


'''simple docstring'''







import sys
from pathlib import Path


SCREAMING_SNAKE_CASE_      =			Path(__file__).resolve().parents[3] / 'src'
sys.path.insert(1, str(git_repo_path))

import dataclasses  # noqa
import io  # noqa
import itertools  # noqa
import json  # noqa
import os  # noqa
import unittest  # noqa
from copy import deepcopy  # noqa

from parameterized import parameterized  # noqa
from transformers import TrainingArguments, is_torch_available  # noqa
from transformers.deepspeed import is_deepspeed_available  # noqa
from transformers.file_utils import WEIGHTS_NAME  # noqa
from transformers.testing_utils import (  # noqa
    CaptureLogger,
    ExtendSysPath,
    TestCasePlus,
    execute_subprocess_async,
    get_gpu_count,
    mockenv_context,
    require_deepspeed,
    require_torch_gpu,
    require_torch_multi_gpu,
    slow,
)
from transformers.trainer_utils import set_seed  # noqa


set_seed(42)

SCREAMING_SNAKE_CASE_      =			{'base': 'patrickvonplaten/wav2vec2_tiny_random', 'robust': 'patrickvonplaten/wav2vec2_tiny_random_robust'}

SCREAMING_SNAKE_CASE_      =			'zero2'
SCREAMING_SNAKE_CASE_      =			'zero3'
SCREAMING_SNAKE_CASE_      =			[ZEROa, ZEROa]




def        __lowercase						(      __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE		)				->					str:
     """simple docstring"""




     __a       =   parameterized.to_safe_name("""_""".join(str(__SCREAMING_SNAKE_CASE		) for x in param.args		)		)
     return F'''{func.__name__}_{param_based_name}'''


# Cartesian-product of zero stages with models to test
SCREAMING_SNAKE_CASE_      =			list(itertools.product(stages, models.keys()))



@slow
@require_deepspeed
@require_torch_gpu
class  lowerCAmelCase_						(       snake_case__      ):





  """simple docstring"""



  @parameterized.expand(SCREAMING_SNAKE_CASE__				,							name_func=SCREAMING_SNAKE_CASE__						)
  def __a						(					self     :							str				,							SCREAMING_SNAKE_CASE__     :							Optional[int]				,							SCREAMING_SNAKE_CASE__     :							Tuple						):





       '''simple docstring'''

       self.run_and_check(
           stage=SCREAMING_SNAKE_CASE__				,							model=SCREAMING_SNAKE_CASE__				,							distributed=SCREAMING_SNAKE_CASE__				,							fpaa=SCREAMING_SNAKE_CASE__				,							)

  @require_torch_multi_gpu
  @parameterized.expand(SCREAMING_SNAKE_CASE__				,							name_func=SCREAMING_SNAKE_CASE__						)
  def __a						(					self     :							List[str]				,							SCREAMING_SNAKE_CASE__     :							List[str]				,							SCREAMING_SNAKE_CASE__     :							Optional[int]						):





       '''simple docstring'''

       self.run_and_check(
           stage=SCREAMING_SNAKE_CASE__				,							model=SCREAMING_SNAKE_CASE__				,							distributed=SCREAMING_SNAKE_CASE__				,							fpaa=SCREAMING_SNAKE_CASE__				,							)

  @parameterized.expand(SCREAMING_SNAKE_CASE__				,							name_func=SCREAMING_SNAKE_CASE__						)
  def __a						(					self     :							Any				,							SCREAMING_SNAKE_CASE__     :							Union[str, Any]				,							SCREAMING_SNAKE_CASE__     :							int						):





       '''simple docstring'''

       self.run_and_check(
           stage=SCREAMING_SNAKE_CASE__				,							model=SCREAMING_SNAKE_CASE__				,							distributed=SCREAMING_SNAKE_CASE__				,							fpaa=SCREAMING_SNAKE_CASE__				,							)

