



"""simple docstring"""

# XXX: we want transformers master here - in the absense of conftest manipulating sys.path:
# hack it in for now:
import sys
from pathlib import Path


lowercase__					:						Union[str, Any]												=			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)

lowercase__					:						List[str]												=			{'''base''': '''patrickvonplaten/wav2vec2_tiny_random''', '''robust''': '''patrickvonplaten/wav2vec2_tiny_random_robust'''}

lowercase__					:						Optional[Any]												=			'''zero2'''
lowercase__					:						Tuple												=			'''zero3'''
lowercase__					:						str												=			[ZEROa, ZEROa]




def __lowercase    (							_a	,    _a	,    _a					):
    # customize the test name generator function as we want both params to appear in the sub-test
    # name, as by default it shows only the first param
    snake_case_				:  int       =			parameterized.to_safe_name('''_'''.join(str(_a					) for x in param.args					)					)
    return f"{func.__name__}_{param_based_name}"


# Cartesian-product of zero stages with models to test
lowercase__					:						Optional[int]												=			list(itertools.product(stages, models.keys()))






@slow
@require_deepspeed
@require_torch_gpu
class  _UpperCAmelCase (    lowerCAmelCase__):

 @parameterized.expand(lowercase_						,  name_func=lowercase_       )
 def 		_snake_case      (  self	: Optional[Any]						,  lowercase_	: List[Any]						,  lowercase_	: int       ):
     self.run_and_check(
         stage=lowercase_						,  model=lowercase_						,  distributed=lowercase_						,  fpaa=lowercase_						,  )

 @require_torch_multi_gpu
 @parameterized.expand(lowercase_						,  name_func=lowercase_       )
 def 		_snake_case      (  self	: List[str]						,  lowercase_	: str						,  lowercase_	: List[Any]       ):
     self.run_and_check(
         stage=lowercase_						,  model=lowercase_						,  distributed=lowercase_						,  fpaa=lowercase_						,  )

 @parameterized.expand(lowercase_						,  name_func=lowercase_       )
 def 		_snake_case      (  self	: Union[str, Any]						,  lowercase_	: Union[str, Any]						,  lowercase_	: Union[str, Any]       ):
     self.run_and_check(
         stage=lowercase_						,  model=lowercase_						,  distributed=lowercase_						,  fpaa=lowercase_						,  )

 @require_torch_multi_gpu
 @parameterized.expand(lowercase_						,  name_func=lowercase_       )
 def 		_snake_case      (  self	: str						,  lowercase_	: List[Any]						,  lowercase_	: List[str]       ):
     self.run_and_check(
         stage=lowercase_						,  model=lowercase_						,  distributed=lowercase_						,  fpaa=lowercase_						,  )

 def 		_snake_case      (  self	: Optional[Any]						,  lowercase_	: Union[str, Any]       ):
     # XXX: run_asr is premature and doesn't save any results
     # so all we check for now is that the process didn't fail
     pass

 def 		_snake_case      (  self	: List[str]						,  lowercase_	: str						,  lowercase_	: str						,  lowercase_	: int = 10						,  lowercase_	: bool = True						,  lowercase_	: bool = True						,  lowercase_	: bool = True						,  ):
     snake_case_				:  Dict       =			models[model]

     snake_case_				:  List[str]       =			self.run_trainer(
         stage=lowercase_						,  model_name=lowercase_						,  eval_steps=lowercase_						,  num_train_epochs=1						,  distributed=lowercase_						,  fpaa=lowercase_						,  )

     self.do_checks(lowercase_       )

     return output_dir

 def 		_snake_case      (  self	: Tuple						,  lowercase_	: str						,  lowercase_	: str						,  lowercase_	: int = 10						,  lowercase_	: int = 1						,  lowercase_	: bool = True						,  lowercase_	: bool = True						,  ):
     snake_case_				:  Any       =			self.get_auto_remove_tmp_dir('''./xxx'''						,  after=lowercase_       )
     snake_case_				:  Any       =			f"\n            --model_name_or_path {model_name}\n            --dataset_name hf-internal-testing/librispeech_asr_dummy\n            --dataset_config_name clean\n            --train_split_name validation\n            --validation_split_name validation\n            --output_dir {output_dir}\n            --num_train_epochs {str(lowercase_       )}\n            --per_device_train_batch_size 2\n            --per_device_eval_batch_size 2\n            --evaluation_strategy steps\n            --learning_rate 5e-4\n            --warmup_steps 8\n            --orthography timit\n            --preprocessing_num_workers 1\n            --group_by_length\n            --freeze_feature_extractor\n            --report_to none\n            --save_steps 0\n            --eval_steps {eval_steps}\n            --report_to none\n        ".split()

