





'''simple docstring'''

import operator as op


lowercase__						:      Union[str, Any]	=    "scaler.pt"
lowercase__						:      Any	=    "pytorch_model"
lowercase__						:      str	=    "random_states"
lowercase__						:      Optional[Any]	=    "optimizer"
lowercase__						:      Optional[int]	=    "scheduler"
lowercase__						:      Tuple	=    "pytorch_model.bin"
lowercase__						:      Union[str, Any]	=    "pytorch_model.bin.index.json"
lowercase__						:      str	=    "model.safetensors"
lowercase__						:      List[str]	=    "model.safetensors.index.json"
lowercase__						:      int	=    "1.10.2"
lowercase__						:      List[str]	=    "py38"
lowercase__						:      str	=    "4.17.0"
lowercase__						:      Optional[Any]	=    ["ml.p3.16xlarge", "ml.p3dn.24xlarge", "ml.p4dn.24xlarge"]
lowercase__						:      Optional[int]	=    ["FULL_SHARD", "SHARD_GRAD_OP", "NO_SHARD", "HYBRID_SHARD", "HYBRID_SHARD_ZERO2"]
lowercase__						:      List[Any]	=    ["TRANSFORMER_BASED_WRAP", "SIZE_BASED_WRAP", "NO_WRAP"]
lowercase__						:      List[Any]	=    ["BACKWARD_PRE", "BACKWARD_POST", "NO_PREFETCH"]
lowercase__						:      Optional[int]	=    ["FULL_STATE_DICT", "LOCAL_STATE_DICT", "SHARDED_STATE_DICT"]
lowercase__						:      List[str]	=    "2.0.1"
lowercase__						:      Dict	=    ["pdsh", "standard", "openmpi", "mvapich"]
lowercase__						:      Union[str, Any]	=    ["default", "reduce-overhead", "max-autotune"]

lowercase__						:      Any	=    {">": op.gt, ">=": op.ge, "==": op.eq, "!=": op.ne, "<=": op.le, "<": op.lt}

# These are the args for `torch.distributed.launch` for pytorch < 1.9
lowercase__						:      Union[str, Any]	=    [
    "nnodes",
    "nproc_per_node",
    "rdzv_backend",
    "rdzv_endpoint",
    "rdzv_id",
    "rdzv_conf",
    "standalone",
    "max_restarts",
    "monitor_interval",
    "start_method",
    "role",
    "module",
    "m",
    "no_python",
    "run_path",
    "log_dir",
    "r",
    "redirects",
    "t",
    "tee",
    "node_rank",
    "master_addr",
    "master_port",
]

lowercase__						:      int	=    ["DEEPSPEED", "MULTI_GPU", "FSDP", "MEGATRON_LM"]
lowercase__						:      Dict	=    ["DEEPSPEED", "MULTI_XPU", "FSDP"]










'''simple docstring'''

import unittest

import numpy as np

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

from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
   from transformers.models.roberta.modeling_flax_roberta import (
       FlaxRobertaForCausalLM,
       FlaxRobertaForMaskedLM,
       FlaxRobertaForMultipleChoice,
       FlaxRobertaForQuestionAnswering,
       FlaxRobertaForSequenceClassification,
       FlaxRobertaForTokenClassification,
       FlaxRobertaModel,
   )
class     __lowerCAmelCase					(   unittest.TestCase			):






       """simple docstring"""





       def __init__(							self       :		Tuple	,			lowerCAmelCase__       :		Tuple	,			lowerCAmelCase__       :		Union[str, Any]=13	,			lowerCAmelCase__       :		Union[str, Any]=7	,			lowerCAmelCase__       :		str=True	,			lowerCAmelCase__       :		Tuple=True	,			lowerCAmelCase__       :		Dict=True	,			lowerCAmelCase__       :		Dict=True	,			lowerCAmelCase__       :		int=99	,			lowerCAmelCase__       :		str=32	,			lowerCAmelCase__       :		str=5	,			lowerCAmelCase__       :		str=4	,			lowerCAmelCase__       :		str=37	,			lowerCAmelCase__       :		int="gelu"	,			lowerCAmelCase__       :		Optional[Any]=0.1	,			lowerCAmelCase__       :		int=0.1	,			lowerCAmelCase__       :		Optional[int]=512	,			lowerCAmelCase__       :		Dict=16	,			lowerCAmelCase__       :		List[Any]=2	,			lowerCAmelCase__       :		Any=0.02	,			lowerCAmelCase__       :		Union[str, Any]=4	,			)   ->					Dict:

