
import itertools
import os
import subprocess
from os.path import dirname

from parameterized import parameterized

from tests.trainer.test_trainer import TrainerIntegrationCommon  # noqa
from transformers import is_torch_available
from transformers.testing_utils import (
    TestCasePlus,
    execute_subprocess_async,
    get_gpu_count,
    get_tests_dir,
    require_deepspeed,
    require_torch_gpu,
    slow,
)
from transformers.trainer_utils import set_seed


if is_torch_available():
							from tests.trainer.test_trainer import (  # noqa
							    RegressionModelConfig,
							    RegressionPreTrainedModel,
							    get_regression_trainer,
							)


set_seed(42)

lowercase__						:Union[str, Any]					    =       get_tests_dir("fixtures")
lowercase__						:int					    =       os.path.join(dirname(get_tests_dir()))
lowercase__						:Tuple					    =       dirname(os.path.abspath(__file__))

# default torch.distributed port
lowercase__						:Optional[int]					    =       "10999"

lowercase__						:int					    =       "t5-small"

# *** Working Models ***
lowercase__						:Tuple					    =       "hf-internal-testing/tiny-albert"
lowercase__						:Any					    =       "sshleifer/bart-tiny-random"
lowercase__						:str					    =       "hf-internal-testing/tiny-bert"
lowercase__						:Optional[int]					    =       "hf-internal-testing/tiny-random-bigbird_pegasus"
lowercase__						:Optional[Any]					    =       "hf-internal-testing/tiny-random-big_bird"
lowercase__						:Tuple					    =       "hf-internal-testing/tiny-random-blenderbot"
lowercase__						:int					    =       "bigscience/bigscience-small-testing"
lowercase__						:Optional[Any]					    =       "hf-internal-testing/tiny-random-deberta"
lowercase__						:str					    =       "hf-internal-testing/tiny-random-deberta-v2"
lowercase__						:List[str]					    =       "sshleifer/tiny-distilbert-base-cased"
lowercase__						:Tuple					    =       "hf-internal-testing/tiny-electra"
lowercase__						:List[str]					    =       "hf-internal-testing/tiny-random-flaubert"
lowercase__						:Any					    =       "stas/tiny-wmt19-en-de"
lowercase__						:int					    =       "hf-internal-testing/tiny-random-funnel"
lowercase__						:Any					    =       "sshleifer/tiny-gpt2"
lowercase__						:str					    =       "hf-internal-testing/tiny-random-gptj"
lowercase__						:str					    =       "hf-internal-testing/tiny-random-gpt_neo"
lowercase__						:str					    =       "hf-internal-testing/tiny-layoutlm"
lowercase__						:List[Any]					    =       "hf-internal-testing/tiny-random-led"
lowercase__						:Optional[int]					    =       "hf-internal-testing/tiny-random-longformer"
lowercase__						:str					    =       "stas/tiny-m2m_100"  # hf tiny model is unsuitable
lowercase__						:Dict					    =       "sshleifer/tiny-marian-en-de"
lowercase__						:Union[str, Any]					    =       "sshleifer/tiny-mbart"
lowercase__						:Any					    =       "hf-internal-testing/tiny-random-mobilebert"
lowercase__						:List[Any]					    =       "hf-internal-testing/tiny-random-mpnet"
lowercase__						:str					    =       "stas/pegasus-cnn_dailymail-tiny-random"
lowercase__						:Tuple					    =       "hf-internal-testing/tiny-random-prophetnet"
lowercase__						:List[Any]					    =       "sshleifer/tiny-distilroberta-base"
lowercase__						:int					    =       "hf-internal-testing/tiny-random-squeezebert"
lowercase__						:Union[str, Any]					    =       "patrickvonplaten/t5-tiny-random"
lowercase__						:Optional[int]					    =       "hf-internal-testing/tiny-random-t5-v1.1"
lowercase__						:Any					    =       "hf-internal-testing/tiny-random-vit"
lowercase__						:Optional[Any]					    =       "hf-internal-testing/tiny-xlm-roberta"
lowercase__						:Optional[Any]					    =       "sshleifer/tiny-xlnet-base-cased"


