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







def       __a	(						__lowerCamelCase,						__lowerCamelCase,						__lowerCamelCase    ):
   # The converted output model.
   UpperCAmelCase_   :   str     	=       {}

   # old versions did not store training args
   UpperCAmelCase_   :   str     	=       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))

      UpperCAmelCase_   :   int     	=       ds_args.tokenizer_type
      UpperCAmelCase_   :   str     	=       ds_args.padded_vocab_size
      UpperCAmelCase_   :   Optional[int]     	=       ds_args.max_position_embeddings
      UpperCAmelCase_   :   Union[str, Any]     	=       ds_args.hidden_size
      UpperCAmelCase_   :   int     	=       ds_args.num_layers
      UpperCAmelCase_   :   Dict     	=       ds_args.num_attention_heads
      UpperCAmelCase_   :   Tuple     	=       ds_args.ffn_hidden_size if "ffn_hidden_size" in ds_args else 4 * ds_args.hidden_size
      # pprint(config)

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

   # The model.
   UpperCAmelCase_   :   Union[str, Any]     	=       input_state_dict["model"]
   # The language model.
   UpperCAmelCase_   :   int     	=       model["language_model"]
   # The embeddings.
   UpperCAmelCase_   :   List[Any]     	=       lm["embedding"]

   # The word embeddings.
   UpperCAmelCase_   :   Tuple     	=       embeddings["word_embeddings"]["weight"]
   # Truncate the embedding table to vocab_size rows.
   UpperCAmelCase_   :   str     	=       word_embeddings[: config.vocab_size, :]
   # Store the word embeddings.
   UpperCAmelCase_   :   List[str]     	=       word_embeddings

   # The position embeddings.
   UpperCAmelCase_   :   Tuple     	=       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.
   UpperCAmelCase_   :   int     	=       pos_embeddings

   # The token-type embeddings.
   UpperCAmelCase_   :   int     	=       embeddings["tokentype_embeddings"]["weight"]
   # Store the position embeddings.
   UpperCAmelCase_   :   List[str]     	=       tokentype_embeddings

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

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

   # The simple map of names for "automated" rules.
   UpperCAmelCase_   :   List[Any]     	=       {
       "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.
   UpperCAmelCase_   :   int     	=       None

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

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

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

      # The name of the layer.
      UpperCAmelCase_   :   Optional[Any]     	=       f"""bert.encoder.layer.{layer_idx}"""

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

         UpperCAmelCase_   :   str     	=       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.
         UpperCAmelCase_   :   List[str]     	=       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.
         UpperCAmelCase_   :   Optional[int]     	=       attention_qkv_weight[0 * config.hidden_size : 1 * config.hidden_size, :]
         UpperCAmelCase_   :   Tuple     	=       attention_qkv_weight[1 * config.hidden_size : 2 * config.hidden_size, :]
         UpperCAmelCase_   :   List[Any]     	=       attention_qkv_weight[2 * config.hidden_size : 3 * config.hidden_size, :]

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

         # Store.
         UpperCAmelCase_   :   Union[str, Any]     	=       q
         UpperCAmelCase_   :   int     	=       q_bias
         UpperCAmelCase_   :   Dict     	=       k
         UpperCAmelCase_   :   int     	=       k_bias
         UpperCAmelCase_   :   int     	=       v
         UpperCAmelCase_   :   Tuple     	=       v_bias

         # Clear the stored tensor.
         UpperCAmelCase_   :   List[str]     	=       None

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

    # The final layernorm.
   UpperCAmelCase_   :   Dict     	=       transformer["final_layernorm.weight"]
   UpperCAmelCase_   :   Dict     	=       transformer["final_layernorm.bias"]

   # The pooler.
   UpperCAmelCase_   :   Optional[int]     	=       lm["pooler"]

   # Store the matrix and the bias.
   UpperCAmelCase_   :   Optional[Any]     	=       pooler["dense.weight"]
   UpperCAmelCase_   :   Any     	=       pooler["dense.bias"]

   # The LM head from Megatron (for RACE).
   UpperCAmelCase_   :   str     	=       model["lm_head"]

   # The transform matrix.
   UpperCAmelCase_   :   int     	=       lm_head["dense.weight"]
   UpperCAmelCase_   :   Optional[Any]     	=       lm_head["dense.bias"]

   # The transform LN.
   UpperCAmelCase_   :   List[Any]     	=       lm_head["layernorm.weight"]
   UpperCAmelCase_   :   List[Any]     	=       lm_head["layernorm.bias"]

