
# 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


snake_case__      :		Optional[int]				=       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(4_2)

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

snake_case__      :		int				=       'zero2'
snake_case__      :		List[Any]				=       'zero3'
snake_case__      :		Optional[Any]				=       [ZEROa, ZEROa]
def 					lowerCamelCase__		(				_lowerCamelCase			,		_lowerCamelCase			,		_lowerCamelCase  )						->Optional[Any]:
 # 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
 _UpperCAmelCase 				=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
snake_case__      :		str				=       list(itertools.product(stages, models.keys()))





@slow
@require_deepspeed
@require_torch_gpu
class 						_a   (						a__       ):

       """simple docstring"""





       @parameterized.expand(__a       ,					name_func=__a	)
       def     SCREAMING_SNAKE_CASE  (					self       ,					_snake_case       ,					_snake_case	):
        self.run_and_check(
            stage=__a       ,					model=__a       ,					distributed=__a       ,					fpaa=__a       ,					)
       @require_torch_multi_gpu
       @parameterized.expand(__a       ,					name_func=__a	)
       def     SCREAMING_SNAKE_CASE  (					self       ,					_snake_case       ,					_snake_case	):
        self.run_and_check(
            stage=__a       ,					model=__a       ,					distributed=__a       ,					fpaa=__a       ,					)
       @parameterized.expand(__a       ,					name_func=__a	)
       def     SCREAMING_SNAKE_CASE  (					self       ,					_snake_case       ,					_snake_case	):
        self.run_and_check(
            stage=__a       ,					model=__a       ,					distributed=__a       ,					fpaa=__a       ,					)
       @require_torch_multi_gpu
       @parameterized.expand(__a       ,					name_func=__a	)
       def     SCREAMING_SNAKE_CASE  (					self       ,					_snake_case       ,					_snake_case	):
        self.run_and_check(
            stage=__a       ,					model=__a       ,					distributed=__a       ,					fpaa=__a       ,					)
       def     SCREAMING_SNAKE_CASE  (					self       ,					_snake_case	):
        # 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     SCREAMING_SNAKE_CASE  (					self       ,					_snake_case       ,					_snake_case       ,					_snake_case = 10       ,					_snake_case = True       ,					_snake_case = True       ,					_snake_case = True       ,					):
        _UpperCAmelCase 				=models[model]

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

        self.do_checks(__a	)

        return output_dir
       def     SCREAMING_SNAKE_CASE  (					self       ,					_snake_case       ,					_snake_case       ,					_snake_case = 10       ,					_snake_case = 1       ,					_snake_case = True       ,					_snake_case = True       ,					):
        _UpperCAmelCase 				=self.get_auto_remove_tmp_dir("./xxx"       ,					after=__a	)
        _UpperCAmelCase 				=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(__a	)}\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
        _UpperCAmelCase 				=F"--deepspeed {self.test_file_dir_str}/ds_config_wav2vec2_{stage}.json".split()
        _UpperCAmelCase 				=[F"{self.examples_dir_str}/research_projects/wav2vec2/run_asr.py"]
        _UpperCAmelCase 				=self.get_launcher(__a	)

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

        return output_dir

       def     SCREAMING_SNAKE_CASE  (					self       ,					_snake_case=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)
        _UpperCAmelCase 				=min(2       ,					get_gpu_count()	) if distributed else 1
        return F"deepspeed --num_nodes 1 --num_gpus {num_gpus}".split()









class   SCREAMING_SNAKE_CASE_			:




       '''simple docstring'''



       def __init__(						self				:				List[Any]							)					->Tuple:
          lowerCamelCase_						:   Optional[Any]		= """"""
          lowerCamelCase_						:   Dict		= """"""
          lowerCamelCase_						:   Optional[Any]		= []



       def 				_lowerCAmelCase							(						self				:				Optional[Any]		,				__a				:				int		,				__a				:				int							)					->int:
          if m == -1:
             return n + 1
          elif n == -1:
             return m + 1
          elif self.dp[m][n] > -1:
             return self.dp[m][n]
          else:
             if self.worda[m] == self.worda[n]:
                lowerCamelCase_						:   Optional[Any]		= self.__min_dist_top_down_dp(m - 1		,				n - 1							)
             else:
                lowerCamelCase_						:   List[Any]		= self.__min_dist_top_down_dp(__a		,				n - 1							)
                lowerCamelCase_						:   Union[str, Any]		= self.__min_dist_top_down_dp(m - 1		,				__a							)
                lowerCamelCase_						:   Any		= self.__min_dist_top_down_dp(m - 1		,				n - 1							)
                lowerCamelCase_						:   Tuple		= 1 + min(__a		,				__a		,				__a							)

             return self.dp[m][n]



       def 				_lowerCAmelCase							(						self				:				Any		,				__a				:				str		,				__a				:				str							)					->int:
          lowerCamelCase_						:   Dict		= worda
          lowerCamelCase_						:   str		= worda
          lowerCamelCase_						:   Union[str, Any]		= [[-1 for _ in range(len(__a							)							)] for _ in range(len(__a							)							)]

          return self.__min_dist_top_down_dp(len(__a							) - 1		,				len(__a							) - 1							)




       def 				_lowerCAmelCase							(						self				:				int		,				__a				:				str		,				__a				:				str							)					->int:
          lowerCamelCase_						:   Tuple		= worda
          lowerCamelCase_						:   Optional[Any]		= worda
          lowerCamelCase_						:   Tuple		= len(__a							)
          lowerCamelCase_						:   Optional[Any]		= len(__a							)
          lowerCamelCase_						:   List[Any]		= [[0 for _ in range(n + 1							)] for _ in range(m + 1							)]

          for i in range(m + 1							):
             for j in range(n + 1							):
                if i == 0:  # first string is empty
                   lowerCamelCase_						:   Optional[Any]		= j
                elif j == 0:  # second string is empty
                   lowerCamelCase_						:   Any		= i
                elif worda[i - 1] == worda[j - 1]:  # last characters are equal
                   lowerCamelCase_						:   Tuple		= self.dp[i - 1][j - 1]
                else:
                   lowerCamelCase_						:   Dict		= self.dp[i][j - 1]
                   lowerCamelCase_						:   Union[str, Any]		= self.dp[i - 1][j]
                   lowerCamelCase_						:   Any		= self.dp[i - 1][j - 1]
                   lowerCamelCase_						:   Optional[int]		= 1 + min(__a		,				__a		,				__a							)
          return self.dp[m][n]


if __name__ == "__main__":
      snake_case__				:	Optional[Any]					       =					EditDistance()

      print('****************** Testing Edit Distance DP Algorithm ******************')
      print()

      snake_case__				:	Union[str, Any]					       =					input('Enter the first string: ').strip()
      snake_case__				:	List[Any]					       =					input('Enter the second string: ').strip()

      print()
      print(F'The minimum edit distance is: {solver.min_dist_top_down(Sa, Sa)}')
      print(F'The minimum edit distance is: {solver.min_dist_bottom_up(Sa, Sa)}')
      print()
      print('*************** End of Testing Edit Distance DP Algorithm ***************')





