
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						(							snake_case__: Optional[int]						,       snake_case__: Dict = False ):


    '''simple docstring'''







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




def     a						(							snake_case__: Optional[int] = None						,       snake_case__: str = None ):


    '''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(snake_case__						,       0 )
        lowercase_					  = rng_state.cpu()
    elif state.distributed_type == DistributedType.MULTI_CPU:
        torch.distributed.broadcast(snake_case__						,       0 )

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




def     a						(							snake_case__: Optional[int]						,       snake_case__: Any = None ):


    '''simple docstring'''







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






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 			lowercase__(    unittest.TestCase							):



	"""simple docstring"""




	def __init__(	self  :      str	,							SCREAMING_SNAKE_CASE_  :      str	,							SCREAMING_SNAKE_CASE_  :      Optional[int]=1_3	,							SCREAMING_SNAKE_CASE_  :      int=7	,							SCREAMING_SNAKE_CASE_  :      Tuple=True	,							SCREAMING_SNAKE_CASE_  :      List[Any]=True	,							SCREAMING_SNAKE_CASE_  :      List[Any]=True	,							SCREAMING_SNAKE_CASE_  :      Dict=True	,							SCREAMING_SNAKE_CASE_  :      Optional[Any]=9_9	,							SCREAMING_SNAKE_CASE_  :      Any=2_4	,							SCREAMING_SNAKE_CASE_  :      int=3_2	,							SCREAMING_SNAKE_CASE_  :      Any=5	,							SCREAMING_SNAKE_CASE_  :      List[str]=4	,							SCREAMING_SNAKE_CASE_  :      Optional[int]=3_7	,							SCREAMING_SNAKE_CASE_  :      Optional[int]="gelu"	,							SCREAMING_SNAKE_CASE_  :      Any=0.1	,							SCREAMING_SNAKE_CASE_  :      List[Any]=0.1	,							SCREAMING_SNAKE_CASE_  :      Dict=5_1_2	,							SCREAMING_SNAKE_CASE_  :      Optional[int]=1_6	,							SCREAMING_SNAKE_CASE_  :      List[Any]=2	,							SCREAMING_SNAKE_CASE_  :      List[str]=0.02	,							SCREAMING_SNAKE_CASE_  :      List[str]=4	,							)	->					Optional[int]:
					lowercase_					  = parent
					lowercase_					  = batch_size
					lowercase_					  = seq_length
					lowercase_					  = is_training
					lowercase_					  = use_attention_mask
					lowercase_					  = use_token_type_ids
					lowercase_					  = use_labels
					lowercase_					  = vocab_size
					lowercase_					  = hidden_size
					lowercase_					  = embedding_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_					  = type_sequence_label_size
					lowercase_					  = initializer_range
					lowercase_					  = num_choices
	def 						_lowercase  (	self  :      Optional[int]	)	->					Tuple:
					lowercase_					  = ids_tensor([self.batch_size, self.seq_length]	,							self.vocab_size	)

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

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

					lowercase_					  = 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  :      Any	)	->					str:
					lowercase_					  = self.prepare_config_and_inputs()
					lowercase_  ,     lowercase_  ,     lowercase_  ,     lowercase_					  = config_and_inputs
					lowercase_					  = {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': attention_mask}
					return config, inputs_dict

@require_flax
class 			lowercase__(    UpperCAmelCase			,						unittest.TestCase							):



	"""simple docstring"""




	a      :List[str]   					=     True

	a      :List[Any]   					=     (
	    (
	        FlaxElectraModel,
	        FlaxElectraForCausalLM,
	        FlaxElectraForMaskedLM,
	        FlaxElectraForPreTraining,
	        FlaxElectraForTokenClassification,
	        FlaxElectraForQuestionAnswering,
	        FlaxElectraForMultipleChoice,
	        FlaxElectraForSequenceClassification,
	    )
	    if is_flax_available()
	    else ()
	)
	def 						_lowercase  (	self  :      List[str]	)	->					Optional[Any]:
					lowercase_					  = FlaxElectraModelTester(self	)




	@slow
	def 						_lowercase  (	self  :      List[str]	)	->					int:
					for model_class_name in self.all_model_classes:
									if model_class_name == FlaxElectraForMaskedLM:
													lowercase_					  = model_class_name.from_pretrained('''google/electra-small-generator'''	)
									else:
													lowercase_					  = model_class_name.from_pretrained('''google/electra-small-discriminator'''	)
									lowercase_					  = model(np.ones((1, 1)	)	)
									self.assertIsNotNone(SCREAMING_SNAKE_CASE_	)