# *** To Fix ***


# *** tiny model issues ***
# missing model files:
lowercase__						:Union[str, Any]					    =       "hf-internal-testing/tiny-random-mt5"
lowercase__						:Any					    =       "hf-internal-testing/tiny-random-camembert"
lowercase__						:Any					    =       "hf-internal-testing/tiny-random-openai-gpt"

# missing tokenizer files
lowercase__						:str					    =       "hf-internal-testing/tiny-random-convbert"
lowercase__						:List[str]					    =       "hf-internal-testing/tiny-random-layoutlmv2"
lowercase__						:Tuple					    =       "hf-internal-testing/tiny-random-hubert"

# issues with tokenizer
lowercase__						:Any					    =       "hf-internal-testing/tiny-random-ctrl"
lowercase__						:Dict					    =       "hf-internal-testing/tiny-random-transfo-xl"  # same as ctrl

# other issues with tiny models
lowercase__						:Optional[int]					    =       "hf-internal-testing/tiny-random-ibert"  # multiple issues with either mlm/qa/clas
lowercase__						:Dict					    =       "hf-internal-testing/tiny-random-reformer"  # multiple issues with either mlm/qa/clas

# *** Lacking official examples to test with ***
# or not working with examples
lowercase__						:Any					    =       "hf-internal-testing/tiny-random-dpr"
# - "dpr"  examples/research_projects/rag-end2end-retriever/
lowercase__						:Tuple					    =       "hf-internal-testing/tiny-random-rag"
# - "rag" research_projects
lowercase__						:Any					    =       ""
# - "luke" Entities classes - no plan to make such example
lowercase__						:int					    =       "hf-internal-testing/tiny-random-lxmert"
# - "lxmert" doesn't work with run_qa.py
lowercase__						:str					    =       "hf-internal-testing/tiny-random-clip"
# - "clip" nothing under pytorch examples - XXX: Suraj is working on adding some - check by end of Sep
lowercase__						:Tuple					    =       "hf-internal-testing/tiny-random-speech_to_text"
# - "speech_to_text", nothing under pytorch examples


# *** Reactive mode ***
# models with low usage, unstable API, things about to change - do nothing about the following until someone runs into a problem
lowercase__						:Union[str, Any]					    =       "hf-internal-testing/tiny-random-tapas"



def 			UpperCamelCase       (				lowerCAmelCase__=False						):
					'''simple docstring'''
					# 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)
					lowercase		      =		min(2					,     get_gpu_count()						) if distributed else 1
					lowercase		      =		os.environ.get('''DS_TEST_PORT'''					,     lowerCAmelCase__						)
					return f'deepspeed --num_nodes 1 --num_gpus {num_gpus} --master_port {master_port}'.split()



def 			UpperCamelCase       (				):
					'''simple docstring'''
					lowercase		      =		f'{FIXTURE_DIRECTORY}/tests_samples'
					lowercase		      =		f'{data_dir_samples}/wmt_en_ro'
					lowercase		      =		f'{data_dir_samples}/xsum'
					lowercase		      =		'''
        --do_train
        --max_train_samples 4
        --per_device_train_batch_size 2
        --num_train_epochs 1
        --fp16
        --report_to none
        --overwrite_output_dir
        '''.split()

