




import unittest

import numpy as np

from transformers.testing_utils import is_flaky, 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 DonutImageProcessor




class       a__	( unittest.TestCase				):







     def __init__(     self  ,		A  ,		A=7  ,		A=3  ,		A=18  ,		A=30  ,		A=400  ,		A=True  ,		A=None  ,		A=True  ,		A=False  ,		A=True  ,		A=True  ,		A=[0.5, 0.5, 0.5]  ,		A=[0.5, 0.5, 0.5]  ,		)  ->   Optional[Any]:



          '''simple docstring'''



          a     =				parent
          a     =				batch_size
          a     =				num_channels
          a     =				image_size
          a     =				min_resolution
          a     =				max_resolution
          a     =				do_resize
          a     =				size if size is not None else {"height": 18, "width": 20}
          a     =				do_thumbnail
          a     =				do_align_axis
          a     =				do_pad
          a     =				do_normalize
          a     =				image_mean
          a     =				image_std







     def       lowerCAmelCase_    (     self			)  ->   Dict:



          '''simple docstring'''



          return {
              "do_resize": self.do_resize,
              "size": self.size,
              "do_thumbnail": self.do_thumbnail,
              "do_align_long_axis": self.do_align_axis,
              "do_pad": self.do_pad,
              "do_normalize": self.do_normalize,
              "image_mean": self.image_mean,
              "image_std": self.image_std,
          }







@require_torch
@require_vision
class       a__	( UpperCamelCase__       , unittest.TestCase				):
     a		:      Optional[Any]		 =     DonutImageProcessor if is_vision_available() else None







     def       lowerCAmelCase_    (     self			)  ->   List[Any]:



          '''simple docstring'''



          a     =				DonutImageProcessingTester(self			)







     @property
     def       lowerCAmelCase_    (     self			)  ->   Tuple:



          '''simple docstring'''



          return self.image_processor_tester.prepare_image_processor_dict()







     def       lowerCAmelCase_    (     self			)  ->   Union[str, Any]:



          '''simple docstring'''



          a     =				self.image_processing_class(**self.image_processor_dict			)
          self.assertTrue(hasattr(A  ,		"do_resize"			)			)
          self.assertTrue(hasattr(A  ,		"size"			)			)
          self.assertTrue(hasattr(A  ,		"do_thumbnail"			)			)
          self.assertTrue(hasattr(A  ,		"do_align_long_axis"			)			)
          self.assertTrue(hasattr(A  ,		"do_pad"			)			)
          self.assertTrue(hasattr(A  ,		"do_normalize"			)			)
          self.assertTrue(hasattr(A  ,		"image_mean"			)			)
          self.assertTrue(hasattr(A  ,		"image_std"			)			)







     def       lowerCAmelCase_    (     self			)  ->   List[str]:



          '''simple docstring'''



          a     =				self.image_processing_class.from_dict(self.image_processor_dict			)
          self.assertEqual(image_processor.size  ,		{"height": 18, "width": 20}			)

          a     =				self.image_processing_class.from_dict(self.image_processor_dict  ,		size=42			)
          self.assertEqual(image_processor.size  ,		{"height": 42, "width": 42}			)

          # Previous config had dimensions in (width, height) order
          a     =				self.image_processing_class.from_dict(self.image_processor_dict  ,		size=(42, 84)			)
          self.assertEqual(image_processor.size  ,		{"height": 84, "width": 42}			)







     def       lowerCAmelCase_    (     self			)  ->   Optional[Any]:



          '''simple docstring'''



          pass







     @is_flaky()
     def       lowerCAmelCase_    (     self			)  ->   Tuple:



          '''simple docstring'''



          a     =				self.image_processing_class(**self.image_processor_dict			)
          # create random PIL images
          a     =				prepare_image_inputs(self.image_processor_tester  ,		equal_resolution=A			)
          for image in image_inputs:
               self.assertIsInstance(A  ,		Image.Image			)

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

          # Test batched
          a     =				image_processing(A  ,		return_tensors="pt"			).pixel_values
          self.assertEqual(
              encoded_images.shape  ,		(
                  self.image_processor_tester.batch_size,
                  self.image_processor_tester.num_channels,
                  self.image_processor_tester.size["height"],
                  self.image_processor_tester.size["width"],
              )  ,		)







     @is_flaky()
     def       lowerCAmelCase_    (     self			)  ->   Optional[Any]:



