






# 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


_UpperCAmelCase			:		str   				=    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)

_UpperCAmelCase			:		str   				=    {"base": "patrickvonplaten/wav2vec2_tiny_random", "robust": "patrickvonplaten/wav2vec2_tiny_random_robust"}

_UpperCAmelCase			:		Optional[Any]   				=    "zero2"
_UpperCAmelCase			:		Union[str, Any]   				=    "zero3"
_UpperCAmelCase			:		List[str]   				=    [ZEROa, ZEROa]





def 	lowerCAmelCase_		(lowercase__						:       List[Any]     ,					lowercase__						:       Optional[int]     ,					lowercase__						:       List[str]       )		->    Any:


				'''simple docstring'''




				lowerCAmelCase__      			=						parameterized.to_safe_name('''_'''.join(str(lowercase__       ) for x in param.args       )       )
				return f'{func.__name__}_{param_based_name}'


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




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




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




							def 						__snake_case					(   self     :   Dict ,							SCREAMING_SNAKE_CASE_     :   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     :   Optional[Any] ,							SCREAMING_SNAKE_CASE_     :   str ,							SCREAMING_SNAKE_CASE_     :   str ,							SCREAMING_SNAKE_CASE_     :   int = 10 ,							SCREAMING_SNAKE_CASE_     :   bool = True ,							SCREAMING_SNAKE_CASE_     :   bool = True ,							SCREAMING_SNAKE_CASE_     :   bool = True ,							):
											lowerCAmelCase__      			=						models[model]

											lowerCAmelCase__      			=						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 						__snake_case					(   self     :   Union[str, Any] ,							SCREAMING_SNAKE_CASE_     :   str ,							SCREAMING_SNAKE_CASE_     :   str ,							SCREAMING_SNAKE_CASE_     :   int = 10 ,							SCREAMING_SNAKE_CASE_     :   int = 1 ,							SCREAMING_SNAKE_CASE_     :   bool = True ,							SCREAMING_SNAKE_CASE_     :   bool = True ,							):
											lowerCAmelCase__      			=						self.get_auto_remove_tmp_dir('''./xxx''' ,							after=SCREAMING_SNAKE_CASE_	)
											lowerCAmelCase__      			=						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(SCREAMING_SNAKE_CASE_	)}\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
											lowerCAmelCase__      			=						f'--deepspeed {self.test_file_dir_str}/ds_config_wav2vec2_{stage}.json'.split()
											lowerCAmelCase__      			=						[f'{self.examples_dir_str}/research_projects/wav2vec2/run_asr.py']
											lowerCAmelCase__      			=						self.get_launcher(SCREAMING_SNAKE_CASE_	)

											lowerCAmelCase__      			=						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 						__snake_case					(   self     :   Optional[Any] ,							SCREAMING_SNAKE_CASE_     :   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)
											lowerCAmelCase__      			=						min(2 ,							get_gpu_count()	) if distributed else 1
											return f'deepspeed --num_nodes 1 --num_gpus {num_gpus}'.split()













from math import isclose, sqrt





def 	lowerCAmelCase_		(lowercase__						:       float     ,					lowercase__						:       float     ,					lowercase__						:       float       )		->    tuple[float, float, float]:


				'''simple docstring'''




				lowerCAmelCase__      			=						point_y / 4 / point_x
				lowerCAmelCase__      			=						2 * normal_gradient / (1 + normal_gradient * normal_gradient)
				lowerCAmelCase__      			=						(1 - normal_gradient * normal_gradient) / (
				    1 + normal_gradient * normal_gradient
				)
				lowerCAmelCase__      			=						(sa - ca * incoming_gradient) / (ca + sa * incoming_gradient)

				# to find the next point, solve the simultaeneous equations:
				# y^2 + 4x^2 = 100
				# y - b = m * (x - a)
				# ==> A x^2 + B x + C = 0
				lowerCAmelCase__      			=						outgoing_gradient**2 + 4
				lowerCAmelCase__      			=						2 * outgoing_gradient * (point_y - outgoing_gradient * point_x)
				lowerCAmelCase__      			=						(point_y - outgoing_gradient * point_x) ** 2 - 1_00

				lowerCAmelCase__      			=						(
				    -linear_term - sqrt(linear_term**2 - 4 * quadratic_term * constant_term       )
				) / (2 * quadratic_term)
				lowerCAmelCase__      			=						(
				    -linear_term + sqrt(linear_term**2 - 4 * quadratic_term * constant_term       )
				) / (2 * quadratic_term)

				# two solutions, one of which is our input point
				lowerCAmelCase__      			=						x_minus if isclose(lowercase__     ,					lowercase__       ) else x_plus
				lowerCAmelCase__      			=						point_y + outgoing_gradient * (next_x - point_x)

				return next_x, next_y, outgoing_gradient





def 	lowerCAmelCase_		(lowercase__						:       float = 1.4     ,					lowercase__						:       float = -9.6       )		->    int:


				'''simple docstring'''




				lowerCAmelCase__      			=						0
				lowerCAmelCase__      			=						first_x_coord
				lowerCAmelCase__      			=						first_y_coord
				lowerCAmelCase__      			=						(10.1 - point_y) / (0.0 - point_x)

				while not (-0.01 <= point_x <= 0.01 and point_y > 0):
								lowerCAmelCase__			,		lowerCAmelCase__			,		lowerCAmelCase__      			=						next_point(lowercase__     ,					lowercase__     ,					lowercase__       )
								num_reflections += 1

				return num_reflections


if __name__ == "__main__":
			print(F'''{solution() = }''')





