
"""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)

__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 		_snake_case			(      lowercase__						:    str=False						)    ->							List[str]:


			'''simple docstring'''





			lowerCAmelCase_       :int     	=    min(2 ,	get_gpu_count()						) if distributed else 1
			lowerCAmelCase_       :Dict     	=    os.environ.get("""DS_TEST_PORT""" ,	UpperCamelCase__						)
			return f"""deepspeed --num_nodes 1 --num_gpus {num_gpus} --master_port {master_port}""".split()






def 		_snake_case			(      )    ->							Any:


			'''simple docstring'''





			lowerCAmelCase_       :Tuple     	=    f"""{FIXTURE_DIRECTORY}/tests_samples"""
			lowerCAmelCase_       :int     	=    f"""{data_dir_samples}/wmt_en_ro"""
			lowerCAmelCase_       :List[str]     	=    f"""{data_dir_samples}/xsum"""
			lowerCAmelCase_       :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.
			lowerCAmelCase_       :List[str]     	=    {
			    '''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_       :int     	=    f"""{ROOT_DIRECTORY}/examples/pytorch"""

			lowerCAmelCase_       :List[str]     	=    {
			    '''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_       :int     	=    get_launcher(distributed=UpperCamelCase__						)

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


__UpperCAmelCase 					=    make_task_cmds()

__UpperCAmelCase 					=    'zero2'
__UpperCAmelCase 					=    'zero3'

__UpperCAmelCase 					=    [ZEROa, ZEROa]






def 		_snake_case			(      lowercase__						:    Tuple ,	lowercase__						:    int ,	lowercase__						:    Optional[int]						)    ->							Any:


			'''simple docstring'''





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




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






			def 					__lowerCAmelCase					(   self			,  __A			,  __A			)     ->	Dict:
						# 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()}"""			)

						lowerCAmelCase_       :List[str]     	=    task_cmds[task]
						lowerCAmelCase_       :Dict     	=    f"""--deepspeed {self.test_file_dir_str}/ds_config_{stage}.json""".split()

						lowerCAmelCase_       :Optional[Any]     	=    self.get_auto_remove_tmp_dir()
						lowerCAmelCase_       :Union[str, 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			,  __A			,  __A			)     ->	List[Any]:
						# testing the ability to do a run followed by recovery of full fp32 weights

						lowerCAmelCase_       :List[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
						lowerCAmelCase_       :Dict     	=    f"""{output_dir}/checkpoint-1"""
						lowerCAmelCase_       :Optional[Any]     	=    f"""{chkpt_dir}/out.bin"""
						lowerCAmelCase_       :Optional[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 argparse
import os
from os.path import isfile

import torch

from transformers import GPTaConfig
def 				_lowerCamelCase(	UpperCamelCase__     :			List[str]     ,							UpperCamelCase__     :			Optional[Any]     ,							UpperCamelCase__     :			List[str]=0		)		->	Optional[int]:
    # Format the message.
    if name is None:
        A							:							Dict				=    None
    else:
        A							:							Optional[Any]				=    '''.''' * max(0     ,							spaces - 2		) + '''# {:''' + str(50 - spaces		) + '''s}'''
        A							:							Tuple				=    fmt.format(UpperCamelCase__		)

