
'''simple docstring'''
import math
import os
import re
import sys
import unittest
from pathlib import Path
from typing import Tuple
from unittest.mock import patch

from parameterized import parameterized

from transformers.testing_utils import (
    CaptureStderr,
    ExtendSysPath,
    TestCasePlus,
    execute_subprocess_async,
    get_gpu_count,
    get_torch_dist_unique_port,
    require_apex,
    require_bitsandbytes,
    require_fairscale,
    require_torch,
    require_torch_gpu,
    require_torch_multi_gpu,
    require_torch_non_multi_gpu,
    slow,
)
from transformers.trainer_callback import TrainerState
from transformers.trainer_utils import set_seed


__lowerCAmelCase												=	os.path.abspath(os.path.dirname(__file__))
with ExtendSysPath(f'''{bindir}/../../examples/pytorch/translation'''):
			from run_translation import main  # noqa


set_seed(42)
__lowerCAmelCase												=	'sshleifer/student_marian_en_ro_6_1'
__lowerCAmelCase												=	'sshleifer/tiny-mbart'


@require_torch
class       _lowerCAmelCase   (   __snake_case				):







							'''simple docstring'''

							def 	lowercase							(self  ,  UpperCAmelCase=False  ,  UpperCAmelCase=None  ,  UpperCAmelCase=True  ,  UpperCAmelCase=True  ,  UpperCAmelCase=True  ,  UpperCAmelCase=True  ,  )					->       Optional[Any]:
														_snake_case       =  self.run_trainer(
														    eval_steps=1  ,  max_len=12  ,  model_name=UpperCAmelCase  ,  num_train_epochs=1  ,  distributed=UpperCAmelCase  ,  extra_args_str=UpperCAmelCase  ,  predict_with_generate=UpperCAmelCase  ,  do_train=UpperCAmelCase  ,  do_eval=UpperCAmelCase  ,  do_predict=UpperCAmelCase  ,  )
														_snake_case       =  TrainerState.load_from_json(os.path.join(UpperCAmelCase  ,  """trainer_state.json"""  )  ).log_history

														if not do_eval:
																					return

														_snake_case       =  [log for log in logs if """eval_loss""" in log.keys()]

														_snake_case       =  eval_metrics[0]
														if predict_with_generate:
																					assert "eval_bleu" in first_step_stats

																					_snake_case       =  eval_metrics[-1]
																					assert isinstance(last_step_stats["""eval_bleu"""]  ,  UpperCAmelCase  )
																					assert not math.isnan(float(last_step_stats["""eval_loss"""]  )  ), "eval_loss must not be `nan`"

							@require_torch_non_multi_gpu
							def 	lowercase							(self  )					->       int:
														self.run_seqaseq_quick()

							@require_torch_multi_gpu
							def 	lowercase							(self  )					->       Any:
														self.run_seqaseq_quick(distributed=UpperCAmelCase  )

							@require_torch_multi_gpu
							def 	lowercase							(self  )					->       List[str]:
														self.run_seqaseq_quick(distributed=UpperCAmelCase  )

							@unittest.skip("""Requires an update of the env running those tests"""  )
							@require_torch_multi_gpu
							@require_fairscale
							def 	lowercase							(self  )					->       List[Any]:
														self.run_seqaseq_quick(distributed=UpperCAmelCase  ,  extra_args_str="""--sharded_ddp simple"""  )

							@unittest.skip("""Requires an update of the env running those tests"""  )
							@require_torch_multi_gpu
							@require_fairscale
							def 	lowercase							(self  )					->       Union[str, Any]:
														self.run_seqaseq_quick(distributed=UpperCAmelCase  ,  extra_args_str="""--sharded_ddp simple --fp16"""  )

