




"""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 		lowercase_      (		_UpperCAmelCase  ,      _UpperCAmelCase  ,      _UpperCAmelCase=0	):




	"""simple docstring"""






	if name is None:
		A_  :				Union[str, Any]	   =		None
	else:
		A_  :				str	   =		'''.''' * max(0  ,      spaces - 2	) + '''# {:''' + str(50 - spaces	) + '''s}'''
		A_  :				List[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 		lowercase_      (		_UpperCAmelCase  ,      _UpperCAmelCase  ,      _UpperCAmelCase  ,      _UpperCAmelCase  ,      _UpperCAmelCase	):




	"""simple docstring"""






	A_  :				Dict	   =		param.size()
	if checkpoint_version == 1.0:
		# version 1.0 stores [num_heads * hidden_size * num_splits, :]
		A_  :				str	   =		(num_heads, hidden_size, num_splits) + input_shape[1:]
		A_  :				List[str]	   =		param.view(*_UpperCAmelCase	)
		A_  :				List[str]	   =		param.transpose(0  ,      2	)
		A_  :				int	   =		param.transpose(1  ,      2	).contiguous()
	elif checkpoint_version >= 2.0:
		# other versions store [num_heads * num_splits * hidden_size, :]
		A_  :				Tuple	   =		(num_heads, num_splits, hidden_size) + input_shape[1:]
		A_  :				Tuple	   =		param.view(*_UpperCAmelCase	)
		A_  :				Union[str, Any]	   =		param.transpose(0  ,      1	).contiguous()
	A_  :				Union[str, Any]	   =		param.view(*_UpperCAmelCase	)
	return param

def 		lowercase_      (		_UpperCAmelCase  ,      _UpperCAmelCase  ,      _UpperCAmelCase	):




	"""simple docstring"""






	A_  :				Union[str, Any]	   =		{}

	# old versions did not store training args
	A_  :				int	   =		input_state_dict.get('''args'''  ,      _UpperCAmelCase	)
	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))

		A_  :				Dict	   =		ds_args.padded_vocab_size
		A_  :				Optional[Any]	   =		ds_args.max_position_embeddings
		A_  :				int	   =		ds_args.hidden_size
		A_  :				Dict	   =		ds_args.num_layers
		A_  :				Dict	   =		ds_args.num_attention_heads
		A_  :				List[str]	   =		ds_args.ffn_hidden_size
		# pprint(config)

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

	# The model.
	A_  :				Any	   =		input_state_dict['''model''']
	# The language model.
	A_  :				List[str]	   =		model['''language_model''']
	# The embeddings.
	A_  :				int	   =		lm['''embedding''']

	# The word embeddings.
	A_  :				List[Any]	   =		embeddings['''word_embeddings''']['''weight''']
	# Truncate the embedding table to vocab_size rows.
	A_  :				List[str]	   =		word_embeddings[: config.vocab_size, :]
	A_  :				str	   =		word_embeddings

	# The position embeddings.
	A_  :				str	   =		embeddings['''position_embeddings''']['''weight''']
	# Read the causal mask dimension (seqlen). [max_sequence_length, hidden_size]
	A_  :				Optional[Any]	   =		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.
	A_  :				Dict	   =		pos_embeddings

	# The transformer.
	A_  :				Optional[Any]	   =		lm['''transformer'''] if '''transformer''' in lm.keys() else lm['''encoder''']

	# The regex to extract layer names.
	A_  :				Optional[int]	   =		re.compile(r'''layers\.(\d+)\.([a-z0-9_.]+)\.([a-z]+)'''	)

	# The simple map of names for "automated" rules.
	A_  :				Dict	   =		{
	    '''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.
		A_  :				Tuple	   =		layer_re.match(_UpperCAmelCase	)

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

		# The index of the layer.
		A_  :				List[Any]	   =		int(m.group(1	)	)
		# The name of the operation.
		A_  :				Dict	   =		m.group(2	)
		# Is it a weight or a bias?
		A_  :				List[Any]	   =		m.group(3	)

