





"""simple docstring"""






import inspect
import os
import unittest
from dataclasses import dataclass

import torch

from accelerate import Accelerator, DistributedDataParallelKwargs, GradScalerKwargs
from accelerate.state import AcceleratorState
from accelerate.test_utils import execute_subprocess_async, require_cuda, require_multi_gpu
from accelerate.utils import KwargsHandler



@dataclass
class 	lowerCAmelCase					(      snake_case							):
       lowerCAmelCase__						 =  0
       lowerCAmelCase__						 =  False
       lowerCAmelCase__						 =  3.0



class 	lowerCAmelCase					(      unittest.TestCase							):






       def 							__A (   self     ):
              # If no defaults are changed, `to_kwargs` returns an empty dict.
              self.assertDictEqual(MockClass().to_kwargs()    ,  {}     )
              self.assertDictEqual(MockClass(a=2     ).to_kwargs()    ,  {'a': 2}     )
              self.assertDictEqual(MockClass(a=2    ,  b=a__     ).to_kwargs()    ,  {'a': 2, 'b': True}     )
              self.assertDictEqual(MockClass(a=2    ,  c=2.25     ).to_kwargs()    ,  {'a': 2, 'c': 2.25}     )






       @require_cuda
       def 							__A (   self     ):
              # If no defaults are changed, `to_kwargs` returns an empty dict.
              _UpperCAmelCase     =     GradScalerKwargs(init_scale=10_24    ,  growth_factor=2     )
              AcceleratorState._reset_state()
              _UpperCAmelCase     =     Accelerator(mixed_precision='fp16'    ,  kwargs_handlers=[scaler_handler]     )
              print(accelerator.use_fpaa     )
              _UpperCAmelCase     =     accelerator.scaler

              # Check the kwargs have been applied
              self.assertEqual(scaler._init_scale    ,  1_024.0     )
              self.assertEqual(scaler._growth_factor    ,  2.0     )

              # Check the other values are at the default
              self.assertEqual(scaler._backoff_factor    ,  0.5     )
              self.assertEqual(scaler._growth_interval    ,  20_00     )
              self.assertEqual(scaler._enabled    ,  a__     )






       @require_multi_gpu
       def 							__A (   self     ):
              _UpperCAmelCase     =     ['torchrun', f"""--nproc_per_node={torch.cuda.device_count()}""", inspect.getfile(self.__class__     )]
              execute_subprocess_async(a__    ,  env=os.environ.copy()     )


if __name__ == "__main__":
    lowerCAmelCase_			=							DistributedDataParallelKwargs(bucket_cap_mb=15, find_unused_parameters=True)
    lowerCAmelCase_			=							Accelerator(kwargs_handlers=[ddp_scaler])
    lowerCAmelCase_			=							torch.nn.Linear(100, 200)
    lowerCAmelCase_			=							accelerator.prepare(model)

    # Check the values changed in kwargs
    lowerCAmelCase_			=							''''''
    lowerCAmelCase_			=							model.bucket_bytes_cap // (1_024 * 1_024)
    if observed_bucket_cap_map != 15:
        error_msg += F"Kwargs badly passed, should have `15` but found {observed_bucket_cap_map}.\n"
    if model.find_unused_parameters is not True:
        error_msg += F"Kwargs badly passed, should have `True` but found {model.find_unused_parameters}.\n"

    # Check the values of the defaults
    if model.dim != 0:
        error_msg += F"Default value not respected, should have `0` but found {model.dim}.\n"
    if model.broadcast_buffers is not True:
        error_msg += F"Default value not respected, should have `True` but found {model.broadcast_buffers}.\n"
    if model.gradient_as_bucket_view is not False:
        error_msg += F"Default value not respected, should have `False` but found {model.gradient_as_bucket_view}.\n"

    # Raise error at the end to make sure we don't stop at the first failure.
    if len(error_msg) > 0:
        raise ValueError(error_msg)













"""simple docstring"""






# Copyright 2023 The HuggingFace Inc. 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 ..models.auto import AutoModelForSeqaSeqLM, AutoTokenizer
from .base import PipelineTool



class 	lowerCAmelCase					(      snake_case							):
       lowerCAmelCase__						 =  """philschmid/bart-large-cnn-samsum"""
       lowerCAmelCase__						 =  (
           """This is a tool that summarizes an English text. It takes an input `text` containing the text to summarize, """
           """and returns a summary of the text."""
       )
       lowerCAmelCase__						 =  """summarizer"""
       lowerCAmelCase__						 =  AutoTokenizer
       lowerCAmelCase__						 =  AutoModelForSeqaSeqLM

       lowerCAmelCase__						 =  ["""text"""]
       lowerCAmelCase__						 =  ["""text"""]






       def 							__A (   self    ,  a__     ):
              return self.pre_processor(a__    ,  return_tensors='pt'    ,  truncation=a__     )






       def 							__A (   self    ,  a__     ):
              return self.model.generate(**a__     )[0]






       def 							__A (   self    ,  a__     ):
              return self.pre_processor.decode(a__    ,  skip_special_tokens=a__    ,  clean_up_tokenization_spaces=a__     )