            '''simple docstring'''




            _UpperCamelCase       =	parent
            _UpperCamelCase       =	batch_size
            _UpperCamelCase       =	seq_length
            _UpperCamelCase       =	is_training
            _UpperCamelCase       =	use_attention_mask
            _UpperCamelCase       =	use_token_type_ids
            _UpperCamelCase       =	use_labels
            _UpperCamelCase       =	vocab_size
            _UpperCamelCase       =	hidden_size
            _UpperCamelCase       =	num_hidden_layers
            _UpperCamelCase       =	num_attention_heads
            _UpperCamelCase       =	intermediate_size
            _UpperCamelCase       =	hidden_act
            _UpperCamelCase       =	hidden_dropout_prob
            _UpperCamelCase       =	attention_probs_dropout_prob
            _UpperCamelCase       =	max_position_embeddings
            _UpperCamelCase       =	type_vocab_size
            _UpperCamelCase       =	type_sequence_label_size
            _UpperCamelCase       =	initializer_range
            _UpperCamelCase       =	num_choices





       def snake_case__						(							self       :		Optional[int]					)   ->					Optional[Any]:

            '''simple docstring'''




            _UpperCamelCase       =	ids_tensor([self.batch_size, self.seq_length]	,			self.vocab_size					)

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

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

            _UpperCamelCase       =	RobertaConfig(
                vocab_size=self.vocab_size	,			hidden_size=self.hidden_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	,			is_decoder=lowerCAmelCase__	,			initializer_range=self.initializer_range	,			)

            return config, input_ids, token_type_ids, attention_mask





       def snake_case__						(							self       :		Optional[int]					)   ->					Any:

            '''simple docstring'''




            _UpperCamelCase       =	self.prepare_config_and_inputs()
            _UpperCamelCase   ,    _UpperCamelCase   ,    _UpperCamelCase   ,    _UpperCamelCase       =	config_and_inputs
            _UpperCamelCase       =	{'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': attention_mask}
            return config, inputs_dict







       def snake_case__						(							self       :		List[str]					)   ->					Any:

            '''simple docstring'''




            _UpperCamelCase       =	self.prepare_config_and_inputs()
            _UpperCamelCase   ,    _UpperCamelCase   ,    _UpperCamelCase   ,    _UpperCamelCase       =	config_and_inputs

            _UpperCamelCase       =	True
            _UpperCamelCase       =	floats_tensor([self.batch_size, self.seq_length, self.hidden_size]					)
            _UpperCamelCase       =	ids_tensor([self.batch_size, self.seq_length]	,			vocab_size=2					)

            return (
                config,
                input_ids,
                token_type_ids,
                encoder_hidden_states,
                encoder_attention_mask,
            )

@require_flax
class     __lowerCAmelCase					(   __magic_name__						,						unittest.TestCase			):






       """simple docstring"""
       _snake_case     :					Optional[int]						    =   True

       _snake_case     :					Optional[Any]						    =   (
           (
               FlaxRobertaModel,
               FlaxRobertaForCausalLM,
               FlaxRobertaForMaskedLM,
               FlaxRobertaForSequenceClassification,
               FlaxRobertaForTokenClassification,
               FlaxRobertaForMultipleChoice,
               FlaxRobertaForQuestionAnswering,
           )
           if is_flax_available()
           else ()
       )





       def snake_case__						(							self       :		Dict					)   ->					List[Any]:

            '''simple docstring'''




            _UpperCamelCase       =	FlaxRobertaModelTester(self					)







       @slow
       def snake_case__						(							self       :		List[str]					)   ->					Union[str, Any]:

            '''simple docstring'''




            for model_class_name in self.all_model_classes:
                 _UpperCamelCase       =	model_class_name.from_pretrained('''roberta-base'''	,			from_pt=lowerCAmelCase__					)
                 _UpperCamelCase       =	model(np.ones((1, 1)					)					)
                 self.assertIsNotNone(lowerCAmelCase__					)



