



"""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 	snake_case__  (						__lowerCamelCase							:       int				,						__lowerCamelCase							:       bool = False      ):
			"""simple docstring"""
			if device_specific:
						seed += AcceleratorState().process_index
			random.seed(__lowerCamelCase      )
			np.random.seed(__lowerCamelCase      )
			torch.manual_seed(__lowerCamelCase      )
			if is_xpu_available():
						torch.xpu.manual_seed_all(__lowerCamelCase      )
			elif is_npu_available():
						torch.npu.manual_seed_all(__lowerCamelCase      )
			else:
						torch.cuda.manual_seed_all(__lowerCamelCase      )
			# ^^ safe to call this function even if cuda is not available
			if is_tpu_available():
						xm.set_rng_state(__lowerCamelCase      )






def 	snake_case__  (						__lowerCamelCase							:       Optional[RNGType] = None				,						__lowerCamelCase							:       Optional[torch.Generator] = None      ):
			"""simple docstring"""
			# Get the proper rng state
			if rng_type == RNGType.TORCH:
						lowerCamelCase__						:						Dict						      =torch.get_rng_state()
			elif rng_type == RNGType.CUDA:
						lowerCamelCase__						:						Optional[Any]						      =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__						:						Any						      =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__						:						List[str]						      =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__						:						Dict						      =torch.xpu.get_rng_state()
			elif rng_type == RNGType.GENERATOR:
						assert generator is not None, "Need a generator to synchronize its seed."
						lowerCamelCase__						:						Union[str, Any]						      =generator.get_state()

			# Broadcast the rng state from device 0 to other devices
			lowerCamelCase__						:						Tuple						      =AcceleratorState()
			if state.distributed_type == DistributedType.TPU:
						lowerCamelCase__						:						Any						      =rng_state.to(xm.xla_device()      )
						xm.collective_broadcast([rng_state]      )
						xm.mark_step()
						lowerCamelCase__						:						int						      =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__						:						Optional[Any]						      =rng_state.to(state.device      )
						torch.distributed.broadcast(__lowerCamelCase				,						0      )
						lowerCamelCase__						:						List[Any]						      =rng_state.cpu()
			elif state.distributed_type == DistributedType.MULTI_CPU:
						torch.distributed.broadcast(__lowerCamelCase				,						0      )

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






def 	snake_case__  (						__lowerCamelCase							:       List[Union[str, RNGType]]				,						__lowerCamelCase							:       Optional[torch.Generator] = None      ):
			"""simple docstring"""
			for rng_type in rng_types:
						synchronize_rng_state(RNGType(__lowerCamelCase      )				,						generator=__lowerCamelCase      )









"""simple docstring"""






from typing import TYPE_CHECKING

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


_lowercase					:					str 		=		{
    "configuration_openai": ["OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP", "OpenAIGPTConfig"],
    "tokenization_openai": ["OpenAIGPTTokenizer"],
}

try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowercase					:					List[Any] 		=		["OpenAIGPTTokenizerFast"]

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowercase					:					Union[str, Any] 		=		[
		    "OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "OpenAIGPTDoubleHeadsModel",
		    "OpenAIGPTForSequenceClassification",
		    "OpenAIGPTLMHeadModel",
		    "OpenAIGPTModel",
		    "OpenAIGPTPreTrainedModel",
		    "load_tf_weights_in_openai_gpt",
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowercase					:					Optional[Any] 		=		[
		    "TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "TFOpenAIGPTDoubleHeadsModel",
		    "TFOpenAIGPTForSequenceClassification",
		    "TFOpenAIGPTLMHeadModel",
		    "TFOpenAIGPTMainLayer",
		    "TFOpenAIGPTModel",
		    "TFOpenAIGPTPreTrainedModel",
		]


if TYPE_CHECKING:
		from .configuration_openai import OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP, OpenAIGPTConfig
		from .tokenization_openai import OpenAIGPTTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_openai_fast import OpenAIGPTTokenizerFast

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_openai import (
				    OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    OpenAIGPTDoubleHeadsModel,
				    OpenAIGPTForSequenceClassification,
				    OpenAIGPTLMHeadModel,
				    OpenAIGPTModel,
				    OpenAIGPTPreTrainedModel,
				    load_tf_weights_in_openai_gpt,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_openai import (
				    TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFOpenAIGPTDoubleHeadsModel,
				    TFOpenAIGPTForSequenceClassification,
				    TFOpenAIGPTLMHeadModel,
				    TFOpenAIGPTMainLayer,
				    TFOpenAIGPTModel,
				    TFOpenAIGPTPreTrainedModel,
				)

else:
		import sys

		_lowercase					:					Dict 		=		_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