  @require_torch_multi_gpu
  @parameterized.expand(SCREAMING_SNAKE_CASE__				,							name_func=SCREAMING_SNAKE_CASE__						)
  def __a						(					self     :							Optional[Any]				,							SCREAMING_SNAKE_CASE__     :							Dict				,							SCREAMING_SNAKE_CASE__     :							int						):





       '''simple docstring'''

       self.run_and_check(
           stage=SCREAMING_SNAKE_CASE__				,							model=SCREAMING_SNAKE_CASE__				,							distributed=SCREAMING_SNAKE_CASE__				,							fpaa=SCREAMING_SNAKE_CASE__				,							)

  def __a						(					self     :							Dict				,							SCREAMING_SNAKE_CASE__     :							Optional[int]						):





       '''simple docstring'''

       pass

  def __a						(					self     :							List[str]				,							SCREAMING_SNAKE_CASE__     :							str				,							SCREAMING_SNAKE_CASE__     :							str				,							SCREAMING_SNAKE_CASE__     :							int = 1_0				,							SCREAMING_SNAKE_CASE__     :							bool = True				,							SCREAMING_SNAKE_CASE__     :							bool = True				,							SCREAMING_SNAKE_CASE__     :							bool = True				,							):





       '''simple docstring'''

       __a       =   models[model]

       __a       =   self.run_trainer(
           stage=SCREAMING_SNAKE_CASE__				,							model_name=SCREAMING_SNAKE_CASE__				,							eval_steps=SCREAMING_SNAKE_CASE__				,							num_train_epochs=1				,							distributed=SCREAMING_SNAKE_CASE__				,							fpaa=SCREAMING_SNAKE_CASE__				,							)

       self.do_checks(SCREAMING_SNAKE_CASE__						)

       return output_dir

  def __a						(					self     :							Union[str, Any]				,							SCREAMING_SNAKE_CASE__     :							str				,							SCREAMING_SNAKE_CASE__     :							str				,							SCREAMING_SNAKE_CASE__     :							int = 1_0				,							SCREAMING_SNAKE_CASE__     :							int = 1				,							SCREAMING_SNAKE_CASE__     :							bool = True				,							SCREAMING_SNAKE_CASE__     :							bool = True				,							):





       '''simple docstring'''

       __a       =   self.get_auto_remove_tmp_dir("""./xxx"""				,							after=SCREAMING_SNAKE_CASE__						)
       __a       =   f'''
            --model_name_or_path {model_name}
            --dataset_name hf-internal-testing/librispeech_asr_dummy
            --dataset_config_name clean
            --train_split_name validation
            --validation_split_name validation
            --output_dir {output_dir}
            --num_train_epochs {str(SCREAMING_SNAKE_CASE__						)}
            --per_device_train_batch_size 2
            --per_device_eval_batch_size 2
            --evaluation_strategy steps
            --learning_rate 5e-4
            --warmup_steps 8
            --orthography timit
            --preprocessing_num_workers 1
            --group_by_length
            --freeze_feature_extractor
            --report_to none
            --save_steps 0
            --eval_steps {eval_steps}
            --report_to none
        '''.split()

       if fpaa:
            args.extend(["""--fp16"""]						)

       # currently ds_config_wav2vec2_zero.json requires "zero_optimization.find_unused_parameters": true,
       # hence the separate config files
       __a       =   f'''--deepspeed {self.test_file_dir_str}/ds_config_wav2vec2_{stage}.json'''.split()
       __a       =   [f'''{self.examples_dir_str}/research_projects/wav2vec2/run_asr.py''']
       __a       =   self.get_launcher(SCREAMING_SNAKE_CASE__						)

       __a       =   launcher + script + args + ds_args
       # keep for quick debug
       # print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
       execute_subprocess_async(SCREAMING_SNAKE_CASE__				,							env=self.get_env()						)

       return output_dir


  def __a						(					self     :							int				,							SCREAMING_SNAKE_CASE__     :							Tuple=False						):





