






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       __UpperCAmelCase		(unittest.TestCase    ):







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







									'''simple docstring'''




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







			def 							UpperCamelCase      (	self:  Dict     ):







									'''simple docstring'''




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

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

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

									_SCREAMING_SNAKE_CASE	      =  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=UpperCAmelCase_      ,	initializer_range=self.initializer_range      ,	)

									return config, input_ids, token_type_ids, attention_mask







			def 							UpperCamelCase      (	self:  Optional[Any]     ):







									'''simple docstring'''




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







			def 							UpperCamelCase      (	self:  Optional[int]     ):







									'''simple docstring'''




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

									_SCREAMING_SNAKE_CASE	      =  True
									_SCREAMING_SNAKE_CASE	      =  floats_tensor([self.batch_size, self.seq_length, self.hidden_size]     )
									_SCREAMING_SNAKE_CASE	      =  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       __UpperCAmelCase		(_UpperCAmelCase       ,unittest.TestCase    ):
			__snake_case				:      Optional[int] 						=							True

			__snake_case				:      Any 						=							(
			    (
			        FlaxRobertaModel,
			        FlaxRobertaForCausalLM,
			        FlaxRobertaForMaskedLM,
			        FlaxRobertaForSequenceClassification,
			        FlaxRobertaForTokenClassification,
			        FlaxRobertaForMultipleChoice,
			        FlaxRobertaForQuestionAnswering,
			    )
			    if is_flax_available()
			    else ()
			)







			def 							UpperCamelCase      (	self:  Optional[Any]     ):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE	      =  FlaxRobertaModelTester(self     )







			@slow
			def 							UpperCamelCase      (	self:  Optional[Any]     ):







									'''simple docstring'''




									for model_class_name in self.all_model_classes:
															_SCREAMING_SNAKE_CASE	      =  model_class_name.from_pretrained("""roberta-base"""      ,	from_pt=UpperCAmelCase_     )
															_SCREAMING_SNAKE_CASE	      =  model(np.ones((1, 1)     )     )
															self.assertIsNotNone(UpperCAmelCase_     )





'''simple docstring'''

# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from argparse import ArgumentParser

from accelerate.commands.config import get_config_parser
from accelerate.commands.env import env_command_parser
from accelerate.commands.launch import launch_command_parser
from accelerate.commands.test import test_command_parser
from accelerate.commands.tpu import tpu_command_parser





def __snake_case					(      ):
 lowerCamelCase_      =  ArgumentParser("Accelerate CLI tool"					,				usage="accelerate <command> [<args>]"					,				allow_abbrev=UpperCAmelCase_		)
 lowerCamelCase_      =  parser.add_subparsers(help="accelerate command helpers"		)

 # Register commands
 get_config_parser(subparsers=UpperCAmelCase_		)
 env_command_parser(subparsers=UpperCAmelCase_		)
 launch_command_parser(subparsers=UpperCAmelCase_		)
 tpu_command_parser(subparsers=UpperCAmelCase_		)
 test_command_parser(subparsers=UpperCAmelCase_		)

 # Let's go
 lowerCamelCase_      =  parser.parse_args()

 if not hasattr(UpperCAmelCase_					,				"func"		):
  parser.print_help()
  exit(1		)

 # Run
 args.func(UpperCAmelCase_		)


if __name__ == "__main__":
     main()



