


from typing import TYPE_CHECKING

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


_UpperCAmelCase     :					Dict       ={"configuration_gptj": ["GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTJConfig", "GPTJOnnxConfig"]}

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

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

try:
						if not is_flax_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_UpperCAmelCase     :					Optional[int]       =[
						    "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

						_UpperCAmelCase     :					List[str]       =_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



from typing import List, Optional, Tuple, Union

from . import logging
from .import_utils import is_torch_available, is_torch_version


if is_torch_available():
						import torch

_UpperCAmelCase     :					List[Any]       =logging.get_logger(__name__)  # pylint: disable=invalid-name

try:
						from torch._dynamo import allow_in_graph as maybe_allow_in_graph
except (ImportError, ModuleNotFoundError):



						def      lowerCAmelCase	(						cls	)->	int:
										return cls



def      lowerCAmelCase	(						lowerCAmelCase_	,				lowerCAmelCase_ = None	,				lowerCAmelCase_ = None	,				lowerCAmelCase_ = None	,				lowerCAmelCase_ = None	,				)->	int:
				lowerCAmelCase_	: Tuple      			=			device
				lowerCAmelCase_	: List[str]      			=			shape[0]

				lowerCAmelCase_	: Union[str, Any]      			=			layout or torch.strided
				lowerCAmelCase_	: Union[str, Any]      			=			device or torch.device('''cpu'''	)

				if generator is not None:
								lowerCAmelCase_	: List[str]      			=			generator.device.type if not isinstance(lowerCAmelCase_	,				lowerCAmelCase_	) else generator[0].device.type
								if gen_device_type != device.type and gen_device_type == "cpu":
												lowerCAmelCase_	: Optional[Any]      			=			'''cpu'''
												if device != "mps":
																logger.info(
																    f"""The passed generator was created on 'cpu' even though a tensor on {device} was expected."""
																    f""" Tensors will be created on 'cpu' and then moved to {device}. Note that one can probably"""
																    f""" slighly speed up this function by passing a generator that was created on the {device} device."""	)
								elif gen_device_type != device.type and gen_device_type == "cuda":
												raise ValueError(f"""Cannot generate a {device} tensor from a generator of type {gen_device_type}."""	)

				if isinstance(lowerCAmelCase_	,				lowerCAmelCase_	):
								lowerCAmelCase_	: Dict      			=			(1,) + shape[1:]
								lowerCAmelCase_	: Union[str, Any]      			=			[
								    torch.randn(lowerCAmelCase_	,				generator=generator[i]	,				device=lowerCAmelCase_	,				dtype=lowerCAmelCase_	,				layout=lowerCAmelCase_	)
								    for i in range(lowerCAmelCase_	)
								]
								lowerCAmelCase_	: Tuple      			=			torch.cat(lowerCAmelCase_	,				dim=0	).to(lowerCAmelCase_	)
				else:
								lowerCAmelCase_	: int      			=			torch.randn(lowerCAmelCase_	,				generator=lowerCAmelCase_	,				device=lowerCAmelCase_	,				dtype=lowerCAmelCase_	,				layout=lowerCAmelCase_	).to(lowerCAmelCase_	)

				return latents




def      lowerCAmelCase	(						lowerCAmelCase_	)->	Optional[Any]:
				if is_torch_version('''<'''	,				'''2.0.0'''	) or not hasattr(lowerCAmelCase_	,				'''_dynamo'''	):
								return False
				return isinstance(lowerCAmelCase_	,				torch._dynamo.eval_frame.OptimizedModule	)