   # For the decoder, we replicate the weights.
   UpperCAmelCase_   :   int     	=       word_embeddings
   UpperCAmelCase_   :   List[str]     	=       lm_head["bias"]

   # The classifier from Megatron (for MLNI).
   UpperCAmelCase_   :   Optional[Any]     	=       model["binary_head"]

   # Store the classifier.
   UpperCAmelCase_   :   Optional[int]     	=       binary_head["weight"]
   UpperCAmelCase_   :   List[Any]     	=       binary_head["bias"]

   # It should be done!
   return output_state_dict







def       __a	(						):
   # Create the argument parser.
   UpperCAmelCase_   :   List[Any]     	=       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.",						)
   UpperCAmelCase_   :   Tuple     	=       parser.parse_args()

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

   if args.config_file == "":
      # Default config of megatron-bert 345m
      UpperCAmelCase_   :   List[str]     	=       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
      UpperCAmelCase_   :   Optional[int]     	=       input_state_dict["model"]["lm_head"]["bias"].numel()
   else:
      UpperCAmelCase_   :   Optional[Any]     	=       MegatronBertConfig.from_json_file(args.config_file    )

   # Convert.
   print("Converting"    )
   UpperCAmelCase_   :   int     	=       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.
   UpperCAmelCase_   :   Optional[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 collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_a									=				logging.get_logger(__name__)

_a									=				{
    'squeezebert/squeezebert-uncased': (
        'https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/config.json'
    ),
    'squeezebert/squeezebert-mnli': 'https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/config.json',
    'squeezebert/squeezebert-mnli-headless': (
        'https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/config.json'
    ),
}


class A_      (lowercase__ ):




  '''simple docstring'''
  SCREAMING_SNAKE_CASE__     :		Any    	=						SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP
  SCREAMING_SNAKE_CASE__     :		Optional[Any]    	=						"""squeezebert"""







  def __init__(						self							,  lowercase_=3_0522							,  lowercase_=768							,  lowercase_=12							,  lowercase_=12							,  lowercase_=3072							,  lowercase_="gelu"							,  lowercase_=0.1							,  lowercase_=0.1							,  lowercase_=512							,  lowercase_=2							,  lowercase_=0.02							,  lowercase_=1E-1_2							,  lowercase_=0							,  lowercase_=768							,  lowercase_=4							,  lowercase_=4							,  lowercase_=4							,  lowercase_=1							,  lowercase_=4							,  lowercase_=4							,  **lowercase_							,  ):

     """simple docstring"""
     super().__init__(pad_token_id=lowercase_							,  **lowercase_    )

     UpperCAmelCase_   :   List[Any]     	=       vocab_size
     UpperCAmelCase_   :   Optional[Any]     	=       hidden_size
     UpperCAmelCase_   :   Tuple     	=       num_hidden_layers
     UpperCAmelCase_   :   Optional[int]     	=       num_attention_heads
     UpperCAmelCase_   :   Optional[Any]     	=       hidden_act
     UpperCAmelCase_   :   Tuple     	=       intermediate_size
     UpperCAmelCase_   :   Union[str, Any]     	=       hidden_dropout_prob
     UpperCAmelCase_   :   List[Any]     	=       attention_probs_dropout_prob
     UpperCAmelCase_   :   int     	=       max_position_embeddings
     UpperCAmelCase_   :   List[str]     	=       type_vocab_size
     UpperCAmelCase_   :   Optional[Any]     	=       initializer_range
     UpperCAmelCase_   :   Optional[int]     	=       layer_norm_eps
     UpperCAmelCase_   :   Tuple     	=       embedding_size
     UpperCAmelCase_   :   Dict     	=       q_groups
     UpperCAmelCase_   :   List[Any]     	=       k_groups
     UpperCAmelCase_   :   List[str]     	=       v_groups
     UpperCAmelCase_   :   Optional[Any]     	=       post_attention_groups
     UpperCAmelCase_   :   List[str]     	=       intermediate_groups
     UpperCAmelCase_   :   List[Any]     	=       output_groups




class A_      (lowercase__ ):




  '''simple docstring'''







  @property
  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     if self.task == "multiple-choice":
        UpperCAmelCase_   :   List[str]     	=       {0: "batch", 1: "choice", 2: "sequence"}
     else:
        UpperCAmelCase_   :   Optional[int]     	=       {0: "batch", 1: "sequence"}
     return OrderedDict(
         [
             ("input_ids", dynamic_axis),
             ("attention_mask", dynamic_axis),
             ("token_type_ids", dynamic_axis),
         ]    )


