"""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_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 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       Optional[Any]			,_lowerCamelCase						:       Union[str, Any]			,_lowerCamelCase						:       Optional[int]=0	)       ->      Dict:
  # Format the message.
  if name is None:
    _lowerCAmelCase						:       int  			=     None
  else:
    _lowerCAmelCase						:       int  			=     """.""" * max(0			,spaces - 2	) + """# {:""" + str(50 - spaces	) + """s}"""
    _lowerCAmelCase						:       Optional[int]  			=     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 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       Optional[Any]			,_lowerCamelCase						:       Optional[int]			,_lowerCamelCase						:       int			,_lowerCamelCase						:       Dict			,_lowerCamelCase						:       Dict	)       ->      Tuple:
  # 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 GPT2.
  _lowerCAmelCase						:       int  			=     param.size()
  if checkpoint_version == 1.0:
    # version 1.0 stores [num_heads * hidden_size * num_splits, :]
    _lowerCAmelCase						:       Union[str, Any]  			=     (num_heads, hidden_size, num_splits) + input_shape[1:]
    _lowerCAmelCase						:       List[str]  			=     param.view(*_lowerCamelCase	)
    _lowerCAmelCase						:       Tuple  			=     param.transpose(0			,2	)
    _lowerCAmelCase						:       List[Any]  			=     param.transpose(1			,2	).contiguous()
  elif checkpoint_version >= 2.0:
    # other versions store [num_heads * num_splits * hidden_size, :]
    _lowerCAmelCase						:       List[Any]  			=     (num_heads, num_splits, hidden_size) + input_shape[1:]
    _lowerCAmelCase						:       str  			=     param.view(*_lowerCamelCase	)
    _lowerCAmelCase						:       int  			=     param.transpose(0			,1	).contiguous()
  _lowerCAmelCase						:       List[Any]  			=     param.view(*_lowerCamelCase	)
  return param





def 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       Optional[Any]			,_lowerCamelCase						:       List[Any]			,_lowerCamelCase						:       Dict	)       ->      Optional[int]:
  # The converted output model.
  _lowerCAmelCase						:       Tuple  			=     {}

  # old versions did not store training args
  _lowerCAmelCase						:       List[Any]  			=     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						:       Any  			=     ds_args.padded_vocab_size
    _lowerCAmelCase						:       Tuple  			=     ds_args.max_position_embeddings
    _lowerCAmelCase						:       Dict  			=     ds_args.hidden_size
    _lowerCAmelCase						:       Dict  			=     ds_args.num_layers
    _lowerCAmelCase						:       str  			=     ds_args.num_attention_heads
    _lowerCAmelCase						:       List[Any]  			=     ds_args.ffn_hidden_size
    # pprint(config)

  # The number of heads.
  _lowerCAmelCase						:       Tuple  			=     config.n_head
  # The hidden_size per head.
  _lowerCAmelCase						:       int  			=     config.n_embd // config.n_head
  # Megatron-LM checkpoint version
  if "checkpoint_version" in input_state_dict.keys():
    _lowerCAmelCase						:       List[Any]  			=     input_state_dict["""checkpoint_version"""]
  else:
    _lowerCAmelCase						:       Union[str, Any]  			=     0.0

  # The model.
  _lowerCAmelCase						:       Tuple  			=     input_state_dict["""model"""]
  # The language model.
  _lowerCAmelCase						:       List[Any]  			=     model["""language_model"""]
  # The embeddings.
  _lowerCAmelCase						:       List[str]  			=     lm["""embedding"""]

  # The word embeddings.
  _lowerCAmelCase						:       Union[str, Any]  			=     embeddings["""word_embeddings"""]["""weight"""]
  # Truncate the embedding table to vocab_size rows.
  _lowerCAmelCase						:       Tuple  			=     word_embeddings[: config.vocab_size, :]
  _lowerCAmelCase						:       Tuple  			=     word_embeddings

  # The position embeddings.
  _lowerCAmelCase						:       List[str]  			=     embeddings["""position_embeddings"""]["""weight"""]
  # Read the causal mask dimension (seqlen). [max_sequence_length, hidden_size]
  _lowerCAmelCase						:       int  			=     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						:       Tuple  			=     pos_embeddings

