






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)

__snake_case			:			int			   =   get_tests_dir("""fixtures""")
__snake_case			:			List[Any]			   =   os.path.join(dirname(get_tests_dir()))
__snake_case			:			Tuple			   =   dirname(os.path.abspath(__file__))

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

__snake_case			:			List[str]			   =   """t5-small"""

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


# *** To Fix ***


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

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

# issues with tokenizer
__snake_case			:			Optional[Any]			   =   """hf-internal-testing/tiny-random-ctrl"""
__snake_case			:			Any			   =   """hf-internal-testing/tiny-random-transfo-xl"""  # same as ctrl

# other issues with tiny models
__snake_case			:			Union[str, Any]			   =   """hf-internal-testing/tiny-random-ibert"""  # multiple issues with either mlm/qa/clas
__snake_case			:			List[Any]			   =   """hf-internal-testing/tiny-random-reformer"""  # multiple issues with either mlm/qa/clas

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







def 		_UpperCamelCase      (			UpperCamelCase_				:    List[Any]=False						)     ->      Dict:

						"""simple docstring"""



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







def 		_UpperCamelCase      (			)     ->      Dict:

						"""simple docstring"""



						lowerCAmelCase__			  =  F"{FIXTURE_DIRECTORY}/tests_samples"
						lowerCAmelCase__			  =  F"{data_dir_samples}/wmt_en_ro"
						lowerCAmelCase__			  =  F"{data_dir_samples}/xsum"
						lowerCAmelCase__			  =  '\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__			  =  {
						    '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__			  =  F"{ROOT_DIRECTORY}/examples/pytorch"

						lowerCAmelCase__			  =  {
						    '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__			  =  get_launcher(distributed=UpperCamelCase_						)

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


__snake_case			:			List[Any]			   =   make_task_cmds()

__snake_case			:			List[str]			   =   """zero2"""
__snake_case			:			Optional[int]			   =   """zero3"""

__snake_case			:			List[Any]			   =   [ZEROa, ZEROa]







def 		_UpperCamelCase      (			UpperCamelCase_				:    Union[str, Any]			,				UpperCamelCase_				:    Dict			,				UpperCamelCase_				:    Tuple						)     ->      int:

						"""simple docstring"""



						lowerCAmelCase__			  =  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
__snake_case			:			Union[str, Any]			   =   list(itertools.product(stages, task_cmds.keys()))


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






						def 					UpperCamelCase__					(		self   ,  _UpperCamelCase   ,  _UpperCamelCase      ):






												"""simple docstring"""





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

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

												lowerCAmelCase__			  =  self.get_auto_remove_tmp_dir()
												lowerCAmelCase__			  =  F"--output_dir {output_dir}".split()

												cmd += args_ds + args_out

												return cmd, output_dir







						@parameterized.expand(_lowercase   ,  name_func=_lowercase      )
						def 					UpperCamelCase__					(		self   ,  _UpperCamelCase   ,  _UpperCamelCase      ):






												"""simple docstring"""
												lowerCAmelCase__  ,				lowerCAmelCase__			  =  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__			  =  F"{output_dir}/checkpoint-1"
												lowerCAmelCase__			  =  F"{chkpt_dir}/out.bin"
												lowerCAmelCase__			  =  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












from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_tokenizers_available


_snake_case				= {}

try:
  if not is_sentencepiece_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _snake_case				= ["""MBart50Tokenizer"""]

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _snake_case				= ["""MBart50TokenizerFast"""]


if TYPE_CHECKING:
  try:
    if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_mbartaa import MBartaaTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_mbartaa_fast import MBartaaTokenizerFast

else:
  import sys

  _snake_case				= _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)






