




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 	A	(						unittest.TestCase						):
     """simple docstring"""

     def __init__(			self     :      str		,     A__     :      Dict		,     A__     :      Tuple=7		,     A__     :      Optional[Any]=3		,     A__     :      Optional[Any]=1_8		,     A__     :      Tuple=3_0		,     A__     :      Any=4_0_0		,     A__     :      Optional[Any]=True		,     A__     :      List[Any]=None		,     A__     :      Any=True		,     A__     :      List[Any]=[0.5, 0.5, 0.5]		,     A__     :      Optional[int]=[0.5, 0.5, 0.5]		,     ) ->				Optional[int]:
           lowerCamelCase__					     = size if size is not None else {'height': 1_8, 'width': 1_8}
           lowerCamelCase__					     = parent
           lowerCamelCase__					     = batch_size
           lowerCamelCase__					     = num_channels
           lowerCamelCase__					     = image_size
           lowerCamelCase__					     = min_resolution
           lowerCamelCase__					     = max_resolution
           lowerCamelCase__					     = do_resize
           lowerCamelCase__					     = size
           lowerCamelCase__					     = do_normalize
           lowerCamelCase__					     = image_mean
           lowerCamelCase__					     = image_std




     def        __snake_case      (			self     :      Union[str, Any]	) ->				Optional[Any]:
           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 	A	(						_lowerCAmelCase	,  unittest.TestCase						):
     """simple docstring"""

     _snake_case:		Dict   =ViTImageProcessor if is_vision_available() else None




     def        __snake_case      (			self     :      Union[str, Any]	) ->				Any:
           lowerCamelCase__					     = ViTImageProcessingTester(self	)




     @property
     def        __snake_case      (			self     :      List[str]	) ->				List[str]:
           return self.image_processor_tester.prepare_image_processor_dict()




     def        __snake_case      (			self     :      Optional[int]	) ->				str:
           lowerCamelCase__					     = 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        __snake_case      (			self     :      List[str]	) ->				Dict:
           lowerCamelCase__					     = self.image_processing_class.from_dict(self.image_processor_dict	)
           self.assertEqual(image_processor.size		,     {'height': 1_8, 'width': 1_8}	)

           lowerCamelCase__					     = self.image_processing_class.from_dict(self.image_processor_dict		,     size=4_2	)
           self.assertEqual(image_processor.size		,     {'height': 4_2, 'width': 4_2}	)




     def        __snake_case      (			self     :      List[str]	) ->				Any:
           pass




     def        __snake_case      (			self     :      Optional[Any]	) ->				int:
           # Initialize image_processing
           lowerCamelCase__					     = self.image_processing_class(**self.image_processor_dict	)
           # create random PIL images
           lowerCamelCase__					     = prepare_image_inputs(self.image_processor_tester		,     equal_resolution=A__	)
           for image in image_inputs:
                 self.assertIsInstance(A__		,     Image.Image	)

           # Test not batched input
           lowerCamelCase__					     = 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
           lowerCamelCase__					     = 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'],
               )		,     )




     def        __snake_case      (			self     :      int	) ->				Dict:
           # Initialize image_processing
           lowerCamelCase__					     = self.image_processing_class(**self.image_processor_dict	)
           # create random numpy tensors
           lowerCamelCase__					     = 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
           lowerCamelCase__					     = 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
           lowerCamelCase__					     = 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'],
               )		,     )




     def        __snake_case      (			self     :      Dict	) ->				Optional[int]:
           # Initialize image_processing
           lowerCamelCase__					     = self.image_processing_class(**self.image_processor_dict	)
           # create random PyTorch tensors
           lowerCamelCase__					     = 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
           lowerCamelCase__					     = 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
           lowerCamelCase__					     = 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 BlenderbotTokenizer, BlenderbotTokenizerFast
from transformers.utils import cached_property






class 	A	(						unittest.TestCase						):
     """simple docstring"""

     @cached_property
     def        __snake_case      (			self     :      Union[str, Any]	) ->				str:
           return BlenderbotTokenizer.from_pretrained('facebook/blenderbot-3B'	)




     @cached_property
     def        __snake_case      (			self     :      Tuple	) ->				Dict:
           return BlenderbotTokenizerFast.from_pretrained('facebook/blenderbot-3B'	)




     def        __snake_case      (			self     :      List[str]	) ->				int:
           lowerCamelCase__					     = self.tokenizer_ab
           lowerCamelCase__					     = ' I am a small frog.'
           lowerCamelCase__					     = tok([src_text]		,     padding=A__		,     truncation=A__	)['input_ids']
           lowerCamelCase__					     = tok.batch_decode(A__		,     skip_special_tokens=A__		,     clean_up_tokenization_spaces=A__	)[0]
           assert src_text == decoded




     def        __snake_case      (			self     :      List[str]	) ->				str:
           lowerCamelCase__					     = self.rust_tokenizer_ab
           lowerCamelCase__					     = ' I am a small frog.'
           lowerCamelCase__					     = tok([src_text]		,     padding=A__		,     truncation=A__	)['input_ids']
           lowerCamelCase__					     = tok.batch_decode(A__		,     skip_special_tokens=A__		,     clean_up_tokenization_spaces=A__	)[0]
           assert src_text == decoded




     def        __snake_case      (			self     :      Any	) ->				str:
           assert self.tokenizer_ab.add_prefix_space
           assert self.tokenizer_ab([' Sam', 'Sam']	).input_ids == [[5_5_0_2, 2], [5_5_0_2, 2]]




     def        __snake_case      (			self     :      str	) ->				Union[str, Any]:
           assert self.rust_tokenizer_ab.add_prefix_space
           assert self.rust_tokenizer_ab([' Sam', 'Sam']	).input_ids == [[5_5_0_2, 2], [5_5_0_2, 2]]