							@unittest.skip("""Requires an update of the env running those tests"""  )
							@require_torch_multi_gpu
							@require_fairscale
							def 	lowercase							(self  )					->       int:
														self.run_seqaseq_quick(distributed=UpperCAmelCase  ,  extra_args_str="""--sharded_ddp zero_dp_2"""  ,  predict_with_generate=UpperCAmelCase  )

							@unittest.skip("""Requires an update of the env running those tests"""  )
							@require_torch_multi_gpu
							@require_fairscale
							def 	lowercase							(self  )					->       Dict:
														self.run_seqaseq_quick(
														    distributed=UpperCAmelCase  ,  extra_args_str="""--sharded_ddp zero_dp_2 --fp16"""  ,  predict_with_generate=UpperCAmelCase  )

							@require_apex
							@require_torch_gpu
							def 	lowercase							(self  )					->       Optional[Any]:
														# XXX: apex breaks the trainer if it's run twice e.g. run_seq2seq.main() from the same
														# program and it breaks other tests that run from the same pytest worker, therefore until this is
														# sorted out it must be run only in an external program, that is distributed=True in this
														# test and only under one or more gpus - if we want cpu will need to make a special test
														#
														# specifically to the problem traced it to self.optimizer.step() - if it's run 2nd time via
														# 2nd main() call it botches the future eval.
														#
														self.run_seqaseq_quick(distributed=UpperCAmelCase  ,  extra_args_str="""--fp16 --fp16_backend=apex"""  )
														# test 2nd time - was getting eval_loss': nan'
														# to reproduce the problem set distributed=False
														self.run_seqaseq_quick(distributed=UpperCAmelCase  ,  extra_args_str="""--fp16 --fp16_backend=apex"""  )

							@parameterized.expand(["""base""", """low""", """high""", """mixed"""]  )
							@require_torch_multi_gpu
							def 	lowercase							(self  ,  UpperCAmelCase  )					->       List[Any]:
														# as each sub-test is slow-ish split into multiple sub-tests to avoid CI timeout
														_snake_case       =  {
														    # test with the default log_level - should be info and thus log info once
														    """base""": {"""extra_args_str""": """""", """n_matches""": 1},
														    # test with low log_level and log_level_replica - should be noisy on all processes
														    # now the info string should appear twice on 2 processes
														    """low""": {"""extra_args_str""": """--log_level debug --log_level_replica debug""", """n_matches""": 2},
														    # test with high log_level and low log_level_replica
														    # now the info string should appear once only on the replica
														    """high""": {"""extra_args_str""": """--log_level error --log_level_replica debug""", """n_matches""": 1},
														    # test with high log_level and log_level_replica - should be quiet on all processes
														    """mixed""": {"""extra_args_str""": """--log_level error --log_level_replica error""", """n_matches""": 0},
														}

														_snake_case       =  experiments[experiment_id]
														_snake_case       =  {"""distributed""": True, """predict_with_generate""": False, """do_eval""": False, """do_predict""": False}
														_snake_case       =  """Running training"""
														with CaptureStderr() as cl:
																					self.run_seqaseq_quick(**UpperCAmelCase  ,  extra_args_str=data["""extra_args_str"""]  )
														_snake_case       =  len(re.findall(UpperCAmelCase  ,  cl.err  )  )
														self.assertEqual(UpperCAmelCase  ,  data["""n_matches"""]  )

							@slow
							def 	lowercase							(self  )					->       List[str]:
														_snake_case       =  self.run_trainer(
														    eval_steps=2  ,  max_len=128  ,  model_name=UpperCAmelCase  ,  learning_rate=3e-4  ,  num_train_epochs=10  ,  distributed=UpperCAmelCase  ,  )

														# Check metrics
														_snake_case       =  TrainerState.load_from_json(os.path.join(UpperCAmelCase  ,  """trainer_state.json"""  )  ).log_history
														_snake_case       =  [log for log in logs if """eval_loss""" in log.keys()]
														_snake_case       =  eval_metrics[0]
														_snake_case       =  eval_metrics[-1]

														assert first_step_stats["eval_loss"] > last_step_stats["eval_loss"], "model learned nothing"
														assert isinstance(last_step_stats["""eval_bleu"""]  ,  UpperCAmelCase  )

