


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 A    (  unittest.TestCase	):






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




             _lowerCamelCase			:						Any            =parent
             _lowerCamelCase			:						Union[str, Any]            =batch_size
             _lowerCamelCase			:						Dict            =seq_length
             _lowerCamelCase			:						Any            =is_training
             _lowerCamelCase			:						Any            =use_attention_mask
             _lowerCamelCase			:						Optional[Any]            =use_token_type_ids
             _lowerCamelCase			:						Tuple            =use_labels
             _lowerCamelCase			:						Optional[int]            =vocab_size
             _lowerCamelCase			:						Optional[Any]            =hidden_size
             _lowerCamelCase			:						List[str]            =num_hidden_layers
             _lowerCamelCase			:						int            =num_attention_heads
             _lowerCamelCase			:						Tuple            =intermediate_size
             _lowerCamelCase			:						Optional[int]            =hidden_act
             _lowerCamelCase			:						List[str]            =hidden_dropout_prob
             _lowerCamelCase			:						Any            =attention_probs_dropout_prob
             _lowerCamelCase			:						Union[str, Any]            =max_position_embeddings
             _lowerCamelCase			:						Dict            =type_vocab_size
             _lowerCamelCase			:						Union[str, Any]            =type_sequence_label_size
             _lowerCamelCase			:						Tuple            =initializer_range
             _lowerCamelCase			:						int            =num_choices






      def 			lowerCamelCase			(   self		:	Union[str, Any]					)   ->		List[str]:
             """simple docstring"""




             _lowerCamelCase			:						Optional[int]            =ids_tensor([self.batch_size, self.seq_length]     ,  self.vocab_size					)

             _lowerCamelCase			:						Tuple            =None
             if self.use_attention_mask:
                    _lowerCamelCase			:						Optional[Any]            =random_attention_mask([self.batch_size, self.seq_length]					)

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

             _lowerCamelCase			:						Tuple            =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=lowercase_     ,  initializer_range=self.initializer_range     ,  )

             return config, input_ids, token_type_ids, attention_mask






      def 			lowerCamelCase			(   self		:	Optional[int]					)   ->		int:
             """simple docstring"""




             _lowerCamelCase			:						Dict            =self.prepare_config_and_inputs()
             _lowerCamelCase      ,	_lowerCamelCase      ,	_lowerCamelCase      ,	_lowerCamelCase			:						int            =config_and_inputs
             _lowerCamelCase			:						str            ={'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': attention_mask}
             return config, inputs_dict






      def 			lowerCamelCase			(   self		:	Dict					)   ->		Optional[Any]:
             """simple docstring"""




             _lowerCamelCase			:						Any            =self.prepare_config_and_inputs()
             _lowerCamelCase      ,	_lowerCamelCase      ,	_lowerCamelCase      ,	_lowerCamelCase			:						Optional[int]            =config_and_inputs

             _lowerCamelCase			:						List[Any]            =True
             _lowerCamelCase			:						Dict            =floats_tensor([self.batch_size, self.seq_length, self.hidden_size]					)
             _lowerCamelCase			:						List[Any]            =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 A    (  UpperCamelCase_       ,				unittest.TestCase	):
      UpperCamelCase__				:    int =True

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






      def 			lowerCamelCase			(   self		:	Optional[int]					)   ->		List[Any]:
             """simple docstring"""




             _lowerCamelCase			:						List[str]            =FlaxRobertaModelTester(self					)






      @slow
      def 			lowerCamelCase			(   self		:	int					)   ->		List[str]:
             """simple docstring"""




             for model_class_name in self.all_model_classes:
                    _lowerCamelCase			:						Any            =model_class_name.from_pretrained('roberta-base'     ,  from_pt=lowercase_					)
                    _lowerCamelCase			:						List[Any]            =model(np.ones((1, 1)					)					)
                    self.assertIsNotNone(lowercase_					)






# Copyright 2022 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
import platform

import numpy as np
import psutil
import torch

from accelerate import __version__ as version
from accelerate.commands.config import default_config_file, load_config_from_file

from ..utils import is_npu_available, is_xpu_available




def       a_ (       SCREAMING_SNAKE_CASE__   : str=None	):
       '''simple docstring'''





       if subparsers is not None:
              _lowerCamelCase			:						str            =subparsers.add_parser('env'	)
       else:
              _lowerCamelCase			:						List[Any]            =argparse.ArgumentParser('Accelerate env command'	)

       parser.add_argument(
           '--config_file'						,     default=SCREAMING_SNAKE_CASE__						,     help='The config file to use for the default values in the launching script.'	)

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




def       a_ (       SCREAMING_SNAKE_CASE__   : int	):
       '''simple docstring'''





       _lowerCamelCase			:						int            =torch.__version__
       _lowerCamelCase			:						Optional[Any]            =torch.cuda.is_available()
       _lowerCamelCase			:						int            =is_xpu_available()
       _lowerCamelCase			:						Union[str, Any]            =is_npu_available()

       _lowerCamelCase			:						Optional[int]            ='Not found'
       # Get the default from the config file.
       if args.config_file is not None or os.path.isfile(SCREAMING_SNAKE_CASE__	):
              _lowerCamelCase			:						Any            =load_config_from_file(args.config_file	).to_dict()

       _lowerCamelCase			:						List[str]            ={
           '`Accelerate` version': version,
           'Platform': platform.platform(),
           'Python version': platform.python_version(),
           'Numpy version': np.__version__,
           'PyTorch version (GPU?)': F'''{pt_version} ({pt_cuda_available})''',
           'PyTorch XPU available': str(SCREAMING_SNAKE_CASE__	),
           'PyTorch NPU available': str(SCREAMING_SNAKE_CASE__	),
           'System RAM': F'''{psutil.virtual_memory().total / 1_024 ** 3:.2f} GB''',
       }
       if pt_cuda_available:
              _lowerCamelCase			:						List[Any]            =torch.cuda.get_device_name()

       print('\nCopy-and-paste the text below in your GitHub issue\n'	)
       print('\n'.join([F'''- {prop}: {val}''' for prop, val in info.items()]	)	)

       print('- `Accelerate` default config:' if args.config_file is None else '- `Accelerate` config passed:'	)
       _lowerCamelCase			:						Dict            =(
           '\n'.join([F'''\t- {prop}: {val}''' for prop, val in accelerate_config.items()]	)
           if isinstance(SCREAMING_SNAKE_CASE__						,     SCREAMING_SNAKE_CASE__	)
           else F'''\t{accelerate_config}'''
       )
       print(SCREAMING_SNAKE_CASE__	)

       _lowerCamelCase			:						str            =accelerate_config

       return info




def       a_ (       ):
       '''simple docstring'''





       _lowerCamelCase			:						Optional[int]            =env_command_parser()
       _lowerCamelCase			:						Any            =parser.parse_args()
       env_command(SCREAMING_SNAKE_CASE__	)
       return 0


if __name__ == "__main__":
 raise SystemExit(main())


