


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     UpperCAmelCase_ (							__snake_case					,						__snake_case = False						)					->      List[Any]:







 """simple docstring"""


 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     UpperCAmelCase_ (							__snake_case = None					,						__snake_case = None						)					->      Optional[Any]:







 """simple docstring"""


 if rng_type == RNGType.TORCH:
  _lowercase											=torch.get_rng_state()
 elif rng_type == RNGType.CUDA:
  _lowercase											=torch.cuda.get_rng_state()
 elif rng_type == RNGType.XLA:
  assert is_tpu_available(), "Can't synchronize XLA seeds on an environment without TPUs."
  _lowercase											=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."
  _lowercase											=torch.npu.get_rng_state()
 elif rng_type == RNGType.XPU:
  assert is_xpu_available(), "Can't synchronize XPU seeds on an environment without XPUs."
  _lowercase											=torch.xpu.get_rng_state()
 elif rng_type == RNGType.GENERATOR:
  assert generator is not None, "Need a generator to synchronize its seed."
  _lowercase											=generator.get_state()

 # Broadcast the rng state from device 0 to other devices
 _lowercase											=AcceleratorState()
 if state.distributed_type == DistributedType.TPU:
  _lowercase											=rng_state.to(xm.xla_device()						)
  xm.collective_broadcast([rng_state]						)
  xm.mark_step()
  _lowercase											=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
 ):
  _lowercase											=rng_state.to(state.device						)
  torch.distributed.broadcast(_UpperCamelCase					,						0						)
  _lowercase											=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     UpperCAmelCase_ (							__snake_case					,						__snake_case = None						)					->      str:







 """simple docstring"""


 for rng_type in rng_types:
  synchronize_rng_state(RNGType(_UpperCamelCase						)					,						generator=_UpperCamelCase						)





from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


if TYPE_CHECKING:
  from ... import FeatureExtractionMixin, PreTrainedTokenizerBase, TensorType


UpperCAmelCase__            =							logging.get_logger(__name__)

UpperCAmelCase__            =							{
    '''microsoft/deberta-base''': '''https://huggingface.co/microsoft/deberta-base/resolve/main/config.json''',
    '''microsoft/deberta-large''': '''https://huggingface.co/microsoft/deberta-large/resolve/main/config.json''',
    '''microsoft/deberta-xlarge''': '''https://huggingface.co/microsoft/deberta-xlarge/resolve/main/config.json''',
    '''microsoft/deberta-base-mnli''': '''https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/config.json''',
    '''microsoft/deberta-large-mnli''': '''https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/config.json''',
    '''microsoft/deberta-xlarge-mnli''': '''https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/config.json''',
}







class 				lowerCamelCase__       (	lowerCAmelCase):
     SCREAMING_SNAKE_CASE__				      =      '''deberta'''





     def __init__(self		,					UpperCAmelCase=5_0_2_6_5		,					UpperCAmelCase=7_6_8		,					UpperCAmelCase=1_2		,					UpperCAmelCase=1_2		,					UpperCAmelCase=3_0_7_2		,					UpperCAmelCase="gelu"		,					UpperCAmelCase=0.1		,					UpperCAmelCase=0.1		,					UpperCAmelCase=5_1_2		,					UpperCAmelCase=0		,					UpperCAmelCase=0.02		,					UpperCAmelCase=1e-7		,					UpperCAmelCase=False		,					UpperCAmelCase=-1		,					UpperCAmelCase=0		,					UpperCAmelCase=True		,					UpperCAmelCase=None		,					UpperCAmelCase=0		,					UpperCAmelCase="gelu"		,					**UpperCAmelCase		,					)					->       Dict:
      super().__init__(**UpperCAmelCase	)

      _lowercase											=hidden_size
      _lowercase											=num_hidden_layers
      _lowercase											=num_attention_heads
      _lowercase											=intermediate_size
      _lowercase											=hidden_act
      _lowercase											=hidden_dropout_prob
      _lowercase											=attention_probs_dropout_prob
      _lowercase											=max_position_embeddings
      _lowercase											=type_vocab_size
      _lowercase											=initializer_range
      _lowercase											=relative_attention
      _lowercase											=max_relative_positions
      _lowercase											=pad_token_id
      _lowercase											=position_biased_input

      # Backwards compatibility
      if type(UpperCAmelCase	) == str:
       _lowercase											=[x.strip() for x in pos_att_type.lower().split('''|'''	)]

      _lowercase											=pos_att_type
      _lowercase											=vocab_size
      _lowercase											=layer_norm_eps

      _lowercase											=kwargs.get('''pooler_hidden_size'''		,					UpperCAmelCase	)
      _lowercase											=pooler_dropout
      _lowercase											=pooler_hidden_act







class 				lowerCamelCase__       (	lowerCAmelCase):





     @property
     def 					__A (self	)					->       Mapping[str, Mapping[int, str]]:
      if self.task == "multiple-choice":
       _lowercase											={0: '''batch''', 1: '''choice''', 2: '''sequence'''}
      else:
       _lowercase											={0: '''batch''', 1: '''sequence'''}
      if self._config.type_vocab_size > 0:
       return OrderedDict(
           [('''input_ids''', dynamic_axis), ('''attention_mask''', dynamic_axis), ('''token_type_ids''', dynamic_axis)]	)
      else:
       return OrderedDict([('''input_ids''', dynamic_axis), ('''attention_mask''', dynamic_axis)]	)





     @property
     def 					__A (self	)					->       int:
      return 1_2





     def 					__A (self		,					UpperCAmelCase		,					UpperCAmelCase = -1		,					UpperCAmelCase = -1		,					UpperCAmelCase = -1		,					UpperCAmelCase = False		,					UpperCAmelCase = None		,					UpperCAmelCase = 3		,					UpperCAmelCase = 4_0		,					UpperCAmelCase = 4_0		,					UpperCAmelCase = None		,					)					->       Mapping[str, Any]:
      _lowercase											=super().generate_dummy_inputs(preprocessor=UpperCAmelCase		,					framework=UpperCAmelCase	)
      if self._config.type_vocab_size == 0 and "token_type_ids" in dummy_inputs:
       del dummy_inputs["token_type_ids"]
      return dummy_inputs

