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

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






def       __lowerCamelCase		(						lowerCAmelCase__	,  lowerCAmelCase__	,  lowerCAmelCase__	,  lowerCAmelCase__	,  lowerCAmelCase__	):
       # 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.
       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(*lowerCAmelCase__	)
              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(*lowerCAmelCase__	)
              lowerCAmelCase__     	=					param.transpose(0	,  1	).contiguous()
       lowerCAmelCase__     	=					param.view(*lowerCAmelCase__	)
       return param






def       __lowerCamelCase		(						lowerCAmelCase__	,  lowerCAmelCase__	,  lowerCAmelCase__	):
       # The converted output model.
       lowerCAmelCase__     	=					{}

       # old versions did not store training args
       lowerCAmelCase__     	=					input_state_dict.get('args'	,  lowerCAmelCase__	)
       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.tokenizer_type
              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 if 'ffn_hidden_size' in ds_args else 4 * ds_args.hidden_size
              # pprint(config)

       # The number of heads.
       lowerCAmelCase__     	=					config.num_attention_heads
       # The hidden_size per head.
       lowerCAmelCase__     	=					config.hidden_size // heads
       # 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, :]
       # Store the word embeddings.
       lowerCAmelCase__     	=					word_embeddings

       # The position embeddings.
       lowerCAmelCase__     	=					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.
       lowerCAmelCase__     	=					pos_embeddings

       # The token-type embeddings.
       lowerCAmelCase__     	=					embeddings['tokentype_embeddings']['weight']
       # Store the position embeddings.
       lowerCAmelCase__     	=					tokentype_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': '.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.
       lowerCAmelCase__     	=					None

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

              # 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"""bert.encoder.layer.{layer_idx}"""

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

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

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

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

                     # Clear the stored tensor.
                     lowerCAmelCase__     	=					None

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

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

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

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

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

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

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

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

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

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

       # It should be done!
       return output_state_dict






def       __lowerCamelCase		(						):
       # Create the argument parser.
       lowerCAmelCase__     	=					argparse.ArgumentParser()
       parser.add_argument('--print-checkpoint-structure'	,  action='store_true'	)
       parser.add_argument('path_to_checkpoint'	,  type=lowerCAmelCase__	,  help='Path to the ZIP file containing the checkpoint'	)
       parser.add_argument(
           '--config_file'	,  default=''	,  type=lowerCAmelCase__	,  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(lowerCAmelCase__	,  map_location='cpu'	)
       else:
              lowerCAmelCase__     	=					torch.load(args.path_to_checkpoint	,  map_location='cpu'	)

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

       # Convert.
       print('Converting'	)
       lowerCAmelCase__     	=					convert_megatron_checkpoint(lowerCAmelCase__	,  lowerCAmelCase__	,  lowerCAmelCase__	)

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

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

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


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

if __name__ == "__main__":
    main()

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

import itertools
from dataclasses import dataclass
from typing import Optional

import pyarrow as pa

import datasets
from datasets.table import table_cast


lowerCAmelCase__							= datasets.utils.logging.get_logger(__name__)
@dataclass
class 				a_ (						datasets.BuilderConfig						):






 '''simple docstring'''






 UpperCAmelCase_	 =					None






class 				a_ (						datasets.ArrowBasedBuilder						):






 '''simple docstring'''






 UpperCAmelCase_	 =					ArrowConfig




 def 		__snake_case						(			self					:  Any):







        '''simple docstring'''



        return datasets.DatasetInfo(features=self.config.features)




 def 		__snake_case						(			self					:  Dict			,    lowercase__					:  str):







        '''simple docstring'''



        if not self.config.data_files:
               raise ValueError(F"""At least one data file must be specified, but got data_files={self.config.data_files}""")
        lowerCAmelCase__     	=					dl_manager.download_and_extract(self.config.data_files)
        if isinstance(lowercase__			,    (str, list, tuple)):
               lowerCAmelCase__     	=					data_files
               if isinstance(lowercase__			,    lowercase__):
                      lowerCAmelCase__     	=					[files]
               # Use `dl_manager.iter_files` to skip hidden files in an extracted archive
               lowerCAmelCase__     	=					[dl_manager.iter_files(lowercase__) for file in files]
               return [datasets.SplitGenerator(name=datasets.Split.TRAIN			,    gen_kwargs={'files': files})]
        lowerCAmelCase__     	=					[]
        for split_name, files in data_files.items():
               if isinstance(lowercase__			,    lowercase__):
                      lowerCAmelCase__     	=					[files]
               # Use `dl_manager.iter_files` to skip hidden files in an extracted archive
               lowerCAmelCase__     	=					[dl_manager.iter_files(lowercase__) for file in files]
               # Infer features is they are stoed in the arrow schema
               if self.info.features is None:
                      for file in itertools.chain.from_iterable(lowercase__):
                             with open(lowercase__			,    'rb') as f:
                                    lowerCAmelCase__     	=					datasets.Features.from_arrow_schema(pa.ipc.open_stream(lowercase__).schema)
                             break
               splits.append(datasets.SplitGenerator(name=lowercase__			,    gen_kwargs={'files': files}))
        return splits




 def 		__snake_case						(			self					:  Dict			,    lowercase__					:  pa.Table):







        '''simple docstring'''



        if self.info.features is not None:
               # more expensive cast to support nested features with keys in a different order
               # allows str <-> int/float or str to Audio for example
               lowerCAmelCase__     	=					table_cast(lowercase__			,    self.info.features.arrow_schema)
        return pa_table




 def 		__snake_case						(			self					:  List[Any]			,    lowercase__					:  str):







        '''simple docstring'''



        for file_idx, file in enumerate(itertools.chain.from_iterable(lowercase__)):
               with open(lowercase__			,    'rb') as f:
                      try:
                             for batch_idx, record_batch in enumerate(pa.ipc.open_stream(lowercase__)):
                                    lowerCAmelCase__     	=					pa.Table.from_batches([record_batch])
                                    # Uncomment for debugging (will print the Arrow table size and elements)
                                    # logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
                                    # logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
                                    yield F"""{file_idx}_{batch_idx}""", self._cast_table(lowercase__)
                      except ValueError as e:
                             logger.error(F"""Failed to read file '{file}' with error {type(lowercase__)}: {e}""")
                             raise