    # Print and recurse (if needed).
    if isinstance(UpperCamelCase__     ,							UpperCamelCase__		):
        if msg is not None:
            print(UpperCamelCase__		)
        for k in val.keys():
            recursive_print(UpperCamelCase__     ,							val[k]     ,							spaces + 2		)
    elif isinstance(UpperCamelCase__     ,							torch.Tensor		):
        print(UpperCamelCase__     ,							''':'''     ,							val.size()		)
    else:
        print(UpperCamelCase__     ,							''':'''     ,							UpperCamelCase__		)
def 				_lowerCamelCase(	UpperCamelCase__     :			Dict     ,							UpperCamelCase__     :			List[Any]     ,							UpperCamelCase__     :			Dict     ,							UpperCamelCase__     :			List[str]		)		->	Dict:
    # Permutes layout of param tensor to [num_splits * num_heads * hidden_size, :]
    # for compatibility with later versions of NVIDIA Megatron-LM.
    # The inverse operation is performed inside Megatron-LM to read checkpoints:
    # https://github.com/NVIDIA/Megatron-LM/blob/v2.4/megatron/checkpointing.py#L209
    # If param is the weight tensor of the self-attention block, the returned tensor
    # will have to be transposed one more time to be read by HuggingFace GPT2.
    A							:							int				=    param.size()
    # other versions store [num_heads * num_splits * hidden_size, :]
    A							:							List[str]				=    (num_heads, num_splits, hidden_size) + input_shape[1:]
    A							:							Optional[int]				=    param.view(*UpperCamelCase__		)
    A							:							Optional[Any]				=    param.transpose(0     ,							1		).contiguous()
    A							:							Dict				=    param.view(*UpperCamelCase__		)
    return param
def 				_lowerCamelCase(	UpperCamelCase__     :			Any     ,							UpperCamelCase__     :			Tuple		)		->	Optional[int]:
    A							:							int				=    config.n_positions
    A							:							Dict				=    config.n_layer
    A							:							List[str]				=    config.vocab_size
    A							:							Optional[Any]				=    config.n_head
    A							:							Union[str, Any]				=    config.n_embd // config.n_head

    A							:							Optional[int]				=    sd_megatron['''model.language_model.embedding.word_embeddings.weight'''][:vocab_size, :]
    A							:							Any				=    {
        '''transformer.wte.weight''': word_embeddings,
        '''transformer.wpe.weight''': sd_megatron['''model.language_model.embedding.position_embeddings.weight'''],
        '''transformer.ln_f.weight''': sd_megatron['''model.language_model.encoder.final_layernorm.weight'''],
        '''transformer.ln_f.bias''': sd_megatron['''model.language_model.encoder.final_layernorm.bias'''],
    }

    A							:							Optional[int]				=    '''model.language_model.encoder.layers.'''
    for i in range(UpperCamelCase__		):
        A							:							Dict				=    torch.tril(torch.ones((n_positions, n_positions)     ,							dtype=torch.bool		)		)
        A							:							Any				=    causal_mask.view(1     ,							1     ,							UpperCamelCase__     ,							UpperCamelCase__		)
        A							:							List[Any]				=    causal_mask
        A							:							List[str]				=    torch.tensor(-1e4     ,							dtype=torch.bfloataa		)

        A							:							Optional[int]				=    sd_megatron[F'''{pf}{i}.input_layernorm.weight''']
        A							:							Optional[int]				=    sd_megatron[F'''{pf}{i}.input_layernorm.bias''']

        A							:							Any				=    sd_megatron[F'''{pf}{i}.self_attention.query_key_value.weight''']
        A							:							List[Any]				=    fix_query_key_value_ordering(UpperCamelCase__     ,							3     ,							UpperCamelCase__     ,							UpperCamelCase__		)
        A							:							List[Any]				=    vala.transpose(0     ,							1		).contiguous()

        A							:							List[Any]				=    sd_megatron[F'''{pf}{i}.self_attention.query_key_value.bias''']
        A							:							Any				=    fix_query_key_value_ordering(UpperCamelCase__     ,							3     ,							UpperCamelCase__     ,							UpperCamelCase__		)
        A							:							List[Any]				=    vala

        A							:							Tuple				=    sd_megatron[F'''{pf}{i}.self_attention.dense.weight'''].transpose(
            0     ,							1		)
        A							:							Optional[int]				=    sd_megatron[F'''{pf}{i}.self_attention.dense.bias''']
        A							:							Any				=    sd_megatron[F'''{pf}{i}.post_attention_layernorm.weight''']
        A							:							Any				=    sd_megatron[F'''{pf}{i}.post_attention_layernorm.bias''']
        A							:							str				=    sd_megatron[F'''{pf}{i}.mlp.dense_h_to_4h.weight'''].transpose(0     ,							1		)
        A							:							List[Any]				=    sd_megatron[F'''{pf}{i}.mlp.dense_h_to_4h.bias''']
        A							:							Tuple				=    sd_megatron[F'''{pf}{i}.mlp.dense_4h_to_h.weight'''].transpose(
            0     ,							1		)
        A							:							Union[str, Any]				=    sd_megatron[F'''{pf}{i}.mlp.dense_4h_to_h.bias''']

