





from typing import List, Union

import numpy as np

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
    from PIL import Image

    from ..image_utils import load_image

if is_torch_available():
    import torch

    from ..models.auto.modeling_auto import MODEL_FOR_DEPTH_ESTIMATION_MAPPING

snake_case_				=			logging.get_logger(__name__)

@add_end_docstrings(_UpperCAmelCase  )
class 		SCREAMING_SNAKE_CASE__				(       _UpperCAmelCase  ):


      def __init__(self       :      Optional[int]				,			*a__       :      Any				,			**a__       :      Dict      ):





         """simple docstring"""



         super().__init__(*a__				,			**a__      )
         requires_backends(self				,			'''vision'''      )
         self.check_model_type(a__      )


      def __call__(self       :      Optional[int]				,			a__       :      Union[str, List[str], "Image.Image", List["Image.Image"]]				,			**a__       :      Tuple      ):





         """simple docstring"""



         return super().__call__(a__				,			**a__      )


      def    a (self       :      Dict				,			**a__       :      Any      ):





         """simple docstring"""



         return {}, {}, {}


      def    a (self       :      List[str]				,			a__       :      Any      ):





         """simple docstring"""



         __snake_case							       =   load_image(a__      )
         __snake_case							       =   image.size
         __snake_case							       =   self.image_processor(images=a__				,			return_tensors=self.framework      )
         return model_inputs


      def    a (self       :      int				,			a__       :      List[Any]      ):





         """simple docstring"""



         __snake_case							       =   self.model(**a__      )
         return model_outputs


      def    a (self       :      int				,			a__       :      str      ):





         """simple docstring"""



         __snake_case							       =   model_outputs.predicted_depth
         __snake_case							       =   torch.nn.functional.interpolate(
             predicted_depth.unsqueeze(1      )				,			size=self.image_size[::-1]				,			mode='''bicubic'''				,			align_corners=a__      )
         __snake_case							       =   prediction.squeeze().cpu().numpy()
         __snake_case							       =   (output * 255 / np.max(a__      )).astype('''uint8'''      )
         __snake_case							       =   Image.fromarray(a__      )
         __snake_case							       =   {}
         __snake_case							       =   predicted_depth
         __snake_case							       =   depth
         return output_dict








import os
import unittest

from transformers.models.cpmant.tokenization_cpmant import VOCAB_FILES_NAMES, CpmAntTokenizer
from transformers.testing_utils import require_jieba, tooslow

from ...test_tokenization_common import TokenizerTesterMixin

@require_jieba
class 		SCREAMING_SNAKE_CASE__				(       _UpperCAmelCase						,      unittest.TestCase  ):
      A_						:				List[str]							=    CpmAntTokenizer
      A_						:				Optional[int]							=    False


      def    a (self       :      Optional[int]      ):





         """simple docstring"""



         super().setUp()

         __snake_case							       =   [
             '''<d>''',
             '''</d>''',
             '''<s>''',
             '''</s>''',
             '''</_>''',
             '''<unk>''',
             '''<pad>''',
             '''</n>''',
             '''我''',
             '''是''',
             '''C''',
             '''P''',
             '''M''',
             '''A''',
             '''n''',
             '''t''',
         ]
         __snake_case							       =   os.path.join(self.tmpdirname				,			VOCAB_FILES_NAMES['''vocab_file''']      )
         with open(self.vocab_file				,			'''w'''				,			encoding='''utf-8'''      ) as vocab_writer:
            vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens]      )      )


      @tooslow
      def    a (self       :      Dict      ):





         """simple docstring"""



         __snake_case							       =   CpmAntTokenizer.from_pretrained('''openbmb/cpm-ant-10b'''      )
         __snake_case							       =   '''今天天气真好！'''
         __snake_case							       =   ['''今天''', '''天气''', '''真''', '''好''', '''！''']
         __snake_case							       =   tokenizer.tokenize(a__      )
         self.assertListEqual(a__				,			a__      )
         __snake_case							       =   '''今天天气真好！'''
         __snake_case							       =   [tokenizer.bos_token] + tokens

         __snake_case							       =   [6, 9802, 1_4962, 2082, 831, 244]
         self.assertListEqual(tokenizer.convert_tokens_to_ids(a__      )				,			a__      )

         __snake_case							       =   tokenizer.decode(a__      )
         self.assertEqual(a__				,			a__      )

