






import unittest

import numpy as np

from transformers.file_utils import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision

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 DPTImageProcessor
class 							A_    (						unittest.TestCase			):






 '''simple docstring'''




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

     """simple docstring"""



     _UpperCAmelCase				:      Any			  =				size if size is not None else {'''height''': 18, '''width''': 18}
     _UpperCAmelCase				:      str			  =				parent
     _UpperCAmelCase				:      List[Any]			  =				batch_size
     _UpperCAmelCase				:      Dict			  =				num_channels
     _UpperCAmelCase				:      List[Any]			  =				image_size
     _UpperCAmelCase				:      Any			  =				min_resolution
     _UpperCAmelCase				:      Any			  =				max_resolution
     _UpperCAmelCase				:      int			  =				do_resize
     _UpperCAmelCase				:      Union[str, Any]			  =				size
     _UpperCAmelCase				:      Tuple			  =				do_normalize
     _UpperCAmelCase				:      Any			  =				image_mean
     _UpperCAmelCase				:      Tuple			  =				image_std


 def  snake_case__				(	self) ->		Tuple:

     """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,
     }



@require_torch
@require_vision
class 							A_    (						__lowercase ,   unittest.TestCase			):






 '''simple docstring'''




 _SCREAMING_SNAKE_CASE							:			List[Any] =					DPTImageProcessor if is_vision_available() else None


 def  snake_case__				(	self) ->		Tuple:

     """simple docstring"""



     _UpperCAmelCase				:      int			  =				DPTImageProcessingTester(self)


 @property
 def  snake_case__				(	self) ->		int:

     """simple docstring"""



     return self.image_processor_tester.prepare_image_processor_dict()


 def  snake_case__				(	self) ->		Tuple:

     """simple docstring"""



     _UpperCAmelCase				:      str			  =				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) ->		str:

     """simple docstring"""



     _UpperCAmelCase				:      List[str]			  =				self.image_processing_class.from_dict(self.image_processor_dict)
     self.assertEqual(image_processor.size							,  {'''height''': 18, '''width''': 18})

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


 def  snake_case__				(	self) ->		int:

     """simple docstring"""



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

     # Test not batched input
     _UpperCAmelCase				:      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
     _UpperCAmelCase				:      Dict			  =				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) ->		List[Any]:

     """simple docstring"""



     _UpperCAmelCase				:      str			  =				self.image_processing_class(**self.image_processor_dict)
     # create random numpy tensors
     _UpperCAmelCase				:      List[str]			  =				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
     _UpperCAmelCase				:      str			  =				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
     _UpperCAmelCase				:      Tuple			  =				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) ->		Tuple:

     """simple docstring"""



     _UpperCAmelCase				:      List[str]			  =				self.image_processing_class(**self.image_processor_dict)
     # create random PyTorch tensors
     _UpperCAmelCase				:      List[Any]			  =				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
     _UpperCAmelCase				:      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
     _UpperCAmelCase				:      int			  =				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 argparse
import os
import re


SCREAMING_SNAKE_CASE 							=						'src/transformers/models/auto'


# re pattern that matches mapping introductions:
#    SUPER_MODEL_MAPPING_NAMES = OrderedDict or SUPER_MODEL_MAPPING = OrderedDict
SCREAMING_SNAKE_CASE 							=						re.compile(R'[A-Z_]+_MAPPING(\s+|_[A-Z_]+\s+)=\s+OrderedDict')
# re pattern that matches identifiers in mappings
SCREAMING_SNAKE_CASE 							=						re.compile(R'\s*\(\s*"(\S[^"]+)"')

def      _lowerCamelCase	(						__A    :							Optional[int]		,		__A    :							bool = False )      ->					int:
    with open(__A		,		'''r'''		,		encoding='''utf-8''' ) as f:
        _UpperCAmelCase				:      Union[str, Any]			  =				f.read()

    _UpperCAmelCase				:      Any			  =				content.split('''\n''' )
    _UpperCAmelCase				:      Any			  =				[]
    _UpperCAmelCase				:      Tuple			  =				0
    while line_idx < len(__A ):
        if _re_intro_mapping.search(lines[line_idx] ) is not None:
            _UpperCAmelCase				:      Union[str, Any]			  =				len(re.search(r'''^(\s*)\S'''		,		lines[line_idx] ).groups()[0] ) + 8
            # Start of a new mapping!
            while not lines[line_idx].startswith(''' ''' * indent + '''(''' ):
                new_lines.append(lines[line_idx] )
                line_idx += 1

            _UpperCAmelCase				:      str			  =				[]
            while lines[line_idx].strip() != "]":
                # Blocks either fit in one line or not
                if lines[line_idx].strip() == "(":
                    _UpperCAmelCase				:      List[str]			  =				line_idx
                    while not lines[line_idx].startswith(''' ''' * indent + ''')''' ):
                        line_idx += 1
                    blocks.append('''\n'''.join(lines[start_idx : line_idx + 1] ) )
                else:
                    blocks.append(lines[line_idx] )
                line_idx += 1

            # Sort blocks by their identifiers
            _UpperCAmelCase				:      Tuple			  =				sorted(__A		,		key=lambda __A : _re_identifier.search(__A ).groups()[0] )
            new_lines += blocks
        else:
            new_lines.append(lines[line_idx] )
            line_idx += 1

    if overwrite:
        with open(__A		,		'''w'''		,		encoding='''utf-8''' ) as f:
            f.write('''\n'''.join(__A ) )
    elif "\n".join(__A ) != content:
        return True

def      _lowerCamelCase	(						__A    :							bool = False )      ->					List[str]:
    _UpperCAmelCase				:      List[str]			  =				[os.path.join(__A		,		__A ) for f in os.listdir(__A ) if f.endswith('''.py''' )]
    _UpperCAmelCase				:      List[Any]			  =				[sort_auto_mapping(__A		,		overwrite=__A ) for fname in fnames]

    if not overwrite and any(__A ):
        _UpperCAmelCase				:      Optional[int]			  =				[f for f, d in zip(__A		,		__A ) if d]
        raise ValueError(
            f'''The following files have auto mappings that need sorting: {', '.join(__A )}. Run `make style` to fix'''
            ''' this.''' )


if __name__ == "__main__":
  SCREAMING_SNAKE_CASE 							=						argparse.ArgumentParser()
  parser.add_argument('--check_only', action='store_true', help='Whether to only check or fix style.')
  SCREAMING_SNAKE_CASE 							=						parser.parse_args()

  sort_all_auto_mappings(not args.check_only)




