
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   lowerCamelCase	(						unittest.TestCase						):





     def __init__(self   :					Any       ,					_A   :					Tuple       ,					_A   :					List[str]=1_3       ,					_A   :					Optional[Any]=3       ,					_A   :					Dict=2_2_4       ,					_A   :					Optional[int]=3_0       ,					_A   :					str=4_0_0       ,					_A   :					List[Any]=True       ,					_A   :					int=None       ,					_A   :					Any=True       ,					_A   :					Tuple=[0.5, 0.5, 0.5]       ,					_A   :					Optional[Any]=[0.5, 0.5, 0.5]       ,					)		->		List[str]:
         snake_case				  =      size if size is not None else {"height": 1_8, "width": 1_8}
         snake_case				  =      parent
         snake_case				  =      batch_size
         snake_case				  =      num_channels
         snake_case				  =      image_size
         snake_case				  =      min_resolution
         snake_case				  =      max_resolution
         snake_case				  =      do_resize
         snake_case				  =      size
         snake_case				  =      do_normalize
         snake_case				  =      image_mean
         snake_case				  =      image_std





     def 	UpperCAmelCase(self   :					str     )		->		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,
         }







@require_torch
@require_vision
class   lowerCamelCase	(						A_							,				unittest.TestCase						):
     UpperCAmelCase__       :							int     =	ViTImageProcessor if is_vision_available() else None





     def 	UpperCAmelCase(self   :					str     )		->		Union[str, Any]:
         snake_case				  =      EfficientFormerImageProcessorTester(self     )





     @property
     def 	UpperCAmelCase(self   :					Dict     )		->		Tuple:
         return self.image_proc_tester.prepare_image_processor_dict()





     def 	UpperCAmelCase(self   :					str     )		->		Union[str, Any]:
         snake_case				  =      self.image_processing_class(**self.image_processor_dict     )
         self.assertTrue(hasattr(_A       ,					"image_mean"     )     )
         self.assertTrue(hasattr(_A       ,					"image_std"     )     )
         self.assertTrue(hasattr(_A       ,					"do_normalize"     )     )
         self.assertTrue(hasattr(_A       ,					"do_resize"     )     )
         self.assertTrue(hasattr(_A       ,					"size"     )     )





     def 	UpperCAmelCase(self   :					str     )		->		Any:
         pass





     def 	UpperCAmelCase(self   :					Dict     )		->		int:
         # Initialize image_processor
         snake_case				  =      self.image_processing_class(**self.image_processor_dict     )
         # create random PIL images
         snake_case				  =      prepare_image_inputs(self.image_proc_tester       ,					equal_resolution=_A     )
         for image in image_inputs:
             self.assertIsInstance(_A       ,					Image.Image     )

         # Test not batched input
         snake_case				  =      image_processor(image_inputs[0]       ,					return_tensors="pt"     ).pixel_values
         self.assertEqual(
             encoded_images.shape       ,					(
                 1,
                 self.image_proc_tester.num_channels,
                 self.image_proc_tester.size["height"],
                 self.image_proc_tester.size["width"],
             )       ,					)

         # Test batched
         snake_case				  =      image_processor(_A       ,					return_tensors="pt"     ).pixel_values
         self.assertEqual(
             encoded_images.shape       ,					(
                 self.image_proc_tester.batch_size,
                 self.image_proc_tester.num_channels,
                 self.image_proc_tester.size["height"],
                 self.image_proc_tester.size["width"],
             )       ,					)





     def 	UpperCAmelCase(self   :					List[str]     )		->		Union[str, Any]:
         # Initialize image_processor
         snake_case				  =      self.image_processing_class(**self.image_processor_dict     )
         # create random numpy tensors
         snake_case				  =      prepare_image_inputs(self.image_proc_tester       ,					equal_resolution=_A       ,					numpify=_A     )
         for image in image_inputs:
             self.assertIsInstance(_A       ,					np.ndarray     )

         # Test not batched input
         snake_case				  =      image_processor(image_inputs[0]       ,					return_tensors="pt"     ).pixel_values
         self.assertEqual(
             encoded_images.shape       ,					(
                 1,
                 self.image_proc_tester.num_channels,
                 self.image_proc_tester.size["height"],
                 self.image_proc_tester.size["width"],
             )       ,					)

         # Test batched
         snake_case				  =      image_processor(_A       ,					return_tensors="pt"     ).pixel_values
         self.assertEqual(
             encoded_images.shape       ,					(
                 self.image_proc_tester.batch_size,
                 self.image_proc_tester.num_channels,
                 self.image_proc_tester.size["height"],
                 self.image_proc_tester.size["width"],
             )       ,					)





     def 	UpperCAmelCase(self   :					str     )		->		Optional[Any]:
         # Initialize image_processor
         snake_case				  =      self.image_processing_class(**self.image_processor_dict     )
         # create random PyTorch tensors
         snake_case				  =      prepare_image_inputs(self.image_proc_tester       ,					equal_resolution=_A       ,					torchify=_A     )
         for image in image_inputs:
             self.assertIsInstance(_A       ,					torch.Tensor     )

         # Test not batched input
         snake_case				  =      image_processor(image_inputs[0]       ,					return_tensors="pt"     ).pixel_values
         self.assertEqual(
             encoded_images.shape       ,					(
                 1,
                 self.image_proc_tester.num_channels,
                 self.image_proc_tester.size["height"],
                 self.image_proc_tester.size["width"],
             )       ,					)

         # Test batched
         snake_case				  =      image_processor(_A       ,					return_tensors="pt"     ).pixel_values
         self.assertEqual(
             encoded_images.shape       ,					(
                 self.image_proc_tester.batch_size,
                 self.image_proc_tester.num_channels,
                 self.image_proc_tester.size["height"],
                 self.image_proc_tester.size["width"],
             )       ,					)



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     )