       '''simple docstring'''

       __a       =   min(2				,							get_gpu_count()						) if distributed else 1
       return f'''deepspeed --num_nodes 1 --num_gpus {num_gpus}'''.split()










'''simple docstring'''







import numpy

# List of input, output pairs
SCREAMING_SNAKE_CASE_      =			(
    ((5, 2, 3), 15),
    ((6, 5, 9), 25),
    ((11, 12, 13), 41),
    ((1, 1, 1), 8),
    ((11, 12, 13), 41),
)
SCREAMING_SNAKE_CASE_      =			(((5_15, 22, 13), 5_55), ((61, 35, 49), 1_50))
SCREAMING_SNAKE_CASE_      =			[2, 4, 1, 5]
SCREAMING_SNAKE_CASE_      =			len(train_data)
SCREAMING_SNAKE_CASE_      =			0.0_0_9




def        __lowercase						(      __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE="train"		)				->					Optional[int]:
     """simple docstring"""




     return calculate_hypothesis_value(__SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE		) - output(
         __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE		)




def        __lowercase						(      __SCREAMING_SNAKE_CASE		)				->					int:
     """simple docstring"""




     __a       =   0
     for i in range(len(__SCREAMING_SNAKE_CASE		) - 1		):
          hyp_val += data_input_tuple[i] * parameter_vector[i + 1]
     hyp_val += parameter_vector[0]
     return hyp_val




def        __lowercase						(      __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE		)				->					Optional[int]:
     """simple docstring"""




     if data_set == "train":
          return train_data[example_no][1]
     elif data_set == "test":
          return test_data[example_no][1]
     return None




def        __lowercase						(      __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE		)				->					Tuple:
     """simple docstring"""




     if data_set == "train":
          return _hypothesis_value(train_data[example_no][0]		)
     elif data_set == "test":
          return _hypothesis_value(test_data[example_no][0]		)
     return None




def        __lowercase						(      __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE=m		)				->					Optional[Any]:
     """simple docstring"""




     __a       =   0
     for i in range(__SCREAMING_SNAKE_CASE		):
          if index == -1:
               summation_value += _error(__SCREAMING_SNAKE_CASE		)
          else:
               summation_value += _error(__SCREAMING_SNAKE_CASE		) * train_data[i][0][index]
     return summation_value




def        __lowercase						(      __SCREAMING_SNAKE_CASE		)				->					List[str]:
     """simple docstring"""




     __a       =   summation_of_cost_derivative(__SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE		) / m
     return cost_derivative_value




def        __lowercase						(      )				->					str:
     """simple docstring"""




     global parameter_vector
     # Tune these values to set a tolerance value for predicted output
     __a       =   0.000_002
     __a       =   0
     __a       =   0
     while True:
          j += 1
          __a       =   [0, 0, 0, 0]
          for i in range(0				,  len(__SCREAMING_SNAKE_CASE		)		):
               __a       =   get_cost_derivative(i - 1		)
               __a       =   (
                   parameter_vector[i] - LEARNING_RATE * cost_derivative
               )
          if numpy.allclose(
              __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE				,  atol=__SCREAMING_SNAKE_CASE				,  rtol=__SCREAMING_SNAKE_CASE				,  ):
               break
          __a       =   temp_parameter_vector
     print(("""Number of iterations:""", j)		)




def        __lowercase						(      )				->					List[Any]:
     """simple docstring"""




     for i in range(len(__SCREAMING_SNAKE_CASE		)		):
          print(("""Actual output value:""", output(__SCREAMING_SNAKE_CASE				,  """test"""		))		)
          print(("""Hypothesis output:""", calculate_hypothesis_value(__SCREAMING_SNAKE_CASE				,  """test"""		))		)


if __name__ == "__main__":
 run_gradient_descent()
 print('\nTesting gradient descent for a linear hypothesis function.\n')
 test_gradient_descent()