														# test if do_predict saves generations and metrics
														_snake_case       =  os.listdir(UpperCAmelCase  )
														_snake_case       =  {os.path.basename(UpperCAmelCase  ) for p in contents}
														assert "generated_predictions.txt" in contents
														assert "predict_results.json" in contents

							@slow
							@require_bitsandbytes
							def 	lowercase							(self  )					->       Any:
														from transformers.training_args import OptimizerNames

														def train_and_return_metrics(UpperCAmelCase  ) -> Tuple[int, float]:
																					_snake_case       =  """--skip_memory_metrics 0"""

																					_snake_case       =  self.run_trainer(
																					    max_len=128  ,  model_name=UpperCAmelCase  ,  learning_rate=3e-4  ,  num_train_epochs=1  ,  optim=UpperCAmelCase  ,  distributed=UpperCAmelCase  ,  extra_args_str=UpperCAmelCase  ,  do_eval=UpperCAmelCase  ,  do_predict=UpperCAmelCase  ,  n_gpus_to_use=1  ,  )

																					# Check metrics
																					_snake_case       =  TrainerState.load_from_json(Path(UpperCAmelCase  ,  """trainer_state.json"""  )  ).log_history
																					_snake_case       =  int(logs[0]["""train_mem_gpu_peaked_delta"""] / 2**20  )
																					_snake_case       =  int(logs[0]["""train_mem_gpu_alloc_delta"""] / 2**20  )

																					_snake_case       =  logs[0]["""train_loss"""]
																					return gpu_peak_mem_mb, gpu_alloc_mem_mb, loss

														_snake_case,							_snake_case,							_snake_case       =  train_and_return_metrics(OptimizerNames.ADAMW_TORCH.value  )
														_snake_case,							_snake_case,							_snake_case       =  train_and_return_metrics(OptimizerNames.ADAMW_BNB.value  )

														_snake_case       =  gpu_alloc_mem_orig - gpu_alloc_mem_bnb

														_snake_case       =  gpu_peak_mem_orig + gpu_alloc_mem_orig
														_snake_case       =  gpu_peak_mem_bnb + gpu_alloc_mem_bnb
														_snake_case       =  gpu_total_mem_orig - gpu_total_mem_bnb

														# sshleifer/student_marian_en_ro_6_1 has 54M parameter, 29M of which is `nn.Embedding` which
														# doesn't get quantized and remains in fp32. Therefore we only have 25M parameters quantized
														# in 2 bytes and the diff in optim memory usage is derived as so:
														#
														# - normal 25*8=~200MB (8 bytes per param)
														# - bnb    25*2= ~50MB (2 bytes per param)
														#
														# Thus we should expect ~150MB total memory saved.
														#
														# Peak memory should be the same - the total should be different by about that same margin
														#
														# After leaving a small margin to accommodate for differences between gpus let's check
														# that we have at least 120MB in savings
														_snake_case       =  120

														# uncomment the following if this test starts failing - requires py38 for a new print feature
														# gpu_peak_mem_diff = gpu_peak_mem_orig - gpu_peak_mem_bnb
														# print(f"{gpu_alloc_mem_orig=}MB {gpu_peak_mem_orig=}MB {gpu_alloc_mem_orig+gpu_peak_mem_orig=}MB")
														# print(f" {gpu_alloc_mem_bnb=}MB  {gpu_peak_mem_bnb=}MB  {gpu_alloc_mem_bnb+gpu_peak_mem_bnb=}MB")
														# print(f"{gpu_alloc_mem_diff=}MB")
														# print(f"{gpu_peak_mem_diff=}MB")
														# print(f"{gpu_total_mem_orig=}MB, {gpu_total_mem_bnb=}MB")
														# print(f"{gpu_total_mem_diff=}MB, {gpu_total_mem_diff=}MB")

