"""simple docstring"""






import unittest

import numpy as np

from transformers import RobertaPreLayerNormConfig, 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():
						import jax.numpy as jnp

						from transformers.models.roberta_prelayernorm.modeling_flax_roberta_prelayernorm import (
						    FlaxRobertaPreLayerNormForCausalLM,
						    FlaxRobertaPreLayerNormForMaskedLM,
						    FlaxRobertaPreLayerNormForMultipleChoice,
						    FlaxRobertaPreLayerNormForQuestionAnswering,
						    FlaxRobertaPreLayerNormForSequenceClassification,
						    FlaxRobertaPreLayerNormForTokenClassification,
						    FlaxRobertaPreLayerNormModel,
						)
class        snake_case (				unittest.TestCase       ):




					"""simple docstring"""

					def __init__(       self     :			Optional[Any]					,lowerCamelCase__     :			Tuple					,lowerCamelCase__     :			List[Any]=13					,lowerCamelCase__     :			Optional[Any]=7					,lowerCamelCase__     :			str=True					,lowerCamelCase__     :			str=True					,lowerCamelCase__     :			List[str]=True					,lowerCamelCase__     :			Any=True					,lowerCamelCase__     :			Union[str, Any]=99					,lowerCamelCase__     :			Dict=32					,lowerCamelCase__     :			List[Any]=5					,lowerCamelCase__     :			Tuple=4					,lowerCamelCase__     :			Any=37					,lowerCamelCase__     :			List[str]="gelu"					,lowerCamelCase__     :			List[Any]=0.1					,lowerCamelCase__     :			int=0.1					,lowerCamelCase__     :			str=512					,lowerCamelCase__     :			Optional[int]=16					,lowerCamelCase__     :			Optional[Any]=2					,lowerCamelCase__     :			Dict=0.0_2					,lowerCamelCase__     :			List[str]=4					,):
									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 	__lowerCAmelCase		(       self     :			str    ):
									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__    =							RobertaPreLayerNormConfig(
									    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 	__lowerCAmelCase		(       self     :			Dict    ):
									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 	__lowerCAmelCase		(       self     :			str    ):
									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
# Copied from tests.models.roberta.test_modelling_flax_roberta.FlaxRobertaPreLayerNormModelTest with ROBERTA->ROBERTA_PRELAYERNORM,Roberta->RobertaPreLayerNorm,roberta-base->andreasmadsen/efficient_mlm_m0.40
class        snake_case (				__UpperCAmelCase   ,					unittest.TestCase       ):




					"""simple docstring"""

					snake_case__       					=				True

					snake_case__       					=				(
					    (
					        FlaxRobertaPreLayerNormModel,
					        FlaxRobertaPreLayerNormForCausalLM,
					        FlaxRobertaPreLayerNormForMaskedLM,
					        FlaxRobertaPreLayerNormForSequenceClassification,
					        FlaxRobertaPreLayerNormForTokenClassification,
					        FlaxRobertaPreLayerNormForMultipleChoice,
					        FlaxRobertaPreLayerNormForQuestionAnswering,
					    )
					    if is_flax_available()
					    else ()
					)







					def 	__lowerCAmelCase		(       self     :			List[str]    ):
									UpperCAmelCase__    =							FlaxRobertaPreLayerNormModelTester(self    )







					@slow
					def 	__lowerCAmelCase		(       self     :			Union[str, Any]    ):
									for model_class_name in self.all_model_classes:
													UpperCAmelCase__    =							model_class_name.from_pretrained('andreasmadsen/efficient_mlm_m0.40'					,from_pt=lowerCamelCase__    )
													UpperCAmelCase__    =							model(np.ones((1, 1)    )    )
													self.assertIsNotNone(lowerCamelCase__    )






@require_flax
class        snake_case (				unittest.TestCase       ):




					"""simple docstring"""

					@slow
					def 	__lowerCAmelCase		(       self     :			Union[str, Any]    ):
									UpperCAmelCase__    =							FlaxRobertaPreLayerNormForMaskedLM.from_pretrained('andreasmadsen/efficient_mlm_m0.40'					,from_pt=lowerCamelCase__    )

