
from typing import Dict, List, Optional, Tuple, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_torch_available, is_torch_tensor, logging


if is_torch_available():
   import torch


_A						      =				logging.get_logger(__name__)







class   lowerCamelCase	(						A_						):
     UpperCAmelCase__       :							Dict     =	["pixel_values"]





     def __init__(self   :					Any       ,					_A   :					bool = True       ,					_A   :					Optional[Dict[str, int]] = None       ,					_A   :					PILImageResampling = PILImageResampling.BILINEAR       ,					_A   :					bool = True       ,					_A   :					Dict[str, int] = None       ,					_A   :					bool = True       ,					_A   :					Union[int, float] = 1 / 2_5_5       ,					_A   :					bool = True       ,					_A   :					Optional[Union[float, List[float]]] = None       ,					_A   :					Optional[Union[float, List[float]]] = None       ,					**_A   :					str       ,					)		->		None:
         super().__init__(**_A     )
         snake_case				  =      size if size is not None else {"shortest_edge": 2_5_6}
         snake_case				  =      get_size_dict(_A       ,					default_to_square=_A     )
         snake_case				  =      crop_size if crop_size is not None else {"height": 2_2_4, "width": 2_2_4}
         snake_case				  =      get_size_dict(_A       ,					param_name="crop_size"     )
         snake_case				  =      do_resize
         snake_case				  =      size
         snake_case				  =      resample
         snake_case				  =      do_center_crop
         snake_case				  =      crop_size
         snake_case				  =      do_rescale
         snake_case				  =      rescale_factor
         snake_case				  =      do_normalize
         snake_case				  =      image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
         snake_case				  =      image_std if image_std is not None else IMAGENET_STANDARD_STD





     def 	UpperCAmelCase(self   :					Any       ,					_A   :					np.ndarray       ,					_A   :					Dict[str, int]       ,					_A   :					PILImageResampling = PILImageResampling.BICUBIC       ,					_A   :					Optional[Union[str, ChannelDimension]] = None       ,					**_A   :					Tuple       ,					)		->		np.ndarray:
         snake_case				  =      get_size_dict(_A       ,					default_to_square=_A     )
         if "shortest_edge" not in size:
             raise ValueError(f'The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'     )
         snake_case				  =      get_resize_output_image_size(_A       ,					size=size["shortest_edge"]       ,					default_to_square=_A     )
         return resize(_A       ,					size=_A       ,					resample=_A       ,					data_format=_A       ,					**_A     )





     def 	UpperCAmelCase(self   :					Dict       ,					_A   :					np.ndarray       ,					_A   :					Dict[str, int]       ,					_A   :					Optional[Union[str, ChannelDimension]] = None       ,					**_A   :					Dict       ,					)		->		np.ndarray:
         snake_case				  =      get_size_dict(_A     )
         if "height" not in size or "width" not in size:
             raise ValueError(f'The `size` parameter must contain the keys `height` and `width`. Got {size.keys()}'     )
         return center_crop(_A       ,					size=(size["height"], size["width"])       ,					data_format=_A       ,					**_A     )





     def 	UpperCAmelCase(self   :					Union[str, Any]       ,					_A   :					np.ndarray       ,					_A   :					float       ,					_A   :					Optional[Union[str, ChannelDimension]] = None       ,					**_A   :					Optional[Any]     )		->		np.ndarray:
         return rescale(_A       ,					scale=_A       ,					data_format=_A       ,					**_A     )





     def 	UpperCAmelCase(self   :					Optional[Any]       ,					_A   :					np.ndarray       ,					_A   :					Union[float, List[float]]       ,					_A   :					Union[float, List[float]]       ,					_A   :					Optional[Union[str, ChannelDimension]] = None       ,					**_A   :					Union[str, Any]       ,					)		->		np.ndarray:
         return normalize(_A       ,					mean=_A       ,					std=_A       ,					data_format=_A       ,					**_A     )