  # The transformer.
  _lowerCAmelCase						:       List[Any]  			=     lm["""transformer"""] if """transformer""" in lm.keys() else lm["""encoder"""]

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

  # The simple map of names for "automated" rules.
  _lowerCAmelCase						:       Any  			=     {
      """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						:       Optional[Any]  			=     layer_re.match(_lowerCamelCase	)

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

    # The index of the layer.
    _lowerCAmelCase						:       Any  			=     int(m.group(1	)	)
    # The name of the operation.
    _lowerCAmelCase						:       Any  			=     m.group(2	)
    # Is it a weight or a bias?
    _lowerCAmelCase						:       Optional[Any]  			=     m.group(3	)

    # The name of the layer.
    _lowerCAmelCase						:       Union[str, Any]  			=     f"transformer.h.{layer_idx}"

    # For layernorm(s), simply store the layer norm.
    if op_name.endswith("""layernorm"""	):
      _lowerCAmelCase						:       Optional[int]  			=     """ln_1""" if op_name.startswith("""input"""	) else """ln_2"""
      _lowerCAmelCase						:       Union[str, Any]  			=     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						:       Any  			=     torch.tril(torch.ones((n_positions, n_positions)			,dtype=torch.floataa	)	).view(
          1			,1			,_lowerCamelCase			,_lowerCamelCase	)
      _lowerCAmelCase						:       str  			=     causal_mask

      # Insert a "dummy" tensor for masked_bias.
      _lowerCAmelCase						:       str  			=     torch.tensor(-1e4			,dtype=torch.floataa	)
      _lowerCAmelCase						:       Union[str, Any]  			=     masked_bias

      _lowerCAmelCase						:       Tuple  			=     fix_query_key_value_ordering(_lowerCamelCase			,_lowerCamelCase			,3			,_lowerCamelCase			,_lowerCamelCase	)
      # Megatron stores (3*D) x D but transformers-GPT2 expects D x 3*D.
      _lowerCAmelCase						:       Dict  			=     out_val.transpose(0			,1	).contiguous()
      # Store.
      _lowerCAmelCase						:       List[Any]  			=     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						:       Dict  			=     fix_query_key_value_ordering(_lowerCamelCase			,_lowerCamelCase			,3			,_lowerCamelCase			,_lowerCamelCase	)
      # Store. No change of shape.
      _lowerCAmelCase						:       List[Any]  			=     out_val

    # Transpose the weights.
    elif weight_or_bias == "weight":
      _lowerCAmelCase						:       Dict  			=     megatron_to_transformers[op_name]
      _lowerCAmelCase						:       Dict  			=     val.transpose(0			,1	)

    # Copy the bias.
    elif weight_or_bias == "bias":
      _lowerCAmelCase						:       Union[str, Any]  			=     megatron_to_transformers[op_name]
      _lowerCAmelCase						:       List[str]  			=     val

    # DEBUG.
  assert config.n_layer == layer_idx + 1

  # The final layernorm.
  _lowerCAmelCase						:       Union[str, Any]  			=     transformer["""final_layernorm.weight"""]
  _lowerCAmelCase						:       Union[str, Any]  			=     transformer["""final_layernorm.bias"""]

  # For LM head, transformers' wants the matrix to weight embeddings.
  _lowerCAmelCase						:       Dict  			=     word_embeddings

  # It should be done!
  return output_state_dict





def 							SCREAMING_SNAKE_CASE     (			)       ->      int:
  # Create the argument parser.
  _lowerCAmelCase						:       Tuple  			=     argparse.ArgumentParser()
  parser.add_argument("""--print-checkpoint-structure"""			,action="""store_true"""	)
  parser.add_argument(
      """path_to_checkpoint"""			,type=_lowerCamelCase			,help="""Path to the checkpoint file (.zip archive or direct .pt file)"""			,)
  parser.add_argument(
      """--config_file"""			,default=""""""			,type=_lowerCamelCase			,help="""An optional config json file describing the pre-trained model."""			,)
  _lowerCAmelCase						:       Dict  			=     parser.parse_args()

