import argparse
import os
from os.path import isfile

import torch

from transformers import GPTaConfig
def 					UpperCamelCase__		(							A__     ,					A__     ,					A__=0       )  ->   Union[str, Any]:
				# Format the message.
				if name is None:
								snake_case__   :      int   				= None
				else:
								snake_case__   :      Any   				= '.' * max(0     ,					spaces - 2       ) + '# {:' + str(50 - spaces       ) + 's}'
								snake_case__   :      List[Any]   				= fmt.format(A__       )

				# Print and recurse (if needed).
				if isinstance(A__     ,					A__       ):
								if msg is not None:
												print(A__       )
								for k in val.keys():
												recursive_print(A__     ,					val[k]     ,					spaces + 2       )
				elif isinstance(A__     ,					torch.Tensor       ):
								print(A__     ,					':'     ,					val.size()       )
				else:
								print(A__     ,					':'     ,					A__       )
def 					UpperCamelCase__		(							A__     ,					A__     ,					A__     ,					A__       )  ->   List[Any]:
				# 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.
				snake_case__   :      str   				= param.size()
				# other versions store [num_heads * num_splits * hidden_size, :]
				snake_case__   :      int   				= (num_heads, num_splits, hidden_size) + input_shape[1:]
				snake_case__   :      Optional[int]   				= param.view(*A__       )
				snake_case__   :      int   				= param.transpose(0     ,					1       ).contiguous()
				snake_case__   :      Tuple   				= param.view(*A__       )
				return param
def 					UpperCamelCase__		(							A__     ,					A__       )  ->   Optional[int]:
				snake_case__   :      int   				= config.n_positions
				snake_case__   :      int   				= config.n_layer
				snake_case__   :      Optional[Any]   				= config.vocab_size
				snake_case__   :      Any   				= config.n_head
				snake_case__   :      List[str]   				= config.n_embd // config.n_head

				snake_case__   :      List[str]   				= sd_megatron['model.language_model.embedding.word_embeddings.weight'][:vocab_size, :]
				snake_case__   :      List[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'],
				}

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

								snake_case__   :      List[Any]   				= sd_megatron[F"""{pf}{i}.input_layernorm.weight"""]
								snake_case__   :      int   				= sd_megatron[F"""{pf}{i}.input_layernorm.bias"""]

								snake_case__   :      Dict   				= sd_megatron[F"""{pf}{i}.self_attention.query_key_value.weight"""]
								snake_case__   :      int   				= fix_query_key_value_ordering(A__     ,					3     ,					A__     ,					A__       )
								snake_case__   :      List[str]   				= vala.transpose(0     ,					1       ).contiguous()

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

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

				# For LM head, transformers' wants the matrix to weight embeddings.
				snake_case__   :      Union[str, Any]   				= word_embeddings

				return sd_hf
def 					UpperCamelCase__		(							A__     ,					A__       )  ->   str:
				snake_case__   :      Dict   				= 6_4000
				snake_case__   :      Dict   				= config_megatron['encoder_seq_length']
				snake_case__   :      List[str]   				= config_megatron['hidden_size']
				snake_case__   :      Any   				= config_megatron['num_layers']
				snake_case__   :      Dict   				= config_megatron['num_attention_heads']
				snake_case__   :      Optional[int]   				= config_megatron['ffn_hidden_size']
				snake_case__   :      Optional[int]   				= 'gelu'
				snake_case__   :      Optional[int]   				= 0.1
				snake_case__   :      Optional[Any]   				= 0.1
				snake_case__   :      Optional[Any]   				= 0.1
				snake_case__   :      List[Any]   				= config_megatron['layernorm_epsilon']  # 1e-5
				snake_case__   :      Optional[Any]   				= config_megatron['init_method_std']  # 0.02
				snake_case__   :      Any   				= config_megatron['apply_query_key_layer_scaling']  # True
				snake_case__   :      List[str]   				= True
				snake_case__   :      Any   				= True

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

				return config_hf
def 					UpperCamelCase__		(							A__       )  ->   Optional[Any]:
				print(A__       )

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

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

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

				snake_case__   :      Any   				= checkpoint['state_dict']

				# Convert.
				print('Converting'       )
				snake_case__   :      List[Any]   				= convert_megatron_checkpoint(A__     ,					A__       )

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

				snake_case__   :      Optional[int]   				= 'GPTSw3Tokenizer'

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

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


if __name__ == "__main__":
				lowerCAmelCase__	:  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''')
				lowerCAmelCase__	:  Dict						=  parser.parse_args()
				main(_args)









