'''simple docstring'''




# Copyright (c) 2021-, NVIDIA CORPORATION.  All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

####################################################################################################

#
# Note: If when running this conversion script you're getting an exception:
#     ModuleNotFoundError: No module named 'megatron.model.enums'
# you need to tell python where to find the clone of Megatron-LM, e.g.:
#
# cd /tmp
# git clone https://github.com/NVIDIA/Megatron-LM
# PYTHONPATH=/tmp/Megatron-LM python src/transformers/models/megatron_gpt2/convert_megatron_gpt2_checkpoint.py ...
#
# if you already have it cloned elsewhere, simply adjust the path to the existing path
#
# If the training was done using a Megatron-LM fork, e.g.,
# https://github.com/microsoft/Megatron-DeepSpeed/ then chances are that you need to have that one
# in your path, i.e., /path/to/Megatron-DeepSpeed/
#

import argparse
import os
import re
import zipfile

import torch

from transformers import AutoTokenizer, GPTaConfig






def 				snake_case__							(			_A:					int      ,   _A:					Tuple      ,   _A:					str=0 )    ->	Union[str, Any]:

		'''simple docstring'''


		if name is None:
				lowerCAmelCase   					=  None
		else:
				lowerCAmelCase   					=  """.""" * max(0      ,   spaces - 2 ) + """# {:""" + str(50 - spaces ) + """s}"""
				lowerCAmelCase   					=  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 				snake_case__							(			_A:					Tuple      ,   _A:					List[Any]      ,   _A:					Dict      ,   _A:					Optional[Any]      ,   _A:					Union[str, Any] )    ->	str:

		'''simple docstring'''


		lowerCAmelCase   					=  param.size()
		if checkpoint_version == 1.0:
				# version 1.0 stores [num_heads * hidden_size * num_splits, :]
				lowerCAmelCase   					=  (num_heads, hidden_size, num_splits) + input_shape[1:]
				lowerCAmelCase   					=  param.view(*_A )
				lowerCAmelCase   					=  param.transpose(0      ,   2 )
				lowerCAmelCase   					=  param.transpose(1      ,   2 ).contiguous()
		elif checkpoint_version >= 2.0:
				# other versions store [num_heads * num_splits * hidden_size, :]
				lowerCAmelCase   					=  (num_heads, num_splits, hidden_size) + input_shape[1:]
				lowerCAmelCase   					=  param.view(*_A )
				lowerCAmelCase   					=  param.transpose(0      ,   1 ).contiguous()
		lowerCAmelCase   					=  param.view(*_A )
		return param






def 				snake_case__							(			_A:					int      ,   _A:					List[Any]      ,   _A:					List[str] )    ->	Tuple:

		'''simple docstring'''


		lowerCAmelCase   					=  {}

		# old versions did not store training args
		lowerCAmelCase   					=  input_state_dict.get("""args"""      ,   _A )
		if ds_args is not None:
				# do not make the user write a config file when the exact dimensions/sizes are already in the checkpoint
				# from pprint import pprint
				# pprint(vars(ds_args))

				lowerCAmelCase   					=  ds_args.padded_vocab_size
				lowerCAmelCase   					=  ds_args.max_position_embeddings
				lowerCAmelCase   					=  ds_args.hidden_size
				lowerCAmelCase   					=  ds_args.num_layers
				lowerCAmelCase   					=  ds_args.num_attention_heads
				lowerCAmelCase   					=  ds_args.ffn_hidden_size
				# pprint(config)

		# The number of heads.
		lowerCAmelCase   					=  config.n_head
		# The hidden_size per head.
		lowerCAmelCase   					=  config.n_embd // config.n_head
		# Megatron-LM checkpoint version
		if "checkpoint_version" in input_state_dict.keys():
				lowerCAmelCase   					=  input_state_dict["""checkpoint_version"""]
		else:
				lowerCAmelCase   					=  0.0

		# The model.
		lowerCAmelCase   					=  input_state_dict["""model"""]
		# The language model.
		lowerCAmelCase   					=  model["""language_model"""]
		# The embeddings.
		lowerCAmelCase   					=  lm["""embedding"""]

		# The word embeddings.
		lowerCAmelCase   					=  embeddings["""word_embeddings"""]["""weight"""]
		# Truncate the embedding table to vocab_size rows.
		lowerCAmelCase   					=  word_embeddings[: config.vocab_size, :]
		lowerCAmelCase   					=  word_embeddings

		# The position embeddings.
		lowerCAmelCase   					=  embeddings["""position_embeddings"""]["""weight"""]
		# Read the causal mask dimension (seqlen). [max_sequence_length, hidden_size]
		lowerCAmelCase   					=  pos_embeddings.size(0 )
		if n_positions != config.n_positions:
				raise ValueError(
				    f"pos_embeddings.max_sequence_length={n_positions} and config.n_positions={config.n_positions} don't match" )
		# Store the position embeddings.
		lowerCAmelCase   					=  pos_embeddings

		# The transformer.
		lowerCAmelCase   					=  lm["""transformer"""] if """transformer""" in lm.keys() else lm["""encoder"""]

