


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 		_snake_case  (      unittest.TestCase							):




 def __init__(		self					,  _lowerCamelCase					,  _lowerCamelCase=7					,  _lowerCamelCase=3					,  _lowerCamelCase=18					,  _lowerCamelCase=30					,  _lowerCamelCase=400					,  _lowerCamelCase=True					,  _lowerCamelCase=None					,  _lowerCamelCase=True					,  _lowerCamelCase=False					,  _lowerCamelCase=True					,  _lowerCamelCase=True					,  _lowerCamelCase=[0.5, 0.5, 0.5]					,  _lowerCamelCase=[0.5, 0.5, 0.5]					,  ):
       a						:Union[str, Any] 							=       parent
       a						:List[Any] 							=       batch_size
       a						:Any 							=       num_channels
       a						:Optional[int] 							=       image_size
       a						:Union[str, Any] 							=       min_resolution
       a						:Optional[Any] 							=       max_resolution
       a						:Tuple 							=       do_resize
       a						:int 							=       size if size is not None else {'''height''': 18, '''width''': 20}
       a						:str 							=       do_thumbnail
       a						:List[Any] 							=       do_align_axis
       a						:Tuple 							=       do_pad
       a						:str 							=       do_normalize
       a						:Dict 							=       image_mean
       a						:Any 							=       image_std




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       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 		_snake_case  (      _snake_case						,    unittest.TestCase							):
 SCREAMING_SNAKE_CASE__      =       DonutImageProcessor if is_vision_available() else None




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       a						:str 							=       DonutImageProcessingTester(self    )




 @property
 def        SCREAMING_SNAKE_CASE__				(		self    ):
       return self.image_processor_tester.prepare_image_processor_dict()




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       a						:Tuple 							=       self.image_processing_class(**self.image_processor_dict    )
       self.assertTrue(hasattr(_lowerCamelCase					,  '''do_resize'''    )    )
       self.assertTrue(hasattr(_lowerCamelCase					,  '''size'''    )    )
       self.assertTrue(hasattr(_lowerCamelCase					,  '''do_thumbnail'''    )    )
       self.assertTrue(hasattr(_lowerCamelCase					,  '''do_align_long_axis'''    )    )
       self.assertTrue(hasattr(_lowerCamelCase					,  '''do_pad'''    )    )
       self.assertTrue(hasattr(_lowerCamelCase					,  '''do_normalize'''    )    )
       self.assertTrue(hasattr(_lowerCamelCase					,  '''image_mean'''    )    )
       self.assertTrue(hasattr(_lowerCamelCase					,  '''image_std'''    )    )




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

       a						:Union[str, Any] 							=       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						:List[Any] 							=       self.image_processing_class.from_dict(self.image_processor_dict					,  size=(42, 84)    )
       self.assertEqual(image_processor.size					,  {'''height''': 84, '''width''': 42}    )




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       pass




 @is_flaky()
 def        SCREAMING_SNAKE_CASE__				(		self    ):
       # Initialize image_processing
       a						:Dict 							=       self.image_processing_class(**self.image_processor_dict    )
       # create random PIL images
       a						:Optional[Any] 							=       prepare_image_inputs(self.image_processor_tester					,  equal_resolution=_lowerCamelCase    )
       for image in image_inputs:
             self.assertIsInstance(_lowerCamelCase					,  Image.Image    )

       # Test not batched input
       a						:Any 							=       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						:Union[str, Any] 							=       image_processing(_lowerCamelCase					,  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        SCREAMING_SNAKE_CASE__				(		self    ):
       # Initialize image_processing
       a						:Any 							=       self.image_processing_class(**self.image_processor_dict    )
       # create random numpy tensors
       a						:Optional[int] 							=       prepare_image_inputs(self.image_processor_tester					,  equal_resolution=_lowerCamelCase					,  numpify=_lowerCamelCase    )
       for image in image_inputs:
             self.assertIsInstance(_lowerCamelCase					,  np.ndarray    )