  # Extract the basename.
  _lowerCAmelCase						:       Optional[int]  			=     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						:       int  			=     torch.load(_lowerCamelCase			,map_location="""cpu"""	)
  else:
    _lowerCAmelCase						:       List[Any]  			=     torch.load(args.path_to_checkpoint			,map_location="""cpu"""	)

  _lowerCAmelCase						:       Union[str, Any]  			=     input_state_dict.get("""args"""			,_lowerCamelCase	)

  # 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						:       Optional[Any]  			=     """gelu_fast"""
      elif ds_args.openai_gelu:
        _lowerCAmelCase						:       Any  			=     """gelu_new"""
      else:
        _lowerCAmelCase						:       int  			=     """gelu"""
    else:
      # in the very early days this used to be "gelu_new"
      _lowerCAmelCase						:       Tuple  			=     """gelu_new"""

    # Spell out all parameters in case the defaults change.
    _lowerCAmelCase						:       Dict  			=     GPTaConfig(
        vocab_size=50257			,n_positions=1024			,n_embd=1024			,n_layer=24			,n_head=16			,n_inner=4096			,activation_function=_lowerCamelCase			,resid_pdrop=0.1			,embd_pdrop=0.1			,attn_pdrop=0.1			,layer_norm_epsilon=1e-5			,initializer_range=0.02			,summary_type="""cls_index"""			,summary_use_proj=_lowerCamelCase			,summary_activation=_lowerCamelCase			,summary_proj_to_labels=_lowerCamelCase			,summary_first_dropout=0.1			,scale_attn_weights=_lowerCamelCase			,use_cache=_lowerCamelCase			,bos_token_id=50256			,eos_token_id=50256			,)
  else:
    _lowerCAmelCase						:       int  			=     GPTaConfig.from_json_file(args.config_file	)

  _lowerCAmelCase						:       Tuple  			=     ["""GPT2LMHeadModel"""]

  # Convert.
  print("""Converting"""	)
  _lowerCAmelCase						:       int  			=     convert_megatron_checkpoint(_lowerCamelCase			,_lowerCamelCase			,_lowerCamelCase	)

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

  # Add tokenizer class info to config
  # see https://github.com/huggingface/transformers/issues/13906)
  if ds_args is not None:
    _lowerCAmelCase						:       str  			=     ds_args.tokenizer_type
    if tokenizer_type == "GPT2BPETokenizer":
      _lowerCAmelCase						:       List[Any]  			=     """gpt2"""
    elif tokenizer_type == "PretrainedFromHF":
      _lowerCAmelCase						:       str  			=     ds_args.tokenizer_name_or_path
    else:
      raise ValueError(f"Unrecognized tokenizer_type {tokenizer_type}"	)
  else:
    _lowerCAmelCase						:       Optional[Any]  			=     """gpt2"""

  _lowerCAmelCase						:       Union[str, Any]  			=     AutoTokenizer.from_pretrained(_lowerCamelCase	)
  _lowerCAmelCase						:       int  			=     type(_lowerCamelCase	).__name__
  _lowerCAmelCase						:       str  			=     tokenizer_class

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

  # Save tokenizer based on args
  print(f"Adding {tokenizer_class} tokenizer files"	)
  tokenizer.save_pretrained(_lowerCamelCase	)

  # Store the state_dict to file.
  _lowerCAmelCase						:       int  			=     os.path.join(_lowerCamelCase			,"""pytorch_model.bin"""	)
  print(f"Saving checkpoint to \"{output_checkpoint_file}\""	)
  torch.save(_lowerCamelCase			,_lowerCamelCase	)


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

if __name__ == "__main__":
     main()

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





"""simple docstring"""




from scipy.stats import pearsonr

import datasets


_a      :		str           =   '\nPearson correlation coefficient and p-value for testing non-correlation.\nThe Pearson correlation coefficient measures the linear relationship between two datasets. The calculation of the p-value relies on the assumption that each dataset is normally distributed. Like other correlation coefficients, this one varies between -1 and +1 with 0 implying no correlation. Correlations of -1 or +1 imply an exact linear relationship. Positive correlations imply that as x increases, so does y. Negative correlations imply that as x increases, y decreases.\nThe p-value roughly indicates the probability of an uncorrelated system producing datasets that have a Pearson correlation at least as extreme as the one computed from these datasets.\n'