"""simple docstring"""
from typing import List, Optional, Union

import numpy as np

from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging


_snake_case	:						List[str]					=					logging.get_logger(__name__)






class    _UpperCAmelCase  (      lowercase_					):
					UpperCamelCase						=				['''input_values''', '''attention_mask''']


					def __init__(   self     :str	,			__UpperCamelCase     :List[str]=1	,			__UpperCamelCase     :Tuple=1_60_00	,			__UpperCamelCase     :Optional[int]=0.0	,			__UpperCamelCase     :Optional[int]=False	,			__UpperCamelCase     :Optional[Any]=True	,			**__UpperCamelCase     :Optional[Any]	,			):
												super().__init__(feature_size=__UpperCamelCase	,			sampling_rate=__UpperCamelCase	,			padding_value=__UpperCamelCase	,			**__UpperCamelCase						)
												A						=    return_attention_mask
												A						=    do_normalize


					@staticmethod
					def       lowerCamelCase			(   __UpperCamelCase     :List[np.ndarray]	,			__UpperCamelCase     :List[np.ndarray]	,			__UpperCamelCase     :float = 0.0						):
												if attention_mask is not None:
																			A						=    np.array(__UpperCamelCase	,			np.intaa						)
																			A						=    []

																			for vector, length in zip(__UpperCamelCase	,			attention_mask.sum(-1						)						):
																										A						=    (vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1e-7						)
																										if length < normed_slice.shape[0]:
																																	A						=    padding_value

																										normed_input_values.append(__UpperCamelCase						)
												else:
																			A						=    [(x - x.mean()) / np.sqrt(x.var() + 1e-7						) for x in input_values]

												return normed_input_values


					def __call__(   self     :Tuple	,			__UpperCamelCase     :Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]	,			__UpperCamelCase     :Union[bool, str, PaddingStrategy] = False	,			__UpperCamelCase     :Optional[int] = None	,			__UpperCamelCase     :bool = False	,			__UpperCamelCase     :Optional[int] = None	,			__UpperCamelCase     :Optional[bool] = None	,			__UpperCamelCase     :Optional[Union[str, TensorType]] = None	,			__UpperCamelCase     :Optional[int] = None	,			**__UpperCamelCase     :List[str]	,			):

												if sampling_rate is not None:
																			if sampling_rate != self.sampling_rate:
																										raise ValueError(
																										    f"The model corresponding to this feature extractor: {self} was trained using a sampling rate of"
																										    f" {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled with"
																										    f" {self.sampling_rate} and not {sampling_rate}."						)
												else:
																			logger.warning(
																			    "It is strongly recommended to pass the ``sampling_rate`` argument to this function. "
																			    "Failing to do so can result in silent errors that might be hard to debug."						)

												A						=    isinstance(__UpperCamelCase	,			np.ndarray						) and len(raw_speech.shape						) > 1
												if is_batched_numpy and len(raw_speech.shape						) > 2:
																			raise ValueError(f"Only mono-channel audio is supported for input to {self}"						)
												A						=    is_batched_numpy or (
												    isinstance(__UpperCamelCase	,			(list, tuple)						) and (isinstance(raw_speech[0]	,			(np.ndarray, tuple, list)						))
												)

												# always return batch
												if not is_batched:
																			A						=    [raw_speech]

												# convert into correct format for padding
												A						=    BatchFeature({"input_values": raw_speech}						)

												A						=    self.pad(
												    __UpperCamelCase	,			padding=__UpperCamelCase	,			max_length=__UpperCamelCase	,			truncation=__UpperCamelCase	,			pad_to_multiple_of=__UpperCamelCase	,			return_attention_mask=__UpperCamelCase	,			)

												# convert input values to correct format
												A						=    padded_inputs["input_values"]
												if not isinstance(input_values[0]	,			np.ndarray						):
																			A						=    [np.asarray(__UpperCamelCase	,			dtype=np.floataa						) for array in input_values]
												elif (
												    not isinstance(__UpperCamelCase	,			np.ndarray						)
												    and isinstance(input_values[0]	,			np.ndarray						)
												    and input_values[0].dtype is np.dtype(np.floataa						)
												):
																			A						=    [array.astype(np.floataa						) for array in input_values]
												elif isinstance(__UpperCamelCase	,			np.ndarray						) and input_values.dtype is np.dtype(np.floataa						):
																			A						=    input_values.astype(np.floataa						)

												# convert attention_mask to correct format
												A						=    padded_inputs.get("attention_mask"						)
												if attention_mask is not None:
																			A						=    [np.asarray(__UpperCamelCase	,			dtype=np.intaa						) for array in attention_mask]

												# zero-mean and unit-variance normalization
												if self.do_normalize:
																			A						=    (
																			    attention_mask
																			    if self._get_padding_strategies(__UpperCamelCase	,			max_length=__UpperCamelCase						) is not PaddingStrategy.DO_NOT_PAD
																			    else None
																			)
																			A						=    self.zero_mean_unit_var_norm(
																			    padded_inputs["input_values"]	,			attention_mask=__UpperCamelCase	,			padding_value=self.padding_value						)

												if return_tensors is not None:
																			A						=    padded_inputs.convert_to_tensors(__UpperCamelCase						)

												return padded_inputs


