



import os
import unittest

import pytest
from parameterized import parameterized

from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device


# skipping in unittest tests

A__						:						int          =      [(1,)]


def     UpperCamelCase(						):
		lowerCAmelCase_    :  List[Any]             =				bool(os.getenv('''RUN_SLOW'''  ,0				)				)
		if run_slow:
				assert True
		else:
				assert False, "should have been skipped"


def     UpperCamelCase(						):
		lowerCAmelCase_    :  Union[str, Any]             =				bool(os.getenv('''RUN_SLOW'''  ,0				)				)
		if run_slow and torch_device == "cuda":
				assert True
		else:
				assert False, "should have been skipped"



@require_torch
class        __snake_case		(  unittest.TestCase		):




							@slow
							@require_torch_gpu
							def      UpperCAmelCase__    ( self  : Optional[int]):
									check_slow_torch_cuda()




							@require_torch_gpu
							@slow
							def      UpperCAmelCase__    ( self  : str):
									check_slow_torch_cuda()







							@parameterized.expand(A_)
							@slow
							def      UpperCAmelCase__    ( self  : List[str]       ,       A_  : Optional[int]=None):
									check_slow()





@slow
@require_torch_gpu
def     UpperCamelCase(						):
		check_slow_torch_cuda()


@require_torch_gpu
@slow
def     UpperCamelCase(						):
		check_slow_torch_cuda()


@slow
@pytest.mark.parametrize('''param'''  ,[1]				)
def     UpperCamelCase(						__UpperCamelCase							:	Optional[int]				):
		check_slow()





@pytest.mark.parametrize('''param'''  ,[1]				)
@slow
def     UpperCamelCase(						__UpperCamelCase							:	Optional[int]				):
		check_slow()










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__						:						int          =      ['''SegformerFeatureExtractor''']
						A__						:						Dict          =      ['''SegformerImageProcessor''']

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A__						:						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__						:						List[Any]          =      [
						    '''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__						:						Dict          =      _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