		# The regex to extract layer names.
		lowerCAmelCase   					=  re.compile(r"""layers\.(\d+)\.([a-z0-9_.]+)\.([a-z]+)""" )

		# The simple map of names for "automated" rules.
		lowerCAmelCase   					=  {
		    """attention.dense""": """.attn.c_proj.""",
		    """self_attention.dense""": """.attn.c_proj.""",
		    """mlp.dense_h_to_4h""": """.mlp.c_fc.""",
		    """mlp.dense_4h_to_h""": """.mlp.c_proj.""",
		}

		# Extract the layers.
		for key, val in transformer.items():
				# Match the name.
				lowerCAmelCase   					=  layer_re.match(_A )

				# Stop if that's not a layer
				if m is None:
						break

				# The index of the layer.
				lowerCAmelCase   					=  int(m.group(1 ) )
				# The name of the operation.
				lowerCAmelCase   					=  m.group(2 )
				# Is it a weight or a bias?
				lowerCAmelCase   					=  m.group(3 )

				# The name of the layer.
				lowerCAmelCase   					=  f"transformer.h.{layer_idx}"

				# For layernorm(s), simply store the layer norm.
				if op_name.endswith("""layernorm""" ):
						lowerCAmelCase   					=  """ln_1""" if op_name.startswith("""input""" ) else """ln_2"""
						lowerCAmelCase   					=  val

				# Transpose the QKV matrix.
				elif (
				    op_name == "attention.query_key_value" or op_name == "self_attention.query_key_value"
				) and weight_or_bias == "weight":
						# Insert a tensor of 1x1xDxD bias.
						lowerCAmelCase   					=  torch.tril(torch.ones((n_positions, n_positions)      ,   dtype=torch.floataa ) ).view(
						    1      ,   1      ,   _A      ,   _A )
						lowerCAmelCase   					=  causal_mask

						# Insert a "dummy" tensor for masked_bias.
						lowerCAmelCase   					=  torch.tensor(-1e4      ,   dtype=torch.floataa )
						lowerCAmelCase   					=  masked_bias

						lowerCAmelCase   					=  fix_query_key_value_ordering(_A      ,   _A      ,   3      ,   _A      ,   _A )
						# Megatron stores (3*D) x D but transformers-GPT2 expects D x 3*D.
						lowerCAmelCase   					=  out_val.transpose(0      ,   1 ).contiguous()
						# Store.
						lowerCAmelCase   					=  out_val

				# Transpose the bias.
				elif (
				    op_name == "attention.query_key_value" or op_name == "self_attention.query_key_value"
				) and weight_or_bias == "bias":
						lowerCAmelCase   					=  fix_query_key_value_ordering(_A      ,   _A      ,   3      ,   _A      ,   _A )
						# Store. No change of shape.
						lowerCAmelCase   					=  out_val

				# Transpose the weights.
				elif weight_or_bias == "weight":
						lowerCAmelCase   					=  megatron_to_transformers[op_name]
						lowerCAmelCase   					=  val.transpose(0      ,   1 )

				# Copy the bias.
				elif weight_or_bias == "bias":
						lowerCAmelCase   					=  megatron_to_transformers[op_name]
						lowerCAmelCase   					=  val

    # DEBUG.
		assert config.n_layer == layer_idx + 1

		# The final layernorm.
		lowerCAmelCase   					=  transformer["""final_layernorm.weight"""]
		lowerCAmelCase   					=  transformer["""final_layernorm.bias"""]

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

		# It should be done!
		return output_state_dict






def 				snake_case__							(			)    ->	Optional[Any]:

		'''simple docstring'''


		lowerCAmelCase   					=  argparse.ArgumentParser()
		parser.add_argument("""--print-checkpoint-structure"""      ,   action="""store_true""" )
		parser.add_argument(
		    """path_to_checkpoint"""      ,   type=_A      ,   help="""Path to the checkpoint file (.zip archive or direct .pt file)"""      ,   )
		parser.add_argument(
		    """--config_file"""      ,   default=""""""      ,   type=_A      ,   help="""An optional config json file describing the pre-trained model."""      ,   )
		lowerCAmelCase   					=  parser.parse_args()

		# Extract the basename.
		lowerCAmelCase   					=  os.path.dirname(args.path_to_checkpoint )

		# Load the model.
		# the .zip is very optional, let's keep it for backward compatibility
		print(f"Extracting PyTorch state dictionary from {args.path_to_checkpoint}" )
		if args.path_to_checkpoint.endswith(""".zip""" ):
				with zipfile.ZipFile(args.path_to_checkpoint      ,   """r""" ) as checkpoint:
						with checkpoint.open("""release/mp_rank_00/model_optim_rng.pt""" ) as pytorch_dict:
								lowerCAmelCase   					=  torch.load(_A      ,   map_location="""cpu""" )
		else:
				lowerCAmelCase   					=  torch.load(args.path_to_checkpoint      ,   map_location="""cpu""" )

		lowerCAmelCase   					=  input_state_dict.get("""args"""      ,   _A )

