




'''simple docstring'''

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__         =     [(1,)]




def    snake_case__     (  )			->       Tuple:





			'''simple docstring'''






			snake_case__          =						bool(os.getenv("""RUN_SLOW"""       ,      0	)	)
			if run_slow:
						assert True
			else:
						assert False, "should have been skipped"




def    snake_case__     (  )			->       Dict:





			'''simple docstring'''






			snake_case__          =						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 __magic_name__(						unittest.TestCase      ):






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



									'''simple docstring'''






									check_slow_torch_cuda()






						@require_torch_gpu
						@slow
						def       __lowerCAmelCase(   self						:						Dict       ):



									'''simple docstring'''






									check_slow_torch_cuda()







						@parameterized.expand(__UpperCamelCase       )
						@slow
						def       __lowerCAmelCase(   self						:						Tuple					,   __UpperCamelCase						:						int=None       ):



									'''simple docstring'''






									check_slow()







@slow
@require_torch_gpu
def    snake_case__     (  )			->       Tuple:





			'''simple docstring'''






			check_slow_torch_cuda()




@require_torch_gpu
@slow
def    snake_case__     (  )			->       List[Any]:





			'''simple docstring'''






			check_slow_torch_cuda()




@slow
@pytest.mark.parametrize("""param"""       ,      [1]	)
def    snake_case__     (  a	)			->       Union[str, Any]:





			'''simple docstring'''






			check_slow()




@pytest.mark.parametrize("""param"""       ,      [1]	)
@slow
def    snake_case__     (  a	)			->       Dict:





			'''simple docstring'''






			check_slow()





'''simple docstring'''

from typing import TYPE_CHECKING

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


a__         =     {'''configuration_gptj''': ['''GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTJConfig''', '''GPTJOnnxConfig''']}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		a__         =     [
		    '''GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''GPTJForCausalLM''',
		    '''GPTJForQuestionAnswering''',
		    '''GPTJForSequenceClassification''',
		    '''GPTJModel''',
		    '''GPTJPreTrainedModel''',
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		a__         =     [
		    '''TFGPTJForCausalLM''',
		    '''TFGPTJForQuestionAnswering''',
		    '''TFGPTJForSequenceClassification''',
		    '''TFGPTJModel''',
		    '''TFGPTJPreTrainedModel''',
		]

try:
		if not is_flax_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		a__         =     [
		    '''FlaxGPTJForCausalLM''',
		    '''FlaxGPTJModel''',
		    '''FlaxGPTJPreTrainedModel''',
		]


if TYPE_CHECKING:
		from .configuration_gptj import GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTJConfig, GPTJOnnxConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_gptj import (
				    GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST,
				    GPTJForCausalLM,
				    GPTJForQuestionAnswering,
				    GPTJForSequenceClassification,
				    GPTJModel,
				    GPTJPreTrainedModel,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_gptj import (
				    TFGPTJForCausalLM,
				    TFGPTJForQuestionAnswering,
				    TFGPTJForSequenceClassification,
				    TFGPTJModel,
				    TFGPTJPreTrainedModel,
				)

		try:
				if not is_flax_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_flax_gptj import FlaxGPTJForCausalLM, FlaxGPTJModel, FlaxGPTJPreTrainedModel

else:
		import sys

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