




'''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 			__a			(     _UpperCamelCase:					int      ,		_UpperCamelCase:					bool = False					)		->				Dict:

     """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 			__a			(     _UpperCamelCase:					Optional[RNGType] = None      ,		_UpperCamelCase:					Optional[torch.Generator] = None					)		->				Any:

     """simple docstring"""




     if rng_type == RNGType.TORCH:
          _snake_case							=   torch.get_rng_state()
     elif rng_type == RNGType.CUDA:
          _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."
          _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."
          _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."
          _snake_case							=   torch.xpu.get_rng_state()
     elif rng_type == RNGType.GENERATOR:
          assert generator is not None, "Need a generator to synchronize its seed."
          _snake_case							=   generator.get_state()

     # Broadcast the rng state from device 0 to other devices
     _snake_case							=   AcceleratorState()
     if state.distributed_type == DistributedType.TPU:
          _snake_case							=   rng_state.to(xm.xla_device()					)
          xm.collective_broadcast([rng_state]					)
          xm.mark_step()
          _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
     ):
          _snake_case							=   rng_state.to(state.device					)
          torch.distributed.broadcast(_UpperCamelCase      ,		0					)
          _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 			__a			(     _UpperCamelCase:					List[Union[str, RNGType]]      ,		_UpperCamelCase:					Optional[torch.Generator] = None					)		->				Optional[int]:

     """simple docstring"""




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








'''simple docstring'''





import unittest

import numpy as np

from transformers import ElectraConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
   from transformers.models.electra.modeling_flax_electra import (
       FlaxElectraForCausalLM,
       FlaxElectraForMaskedLM,
       FlaxElectraForMultipleChoice,
       FlaxElectraForPreTraining,
       FlaxElectraForQuestionAnswering,
       FlaxElectraForSequenceClassification,
       FlaxElectraForTokenClassification,
       FlaxElectraModel,
   )

class      _a       (		unittest.TestCase							):






     def __init__(				self      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE=13      ,_SCREAMING_SNAKE_CASE=7      ,_SCREAMING_SNAKE_CASE=True      ,_SCREAMING_SNAKE_CASE=True      ,_SCREAMING_SNAKE_CASE=True      ,_SCREAMING_SNAKE_CASE=True      ,_SCREAMING_SNAKE_CASE=99      ,_SCREAMING_SNAKE_CASE=24      ,_SCREAMING_SNAKE_CASE=32      ,_SCREAMING_SNAKE_CASE=5      ,_SCREAMING_SNAKE_CASE=4      ,_SCREAMING_SNAKE_CASE=37      ,_SCREAMING_SNAKE_CASE="gelu"      ,_SCREAMING_SNAKE_CASE=0.1      ,_SCREAMING_SNAKE_CASE=0.1      ,_SCREAMING_SNAKE_CASE=512      ,_SCREAMING_SNAKE_CASE=16      ,_SCREAMING_SNAKE_CASE=2      ,_SCREAMING_SNAKE_CASE=0.0_2      ,_SCREAMING_SNAKE_CASE=4      ,)							->    List[str]:
          _snake_case							=   parent
          _snake_case							=   batch_size
          _snake_case							=   seq_length
          _snake_case							=   is_training
          _snake_case							=   use_attention_mask
          _snake_case							=   use_token_type_ids
          _snake_case							=   use_labels
          _snake_case							=   vocab_size
          _snake_case							=   hidden_size
          _snake_case							=   embedding_size
          _snake_case							=   num_hidden_layers
          _snake_case							=   num_attention_heads
          _snake_case							=   intermediate_size
          _snake_case							=   hidden_act
          _snake_case							=   hidden_dropout_prob
          _snake_case							=   attention_probs_dropout_prob
          _snake_case							=   max_position_embeddings
          _snake_case							=   type_vocab_size
          _snake_case							=   type_sequence_label_size
          _snake_case							=   initializer_range
          _snake_case							=   num_choices






     def        _lowercase			(				self					)							->    int:
          _snake_case							=   ids_tensor([self.batch_size, self.seq_length]      ,self.vocab_size					)

          _snake_case							=   None
          if self.use_attention_mask:
               _snake_case							=   random_attention_mask([self.batch_size, self.seq_length]					)

          _snake_case							=   None
          if self.use_token_type_ids:
               _snake_case							=   ids_tensor([self.batch_size, self.seq_length]      ,self.type_vocab_size					)

          _snake_case							=   ElectraConfig(
              vocab_size=self.vocab_size      ,hidden_size=self.hidden_size      ,embedding_size=self.embedding_size      ,num_hidden_layers=self.num_hidden_layers      ,num_attention_heads=self.num_attention_heads      ,intermediate_size=self.intermediate_size      ,hidden_act=self.hidden_act      ,hidden_dropout_prob=self.hidden_dropout_prob      ,attention_probs_dropout_prob=self.attention_probs_dropout_prob      ,max_position_embeddings=self.max_position_embeddings      ,type_vocab_size=self.type_vocab_size      ,initializer_range=self.initializer_range      ,)

          return config, input_ids, token_type_ids, attention_mask






     def        _lowercase			(				self					)							->    List[str]:
          _snake_case							=   self.prepare_config_and_inputs()
          _snake_case							,      _snake_case							,      _snake_case							,      _snake_case							=   config_and_inputs
          _snake_case							=   {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": attention_mask}
          return config, inputs_dict







@require_flax
class      _a       (		__lowerCAmelCase      ,      unittest.TestCase							):
     SCREAMING_SNAKE_CASE_							:					List[str]					       =		True

     SCREAMING_SNAKE_CASE_							:					Any					       =		(
         (
             FlaxElectraModel,
             FlaxElectraForCausalLM,
             FlaxElectraForMaskedLM,
             FlaxElectraForPreTraining,
             FlaxElectraForTokenClassification,
             FlaxElectraForQuestionAnswering,
             FlaxElectraForMultipleChoice,
             FlaxElectraForSequenceClassification,
         )
         if is_flax_available()
         else ()
     )






     def        _lowercase			(				self					)							->    List[Any]:
          _snake_case							=   FlaxElectraModelTester(self					)






     @slow
     def        _lowercase			(				self					)							->    List[Any]:
          for model_class_name in self.all_model_classes:
               if model_class_name == FlaxElectraForMaskedLM:
                    _snake_case							=   model_class_name.from_pretrained("google/electra-small-generator"					)
               else:
                    _snake_case							=   model_class_name.from_pretrained("google/electra-small-discriminator"					)
               _snake_case							=   model(np.ones((1, 1)					)					)
               self.assertIsNotNone(_SCREAMING_SNAKE_CASE					)