														self.assertGreater(
														    UpperCAmelCase  ,  UpperCAmelCase  ,  """should use ~150MB less alloc gpu memory with BNB, compared to without it for this model but got"""
														    f""" a difference of {gpu_alloc_mem_diff}MB, with gpu_alloc_mem_orig={gpu_alloc_mem_orig}MB and"""
														    f""" gpu_alloc_mem_bnb={gpu_alloc_mem_bnb}MB"""  ,  )

														self.assertGreater(
														    UpperCAmelCase  ,  UpperCAmelCase  ,  """should use ~150MB less total gpu memory with BNB, compared to without it for this model but got"""
														    f""" a difference of {gpu_total_mem_diff}MB, with gpu_total_mem_orig={gpu_total_mem_orig}MB and"""
														    f""" gpu_total_mem_bnb={gpu_total_mem_bnb}MB"""  ,  )

														self.assertEqual(
														    UpperCAmelCase  ,  UpperCAmelCase  ,  f"""loss should be the same, but got loss_orig={loss_orig}, loss_bnb={loss_bnb}"""  )





							def 	lowercase							(self  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase = 3e-3  ,  UpperCAmelCase = "adafactor"  ,  UpperCAmelCase = False  ,  UpperCAmelCase = None  ,  UpperCAmelCase = 0  ,  UpperCAmelCase = True  ,  UpperCAmelCase = True  ,  UpperCAmelCase = True  ,  UpperCAmelCase = True  ,  UpperCAmelCase = None  ,  )					->       List[Any]:
														_snake_case       =  self.test_file_dir / """../fixtures/tests_samples/wmt_en_ro"""
														_snake_case       =  self.get_auto_remove_tmp_dir()
														_snake_case       =  f"""
            --model_name_or_path {model_name}
            --train_file {data_dir}/train.json
            --validation_file {data_dir}/val.json
            --test_file {data_dir}/test.json
            --output_dir {output_dir}
            --overwrite_output_dir
            --max_train_samples 8
            --max_source_length {max_len}
            --max_target_length {max_len}
            --do_train
            --num_train_epochs {str(UpperCAmelCase  )}
            --per_device_train_batch_size 4
            --learning_rate {learning_rate}
            --warmup_steps 8
            --logging_steps 0
            --logging_strategy no
            --save_steps {str(UpperCAmelCase  )}
            --group_by_length
            --label_smoothing_factor 0.1
            --target_lang ro_RO
            --source_lang en_XX
        """.split()

														_snake_case       =  f"""
            --do_eval
            --per_device_eval_batch_size 4
            --max_eval_samples 8
            --val_max_target_length {max_len}
            --evaluation_strategy steps
            --eval_steps {str(UpperCAmelCase  )}
        """.split()

														_snake_case       =  """
            --do_predict
        """.split()

														_snake_case       =  []
														if do_train:
																					args += args_train

														if do_eval:
																					args += args_eval

														if do_predict:
																					args += args_predict

														if predict_with_generate:
																					args += "--predict_with_generate".split()

														if do_train:
																					if optim == "adafactor":
																												args += "--adafactor".split()
																					else:
																												args += f"""--optim {optim}""".split()

														if extra_args_str is not None:
																					args += extra_args_str.split()

														if distributed:
																					if n_gpus_to_use is None:
																												_snake_case       =  get_gpu_count()
																					_snake_case       =  get_torch_dist_unique_port()
																					_snake_case       =  f"""
                -m torch.distributed.run
                --nproc_per_node={n_gpus_to_use}
                --master_port={master_port}
                {self.examples_dir_str}/pytorch/translation/run_translation.py
            """.split()
																					_snake_case       =  [sys.executable] + distributed_args + args
																					# keep for quick debug
																					# print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
																					execute_subprocess_async(UpperCAmelCase  ,  env=self.get_env()  )
														else:
																					_snake_case       =  ["""run_translation.py"""] + args
																					with patch.object(UpperCAmelCase  ,  """argv"""  ,  UpperCAmelCase  ):
																												main()

														return output_dir

'''simple docstring'''
import argparse
import json
import os
import re

import torch

from transformers import BloomConfig, BloomModel
from transformers.file_utils import CONFIG_NAME, WEIGHTS_NAME
from transformers.utils import logging


logging.set_verbosity_info()