       # Test not batched input
       a						:Optional[Any] 							=       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						:int 							=       image_processing(_lowerCamelCase					,  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        SCREAMING_SNAKE_CASE__				(		self    ):
       # Initialize image_processing
       a						:Any 							=       self.image_processing_class(**self.image_processor_dict    )
       # create random PyTorch tensors
       a						:Any 							=       prepare_image_inputs(self.image_processor_tester					,  equal_resolution=_lowerCamelCase					,  torchify=_lowerCamelCase    )
       for image in image_inputs:
             self.assertIsInstance(_lowerCamelCase					,  torch.Tensor    )

       # Test not batched input
       a						:Tuple 							=       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						:Tuple 							=       image_processing(_lowerCamelCase					,  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'''],
           )					,  )









from pathlib import PurePosixPath
from typing import Optional

import fsspec
from fsspec import AbstractFileSystem
from huggingface_hub.hf_api import DatasetInfo

from ..utils.file_utils import get_authentication_headers_for_url
from ..utils.hub import hf_hub_url







class 		_snake_case  (      _snake_case							):
 SCREAMING_SNAKE_CASE__      =       ''
 SCREAMING_SNAKE_CASE__      =       'hf-legacy'  # "hf://"" is reserved for hffs




 def __init__(		self					,  _lowerCamelCase = None					,  _lowerCamelCase = None					,  **_lowerCamelCase					,  ):
       super().__init__(self					,  **_lowerCamelCase    )
       a						:Union[str, Any] 							=       repo_info
       a						:int 							=       token
       a						:int 							=       None




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       if self.dir_cache is None:
             a						:Dict 							=       {}
             for hf_file in self.repo_info.siblings:
                   # TODO(QL): add sizes
                   a						:List[Any] 							=       {
                       '''name''': hf_file.rfilename,
                       '''size''': None,
                       '''type''': '''file''',
                   }
                   self.dir_cache.update(
                       {
                           str(_lowerCamelCase    ): {'''name''': str(_lowerCamelCase    ), '''size''': None, '''type''': '''directory'''}
                           for d in list(PurePosixPath(hf_file.rfilename    ).parents    )[:-1]
                       }    )




 def        SCREAMING_SNAKE_CASE__				(		self					,  _lowerCamelCase					,  _lowerCamelCase = "rb"					,  **_lowerCamelCase					,  ):
       if not isinstance(self.repo_info					,  _lowerCamelCase    ):
             raise NotImplementedError(F'''Open is only implemented for dataset repositories, but got {self.repo_info}'''    )
       a						:Optional[int] 							=       hf_hub_url(self.repo_info.id					,  _lowerCamelCase					,  revision=self.repo_info.sha    )
       return fsspec.open(
           _lowerCamelCase					,  mode=_lowerCamelCase					,  headers=get_authentication_headers_for_url(_lowerCamelCase					,  use_auth_token=self.token    )					,  client_kwargs={'''trust_env''': True}					,  ).open()




 def        SCREAMING_SNAKE_CASE__				(		self					,  _lowerCamelCase					,  **_lowerCamelCase    ):
       self._get_dirs()
       a						:Union[str, Any] 							=       self._strip_protocol(_lowerCamelCase    )
       if path in self.dir_cache:
             return self.dir_cache[path]
       else:
             raise FileNotFoundError(_lowerCamelCase    )




 def        SCREAMING_SNAKE_CASE__				(		self					,  _lowerCamelCase					,  _lowerCamelCase=False					,  **_lowerCamelCase    ):
       self._get_dirs()
       a						:str 							=       PurePosixPath(path.strip('''/'''    )    )
       a						:Tuple 							=       {}
       for p, f in self.dir_cache.items():
             a						:Optional[int] 							=       PurePosixPath(p.strip('''/'''    )    )
             a						:str 							=       p.parent
             if root == path:
                   a						:List[str] 							=       f
       a						:Any 							=       list(paths.values()    )
       if detail:
             return out
       else:
             return sorted(f['''name'''] for f in out    )





