




import tempfile
import unittest

from transformers import TaConfig, is_torch_available
from transformers.testing_utils import (
    require_sentencepiece,
    require_tokenizers,
    require_torch,
    slow,
    torch_device,
)

from ...generation.test_utils import GenerationTesterMixin
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
  import torch

  from transformers import AutoTokenizer, UMTaForConditionalGeneration, UMTaForQuestionAnswering, UMTaModel


class 							UpperCamelCase_       :

    '''simple docstring'''






    def __init__(     self :      int   ,					UpperCAmelCase__ :      Optional[int]   ,					UpperCAmelCase__ :      int=99   ,					UpperCAmelCase__ :      Optional[Any]=13   ,					UpperCAmelCase__ :      Tuple=7   ,					UpperCAmelCase__ :      Union[str, Any]=9   ,					UpperCAmelCase__ :      Tuple=True   ,					UpperCAmelCase__ :      int=True   ,					UpperCAmelCase__ :      Tuple=False   ,					UpperCAmelCase__ :      Any=32   ,					UpperCAmelCase__ :      Tuple=5   ,					UpperCAmelCase__ :      Any=4   ,					UpperCAmelCase__ :      str=37   ,					UpperCAmelCase__ :      Union[str, Any]=8   ,					UpperCAmelCase__ :      Tuple=0.1   ,					UpperCAmelCase__ :      Dict=0.002   ,					UpperCAmelCase__ :      str=1   ,					UpperCAmelCase__ :      str=0   ,					UpperCAmelCase__ :      Tuple=0   ,					UpperCAmelCase__ :      Union[str, Any]=None   ,					UpperCAmelCase__ :      str=None   ,					)		->Optional[Any]:





           '''simple docstring'''

           A__ 					=       parent
           A__ 					=       batch_size
           A__ 					=       encoder_seq_length
           A__ 					=       decoder_seq_length
           # For common tests
           A__ 					=       self.decoder_seq_length
           A__ 					=       is_training
           A__ 					=       use_attention_mask
           A__ 					=       use_labels
           A__ 					=       vocab_size
           A__ 					=       hidden_size
           A__ 					=       num_hidden_layers
           A__ 					=       num_attention_heads
           A__ 					=       d_ff
           A__ 					=       relative_attention_num_buckets
           A__ 					=       dropout_rate
           A__ 					=       initializer_factor
           A__ 					=       eos_token_id
           A__ 					=       pad_token_id
           A__ 					=       decoder_start_token_id
           A__ 					=       None
           A__ 					=       decoder_layers


    def 			SCREAMING_SNAKE_CASE				(     self :      Tuple)		->Optional[int]:





           '''simple docstring'''

           return TaConfig.from_pretrained('''google/umt5-base''')


    def 			SCREAMING_SNAKE_CASE				(     self :      Union[str, Any]   ,					UpperCAmelCase__ :      List[str]   ,					UpperCAmelCase__ :      Tuple   ,					UpperCAmelCase__ :      Optional[int]   ,					UpperCAmelCase__ :      Optional[Any]=None   ,					UpperCAmelCase__ :      List[Any]=None   ,					UpperCAmelCase__ :      Union[str, Any]=None   ,					UpperCAmelCase__ :      int=None   ,					UpperCAmelCase__ :      Optional[int]=None   ,					)		->Optional[Any]:





           '''simple docstring'''

           if attention_mask is None:
                  A__ 					=       input_ids.ne(config.pad_token_id)
           if decoder_attention_mask is None:
                  A__ 					=       decoder_input_ids.ne(config.pad_token_id)
           if head_mask is None:
                  A__ 					=       torch.ones(config.num_hidden_layers   ,					config.num_attention_heads   ,					device=UpperCAmelCase__)
           if decoder_head_mask is None:
                  A__ 					=       torch.ones(config.num_decoder_layers   ,					config.num_attention_heads   ,					device=UpperCAmelCase__)
           if cross_attn_head_mask is None:
                  A__ 					=       torch.ones(
                      config.num_decoder_layers   ,					config.num_attention_heads   ,					device=UpperCAmelCase__)
           return {
               "input_ids": input_ids,
               "decoder_input_ids": decoder_input_ids,
               "attention_mask": attention_mask,
               "decoder_attention_mask": decoder_attention_mask,
               "head_mask": head_mask,
               "decoder_head_mask": decoder_head_mask,
               "cross_attn_head_mask": cross_attn_head_mask,
           }


    def 			SCREAMING_SNAKE_CASE				(     self :      List[str])		->Any:





