
'''simple docstring'''
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


A__ :		List[str]					={
    'configuration_segformer': ['SEGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'SegformerConfig', 'SegformerOnnxConfig']
}

try:
     if not is_vision_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     A__ :		Any					=['SegformerFeatureExtractor']
     A__ :		Union[str, Any]					=['SegformerImageProcessor']

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     A__ :		Optional[Any]					=[
         'SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
         'SegformerDecodeHead',
         'SegformerForImageClassification',
         'SegformerForSemanticSegmentation',
         'SegformerLayer',
         'SegformerModel',
         'SegformerPreTrainedModel',
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     A__ :		Optional[int]					=[
         'TF_SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
         'TFSegformerDecodeHead',
         'TFSegformerForImageClassification',
         'TFSegformerForSemanticSegmentation',
         'TFSegformerModel',
         'TFSegformerPreTrainedModel',
     ]


if TYPE_CHECKING:
     from .configuration_segformer import SEGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, SegformerConfig, SegformerOnnxConfig

     try:
          if not is_vision_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .feature_extraction_segformer import SegformerFeatureExtractor
          from .image_processing_segformer import SegformerImageProcessor

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_segformer import (
              SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
              SegformerDecodeHead,
              SegformerForImageClassification,
              SegformerForSemanticSegmentation,
              SegformerLayer,
              SegformerModel,
              SegformerPreTrainedModel,
          )
     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_segformer import (
              TF_SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
              TFSegformerDecodeHead,
              TFSegformerForImageClassification,
              TFSegformerForSemanticSegmentation,
              TFSegformerModel,
              TFSegformerPreTrainedModel,
          )

else:
     import sys

     A__ :		Tuple					=_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)








import os

from apache_beam.io.filesystems import FileSystems
from apache_beam.pipeline import Pipeline

from .logging import get_logger


lowerCAmelCase					:				Union[str, Any]   	=2 << 20  # 2mb
lowerCAmelCase					:				Union[str, Any]   	=get_logger(__name__)






class 	_a			(      snake_case_  ):






			def 							_snake_case   (    self )	->					Dict:
									lowerCAmelCase	:   Optional[int]					     =					self._options.get_all_options().get("""runner""" )
									return runner in [None, "DirectRunner", "PortableRunner"]







def  _UpperCAmelCase  (				SCREAMING_SNAKE_CASE__			,SCREAMING_SNAKE_CASE__			,SCREAMING_SNAKE_CASE__=False  ):


						'''simple docstring'''




						lowerCAmelCase	:   Union[str, Any]					     =					FileSystems
						if fs.exists(SCREAMING_SNAKE_CASE__  ):
												if force_upload:
																		logger.info(F"""Remote path already exist: {remote_file_path}. Overwriting it as force_upload=True."""  )
												else:
																		logger.info(F"""Remote path already exist: {remote_file_path}. Skipping it as force_upload=False."""  )
																		return
						with fs.create(SCREAMING_SNAKE_CASE__  ) as remote_file:
												with open(SCREAMING_SNAKE_CASE__			,"""rb"""  ) as local_file:
																		lowerCAmelCase	:   Any					     =					local_file.read(SCREAMING_SNAKE_CASE__  )
																		while chunk:
																								remote_file.write(SCREAMING_SNAKE_CASE__  )
																								lowerCAmelCase	:   List[str]					     =					local_file.read(SCREAMING_SNAKE_CASE__  )







def  _UpperCAmelCase  (				SCREAMING_SNAKE_CASE__			,SCREAMING_SNAKE_CASE__			,SCREAMING_SNAKE_CASE__=False  ):


						'''simple docstring'''




						lowerCAmelCase	:   Optional[int]					     =					FileSystems
						if os.path.exists(SCREAMING_SNAKE_CASE__  ):
												if force_download:
																		logger.info(F"""Local path already exist: {remote_file_path}. Overwriting it as force_upload=True."""  )
												else:
																		logger.info(F"""Local path already exist: {remote_file_path}. Skipping it as force_upload=False."""  )
																		return
						with fs.open(SCREAMING_SNAKE_CASE__  ) as remote_file:
												with open(SCREAMING_SNAKE_CASE__			,"""wb"""  ) as local_file:
																		lowerCAmelCase	:   Optional[int]					     =					remote_file.read(SCREAMING_SNAKE_CASE__  )
																		while chunk:
																								local_file.write(SCREAMING_SNAKE_CASE__  )
																								lowerCAmelCase	:   Union[str, Any]					     =					remote_file.read(SCREAMING_SNAKE_CASE__  )

