

"""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      __SCREAMING_SNAKE_CASE		(		lowercase__    ,							lowercase__    ,							lowercase__=0   ):
					"""simple docstring"""
					# Format the message.
					if name is None:
										A    =      None
					else:
										A    =      '.' * max(0    ,							spaces - 2   ) + '# {:' + str(50 - spaces   ) + 's}'
										A    =      fmt.format(snake_case__   )

					# Print and recurse (if needed).
					if isinstance(snake_case__    ,							snake_case__   ):
										if msg is not None:
															print(snake_case__   )
										for k in val.keys():
															recursive_print(snake_case__    ,							val[k]    ,							spaces + 2   )
					elif isinstance(snake_case__    ,							torch.Tensor   ):
										print(snake_case__    ,							":"    ,							val.size()   )
					else:
										print(snake_case__    ,							":"    ,							snake_case__   )
def      __SCREAMING_SNAKE_CASE		(		lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__   ):
					"""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 BERT.
					A    =      param.size()
					if checkpoint_version == 1.0:
										# version 1.0 stores [num_heads * hidden_size * num_splits, :]
										A    =      (num_heads, hidden_size, num_splits) + input_shape[1:]
										A    =      param.view(*snake_case__   )
										A    =      param.transpose(0    ,							2   )
										A    =      param.transpose(1    ,							2   ).contiguous()
					elif checkpoint_version >= 2.0:
										# other versions store [num_heads * num_splits * hidden_size, :]
										A    =      (num_heads, num_splits, hidden_size) + input_shape[1:]
										A    =      param.view(*snake_case__   )
										A    =      param.transpose(0    ,							1   ).contiguous()
					A    =      param.view(*snake_case__   )
					return param
def      __SCREAMING_SNAKE_CASE		(		lowercase__    ,							lowercase__    ,							lowercase__   ):
					"""simple docstring"""
					# The converted output model.
					A    =      {}

					# old versions did not store training args
					A    =      input_state_dict.get("args"    ,							snake_case__   )
					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    =      ds_args.tokenizer_type
										A    =      ds_args.padded_vocab_size
										A    =      ds_args.max_position_embeddings
										A    =      ds_args.hidden_size
										A    =      ds_args.num_layers
										A    =      ds_args.num_attention_heads
										A    =      ds_args.ffn_hidden_size if 'ffn_hidden_size' in ds_args else 4 * ds_args.hidden_size
										# pprint(config)

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

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

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

					# The position embeddings.
					A    =      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.
					A    =      pos_embeddings

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

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

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

					# The simple map of names for "automated" rules.
					A    =      {
					    '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.
					A    =      None

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

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

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

										# The name of the layer.
										A    =      F"""bert.encoder.layer.{layer_idx}"""

										# For layernorm(s), simply store the layer norm.
										if op_name.endswith("layernorm"   ):
															A    =      'attention.ln' if op_name.startswith("input"   ) else 'ln'
															A    =      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, ""

															A    =      fix_query_key_value_ordering(snake_case__    ,							snake_case__    ,							3    ,							snake_case__    ,							snake_case__   )
															# Store the tensor as we need the bias as well to interleave QKV and biases.
															A    =      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.
															A    =      attention_qkv_weight[0 * config.hidden_size : 1 * config.hidden_size, :]
															A    =      attention_qkv_weight[1 * config.hidden_size : 2 * config.hidden_size, :]
															A    =      attention_qkv_weight[2 * config.hidden_size : 3 * config.hidden_size, :]

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

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

															# Clear the stored tensor.
															A    =      None

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

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

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

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

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

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

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

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

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

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

					# It should be done!
					return output_state_dict
def      __SCREAMING_SNAKE_CASE		(		):
					"""simple docstring"""
					# Create the argument parser.
					A    =      argparse.ArgumentParser()
					parser.add_argument("--print-checkpoint-structure"    ,							action="store_true"   )
					parser.add_argument("path_to_checkpoint"    ,							type=snake_case__    ,							help="Path to the ZIP file containing the checkpoint"   )
					parser.add_argument(
					    "--config_file"    ,							default=""    ,							type=snake_case__    ,							help="An optional config json file describing the pre-trained model."    ,							)
					A    =      parser.parse_args()

					# Extract the basename.
					A    =      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    =      torch.load(snake_case__    ,							map_location="cpu"   )
					else:
										A    =      torch.load(args.path_to_checkpoint    ,							map_location="cpu"   )

					if args.config_file == "":
										# Default config of megatron-bert 345m
										A    =      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
										A    =      input_state_dict['model']['lm_head']['bias'].numel()
					else:
										A    =      MegatronBertConfig.from_json_file(args.config_file   )

					# Convert.
					print("Converting"   )
					A    =      convert_megatron_checkpoint(snake_case__    ,							snake_case__    ,							snake_case__   )

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

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

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


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

if __name__ == "__main__":
	main()

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








"""simple docstring"""




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


__A  :					int												=	{
    'configuration_nystromformer': ['NYSTROMFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'NystromformerConfig'],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__A  :					Any												=	[
	    'NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'NystromformerForMaskedLM',
	    'NystromformerForMultipleChoice',
	    'NystromformerForQuestionAnswering',
	    'NystromformerForSequenceClassification',
	    'NystromformerForTokenClassification',
	    'NystromformerLayer',
	    'NystromformerModel',
	    'NystromformerPreTrainedModel',
	]


if TYPE_CHECKING:
	from .configuration_nystromformer import NYSTROMFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, NystromformerConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_nystromformer import (
		    NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
		    NystromformerForMaskedLM,
		    NystromformerForMultipleChoice,
		    NystromformerForQuestionAnswering,
		    NystromformerForSequenceClassification,
		    NystromformerForTokenClassification,
		    NystromformerLayer,
		    NystromformerModel,
		    NystromformerPreTrainedModel,
		)


else:
	import sys

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





