
"""simple docstring"""




import argparse
import os
import re
import zipfile

import torch

from transformers import MegatronBertConfig

def     UpperCamelCase_    (						SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_=0     ):
					# Format the message.
					if name is None:
										SCREAMING_SNAKE_CASE           =      None
					else:
										SCREAMING_SNAKE_CASE           =      '.' * max(0,	spaces - 2     ) + '# {:' + str(5_0 - spaces     ) + 's}'
										SCREAMING_SNAKE_CASE           =      fmt.format(SCREAMING_SNAKE_CASE_     )

					# Print and recurse (if needed).
					if isinstance(SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_     ):
										if msg is not None:
															print(SCREAMING_SNAKE_CASE_     )
										for k in val.keys():
															recursive_print(SCREAMING_SNAKE_CASE_,	val[k],	spaces + 2     )
					elif isinstance(SCREAMING_SNAKE_CASE_,	torch.Tensor     ):
										print(SCREAMING_SNAKE_CASE_,	':',	val.size()     )
					else:
										print(SCREAMING_SNAKE_CASE_,	':',	SCREAMING_SNAKE_CASE_     )

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

def     UpperCamelCase_    (						SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_     ):
					SCREAMING_SNAKE_CASE           =      {}

					# old versions did not store training args
					SCREAMING_SNAKE_CASE           =      input_state_dict.get('args',	SCREAMING_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))

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

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

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

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

					# The position embeddings.
					SCREAMING_SNAKE_CASE           =      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.
					SCREAMING_SNAKE_CASE           =      pos_embeddings

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

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

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

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

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

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

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

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

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

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

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

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

															# Clear the stored tensor.
															SCREAMING_SNAKE_CASE           =      None

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

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

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

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

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

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

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

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

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

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

					# It should be done!
					return output_state_dict

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

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

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

					# Convert.
					print('Converting'     )
					SCREAMING_SNAKE_CASE           =      convert_megatron_checkpoint(SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_     )

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

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

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


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

if __name__ == "__main__":
	main()

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


'''simple docstring'''





import argparse

import torch

from transformers import RoFormerConfig, RoFormerForMaskedLM, load_tf_weights_in_roformer
from transformers.utils import logging


logging.set_verbosity_info()





def 							snake_case				(		UpperCAmelCase   ,	UpperCAmelCase   ,	UpperCAmelCase						)->      Union[str, Any]:







      """simple docstring"""
      # Initialise PyTorch model
      __A													=      RoFormerConfig.from_json_file(UpperCAmelCase						)
      print(f'Building PyTorch model from configuration: {config}'						)
      __A													=      RoFormerForMaskedLM(UpperCAmelCase						)

      # Load weights from tf checkpoint
      load_tf_weights_in_roformer(UpperCAmelCase   ,	UpperCAmelCase   ,	UpperCAmelCase						)

      # Save pytorch-model
      print(f'Save PyTorch model to {pytorch_dump_path}'						)
      torch.save(model.state_dict()   ,	UpperCAmelCase   ,	_use_new_zipfile_serialization=UpperCAmelCase						)


if __name__ == "__main__":
 a__		: Tuple  							=							argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     "--tf_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
 )
 parser.add_argument(
     "--bert_config_file",
     default=None,
     type=str,
     required=True,
     help=(
         "The config json file corresponding to the pre-trained BERT model. \n"
         "This specifies the model architecture."
     ),
 )
 parser.add_argument(
     "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
 )
 a__		: Union[str, Any]  							=							parser.parse_args()
 convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)






