






"""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


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





def    SCREAMING_SNAKE_CASE       (     snake_case=None):




     if subparsers is not None:
          __snake_case				=							subparsers.add_parser('''config''',      description=__lowercase)
     else:
          __snake_case				=							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    SCREAMING_SNAKE_CASE       (     snake_case):
     __snake_case				=							get_user_input()
     if args.config_file is not None:
          __snake_case				=							args.config_file
     else:
          if not os.path.isdir(__lowercase):
               os.makedirs(__lowercase)
          __snake_case				=							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    SCREAMING_SNAKE_CASE       (     ):
     __snake_case				=							config_command_parser()
     __snake_case				=							parser.parse_args()
     config_command(__lowercase)


if __name__ == "__main__":
       main()
"""simple docstring"""



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      _A	(						unittest.TestCase					):




    """simple docstring"""




    def __init__(		self							:     Optional[Any]	,      A_							:     Dict	,      A_							:     List[Any]=13	,      A_							:     Dict=7	,      A_							:     Optional[int]=True	,      A_							:     Optional[int]=True	,      A_							:     List[Any]=True	,      A_							:     Union[str, Any]=True	,      A_							:     str=99	,      A_							:     Union[str, Any]=32	,      A_							:     Optional[int]=5	,      A_							:     Union[str, Any]=4	,      A_							:     Dict=37	,      A_							:     Any="gelu"	,      A_							:     Tuple=0.1	,      A_							:     str=0.1	,      A_							:     List[str]=512	,      A_							:     List[str]=16	,      A_							:     Optional[int]=2	,      A_							:     Optional[Any]=0.02	,      A_							:     str=4	,      )    ->       Any:
         __snake_case				=							parent
         __snake_case				=							batch_size
         __snake_case				=							seq_length
         __snake_case				=							is_training
         __snake_case				=							use_attention_mask
         __snake_case				=							use_token_type_ids
         __snake_case				=							use_labels
         __snake_case				=							vocab_size
         __snake_case				=							hidden_size
         __snake_case				=							num_hidden_layers
         __snake_case				=							num_attention_heads
         __snake_case				=							intermediate_size
         __snake_case				=							hidden_act
         __snake_case				=							hidden_dropout_prob
         __snake_case				=							attention_probs_dropout_prob
         __snake_case				=							max_position_embeddings
         __snake_case				=							type_vocab_size
         __snake_case				=							type_sequence_label_size
         __snake_case				=							initializer_range
         __snake_case				=							num_choices
    def  lowercase					(		self							:     List[Any]    )    ->       Any:
         __snake_case				=							ids_tensor([self.batch_size, self.seq_length]	,      self.vocab_size    )

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

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

         __snake_case				=							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=A_	,      initializer_range=self.initializer_range	,      )

         return config, input_ids, token_type_ids, attention_mask







    def  lowercase					(		self							:     Dict    )    ->       Union[str, Any]:
         __snake_case				=							self.prepare_config_and_inputs()
         __snake_case		,	__snake_case		,	__snake_case		,	__snake_case				=							config_and_inputs
         __snake_case				=							{'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': attention_mask}
         return config, inputs_dict






@require_flax
class      _A	(						_UpperCAmelCase							, unittest.TestCase					):




    """simple docstring"""




    UpperCamelCase_	:					List[str]							=					True

    UpperCamelCase_	:					List[str]							=					(
        (
            FlaxRoFormerModel,
            FlaxRoFormerForMaskedLM,
            FlaxRoFormerForSequenceClassification,
            FlaxRoFormerForTokenClassification,
            FlaxRoFormerForMultipleChoice,
            FlaxRoFormerForQuestionAnswering,
        )
        if is_flax_available()
        else ()
    )
    def  lowercase					(		self							:     str    )    ->       List[str]:
         __snake_case				=							FlaxRoFormerModelTester(self    )







    @slow
    def  lowercase					(		self							:     Optional[Any]    )    ->       List[Any]:
         for model_class_name in self.all_model_classes:
              __snake_case				=							model_class_name.from_pretrained('''junnyu/roformer_chinese_small'''	,      from_pt=A_    )
              __snake_case				=							model(np.ones((1, 1)    )    )
              self.assertIsNotNone(A_    )






@require_flax
class      _A	(						unittest.TestCase					):




    """simple docstring"""




    @slow
    def  lowercase					(		self							:     List[str]    )    ->       List[str]:
         __snake_case				=							FlaxRoFormerForMaskedLM.from_pretrained('''junnyu/roformer_chinese_base'''    )
         __snake_case				=							jnp.array([[0, 1, 2, 3, 4, 5]]    )
         __snake_case				=							model(A_    )[0]

         __snake_case				=							50_000

         __snake_case				=							(1, 6, vocab_size)
         self.assertEqual(output.shape	,      A_    )

         __snake_case				=							jnp.array(
             [[[-0.12_05, -1.02_65, 0.29_22], [-1.51_34, 0.19_74, 0.15_19], [-5.01_35, -3.90_03, -0.84_04]]]    )

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