


'''simple docstring'''







import argparse
import os
from os.path import isfile

import torch

from transformers import GPTaConfig



def 						_UpperCamelCase							( UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__=0		):






				"""simple docstring"""



				if name is None:
								__magic_name__				:					Union[str, Any]						       =       None
				else:
								__magic_name__				:					int						       =       "." * max(0     ,   spaces - 2		) + "# {:" + str(50 - spaces		) + "s}"
								__magic_name__				:					Union[str, Any]						       =       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 						_UpperCamelCase							( UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__		):






				"""simple docstring"""



				__magic_name__				:					List[Any]						       =       param.size()
				# other versions store [num_heads * num_splits * hidden_size, :]
				__magic_name__				:					Optional[int]						       =       (num_heads, num_splits, hidden_size) + input_shape[1:]
				__magic_name__				:					Dict						       =       param.view(*UpperCamelCase__		)
				__magic_name__				:					Optional[int]						       =       param.transpose(0     ,   1		).contiguous()
				__magic_name__				:					List[str]						       =       param.view(*UpperCamelCase__		)
				return param



def 						_UpperCamelCase							( UpperCamelCase__     ,   UpperCamelCase__		):






				"""simple docstring"""



				__magic_name__				:					Optional[Any]						       =       config.n_positions
				__magic_name__				:					Optional[int]						       =       config.n_layer
				__magic_name__				:					Tuple						       =       config.vocab_size
				__magic_name__				:					Any						       =       config.n_head
				__magic_name__				:					Optional[int]						       =       config.n_embd // config.n_head

				__magic_name__				:					int						       =       sd_megatron["model.language_model.embedding.word_embeddings.weight"][:vocab_size, :]
				__magic_name__				:					str						       =       {
				    "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"],
				}

				__magic_name__				:					List[Any]						       =       "model.language_model.encoder.layers."
				for i in range(UpperCamelCase__		):
								__magic_name__				:					Dict						       =       torch.tril(torch.ones((n_positions, n_positions)     ,   dtype=torch.bool		)		)
								__magic_name__				:					Optional[Any]						       =       causal_mask.view(1     ,   1     ,   UpperCamelCase__     ,   UpperCamelCase__		)
								__magic_name__				:					List[str]						       =       causal_mask
								__magic_name__				:					Any						       =       torch.tensor(-1E4     ,   dtype=torch.bfloataa		)

								__magic_name__				:					Union[str, Any]						       =       sd_megatron[F"""{pf}{i}.input_layernorm.weight"""]
								__magic_name__				:					Optional[Any]						       =       sd_megatron[F"""{pf}{i}.input_layernorm.bias"""]

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

								__magic_name__				:					int						       =       sd_megatron[F"""{pf}{i}.self_attention.query_key_value.bias"""]
								__magic_name__				:					List[Any]						       =       fix_query_key_value_ordering(UpperCamelCase__     ,   3     ,   UpperCamelCase__     ,   UpperCamelCase__		)
								__magic_name__				:					List[str]						       =       vala

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

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

				return sd_hf



def 						_UpperCamelCase							( UpperCamelCase__     ,   UpperCamelCase__		):






				"""simple docstring"""



				__magic_name__				:					Tuple						       =       64000
				__magic_name__				:					Optional[Any]						       =       config_megatron["encoder_seq_length"]
				__magic_name__				:					Union[str, Any]						       =       config_megatron["hidden_size"]
				__magic_name__				:					str						       =       config_megatron["num_layers"]
				__magic_name__				:					List[Any]						       =       config_megatron["num_attention_heads"]
				__magic_name__				:					str						       =       config_megatron["ffn_hidden_size"]
				__magic_name__				:					str						       =       "gelu"
				__magic_name__				:					Any						       =       0.1
				__magic_name__				:					Optional[Any]						       =       0.1
				__magic_name__				:					Union[str, Any]						       =       0.1
				__magic_name__				:					List[str]						       =       config_megatron["layernorm_epsilon"]  # 1e-5
				__magic_name__				:					Tuple						       =       config_megatron["init_method_std"]  # 0.02
				__magic_name__				:					Union[str, Any]						       =       config_megatron["apply_query_key_layer_scaling"]  # True
				__magic_name__				:					Tuple						       =       True
				__magic_name__				:					Optional[Any]						       =       True

