



# 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:
            lowerCamelCase             =		None
      else:
            lowerCamelCase             =		"." * max(0 , spaces - 2			) + "# {:" + str(50 - spaces			) + "s}"
            lowerCamelCase             =		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"""



      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(*UpperCAmelCase__			)
            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(*UpperCAmelCase__			)
            lowerCamelCase             =		param.transpose(0 , 1			).contiguous()
      lowerCamelCase             =		param.view(*UpperCAmelCase__			)
      return param

def      __lowercase(						UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__			):


      """simple docstring"""



      lowerCamelCase             =		{}

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

            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(UpperCAmelCase__			)

            # 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 , UpperCAmelCase__ , UpperCAmelCase__			)
                  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(UpperCAmelCase__ , UpperCAmelCase__ , 3 , UpperCAmelCase__ , UpperCAmelCase__			)
                  # 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(UpperCAmelCase__ , UpperCAmelCase__ , 3 , UpperCAmelCase__ , UpperCAmelCase__			)
                  # 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      __lowercase(						):


      """simple docstring"""



      lowerCamelCase             =		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." , )
      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(UpperCAmelCase__ , map_location="cpu"			)
      else:
            lowerCamelCase             =		torch.load(args.path_to_checkpoint , map_location="cpu"			)

      lowerCamelCase             =		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:
                        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=UpperCAmelCase__ , resid_pdrop=0.1 , embd_pdrop=0.1 , attn_pdrop=0.1 , layer_norm_epsilon=1E-5 , initializer_range=0.0_2 , 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=50256 , eos_token_id=50256 , )
      else:
            lowerCamelCase             =		GPTaConfig.from_json_file(args.config_file			)

      lowerCamelCase             =		["GPT2LMHeadModel"]

      # Convert.
      print("Converting"			)
      lowerCamelCase             =		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:
            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(UpperCAmelCase__			)
      lowerCamelCase             =		type(UpperCAmelCase__			).__name__
      lowerCamelCase             =		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.
      lowerCamelCase             =		os.path.join(UpperCAmelCase__ , "pytorch_model.bin"			)
      print(F"""Saving checkpoint to \"{output_checkpoint_file}\""""			)
      torch.save(UpperCAmelCase__ , UpperCAmelCase__			)


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

if __name__ == "__main__":
     main()

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




import argparse
import json
import os

import torch

from transformers import LukeConfig, LukeModel, LukeTokenizer, RobertaTokenizer
from transformers.tokenization_utils_base import AddedToken

@torch.no_grad()
def      __lowercase(						UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__			):


      """simple docstring"""



      with open(UpperCAmelCase__			) as metadata_file:
            lowerCamelCase             =		json.load(UpperCAmelCase__			)
      lowerCamelCase             =		LukeConfig(use_entity_aware_attention=UpperCAmelCase__ , **metadata["model_config"]			)

      # Load in the weights from the checkpoint_path
      lowerCamelCase             =		torch.load(UpperCAmelCase__ , map_location="cpu"			)

      # Load the entity vocab file
      lowerCamelCase             =		load_entity_vocab(UpperCAmelCase__			)

      lowerCamelCase             =		RobertaTokenizer.from_pretrained(metadata["model_config"]["bert_model_name"]			)

      # Add special tokens to the token vocabulary for downstream tasks
      lowerCamelCase             =		AddedToken("<ent>" , lstrip=UpperCAmelCase__ , rstrip=UpperCAmelCase__			)
      lowerCamelCase             =		AddedToken("<ent2>" , lstrip=UpperCAmelCase__ , rstrip=UpperCAmelCase__			)
      tokenizer.add_special_tokens({"additional_special_tokens": [entity_token_a, entity_token_a]}			)
      config.vocab_size += 2

      print(F"""Saving tokenizer to {pytorch_dump_folder_path}"""			)
      tokenizer.save_pretrained(UpperCAmelCase__			)
      with open(os.path.join(UpperCAmelCase__ , LukeTokenizer.vocab_files_names["entity_vocab_file"]			) , "w"			) as f:
            json.dump(UpperCAmelCase__ , UpperCAmelCase__			)

      lowerCamelCase             =		LukeTokenizer.from_pretrained(UpperCAmelCase__			)

      # Initialize the embeddings of the special tokens
      lowerCamelCase             =		state_dict["embeddings.word_embeddings.weight"]
      lowerCamelCase             =		word_emb[tokenizer.convert_tokens_to_ids(["@"]			)[0]].unsqueeze(0			)
      lowerCamelCase             =		word_emb[tokenizer.convert_tokens_to_ids(["#"]			)[0]].unsqueeze(0			)
      lowerCamelCase             =		torch.cat([word_emb, ent_emb, enta_emb]			)

      # Initialize the query layers of the entity-aware self-attention mechanism
      for layer_index in range(config.num_hidden_layers			):
            for matrix_name in ["query.weight", "query.bias"]:
                  lowerCamelCase             =		F"""encoder.layer.{layer_index}.attention.self."""
                  lowerCamelCase             =		state_dict[prefix + matrix_name]
                  lowerCamelCase             =		state_dict[prefix + matrix_name]
                  lowerCamelCase             =		state_dict[prefix + matrix_name]

