



"""simple docstring"""







import argparse
from pathlib import Path

import torch

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


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

def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							Optional[Any] ):
						"""simple docstring"""






						snake_case   :			str						    = torch.load(lowerCamelCase_     ,		map_location="cpu" )
						if "model" in sd.keys():
												snake_case   :			Optional[Any]						    = torch.load(lowerCamelCase_     ,		map_location="cpu" )["model"]

						# pop unnecessary weights
						snake_case   :			Dict						    = [
						    "decoder.version",
						    "decoder.output_projection.weight",
						]
						for key in keys_to_delete:
												if key in sd:
																		sd.pop(lowerCamelCase_ )

						snake_case   :			List[str]						    = {
						    "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:
																		snake_case   :			Optional[int]						    = sd.pop(lowerCamelCase_ )

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

																		snake_case   :			Any						    = key.replace(".qkv_proj."     ,		".q_proj." )
																		snake_case   :			str						    = key.replace(".qkv_proj."     ,		".k_proj." )
																		snake_case   :			Dict						    = key.replace(".qkv_proj."     ,		".v_proj." )

																		snake_case   :			Optional[Any]						    = 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
																		snake_case    ,			snake_case    ,			snake_case   :			Dict						    = torch.split(lowerCamelCase_     ,		depth // 3     ,		dim=0 )

																		snake_case   :			str						    = q
																		snake_case   :			Optional[int]						    = k
																		snake_case   :			Dict						    = v
																		del sd[key]

						return sd

@torch.no_grad()
def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							Dict     ,		lowerCamelCase_:							Optional[Any]     ,		lowerCamelCase_:							Optional[int]=None ):
						"""simple docstring"""






						snake_case   :			Tuple						    = load_checkpoint(lowerCamelCase_ )

						if config is not None:
												snake_case   :			Optional[Any]						    = OPTConfig.from_pretrained(lowerCamelCase_ )
						else:
												snake_case   :			Tuple						    = OPTConfig()

						snake_case   :			Optional[int]						    = OPTModel(lowerCamelCase_ ).half().eval()
						model.load_state_dict(lowerCamelCase_ )

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


if __name__ == "__main__":
			A		=						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.')
			A		=						parser.parse_args()
			convert_opt_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, config=args.hf_config)








"""simple docstring"""







from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional

from packaging import version


if TYPE_CHECKING:
			from ... import PreTrainedTokenizer, TensorType

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import is_torch_available, logging


A		=						logging.get_logger(__name__)

A		=						{
    'bigscience/bloom': 'https://huggingface.co/bigscience/bloom/resolve/main/config.json',
    'bigscience/bloom-560m': 'https://huggingface.co/bigscience/bloom-560m/blob/main/config.json',
    'bigscience/bloom-1b1': 'https://huggingface.co/bigscience/bloom-1b1/blob/main/config.json',
    'bigscience/bloom-1b7': 'https://huggingface.co/bigscience/bloom-1b7/blob/main/config.json',
    'bigscience/bloom-3b': 'https://huggingface.co/bigscience/bloom-3b/blob/main/config.json',
    'bigscience/bloom-7b1': 'https://huggingface.co/bigscience/bloom-7b1/blob/main/config.json',
}

class       _a  ( SCREAMING_SNAKE_CASE__):
			__magic_name__     					=     """bloom"""
			__magic_name__     					=     ["""past_key_values"""]
			__magic_name__     					=     {
			    """num_hidden_layers""": """n_layer""",
			    """num_attention_heads""": """n_head""",
			}



			def __init__(			self							:				Tuple   ,	_lowercase							:				List[Any]=250880   ,	_lowercase							:				int=64   ,	_lowercase							:				Optional[Any]=2   ,	_lowercase							:				Dict=8   ,	_lowercase							:				List[str]=1E-5   ,	_lowercase							:				List[Any]=0.02   ,	_lowercase							:				Optional[Any]=True   ,	_lowercase							:				Dict=1   ,	_lowercase							:				Union[str, Any]=2   ,	_lowercase							:				str=False   ,	_lowercase							:				List[Any]=0.0   ,	_lowercase							:				Tuple=0.0   ,	_lowercase							:				Dict=1   ,	_lowercase							:				int=False   ,	**_lowercase							:				int   ,	)							->					List[str]:
									snake_case   :			Any						    = vocab_size
									# Backward compatibility with n_embed kwarg
									snake_case   :			Any						    = kwargs.pop("n_embed"   ,	_lowercase		)
									snake_case   :			Tuple						    = hidden_size if n_embed is None else n_embed
									snake_case   :			Optional[Any]						    = n_layer
									snake_case   :			Optional[Any]						    = n_head
									snake_case   :			Union[str, Any]						    = layer_norm_epsilon
									snake_case   :			int						    = initializer_range
									snake_case   :			int						    = use_cache
									snake_case   :			int						    = pretraining_tp
									snake_case   :			Tuple						    = apply_residual_connection_post_layernorm
									snake_case   :			Union[str, Any]						    = hidden_dropout
									snake_case   :			Optional[Any]						    = attention_dropout