					# try to cover as many models as possible once (it's enough to run on one task per model)
					# but need a tiny model for each
					#
					# should have "{model_type.upper()}_TINY" corresponding vars defined, e.g., T5_TINY, etc.
					lowercase		      =		{
					    '''trans''': [
					        '''bart''',
					        '''fsmt''',
					        '''m2m_100''',
					        '''marian''',
					        '''mbart''',
					        '''t5''',
					        '''t5_v1''',
					        # "mt5", missing model files
					    ],
					    '''sum''': [
					        '''pegasus''',
					    ],
					    '''clm''': [
					        '''big_bird''',
					        '''bigbird_pegasus''',
					        '''blenderbot''',
					        '''bloom''',
					        '''gpt2''',
					        '''gpt_neo''',
					        '''gptj''',
					        '''xlm-roberta''',
					        '''prophetnet''',
					        # "camembert", missing model files
					    ],
					    '''mlm''': [
					        '''albert''',
					        '''deberta''',
					        '''deberta-v2''',
					        '''distilbert''',
					        '''electra''',
					        '''flaubert''',
					        '''funnel''',
					        '''layoutlm''',
					        # "reformer", # multiple issues with either mlm/qa/clas
					    ],
					    '''qa''': [
					        '''led''',
					        '''longformer''',
					        '''mobilebert''',
					        '''mpnet''',
					        '''roberta''',
					        '''squeezebert''',
					        # "convbert", # missing tokenizer files
					        # "layoutlmv2", missing model files
					    ],
					    '''clas''': [
					        '''bert''',
					        '''xlnet''',
					        # "hubert", # missing tokenizer files
					        # "ibert", # multiple issues with either mlm/qa/clas
					        # "transfo-xl", # tokenizer issues as ctrl
					        # "ctrl", # tokenizer issues
					        # "openai-gpt", missing model files
					        # "tapas", multiple issues
					    ],
					    '''img_clas''': [
					        '''vit''',
					    ],
					}

					lowercase		      =		f'{ROOT_DIRECTORY}/examples/pytorch'

					lowercase		      =		{
					    '''trans''': f'\n        {scripts_dir}/translation/run_translation.py\n        --train_file {data_dir_wmt}/train.json\n        --source_lang en\n        --target_lang ro\n        ',
					    '''sum''': f'\n        {scripts_dir}/summarization/run_summarization.py\n        --train_file {data_dir_xsum}/sample.json\n        --max_source_length 12\n        --max_target_length 12\n        --lang en\n        ',
					    '''clm''': f'\n        {scripts_dir}/language-modeling/run_clm.py\n        --train_file {FIXTURE_DIRECTORY}/sample_text.txt\n        --block_size 8\n        ',
					    '''mlm''': f'\n        {scripts_dir}/language-modeling/run_mlm.py\n        --train_file {FIXTURE_DIRECTORY}/sample_text.txt\n        ',
					    '''qa''': f'\n        {scripts_dir}/question-answering/run_qa.py\n        --train_file {data_dir_samples}/SQUAD/sample.json\n        ',
					    '''clas''': f'\n        {scripts_dir}/text-classification/run_glue.py\n        --train_file {data_dir_samples}/MRPC/train.csv\n        --max_seq_length 12\n        --task_name MRPC\n        ',
					    '''img_clas''': f'\n        {scripts_dir}/image-classification/run_image_classification.py\n            --dataset_name hf-internal-testing/cats_vs_dogs_sample\n            --remove_unused_columns False\n            --max_steps 10\n            --image_processor_name {DS_TESTS_DIRECTORY}/vit_feature_extractor.json\n        ',
					}

					lowercase		      =		get_launcher(distributed=lowerCAmelCase__						)

					lowercase		      =		{}
					for task, args in tasks.items():
										lowercase		      =		args.split()
										for model in tasksamodels[task]:
															lowercase		      =		globals()[f'{model.upper().replace("-"					,     "_"						)}_TINY']
															lowercase		      =		f'--model_name_or_path {model_name}'.split()
															lowercase		      =		launcher + args + args_model + args_main

															# # generation special case
															# if task == "gen":
															#     launcher = f"deepspeed --num_nodes 1 --num_gpus 1".split()
															#     args_model += f"--model_type {model}".split()
															#     cmds[f"{task}_{model}"] = launcher + args + args_model
															# else:

					return cmds


lowercase__						:Optional[int]					    =       make_task_cmds()

lowercase__						:Any					    =       "zero2"
lowercase__						:str					    =       "zero3"

lowercase__						:str					    =       [ZEROa, ZEROa]



def 			UpperCamelCase       (				lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__						):
					'''simple docstring'''
					# 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
					lowercase		      =		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
lowercase__						:Optional[int]					    =       list(itertools.product(stages, task_cmds.keys()))







