

"""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_bert/convert_megatron_bert_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 MegatronBertConfig





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




					"""simple docstring"""



					if name is None:
										__lowercase        =      None
					else:
										__lowercase        =      '''.''' * max(0			,						spaces - 2      ) + '''# {:''' + str(50 - spaces      ) + '''s}'''
										__lowercase        =      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__:			Tuple			,						a__:			Optional[Any]			,						a__:			List[Any]			,						a__:			Union[str, Any]			,						a__:			Optional[Any]      )					->      List[Any]:




					"""simple docstring"""



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





def 					__UpperCamelCase (     a__:			Optional[int]			,						a__:			Union[str, Any]			,						a__:			List[Any]      )					->      List[str]:




					"""simple docstring"""



					__lowercase        =      {}

					# old versions did not store training args
					__lowercase        =      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))

										__lowercase        =      ds_args.tokenizer_type
										__lowercase        =      ds_args.padded_vocab_size
										__lowercase        =      ds_args.max_position_embeddings
										__lowercase        =      ds_args.hidden_size
										__lowercase        =      ds_args.num_layers
										__lowercase        =      ds_args.num_attention_heads
										__lowercase        =      ds_args.ffn_hidden_size if '''ffn_hidden_size''' in ds_args else 4 * ds_args.hidden_size
										# pprint(config)

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

					# The model.
					__lowercase        =      input_state_dict['''model''']
					# The language model.
					__lowercase        =      model['''language_model''']
					# The embeddings.
					__lowercase        =      lm['''embedding''']

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

					# The position embeddings.
					__lowercase        =      embeddings['''position_embeddings''']['''weight''']
					assert pos_embeddings.size(0      ) == config.max_position_embeddings and pos_embeddings.size(1      ) == config.hidden_size
					# Store the position embeddings.
					__lowercase        =      pos_embeddings

					# The token-type embeddings.
					__lowercase        =      embeddings['''tokentype_embeddings''']['''weight''']
					# Store the position embeddings.
					__lowercase        =      tokentype_embeddings

					# The transformer.
					__lowercase        =      lm['''transformer'''] if '''transformer''' in lm.keys() else lm['''encoder''']

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

					# The simple map of names for "automated" rules.
					__lowercase        =      {
					    '''attention.dense''': '''.attention.output.dense.''',
					    '''self_attention.dense''': '''.attention.output.dense.''',
					    '''mlp.dense_h_to_4h''': '''.intermediate.dense.''',
					    '''mlp.dense_4h_to_h''': '''.output.dense.''',
					}

					# Keep track of the attention/query/value tensor.
					__lowercase        =      None

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

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

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

										# The name of the layer.
										__lowercase        =      f'''bert.encoder.layer.{layer_idx}'''

										# For layernorm(s), simply store the layer norm.
										if op_name.endswith('''layernorm'''      ):
															__lowercase        =      '''attention.ln''' if op_name.startswith('''input'''      ) else '''ln'''
															__lowercase        =      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":
															# Make sure the QKV pointer is nil.
															assert attention_qkv_weight is None, ""

															__lowercase        =      fix_query_key_value_ordering(a__			,						a__			,						3			,						a__			,						a__      )
															# Store the tensor as we need the bias as well to interleave QKV and biases.
															__lowercase        =      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":
															# Make sure we read the weight tensor.
															assert attention_qkv_weight is not None, ""

															# Split the QKV matrix into Q, K and V. Megatron stores Q,K,V interleaved.
															__lowercase        =      attention_qkv_weight[0 * config.hidden_size : 1 * config.hidden_size, :]
															__lowercase        =      attention_qkv_weight[1 * config.hidden_size : 2 * config.hidden_size, :]
															__lowercase        =      attention_qkv_weight[2 * config.hidden_size : 3 * config.hidden_size, :]

															__lowercase        =      fix_query_key_value_ordering(a__			,						a__			,						3			,						a__			,						a__      )
															# Split the bias.
															__lowercase        =      out_val[0 * config.hidden_size : 1 * config.hidden_size]
															__lowercase        =      out_val[1 * config.hidden_size : 2 * config.hidden_size]
															__lowercase        =      out_val[2 * config.hidden_size : 3 * config.hidden_size]

