




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)

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       _a (		SCREAMING_SNAKE_CASE_				:							str=False	):
					# 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)
					__lowerCAmelCase							  =			min(2 , get_gpu_count()	) if distributed else 1
					__lowerCAmelCase							  =			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       _a (		):
					__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"""
        {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
        """,
					}

					__lowerCAmelCase							  =			get_launcher(distributed=SCREAMING_SNAKE_CASE_	)

					__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


UpperCamelCase__            =     make_task_cmds()

UpperCamelCase__            =     """zero2"""
UpperCamelCase__            =     """zero3"""

UpperCamelCase__            =     [ZEROa, ZEROa]







def       _a (		SCREAMING_SNAKE_CASE_				:							Any , SCREAMING_SNAKE_CASE_				:							Tuple , SCREAMING_SNAKE_CASE_				:							List[Any]	):
					# 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
					__lowerCAmelCase							  =			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
UpperCamelCase__            =     list(itertools.product(stages, task_cmds.keys()))


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






						def 							__SCREAMING_SNAKE_CASE(  self						,     _A						,     _A     ):




											"""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(_A						,     name_func=_A     )
						def 							__SCREAMING_SNAKE_CASE(  self						,     _A						,     _A     ):




											"""simple docstring"""



											__lowerCAmelCase  ,				__lowerCAmelCase							  =			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
											__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(_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 typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCamelCase__            =     {
    """configuration_marian""": ["""MARIAN_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MarianConfig""", """MarianOnnxConfig"""],
}

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

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCamelCase__            =     [
					    """MARIAN_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """MarianForCausalLM""",
					    """MarianModel""",
					    """MarianMTModel""",
					    """MarianPreTrainedModel""",
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCamelCase__            =     ["""TFMarianModel""", """TFMarianMTModel""", """TFMarianPreTrainedModel"""]

try:
					if not is_flax_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCamelCase__            =     ["""FlaxMarianModel""", """FlaxMarianMTModel""", """FlaxMarianPreTrainedModel"""]

if TYPE_CHECKING:
					from .configuration_marian import MARIAN_PRETRAINED_CONFIG_ARCHIVE_MAP, MarianConfig, MarianOnnxConfig

					try:
										if not is_sentencepiece_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_marian import MarianTokenizer

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_marian import (
										    MARIAN_PRETRAINED_MODEL_ARCHIVE_LIST,
										    MarianForCausalLM,
										    MarianModel,
										    MarianMTModel,
										    MarianPreTrainedModel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_marian import TFMarianModel, TFMarianMTModel, TFMarianPreTrainedModel

					try:
										if not is_flax_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_flax_marian import FlaxMarianModel, FlaxMarianMTModel, FlaxMarianPreTrainedModel

else:
					import sys

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




