




"""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      UpperCAmelCase_  (						unittest.TestCase							):




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




     '''simple docstring'''

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




    def 							__magic_name__							(   self						:	Optional[int]				)				->							List[str]:




     '''simple docstring'''

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

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

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

     A__	=    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=snake_case_     ,			initializer_range=self.initializer_range     ,			)

     return config, input_ids, token_type_ids, attention_mask




    def 							__magic_name__							(   self						:	List[str]				)				->							List[str]:




     '''simple docstring'''

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




    def 							__magic_name__							(   self						:	Optional[int]				)				->							Dict:




     '''simple docstring'''

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

     A__	=    True
     A__	=    floats_tensor([self.batch_size, self.seq_length, self.hidden_size]				)
     A__	=    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_  (						A_,       unittest.TestCase							):
    lowercase__						 =				True

    lowercase__						 =				(
        (
            FlaxRobertaModel,
            FlaxRobertaForCausalLM,
            FlaxRobertaForMaskedLM,
            FlaxRobertaForSequenceClassification,
            FlaxRobertaForTokenClassification,
            FlaxRobertaForMultipleChoice,
            FlaxRobertaForQuestionAnswering,
        )
        if is_flax_available()
        else ()
    )




    def 							__magic_name__							(   self						:	str				)				->							Union[str, Any]:




     '''simple docstring'''

     A__	=    FlaxRobertaModelTester(self				)




    @slow
    def 							__magic_name__							(   self						:	str				)				->							Optional[Any]:




     '''simple docstring'''

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






"""simple docstring"""



# 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   _SCREAMING_SNAKE_CASE       (							lowercase_=None						)						->		Any:
 if subparsers is not None:
  A__	=    subparsers.add_parser("env"						)
 else:
  A__	=    argparse.ArgumentParser("Accelerate env command"						)

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

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







def   _SCREAMING_SNAKE_CASE       (							lowercase_						)						->		Dict:
 A__	=    torch.__version__
 A__	=    torch.cuda.is_available()
 A__	=    is_xpu_available()
 A__	=    is_npu_available()

 A__	=    "Not found"
 # Get the default from the config file.
 if args.config_file is not None or os.path.isfile(lowercase_						):
  A__	=    load_config_from_file(args.config_file						).to_dict()

 A__	=    {
     "`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(lowercase_						),
     "PyTorch NPU available": str(lowercase_						),
     "System RAM": f"""{psutil.virtual_memory().total / 10_24 ** 3:.2f} GB""",
 }
 if pt_cuda_available:
  A__	=    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:"						)
 A__	=    (
     "\n".join([f"""\t- {prop}: {val}""" for prop, val in accelerate_config.items()]						)
     if isinstance(lowercase_      ,			lowercase_						)
     else f"""\t{accelerate_config}"""
 )
 print(lowercase_						)

 A__	=    accelerate_config

 return info







def   _SCREAMING_SNAKE_CASE       (							)						->		int:
 A__	=    env_command_parser()
 A__	=    parser.parse_args()
 env_command(lowercase_						)
 return 0


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





