




import argparse
import os
import re


snake_case_ : Union[str, Any]    		=       "src/transformers/models/auto"


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


def    A						(__A   :    Tuple   ,				__A   :    bool = False       )   ->      Dict:







     """simple docstring"""


     with open(__A   ,				'''r'''   ,				encoding='''utf-8'''       ) as f:
          UpperCAmelCase_          =							f.read()

     UpperCAmelCase_          =							content.split('''\n'''       )
     UpperCAmelCase_          =							[]
     UpperCAmelCase_          =							0
     while line_idx < len(__A       ):
          if _re_intro_mapping.search(lines[line_idx]       ) is not None:
               UpperCAmelCase_          =							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_          =							[]
               while lines[line_idx].strip() != "]":
                    # Blocks either fit in one line or not
                    if lines[line_idx].strip() == "(":
                         UpperCAmelCase_          =							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_          =							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    A						(__A   :    bool = False       )   ->      List[Any]:







     """simple docstring"""


     UpperCAmelCase_          =							[os.path.join(__A   ,				__A       ) for f in os.listdir(__A       ) if f.endswith('''.py'''       )]
     UpperCAmelCase_          =							[sort_auto_mapping(__A   ,				overwrite=__A       ) for fname in fnames]

     if not overwrite and any(__A       ):
          UpperCAmelCase_          =							[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__":
    snake_case_ : Optional[Any]    		=       argparse.ArgumentParser()
    parser.add_argument("--check_only", action="store_true", help="Whether to only check or fix style.")
    snake_case_ : Union[str, Any]    		=       parser.parse_args()

    sort_all_auto_mappings(not args.check_only)












import hashlib
import unittest

from transformers import MODEL_FOR_DEPTH_ESTIMATION_MAPPING, is_torch_available, is_vision_available
from transformers.pipelines import DepthEstimationPipeline, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_timm,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_torch_available():
    import torch

if is_vision_available():
    from PIL import Image
else:







    class __snake_case			:







       @staticmethod
       def lowerCamelCase						(      *_snake_case		:						Optional[int]					,						**_snake_case		:						int):

            """simple docstring"""


            pass


def    A						(__A   :    Image       )   ->      str:







     """simple docstring"""


     UpperCAmelCase_          =							hashlib.mda(image.tobytes()       )
     return m.hexdigest()







@is_pipeline_test
@require_vision
@require_timm
@require_torch
class __snake_case			(			unittest.TestCase		):
   UpperCAmelCase__  :    Tuple          =  MODEL_FOR_DEPTH_ESTIMATION_MAPPING







   def lowerCamelCase						(      self		:						Tuple					,						_snake_case		:						Optional[Any]					,						_snake_case		:						Optional[Any]					,						_snake_case		:						List[str]):

        """simple docstring"""


        UpperCAmelCase_          =							DepthEstimationPipeline(model=_snake_case					,						image_processor=_snake_case)
        return depth_estimator, [
            "./tests/fixtures/tests_samples/COCO/000000039769.png",
            "./tests/fixtures/tests_samples/COCO/000000039769.png",
        ]







   def lowerCamelCase						(      self		:						str					,						_snake_case		:						Optional[int]					,						_snake_case		:						List[str]):

        """simple docstring"""


        UpperCAmelCase_          =							depth_estimator('''./tests/fixtures/tests_samples/COCO/000000039769.png''')
        self.assertEqual({'''predicted_depth''': ANY(torch.Tensor), '''depth''': ANY(Image.Image)}					,						_snake_case)
        import datasets

        UpperCAmelCase_          =							datasets.load_dataset('''hf-internal-testing/fixtures_image_utils'''					,						'''image'''					,						split='''test''')
        UpperCAmelCase_          =							depth_estimator(
            [
                Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''),
                '''http://images.cocodataset.org/val2017/000000039769.jpg''',
                # RGBA
                dataset[0]['''file'''],
                # LA
                dataset[1]['''file'''],
                # L
                dataset[2]['''file'''],
            ])
        self.assertEqual(
            [
                {'''predicted_depth''': ANY(torch.Tensor), '''depth''': ANY(Image.Image)},
                {'''predicted_depth''': ANY(torch.Tensor), '''depth''': ANY(Image.Image)},
                {'''predicted_depth''': ANY(torch.Tensor), '''depth''': ANY(Image.Image)},
                {'''predicted_depth''': ANY(torch.Tensor), '''depth''': ANY(Image.Image)},
                {'''predicted_depth''': ANY(torch.Tensor), '''depth''': ANY(Image.Image)},
            ]					,						_snake_case					,						)







   @require_tf
   @unittest.skip('''Depth estimation is not implemented in TF''')
   def lowerCamelCase						(      self		:						Union[str, Any]):

        """simple docstring"""


        pass







   @slow
   @require_torch
   def lowerCamelCase						(      self		:						List[str]):

        """simple docstring"""


        UpperCAmelCase_          =							'''Intel/dpt-large'''
        UpperCAmelCase_          =							pipeline('''depth-estimation'''					,						model=_snake_case)
        UpperCAmelCase_          =							depth_estimator('''http://images.cocodataset.org/val2017/000000039769.jpg''')
        UpperCAmelCase_          =							hashimage(outputs['''depth'''])

        # This seems flaky.
        # self.assertEqual(outputs["depth"], "1a39394e282e9f3b0741a90b9f108977")
        self.assertEqual(nested_simplify(outputs['''predicted_depth'''].max().item())					,						2_9.3_0_4)
        self.assertEqual(nested_simplify(outputs['''predicted_depth'''].min().item())					,						2.6_6_2)







   @require_torch
   def lowerCamelCase						(      self		:						Optional[Any]):

        """simple docstring"""


        self.skipTest('''There is not hf-internal-testing tiny model for either GLPN nor DPT''')






