


'''simple docstring'''







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 			_lowercase						(       unittest.TestCase					):







     def __init__( self    ,   A__    ,   A__=7    ,   A__=3    ,   A__=18    ,   A__=30    ,   A__=4_00    ,   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]    ,   ) ->	Dict:
      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 if size is not None else {'''height''': 18, '''width''': 20}
      snake_case				=			do_thumbnail
      snake_case				=			do_align_axis
      snake_case				=			do_pad
      snake_case				=			do_normalize
      snake_case				=			image_mean
      snake_case				=			image_std







     def     UpperCamelCase			( self    ) ->	List[Any]:
      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 			_lowercase						(       __a				,		unittest.TestCase					):
     _UpperCAmelCase										=					DonutImageProcessor if is_vision_available() else None







     def     UpperCamelCase			( self    ) ->	Tuple:
      snake_case				=			DonutImageProcessingTester(self    )







     @property
     def     UpperCamelCase			( self    ) ->	Dict:
      return self.image_processor_tester.prepare_image_processor_dict()







     def     UpperCamelCase			( self    ) ->	List[Any]:
      snake_case				=			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     UpperCamelCase			( self    ) ->	int:
      snake_case				=			self.image_processing_class.from_dict(self.image_processor_dict    )
      self.assertEqual(image_processor.size    ,   {'''height''': 18, '''width''': 20}    )

      snake_case				=			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
      snake_case				=			self.image_processing_class.from_dict(self.image_processor_dict    ,   size=(42, 84)    )
      self.assertEqual(image_processor.size    ,   {'''height''': 84, '''width''': 42}    )







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







     @is_flaky()
     def     UpperCamelCase			( self    ) ->	List[str]:
      # Initialize image_processing
      snake_case				=			self.image_processing_class(**self.image_processor_dict    )
      # create random PIL images
      snake_case				=			prepare_image_inputs(self.image_processor_tester    ,   equal_resolution=A__    )
      for image in image_inputs:
       self.assertIsInstance(A__    ,   Image.Image    )

      # Test not batched input
      snake_case				=			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
      snake_case				=			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     UpperCamelCase			( self    ) ->	str:
      # Initialize image_processing
      snake_case				=			self.image_processing_class(**self.image_processor_dict    )
      # create random numpy tensors
      snake_case				=			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
      snake_case				=			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
      snake_case				=			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     UpperCamelCase			( self    ) ->	Optional[Any]:
      # Initialize image_processing
      snake_case				=			self.image_processing_class(**self.image_processor_dict    )
      # create random PyTorch tensors
      snake_case				=			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
      snake_case				=			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
      snake_case				=			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'''],
          )    ,   )








'''simple docstring'''







import inspect
import re
from hashlib import shaaaa
from typing import Dict, List

from .arrow import arrow
from .audiofolder import audiofolder
from .csv import csv
from .imagefolder import imagefolder
from .json import json
from .pandas import pandas
from .parquet import parquet
from .sql import sql  # noqa F401
from .text import text

def    __UpperCamelCase	(	a    : List[str]     ) ->str:
 snake_case				=			[]
 for line in lines:
  snake_case				=			re.sub(R'''#.*'''					,   ''''''					,   a     )  # remove comments
  if line:
   filtered_lines.append(a     )
 snake_case				=			'''\n'''.join(a     )

 # Make a hash from all this code
 snake_case				=			full_str.encode('''utf-8'''     )
 return shaaaa(a     ).hexdigest()


# get importable module names and hash for caching
_lowercase       						=			{
    'csv': (csv.__name__, _hash_python_lines(inspect.getsource(csv).splitlines())),
    'json': (json.__name__, _hash_python_lines(inspect.getsource(json).splitlines())),
    'pandas': (pandas.__name__, _hash_python_lines(inspect.getsource(pandas).splitlines())),
    'parquet': (parquet.__name__, _hash_python_lines(inspect.getsource(parquet).splitlines())),
    'arrow': (arrow.__name__, _hash_python_lines(inspect.getsource(arrow).splitlines())),
    'text': (text.__name__, _hash_python_lines(inspect.getsource(text).splitlines())),
    'imagefolder': (imagefolder.__name__, _hash_python_lines(inspect.getsource(imagefolder).splitlines())),
    'audiofolder': (audiofolder.__name__, _hash_python_lines(inspect.getsource(audiofolder).splitlines())),
}

# Used to infer the module to use based on the data files extensions
_lowercase       						=			{
    '.csv': ('csv', {}),
    '.tsv': ('csv', {'sep': '\t'}),
    '.json': ('json', {}),
    '.jsonl': ('json', {}),
    '.parquet': ('parquet', {}),
    '.arrow': ('arrow', {}),
    '.txt': ('text', {}),
}
_EXTENSION_TO_MODULE.update({ext: ('imagefolder', {}) for ext in imagefolder.ImageFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext.upper(): ('imagefolder', {}) for ext in imagefolder.ImageFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext: ('audiofolder', {}) for ext in audiofolder.AudioFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext.upper(): ('audiofolder', {}) for ext in audiofolder.AudioFolder.EXTENSIONS})
_lowercase       						=			{'imagefolder', 'audiofolder'}

# Used to filter data files based on extensions given a module name
_lowercase       						=			{}
for _ext, (_module, _) in _EXTENSION_TO_MODULE.items():
   _MODULE_TO_EXTENSIONS.setdefault(_module, []).append(_ext)

_MODULE_TO_EXTENSIONS["imagefolder"].append('.zip')






_MODULE_TO_EXTENSIONS["audiofolder"].append('.zip')

