
'''simple docstring'''






import random
from typing import List, Optional, Union

import numpy as np
import torch

from ..state import AcceleratorState
from .constants import CUDA_DISTRIBUTED_TYPES
from .dataclasses import DistributedType, RNGType
from .imports import is_npu_available, is_tpu_available, is_xpu_available


if is_tpu_available(check_device=False):
							import torch_xla.core.xla_model as xm







def 		SCREAMING_SNAKE_CASE__					(							_SCREAMING_SNAKE_CASE  ,					_SCREAMING_SNAKE_CASE = False  ):
		if device_specific:
				seed += AcceleratorState().process_index
		random.seed(_SCREAMING_SNAKE_CASE  )
		np.random.seed(_SCREAMING_SNAKE_CASE  )
		torch.manual_seed(_SCREAMING_SNAKE_CASE  )
		if is_xpu_available():
				torch.xpu.manual_seed_all(_SCREAMING_SNAKE_CASE  )
		elif is_npu_available():
				torch.npu.manual_seed_all(_SCREAMING_SNAKE_CASE  )
		else:
				torch.cuda.manual_seed_all(_SCREAMING_SNAKE_CASE  )
		# ^^ safe to call this function even if cuda is not available
		if is_tpu_available():
				xm.set_rng_state(_SCREAMING_SNAKE_CASE  )







def 		SCREAMING_SNAKE_CASE__					(							_SCREAMING_SNAKE_CASE = None  ,					_SCREAMING_SNAKE_CASE = None  ):
		# Get the proper rng state
		if rng_type == RNGType.TORCH:
				lowerCAmelCase_   :       str    							=torch.get_rng_state()
		elif rng_type == RNGType.CUDA:
				lowerCAmelCase_   :       Optional[int]    							=torch.cuda.get_rng_state()
		elif rng_type == RNGType.XLA:
				assert is_tpu_available(), "Can't synchronize XLA seeds on an environment without TPUs."
				lowerCAmelCase_   :       Optional[int]    							=torch.tensor(xm.get_rng_state()  )
		elif rng_type == RNGType.NPU:
				assert is_npu_available(), "Can't synchronize NPU seeds on an environment without NPUs."
				lowerCAmelCase_   :       Optional[Any]    							=torch.npu.get_rng_state()
		elif rng_type == RNGType.XPU:
				assert is_xpu_available(), "Can't synchronize XPU seeds on an environment without XPUs."
				lowerCAmelCase_   :       List[str]    							=torch.xpu.get_rng_state()
		elif rng_type == RNGType.GENERATOR:
				assert generator is not None, "Need a generator to synchronize its seed."
				lowerCAmelCase_   :       Tuple    							=generator.get_state()

		# Broadcast the rng state from device 0 to other devices
		lowerCAmelCase_   :       List[str]    							=AcceleratorState()
		if state.distributed_type == DistributedType.TPU:
				lowerCAmelCase_   :       Union[str, Any]    							=rng_state.to(xm.xla_device()  )
				xm.collective_broadcast([rng_state]  )
				xm.mark_step()
				lowerCAmelCase_   :       Any    							=rng_state.cpu()
		elif (
		    state.distributed_type in CUDA_DISTRIBUTED_TYPES
		    or state.distributed_type == DistributedType.MULTI_NPU
		    or state.distributed_type == DistributedType.MULTI_XPU
		):
				lowerCAmelCase_   :       Tuple    							=rng_state.to(state.device  )
				torch.distributed.broadcast(_SCREAMING_SNAKE_CASE  ,					0  )
				lowerCAmelCase_   :       List[str]    							=rng_state.cpu()
		elif state.distributed_type == DistributedType.MULTI_CPU:
				torch.distributed.broadcast(_SCREAMING_SNAKE_CASE  ,					0  )

		# Set the broadcast rng state
		if rng_type == RNGType.TORCH:
				torch.set_rng_state(_SCREAMING_SNAKE_CASE  )
		elif rng_type == RNGType.CUDA:
				torch.cuda.set_rng_state(_SCREAMING_SNAKE_CASE  )
		elif rng_type == RNGType.NPU:
				torch.npu.set_rng_state(_SCREAMING_SNAKE_CASE  )
		elif rng_type == RNGType.XPU:
				torch.xpu.set_rng_state(_SCREAMING_SNAKE_CASE  )
		elif rng_type == RNGType.XLA:
				xm.set_rng_state(rng_state.item()  )
		elif rng_type == RNGType.GENERATOR:
				generator.set_state(_SCREAMING_SNAKE_CASE  )







def 		SCREAMING_SNAKE_CASE__					(							_SCREAMING_SNAKE_CASE  ,					_SCREAMING_SNAKE_CASE = None  ):
		for rng_type in rng_types:
				synchronize_rng_state(RNGType(_SCREAMING_SNAKE_CASE  )  ,					generator=_SCREAMING_SNAKE_CASE  )








'''simple docstring'''






from typing import TYPE_CHECKING

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


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

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

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

try:
							if not is_flax_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__lowercase	=    [
							    '''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

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