															# Store.
															__lowercase        =      q
															__lowercase        =      q_bias
															__lowercase        =      k
															__lowercase        =      k_bias
															__lowercase        =      v
															__lowercase        =      v_bias

															# Clear the stored tensor.
															__lowercase        =      None

										# Copy weights and biases as is.
										elif weight_or_bias in ["weight", "bias"]:
															__lowercase        =      megatron_to_transformers[op_name]
															__lowercase        =      val

    # The final layernorm.
					__lowercase        =      transformer['''final_layernorm.weight''']
					__lowercase        =      transformer['''final_layernorm.bias''']

					# The pooler.
					__lowercase        =      lm['''pooler''']

					# Store the matrix and the bias.
					__lowercase        =      pooler['''dense.weight''']
					__lowercase        =      pooler['''dense.bias''']

					# The LM head from Megatron (for RACE).
					__lowercase        =      model['''lm_head''']

					# The transform matrix.
					__lowercase        =      lm_head['''dense.weight''']
					__lowercase        =      lm_head['''dense.bias''']

					# The transform LN.
					__lowercase        =      lm_head['''layernorm.weight''']
					__lowercase        =      lm_head['''layernorm.bias''']

					# For the decoder, we replicate the weights.
					__lowercase        =      word_embeddings
					__lowercase        =      lm_head['''bias''']

					# The classifier from Megatron (for MLNI).
					__lowercase        =      model['''binary_head''']

					# Store the classifier.
					__lowercase        =      binary_head['''weight''']
					__lowercase        =      binary_head['''bias''']

					# It should be done!
					return output_state_dict





def 					__UpperCamelCase (     )					->      Any:




					"""simple docstring"""



					__lowercase        =      argparse.ArgumentParser()
					parser.add_argument('''--print-checkpoint-structure'''			,						action='''store_true'''      )
					parser.add_argument('''path_to_checkpoint'''			,						type=a__			,						help='''Path to the ZIP file containing the checkpoint'''      )
					parser.add_argument(
					    '''--config_file'''			,						default=''''''			,						type=a__			,						help='''An optional config json file describing the pre-trained model.'''			,						)
					__lowercase        =      parser.parse_args()

					# Extract the basename.
					__lowercase        =      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:
																				__lowercase        =      torch.load(a__			,						map_location='''cpu'''      )
					else:
										__lowercase        =      torch.load(args.path_to_checkpoint			,						map_location='''cpu'''      )

					if args.config_file == "":
										# Default config of megatron-bert 345m
										__lowercase        =      MegatronBertConfig()

										# different megatron-bert-*-345m models have different vocab sizes, so override the default
										# config (which is for megatron-bert-cased-345m) with the actual vocab dimension
										__lowercase        =      input_state_dict['''model''']['''lm_head''']['''bias'''].numel()
					else:
										__lowercase        =      MegatronBertConfig.from_json_file(args.config_file      )

					# Convert.
					print('''Converting'''      )
					__lowercase        =      convert_megatron_checkpoint(a__			,						a__			,						a__      )

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

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

					# Store the state_dict to file.
					__lowercase        =      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 typing import TYPE_CHECKING

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


a__			:						str							=			{
    """configuration_rwkv""": ["""RWKV_PRETRAINED_CONFIG_ARCHIVE_MAP""", """RwkvConfig""", """RwkvOnnxConfig"""],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							a__			:						Optional[int]							=			[
							    """RWKV_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """RwkvForCausalLM""",
							    """RwkvModel""",
							    """RwkvPreTrainedModel""",
							]


if TYPE_CHECKING:
							from .configuration_rwkv import RWKV_PRETRAINED_CONFIG_ARCHIVE_MAP, RwkvConfig, RwkvOnnxConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_rwkv import (
														    RWKV_PRETRAINED_MODEL_ARCHIVE_LIST,
														    RwkvForCausalLM,
														    RwkvModel,
														    RwkvPreTrainedModel,
														)
else:
							import sys

							a__			:						List[str]							=			_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


