
"""simple docstring"""



import re
from pathlib import Path
from unittest import TestCase

import pytest

@pytest.mark.integration
class       lowerCAmelCase__				(						UpperCamelCase__	):







     def      lowercase	(							self			:			Dict    ,      _lowerCamelCase			:			str			):







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

       return match







     def      lowercase	(							self			:			List[str]    ,      _lowerCamelCase			:			str			):







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

       _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      lowercase	(							self			:			List[Any]			):
       _snake_case					     =       Path('''./datasets'''			)
       _snake_case					     =       list(dataset_paths.absolute().glob('''**/*.py'''			)			)

       for dataset in dataset_files:
         if self._no_encoding_on_file_open(str(UpperCamelCase_			)			):
           raise AssertionError(f'''open(...) must use utf-8 encoding in {dataset}'''			)







     def      lowercase	(							self			:			Any			):
       _snake_case					     =       Path('''./datasets'''			)
       _snake_case					     =       list(dataset_paths.absolute().glob('''**/*.py'''			)			)

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







from typing import TYPE_CHECKING

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


lowerCAmelCase       =							{'configuration_dpt': ['DPT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DPTConfig']}

try:
			if not is_vision_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowerCAmelCase       =							['DPTFeatureExtractor']
			lowerCAmelCase       =							['DPTImageProcessor']

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowerCAmelCase       =							[
			    'DPT_PRETRAINED_MODEL_ARCHIVE_LIST',
			    'DPTForDepthEstimation',
			    'DPTForSemanticSegmentation',
			    'DPTModel',
			    'DPTPreTrainedModel',
			]


if TYPE_CHECKING:
			from .configuration_dpt import DPT_PRETRAINED_CONFIG_ARCHIVE_MAP, DPTConfig

			try:
						if not is_vision_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .feature_extraction_dpt import DPTFeatureExtractor
						from .image_processing_dpt import DPTImageProcessor

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_dpt import (
						    DPT_PRETRAINED_MODEL_ARCHIVE_LIST,
						    DPTForDepthEstimation,
						    DPTForSemanticSegmentation,
						    DPTModel,
						    DPTPreTrainedModel,
						)


else:
			import sys

			lowerCAmelCase       =							_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