           '''simple docstring'''

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

           # we need to clamp the input ids here to avoid having pad token in between
           # this is because for NllbMoe the position_ids are prepared such that
           # all pad tokens have pos id = 2 and rest are between 2..seq_length
           # and the seq_length here is seq_length - num_pad_tokens
           # but when using past, there is no way of knowing if the past input ids had
           # pad tokens in them, which results in incorrect seq_lenth and which in turn results in
           # position_ids being off by num_pad_tokens in past input
           A__ 					=       input_ids.clamp(self.pad_token_id + 1)
           A__ 					=       decoder_input_ids.clamp(self.pad_token_id + 1)

           A__ 					=       self.get_config()
           A__ 					=       config.num_attention_heads
           A__ 					=       self.prepare_inputs_dict(UpperCAmelCase__   ,					UpperCAmelCase__   ,					UpperCAmelCase__)
           return config, input_dict


    def 			SCREAMING_SNAKE_CASE				(     self :      Dict)		->Tuple:





           '''simple docstring'''

           A__					,				A__ 					=       self.prepare_config_and_inputs()
           return config, inputs_dict


    def 			SCREAMING_SNAKE_CASE				(     self :      Dict)		->List[str]:





           '''simple docstring'''

           return TaConfig(
               vocab_size=166   ,					d_model=self.hidden_size   ,					d_ff=self.d_ff   ,					d_kv=self.hidden_size // self.num_attention_heads   ,					num_layers=self.num_hidden_layers   ,					num_decoder_layers=self.decoder_layers   ,					num_heads=self.num_attention_heads   ,					relative_attention_num_buckets=self.relative_attention_num_buckets   ,					dropout_rate=self.dropout_rate   ,					initializer_factor=self.initializer_factor   ,					eos_token_id=self.eos_token_id   ,					bos_token_id=self.pad_token_id   ,					pad_token_id=self.pad_token_id   ,					decoder_start_token_id=self.decoder_start_token_id   ,					)


    def 			SCREAMING_SNAKE_CASE				(     self :      Optional[int])		->Optional[Any]:





           '''simple docstring'''

           return TaConfig(
               vocab_size=self.vocab_size   ,					d_model=self.hidden_size   ,					d_ff=self.d_ff   ,					d_kv=self.hidden_size // self.num_attention_heads   ,					num_layers=self.num_hidden_layers   ,					num_decoder_layers=self.decoder_layers   ,					num_heads=self.num_attention_heads   ,					relative_attention_num_buckets=self.relative_attention_num_buckets   ,					dropout_rate=self.dropout_rate   ,					initializer_factor=self.initializer_factor   ,					eos_token_id=self.eos_token_id   ,					bos_token_id=self.pad_token_id   ,					pad_token_id=self.pad_token_id   ,					decoder_start_token_id=self.decoder_start_token_id   ,					)


    def 			SCREAMING_SNAKE_CASE				(     self :      List[str]   ,					UpperCAmelCase__ :      List[str]   ,					UpperCAmelCase__ :      int   ,					UpperCAmelCase__ :      Any   ,					UpperCAmelCase__ :      Optional[int]   ,					UpperCAmelCase__ :      Dict   ,					UpperCAmelCase__ :      Optional[int]   ,					)		->Optional[Any]:





           '''simple docstring'''