          '''simple docstring'''



          a     =				self.image_processing_class(**self.image_processor_dict			)
          # create random numpy tensors
          a     =				prepare_image_inputs(self.image_processor_tester  ,		equal_resolution=A  ,		numpify=A			)
          for image in image_inputs:
               self.assertIsInstance(A  ,		np.ndarray			)

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

          # Test batched
          a     =				image_processing(A  ,		return_tensors="pt"			).pixel_values
          self.assertEqual(
              encoded_images.shape  ,		(
                  self.image_processor_tester.batch_size,
                  self.image_processor_tester.num_channels,
                  self.image_processor_tester.size["height"],
                  self.image_processor_tester.size["width"],
              )  ,		)







     @is_flaky()
     def       lowerCAmelCase_    (     self			)  ->   List[Any]:



          '''simple docstring'''



          a     =				self.image_processing_class(**self.image_processor_dict			)
          # create random PyTorch tensors
          a     =				prepare_image_inputs(self.image_processor_tester  ,		equal_resolution=A  ,		torchify=A			)
          for image in image_inputs:
               self.assertIsInstance(A  ,		torch.Tensor			)

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

          # Test batched
          a     =				image_processing(A  ,		return_tensors="pt"			).pixel_values
          self.assertEqual(
              encoded_images.shape  ,		(
                  self.image_processor_tester.batch_size,
                  self.image_processor_tester.num_channels,
                  self.image_processor_tester.size["height"],
                  self.image_processor_tester.size["width"],
              )  ,		)







import unittest

from transformers import RoFormerTokenizer, RoFormerTokenizerFast
from transformers.testing_utils import require_rjieba, require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin




@require_rjieba
@require_tokenizers
class       a__	( UpperCamelCase__       , unittest.TestCase				):
     a		:      List[Any]		 =     RoFormerTokenizer
     a		:      Tuple		 =     RoFormerTokenizerFast
     a		:      Dict		 =     True
     a		:      Optional[Any]		 =     True







     def       lowerCAmelCase_    (     self			)  ->   List[Any]:



          '''simple docstring'''



          super().setUp()







     def       lowerCAmelCase_    (     self  ,		**A			)  ->   Tuple:



          '''simple docstring'''



          return self.tokenizer_class.from_pretrained("junnyu/roformer_chinese_base"  ,		**A			)







     def       lowerCAmelCase_    (     self  ,		**A			)  ->   Union[str, Any]:



          '''simple docstring'''



          return self.rust_tokenizer_class.from_pretrained("junnyu/roformer_chinese_base"  ,		**A			)







     def       lowerCAmelCase_    (     self			)  ->   Tuple:



          '''simple docstring'''



          a     =				"永和服装饰品有限公司,今天天气非常好"
          a     =				"永和 服装 饰品 有限公司 , 今 天 天 气 非常 好"
          return input_text, output_text







     def       lowerCAmelCase_    (     self			)  ->   List[Any]:



          '''simple docstring'''



          a     =				self.get_tokenizer()
          a  ,				a     =				self.get_chinese_input_output_texts()
          a     =				tokenizer.tokenize(A			)

          self.assertListEqual(A  ,		output_text.split()			)

          a     =				tokens + [tokenizer.unk_token]
          a     =				[22943, 21332, 34431, 45904, 117, 306, 1231, 1231, 2653, 33994, 1266, 100]
          self.assertListEqual(tokenizer.convert_tokens_to_ids(A			)  ,		A			)







     def       lowerCAmelCase_    (     self			)  ->   Optional[int]:



          '''simple docstring'''



          a     =				self.get_rust_tokenizer()
          a  ,				a     =				self.get_chinese_input_output_texts()
          a     =				tokenizer.tokenize(A			)
          self.assertListEqual(A  ,		output_text.split()			)
          a     =				tokens + [tokenizer.unk_token]
          a     =				[22943, 21332, 34431, 45904, 117, 306, 1231, 1231, 2653, 33994, 1266, 100]
          self.assertListEqual(tokenizer.convert_tokens_to_ids(A			)  ,		A			)







     def       lowerCAmelCase_    (     self			)  ->   Any:



          '''simple docstring'''



          pass







     def       lowerCAmelCase_    (     self			)  ->   Optional[int]:



          '''simple docstring'''



          pass







     def       lowerCAmelCase_    (     self			)  ->   Optional[int]:



          '''simple docstring'''



          pass

