


import csv
import json
import logging
import os
import re
import subprocess
import tempfile
import unittest
import zipfile
from pathlib import Path
from typing import Optional
from unittest import mock

import torch

# We use TF to parse the logs
from accelerate import Accelerator
from accelerate.test_utils.testing import (
    MockingTestCase,
    TempDirTestCase,
    require_comet_ml,
    require_tensorboard,
    require_wandb,
    skip,
)
from accelerate.tracking import CometMLTracker, GeneralTracker
from accelerate.utils import ProjectConfiguration, is_comet_ml_available, is_tensorboard_available


if is_comet_ml_available():
		from comet_ml import OfflineExperiment

if is_tensorboard_available():
		import struct

		import tensorboard.compat.proto.event_pba as event_pba

lowerCamelCase		=						logging.getLogger(__name__)





@require_tensorboard
class    _a				(							unittest.TestCase):




	def 				UpperCAmelCase__(   self		:						List[Any]						)->			Any:
			lowerCAmelCase__  :     Tuple       	=   '''test_project_with_config'''
			with tempfile.TemporaryDirectory() as dirpath:
					lowerCAmelCase__  :     Optional[int]       	=   Accelerator(log_with='''tensorboard'''						,   project_dir=A_						)
					lowerCAmelCase__  :     Any       	=   {'''num_iterations''': 12, '''learning_rate''': 1E-2, '''some_boolean''': False, '''some_string''': '''some_value'''}
					accelerator.init_trackers(A_						,   A_						)
					accelerator.end_training()
					for child in Path(F'{dirpath}/{project_name}'						).glob('''*/**'''						):
							lowerCAmelCase__  :     List[str]       	=   list(filter(lambda _SCREAMING_SNAKE_CASE						: x.is_file()						,   child.iterdir()						)						)[0]
					self.assertNotEqual(str(A_						)						,   ''''''						)




	def 				UpperCAmelCase__(   self		:						List[Any]						)->			Optional[Any]:
			lowerCAmelCase__  :     List[str]       	=   '''test_project_with_log'''
			with tempfile.TemporaryDirectory() as dirpath:
					lowerCAmelCase__  :     Union[str, Any]       	=   Accelerator(log_with='''tensorboard'''						,   project_dir=A_						)
					accelerator.init_trackers(A_						)
					lowerCAmelCase__  :     Tuple       	=   {'''total_loss''': 0.1, '''iteration''': 1, '''my_text''': '''some_value'''}
					accelerator.log(A_						,   step=0						)
					accelerator.end_training()
					# Logged values are stored in the outermost-tfevents file and can be read in as a TFRecord
					# Names are randomly generated each time
					lowerCAmelCase__  :     int       	=   list(filter(lambda _SCREAMING_SNAKE_CASE						: x.is_file()						,   Path(F'{dirpath}/{project_name}'						).iterdir()						)						)[0]
					self.assertNotEqual(str(A_						)						,   ''''''						)




	def 				UpperCAmelCase__(   self		:						Tuple						)->			List[Any]:
			lowerCAmelCase__  :     Dict       	=   '''test_project_with_log'''
			with tempfile.TemporaryDirectory() as dirpath:
					lowerCAmelCase__  :     Dict       	=   Accelerator(log_with='''tensorboard'''						,   project_dir=A_						)
					accelerator.init_trackers(A_						)
					lowerCAmelCase__  :     Tuple       	=   {'''tensor''': torch.tensor(1						)}
					accelerator.log(A_						,   step=0						)
					accelerator.end_training()
					# Logged values are stored in the outermost-tfevents file and can be read in as a TFRecord
					# Names are randomly generated each time
					lowerCAmelCase__  :     Tuple       	=   list(filter(lambda _SCREAMING_SNAKE_CASE						: x.is_file()						,   Path(F'{dirpath}/{project_name}'						).iterdir()						)						)[0]
					# Reading implementation based on https://github.com/pytorch/pytorch/issues/45327#issuecomment-703757685
					with open(A_						,   '''rb'''						) as f:
							lowerCAmelCase__  :     Union[str, Any]       	=   f.read()
					lowerCAmelCase__  :     List[Any]       	=   False
					while data:
							lowerCAmelCase__  :     str       	=   struct.unpack('''Q'''						,   data[:8]						)

							lowerCAmelCase__  :     str       	=   data[12 : 12 + int(header[0]						)]  # 8+4
							lowerCAmelCase__  :     List[Any]       	=   data[12 + int(header[0]						) + 4 :]
							lowerCAmelCase__  :     Optional[Any]       	=   event_pba.Event()