__lowerCAmelCase												=	[
    'word_embeddings_layernorm.weight',
    'word_embeddings_layernorm.bias',
    'input_layernorm.weight',
    'input_layernorm.bias',
    'post_attention_layernorm.weight',
    'post_attention_layernorm.bias',
    'self_attention.dense.bias',
    'mlp.dense_4h_to_h.bias',
    'ln_f.weight',
    'ln_f.bias',
]

__lowerCAmelCase												=	[
    'mlp.dense_4h_to_h.weight',
    'self_attention.dense.weight',
]






def  __SCREAMING_SNAKE_CASE				(  _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE				):
							_snake_case       =  {
							    """word_embeddings.weight""": """word_embeddings.weight""",
							    """word_embeddings.norm.weight""": """word_embeddings_layernorm.weight""",
							    """word_embeddings.norm.bias""": """word_embeddings_layernorm.bias""",
							    """weight""": """ln_f.weight""",
							    """bias""": """ln_f.bias""",
							}

							if key in layer_rename_map:
														return layer_rename_map[key]

							# Handle transformer blocks
							_snake_case       =  int(re.match(R""".*layer_(\d*).*"""							, _SCREAMING_SNAKE_CASE				)[1]				)
							layer_number -= 3
							return f"""h.{layer_number}.""" + key






def  __SCREAMING_SNAKE_CASE				(  _SCREAMING_SNAKE_CASE				):
							if dtype == torch.bool:
														return 1 / 8
							_snake_case       =  re.search(R"""[^\d](\d+)$"""							, str(_SCREAMING_SNAKE_CASE				)				)
							if bit_search is None:
														raise ValueError(f"""`dtype` is not a valid dtype: {dtype}."""				)
							_snake_case       =  int(bit_search.groups()[0]				)
							return bit_size // 8






def  __SCREAMING_SNAKE_CASE				(  _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE				):
							# Construct model
							if bloom_config_file == "":
														_snake_case       =  BloomConfig()
							else:
														_snake_case       =  BloomConfig.from_json_file(_SCREAMING_SNAKE_CASE				)

							if shard_model:
														_snake_case       =  os.listdir(_SCREAMING_SNAKE_CASE				)
														_snake_case       =  sorted(filter(lambda _SCREAMING_SNAKE_CASE				: s.startswith("""layer"""				) and "model_00" in s							, _SCREAMING_SNAKE_CASE				)				)

														_snake_case       =  {"""weight_map""": {}, """metadata""": {}}
														_snake_case       =  0

														_snake_case       =  None

														_snake_case       =  BloomConfig()

														for j, file in enumerate(_SCREAMING_SNAKE_CASE				):
																					print("""Processing file: {}""".format(_SCREAMING_SNAKE_CASE				)				)
																					_snake_case       =  None

																					for i in range(_SCREAMING_SNAKE_CASE				):
																												# load all TP files
																												_snake_case       =  file.replace("""model_00"""							, f"""model_0{i}"""				)
																												_snake_case       =  torch.load(os.path.join(_SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE				)							, map_location="""cpu"""				)

																												# Rename keys in the transformers names
																												_snake_case       =  list(temp.keys()				)
																												for key in keys:
																																			_snake_case       =  temp.pop(_SCREAMING_SNAKE_CASE				)