				# This identifies the 6.7B (7B) model which uses a different tokenizer
				if config_megatron["hidden_size"] == 4096:
								__magic_name__				:					Optional[int]						       =       1  # <|endoftext|>
								__magic_name__				:					Tuple						       =       1  # <|endoftext|>
								__magic_name__				:					Optional[int]						       =       0  # <unk>
				else:
								__magic_name__				:					List[str]						       =       2  # <s>
								__magic_name__				:					List[Any]						       =       3  # <|endoftext|>
								__magic_name__				:					Union[str, Any]						       =       0  # <pad>

				return config_hf



def 						_UpperCamelCase							( UpperCamelCase__		):






				"""simple docstring"""



				print(UpperCamelCase__		)

				__magic_name__				:					Optional[Any]						       =       args.checkpoint_path
				__magic_name__				:					int						       =       args.save_path
				if isfile(UpperCamelCase__		):
								raise FileNotFoundError(F"""ERROR! could not find file {checkpoint_path}"""		)

				# Load the model.
				__magic_name__				:					Union[str, Any]						       =       torch.load(UpperCamelCase__     ,   map_location="cpu"		)

				# Load the config.
				__magic_name__				:					Dict						       =       checkpoint["hyper_parameters"]["cfg"]
				__magic_name__				:					Tuple						       =       GPTaConfig()
				__magic_name__				:					Optional[Any]						       =       copy_config(config_hf=UpperCamelCase__     ,   config_megatron=UpperCamelCase__		)
				__magic_name__				:					List[Any]						       =       ["GPT2LMHeadModel"]

				__magic_name__				:					List[str]						       =       checkpoint["state_dict"]

				# Convert.
				print("Converting"		)
				__magic_name__				:					Optional[int]						       =       convert_megatron_checkpoint(UpperCamelCase__     ,   UpperCamelCase__		)

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

				__magic_name__				:					Optional[Any]						       =       "GPTSw3Tokenizer"

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

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


if __name__ == "__main__":
		_SCREAMING_SNAKE_CASE       :   str        = 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")
		_SCREAMING_SNAKE_CASE       :   Optional[int]        = parser.parse_args()
		main(_args)



'''simple docstring'''







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_SCREAMING_SNAKE_CASE       :   Any        = {
    "configuration_deberta_v2": ["DEBERTA_V2_PRETRAINED_CONFIG_ARCHIVE_MAP", "DebertaV2Config", "DebertaV2OnnxConfig"],
    "tokenization_deberta_v2": ["DebertaV2Tokenizer"],
}

try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_SCREAMING_SNAKE_CASE       :   List[Any]        = ["DebertaV2TokenizerFast"]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_SCREAMING_SNAKE_CASE       :   int        = [
		    "TF_DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "TFDebertaV2ForMaskedLM",
		    "TFDebertaV2ForQuestionAnswering",
		    "TFDebertaV2ForSequenceClassification",
		    "TFDebertaV2ForTokenClassification",
		    "TFDebertaV2Model",
		    "TFDebertaV2PreTrainedModel",
		]

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_SCREAMING_SNAKE_CASE       :   Union[str, Any]        = [
		    "DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "DebertaV2ForMaskedLM",
		    "DebertaV2ForMultipleChoice",
		    "DebertaV2ForQuestionAnswering",
		    "DebertaV2ForSequenceClassification",
		    "DebertaV2ForTokenClassification",
		    "DebertaV2Model",
		    "DebertaV2PreTrainedModel",
		]


if TYPE_CHECKING:
		from .configuration_deberta_va import (
		    DEBERTA_V2_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    DebertaVaConfig,
		    DebertaVaOnnxConfig,
		)
		from .tokenization_deberta_va import DebertaVaTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_deberta_va_fast import DebertaVaTokenizerFast

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_deberta_va import (
				    TF_DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFDebertaVaForMaskedLM,
				    TFDebertaVaForQuestionAnswering,
				    TFDebertaVaForSequenceClassification,
				    TFDebertaVaForTokenClassification,
				    TFDebertaVaModel,
				    TFDebertaVaPreTrainedModel,
				)

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_deberta_va import (
				    DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
				    DebertaVaForMaskedLM,
				    DebertaVaForMultipleChoice,
				    DebertaVaForQuestionAnswering,
				    DebertaVaForSequenceClassification,
				    DebertaVaForTokenClassification,
				    DebertaVaModel,
				    DebertaVaPreTrainedModel,
				)

else:
		import sys

		_SCREAMING_SNAKE_CASE       :   Dict        = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)