     def 	UpperCAmelCase(self   :					List[Any]       ,					_A   :					ImageInput       ,					_A   :					Optional[bool] = None       ,					_A   :					Dict[str, int] = None       ,					_A   :					PILImageResampling = None       ,					_A   :					bool = None       ,					_A   :					Dict[str, int] = None       ,					_A   :					Optional[bool] = None       ,					_A   :					Optional[float] = None       ,					_A   :					Optional[bool] = None       ,					_A   :					Optional[Union[float, List[float]]] = None       ,					_A   :					Optional[Union[float, List[float]]] = None       ,					_A   :					Optional[Union[str, TensorType]] = None       ,					_A   :					Union[str, ChannelDimension] = ChannelDimension.FIRST       ,					**_A   :					str       ,					)		->		str:
         snake_case				  =      do_resize if do_resize is not None else self.do_resize
         snake_case				  =      size if size is not None else self.size
         snake_case				  =      get_size_dict(_A       ,					default_to_square=_A     )
         snake_case				  =      resample if resample is not None else self.resample
         snake_case				  =      do_center_crop if do_center_crop is not None else self.do_center_crop
         snake_case				  =      crop_size if crop_size is not None else self.crop_size
         snake_case				  =      get_size_dict(_A       ,					param_name="crop_size"     )
         snake_case				  =      do_rescale if do_rescale is not None else self.do_rescale
         snake_case				  =      rescale_factor if rescale_factor is not None else self.rescale_factor
         snake_case				  =      do_normalize if do_normalize is not None else self.do_normalize
         snake_case				  =      image_mean if image_mean is not None else self.image_mean
         snake_case				  =      image_std if image_std is not None else self.image_std

         snake_case				  =      make_list_of_images(_A     )

         if not valid_images(_A     ):
             raise ValueError(
                 "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
                 "torch.Tensor, tf.Tensor or jax.ndarray."     )

         if do_resize and size is None:
             raise ValueError("Size must be specified if do_resize is True."     )

         if do_center_crop and crop_size is None:
             raise ValueError("Crop size must be specified if do_center_crop is True."     )

         if do_rescale and rescale_factor is None:
             raise ValueError("Rescale factor must be specified if do_rescale is True."     )

         if do_normalize and (image_mean is None or image_std is None):
             raise ValueError("Image mean and std must be specified if do_normalize is True."     )

         # All transformations expect numpy arrays.
         snake_case				  =      [to_numpy_array(_A     ) for image in images]

         if do_resize:
             snake_case				  =      [self.resize(image=_A       ,					size=_A       ,					resample=_A     ) for image in images]

         if do_center_crop:
             snake_case				  =      [self.center_crop(image=_A       ,					size=_A     ) for image in images]

         if do_rescale:
             snake_case				  =      [self.rescale(image=_A       ,					scale=_A     ) for image in images]

         if do_normalize:
             snake_case				  =      [self.normalize(image=_A       ,					mean=_A       ,					std=_A     ) for image in images]

         snake_case				  =      [to_channel_dimension_format(_A       ,					_A     ) for image in images]

         snake_case				  =      {"pixel_values": images}
         return BatchFeature(data=_A       ,					tensor_type=_A     )





     def 	UpperCAmelCase(self   :					Optional[Any]       ,					_A   :					Tuple       ,					_A   :					List[Tuple] = None     )		->		Optional[Any]:
         snake_case				  =      outputs.logits

         # Resize logits and compute semantic segmentation maps
         if target_sizes is not None:
             if len(_A     ) != len(_A     ):
                 raise ValueError(
                     "Make sure that you pass in as many target sizes as the batch dimension of the logits"     )

             if is_torch_tensor(_A     ):
                 snake_case				  =      target_sizes.numpy()

             snake_case				  =      []

             for idx in range(len(_A     )     ):
                 snake_case				  =      torch.nn.functional.interpolate(
                     logits[idx].unsqueeze(dim=0     )       ,					size=target_sizes[idx]       ,					mode="bilinear"       ,					align_corners=_A     )
                 snake_case				  =      resized_logits[0].argmax(dim=0     )
                 semantic_segmentation.append(_A     )
         else:
             snake_case				  =      logits.argmax(dim=1     )
             snake_case				  =      [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]     )]

         return semantic_segmentation



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   lowerCamelCase	(						A_							,				unittest.TestCase						):
     UpperCAmelCase__       :							Any     =	CpmAntTokenizer
     UpperCAmelCase__       :							Optional[Any]     =	False





     def 	UpperCAmelCase(self   :					Optional[Any]     )		->		Dict:
         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 	UpperCAmelCase(self   :					Optional[Any]     )		->		Optional[int]:
         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, 9_8_0_2, 1_4_9_6_2, 2_0_8_2, 8_3_1, 2_4_4]
         self.assertListEqual(tokenizer.convert_tokens_to_ids(_A     )       ,					_A     )

         snake_case				  =      tokenizer.decode(_A     )
         self.assertEqual(_A       ,					_A     )