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




							def 					A__ (							self  ,A__  ,A__):
												# return a ready to run train cmd
												if task not in task_cmds:
																	raise ValueError(f'don\'t know of task {task}, have {task_cmds.keys()}')

												lowercase		      =		task_cmds[task]
												lowercase		      =		f'--deepspeed {self.test_file_dir_str}/ds_config_{stage}.json'.split()

												lowercase		      =		self.get_auto_remove_tmp_dir()
												lowercase		      =		f'--output_dir {output_dir}'.split()

												cmd += args_ds + args_out

												return cmd, output_dir





							@parameterized.expand(A__  ,name_func=A__)
							def 					A__ (							self  ,A__  ,A__):
												# testing the ability to do a run followed by recovery of full fp32 weights

												lowercase      ,		lowercase		      =		self.get_task_cmd(A__  ,A__)

												# 1. generate the checkpoint
												cmd += "--save_steps 1".split()
												# keep for quick debug
												# print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] + cmd)); die
												execute_subprocess_async(A__  ,env=self.get_env())

												# 2. test that the fp32 weights get reconsolidated
												lowercase		      =		f'{output_dir}/checkpoint-1'
												lowercase		      =		f'{chkpt_dir}/out.bin'
												lowercase		      =		f'{chkpt_dir}/zero_to_fp32.py {chkpt_dir} {recovered_model_path}'
												# keep for quick debug
												# print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
												subprocess.check_call(A__  ,shell=A__)
												assert os.path.exists(A__), f'{recovered_model_path} was not found'

												# possibly could also test that the resulting saved model is usable but given that we use
												# random models we won't know if it's any good






from __future__ import annotations

import random
import string







class 							lowercase						:




							def __init__(							self  ,A__ = None):
												lowercase		      =		passcode or self.__passcode_creator()
												lowercase		      =		self.__make_key_list()
												lowercase		      =		self.__make_shift_key()




							def __str__(							self):
												return "".join(self.__passcode)




							def 					A__ (							self  ,A__):
												for i in range(1  ,len(A__)  ,2):
																	iterlist[i] *= -1
												return iterlist




							def 					A__ (							self):
												lowercase		      =		string.ascii_letters + string.digits
												lowercase		      =		[random.choice(A__) for _ in range(random.randint(1_0  ,2_0))]
												return password




							def 					A__ (							self):
												lowercase		      =		(
												    string.ascii_letters + string.digits + string.punctuation + ''' \t\n'''
												)

												lowercase		      =		[]

												# creates points known as breakpoints to break the key_list_options at those
												# points and pivot each substring
												lowercase		      =		sorted(set(self.__passcode))
												lowercase		      =		[]

												# algorithm for creating a new shuffled list, keys_l, out of key_list_options
												for i in key_list_options:
																	temp_list.extend(A__)

																	# checking breakpoints at which to pivot temporary sublist and add it into
																	# keys_l
																	if i in breakpoints or i == key_list_options[-1]:
																						keys_l.extend(temp_list[::-1])
																						temp_list.clear()

        # returning a shuffled keys_l to prevent brute force guessing of shift key
												return keys_l




							def 					A__ (							self):
												lowercase		      =		sum(self.__neg_pos([ord(A__) for x in self.__passcode]))
												return num if num > 0 else len(self.__passcode)




							def 					A__ (							self  ,A__):
												lowercase		      =		''''''

												# decoding shift like Caesar cipher algorithm implementing negative shift or
												# reverse shift or left shift
												for i in encoded_message:
																	lowercase		      =		self.__key_list.index(A__)
																	decoded_message += self.__key_list[
																	    (position - self.__shift_key) % -len(self.__key_list)
																	]

												return decoded_message





							def 					A__ (							self  ,A__):
												lowercase		      =		''''''

												# encoding shift like Caesar cipher algorithm implementing positive shift or
												# forward shift or right shift
												for i in plaintext:
																	lowercase		      =		self.__key_list.index(A__)
																	encoded_message += self.__key_list[
																	    (position + self.__shift_key) % len(self.__key_list)
																	]

												return encoded_message



def 			UpperCamelCase       (				lowerCAmelCase__ = "Hello, this is a modified Caesar cipher"						):
					'''simple docstring'''
					lowercase		      =		ShuffledShiftCipher()
					return cipa.decrypt(cipa.encrypt(lowerCAmelCase__						)						)


if __name__ == "__main__":
							import doctest

							doctest.testmod()