																												if tensors is None:
																																			_snake_case       =  temp
																												else:
																																			for key in tensors.keys():
																																										if any(key.endswith(_SCREAMING_SNAKE_CASE				) for end in WEIGHTS_TO_AVERAGE_ENDSWITH				):
																																																	# We average (sum and then divide) some weights accross TP ranks (see https://github.com/bigscience-workshop/Megatron-DeepSpeed/blob/olruwase/sync_layer_norms/megatron/training.py#L425)
																																																	tensors[key] += temp[key]
																																										else:
																																																	# Some weights are RowParallelLinear in Megatron-Deepspeed, others are ColumnParallel
																																																	_snake_case       =  1 if any(text in key for text in WEIGHTS_WITH_ROW_PARALLELISM_CONTAIN				) else 0
																																																	# We concatenate these weights accross TP ranks
																																																	_snake_case       =  torch.cat([tensors[key], temp[key]]							, dim=_SCREAMING_SNAKE_CASE				)

            # Divide by the number of TP the weights we want to average
																					for key in tensors.keys():
																												if any(key.endswith(_SCREAMING_SNAKE_CASE				) for end in WEIGHTS_TO_AVERAGE_ENDSWITH				):
																																			_snake_case       =  tensors[key] / pretraining_tp
																					torch.save(
																					    _SCREAMING_SNAKE_CASE							, os.path.join(
																					        _SCREAMING_SNAKE_CASE							, """pytorch_model_{}-of-{}.bin""".format(str(j + 1				).zfill(5				)							, str(len(_SCREAMING_SNAKE_CASE				)				).zfill(5				)				)							, )							, )

																					for key in tensors.keys():
																												_snake_case       =  tensors[key]
																												total_size += value.numel() * get_dtype_size(value.dtype				)
																												if key not in index_dict["weight_map"]:
																																			_snake_case       =  """pytorch_model_{}-of-{}.bin""".format(
																																			    str(j + 1				).zfill(5				)							, str(len(_SCREAMING_SNAKE_CASE				)				).zfill(5				)				)

														_snake_case       =  BloomConfig()
														_snake_case       =  pytorch_dump_folder_path + """/""" + CONFIG_NAME
														_snake_case       =  total_size
														with open(_SCREAMING_SNAKE_CASE							, """w"""							, encoding="""utf-8"""				) as f:
																					f.write(config.to_json_string()				)
														with open(os.path.join(_SCREAMING_SNAKE_CASE							, WEIGHTS_NAME + """.index.json"""				)							, """w"""							, encoding="""utf-8"""				) as f:
																					_snake_case       =  json.dumps(_SCREAMING_SNAKE_CASE							, indent=2							, sort_keys=_SCREAMING_SNAKE_CASE				) + """\n"""
																					f.write(_SCREAMING_SNAKE_CASE				)
							else:
														_snake_case       =  BloomModel(_SCREAMING_SNAKE_CASE				)

														_snake_case       =  os.listdir(_SCREAMING_SNAKE_CASE				)
														_snake_case       =  sorted(filter(lambda _SCREAMING_SNAKE_CASE				: s.startswith("""layer"""				) and "model_00" in s							, _SCREAMING_SNAKE_CASE				)				)

														_snake_case       =  None
														for i, file in enumerate(_SCREAMING_SNAKE_CASE				):
																					_snake_case       =  None
																					for i in range(_SCREAMING_SNAKE_CASE				):
																												# load all TP files
																												_snake_case       =  file.replace("""model_00"""							, f"""model_0{i}"""				)
																												_snake_case       =  torch.load(os.path.join(_SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE				)							, map_location="""cpu"""				)

																												# Rename keys in the transformers names
																												_snake_case       =  list(temp.keys()				)
																												for key in keys:
																																			_snake_case       =  temp.pop(_SCREAMING_SNAKE_CASE				)

