


"""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


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

_lowerCamelCase     =  'zero2'
_lowerCamelCase     =  'zero3'
_lowerCamelCase     =  [ZEROa, ZEROa]


def __lowercase     (      lowerCamelCase_  :      Dict						,					lowerCamelCase_  :      Dict						,					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
				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(itertools.product(stages, models.keys()))


@slow
@require_deepspeed
@require_torch_gpu
class 			lowerCamelCase_		(			lowercase   ):





				"""simple docstring"""







				@parameterized.expand(UpperCAmelCase__       ,		name_func=UpperCAmelCase__     )
				def   lowerCAmelCase__		(						self       ,		UpperCAmelCase__       ,		UpperCAmelCase__     ):
								self.run_and_check(
								    stage=UpperCAmelCase__       ,		model=UpperCAmelCase__       ,		distributed=UpperCAmelCase__       ,		fpaa=UpperCAmelCase__       ,		)



				@require_torch_multi_gpu
				@parameterized.expand(UpperCAmelCase__       ,		name_func=UpperCAmelCase__     )
				def   lowerCAmelCase__		(						self       ,		UpperCAmelCase__       ,		UpperCAmelCase__     ):
								self.run_and_check(
								    stage=UpperCAmelCase__       ,		model=UpperCAmelCase__       ,		distributed=UpperCAmelCase__       ,		fpaa=UpperCAmelCase__       ,		)



				@parameterized.expand(UpperCAmelCase__       ,		name_func=UpperCAmelCase__     )
				def   lowerCAmelCase__		(						self       ,		UpperCAmelCase__       ,		UpperCAmelCase__     ):
								self.run_and_check(
								    stage=UpperCAmelCase__       ,		model=UpperCAmelCase__       ,		distributed=UpperCAmelCase__       ,		fpaa=UpperCAmelCase__       ,		)



				@require_torch_multi_gpu
				@parameterized.expand(UpperCAmelCase__       ,		name_func=UpperCAmelCase__     )
				def   lowerCAmelCase__		(						self       ,		UpperCAmelCase__       ,		UpperCAmelCase__     ):
								self.run_and_check(
								    stage=UpperCAmelCase__       ,		model=UpperCAmelCase__       ,		distributed=UpperCAmelCase__       ,		fpaa=UpperCAmelCase__       ,		)



				def   lowerCAmelCase__		(						self       ,		UpperCAmelCase__     ):
								# 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   lowerCAmelCase__		(						self       ,		UpperCAmelCase__       ,		UpperCAmelCase__       ,		UpperCAmelCase__ = 10       ,		UpperCAmelCase__ = True       ,		UpperCAmelCase__ = True       ,		UpperCAmelCase__ = True       ,		):
								SCREAMING_SNAKE_CASE__     							=				models[model]

								SCREAMING_SNAKE_CASE__     							=				self.run_trainer(
								    stage=UpperCAmelCase__       ,		model_name=UpperCAmelCase__       ,		eval_steps=UpperCAmelCase__       ,		num_train_epochs=1       ,		distributed=UpperCAmelCase__       ,		fpaa=UpperCAmelCase__       ,		)

								self.do_checks(UpperCAmelCase__     )

								return output_dir



				def   lowerCAmelCase__		(						self       ,		UpperCAmelCase__       ,		UpperCAmelCase__       ,		UpperCAmelCase__ = 10       ,		UpperCAmelCase__ = 1       ,		UpperCAmelCase__ = True       ,		UpperCAmelCase__ = True       ,		):
								SCREAMING_SNAKE_CASE__     							=				self.get_auto_remove_tmp_dir("./xxx"       ,		after=UpperCAmelCase__     )
								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(UpperCAmelCase__     )}
            --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(UpperCAmelCase__     )

								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(UpperCAmelCase__       ,		env=self.get_env()     )

								return output_dir






				def   lowerCAmelCase__		(						self       ,		UpperCAmelCase__=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)
								SCREAMING_SNAKE_CASE__     							=				min(2       ,		get_gpu_count()     ) if distributed else 1
								return f'''deepspeed --num_nodes 1 --num_gpus {num_gpus}'''.split()




"""simple docstring"""






from __future__ import annotations

from collections.abc import Sequence
from typing import Literal


def __lowercase     (      lowerCamelCase_  :      str						,					lowerCamelCase_  :      str  ):
				SCREAMING_SNAKE_CASE__     							=				list(lowerCamelCase_  )
				SCREAMING_SNAKE_CASE__     							=				list(lowerCamelCase_  )
				SCREAMING_SNAKE_CASE__     							=				0
				for i in range(len(lowerCamelCase_  )  ):
								if lista[i] != lista[i]:
												count += 1
												SCREAMING_SNAKE_CASE__     							=				"_"
				if count > 1:
								return False
				else:
								return "".join(lowerCamelCase_  )


