import unittest

import numpy as np

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

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
      import jax.numpy as jnp

      from transformers.models.roformer.modeling_flax_roformer import (
          FlaxRoFormerForMaskedLM,
          FlaxRoFormerForMultipleChoice,
          FlaxRoFormerForQuestionAnswering,
          FlaxRoFormerForSequenceClassification,
          FlaxRoFormerForTokenClassification,
          FlaxRoFormerModel,
      )



class  SCREAMING_SNAKE_CASE_ (					unittest.TestCase			):
  '''simple docstring'''


  def __init__(				self    :					List[str]      ,	SCREAMING_SNAKE_CASE__    :					Union[str, Any]      ,	SCREAMING_SNAKE_CASE__    :					Dict=13      ,	SCREAMING_SNAKE_CASE__    :					List[Any]=7      ,	SCREAMING_SNAKE_CASE__    :					Dict=True      ,	SCREAMING_SNAKE_CASE__    :					str=True      ,	SCREAMING_SNAKE_CASE__    :					Optional[int]=True      ,	SCREAMING_SNAKE_CASE__    :					int=True      ,	SCREAMING_SNAKE_CASE__    :					Optional[int]=99      ,	SCREAMING_SNAKE_CASE__    :					Optional[Any]=32      ,	SCREAMING_SNAKE_CASE__    :					str=5      ,	SCREAMING_SNAKE_CASE__    :					Any=4      ,	SCREAMING_SNAKE_CASE__    :					Any=37      ,	SCREAMING_SNAKE_CASE__    :					Optional[int]="gelu"      ,	SCREAMING_SNAKE_CASE__    :					Any=0.1      ,	SCREAMING_SNAKE_CASE__    :					int=0.1      ,	SCREAMING_SNAKE_CASE__    :					str=5_12      ,	SCREAMING_SNAKE_CASE__    :					Tuple=16      ,	SCREAMING_SNAKE_CASE__    :					Tuple=2      ,	SCREAMING_SNAKE_CASE__    :					Dict=0.0_2      ,	SCREAMING_SNAKE_CASE__    :					str=4      ,	)    ->				Union[str, Any]:
         A						:     Union[str, Any]         =parent
         A						:     List[str]         =batch_size
         A						:     Any         =seq_length
         A						:     Optional[Any]         =is_training
         A						:     Dict         =use_attention_mask
         A						:     str         =use_token_type_ids
         A						:     Union[str, Any]         =use_labels
         A						:     Optional[int]         =vocab_size
         A						:     List[Any]         =hidden_size
         A						:     Dict         =num_hidden_layers
         A						:     str         =num_attention_heads
         A						:     Dict         =intermediate_size
         A						:     Dict         =hidden_act
         A						:     Optional[int]         =hidden_dropout_prob
         A						:     List[Any]         =attention_probs_dropout_prob
         A						:     List[Any]         =max_position_embeddings
         A						:     Any         =type_vocab_size
         A						:     Optional[Any]         =type_sequence_label_size
         A						:     Any         =initializer_range
         A						:     Tuple         =num_choices







  def 		SCREAMING_SNAKE_CASE_		(				self    :					str     )    ->				str:
         A						:     Union[str, Any]         =ids_tensor([self.batch_size, self.seq_length]      ,	self.vocab_size     )

         A						:     Tuple         =None
         if self.use_attention_mask:
                A						:     List[str]         =random_attention_mask([self.batch_size, self.seq_length]     )

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

         A						:     Optional[Any]         =RoFormerConfig(
             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 		SCREAMING_SNAKE_CASE_		(				self    :					Dict     )    ->				Optional[int]:
         A						:     str         =self.prepare_config_and_inputs()
         A  ,  A  ,  A  ,  A						:     Union[str, Any]         =config_and_inputs
         A						:     int         ={'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': attention_mask}
         return config, inputs_dict







@require_flax
class  SCREAMING_SNAKE_CASE_ (					lowerCAmelCase_   ,						unittest.TestCase			):
  '''simple docstring'''


  lowercase		:     Union[str, Any]            =				True

  lowercase		:     str            =				(
      (
          FlaxRoFormerModel,
          FlaxRoFormerForMaskedLM,
          FlaxRoFormerForSequenceClassification,
          FlaxRoFormerForTokenClassification,
          FlaxRoFormerForMultipleChoice,
          FlaxRoFormerForQuestionAnswering,
      )
      if is_flax_available()
      else ()
  )







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Optional[int]     )    ->				str:
         A						:     List[str]         =FlaxRoFormerModelTester(self     )







  @slow
  def 		SCREAMING_SNAKE_CASE_		(				self    :					Dict     )    ->				Dict:
         for model_class_name in self.all_model_classes:
                A						:     str         =model_class_name.from_pretrained('junnyu/roformer_chinese_small'      ,	from_pt=_lowerCAmelCase     )
                A						:     int         =model(np.ones((1, 1)     )     )
                self.assertIsNotNone(_lowerCAmelCase     )







@require_flax
class  SCREAMING_SNAKE_CASE_ (					unittest.TestCase			):
  '''simple docstring'''


  @slow
  def 		SCREAMING_SNAKE_CASE_		(				self    :					Dict     )    ->				Any:
         A						:     Tuple         =FlaxRoFormerForMaskedLM.from_pretrained('junnyu/roformer_chinese_base'     )
         A						:     List[str]         =jnp.array([[0, 1, 2, 3, 4, 5]]     )
         A						:     Union[str, Any]         =model(_lowerCAmelCase     )[0]

         A						:     Tuple         =5_00_00

         A						:     Dict         =(1, 6, vocab_size)
         self.assertEqual(output.shape      ,	_lowerCAmelCase     )

         A						:     List[str]         =jnp.array(
             [[[-0.1_2_0_5, -1.0_2_6_5, 0.2_9_2_2], [-1.5_1_3_4, 0.1_9_7_4, 0.1_5_1_9], [-5.0_1_3_5, -3.9_0_0_3, -0.8_4_0_4]]]     )

         self.assertTrue(jnp.allclose(output[:, :3, :3]      ,	_lowerCAmelCase      ,	atol=1e-4     )     )







# 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


_lowercase							:      List[str]      							='''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__  (			)				->		List[Any]:
       A						:     Any         =_ask_options(
           'In which compute environment are you running?',		['This machine', 'AWS (Amazon SageMaker)'],		_convert_compute_environment,		)
       if compute_environment == ComputeEnvironment.AMAZON_SAGEMAKER:
              A						:     Tuple         =get_sagemaker_input()
       else:
              A						:     str         =get_cluster_input()
       return config



def A__  (			lowercase:     int=None					)				->		str:
       if subparsers is not None:
              A						:     List[str]         =subparsers.add_parser('config',		description=lowercase					)
       else:
              A						:     Union[str, Any]         =argparse.ArgumentParser('Accelerate config command',		description=lowercase					)

       parser.add_argument(
           '--config_file',		default=lowercase,		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=lowercase					)
       return parser



def A__  (			lowercase:     Tuple					)				->		List[Any]:
       A						:     Union[str, Any]         =get_user_input()
       if args.config_file is not None:
              A						:     Optional[Any]         =args.config_file
       else:
              if not os.path.isdir(lowercase					):
                     os.makedirs(lowercase					)
              A						:     Union[str, Any]         =default_yaml_config_file

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



def A__  (			)				->		Optional[int]:
       A						:     Any         =config_command_parser()
       A						:     int         =parser.parse_args()
       config_command(lowercase					)


if __name__ == "__main__":
    main()






