
'''simple docstring'''
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)

lowercase__		    =	get_tests_dir("fixtures")
lowercase__		    =	os.path.join(dirname(get_tests_dir()))
lowercase__		    =	dirname(os.path.abspath(__file__))

# default torch.distributed port
lowercase__		    =	"10999"

lowercase__		    =	"t5-small"

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


# *** To Fix ***


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

# missing tokenizer files
lowercase__		    =	"hf-internal-testing/tiny-random-convbert"
lowercase__		    =	"hf-internal-testing/tiny-random-layoutlmv2"
lowercase__		    =	"hf-internal-testing/tiny-random-hubert"

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

# other issues with tiny models
lowercase__		    =	"hf-internal-testing/tiny-random-ibert"  # multiple issues with either mlm/qa/clas
lowercase__		    =	"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__		    =	"hf-internal-testing/tiny-random-dpr"
# - "dpr"  examples/research_projects/rag-end2end-retriever/
lowercase__		    =	"hf-internal-testing/tiny-random-rag"
# - "rag" research_projects
lowercase__		    =	""
# - "luke" Entities classes - no plan to make such example
lowercase__		    =	"hf-internal-testing/tiny-random-lxmert"
# - "lxmert" doesn't work with run_qa.py
lowercase__		    =	"hf-internal-testing/tiny-random-clip"
# - "clip" nothing under pytorch examples - XXX: Suraj is working on adding some - check by end of Sep
lowercase__		    =	"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__		    =	"hf-internal-testing/tiny-random-tapas"




def 					_UpperCamelCase		(    SCREAMING_SNAKE_CASE__=False       )   ->				str:


		'''simple docstring'''


		snake_case   :    Optional[int]			       =		min(2      ,    get_gpu_count()       ) if distributed else 1
		snake_case   :    Dict			       =		os.environ.get('''DS_TEST_PORT'''      ,    SCREAMING_SNAKE_CASE__       )
		return F'deepspeed --num_nodes 1 --num_gpus {num_gpus} --master_port {master_port}'.split()




def 					_UpperCamelCase		(    )   ->				Optional[int]:


		'''simple docstring'''


		snake_case   :    Union[str, Any]			       =		F'{FIXTURE_DIRECTORY}/tests_samples'
		snake_case   :    str			       =		F'{data_dir_samples}/wmt_en_ro'
		snake_case   :    str			       =		F'{data_dir_samples}/xsum'
		snake_case   :    int			       =		'''
        --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.
		snake_case   :    Tuple			       =		{
		    '''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''',
		    ],
		}

		snake_case   :    int			       =		F'{ROOT_DIRECTORY}/examples/pytorch'

		snake_case   :    Union[str, Any]			       =		{
		    '''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        ',
		}

		snake_case   :    int			       =		get_launcher(distributed=SCREAMING_SNAKE_CASE__       )

		snake_case   :    int			       =		{}
		for task, args in tasks.items():
				snake_case   :    Union[str, Any]			       =		args.split()
				for model in tasksamodels[task]:
						snake_case   :    List[Any]			       =		globals()[F'{model.upper().replace("-"      ,    "_"       )}_TINY']
						snake_case   :    str			       =		F'--model_name_or_path {model_name}'.split()
						snake_case   :    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


lowercase__		    =	make_task_cmds()

lowercase__		    =	"zero2"
lowercase__		    =	"zero3"

lowercase__		    =	[ZEROa, ZEROa]




def 					_UpperCamelCase		(    SCREAMING_SNAKE_CASE__      ,    SCREAMING_SNAKE_CASE__      ,    SCREAMING_SNAKE_CASE__       )   ->				Optional[Any]:


		'''simple docstring'''


		snake_case   :    int			       =		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, task_cmds.keys()))






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







		"""simple docstring"""

		def 	lowerCAmelCase							(	self							:       List[str]   ,     UpperCamelCase__							:       Union[str, Any]   ,     UpperCamelCase__							:       Dict	)		->       Dict:






				"""simple docstring"""







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

				snake_case   :    List[str]			       =		task_cmds[task]
				snake_case   :    int			       =		f'--deepspeed {self.test_file_dir_str}/ds_config_{stage}.json'.split()

				snake_case   :    int			       =		self.get_auto_remove_tmp_dir()
				snake_case   :    Any			       =		f'--output_dir {output_dir}'.split()

				cmd += args_ds + args_out

				return cmd, output_dir



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






				"""simple docstring"""







				snake_case       ,snake_case   :    str			       =		self.get_task_cmd(UpperCamelCase__   ,     UpperCamelCase__	)

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

				# 2. test that the fp32 weights get reconsolidated
				snake_case   :    Union[str, Any]			       =		f'{output_dir}/checkpoint-1'
				snake_case   :    Any			       =		f'{chkpt_dir}/out.bin'
				snake_case   :    List[Any]			       =		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(UpperCamelCase__   ,     shell=UpperCamelCase__	)
				assert os.path.exists(UpperCamelCase__	), 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




'''simple docstring'''
import bsa
import requests




def 					_UpperCamelCase		(    SCREAMING_SNAKE_CASE__       )   ->				dict[str, str]:


		'''simple docstring'''


		return {
		    "name": soup.ha.a.text,
		    "genre": soup.find('''span'''      ,    class_='''genre'''       ).text.strip(),
		    "rating": soup.strong.text,
		    "page_link": F'https://www.imdb.com{soup.a.get("href"       )}',
		}




def 					_UpperCamelCase		(    SCREAMING_SNAKE_CASE__ = 5       )   ->				tuple:


		'''simple docstring'''


		snake_case   :    List[Any]			       =		int(float(SCREAMING_SNAKE_CASE__       )       )
		if num_movies < 1:
				return ()
		snake_case   :    int			       =		(
		    '''https://www.imdb.com/search/title?title_type='''
		    F'feature&sort=num_votes,desc&count={num_movies}'
		)
		snake_case   :    Optional[Any]			       =		bsa.BeautifulSoup(requests.get(SCREAMING_SNAKE_CASE__       ).content      ,    '''html.parser'''       )
		return tuple(
		    get_movie_data_from_soup(SCREAMING_SNAKE_CASE__       )
		    for movie in source.find_all('''div'''      ,    class_='''lister-item mode-advanced'''       )       )


if __name__ == "__main__":
						import json

						lowercase__		    =	int(input("How many movies would you like to see? "))
						print(
						    ", ".join(
						        json.dumps(movie, indent=4) for movie in get_imdb_top_movies(num_movies)
						    )
						)