def __lowercase     (      lowerCamelCase_  :      list[str]  ):
				SCREAMING_SNAKE_CASE__     							=				[]
				while True:
								SCREAMING_SNAKE_CASE__     							=				["$"] * len(lowerCamelCase_  )
								SCREAMING_SNAKE_CASE__     							=				[]
								for i in range(len(lowerCamelCase_  )  ):
												for j in range(i + 1						,					len(lowerCamelCase_  )  ):
																SCREAMING_SNAKE_CASE__     							=				compare_string(binary[i]						,					binary[j]  )
																if k is False:
																				SCREAMING_SNAKE_CASE__     							=				"*"
																				SCREAMING_SNAKE_CASE__     							=				"*"
																				temp.append("X"  )
								for i in range(len(lowerCamelCase_  )  ):
												if checka[i] == "$":
																pi.append(binary[i]  )
								if len(lowerCamelCase_  ) == 0:
												return pi
								SCREAMING_SNAKE_CASE__     							=				list(set(lowerCamelCase_  )  )


def __lowercase     (      lowerCamelCase_  :      int						,					lowerCamelCase_  :      Sequence[float]  ):
				SCREAMING_SNAKE_CASE__     							=				[]
				for minterm in minterms:
								SCREAMING_SNAKE_CASE__     							=				""
								for _ in range(lowerCamelCase_  ):
												SCREAMING_SNAKE_CASE__     							=				str(minterm % 2  ) + string
												minterm //= 2
								temp.append(lowerCamelCase_  )
				return temp


def __lowercase     (      lowerCamelCase_  :      str						,					lowerCamelCase_  :      str						,					lowerCamelCase_  :      int  ):
				SCREAMING_SNAKE_CASE__     							=				list(lowerCamelCase_  )
				SCREAMING_SNAKE_CASE__     							=				list(lowerCamelCase_  )
				SCREAMING_SNAKE_CASE__     							=				0
				for i in range(len(lowerCamelCase_  )  ):
								if lista[i] != lista[i]:
												count_n += 1
				return count_n == count


def __lowercase     (      lowerCamelCase_  :      list[list[int]]						,					lowerCamelCase_  :      list[str]  ):
				SCREAMING_SNAKE_CASE__     							=				[]
				SCREAMING_SNAKE_CASE__     							=				[0] * len(lowerCamelCase_  )
				for i in range(len(chart[0]  )  ):
								SCREAMING_SNAKE_CASE__     							=				0
								SCREAMING_SNAKE_CASE__     							=				-1
								for j in range(len(lowerCamelCase_  )  ):
												if chart[j][i] == 1:
																count += 1
																SCREAMING_SNAKE_CASE__     							=				j
								if count == 1:
												SCREAMING_SNAKE_CASE__     							=				1
				for i in range(len(lowerCamelCase_  )  ):
								if select[i] == 1:
												for j in range(len(chart[0]  )  ):
																if chart[i][j] == 1:
																				for k in range(len(lowerCamelCase_  )  ):
																								SCREAMING_SNAKE_CASE__     							=				0
												temp.append(prime_implicants[i]  )
				while True:
								SCREAMING_SNAKE_CASE__     							=				0
								SCREAMING_SNAKE_CASE__     							=				-1
								SCREAMING_SNAKE_CASE__     							=				0
								for i in range(len(lowerCamelCase_  )  ):
												SCREAMING_SNAKE_CASE__     							=				chart[i].count(1  )
												if count_n > max_n:
																SCREAMING_SNAKE_CASE__     							=				count_n
																SCREAMING_SNAKE_CASE__     							=				i

								if max_n == 0:
												return temp

								temp.append(prime_implicants[rem]  )

								for i in range(len(chart[0]  )  ):
												if chart[rem][i] == 1:
																for j in range(len(lowerCamelCase_  )  ):
																				SCREAMING_SNAKE_CASE__     							=				0


def __lowercase     (      lowerCamelCase_  :      list[str]						,					lowerCamelCase_  :      list[str]  ):
				SCREAMING_SNAKE_CASE__     							=				[[0 for x in range(len(lowerCamelCase_  )  )] for x in range(len(lowerCamelCase_  )  )]
				for i in range(len(lowerCamelCase_  )  ):
								SCREAMING_SNAKE_CASE__     							=				prime_implicants[i].count("_"  )
								for j in range(len(lowerCamelCase_  )  ):
												if is_for_table(prime_implicants[i]						,					binary[j]						,					lowerCamelCase_  ):
																SCREAMING_SNAKE_CASE__     							=				1

				return chart


def __lowercase     (      ):
				SCREAMING_SNAKE_CASE__     							=				int(input("Enter the no. of variables\n"  )  )
				SCREAMING_SNAKE_CASE__     							=				[
				    float(lowerCamelCase_  )
				    for x in input(
				        "Enter the decimal representation of Minterms 'Spaces Separated'\n"  ).split()
				]
				SCREAMING_SNAKE_CASE__     							=				decimal_to_binary(lowerCamelCase_						,					lowerCamelCase_  )

				SCREAMING_SNAKE_CASE__     							=				check(lowerCamelCase_  )
				print("Prime Implicants are:"  )
				print(lowerCamelCase_  )
				SCREAMING_SNAKE_CASE__     							=				prime_implicant_chart(lowerCamelCase_						,					lowerCamelCase_  )

				SCREAMING_SNAKE_CASE__     							=				selection(lowerCamelCase_						,					lowerCamelCase_  )
				print("Essential Prime Implicants are:"  )
				print(lowerCamelCase_  )


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	main()