																												if tensors is None:
																																			_snake_case       =  temp
																												else:
																																			for key in tensors.keys():
																																										# We average (sum and then divide) some weights accross TP ranks (see https://github.com/bigscience-workshop/Megatron-DeepSpeed/blob/olruwase/sync_layer_norms/megatron/training.py#L425)
																																										if any(key.endswith(_SCREAMING_SNAKE_CASE				) for end in WEIGHTS_TO_AVERAGE_ENDSWITH				):
																																																	tensors[key] += temp[key]
																																										else:
																																																	# Some weights are RowParallelLinear in Megatron-Deepspeed, others are ColumnParallel
																																																	_snake_case       =  1 if any(text in key for text in WEIGHTS_WITH_ROW_PARALLELISM_CONTAIN				) else 0
																																																	# We concatenate these weights accross TP ranks
																																																	_snake_case       =  torch.cat([tensors[key], temp[key]]							, dim=_SCREAMING_SNAKE_CASE				)

            # Divide by the number of TP the weights we want to average
																					for key in tensors.keys():
																												if any(key.endswith(_SCREAMING_SNAKE_CASE				) for end in WEIGHTS_TO_AVERAGE_ENDSWITH				):
																																			_snake_case       =  tensors[key] / pretraining_tp

																					_snake_case       =  model.load_state_dict(_SCREAMING_SNAKE_CASE							, strict=_SCREAMING_SNAKE_CASE				)
																					assert not other_keys.unexpected_keys, f"""The keys {other_keys.unexpected_keys} are unexpected"""
																					if missing_keys is None:
																												_snake_case       =  set(other_keys.missing_keys				)
																					else:
																												_snake_case       =  missing_keys.intersection(set(other_keys.missing_keys				)				)

														assert not missing_keys, f"""The keys {missing_keys} are missing"""

														# Save pytorch-model
														os.makedirs(_SCREAMING_SNAKE_CASE							, exist_ok=_SCREAMING_SNAKE_CASE				)
														_snake_case       =  pytorch_dump_folder_path + """/""" + WEIGHTS_NAME
														_snake_case       =  pytorch_dump_folder_path + """/""" + CONFIG_NAME
														print(f"""Save PyTorch model to {pytorch_weights_dump_path} with dtype {config.torch_dtype}"""				)
														if config.torch_dtype is not None:
																					_snake_case       =  model.to(config.torch_dtype				)
														torch.save(model.state_dict()							, _SCREAMING_SNAKE_CASE				)
														print(f"""Save configuration file to {pytorch_config_dump_path}"""				)
														with open(_SCREAMING_SNAKE_CASE							, """w"""							, encoding="""utf-8"""				) as f:
																					f.write(config.to_json_string()				)


if __name__ == "__main__":
				__lowerCAmelCase												=	argparse.ArgumentParser()
				# Required parameters
				parser.add_argument(
				    '--bloom_checkpoint_path',
				    default=None,
				    type=str,
				    required=True,
				    help='Path to the Megatron-LM checkpoint path.',
				)
				parser.add_argument(
				    '--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
				)
				parser.add_argument(
				    '--bloom_config_file',
				    default='',
				    type=str,
				    help=(
				        'An optional config json file corresponding to the pre-trained model. \n'
				        'This specifies the model architecture.'
				    ),
				)
				parser.add_argument(
				    '--shard_model',
				    action='store_true',
				    help='An optional setting to shard the output model \nThis enables sharding the converted checkpoint',
				)
				parser.add_argument(
				    '--pretraining_tp',
				    default=4,
				    type=int,
				    help='Pretraining TP rank that has been used when training the model in Megatron-LM \n',
				)
				__lowerCAmelCase												=	parser.parse_args()
				convert_bloom_checkpoint_to_pytorch(
				    args.bloom_checkpoint_path,
				    args.bloom_config_file,
				    args.pytorch_dump_folder_path,
				    args.shard_model,
				    args.pretraining_tp,
				)