





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






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

		# Broadcast the rng state from device 0 to other devices
		SCREAMING_SNAKE_CASE__								=					AcceleratorState()
		if state.distributed_type == DistributedType.TPU:
				SCREAMING_SNAKE_CASE__								=					rng_state.to(xm.xla_device()			)
				xm.collective_broadcast([rng_state]			)
				xm.mark_step()
				SCREAMING_SNAKE_CASE__								=					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
		):
				SCREAMING_SNAKE_CASE__								=					rng_state.to(state.device			)
				torch.distributed.broadcast(UpperCamelCase__    ,      0			)
				SCREAMING_SNAKE_CASE__								=					rng_state.cpu()
		elif state.distributed_type == DistributedType.MULTI_CPU:
				torch.distributed.broadcast(UpperCamelCase__    ,      0			)

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






def  SCREAMING_SNAKE_CASE__							(				UpperCamelCase__:							List[Union[str, RNGType]]    ,      UpperCamelCase__:							Optional[torch.Generator] = None			):
		for rng_type in rng_types:
				synchronize_rng_state(RNGType(UpperCamelCase__			)    ,      generator=UpperCamelCase__			)
from typing import TYPE_CHECKING

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


lowerCAmelCase__							= {
    'configuration_deberta_v2': ['DEBERTA_V2_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DebertaV2Config', 'DebertaV2OnnxConfig'],
    'tokenization_deberta_v2': ['DebertaV2Tokenizer'],
}

try:
    if not is_tokenizers_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCAmelCase__							= ['DebertaV2TokenizerFast']

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCAmelCase__							= [
        'TF_DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST',
        'TFDebertaV2ForMaskedLM',
        'TFDebertaV2ForQuestionAnswering',
        'TFDebertaV2ForSequenceClassification',
        'TFDebertaV2ForTokenClassification',
        'TFDebertaV2Model',
        'TFDebertaV2PreTrainedModel',
    ]

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCAmelCase__							= [
        'DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST',
        'DebertaV2ForMaskedLM',
        'DebertaV2ForMultipleChoice',
        'DebertaV2ForQuestionAnswering',
        'DebertaV2ForSequenceClassification',
        'DebertaV2ForTokenClassification',
        'DebertaV2Model',
        'DebertaV2PreTrainedModel',
    ]


if TYPE_CHECKING:
    from .configuration_deberta_va import (
        DEBERTA_V2_PRETRAINED_CONFIG_ARCHIVE_MAP,
        DebertaVaConfig,
        DebertaVaOnnxConfig,
    )
    from .tokenization_deberta_va import DebertaVaTokenizer

    try:
        if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_deberta_va_fast import DebertaVaTokenizerFast

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_deberta_va import (
            TF_DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
            TFDebertaVaForMaskedLM,
            TFDebertaVaForQuestionAnswering,
            TFDebertaVaForSequenceClassification,
            TFDebertaVaForTokenClassification,
            TFDebertaVaModel,
            TFDebertaVaPreTrainedModel,
        )

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_deberta_va import (
            DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
            DebertaVaForMaskedLM,
            DebertaVaForMultipleChoice,
            DebertaVaForQuestionAnswering,
            DebertaVaForSequenceClassification,
            DebertaVaForTokenClassification,
            DebertaVaModel,
            DebertaVaPreTrainedModel,
        )

else:
    import sys

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