           A__ 					=       UMTaModel(config=UpperCAmelCase__)
           model.to(UpperCAmelCase__)
           model.eval()
           A__ 					=       model(
               input_ids=UpperCAmelCase__   ,					decoder_input_ids=UpperCAmelCase__   ,					attention_mask=UpperCAmelCase__   ,					decoder_attention_mask=UpperCAmelCase__   ,					)
           A__ 					=       model(input_ids=UpperCAmelCase__   ,					decoder_input_ids=UpperCAmelCase__)
           A__ 					=       result.last_hidden_state
           A__ 					=       result.past_key_values
           A__ 					=       result.encoder_last_hidden_state

           self.parent.assertEqual(encoder_output.size()   ,					(self.batch_size, self.encoder_seq_length, self.hidden_size))
           self.parent.assertEqual(decoder_output.size()   ,					(self.batch_size, self.decoder_seq_length, self.hidden_size))
           # There should be `num_layers` key value embeddings stored in decoder_past
           self.parent.assertEqual(len(UpperCAmelCase__)   ,					config.num_layers)
           # There should be a self attn key, a self attn value, a cross attn key and a cross attn value stored in each decoder_past tuple
           self.parent.assertEqual(len(decoder_past[0])   ,					4)


    def 			SCREAMING_SNAKE_CASE				(     self :      List[str]   ,					UpperCAmelCase__ :      Dict   ,					UpperCAmelCase__ :      Union[str, Any]   ,					UpperCAmelCase__ :      List[str]   ,					UpperCAmelCase__ :      Optional[int]   ,					UpperCAmelCase__ :      Optional[int]   ,					UpperCAmelCase__ :      Optional[int]   ,					)		->Optional[Any]:





           '''simple docstring'''

           A__ 					=       UMTaModel(config=UpperCAmelCase__).get_decoder().to(UpperCAmelCase__).eval()
           # first forward pass
           A__ 					=       model(UpperCAmelCase__   ,					use_cache=UpperCAmelCase__)
           A__ 					=       model(UpperCAmelCase__)
           A__ 					=       model(UpperCAmelCase__   ,					use_cache=UpperCAmelCase__)

           self.parent.assertTrue(len(UpperCAmelCase__) == len(UpperCAmelCase__))
           self.parent.assertTrue(len(UpperCAmelCase__) == len(UpperCAmelCase__) + 1)

           A__					,				A__ 					=       outputs.to_tuple()

           # create hypothetical next token and extent to next_input_ids
           A__ 					=       ids_tensor((self.batch_size, 1)   ,					config.vocab_size)

           # append to next input_ids and
           A__ 					=       torch.cat([input_ids, next_tokens]   ,					dim=-1)

           A__ 					=       model(UpperCAmelCase__)['''last_hidden_state''']
           A__ 					=       model(UpperCAmelCase__   ,					past_key_values=UpperCAmelCase__)['''last_hidden_state''']

           # select random slice
           A__ 					=       ids_tensor((1,)   ,					output_from_past.shape[-1]).item()
           A__ 					=       output_from_no_past[:, -1, random_slice_idx].detach()
           A__ 					=       output_from_past[:, 0, random_slice_idx].detach()

           # test that outputs are equal for slice
           self.parent.assertTrue(torch.allclose(UpperCAmelCase__   ,					UpperCAmelCase__   ,					atol=1e-3))





    def 			SCREAMING_SNAKE_CASE				(     self :      str   ,					UpperCAmelCase__ :      Dict   ,					UpperCAmelCase__ :      Any   ,					)		->Union[str, Any]:





           '''simple docstring'''

           A__ 					=       UMTaModel(config=UpperCAmelCase__).to(UpperCAmelCase__).half().eval()
           A__ 					=       model(**UpperCAmelCase__)['''last_hidden_state''']
           self.parent.assertFalse(torch.isnan(UpperCAmelCase__).any().item())


@require_torch
class 							UpperCamelCase_       (					UpperCAmelCase__						,   UpperCAmelCase__						,   UpperCAmelCase__						,   unittest.TestCase	):

