




import re
from pathlib import Path
from unittest import TestCase

import pytest





@pytest.mark.integration
class        lowerCamelCase_							(      _SCREAMING_SNAKE_CASE					):


 '''simple docstring'''







 def       lowerCAmelCase_  (							self  :		str				,				_lowerCAmelCase  :		str						):

       with open(_lowerCAmelCase				,				encoding='utf-8'						) as input_file:
             SCREAMING_SNAKE_CASE_							=			re.compile(R'(?!.*\b(?:encoding|rb|w|wb|w+|wb+|ab|ab+)\b)(?<=\s)(open)\((.*)\)'						)
             SCREAMING_SNAKE_CASE_							=			input_file.read()
             SCREAMING_SNAKE_CASE_							=			regexp.search(_lowerCAmelCase						)

       return match



 def       lowerCAmelCase_  (							self  :		List[str]				,				_lowerCAmelCase  :		str						):

       with open(_lowerCAmelCase				,				encoding='utf-8'						) as input_file:
             SCREAMING_SNAKE_CASE_							=			re.compile(R'#[^\r\n]*print\(|\"[^\r\n]*print\(|\"\"\".*?print\(.*?\"\"\"|(print\()'				,				re.DOTALL						)
             SCREAMING_SNAKE_CASE_							=			input_file.read()
             # use `re.finditer` to handle the case where the ignored groups would be matched first by `re.search`
             SCREAMING_SNAKE_CASE_							=			regexp.finditer(_lowerCAmelCase						)

       SCREAMING_SNAKE_CASE_							=			[match for match in matches if match is not None and match.group(1						) is not None]
       return matches[0] if matches else None



 def       lowerCAmelCase_  (							self  :		str						):
       SCREAMING_SNAKE_CASE_							=			Path('./datasets'						)
       SCREAMING_SNAKE_CASE_							=			list(dataset_paths.absolute().glob('**/*.py'						)						)

       for dataset in dataset_files:
             if self._no_encoding_on_file_open(str(_lowerCAmelCase						)						):
                   raise AssertionError(F"open(...) must use utf-8 encoding in {dataset}"						)




 def       lowerCAmelCase_  (							self  :		int						):
       SCREAMING_SNAKE_CASE_							=			Path('./datasets'						)
       SCREAMING_SNAKE_CASE_							=			list(dataset_paths.absolute().glob('**/*.py'						)						)

       for dataset in dataset_files:
             if self._no_print_statements(str(_lowerCAmelCase						)						):
                   raise AssertionError(F"print statement found in {dataset}. Use datasets.logger/logging instead."						)





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


lowerCamelCase__ :   str					=							{
    'configuration_mask2former': [
        'MASK2FORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'Mask2FormerConfig',
    ],
}

try:
   if not is_vision_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCamelCase__ :   int					=							['Mask2FormerImageProcessor']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCamelCase__ :   Dict					=							[
       'MASK2FORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
       'Mask2FormerForUniversalSegmentation',
       'Mask2FormerModel',
       'Mask2FormerPreTrainedModel',
   ]

if TYPE_CHECKING:
   from .configuration_maskaformer import MASK2FORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, MaskaFormerConfig

   try:
      if not is_vision_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .image_processing_maskaformer import MaskaFormerImageProcessor

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_maskaformer import (
          MASK2FORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
          MaskaFormerForUniversalSegmentation,
          MaskaFormerModel,
          MaskaFormerPreTrainedModel,
      )


else:
   import sys

   lowerCamelCase__ :   Tuple					=							_LazyModule(__name__, globals()['__file__'], _import_structure)