_a      :		List[str]           =   '\nArgs:\n    predictions (`list` of `int`): Predicted class labels, as returned by a model.\n    references (`list` of `int`): Ground truth labels.\n    return_pvalue (`boolean`): If `True`, returns the p-value, along with the correlation coefficient. If `False`, returns only the correlation coefficient. Defaults to `False`.\n\nReturns:\n    pearsonr (`float`): Pearson correlation coefficient. Minimum possible value is -1. Maximum possible value is 1. Values of 1 and -1 indicate exact linear positive and negative relationships, respectively. A value of 0 implies no correlation.\n    p-value (`float`): P-value, which roughly indicates the probability of an The p-value roughly indicates the probability of an uncorrelated system producing datasets that have a Pearson correlation at least as extreme as the one computed from these datasets. Minimum possible value is 0. Maximum possible value is 1. Higher values indicate higher probabilities.\n\nExamples:\n\n    Example 1-A simple example using only predictions and references.\n        >>> pearsonr_metric = datasets.load_metric("pearsonr")\n        >>> results = pearsonr_metric.compute(predictions=[10, 9, 2.5, 6, 4], references=[1, 2, 3, 4, 5])\n        >>> print(round(results[\'pearsonr\'], 2))\n        -0.74\n\n    Example 2-The same as Example 1, but that also returns the `p-value`.\n        >>> pearsonr_metric = datasets.load_metric("pearsonr")\n        >>> results = pearsonr_metric.compute(predictions=[10, 9, 2.5, 6, 4], references=[1, 2, 3, 4, 5], return_pvalue=True)\n        >>> print(sorted(list(results.keys())))\n        [\'p-value\', \'pearsonr\']\n        >>> print(round(results[\'pearsonr\'], 2))\n        -0.74\n        >>> print(round(results[\'p-value\'], 2))\n        0.15\n'


_a      :		List[Any]           =   '\n@article{2020SciPy-NMeth,\nauthor  = {Virtanen, Pauli and Gommers, Ralf and Oliphant, Travis E. and\n      Haberland, Matt and Reddy, Tyler and Cournapeau, David and\n      Burovski, Evgeni and Peterson, Pearu and Weckesser, Warren and\n      Bright, Jonathan and {van der Walt}, St{\'e}fan J. and\n      Brett, Matthew and Wilson, Joshua and Millman, K. Jarrod and\n      Mayorov, Nikolay and Nelson, Andrew R. J. and Jones, Eric and\n      Kern, Robert and Larson, Eric and Carey, C J and\n      Polat, Ilhan and Feng, Yu and Moore, Eric W. and\n      {VanderPlas}, Jake and Laxalde, Denis and Perktold, Josef and\n      Cimrman, Robert and Henriksen, Ian and Quintero, E. A. and\n      Harris, Charles R. and Archibald, Anne M. and\n      Ribeiro, Antonio H. and Pedregosa, Fabian and\n      {van Mulbregt}, Paul and {SciPy 1.0 Contributors}},\ntitle   = {{{SciPy} 1.0: Fundamental Algorithms for Scientific\n      Computing in Python}},\njournal = {Nature Methods},\nyear    = {2020},\nvolume  = {17},\npages   = {261--272},\nadsurl  = {https://rdcu.be/b08Wh},\ndoi = {10.1038/s41592-019-0686-2},\n}\n'
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION    ,	_KWARGS_DESCRIPTION		)
class 						__A							( datasets.Metric		):






     def     __A  (		self  ):
       return datasets.MetricInfo(
           description=_DESCRIPTION				,     citation=_CITATION				,     inputs_description=_KWARGS_DESCRIPTION				,     features=datasets.Features(
               {
                   """predictions""": datasets.Value("""float"""  ),
                   """references""": datasets.Value("""float"""  ),
               }  )				,     reference_urls=["""https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.pearsonr.html"""]				,     )






     def     __A  (		self				,     a__				,     a__				,     a__=False  ):
       if return_pvalue:
         _lowerCAmelCase						:       List[Any]  			=     pearsonr(a__				,     a__  )
         return {"pearsonr": results[0], "p-value": results[1]}
       else:
         return {"pearsonr": float(pearsonr(a__				,     a__  )[0]  )}