    # For LM head, transformers' wants the matrix to weight embeddings.
    A							:							Optional[int]				=    word_embeddings

    return sd_hf
def 				_lowerCamelCase(	UpperCamelCase__     :			Union[str, Any]     ,							UpperCamelCase__     :			Optional[Any]		)		->	Union[str, Any]:
    A							:							List[str]				=    64_000
    A							:							str				=    config_megatron['''encoder_seq_length''']
    A							:							Union[str, Any]				=    config_megatron['''hidden_size''']
    A							:							Tuple				=    config_megatron['''num_layers''']
    A							:							List[str]				=    config_megatron['''num_attention_heads''']
    A							:							List[Any]				=    config_megatron['''ffn_hidden_size''']
    A							:							List[str]				=    '''gelu'''
    A							:							int				=    0.1
    A							:							Union[str, Any]				=    0.1
    A							:							Optional[int]				=    0.1
    A							:							Optional[int]				=    config_megatron['''layernorm_epsilon''']  # 1e-5
    A							:							Tuple				=    config_megatron['''init_method_std''']  # 0.02
    A							:							Any				=    config_megatron['''apply_query_key_layer_scaling''']  # True
    A							:							Tuple				=    True
    A							:							str				=    True

    # This identifies the 6.7B (7B) model which uses a different tokenizer
    if config_megatron["hidden_size"] == 4_096:
        A							:							Tuple				=    1  # <|endoftext|>
        A							:							Any				=    1  # <|endoftext|>
        A							:							List[str]				=    0  # <unk>
    else:
        A							:							Any				=    2  # <s>
        A							:							str				=    3  # <|endoftext|>
        A							:							Tuple				=    0  # <pad>

    return config_hf
def 				_lowerCamelCase(	UpperCamelCase__     :			List[Any]		)		->	Tuple:
    print(UpperCamelCase__		)

    A							:							Tuple				=    args.checkpoint_path
    A							:							Dict				=    args.save_path
    if isfile(UpperCamelCase__		):
        raise FileNotFoundError(F'''ERROR! could not find file {checkpoint_path}'''		)

    # Load the model.
    A							:							str				=    torch.load(UpperCamelCase__     ,							map_location='''cpu'''		)

    # Load the config.
    A							:							List[str]				=    checkpoint['''hyper_parameters''']['''cfg''']
    A							:							Tuple				=    GPTaConfig()
    A							:							int				=    copy_config(config_hf=UpperCamelCase__     ,							config_megatron=UpperCamelCase__		)
    A							:							Any				=    ['''GPT2LMHeadModel''']

    A							:							Optional[int]				=    checkpoint['''state_dict''']

    # Convert.
    print('''Converting'''		)
    A							:							Any				=    convert_megatron_checkpoint(UpperCamelCase__     ,							UpperCamelCase__		)

    # Print the structure of converted state dict.
    if args.print_checkpoint_structure:
        recursive_print(UpperCamelCase__     ,							UpperCamelCase__		)

    A							:							Union[str, Any]				=    '''GPTSw3Tokenizer'''

    # Store the config to file.
    print('''Saving config'''		)
    config_hf.save_pretrained(UpperCamelCase__		)

    # Store the state_dict to file.
    A							:							int				=    os.path.join(UpperCamelCase__     ,							'''pytorch_model.bin'''		)
    print(F'''Saving checkpoint to "{output_checkpoint_file}"'''		)
    torch.save(UpperCamelCase__     ,							UpperCamelCase__		)


if __name__ == "__main__":
     snake_case_     			=						argparse.ArgumentParser()
     parser.add_argument(
         """--checkpoint_path""",
         type=str,
         required=True,
         help="""e.g. megatron_gpt--val_loss=2.42-step=38000-consumed_samples=54720000""",
     )
     parser.add_argument("""--save_path""", type=str, required=True, help="""e.g. /home/user/gpt-sw3/hf""")
     parser.add_argument("""--print-checkpoint-structure""", action="""store_true""")
     snake_case_     			=						parser.parse_args()
     main(_args)