									UpperCAmelCase__    =							np.array([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]]					,dtype=jnp.intaa    )
									UpperCAmelCase__    =							model(lowerCamelCase__    )[0]
									UpperCAmelCase__    =							[1, 11, 50_265]
									self.assertEqual(list(output.shape    )					,lowerCamelCase__    )
									# compare the actual values for a slice.
									UpperCAmelCase__    =							np.array(
									    [[[4_0.4_8_8_0, 1_8.0_1_9_9, -5.2_3_6_7], [-1.8_8_7_7, -4.0_8_8_5, 1_0.7_0_8_5], [-2.2_6_1_3, -5.6_1_1_0, 7.2_6_6_5]]]					,dtype=np.floataa    )
									self.assertTrue(np.allclose(output[:, :3, :3]					,lowerCamelCase__					,atol=1e-4    )    )







					@slow
					def 	__lowerCAmelCase		(       self     :			Union[str, Any]    ):
									UpperCAmelCase__    =							FlaxRobertaPreLayerNormModel.from_pretrained('andreasmadsen/efficient_mlm_m0.40'					,from_pt=lowerCamelCase__    )

									UpperCAmelCase__    =							np.array([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]]					,dtype=jnp.intaa    )
									UpperCAmelCase__    =							model(lowerCamelCase__    )[0]
									# compare the actual values for a slice.
									UpperCAmelCase__    =							np.array(
									    [[[0.0_2_0_8, -0.0_3_5_6, 0.0_2_3_7], [-0.1_5_6_9, -0.0_4_1_1, -0.2_6_2_6], [0.1_8_7_9, 0.0_1_2_5, -0.0_0_8_9]]]					,dtype=np.floataa    )
									self.assertTrue(np.allclose(output[:, :3, :3]					,lowerCamelCase__					,atol=1e-4    )    )




"""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.

import argparse
import os

from accelerate.utils import ComputeEnvironment

from .cluster import get_cluster_input
from .config_args import cache_dir, default_config_file, default_yaml_config_file, load_config_from_file  # noqa: F401
from .config_utils import _ask_field, _ask_options, _convert_compute_environment  # noqa: F401
from .sagemaker import get_sagemaker_input


lowerCAmelCase__						:						Dict		  =			'Launches a series of prompts to create and save a `default_config.yaml` configuration file for your training system. Should always be ran first on your machine'



def 						a_		(      ):
				UpperCAmelCase__    =							_ask_options(
				    'In which compute environment are you running?'      ,    ['This machine', 'AWS (Amazon SageMaker)']      ,    _convert_compute_environment      ,    )
				if compute_environment == ComputeEnvironment.AMAZON_SAGEMAKER:
								UpperCAmelCase__    =							get_sagemaker_input()
				else:
								UpperCAmelCase__    =							get_cluster_input()
				return config



def 						a_		(      lowerCamelCase=None ):
				if subparsers is not None:
								UpperCAmelCase__    =							subparsers.add_parser('config'      ,    description=lowerCamelCase )
				else:
								UpperCAmelCase__    =							argparse.ArgumentParser('Accelerate config command'      ,    description=lowerCamelCase )

				parser.add_argument(
				    '--config_file'      ,    default=lowerCamelCase      ,    help=(
				        'The path to use to store the config file. Will default to a file named default_config.yaml in the cache '
				        'location, which is the content of the environment `HF_HOME` suffixed with \'accelerate\', or if you don\'t have '
				        'such an environment variable, your cache directory (\'~/.cache\' or the content of `XDG_CACHE_HOME`) suffixed '
				        'with \'huggingface\'.'
				    )      ,    )

				if subparsers is not None:
								parser.set_defaults(func=lowerCamelCase )
				return parser



def 						a_		(      lowerCamelCase ):
				UpperCAmelCase__    =							get_user_input()
				if args.config_file is not None:
								UpperCAmelCase__    =							args.config_file
				else:
								if not os.path.isdir(lowerCamelCase ):
												os.makedirs(lowerCamelCase )
								UpperCAmelCase__    =							default_yaml_config_file

				if config_file.endswith('.json' ):
								config.to_json_file(lowerCamelCase )
				else:
								config.to_yaml_file(lowerCamelCase )
				print(f'''accelerate configuration saved at {config_file}''' )



def 						a_		(      ):
				UpperCAmelCase__    =							config_command_parser()
				UpperCAmelCase__    =							parser.parse_args()
				config_command(lowerCamelCase )


if __name__ == "__main__":
						main()