    '''simple docstring'''






    UpperCAmelCase__				    = (
        (UMTaModel, UMTaForConditionalGeneration, UMTaForQuestionAnswering) if is_torch_available() else ()
    )
    UpperCAmelCase__				    = (UMTaForConditionalGeneration,) if is_torch_available() else ()
    UpperCAmelCase__				    = (
        {
            '''conversational''': UMTaForConditionalGeneration,
            '''feature-extraction''': UMTaModel,
            '''summarization''': UMTaForConditionalGeneration,
            '''text2text-generation''': UMTaForConditionalGeneration,
            '''translation''': UMTaForConditionalGeneration,
            '''question-answering''': UMTaForQuestionAnswering,
        }
        if is_torch_available()
        else {}
    )
    UpperCAmelCase__				    = True
    UpperCAmelCase__				    = False
    UpperCAmelCase__				    = False
    UpperCAmelCase__				    = True
    UpperCAmelCase__				    = True
    # The small UMT5 model needs higher percentages for CPU/MP tests
    UpperCAmelCase__				    = [0.8, 0.9]


    def 			SCREAMING_SNAKE_CASE				(     self :      int)		->Dict:





           '''simple docstring'''

           A__ 					=       UMTaModelTester(self)


    @unittest.skip('''Test has a segmentation fault on torch 1.8.0''')
    def 			SCREAMING_SNAKE_CASE				(     self :      Union[str, Any])		->List[str]:





           '''simple docstring'''

           A__ 					=       self.model_tester.prepare_config_and_inputs()
           A__ 					=       UMTaModel(config_and_inputs[0]).to(UpperCAmelCase__)
           with tempfile.TemporaryDirectory() as tmpdirname:
                  torch.onnx.export(
                      UpperCAmelCase__   ,					(config_and_inputs[1], config_and_inputs[3], config_and_inputs[2])   ,					f"""{tmpdirname}/t5_test.onnx"""   ,					export_params=UpperCAmelCase__   ,					opset_version=9   ,					input_names=['''input_ids''', '''decoder_input_ids''']   ,					)


    @unittest.skipIf(torch_device == '''cpu'''   ,					'''Cant do half precision''')
    def 			SCREAMING_SNAKE_CASE				(     self :      Any)		->List[str]:





           '''simple docstring'''

           A__ 					=       self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model_fpaa_forward(*UpperCAmelCase__)


    def 			SCREAMING_SNAKE_CASE				(     self :      Optional[Any])		->List[Any]:





           '''simple docstring'''

           A__ 					=       ['''encoder_attentions''', '''decoder_attentions''', '''cross_attentions''']
           A__ 					=       self.model_tester.prepare_config_and_inputs()
           A__ 					=       config_and_inputs[0]
           A__ 					=       UMTaForConditionalGeneration(UpperCAmelCase__).eval()
           model.to(UpperCAmelCase__)

           A__ 					=       {
               '''head_mask''': torch.zeros(config.num_layers   ,					config.num_heads   ,					device=UpperCAmelCase__),
               '''decoder_head_mask''': torch.zeros(config.num_decoder_layers   ,					config.num_heads   ,					device=UpperCAmelCase__),
               '''cross_attn_head_mask''': torch.zeros(config.num_decoder_layers   ,					config.num_heads   ,					device=UpperCAmelCase__),
           }

           for attn_name, (name, mask) in zip(UpperCAmelCase__   ,					head_masking.items()):
                  A__ 					=       {name: mask}
                  # Explicitly pass decoder_head_mask as it is required from T5 model when head_mask specified
                  if name == "head_mask":
                         A__ 					=       torch.ones(
                             config.num_decoder_layers   ,					config.num_heads   ,					device=UpperCAmelCase__)

