





'''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(42)

_lowerCamelCase					:						Optional[int]					     =							get_tests_dir('fixtures')
_lowerCamelCase					:						str					     =							os.path.join(dirname(get_tests_dir()))
_lowerCamelCase					:						int					     =							dirname(os.path.abspath(__file__))

# default torch.distributed port
_lowerCamelCase					:						Union[str, Any]					     =							'10999'

_lowerCamelCase					:						Optional[Any]					     =							't5-small'

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


# *** To Fix ***


# *** tiny model issues ***
# missing model files:
_lowerCamelCase					:						List[Any]					     =							'hf-internal-testing/tiny-random-mt5'
_lowerCamelCase					:						int					     =							'hf-internal-testing/tiny-random-camembert'
_lowerCamelCase					:						List[Any]					     =							'hf-internal-testing/tiny-random-openai-gpt'

# missing tokenizer files
_lowerCamelCase					:						Tuple					     =							'hf-internal-testing/tiny-random-convbert'
_lowerCamelCase					:						List[Any]					     =							'hf-internal-testing/tiny-random-layoutlmv2'
_lowerCamelCase					:						Any					     =							'hf-internal-testing/tiny-random-hubert'

# issues with tokenizer
_lowerCamelCase					:						Dict					     =							'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					:						List[Any]					     =							'hf-internal-testing/tiny-random-ibert'  # multiple issues with either mlm/qa/clas
_lowerCamelCase					:						Union[str, 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
_lowerCamelCase					:						Tuple					     =							'hf-internal-testing/tiny-random-dpr'
# - "dpr"  examples/research_projects/rag-end2end-retriever/
_lowerCamelCase					:						Tuple					     =							'hf-internal-testing/tiny-random-rag'
# - "rag" research_projects
_lowerCamelCase					:						Optional[int]					     =							''
# - "luke" Entities classes - no plan to make such example
_lowerCamelCase					:						Dict					     =							'hf-internal-testing/tiny-random-lxmert'
# - "lxmert" doesn't work with run_qa.py
_lowerCamelCase					:						Tuple					     =							'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     )   ->Union[str, Any]:



	"""simple docstring"""







	A					=   min(2							,      get_gpu_count()     ) if distributed else 1
	A					=   os.environ.get("""DS_TEST_PORT"""							,      UpperCAmelCase     )
	return f"""deepspeed --num_nodes 1 --num_gpus {num_gpus} --master_port {master_port}""".split()




def  __a			(  )   ->Optional[Any]:



	"""simple docstring"""







	A					=   f"""{FIXTURE_DIRECTORY}/tests_samples"""
	A					=   f"""{data_dir_samples}/wmt_en_ro"""
	A					=   f"""{data_dir_samples}/xsum"""
	A					=   """
        --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.
	A					=   {
	    """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""",
	    ],
	}

	A					=   f"""{ROOT_DIRECTORY}/examples/pytorch"""

	A					=   {
	    """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
        """,
	}

	A					=   get_launcher(distributed=UpperCAmelCase     )

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

_lowerCamelCase					:						Tuple					     =							'zero2'
_lowerCamelCase					:						List[Any]					     =							'zero3'

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




def  __a			(  UpperCAmelCase							,      UpperCAmelCase							,      UpperCAmelCase     )   ->List[str]:



	"""simple docstring"""







	A					=   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					:						Tuple					     =							list(itertools.product(stages, task_cmds.keys()))



@slow
@require_deepspeed
@require_torch_gpu
class 				__UpperCAmelCase	(							A__    ):







		'''simple docstring'''







		def 							A  (self    :   Any   ,   _lowerCAmelCase    :   Tuple   ,   _lowerCAmelCase    :   Optional[Any]   ):
			# return a ready to run train cmd
			if task not in task_cmds:
				raise ValueError(F"""don't know of task {task}, have {task_cmds.keys()}"""   )

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

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

			cmd += args_ds + args_out

			return cmd, output_dir







		@parameterized.expand(_lowerCAmelCase   ,   name_func=_lowerCAmelCase   )
		def 							A  (self    :   int   ,   _lowerCAmelCase    :   List[str]   ,   _lowerCAmelCase    :   Optional[int]   ):
			# testing the ability to do a run followed by recovery of full fp32 weights

			A	,     A					=   self.get_task_cmd(_lowerCAmelCase   ,   _lowerCAmelCase   )

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

			# 2. test that the fp32 weights get reconsolidated
			A					=   F"""{output_dir}/checkpoint-1"""
			A					=   F"""{chkpt_dir}/out.bin"""
			A					=   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(_lowerCAmelCase   ,   shell=_lowerCAmelCase   )
			assert os.path.exists(_lowerCAmelCase   ), 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 argparse

import torch

from transformers import HubertConfig, HubertForSequenceClassification, WavaVecaFeatureExtractor, logging


logging.set_verbosity_info()
_lowerCamelCase					:						Dict					     =							logging.get_logger(__name__)

_lowerCamelCase					:						List[Any]					     =							['UtteranceLevel']




@torch.no_grad()
def  __a			(  UpperCAmelCase							,      UpperCAmelCase							,      UpperCAmelCase							,      UpperCAmelCase     )   ->Union[str, Any]:



	"""simple docstring"""







	A					=   torch.load(UpperCAmelCase							,      map_location="""cpu"""     )
	if checkpoint["Config"]["downstream_expert"]["modelrc"]["select"] not in SUPPORTED_MODELS:
		raise NotImplementedError(f"""The supported s3prl models are {SUPPORTED_MODELS}"""     )

	A					=   checkpoint["""Downstream"""]

	A					=   HubertConfig.from_pretrained(UpperCAmelCase     )
	A					=   HubertForSequenceClassification.from_pretrained(UpperCAmelCase							,      config=UpperCAmelCase     )
	A					=   WavaVecaFeatureExtractor.from_pretrained(
	    UpperCAmelCase							,      return_attention_mask=UpperCAmelCase							,      do_normalize=UpperCAmelCase     )

	if hf_congfig.use_weighted_layer_sum:
		A					=   checkpoint["""Featurizer"""]["""weights"""]

	A					=   downstream_dict["""projector.weight"""]
	A					=   downstream_dict["""projector.bias"""]
	A					=   downstream_dict["""model.post_net.linear.weight"""]
	A					=   downstream_dict["""model.post_net.linear.bias"""]

	hf_feature_extractor.save_pretrained(UpperCAmelCase     )
	hf_model.save_pretrained(UpperCAmelCase     )


if __name__ == "__main__":
				_lowerCamelCase					:						List[str]					     =							argparse.ArgumentParser()
				parser.add_argument(
				    '--base_model_name', default=None, type=str, help='Name of the huggingface pretrained base model.'
				)
				parser.add_argument('--config_path', default=None, type=str, help='Path to the huggingface classifier config.')
				parser.add_argument('--checkpoint_path', default=None, type=str, help='Path to the s3prl checkpoint.')
				parser.add_argument('--model_dump_path', default=None, type=str, help='Path to the final converted model.')
				_lowerCamelCase					:						List[Any]					     =							parser.parse_args()
				convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)