		# The name of the layer.
		A_  :				List[str]	   =		f"""transformer.h.{layer_idx}"""

		# For layernorm(s), simply store the layer norm.
		if op_name.endswith('''layernorm'''	):
			A_  :				Optional[Any]	   =		'''ln_1''' if op_name.startswith('''input'''	) else '''ln_2'''
			A_  :				List[str]	   =		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.
			A_  :				Optional[Any]	   =		torch.tril(torch.ones((n_positions, n_positions)  ,      dtype=torch.floataa	)	).view(
			    1  ,      1  ,      _UpperCAmelCase  ,      _UpperCAmelCase	)
			A_  :				List[Any]	   =		causal_mask

			# Insert a "dummy" tensor for masked_bias.
			A_  :				Optional[int]	   =		torch.tensor(-1E4  ,      dtype=torch.floataa	)
			A_  :				Optional[Any]	   =		masked_bias

			A_  :				List[Any]	   =		fix_query_key_value_ordering(_UpperCAmelCase  ,      _UpperCAmelCase  ,      3  ,      _UpperCAmelCase  ,      _UpperCAmelCase	)
			# Megatron stores (3*D) x D but transformers-GPT2 expects D x 3*D.
			A_  :				str	   =		out_val.transpose(0  ,      1	).contiguous()
			# Store.
			A_  :				Optional[Any]	   =		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":
			A_  :				Any	   =		fix_query_key_value_ordering(_UpperCAmelCase  ,      _UpperCAmelCase  ,      3  ,      _UpperCAmelCase  ,      _UpperCAmelCase	)
			# Store. No change of shape.
			A_  :				List[str]	   =		out_val

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

		# Copy the bias.
		elif weight_or_bias == "bias":
			A_  :				Tuple	   =		megatron_to_transformers[op_name]
			A_  :				Any	   =		val

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

	# The final layernorm.
	A_  :				Any	   =		transformer['''final_layernorm.weight''']
	A_  :				int	   =		transformer['''final_layernorm.bias''']

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

	# It should be done!
	return output_state_dict

def 		lowercase_      (		):




	"""simple docstring"""






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

	# Extract the basename.
	A_  :				Union[str, Any]	   =		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:
				A_  :				Tuple	   =		torch.load(_UpperCAmelCase  ,      map_location='''cpu'''	)
	else:
		A_  :				Tuple	   =		torch.load(args.path_to_checkpoint  ,      map_location='''cpu'''	)

	A_  :				str	   =		input_state_dict.get('''args'''  ,      _UpperCAmelCase	)

	# 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:
				A_  :				int	   =		'''gelu_fast'''
			elif ds_args.openai_gelu:
				A_  :				Union[str, Any]	   =		'''gelu_new'''
			else:
				A_  :				Union[str, Any]	   =		'''gelu'''
		else:
			# in the very early days this used to be "gelu_new"
			A_  :				Any	   =		'''gelu_new'''

		# Spell out all parameters in case the defaults change.
		A_  :				Dict	   =		GPTaConfig(
		    vocab_size=5_0257  ,      n_positions=1024  ,      n_embd=1024  ,      n_layer=24  ,      n_head=16  ,      n_inner=4096  ,      activation_function=_UpperCAmelCase  ,      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=_UpperCAmelCase  ,      summary_activation=_UpperCAmelCase  ,      summary_proj_to_labels=_UpperCAmelCase  ,      summary_first_dropout=0.1  ,      scale_attn_weights=_UpperCAmelCase  ,      use_cache=_UpperCAmelCase  ,      bos_token_id=5_0256  ,      eos_token_id=5_0256  ,      )
	else:
		A_  :				Tuple	   =		GPTaConfig.from_json_file(args.config_file	)

	A_  :				Any	   =		['''GPT2LMHeadModel''']

	# Convert.
	print('''Converting'''	)
	A_  :				Optional[Any]	   =		convert_megatron_checkpoint(_UpperCAmelCase  ,      _UpperCAmelCase  ,      _UpperCAmelCase	)

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

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

	A_  :				List[str]	   =		AutoTokenizer.from_pretrained(_UpperCAmelCase	)
	A_  :				Union[str, Any]	   =		type(_UpperCAmelCase	).__name__
	A_  :				Tuple	   =		tokenizer_class

