

'''simple docstring'''
import unittest
from typing import Dict, List, Optional, Union

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 BridgeTowerImageProcessor


class   a__    (				unittest.TestCase    ):


   def __init__(					self     ,							_UpperCamelCase     ,							_UpperCamelCase = True     ,							_UpperCamelCase = None     ,							_UpperCamelCase = 32     ,							_UpperCamelCase = True     ,							_UpperCamelCase = 1 / 255     ,							_UpperCamelCase = True     ,							_UpperCamelCase = True     ,							_UpperCamelCase = [0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3]     ,							_UpperCamelCase = [0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1]     ,							_UpperCamelCase = True     ,							_UpperCamelCase=7     ,							_UpperCamelCase=30     ,							_UpperCamelCase=400     ,							_UpperCamelCase=3     ,							):





      """simple docstring"""





      _lowercase     : str     	=  parent
      _lowercase     : List[str]     	=  do_resize
      _lowercase     : Optional[int]     	=  size if size is not None else {"shortest_edge": 288}
      _lowercase     : List[str]     	=  size_divisor
      _lowercase     : Optional[Any]     	=  do_rescale
      _lowercase     : List[Any]     	=  rescale_factor
      _lowercase     : List[Any]     	=  do_normalize
      _lowercase     : int     	=  do_center_crop
      _lowercase     : List[str]     	=  image_mean
      _lowercase     : Dict     	=  image_std
      _lowercase     : List[Any]     	=  do_pad
      _lowercase     : Dict     	=  batch_size
      _lowercase     : Union[str, Any]     	=  num_channels
      _lowercase     : Union[str, Any]     	=  min_resolution
      _lowercase     : Union[str, Any]     	=  max_resolution


   def 		_lowerCamelCase  (					self    ):





      """simple docstring"""





      return {
          "image_mean": self.image_mean,
          "image_std": self.image_std,
          "do_normalize": self.do_normalize,
          "do_resize": self.do_resize,
          "size": self.size,
          "size_divisor": self.size_divisor,
      }






   def 		_lowerCamelCase  (					self     ,							_UpperCamelCase     ,							_UpperCamelCase=False    ):





      """simple docstring"""





      if not batched:
         _lowercase     : Optional[int]     	=  self.size["shortest_edge"]
         _lowercase     : int     	=  image_inputs[0]
         if isinstance(_UpperCamelCase     ,							Image.Image    ):
            _lowercase	, _lowercase     : List[str]     	=  image.size
         else:
            _lowercase	, _lowercase     : List[Any]     	=  image.shape[1], image.shape[2]
         _lowercase     : List[str]     	=  size / min(_UpperCamelCase     ,							_UpperCamelCase    )
         if h < w:
            _lowercase	, _lowercase     : int     	=  size, scale * w
         else:
            _lowercase	, _lowercase     : Optional[int]     	=  scale * h, size

         _lowercase     : List[str]     	=  int((1333 / 800) * size    )
         if max(_UpperCamelCase     ,							_UpperCamelCase    ) > max_size:
            _lowercase     : List[str]     	=  max_size / max(_UpperCamelCase     ,							_UpperCamelCase    )
            _lowercase     : List[str]     	=  newh * scale
            _lowercase     : Dict     	=  neww * scale

         _lowercase	, _lowercase     : Tuple     	=  int(newh + 0.5    ), int(neww + 0.5    )
         _lowercase	, _lowercase     : int     	=  (
             newh // self.size_divisor * self.size_divisor,
             neww // self.size_divisor * self.size_divisor,
         )

      else:
         _lowercase     : Any     	=  []
         for image in image_inputs:
            _lowercase	, _lowercase     : Union[str, Any]     	=  self.get_expected_values([image]    )
            expected_values.append((expected_height, expected_width)    )
         _lowercase     : Any     	=  max(_UpperCamelCase     ,							key=lambda _UpperCamelCase    : item[0]    )[0]
         _lowercase     : int     	=  max(_UpperCamelCase     ,							key=lambda _UpperCamelCase    : item[1]    )[1]

      return expected_height, expected_width




@require_torch
@require_vision
class   a__    (				_snake_case    , unittest.TestCase    ):
   _SCREAMING_SNAKE_CASE       :	Optional[int]      			=				BridgeTowerImageProcessor if is_vision_available() else None


   def 		_lowerCamelCase  (					self    ):