		# Read the config, or default to the model released by NVIDIA.
		if args.config_file == "":
				if ds_args is not None:
						if ds_args.bias_gelu_fusion:
								lowerCAmelCase   					=  """gelu_fast"""
						elif ds_args.openai_gelu:
								lowerCAmelCase   					=  """gelu_new"""
						else:
								lowerCAmelCase   					=  """gelu"""
				else:
						# in the very early days this used to be "gelu_new"
						lowerCAmelCase   					=  """gelu_new"""

				# Spell out all parameters in case the defaults change.
				lowerCAmelCase   					=  GPTaConfig(
				    vocab_size=50257      ,   n_positions=1024      ,   n_embd=1024      ,   n_layer=24      ,   n_head=16      ,   n_inner=4096      ,   activation_function=_A      ,   resid_pdrop=0.1      ,   embd_pdrop=0.1      ,   attn_pdrop=0.1      ,   layer_norm_epsilon=1e-5      ,   initializer_range=0.02      ,   summary_type="""cls_index"""      ,   summary_use_proj=_A      ,   summary_activation=_A      ,   summary_proj_to_labels=_A      ,   summary_first_dropout=0.1      ,   scale_attn_weights=_A      ,   use_cache=_A      ,   bos_token_id=50256      ,   eos_token_id=50256      ,   )
		else:
				lowerCAmelCase   					=  GPTaConfig.from_json_file(args.config_file )

		lowerCAmelCase   					=  ["""GPT2LMHeadModel"""]

		# Convert.
		print("""Converting""" )
		lowerCAmelCase   					=  convert_megatron_checkpoint(_A      ,   _A      ,   _A )

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

		# Add tokenizer class info to config
		# see https://github.com/huggingface/transformers/issues/13906)
		if ds_args is not None:
				lowerCAmelCase   					=  ds_args.tokenizer_type
				if tokenizer_type == "GPT2BPETokenizer":
						lowerCAmelCase   					=  """gpt2"""
				elif tokenizer_type == "PretrainedFromHF":
						lowerCAmelCase   					=  ds_args.tokenizer_name_or_path
				else:
						raise ValueError(f"Unrecognized tokenizer_type {tokenizer_type}" )
		else:
				lowerCAmelCase   					=  """gpt2"""

		lowerCAmelCase   					=  AutoTokenizer.from_pretrained(_A )
		lowerCAmelCase   					=  type(_A ).__name__
		lowerCAmelCase   					=  tokenizer_class

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

		# Save tokenizer based on args
		print(f"Adding {tokenizer_class} tokenizer files" )
		tokenizer.save_pretrained(_A )

		# Store the state_dict to file.
		lowerCAmelCase   					=  os.path.join(_A      ,   """pytorch_model.bin""" )
		print(f"Saving checkpoint to \"{output_checkpoint_file}\"" )
		torch.save(_A      ,   _A )


####################################################################################################

if __name__ == "__main__":
						main()

####################################################################################################





'''simple docstring'''




from __future__ import annotations

from typing import Dict

from ...configuration_utils import PretrainedConfig


__lowercase          =  {
    '''susnato/ernie-m-base_pytorch''': '''https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/config.json''',
    '''susnato/ernie-m-large_pytorch''': '''https://huggingface.co/susnato/ernie-m-large_pytorch/blob/main/config.json''',
}






class 				a__(					lowerCAmelCase__	):


			'''simple docstring'''






			UpperCAmelCase_			:     List[str]      =       '''ernie_m'''
			UpperCAmelCase_			:     Dict[str, str]      =       {"dropout": "classifier_dropout", "num_classes": "num_labels"}



			def __init__(   self       ,    __lowerCAmelCase = 250002       ,    __lowerCAmelCase = 768       ,    __lowerCAmelCase = 12       ,    __lowerCAmelCase = 12       ,    __lowerCAmelCase = 3072       ,    __lowerCAmelCase = "gelu"       ,    __lowerCAmelCase = 0.1       ,    __lowerCAmelCase = 0.1       ,    __lowerCAmelCase = 514       ,    __lowerCAmelCase = 0.02       ,    __lowerCAmelCase = 1       ,    __lowerCAmelCase = 1E-0_5       ,    __lowerCAmelCase=None       ,    __lowerCAmelCase=False       ,    __lowerCAmelCase=0.0       ,    **__lowerCAmelCase       ,    ):




					"""simple docstring"""


					super().__init__(pad_token_id=__lowerCAmelCase       ,    **__lowerCAmelCase)
					lowerCAmelCase   					=  vocab_size
					lowerCAmelCase   					=  hidden_size
					lowerCAmelCase   					=  num_hidden_layers
					lowerCAmelCase   					=  num_attention_heads
					lowerCAmelCase   					=  intermediate_size
					lowerCAmelCase   					=  hidden_act
					lowerCAmelCase   					=  hidden_dropout_prob
					lowerCAmelCase   					=  attention_probs_dropout_prob
					lowerCAmelCase   					=  max_position_embeddings
					lowerCAmelCase   					=  initializer_range
					lowerCAmelCase   					=  layer_norm_eps
					lowerCAmelCase   					=  classifier_dropout
					lowerCAmelCase   					=  is_decoder
					lowerCAmelCase   					=  act_dropout