							event.ParseFromString(A_						)
							if event.HasField('''summary'''						):
									for value in event.summary.value:
											if value.simple_value == 1.0 and value.tag == "tensor":
													lowerCAmelCase__  :     Tuple       	=   True
					self.assertTrue(A_						,   '''Converted tensor was not found in the log file!'''						)




	def 				UpperCAmelCase__(   self		:						Optional[Any]						)->			int:



			with self.assertRaisesRegex(A_						,   '''Logging with `tensorboard` requires a `logging_dir`'''						):
					lowerCAmelCase__  :     Union[str, Any]       	=   Accelerator(log_with='''tensorboard'''						)
			with tempfile.TemporaryDirectory() as dirpath:
					lowerCAmelCase__  :     List[str]       	=   Accelerator(log_with='''tensorboard'''						,   project_dir=A_						)




	def 				UpperCAmelCase__(   self		:						Dict						)->			Any:
			lowerCAmelCase__  :     Optional[Any]       	=   ProjectConfiguration(total_limit=30						)
			with tempfile.TemporaryDirectory() as dirpath:
					lowerCAmelCase__  :     Any       	=   Accelerator(log_with='''tensorboard'''						,   project_dir=A_						,   project_config=A_						)





@require_wandb
@mock.patch.dict(os.environ	,		{'''WANDB_MODE''': '''offline'''})
class    _a				(							__A	,		__A):




	def 				UpperCAmelCase__(   self		:						List[str]						)->			Tuple:
			super().setUp()
			# wandb let's us override where logs are stored to via the WANDB_DIR env var
			self.add_mocks(mock.patch.dict(os.environ						,   {'''WANDB_DIR''': self.tmpdir}						)						)




	@staticmethod
	def 				UpperCAmelCase__(   _SCREAMING_SNAKE_CASE		:						str						,   _SCREAMING_SNAKE_CASE		:						Optional[int]						,   _SCREAMING_SNAKE_CASE		:						Optional[Any] = True						)->			List[str]:
			lowerCAmelCase__  :     Any       	=   rF'{section} ([\S\s]*?)\n\n'
			if record:
					lowerCAmelCase__  :     str       	=   rF'Record: {pattern}'
			lowerCAmelCase__  :     Dict       	=   re.findall(A_						,   A_						)[0]
			# A config
			if section == "config" or section == "history":
					lowerCAmelCase__  :     str       	=   re.findall(r'''"([a-zA-Z0-9_.,]+)'''						,   A_						)
					return {key: val for key, val in zip(cleaned_record[0::2]						,   cleaned_record[1::2]						)}
			# Everything else
			else:
					return dict(re.findall(r'''(\w+): "([^\s]+)"'''						,   A_						)						)




	@skip
	def 				UpperCAmelCase__(   self		:						List[str]						)->			Tuple:
			lowerCAmelCase__  :     int       	=   '''test_project_with_config'''
			lowerCAmelCase__  :     Tuple       	=   Accelerator(log_with='''wandb'''						)
			lowerCAmelCase__  :     Union[str, Any]       	=   {'''num_iterations''': 12, '''learning_rate''': 1E-2, '''some_boolean''': False, '''some_string''': '''some_value'''}
			lowerCAmelCase__  :     Any       	=   {'''wandb''': {'''tags''': ['''my_tag''']}}
			accelerator.init_trackers(A_						,   A_						,   A_						)
			lowerCAmelCase__  :     Optional[int]       	=   {'''total_loss''': 0.1, '''iteration''': 1, '''my_text''': '''some_value'''}
			accelerator.log(A_						,   step=0						)
			accelerator.end_training()
			# The latest offline log is stored at wandb/latest-run/*.wandb
			for child in Path(F'{self.tmpdir}/wandb/latest-run'						).glob('''*'''						):
					if child.is_file() and child.suffix == ".wandb":
							lowerCAmelCase__  :     str       	=   subprocess.check_output(
							    ['''wandb''', '''sync''', '''--view''', '''--verbose''', str(A_						)]						,   env=os.environ.copy()						).decode('''utf8'''						,   '''ignore'''						)
							break

					# Check HPS through careful parsing and cleaning
			lowerCAmelCase__  :     Tuple       	=   self.parse_log(A_						,   '''config'''						)
			self.assertEqual(logged_items['''num_iterations''']						,   '''12'''						)
			self.assertEqual(logged_items['''learning_rate''']						,   '''0.01'''						)
			self.assertEqual(logged_items['''some_boolean''']						,   '''false'''						)
			self.assertEqual(logged_items['''some_string''']						,   '''some_value'''						)
			self.assertEqual(logged_items['''some_string''']						,   '''some_value'''						)