      """simple docstring"""





      _lowercase     : Union[str, Any]     	=  BridgeTowerImageProcessingTester(self    )


   @property
   def 		_lowerCamelCase  (					self    ):





      """simple docstring"""





      return self.image_processor_tester.prepare_image_processor_dict()


   def 		_lowerCamelCase  (					self    ):





      """simple docstring"""





      _lowercase     : int     	=  self.image_processing_class(**self.image_processor_dict    )
      self.assertTrue(hasattr(_UpperCamelCase     ,							"image_mean"    )    )
      self.assertTrue(hasattr(_UpperCamelCase     ,							"image_std"    )    )
      self.assertTrue(hasattr(_UpperCamelCase     ,							"do_normalize"    )    )
      self.assertTrue(hasattr(_UpperCamelCase     ,							"do_resize"    )    )
      self.assertTrue(hasattr(_UpperCamelCase     ,							"size"    )    )
      self.assertTrue(hasattr(_UpperCamelCase     ,							"size_divisor"    )    )


   def 		_lowerCamelCase  (					self    ):





      """simple docstring"""





      pass


   def 		_lowerCamelCase  (					self    ):





      """simple docstring"""





      _lowercase     : List[str]     	=  self.image_processing_class(**self.image_processor_dict    )
      # create random PIL images
      _lowercase     : Union[str, Any]     	=  prepare_image_inputs(self.image_processor_tester     ,							equal_resolution=_UpperCamelCase    )
      for image in image_inputs:
         self.assertIsInstance(_UpperCamelCase     ,							Image.Image    )

      # Test not batched input
      _lowercase     : Optional[Any]     	=  image_processing(image_inputs[0]     ,							return_tensors="pt"    ).pixel_values

      _lowercase	, _lowercase     : Union[str, Any]     	=  self.image_processor_tester.get_expected_values(_UpperCamelCase    )
      self.assertEqual(
          encoded_images.shape     ,							(1, self.image_processor_tester.num_channels, expected_height, expected_width)     ,							)

      # Test batched
      _lowercase     : str     	=  image_processing(_UpperCamelCase     ,							return_tensors="pt"    ).pixel_values

      _lowercase	, _lowercase     : int     	=  self.image_processor_tester.get_expected_values(_UpperCamelCase     ,							batched=_UpperCamelCase    )
      self.assertEqual(
          encoded_images.shape     ,							(
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              expected_height,
              expected_width,
          )     ,							)


   def 		_lowerCamelCase  (					self    ):





      """simple docstring"""





      _lowercase     : List[str]     	=  self.image_processing_class(**self.image_processor_dict    )
      # create random numpy tensors
      _lowercase     : Dict     	=  prepare_image_inputs(self.image_processor_tester     ,							equal_resolution=_UpperCamelCase     ,							numpify=_UpperCamelCase    )
      for image in image_inputs:
         self.assertIsInstance(_UpperCamelCase     ,							np.ndarray    )

      # Test not batched input
      _lowercase     : Union[str, Any]     	=  image_processing(image_inputs[0]     ,							return_tensors="pt"    ).pixel_values

      _lowercase	, _lowercase     : Tuple     	=  self.image_processor_tester.get_expected_values(_UpperCamelCase    )
      self.assertEqual(
          encoded_images.shape     ,							(1, self.image_processor_tester.num_channels, expected_height, expected_width)     ,							)

      # Test batched
      _lowercase     : Dict     	=  image_processing(_UpperCamelCase     ,							return_tensors="pt"    ).pixel_values