                  A__ 					=       model.generate(
                      config_and_inputs[1]['''input_ids''']   ,					num_beams=1   ,					max_length=3   ,					output_attentions=UpperCAmelCase__   ,					return_dict_in_generate=UpperCAmelCase__   ,					**UpperCAmelCase__   ,					)
                  # We check the state of decoder_attentions and cross_attentions just from the last step
                  A__ 					=       out[attn_name] if attn_name == attention_names[0] else out[attn_name][-1]
                  self.assertEqual(sum([w.sum().item() for w in attn_weights])   ,					0.0)





    @unittest.skip('''Does not work on the tiny model as we keep hitting edge cases.''')
    def 			SCREAMING_SNAKE_CASE				(     self :      Optional[int])		->Optional[Any]:





           '''simple docstring'''

           pass


@require_torch
@require_sentencepiece
@require_tokenizers
class 							UpperCamelCase_       (					unittest.TestCase	):

    '''simple docstring'''






    @slow
    @unittest.skip(
        '''Unless we stop stripping left and right by default for all special tokens, the expected ids obtained here will not match the original ones. Wait for https://github.com/huggingface/transformers/pull/23909 to be merged''')
    def 			SCREAMING_SNAKE_CASE				(     self :      int)		->Tuple:





           '''simple docstring'''

           A__ 					=       UMTaForConditionalGeneration.from_pretrained('''google/umt5-small'''   ,					return_dict=UpperCAmelCase__).to(UpperCAmelCase__)
           A__ 					=       AutoTokenizer.from_pretrained('''google/umt5-small'''   ,					use_fast=UpperCAmelCase__   ,					legacy=UpperCAmelCase__)
           A__ 					=       [
               '''Bonjour monsieur <extra_id_0> bien <extra_id_1>.''',
               '''No se como puedo <extra_id_0>.''',
               '''This is the reason why we <extra_id_0> them.''',
               '''The <extra_id_0> walks in <extra_id_1>, seats''',
               '''A <extra_id_0> walks into a bar and orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>.''',
           ]
           A__ 					=       tokenizer(UpperCAmelCase__   ,					return_tensors='''pt'''   ,					padding=UpperCAmelCase__).input_ids
           # fmt: off
           A__ 					=       torch.tensor(
               [
                   [ 38_530, 210_703, 256_299, 1_410, 256_298, 274, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
                   [   826, 321, 671, 25_922, 256_299, 274, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
                   [  1_460, 339, 312, 19_014, 10_620, 758, 256_299, 2_355,274, 1, 0, 0, 0, 0, 0, 0,0, 0],
                   [   517, 256_299, 14_869, 281, 301, 256_298, 275, 119_983,1, 0, 0, 0, 0, 0, 0, 0,0, 0],
                   [   320, 256_299, 14_869, 281, 2_234, 289, 2_275, 333,61_391, 289, 256_298, 543, 256_297, 168_714, 329, 256_296,274, 1],
               ])
           # fmt: on
           torch.testing.assert_allclose(UpperCAmelCase__   ,					UpperCAmelCase__)

           A__ 					=       model.generate(input_ids.to(UpperCAmelCase__))
           A__ 					=       [
               '''<pad><extra_id_0> et<extra_id_1> [eod] <extra_id_2><extra_id_55>.. [eod] 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 <extra_id_56>ajšietosto<extra_id_56>lleux<extra_id_19><extra_id_6>ajšie</s>''',
               '''<pad><extra_id_0>.<extra_id_1>.,<0x0A>...spech <0x0A><extra_id_20> <extra_id_21></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>''',
               '''<pad><extra_id_0> are not going to be a part of the world. We are not going to be a part of<extra_id_1> and<extra_id_2><0x0A><extra_id_48>.<extra_id_48></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>''',
               '''<pad><extra_id_0> door<extra_id_1>, the door<extra_id_2> 피해[/</s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>''',
               '''<pad><extra_id_0>nyone who<extra_id_1> drink<extra_id_2> a<extra_id_3> alcohol<extra_id_4> A<extra_id_5> A. This<extra_id_6> I<extra_id_7><extra_id_52><extra_id_53></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>''',
           ]
           A__ 					=       tokenizer.batch_decode(UpperCAmelCase__)
           self.assertEqual(UpperCAmelCase__   ,					UpperCAmelCase__)




