





import argparse
import os
from os.path import isfile

import torch

from transformers import GPTaConfig


def 			SCREAMING_SNAKE_CASE__     (					_lowercase    :   Any      ,						_lowercase    :   Tuple      ,						_lowercase    :   Optional[int]=0 )      ->       Optional[Any]:




		'''simple docstring'''
		# Format the message.
		if name is None:
				lowercase__				:			Optional[int]            =		None
		else:
				lowercase__				:			Union[str, Any]            =		'.' * max(0      ,						spaces - 2 ) + '# {:' + str(50 - spaces ) + 's}'
				lowercase__				:			Optional[Any]            =		fmt.format(_lowercase )

		# Print and recurse (if needed).
		if isinstance(_lowercase      ,						_lowercase ):
				if msg is not None:
						print(_lowercase )
				for k in val.keys():
						recursive_print(_lowercase      ,						val[k]      ,						spaces + 2 )
		elif isinstance(_lowercase      ,						torch.Tensor ):
				print(_lowercase      ,						':'      ,						val.size() )
		else:
				print(_lowercase      ,						':'      ,						_lowercase )


def 			SCREAMING_SNAKE_CASE__     (					_lowercase    :   Optional[int]      ,						_lowercase    :   List[str]      ,						_lowercase    :   Any      ,						_lowercase    :   Any )      ->       Any:




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


def 			SCREAMING_SNAKE_CASE__     (					_lowercase    :   Any      ,						_lowercase    :   Dict )      ->       Dict:




		'''simple docstring'''
		lowercase__				:			List[str]            =		config.n_positions
		lowercase__				:			Optional[int]            =		config.n_layer
		lowercase__				:			Optional[int]            =		config.vocab_size
		lowercase__				:			List[Any]            =		config.n_head
		lowercase__				:			List[str]            =		config.n_embd // config.n_head

		lowercase__				:			Optional[Any]            =		sd_megatron['model.language_model.embedding.word_embeddings.weight'][:vocab_size, :]
		lowercase__				:			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'],
		}

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

				lowercase__				:			int            =		sd_megatron[f"""{pf}{i}.input_layernorm.weight"""]
				lowercase__				:			str            =		sd_megatron[f"""{pf}{i}.input_layernorm.bias"""]

				lowercase__				:			List[Any]            =		sd_megatron[f"""{pf}{i}.self_attention.query_key_value.weight"""]
				lowercase__				:			Dict            =		fix_query_key_value_ordering(_lowercase      ,						3      ,						_lowercase      ,						_lowercase )
				lowercase__				:			Optional[int]            =		vala.transpose(0      ,						1 ).contiguous()

				lowercase__				:			int            =		sd_megatron[f"""{pf}{i}.self_attention.query_key_value.bias"""]
				lowercase__				:			Dict            =		fix_query_key_value_ordering(_lowercase      ,						3      ,						_lowercase      ,						_lowercase )
				lowercase__				:			Any            =		vala

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

		# For LM head, transformers' wants the matrix to weight embeddings.
		lowercase__				:			Any            =		word_embeddings

		return sd_hf


def 			SCREAMING_SNAKE_CASE__     (					_lowercase    :   str      ,						_lowercase    :   int )      ->       Union[str, Any]:




		'''simple docstring'''
		lowercase__				:			Tuple            =		64_000
		lowercase__				:			Any            =		config_megatron['encoder_seq_length']
		lowercase__				:			str            =		config_megatron['hidden_size']
		lowercase__				:			Any            =		config_megatron['num_layers']
		lowercase__				:			List[str]            =		config_megatron['num_attention_heads']
		lowercase__				:			Optional[Any]            =		config_megatron['ffn_hidden_size']
		lowercase__				:			Tuple            =		'gelu'
		lowercase__				:			Any            =		0.1
		lowercase__				:			Any            =		0.1
		lowercase__				:			List[Any]            =		0.1
		lowercase__				:			Any            =		config_megatron['layernorm_epsilon']  # 1e-5
		lowercase__				:			Any            =		config_megatron['init_method_std']  # 0.02
		lowercase__				:			Optional[Any]            =		config_megatron['apply_query_key_layer_scaling']  # True
		lowercase__				:			List[str]            =		True
		lowercase__				:			Union[str, Any]            =		True

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

		return config_hf


def 			SCREAMING_SNAKE_CASE__     (					_lowercase    :   int )      ->       int:




		'''simple docstring'''
		print(_lowercase )

		lowercase__				:			Union[str, Any]            =		args.checkpoint_path
		lowercase__				:			Optional[int]            =		args.save_path
		if isfile(_lowercase ):
				raise FileNotFoundError(f"""ERROR! could not find file {checkpoint_path}""" )

		# Load the model.
		lowercase__				:			Optional[int]            =		torch.load(_lowercase      ,						map_location='cpu' )

		# Load the config.
		lowercase__				:			Optional[int]            =		checkpoint['hyper_parameters']['cfg']
		lowercase__				:			List[Any]            =		GPTaConfig()
		lowercase__				:			Union[str, Any]            =		copy_config(config_hf=_lowercase      ,						config_megatron=_lowercase )
		lowercase__				:			List[Any]            =		['GPT2LMHeadModel']

		lowercase__				:			Any            =		checkpoint['state_dict']

		# Convert.
		print('Converting' )
		lowercase__				:			Tuple            =		convert_megatron_checkpoint(_lowercase      ,						_lowercase )

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

		lowercase__				:			Dict            =		'GPTSw3Tokenizer'

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

		# Store the state_dict to file.
		lowercase__				:			Union[str, Any]            =		os.path.join(_lowercase      ,						'pytorch_model.bin' )
		print(f"""Saving checkpoint to \"{output_checkpoint_file}\"""" )
		torch.save(_lowercase      ,						_lowercase )


if __name__ == "__main__":
			__UpperCamelCase:  int   =   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""")
			__UpperCamelCase:  List[Any]   =   parser.parse_args()
			main(_args)









from typing import TYPE_CHECKING

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


__UpperCamelCase:  Dict   =   {
    """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:
			__UpperCamelCase:  List[Any]   =   ["""DebertaV2TokenizerFast"""]

try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__UpperCamelCase:  str   =   [
			    """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:
			__UpperCamelCase:  Tuple   =   [
			    """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

			__UpperCamelCase:  Optional[Any]   =   _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


