






import itertools
import string
from collections.abc import Generator, Iterable
def 						_snake_case(   SCREAMING_SNAKE_CASE__      :     Iterable[str]   ,      SCREAMING_SNAKE_CASE__      :     int				)							->				Generator[tuple[str, ...], None, None]:





    '''simple docstring'''






    A__   				=       iter(SCREAMING_SNAKE_CASE__				)
    while True:
        A__   				=       tuple(itertools.islice(SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__				)				)
        if not chunk:
            return
        yield chunk
def 						_snake_case(   SCREAMING_SNAKE_CASE__      :     str				)							->				str:





    '''simple docstring'''






    A__   				=       ''.join([c.upper() for c in dirty if c in string.ascii_letters]				)
    A__   				=       ''

    if len(SCREAMING_SNAKE_CASE__				) < 2:
        return dirty

    for i in range(len(SCREAMING_SNAKE_CASE__				) - 1				):
        clean += dirty[i]

        if dirty[i] == dirty[i + 1]:
            clean += "X"

    clean += dirty[-1]

    if len(SCREAMING_SNAKE_CASE__				) & 1:
        clean += "X"

    return clean
def 						_snake_case(   SCREAMING_SNAKE_CASE__      :     str				)							->				list[str]:





    '''simple docstring'''






    A__   				=       'ABCDEFGHIKLMNOPQRSTUVWXYZ'
    # we're using a list instead of a '2d' array because it makes the math
    # for setting up the table and doing the actual encoding/decoding simpler
    A__   				=       []

    # copy key chars into the table if they are in `alphabet` ignoring duplicates
    for char in key.upper():
        if char not in table and char in alphabet:
            table.append(SCREAMING_SNAKE_CASE__				)

    # fill the rest of the table in with the remaining alphabet chars
    for char in alphabet:
        if char not in table:
            table.append(SCREAMING_SNAKE_CASE__				)

    return table
def 						_snake_case(   SCREAMING_SNAKE_CASE__      :     str   ,      SCREAMING_SNAKE_CASE__      :     str				)							->				str:





    '''simple docstring'''






    A__   				=       generate_table(SCREAMING_SNAKE_CASE__				)
    A__   				=       prepare_input(SCREAMING_SNAKE_CASE__				)
    A__   				=       ''

    # https://en.wikipedia.org/wiki/Playfair_cipher#Description
    for chara, chara in chunker(SCREAMING_SNAKE_CASE__   ,      2				):
        A__				,						A__   				=       divmod(table.index(SCREAMING_SNAKE_CASE__				)   ,      5				)
        A__				,						A__   				=       divmod(table.index(SCREAMING_SNAKE_CASE__				)   ,      5				)

        if rowa == rowa:
            ciphertext += table[rowa * 5 + (cola + 1) % 5]
            ciphertext += table[rowa * 5 + (cola + 1) % 5]
        elif cola == cola:
            ciphertext += table[((rowa + 1) % 5) * 5 + cola]
            ciphertext += table[((rowa + 1) % 5) * 5 + cola]
        else:  # rectangle
            ciphertext += table[rowa * 5 + cola]
            ciphertext += table[rowa * 5 + cola]

    return ciphertext

def 						_snake_case(   SCREAMING_SNAKE_CASE__      :     str   ,      SCREAMING_SNAKE_CASE__      :     str				)							->				str:





    '''simple docstring'''






    A__   				=       generate_table(SCREAMING_SNAKE_CASE__				)
    A__   				=       ''

    # https://en.wikipedia.org/wiki/Playfair_cipher#Description
    for chara, chara in chunker(SCREAMING_SNAKE_CASE__   ,      2				):
        A__				,						A__   				=       divmod(table.index(SCREAMING_SNAKE_CASE__				)   ,      5				)
        A__				,						A__   				=       divmod(table.index(SCREAMING_SNAKE_CASE__				)   ,      5				)

        if rowa == rowa:
            plaintext += table[rowa * 5 + (cola - 1) % 5]
            plaintext += table[rowa * 5 + (cola - 1) % 5]
        elif cola == cola:
            plaintext += table[((rowa - 1) % 5) * 5 + cola]
            plaintext += table[((rowa - 1) % 5) * 5 + cola]
        else:  # rectangle
            plaintext += table[rowa * 5 + cola]
            plaintext += table[rowa * 5 + cola]

    return plaintext




# 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_			=  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_			=  {"base": "patrickvonplaten/wav2vec2_tiny_random", "robust": "patrickvonplaten/wav2vec2_tiny_random_robust"}

lowercase_			=  "zero2"
lowercase_			=  "zero3"
lowercase_			=  [ZEROa, ZEROa]
def 						_snake_case(   SCREAMING_SNAKE_CASE__      :     Optional[int]   ,      SCREAMING_SNAKE_CASE__      :     Tuple   ,      SCREAMING_SNAKE_CASE__      :     List[Any]				)							->				Dict:





    '''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
lowercase_			=  list(itertools.product(stages, models.keys()))


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







    """simple docstring"""





    @parameterized.expand(lowercase_,name_func=lowercase_		)
    def 						snake_case__					(				self			:	int,lowercase_			:	str,lowercase_			:	Any		)->   Optional[int]:





        '''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 						snake_case__					(				self			:	Union[str, Any],lowercase_			:	Optional[Any],lowercase_			:	List[Any]		)->   int:





        '''simple docstring'''







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


    @parameterized.expand(lowercase_,name_func=lowercase_		)
    def 						snake_case__					(				self			:	List[str],lowercase_			:	List[str],lowercase_			:	List[Any]		)->   Any:





        '''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 						snake_case__					(				self			:	Dict,lowercase_			:	Optional[Any],lowercase_			:	List[Any]		)->   Optional[int]:





        '''simple docstring'''







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


    def 						snake_case__					(				self			:	Tuple,lowercase_			:	Any		)->   Union[str, Any]:





        '''simple docstring'''







        pass


    def 						snake_case__					(				self			:	int,lowercase_			:	str,lowercase_			:	str,lowercase_			:	int = 1_0,lowercase_			:	bool = True,lowercase_			:	bool = True,lowercase_			:	bool = True,)->   Union[str, Any]:





        '''simple docstring'''







        A__   				=       models[model]

        A__   				=       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			:	Union[str, Any],lowercase_			:	str,lowercase_			:	str,lowercase_			:	int = 1_0,lowercase_			:	int = 1,lowercase_			:	bool = True,lowercase_			:	bool = True,)->   Any:





        '''simple docstring'''







        A__   				=       self.get_auto_remove_tmp_dir('./xxx',after=lowercase_		)
        A__   				=       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
        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(lowercase_		)

        A__   				=       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			:	Any,lowercase_			:	int=False		)->   Tuple:





        '''simple docstring'''







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