    # Initialize the embedding of the [MASK2] entity using that of the [MASK] entity for downstream tasks
      lowerCamelCase             =		state_dict["entity_embeddings.entity_embeddings.weight"]
      lowerCamelCase             =		entity_emb[entity_vocab["[MASK]"]]

      lowerCamelCase             =		LukeModel(config=UpperCAmelCase__			).eval()

      lowerCamelCase      ,				lowerCamelCase             =		model.load_state_dict(UpperCAmelCase__ , strict=UpperCAmelCase__			)
      if not (len(UpperCAmelCase__			) == 1 and missing_keys[0] == "embeddings.position_ids"):
            raise ValueError(F"""Missing keys {", ".join(UpperCAmelCase__			)}. Expected only missing embeddings.position_ids"""			)
      if not (all(key.startswith("entity_predictions"			) or key.startswith("lm_head"			) for key in unexpected_keys			)):
            raise ValueError(
                "Unexpected keys"
                F""" {", ".join([key for key in unexpected_keys if not (key.startswith("entity_predictions"			) or key.startswith("lm_head"			))]			)}"""			)

      # Check outputs
      lowerCamelCase             =		LukeTokenizer.from_pretrained(UpperCAmelCase__ , task="entity_classification"			)

      lowerCamelCase             =		(
          "Top seed Ana Ivanovic said on Thursday she could hardly believe her luck as a fortuitous netcord helped the"
          " new world number one avoid a humiliating second- round exit at Wimbledon ."
      )
      lowerCamelCase             =		(39, 42)
      lowerCamelCase             =		tokenizer(UpperCAmelCase__ , entity_spans=[span] , add_prefix_space=UpperCAmelCase__ , return_tensors="pt"			)

      lowerCamelCase             =		model(**UpperCAmelCase__			)

      # Verify word hidden states
      if model_size == "large":
            lowerCamelCase             =		torch.Size((1, 42, 1024)			)
            lowerCamelCase             =		torch.tensor(
                [[0.0_1_3_3, 0.0_8_6_5, 0.0_0_9_5], [0.3_0_9_3, -0.2_5_7_6, -0.7_4_1_8], [-0.1_7_2_0, -0.2_1_1_7, -0.2_8_6_9]]			)
      else:  # base
            lowerCamelCase             =		torch.Size((1, 42, 768)			)
            lowerCamelCase             =		torch.tensor([[0.0_0_3_7, 0.1_3_6_8, -0.0_0_9_1], [0.1_0_9_9, 0.3_3_2_9, -0.1_0_9_5], [0.0_7_6_5, 0.5_3_3_5, 0.1_1_7_9]]			)

      if not (outputs.last_hidden_state.shape == expected_shape):
            raise ValueError(
                F"""Outputs.last_hidden_state.shape is {outputs.last_hidden_state.shape}, Expected shape is {expected_shape}"""			)
      if not torch.allclose(outputs.last_hidden_state[0, :3, :3] , UpperCAmelCase__ , atol=1E-4			):
            raise ValueError

      # Verify entity hidden states
      if model_size == "large":
            lowerCamelCase             =		torch.Size((1, 1, 1024)			)
            lowerCamelCase             =		torch.tensor([[0.0_4_6_6, -0.0_1_0_6, -0.0_1_7_9]]			)
      else:  # base
            lowerCamelCase             =		torch.Size((1, 1, 768)			)
            lowerCamelCase             =		torch.tensor([[0.1_4_5_7, 0.1_0_4_4, 0.0_1_7_4]]			)

      if not (outputs.entity_last_hidden_state.shape != expected_shape):
            raise ValueError(
                F"""Outputs.entity_last_hidden_state.shape is {outputs.entity_last_hidden_state.shape}, Expected shape is"""
                F""" {expected_shape}"""			)
      if not torch.allclose(outputs.entity_last_hidden_state[0, :3, :3] , UpperCAmelCase__ , atol=1E-4			):
            raise ValueError

      # Finally, save our PyTorch model and tokenizer
      print("Saving PyTorch model to {}".format(UpperCAmelCase__			)			)
      model.save_pretrained(UpperCAmelCase__			)

def      __lowercase(						UpperCAmelCase__			):


      """simple docstring"""



      lowerCamelCase             =		{}
      with open(UpperCAmelCase__ , "r" , encoding="utf-8"			) as f:
            for index, line in enumerate(UpperCAmelCase__			):
                  lowerCamelCase      ,				lowerCamelCase             =		line.rstrip().split("\t"			)
                  lowerCamelCase             =		index

      return entity_vocab


if __name__ == "__main__":
     a_    :  Tuple						  =		argparse.ArgumentParser()
     # Required parameters
     parser.add_argument('--checkpoint_path', type=str, help='Path to a pytorch_model.bin file.')
     parser.add_argument(
         '--metadata_path', default=None, type=str, help='Path to a metadata.json file, defining the configuration.'
     )
     parser.add_argument(
         '--entity_vocab_path',
         default=None,
         type=str,
         help='Path to an entity_vocab.tsv file, containing the entity vocabulary.',
     )
     parser.add_argument(
         '--pytorch_dump_folder_path', default=None, type=str, help='Path to where to dump the output PyTorch model.'
     )
     parser.add_argument(
         '--model_size', default='base', type=str, choices=['base', 'large'], help='Size of the model to be converted.'
     )
     a_    :  Dict						  =		parser.parse_args()
     convert_luke_checkpoint(
         args.checkpoint_path,
         args.metadata_path,
         args.entity_vocab_path,
         args.pytorch_dump_folder_path,
         args.model_size,
     )