


import argparse
from pathlib import Path

import torch

from transformers import OPTConfig, OPTModel
from transformers.utils import logging


logging.set_verbosity_info()
lowerCAmelCase           =		logging.get_logger(__name__)







def        _lowerCamelCase(	lowercase__	)						->	Dict:

		'''simple docstring'''







		__lowercase=			torch.load(lowercase__   , map_location='cpu'	)
		if "model" in sd.keys():
				__lowercase=			torch.load(lowercase__   , map_location='cpu'	)['model']

		# pop unnecessary weights
		__lowercase=			[
		    'decoder.version',
		    'decoder.output_projection.weight',
		]
		for key in keys_to_delete:
				if key in sd:
						sd.pop(lowercase__	)

		__lowercase=			{
		    'decoder.project_in_dim.weight': 'decoder.project_in.weight',
		    'decoder.project_out_dim.weight': 'decoder.project_out.weight',
		    'decoder.layer_norm.weight': 'decoder.final_layer_norm.weight',
		    'decoder.layer_norm.bias': 'decoder.final_layer_norm.bias',
		}
		for old_key, new_key in keys_to_rename.items():
				if old_key in sd:
						__lowercase=			sd.pop(lowercase__	)

		__lowercase=			list(sd.keys()	)
		for key in keys:
				if ".qkv_proj." in key:
						__lowercase=			sd[key]
						# We split QKV in separate Q,K,V

						__lowercase=			key.replace('.qkv_proj.'   , '.q_proj.'	)
						__lowercase=			key.replace('.qkv_proj.'   , '.k_proj.'	)
						__lowercase=			key.replace('.qkv_proj.'   , '.v_proj.'	)

						__lowercase=			value.shape[0]
						assert depth % 3 == 0
						# `SequeuceParallelTransformerBlock` has QKV weight is separated in K,V,Q despite the naming:
						# https://cs.github.com/facebookresearch/metaseq/blob/51871bd73cd04c038f239ea2a26db1d7f6b37927/metaseq/modules/sequence_parallel_transformer_layer.py#L97
						__lowercase,							__lowercase,							__lowercase=			torch.split(lowercase__   , depth // 3   , dim=0	)

						__lowercase=			q
						__lowercase=			k
						__lowercase=			v
						del sd[key]

		return sd







@torch.no_grad()
def        _lowerCamelCase(	lowercase__   , lowercase__   , lowercase__=None	)						->	Union[str, Any]:

		'''simple docstring'''







		__lowercase=			load_checkpoint(lowercase__	)

		if config is not None:
				__lowercase=			OPTConfig.from_pretrained(lowercase__	)
		else:
				__lowercase=			OPTConfig()

		__lowercase=			OPTModel(lowercase__	).half().eval()
		model.load_state_dict(lowercase__	)

		# Check results
		Path(lowercase__	).mkdir(exist_ok=lowercase__	)
		model.save_pretrained(lowercase__	)


if __name__ == "__main__":
	lowerCAmelCase           =		argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    '''--fairseq_path''',
	    type=str,
	    help=(
	        '''path to fairseq checkpoint in correct format. You can find all checkpoints in the correct format here:'''
	        ''' https://huggingface.co/models?other=opt_metasq'''
	    ),
	)
	parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
	parser.add_argument('''--hf_config''', default=None, type=str, help='''Define HF config.''')
	lowerCAmelCase           =		parser.parse_args()
	convert_opt_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, config=args.hf_config)










from typing import TYPE_CHECKING

from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable


lowerCAmelCase           =		{'''configuration_gpt_neox''': ['''GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTNeoXConfig''']}

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowerCAmelCase           =		['''GPTNeoXTokenizerFast''']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowerCAmelCase           =		[
	    '''GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''GPTNeoXForCausalLM''',
	    '''GPTNeoXForQuestionAnswering''',
	    '''GPTNeoXForSequenceClassification''',
	    '''GPTNeoXForTokenClassification''',
	    '''GPTNeoXLayer''',
	    '''GPTNeoXModel''',
	    '''GPTNeoXPreTrainedModel''',
	]


if TYPE_CHECKING:
	from .configuration_gpt_neox import GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoXConfig

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_gpt_neox_fast import GPTNeoXTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_gpt_neox import (
		    GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST,
		    GPTNeoXForCausalLM,
		    GPTNeoXForQuestionAnswering,
		    GPTNeoXForSequenceClassification,
		    GPTNeoXForTokenClassification,
		    GPTNeoXLayer,
		    GPTNeoXModel,
		    GPTNeoXPreTrainedModel,
		)


else:
	import sys

	lowerCAmelCase           =		_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