      _lowercase	, _lowercase     : Optional[int]     	=  self.image_processor_tester.get_expected_values(_UpperCamelCase     ,							batched=_UpperCamelCase    )
      self.assertEqual(
          encoded_images.shape     ,							(
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              expected_height,
              expected_width,
          )     ,							)






   def 		_lowerCamelCase  (					self    ):





      """simple docstring"""





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

      # Test not batched input
      _lowercase     : List[Any]     	=  image_processing(image_inputs[0]     ,							return_tensors="pt"    ).pixel_values

      _lowercase	, _lowercase     : str     	=  self.image_processor_tester.get_expected_values(_UpperCamelCase    )
      self.assertEqual(
          encoded_images.shape     ,							(1, self.image_processor_tester.num_channels, expected_height, expected_width)     ,							)

      # Test batched
      _lowercase     : str     	=  image_processing(_UpperCamelCase     ,							return_tensors="pt"    ).pixel_values

      _lowercase	, _lowercase     : Optional[int]     	=  self.image_processor_tester.get_expected_values(_UpperCamelCase     ,							batched=_UpperCamelCase    )
      self.assertEqual(
          encoded_images.shape     ,							(
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              expected_height,
              expected_width,
          )     ,							)













'''simple docstring'''

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 __a			( _snake_case      ):
   __UpperCamelCase :			Any						   =						''
   __UpperCamelCase :			int						   =						'hf-legacy'  # "hf://"" is reserved for hffs





   def __init__(       self					:      Any					,lowerCamelCase					:      Optional[DatasetInfo] = None					,lowerCamelCase					:      Optional[str] = None					,**lowerCamelCase					:      Dict					,):

      '''simple docstring'''

      super().__init__(self					,**lowerCamelCase     )
      __SCREAMING_SNAKE_CASE   				=							repo_info
      __SCREAMING_SNAKE_CASE   				=							token
      __SCREAMING_SNAKE_CASE   				=							None





   def   UpperCAmelCase__  (       self					:      Optional[int]     ):

      '''simple docstring'''

      if self.dir_cache is None:
         __SCREAMING_SNAKE_CASE   				=							{}
         for hf_file in self.repo_info.siblings:
            # TODO(QL): add sizes
            __SCREAMING_SNAKE_CASE   				=							{
                """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   UpperCAmelCase__  (       self					:      List[Any]					,lowerCamelCase					:      str					,lowerCamelCase					:      str = "rb"					,**lowerCamelCase					:      Optional[Any]					,):

      '''simple docstring'''

      if not isinstance(self.repo_info					,lowerCamelCase     ):
         raise NotImplementedError(f"""Open is only implemented for dataset repositories, but got {self.repo_info}"""     )
      __SCREAMING_SNAKE_CASE   				=							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   UpperCAmelCase__  (       self					:      Tuple					,lowerCamelCase					:      Any					,**lowerCamelCase					:      Optional[Any]     ):

      '''simple docstring'''

      self._get_dirs()
      __SCREAMING_SNAKE_CASE   				=							self._strip_protocol(lowerCamelCase     )
      if path in self.dir_cache:
         return self.dir_cache[path]
      else:
         raise FileNotFoundError(lowerCamelCase     )





   def   UpperCAmelCase__  (       self					:      str					,lowerCamelCase					:      Any					,lowerCamelCase					:      str=False					,**lowerCamelCase					:      Any     ):

      '''simple docstring'''

      self._get_dirs()
      __SCREAMING_SNAKE_CASE   				=							PurePosixPath(path.strip("""/"""     )     )
      __SCREAMING_SNAKE_CASE   				=							{}
      for p, f in self.dir_cache.items():
         __SCREAMING_SNAKE_CASE   				=							PurePosixPath(p.strip("""/"""     )     )
         __SCREAMING_SNAKE_CASE   				=							p.parent
         if root == path:
            __SCREAMING_SNAKE_CASE   				=							f
      __SCREAMING_SNAKE_CASE   				=							list(paths.values()     )
      if detail:
         return out
      else:
         return sorted(f["""name"""] for f in out     )