# 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.

from pathlib import Path

import torch

from ...utils import is_npu_available, is_xpu_available
from .config_args import ClusterConfig, default_json_config_file
from .config_utils import SubcommandHelpFormatter


lowerCamelCase__     :		Any        = """Create a default config file for Accelerate with only a few flags set."""

def 		UpperCamelCase						(   lowercase_="no"			,			lowercase_ = default_json_config_file			,			lowercase_ = False )		->						Any:


  '''simple docstring'''






  lowercase__	: Any     		=   Path(lowercase_ )
  path.parent.mkdir(parents=lowercase_			,			exist_ok=lowercase_ )
  if path.exists():
    print(
        F'Configuration already exists at {save_location}, will not override. Run `accelerate config` manually or pass a different `save_location`.' )
    return False
  lowercase__	: int     		=   mixed_precision.lower()
  if mixed_precision not in ["no", "fp16", "bf16", "fp8"]:
    raise ValueError(
        F'`mixed_precision` should be one of \'no\', \'fp16\', \'bf16\', or \'fp8\'. Received {mixed_precision}' )
  lowercase__	: Dict     		=   {
      """compute_environment""": """LOCAL_MACHINE""",
      """mixed_precision""": mixed_precision,
  }
  if torch.cuda.is_available():
    lowercase__	: Any     		=   torch.cuda.device_count()
    lowercase__	: Any     		=   num_gpus
    lowercase__	: Optional[int]     		=   False
    if num_gpus > 1:
      lowercase__	: Tuple     		=   """MULTI_GPU"""
    else:
      lowercase__	: Optional[Any]     		=   """NO"""
  elif is_xpu_available() and use_xpu:
    lowercase__	: Union[str, Any]     		=   torch.xpu.device_count()
    lowercase__	: str     		=   num_xpus
    lowercase__	: List[Any]     		=   False
    if num_xpus > 1:
      lowercase__	: str     		=   """MULTI_XPU"""
    else:
      lowercase__	: Optional[Any]     		=   """NO"""
  elif is_npu_available():
    lowercase__	: Tuple     		=   torch.npu.device_count()
    lowercase__	: Union[str, Any]     		=   num_npus
    lowercase__	: Union[str, Any]     		=   False
    if num_npus > 1:
      lowercase__	: List[Any]     		=   """MULTI_NPU"""
    else:
      lowercase__	: int     		=   """NO"""
  else:
    lowercase__	: Union[str, Any]     		=   0
    lowercase__	: str     		=   True
    lowercase__	: Union[str, Any]     		=   1
    lowercase__	: int     		=   """NO"""
  lowercase__	: Tuple     		=   ClusterConfig(**lowercase_ )
  config.to_json_file(lowercase_ )
  return path

def 		UpperCamelCase						(   lowercase_			,			lowercase_ )		->						Optional[Any]:


  '''simple docstring'''






  lowercase__	: List[str]     		=   parser.add_parser("""default"""			,			parents=lowercase_			,			help=lowercase_			,			formatter_class=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'."""
      )			,			dest="""save_location"""			,			)

  parser.add_argument(
      """--mixed_precision"""			,			choices=["""no""", """fp16""", """bf16"""]			,			type=lowercase_			,			help="""Whether or not to use mixed precision training. """
      """Choose between FP16 and BF16 (bfloat16) training. """
      """BF16 training is only supported on Nvidia Ampere GPUs and PyTorch 1.10 or later."""			,			default="""no"""			,			)
  parser.set_defaults(func=lowercase_ )
  return parser



def 		UpperCamelCase						(   lowercase_ )		->						Any:


  '''simple docstring'''






  lowercase__	: Optional[Any]     		=   write_basic_config(args.mixed_precision			,			args.save_location )
  if config_file:
    print(F'accelerate configuration saved at {config_file}' )






