



# 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


lowerCAmelCase__		:					Dict							=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)

lowerCAmelCase__		:					Optional[Any]							={'''base''': '''patrickvonplaten/wav2vec2_tiny_random''', '''robust''': '''patrickvonplaten/wav2vec2_tiny_random_robust'''}

lowerCAmelCase__		:					Optional[Any]							='''zero2'''
lowerCAmelCase__		:					str							='''zero3'''
lowerCAmelCase__		:					Optional[int]							=[ZEROa, ZEROa]






def      __lowercase       (			a__							,       a__							,       a__       )    ->			int:
							__SCREAMING_SNAKE_CASE									= parameterized.to_safe_name('_'.join(str(__lowerCAmelCase       ) for x in param.args       )       )
							return f"""{func.__name__}_{param_based_name}"""


# Cartesian-product of zero stages with models to test
lowerCAmelCase__		:					List[Any]							=list(itertools.product(stages, models.keys()))

@slow
@require_deepspeed
@require_torch_gpu
class        UpperCAmelCase_  (       __lowerCamelCase	):

						'''simple docstring'''




						@parameterized.expand(__lowercase				,							name_func=__lowercase		)
						def 	_A     (   self				,							_A				,							_A		):







													'''simple docstring'''

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






						@require_torch_multi_gpu
						@parameterized.expand(__lowercase				,							name_func=__lowercase		)
						def 	_A     (   self				,							_A				,							_A		):







													'''simple docstring'''

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






						@parameterized.expand(__lowercase				,							name_func=__lowercase		)
						def 	_A     (   self				,							_A				,							_A		):







													'''simple docstring'''

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






						@require_torch_multi_gpu
						@parameterized.expand(__lowercase				,							name_func=__lowercase		)
						def 	_A     (   self				,							_A				,							_A		):







													'''simple docstring'''

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






						def 	_A     (   self				,							_A		):







													'''simple docstring'''

													pass






						def 	_A     (   self				,							_A				,							_A				,							_A = 10				,							_A = True				,							_A = True				,							_A = True				,							):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= models[model]

													__SCREAMING_SNAKE_CASE									= 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 	_A     (   self				,							_A				,							_A				,							_A = 10				,							_A = 1				,							_A = True				,							_A = True				,							):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= self.get_auto_remove_tmp_dir('./xxx'				,							after=__lowercase		)
													__SCREAMING_SNAKE_CASE									= 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(__lowercase		)}
            --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
													__SCREAMING_SNAKE_CASE									= f"""--deepspeed {self.test_file_dir_str}/ds_config_wav2vec2_{stage}.json""".split()
													__SCREAMING_SNAKE_CASE									= [f"""{self.examples_dir_str}/research_projects/wav2vec2/run_asr.py"""]
													__SCREAMING_SNAKE_CASE									= self.get_launcher(__lowercase		)

													__SCREAMING_SNAKE_CASE									= 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 	_A     (   self				,							_A=False		):







													'''simple docstring'''

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











import os






def      __lowercase       (			a__ = "input.txt"       )    ->			int:

							with open(os.path.join(os.path.dirname(a__       )							,       a__       )       ) as input_file:
														__SCREAMING_SNAKE_CASE									= [
														    [int(a__       ) for element in line.split(','       )]
														    for line in input_file.readlines()
														]

							__SCREAMING_SNAKE_CASE									= len(a__       )
							__SCREAMING_SNAKE_CASE									= len(matrix[0]       )

							__SCREAMING_SNAKE_CASE									= [[-1 for _ in range(a__       )] for _ in range(a__       )]
							for i in range(a__       ):
														__SCREAMING_SNAKE_CASE									= matrix[i][0]

							for j in range(1							,       a__       ):
														for i in range(a__       ):
																					__SCREAMING_SNAKE_CASE									= minimal_path_sums[i][j - 1] + matrix[i][j]

														for i in range(1							,       a__       ):
																					__SCREAMING_SNAKE_CASE									= min(
																					    minimal_path_sums[i][j]							,       minimal_path_sums[i - 1][j] + matrix[i][j]       )

														for i in range(rows - 2							,       -1							,       -1       ):
																					__SCREAMING_SNAKE_CASE									= min(
																					    minimal_path_sums[i][j]							,       minimal_path_sums[i + 1][j] + matrix[i][j]       )

							return min(minimal_path_sums_row[-1] for minimal_path_sums_row in minimal_path_sums       )


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