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

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

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

     return output_dir






 def 		_snake_case      (  self	: List[str]						,  lowercase_	: Union[str, Any]=False       ):
     # 1. explicitly set --num_nodes=1 just in case these tests end up run on a multi-node setup
     # - it won't be able to handle that
     # 2. for now testing with just 2 gpus max (since some quality tests may give different
     # results with mode gpus because we use very little data)
     snake_case_				:  Union[str, Any]       =			min(2						,  get_gpu_count()       ) if distributed else 1
     return f"deepspeed --num_nodes 1 --num_gpus {num_gpus}".split()










"""simple docstring"""

import numpy as np
from numpy import ndarray
from scipy.optimize import Bounds, LinearConstraint, minimize




def __lowercase    (							_a					):
    return np.dot(_a	,    _a					)




class  _UpperCAmelCase :

 def __init__(  self	: int						,  *,
     lowercase_	: float = np.inf						,  lowercase_	: str = "linear"						,  lowercase_	: float = 0.0						,  ):
     snake_case_				:  Optional[Any]       =			regularization
     snake_case_				:  Tuple       =			gamma
     if kernel == "linear":
         snake_case_				:  int       =			self.__linear
     elif kernel == "rbf":
         if self.gamma == 0:
             raise ValueError('''rbf kernel requires gamma'''       )
         if not isinstance(self.gamma						,  (float, int)       ):
             raise ValueError('''gamma must be float or int'''       )
         if not self.gamma > 0:
             raise ValueError('''gamma must be > 0'''       )
         snake_case_				:  int       =			self.__rbf
         # in the future, there could be a default value like in sklearn
         # sklear: def_gamma = 1/(n_features * X.var()) (wiki)
         # previously it was 1/(n_features)
     else:
         snake_case_				:  List[Any]       =			f"Unknown kernel: {kernel}"
         raise ValueError(lowercase_       )

 def 		_snake_case      (  self	: int						,  lowercase_	: ndarray						,  lowercase_	: ndarray       ):
     return np.dot(lowercase_						,  lowercase_       )

 def 		_snake_case      (  self	: int						,  lowercase_	: ndarray						,  lowercase_	: ndarray       ):
     return np.exp(-(self.gamma * norm_squared(vectora - vectora       ))       )

 def 		_snake_case      (  self	: Any						,  lowercase_	: list[ndarray]						,  lowercase_	: ndarray       ):
     snake_case_				:  Union[str, Any]       =			observations
     snake_case_				:  int       =			classes

     # using Wolfe's Dual to calculate w.
     # Primal problem: minimize 1/2*norm_squared(w)
     #   constraint: yn(w . xn + b) >= 1
     #
     # With l a vector
     # Dual problem: maximize sum_n(ln) -
     #       1/2 * sum_n(sum_m(ln*lm*yn*ym*xn . xm))
     #   constraint: self.C >= ln >= 0
     #           and sum_n(ln*yn) = 0
     # Then we get w using w = sum_n(ln*yn*xn)
     # At the end we can get b ~= mean(yn - w . xn)
     #
     # Since we use kernels, we only need l_star to calculate b
     # and to classify observations

     ((snake_case_),			)				:  List[str]       =			np.shape(lowercase_       )

     def to_minimize(lowercase_	: ndarray       ) -> float:
         snake_case_				:  Tuple       =			0
         ((snake_case_),			)				:  Optional[Any]       =			np.shape(lowercase_       )
         for i in range(lowercase_       ):
             for j in range(lowercase_       ):
                 s += (
                     candidate[i]
                     * candidate[j]
                     * classes[i]
                     * classes[j]
                     * self.kernel(observations[i]						,  observations[j]       )
                 )
         return 1 / 2 * s - sum(lowercase_       )

     snake_case_				:  Optional[Any]       =			LinearConstraint(lowercase_						,  0						,  0       )
     snake_case_				:  str       =			Bounds(0						,  self.regularization       )

     snake_case_				:  int       =			minimize(
         lowercase_						,  np.ones(lowercase_       )						,  bounds=lowercase_						,  constraints=[ly_contraint]       ).x
     snake_case_				:  Optional[Any]       =			l_star

     # calculating mean offset of separation plane to points
     snake_case_				:  List[Any]       =			0
     for i in range(lowercase_       ):
         for j in range(lowercase_       ):
             s += classes[i] - classes[i] * self.optimum[i] * self.kernel(
                 observations[i]						,  observations[j]       )
     snake_case_				:  Union[str, Any]       =			s / n






 def 		_snake_case      (  self	: List[str]						,  lowercase_	: ndarray       ):
     snake_case_				:  int       =			sum(
         self.optimum[n]
         * self.classes[n]
         * self.kernel(self.observations[n]						,  lowercase_       )
         for n in range(len(self.classes       )       )       )
     return 1 if s + self.offset >= 0 else -1


if __name__ == "__main__":
     import doctest

     doctest.testmod()