			# Run tags
			lowerCAmelCase__  :     int       	=   self.parse_log(A_						,   '''run'''						,   A_						)
			self.assertEqual(logged_items['''tags''']						,   '''my_tag'''						)

			# Actual logging
			lowerCAmelCase__  :     List[str]       	=   self.parse_log(A_						,   '''history'''						)
			self.assertEqual(logged_items['''total_loss''']						,   '''0.1'''						)
			self.assertEqual(logged_items['''iteration''']						,   '''1'''						)
			self.assertEqual(logged_items['''my_text''']						,   '''some_value'''						)
			self.assertEqual(logged_items['''_step''']						,   '''0'''						)






def 	lowerCamelCase_       (			self      ,  _a      ,  _a			):




		"""simple docstring"""






		lowerCAmelCase__  :     List[str]       	=   run_name
		lowerCAmelCase__  :     Any       	=   OfflineExperiment(project_name=UpperCamelCase__      ,  offline_directory=UpperCamelCase__			)
		logger.info(f'Initialized offline CometML project {self.run_name}'			)
		logger.info('''Make sure to log any initial configurations with `self.store_init_configuration` before training!'''			)





@require_comet_ml
@mock.patch.object(__A	,		'''__init__'''	,		__A)
class    _a				(							unittest.TestCase):




	@staticmethod
	def 				UpperCAmelCase__(   _SCREAMING_SNAKE_CASE		:						Dict						,   _SCREAMING_SNAKE_CASE		:						Tuple						,   _SCREAMING_SNAKE_CASE		:						List[str] = False						)->			str:



			for log in log_list:
					lowerCAmelCase__  :     Optional[Any]       	=   json.loads(A_						)['''payload''']
					if is_param and "param" in j.keys():
							if j["param"]["paramName"] == key:
									return j["param"]["paramValue"]
					if "log_other" in j.keys():
							if j["log_other"]["key"] == key:
									return j["log_other"]["val"]
					if "metric" in j.keys():
							if j["metric"]["metricName"] == key:
									return j["metric"]["metricValue"]




	def 				UpperCAmelCase__(   self		:						Any						)->			List[Any]:



			with tempfile.TemporaryDirectory() as d:
					lowerCAmelCase__  :     int       	=   CometMLTracker('''test_project_with_config'''						,   A_						)
					lowerCAmelCase__  :     Dict       	=   Accelerator(log_with=A_						)
					lowerCAmelCase__  :     Tuple       	=   {'''num_iterations''': 12, '''learning_rate''': 1E-2, '''some_boolean''': False, '''some_string''': '''some_value'''}
					accelerator.init_trackers(A_						,   A_						)
					accelerator.end_training()
					lowerCAmelCase__  :     Dict       	=   os.listdir(A_						)[0]  # Comet is nice, it's just a zip file here
					# We parse the raw logs
					lowerCAmelCase__  :     int       	=   os.path.join(A_						,   A_						)
					lowerCAmelCase__  :     Any       	=   zipfile.ZipFile(A_						,   '''r'''						)
					lowerCAmelCase__  :     List[str]       	=   archive.open('''messages.json'''						).read().decode('''utf-8'''						)
			lowerCAmelCase__  :     Dict       	=   log.split('''\n'''						)[:-1]
			self.assertEqual(self.get_value_from_key(A_						,   '''num_iterations'''						,   A_						)						,   12						)
			self.assertEqual(self.get_value_from_key(A_						,   '''learning_rate'''						,   A_						)						,   0.01						)
			self.assertEqual(self.get_value_from_key(A_						,   '''some_boolean'''						,   A_						)						,   A_						)
			self.assertEqual(self.get_value_from_key(A_						,   '''some_string'''						,   A_						)						,   '''some_value'''						)




	def 				UpperCAmelCase__(   self		:						Optional[int]						)->			int:



			with tempfile.TemporaryDirectory() as d:
					lowerCAmelCase__  :     Tuple       	=   CometMLTracker('''test_project_with_config'''						,   A_						)
					lowerCAmelCase__  :     int       	=   Accelerator(log_with=A_						)
					accelerator.init_trackers(A_						)
					lowerCAmelCase__  :     Tuple       	=   {'''total_loss''': 0.1, '''iteration''': 1, '''my_text''': '''some_value'''}
					accelerator.log(A_						,   step=0						)
					accelerator.end_training()
					lowerCAmelCase__  :     Dict       	=   os.listdir(A_						)[0]  # Comet is nice, it's just a zip file here
					# We parse the raw logs
					lowerCAmelCase__  :     str       	=   os.path.join(A_						,   A_						)
					lowerCAmelCase__  :     Optional[Any]       	=   zipfile.ZipFile(A_						,   '''r'''						)
					lowerCAmelCase__  :     str       	=   archive.open('''messages.json'''						).read().decode('''utf-8'''						)
			lowerCAmelCase__  :     int       	=   log.split('''\n'''						)[:-1]
			self.assertEqual(self.get_value_from_key(A_						,   '''curr_step'''						,   A_						)						,   0						)
			self.assertEqual(self.get_value_from_key(A_						,   '''total_loss'''						)						,   0.1						)
			self.assertEqual(self.get_value_from_key(A_						,   '''iteration'''						)						,   1						)
			self.assertEqual(self.get_value_from_key(A_						,   '''my_text'''						)						,   '''some_value'''						)





class    _a				(							__A):
	_a    :    Optional[Any]        =				[
	    '''total_loss''',
	    '''iteration''',
	    '''my_text''',
	    '''learning_rate''',
	    '''num_iterations''',
	    '''some_boolean''',
	    '''some_string''',
	]

	_a    :    Optional[int]        =				'''my_custom_tracker'''
	_a    :    List[Any]        =				False




	def __init__(   self		:						List[Any]						,   _SCREAMING_SNAKE_CASE		:						str						)->			List[Any]:
			lowerCAmelCase__  :     List[str]       	=   open(F'{dir}/log.csv'						,   '''w+'''						)
			lowerCAmelCase__  :     Dict       	=   csv.DictWriter(self.f						,   fieldnames=self._col_names						)
			self.writer.writeheader()




	@property
	def 				UpperCAmelCase__(   self		:						int						)->			List[str]:
			return self.writer




	def 				UpperCAmelCase__(   self		:						List[str]						,   _SCREAMING_SNAKE_CASE		:						Any						)->			Optional[Any]:
			logger.info('''Call init'''						)
			self.writer.writerow(A_						)




	def 				UpperCAmelCase__(   self		:						List[Any]						,   _SCREAMING_SNAKE_CASE		:						str						,   _SCREAMING_SNAKE_CASE		:						List[str]						)->			Tuple:
			logger.info('''Call log'''						)
			self.writer.writerow(A_						)




	def 				UpperCAmelCase__(   self		:						Tuple						)->			Union[str, Any]:
			self.f.close()





class    _a				(							unittest.TestCase):




	def 				UpperCAmelCase__(   self		:						Tuple						)->			Tuple:



			with tempfile.TemporaryDirectory() as d:
					lowerCAmelCase__  :     Union[str, Any]       	=   MyCustomTracker(A_						)
					lowerCAmelCase__  :     int       	=   Accelerator(log_with=A_						)
					lowerCAmelCase__  :     Tuple       	=   {'''num_iterations''': 12, '''learning_rate''': 1E-2, '''some_boolean''': False, '''some_string''': '''some_value'''}
					accelerator.init_trackers('''Some name'''						,   A_						)
					accelerator.end_training()
					with open(F'{d}/log.csv'						,   '''r'''						) as f:
							lowerCAmelCase__  :     str       	=   csv.DictReader(A_						)
							lowerCAmelCase__  :     Optional[Any]       	=   next(A_						)
							lowerCAmelCase__  :     Dict       	=   {
							    '''total_loss''': '''''',
							    '''iteration''': '''''',
							    '''my_text''': '''''',
							    '''learning_rate''': '''0.01''',
							    '''num_iterations''': '''12''',
							    '''some_boolean''': '''False''',
							    '''some_string''': '''some_value''',
							}
							self.assertDictEqual(A_						,   A_						)




	def 				UpperCAmelCase__(   self		:						Union[str, Any]						)->			Union[str, Any]:



			with tempfile.TemporaryDirectory() as d:
					lowerCAmelCase__  :     List[str]       	=   MyCustomTracker(A_						)
					lowerCAmelCase__  :     int       	=   Accelerator(log_with=A_						)
					accelerator.init_trackers('''Some name'''						)
					lowerCAmelCase__  :     Union[str, Any]       	=   {'''total_loss''': 0.1, '''iteration''': 1, '''my_text''': '''some_value'''}
					accelerator.log(A_						,   step=0						)
					accelerator.end_training()
					with open(F'{d}/log.csv'						,   '''r'''						) as f:
							lowerCAmelCase__  :     str       	=   csv.DictReader(A_						)
							lowerCAmelCase__  :     Dict       	=   next(A_						)
							lowerCAmelCase__  :     int       	=   {
							    '''total_loss''': '''0.1''',
							    '''iteration''': '''1''',
							    '''my_text''': '''some_value''',
							    '''learning_rate''': '''''',
							    '''num_iterations''': '''''',
							    '''some_boolean''': '''''',
							    '''some_string''': '''''',
							}
							self.assertDictEqual(A_						,   A_						)









"""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)

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

# default torch.distributed port
lowerCamelCase				:     Optional[Any]								=  """10999"""

lowerCamelCase				:     Optional[Any]								=  """t5-small"""

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


# *** To Fix ***


# *** tiny model issues ***
# missing model files:
lowerCamelCase				:     Optional[int]								=  """hf-internal-testing/tiny-random-mt5"""
lowerCamelCase				:     Optional[int]								=  """hf-internal-testing/tiny-random-camembert"""
lowerCamelCase				:     Any								=  """hf-internal-testing/tiny-random-openai-gpt"""

# missing tokenizer files
lowerCamelCase				:     Optional[int]								=  """hf-internal-testing/tiny-random-convbert"""
lowerCamelCase				:     str								=  """hf-internal-testing/tiny-random-layoutlmv2"""
lowerCamelCase				:     List[Any]								=  """hf-internal-testing/tiny-random-hubert"""

# issues with tokenizer
lowerCamelCase				:     List[str]								=  """hf-internal-testing/tiny-random-ctrl"""
lowerCamelCase				:     Optional[int]								=  """hf-internal-testing/tiny-random-transfo-xl"""  # same as ctrl

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


def 							A__ (     UpperCamelCase__=False    ):


  '''simple docstring'''


  _SCREAMING_SNAKE_CASE        =			min(2       ,							get_gpu_count()    ) if distributed else 1
  _SCREAMING_SNAKE_CASE        =			os.environ.get('''DS_TEST_PORT'''       ,							UpperCamelCase__    )
  return F'''deepspeed --num_nodes 1 --num_gpus {num_gpus} --master_port {master_port}'''.split()


def 							A__ (     ):


  '''simple docstring'''


  _SCREAMING_SNAKE_CASE        =			F'''{FIXTURE_DIRECTORY}/tests_samples'''
  _SCREAMING_SNAKE_CASE        =			F'''{data_dir_samples}/wmt_en_ro'''
  _SCREAMING_SNAKE_CASE        =			F'''{data_dir_samples}/xsum'''
  _SCREAMING_SNAKE_CASE        =			'''
        --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.
  _SCREAMING_SNAKE_CASE        =			{
      '''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''',
      ],
  }

  _SCREAMING_SNAKE_CASE        =			F'''{ROOT_DIRECTORY}/examples/pytorch'''

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

  _SCREAMING_SNAKE_CASE        =			get_launcher(distributed=UpperCamelCase__    )

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


lowerCamelCase				:     List[str]								=  make_task_cmds()

lowerCamelCase				:     Any								=  """zero2"""
lowerCamelCase				:     List[Any]								=  """zero3"""

lowerCamelCase				:     int								=  [ZEROa, ZEROa]


def 							A__ (     UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__    ):


  '''simple docstring'''


  _SCREAMING_SNAKE_CASE        =			parameterized.to_safe_name('''_'''.join(str(UpperCamelCase__    ) for x in param.args    )    )
  return F'''{func.__name__}_{param_based_name}'''


# Cartesian-product of zero stages with models to test
lowerCamelCase				:     Optional[Any]								=  list(itertools.product(stages, task_cmds.keys()))







@slow
@require_deepspeed
@require_torch_gpu
class    __snake_case(					__A       ):




    def   A   (   self    ,							A_    ,							A_			):





      '''simple docstring'''



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

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

      _SCREAMING_SNAKE_CASE        =			self.get_auto_remove_tmp_dir()
      _SCREAMING_SNAKE_CASE        =			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_			):





      '''simple docstring'''



      _SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE        =			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
      _SCREAMING_SNAKE_CASE        =			F'''{output_dir}/checkpoint-1'''
      _SCREAMING_SNAKE_CASE        =			F'''{chkpt_dir}/out.bin'''
      _SCREAMING_SNAKE_CASE        =			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