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

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

	# Store the state_dict to file.
	A_  :				Optional[int]	   =		os.path.join(_UpperCAmelCase  ,      '''pytorch_model.bin'''	)
	print(f"""Saving checkpoint to \"{output_checkpoint_file}\""""	)
	torch.save(_UpperCAmelCase  ,      _UpperCAmelCase	)


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

if __name__ == "__main__":
		main()

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











"""simple docstring"""

from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_lowerCamelCase  :			List[Any]        =       logging.get_logger(__name__)

_lowerCamelCase  :			Tuple        =       {
    'camembert-base': 'https://huggingface.co/camembert-base/resolve/main/config.json',
    'umberto-commoncrawl-cased-v1': (
        'https://huggingface.co/Musixmatch/umberto-commoncrawl-cased-v1/resolve/main/config.json'
    ),
    'umberto-wikipedia-uncased-v1': (
        'https://huggingface.co/Musixmatch/umberto-wikipedia-uncased-v1/resolve/main/config.json'
    ),
}

class        lowercase      (    __UpperCAmelCase):
				__lowerCAmelCase    :    Optional[Any]         =   """camembert"""







				def __init__(							self   :					List[Any]      ,     _lowerCamelCase   :					Optional[Any]=3_05_22      ,     _lowerCamelCase   :					List[str]=7_68      ,     _lowerCamelCase   :					Optional[Any]=12      ,     _lowerCamelCase   :					List[str]=12      ,     _lowerCamelCase   :					Any=30_72      ,     _lowerCamelCase   :					List[str]="gelu"      ,     _lowerCamelCase   :					Optional[Any]=0.1      ,     _lowerCamelCase   :					Optional[int]=0.1      ,     _lowerCamelCase   :					Union[str, Any]=5_12      ,     _lowerCamelCase   :					str=2      ,     _lowerCamelCase   :					Optional[Any]=0.02      ,     _lowerCamelCase   :					int=1E-12      ,     _lowerCamelCase   :					str=1      ,     _lowerCamelCase   :					Union[str, Any]=0      ,     _lowerCamelCase   :					Optional[int]=2      ,     _lowerCamelCase   :					List[str]="absolute"      ,     _lowerCamelCase   :					str=True      ,     _lowerCamelCase   :					List[Any]=None      ,     **_lowerCamelCase   :					Union[str, Any]      ,     ):



					"""simple docstring"""

					super().__init__(pad_token_id=_lowerCamelCase      ,     bos_token_id=_lowerCamelCase      ,     eos_token_id=_lowerCamelCase      ,     **_lowerCamelCase       )

					A_  :				Any	   =		vocab_size
					A_  :				Optional[Any]	   =		hidden_size
					A_  :				Any	   =		num_hidden_layers
					A_  :				List[Any]	   =		num_attention_heads
					A_  :				List[Any]	   =		hidden_act
					A_  :				Optional[int]	   =		intermediate_size
					A_  :				Tuple	   =		hidden_dropout_prob
					A_  :				Union[str, Any]	   =		attention_probs_dropout_prob
					A_  :				int	   =		max_position_embeddings
					A_  :				Tuple	   =		type_vocab_size
					A_  :				Dict	   =		initializer_range
					A_  :				Tuple	   =		layer_norm_eps
					A_  :				List[Any]	   =		position_embedding_type
					A_  :				Any	   =		use_cache
					A_  :				Dict	   =		classifier_dropout

class        lowercase      (    __UpperCAmelCase):







				@property
				def 					a_  (							self   :					Optional[int]       ):



					"""simple docstring"""

					if self.task == "multiple-choice":
						A_  :				Union[str, Any]	   =		{0: '''batch''', 1: '''choice''', 2: '''sequence'''}
					else:
						A_  :				Dict	   =		{0: '''batch''', 1: '''sequence'''}
					return OrderedDict(
					    [
					        ('''input_ids''', dynamic_axis),
					        ('''attention_mask''', dynamic_axis),
					    ]       )