									snake_case   :			List[Any]						    = bos_token_id
									snake_case   :			Any						    = eos_token_id
									snake_case   :			Optional[Any]						    = slow_but_exact

									super().__init__(bos_token_id=_lowercase   ,	eos_token_id=_lowercase   ,	**_lowercase		)



class       _a  ( SCREAMING_SNAKE_CASE__):
			__magic_name__     					=     version.parse("""1.12""")



			def __init__(			self							:				int   ,	_lowercase							:				PretrainedConfig   ,	_lowercase							:				str = "default"   ,	_lowercase							:				List[PatchingSpec] = None   ,	_lowercase							:				bool = False   ,	)							->					Dict:
									super().__init__(_lowercase   ,	task=_lowercase   ,	patching_specs=_lowercase   ,	use_past=_lowercase		)
									if not getattr(self._config   ,	"pad_token_id"   ,	_lowercase		):
															# TODO: how to do that better?
															snake_case   :			int						    = 0



			@property
			def 				__lowercase					(			self							:				str		)							->					Mapping[str, Mapping[int, str]]:
									snake_case   :			int						    = OrderedDict({"input_ids": {0: "batch", 1: "sequence"}}		)
									if self.use_past:
															# BLOOM stores values on dynamic axis 2. For more details see: https://github.com/huggingface/transformers/pull/18344
															self.fill_with_past_key_values_(_lowercase   ,	direction="inputs"   ,	inverted_values_shape=_lowercase		)
															snake_case   :			Optional[int]						    = {0: "batch", 1: "past_sequence + sequence"}
									else:
															snake_case   :			Dict						    = {0: "batch", 1: "sequence"}

									return common_inputs



			@property
			def 				__lowercase					(			self							:				int		)							->					int:
									return self._config.n_layer



			@property
			def 				__lowercase					(			self							:				Dict		)							->					int:
									return self._config.n_head



			@property
			def 				__lowercase					(			self							:				Union[str, Any]		)							->					float:
									return 1E-3



			def 				__lowercase					(			self							:				List[str]   ,	_lowercase							:				"PreTrainedTokenizer"   ,	_lowercase							:				int = -1   ,	_lowercase							:				int = -1   ,	_lowercase							:				bool = False   ,	_lowercase							:				Optional["TensorType"] = None   ,	)							->					Mapping[str, Any]:
									snake_case   :			int						    = super(_lowercase   ,	self		).generate_dummy_inputs(
									    _lowercase   ,	batch_size=_lowercase   ,	seq_length=_lowercase   ,	is_pair=_lowercase   ,	framework=_lowercase		)

									# We need to order the input in the way they appears in the forward()
									snake_case   :			Any						    = OrderedDict({"input_ids": common_inputs["input_ids"]}		)

									# Need to add the past_keys
									if self.use_past:
															if not is_torch_available():
																					raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed."		)
															else:
																					import torch

																					snake_case    ,			snake_case   :			List[str]						    = common_inputs["input_ids"].shape
																					# Not using the same length for past_key_values
																					snake_case   :			Optional[Any]						    = seqlen + 2
																					snake_case   :			Optional[int]						    = self._config.hidden_size // self.num_attention_heads
																					snake_case   :			List[str]						    = (
																					    batch * self.num_attention_heads,
																					    head_dim,
																					    past_key_values_length,
																					)
																					snake_case   :			Any						    = (
																					    batch * self.num_attention_heads,
																					    past_key_values_length,
																					    head_dim,
																					)
																					snake_case   :			Dict						    = [
																					    (torch.zeros(_lowercase		), torch.zeros(_lowercase		)) for _ in range(self.num_layers		)
																					]

									snake_case   :			Tuple						    = common_inputs["attention_mask"]
									if self.use_past:
															snake_case   :			Any						    = ordered_inputs["attention_mask"].dtype
															snake_case   :			List[str]						    = torch.cat(
															    [ordered_inputs["attention_mask"], torch.ones(_lowercase   ,	_lowercase   ,	dtype=_lowercase		)]   ,	dim=1		)

									return ordered_inputs



			@property
			def 				__lowercase					(			self							:				str		)							->					int:
									return 13



