict									=  image_std
        __UpperCAmelCase							:     Union[str, Any]									=  crop_size




      def      UpperCAmelCase				(				self :      Tuple )							->				List[str]:
        return {
            "image_mean": self.image_mean,
            "image_std": self.image_std,
            "do_normalize": self.do_normalize,
            "do_resize": self.do_resize,
            "size": self.size,
            "crop_size": self.crop_size,
        }


@require_torch
@require_vision
class    a   (			lowercase__							,	unittest.TestCase       ):



      """simple docstring"""


      a  :			List[str]								=     VideoMAEImageProcessor if is_vision_available() else None
      def      UpperCAmelCase				(				self :      Optional[Any] )							->				Optional[Any]:
        __UpperCAmelCase							:     Dict									=  VideoMAEImageProcessingTester(self )
      @property
      def      UpperCAmelCase				(				self :      str )							->				Any:
        return self.image_processor_tester.prepare_image_processor_dict()
      def      UpperCAmelCase				(				self :      Optional[Any] )							->				Union[str, Any]:
        __UpperCAmelCase							:     Optional[int]									=  self.image_processing_class(**self.image_processor_dict )
        self.assertTrue(hasattr(__lowercase		,				"""image_mean""" ) )
        self.assertTrue(hasattr(__lowercase		,				"""image_std""" ) )
        self.assertTrue(hasattr(__lowercase		,				"""do_normalize""" ) )
        self.assertTrue(hasattr(__lowercase		,				"""do_resize""" ) )
        self.assertTrue(hasattr(__lowercase		,				"""do_center_crop""" ) )
        self.assertTrue(hasattr(__lowercase		,				"""size""" ) )
      def      UpperCAmelCase				(				self :      Union[str, Any] )							->				int:
        __UpperCAmelCase							:     int									=  self.image_processing_class.from_dict(self.image_processor_dict )
        self.assertEqual(image_processor.size		,				{"""shortest_edge""": 18} )
        self.assertEqual(image_processor.crop_size		,				{"""height""": 18, """width""": 18} )

        __UpperCAmelCase							:     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      UpperCAmelCase				(				self :      List[Any] )							->				List[str]:
        pass
      def      UpperCAmelCase				(				self :      Optional[int] )							->				List[Any]:
        # Initialize image_processing
        __UpperCAmelCase							:     List[Any]									=  self.image_processing_class(**self.image_processor_dict )
        # create random PIL videos
        __UpperCAmelCase							:     Union[str, Any]									=  prepare_video_inputs(self.image_processor_tester		,				equal_resolution=__lowercase )
        for video in video_inputs:
          self.assertIsInstance(__lowercase		,				__lowercase )
          self.assertIsInstance(video[0]		,				Image.Image )

        # Test not batched input
        __UpperCAmelCase							:     str									=  image_processing(video_inputs[0]		,				return_tensors="""pt""" ).pixel_values
        self.assertEqual(
            encoded_videos.shape		,				(
                1,
                self.image_processor_tester.num_frames,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size["""height"""],
                self.image_processor_tester.crop_size["""width"""],
            )		,				)

        # Test batched
        __UpperCAmelCase							:     Optional[int]									=  image_processing(__lowercase		,				return_tensors="""pt""" ).pixel_values
        self.assertEqual(
            encoded_videos.shape		,				(
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_frames,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size["""height"""],
                self.image_processor_tester.crop_size["""width"""],
            )		,				)
      def      UpperCAmelCase				(				self :      List[Any] )							->				Tuple:
        # Initialize image_processing
        __UpperCAmelCase							:     Optional[Any]									=  self.image_processing_class(**self.image_processor_dict )
        # create random numpy tensors
        __UpperCAmelCase							:     Union[str, Any]									=  prepare_video_inputs(self.image_processor_tester		,				equal_resolution=__lowercase		,				numpify=__lowercase )
        for video in video_inputs:
          self.assertIsInstance(__lowercase		,				__lowercase )
          self.assertIsInstance(video[0]		,				np.ndarray )

        # Test not batched input
        __UpperCAmelCase							:     Union[str, Any]									=  image_processing(video_inputs[0]		,				return_tensors="""pt""" ).pixel_values
        self.assertEqual(
            encoded_videos.shape		,				(
                1,
                self.image_processor_tester.num_frames,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size["""height"""],
                self.image_processor_tester.crop_size["""width"""],
            )		,				)

        # Test batched
        __UpperCAmelCase							:     Optional[int]									=  image_processing(__lowercase		,				return_tensors="""pt""" ).pixel_values
        self.assertEqual(
            encoded_videos.shape		,				(
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_frames,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size["""height"""],
                self.image_processor_tester.crop_size["""width"""],
            )		,				)




      def      UpperCAmelCase				(				self :      Optional[int] )							->				str:
        # Initialize image_processing
        __UpperCAmelCase							:     int									=  self.image_processing_class(**self.image_processor_dict )
        # create random PyTorch tensors
        __UpperCAmelCase							:     Dict									=  prepare_video_inputs(self.image_processor_tester		,				equal_resolution=__lowercase		,				torchify=__lowercase )
        for video in video_inputs:
          self.assertIsInstance(__lowercase		,				__lowercase )
          self.assertIsInstance(video[0]		,				torch.Tensor )

        # Test not batched input
        __UpperCAmelCase							:     List[Any]									=  image_processing(video_inputs[0]		,				return_tensors="""pt""" ).pixel_values
        self.assertEqual(
            encoded_videos.shape		,				(
                1,
                self.image_processor_tester.num_frames,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size["""height"""],
                self.image_processor_tester.crop_size["""width"""],
            )		,				)

        # Test batched
        __UpperCAmelCase							:     Dict									=  image_processing(__lowercase		,				return_tensors="""pt""" ).pixel_values
        self.assertEqual(
            encoded_videos.shape		,				(
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_frames,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size["""height"""],
                self.image_processor_tester.crop_size["""width"""],
            )		,				)










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


