




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(4_2)

UpperCamelCase__              =						get_tests_dir('fixtures')
UpperCamelCase__              =						os.path.join(dirname(get_tests_dir()))
UpperCamelCase__              =						dirname(os.path.abspath(__file__))

# default torch.distributed port
UpperCamelCase__              =						'10999'

UpperCamelCase__              =						't5-small'

# *** Working Models ***
UpperCamelCase__              =						'hf-internal-testing/tiny-albert'
UpperCamelCase__              =						'sshleifer/bart-tiny-random'
UpperCamelCase__              =						'hf-internal-testing/tiny-bert'
UpperCamelCase__              =						'hf-internal-testing/tiny-random-bigbird_pegasus'
UpperCamelCase__              =						'hf-internal-testing/tiny-random-big_bird'
UpperCamelCase__              =						'hf-internal-testing/tiny-random-blenderbot'
UpperCamelCase__              =						'bigscience/bigscience-small-testing'
UpperCamelCase__              =						'hf-internal-testing/tiny-random-deberta'
UpperCamelCase__              =						'hf-internal-testing/tiny-random-deberta-v2'
UpperCamelCase__              =						'sshleifer/tiny-distilbert-base-cased'
UpperCamelCase__              =						'hf-internal-testing/tiny-electra'
UpperCamelCase__              =						'hf-internal-testing/tiny-random-flaubert'
UpperCamelCase__              =						'stas/tiny-wmt19-en-de'
UpperCamelCase__              =						'hf-internal-testing/tiny-random-funnel'
UpperCamelCase__              =						'sshleifer/tiny-gpt2'
UpperCamelCase__              =						'hf-internal-testing/tiny-random-gptj'
UpperCamelCase__              =						'hf-internal-testing/tiny-random-gpt_neo'
UpperCamelCase__              =						'hf-internal-testing/tiny-layoutlm'
UpperCamelCase__              =						'hf-internal-testing/tiny-random-led'
UpperCamelCase__              =						'hf-internal-testing/tiny-random-longformer'
UpperCamelCase__              =						'stas/tiny-m2m_100'  # hf tiny model is unsuitable
UpperCamelCase__              =						'sshleifer/tiny-marian-en-de'
UpperCamelCase__              =						'sshleifer/tiny-mbart'
UpperCamelCase__              =						'hf-internal-testing/tiny-random-mobilebert'
UpperCamelCase__              =						'hf-internal-testing/tiny-random-mpnet'
UpperCamelCase__              =						'stas/pegasus-cnn_dailymail-tiny-random'
UpperCamelCase__              =						'hf-internal-testing/tiny-random-prophetnet'
UpperCamelCase__              =						'sshleifer/tiny-distilroberta-base'
UpperCamelCase__              =						'hf-internal-testing/tiny-random-squeezebert'
UpperCamelCase__              =						'patrickvonplaten/t5-tiny-random'
UpperCamelCase__              =						'hf-internal-testing/tiny-random-t5-v1.1'
UpperCamelCase__              =						'hf-internal-testing/tiny-random-vit'
UpperCamelCase__              =						'hf-internal-testing/tiny-xlm-roberta'
UpperCamelCase__              =						'sshleifer/tiny-xlnet-base-cased'


# *** To Fix ***


# *** tiny model issues ***
# missing model files:
UpperCamelCase__              =						'hf-internal-testing/tiny-random-mt5'
UpperCamelCase__              =						'hf-internal-testing/tiny-random-camembert'
UpperCamelCase__              =						'hf-internal-testing/tiny-random-openai-gpt'

# missing tokenizer files
UpperCamelCase__              =						'hf-internal-testing/tiny-random-convbert'
UpperCamelCase__              =						'hf-internal-testing/tiny-random-layoutlmv2'
UpperCamelCase__              =						'hf-internal-testing/tiny-random-hubert'

# issues with tokenizer
UpperCamelCase__              =						'hf-internal-testing/tiny-random-ctrl'
UpperCamelCase__              =						'hf-internal-testing/tiny-random-transfo-xl'  # same as ctrl

# other issues with tiny models
UpperCamelCase__              =						'hf-internal-testing/tiny-random-ibert'  # multiple issues with either mlm/qa/clas
UpperCamelCase__              =						'hf-internal-testing/tiny-random-reformer'  # multiple issues with either mlm/qa/clas

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






def 					snake_case					(							A=False			)							->							List[str]:




	'''simple docstring'''


	lowerCamelCase_							:		List[Any]							   =							min(2						,   get_gpu_count()			) if distributed else 1
	lowerCamelCase_							:		Union[str, Any]							   =							os.environ.get('DS_TEST_PORT'						,   A			)
	return f"deepspeed --num_nodes 1 --num_gpus {num_gpus} --master_port {master_port}".split()






def 					snake_case					(							)							->							Optional[int]:




	'''simple docstring'''


	lowerCamelCase_							:		List[str]							   =							f"{FIXTURE_DIRECTORY}/tests_samples"
	lowerCamelCase_							:		str							   =							f"{data_dir_samples}/wmt_en_ro"
	lowerCamelCase_							:		Any							   =							f"{data_dir_samples}/xsum"
	lowerCamelCase_							:		Optional[int]							   =							'\n        --do_train\n        --max_train_samples 4\n        --per_device_train_batch_size 2\n        --num_train_epochs 1\n        --fp16\n        --report_to none\n        --overwrite_output_dir\n        '.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.
	lowerCamelCase_							:		List[str]							   =							{
	    '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',
	    ],
	}

	lowerCamelCase_							:		Optional[Any]							   =							f"{ROOT_DIRECTORY}/examples/pytorch"

	lowerCamelCase_							:		List[str]							   =							{
	    '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        ",
	}

	lowerCamelCase_							:		List[str]							   =							get_launcher(distributed=A			)

	lowerCamelCase_							:		Tuple							   =							{}
	for task, args in tasks.items():
		lowerCamelCase_							:		str							   =							args.split()
		for model in tasksamodels[task]:
			lowerCamelCase_							:		str							   =							globals()[f"{model.upper().replace('-'						,   '_'			)}_TINY"]
			lowerCamelCase_							:		Dict							   =							f"--model_name_or_path {model_name}".split()
			lowerCamelCase_							:		Optional[Any]							   =							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


UpperCamelCase__              =						make_task_cmds()

UpperCamelCase__              =						'zero2'
UpperCamelCase__              =						'zero3'

UpperCamelCase__              =						[ZEROa, ZEROa]






def 					snake_case					(							A						,   A						,   A			)							->							List[Any]:




	'''simple docstring'''


	lowerCamelCase_							:		Any							   =							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
UpperCamelCase__              =						list(itertools.product(stages, task_cmds.keys()))



@slow
@require_deepspeed
@require_torch_gpu
class 				__SCREAMING_SNAKE_CASE							(		lowerCamelCase  ):

					def 	lowercase_							(				self ,       __lowercase ,       __lowercase					):







						'''simple docstring'''




						if task not in task_cmds:
							raise ValueError(f"don't know of task {task}, have {task_cmds.keys()}"					)

						lowerCamelCase_							:		Union[str, Any]							   =							task_cmds[task]
						lowerCamelCase_							:		Union[str, Any]							   =							f"--deepspeed {self.test_file_dir_str}/ds_config_{stage}.json".split()

						lowerCamelCase_							:		List[Any]							   =							self.get_auto_remove_tmp_dir()
						lowerCamelCase_							:		Tuple							   =							f"--output_dir {output_dir}".split()

						cmd += args_ds + args_out

						return cmd, output_dir





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







						'''simple docstring'''




						lowerCamelCase_   ,			lowerCamelCase_							:		int							   =							self.get_task_cmd(__lowercase ,       __lowercase					)

						# 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(__lowercase ,       env=self.get_env()					)

						# 2. test that the fp32 weights get reconsolidated
						lowerCamelCase_							:		Tuple							   =							f"{output_dir}/checkpoint-1"
						lowerCamelCase_							:		Optional[int]							   =							f"{chkpt_dir}/out.bin"
						lowerCamelCase_							:		Dict							   =							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(__lowercase ,       shell=__lowercase					)
						assert os.path.exists(__lowercase					), 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






# as due to their complexity multi-gpu tests could impact other tests, and to aid debug we have those in a separate module.

import os
import sys

from transformers.testing_utils import TestCasePlus, execute_subprocess_async, get_gpu_count, require_torch_gpu, slow

from .utils import load_json


class 				__SCREAMING_SNAKE_CASE							(		lowerCamelCase  ):

					@classmethod
					def 	lowercase_							(				cls					):







						'''simple docstring'''




						return cls





					@slow
					@require_torch_gpu
					def 	lowercase_							(				self					):







						'''simple docstring'''




						lowerCamelCase_							:		Dict							   =							self.get_auto_remove_tmp_dir()
						lowerCamelCase_							:		Tuple							   =							f"\n            --model_name Helsinki-NLP/opus-mt-en-ro\n            --save_dir {output_dir}\n            --data_dir {self.test_file_dir_str}/test_data/wmt_en_ro\n            --num_beams 2\n            --task translation\n        ".split()

						# we want this test to run even if there is only one GPU, but if there are more we use them all
						lowerCamelCase_							:		List[Any]							   =							get_gpu_count()
						lowerCamelCase_							:		Union[str, Any]							   =							f"\n            -m torch.distributed.launch\n            --nproc_per_node={n_gpu}\n            {self.test_file_dir}/run_distributed_eval.py\n        ".split()
						lowerCamelCase_							:		int							   =							[sys.executable] + distributed_args + args
						execute_subprocess_async(__lowercase ,       env=self.get_env()					)

						lowerCamelCase_							:		Any							   =							os.path.join(__lowercase ,       'test_bleu.json'					)
						lowerCamelCase_							:		List[Any]							   =							load_json(__lowercase					)
						# print(metrics)
						self.assertGreaterEqual(metrics['bleu'] ,       25					)
