

'''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 			__UpperCamelCase							(		lowercase__  :					Optional[int],   lowercase__  :					Tuple,   lowercase__  :					Tuple=0	):






     '''simple docstring'''





     if name is None:
          __lowercase     						=None
     else:
          __lowercase     						="." * max(0,   spaces - 2	) + "# {:" + str(50 - spaces	) + "s}"
          __lowercase     						=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							(		lowercase__  :					Any,   lowercase__  :					Tuple,   lowercase__  :					int,   lowercase__  :					Any,   lowercase__  :					str	):






     '''simple docstring'''





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

def 			__UpperCamelCase							(		lowercase__  :					List[str],   lowercase__  :					str,   lowercase__  :					List[Any]	):






     '''simple docstring'''





     __lowercase     						={}

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

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

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

     # The model.
     __lowercase     						=input_state_dict["model"]
     # The language model.
     __lowercase     						=model["language_model"]
     # The embeddings.
     __lowercase     						=lm["embedding"]

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

     # The position embeddings.
     __lowercase     						=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.
     __lowercase     						=pos_embeddings

     # The token-type embeddings.
     __lowercase     						=embeddings["tokentype_embeddings"]["weight"]
     # Store the position embeddings.
     __lowercase     						=tokentype_embeddings

     # The transformer.
     __lowercase     						=lm["transformer"] if "transformer" in lm.keys() else lm["encoder"]

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

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

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

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

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

          # The name of the layer.
          __lowercase     						=F'''bert.encoder.layer.{layer_idx}'''

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

               __lowercase     						=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.
               __lowercase     						=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.
               __lowercase     						=attention_qkv_weight[0 * config.hidden_size : 1 * config.hidden_size, :]
               __lowercase     						=attention_qkv_weight[1 * config.hidden_size : 2 * config.hidden_size, :]
               __lowercase     						=attention_qkv_weight[2 * config.hidden_size : 3 * config.hidden_size, :]

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

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

               # Clear the stored tensor.
               __lowercase     						=None

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

    # The final layernorm.
     __lowercase     						=transformer["final_layernorm.weight"]
     __lowercase     						=transformer["final_layernorm.bias"]

     # The pooler.
     __lowercase     						=lm["pooler"]

     # Store the matrix and the bias.
     __lowercase     						=pooler["dense.weight"]
     __lowercase     						=pooler["dense.bias"]

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

     # The transform matrix.
     __lowercase     						=lm_head["dense.weight"]
     __lowercase     						=lm_head["dense.bias"]

     # The transform LN.
     __lowercase     						=lm_head["layernorm.weight"]
     __lowercase     						=lm_head["layernorm.bias"]

     # For the decoder, we replicate the weights.
     __lowercase     						=word_embeddings
     __lowercase     						=lm_head["bias"]

     # The classifier from Megatron (for MLNI).
     __lowercase     						=model["binary_head"]

     # Store the classifier.
     __lowercase     						=binary_head["weight"]
     __lowercase     						=binary_head["bias"]

     # It should be done!
     return output_state_dict

def 			__UpperCamelCase							(		):






     '''simple docstring'''





     __lowercase     						=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.',   )
     __lowercase     						=parser.parse_args()

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

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

     # Convert.
     print('Converting'	)
     __lowercase     						=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.
     __lowercase     						=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 math
from typing import Optional

import torch
import torch.utils.checkpoint as checkpoint
from torch import nn

from ....modeling_utils import PreTrainedModel
from ....utils import add_start_docstrings, logging
from ...bert.modeling_bert import BertModel
from .configuration_retribert import RetriBertConfig


UpperCAmelCase							=					logging.get_logger(__name__)

UpperCAmelCase							=					[
    '''yjernite/retribert-base-uncased''',
    # See all RetriBert models at https://huggingface.co/models?filter=retribert
]


class 					lowerCAmelCase       ( A				):
       lowerCAmelCase_				=	RetriBertConfig
       lowerCAmelCase_				=	None
       lowerCAmelCase_				=	"retribert"







       def      snake_case				(							self     :	Tuple  , __lowercase     :	List[str] ):







            """simple docstring"""







            if isinstance(__lowercase  , nn.Linear ):
                 module.weight.data.normal_(mean=0.0  , std=self.config.initializer_range )
                 if module.bias is not None:
                      module.bias.data.zero_()
            elif isinstance(__lowercase  , nn.Embedding ):
                 module.weight.data.normal_(mean=0.0  , std=self.config.initializer_range )
                 if module.padding_idx is not None:
                      module.weight.data[module.padding_idx].zero_()
            elif isinstance(__lowercase  , nn.LayerNorm ):
                 module.bias.data.zero_()
                 module.weight.data.fill_(1.0 )






UpperCAmelCase							=					r'''

    This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
    library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
    etc.)

    This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
    Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
    and behavior.

    Parameters:
        config ([`RetriBertConfig`]): Model configuration class with all the parameters of the model.
            Initializing with a config file does not load the weights associated with the model, only the
            configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
'''


@add_start_docstrings(
    "Bert Based model to embed queries or document for document retrieval."				,     A				,     )
class 					lowerCAmelCase       ( A				):







       def __init__(							self     :	int  , __lowercase     :	RetriBertConfig ):







            """simple docstring"""







            super().__init__(__lowercase )
            __lowercase     						=config.projection_dim

            __lowercase     						=BertModel(__lowercase )
            __lowercase     						=None if config.share_encoders else BertModel(__lowercase )
            __lowercase     						=nn.Dropout(config.hidden_dropout_prob )
            __lowercase     						=nn.Linear(config.hidden_size  , config.projection_dim  , bias=__lowercase )
            __lowercase     						=nn.Linear(config.hidden_size  , config.projection_dim  , bias=__lowercase )

            __lowercase     						=nn.CrossEntropyLoss(reduction='mean' )

            # Initialize weights and apply final processing
            self.post_init()







       def      snake_case				(							self     :	Dict  , __lowercase     :	List[Any]  , __lowercase     :	Any  , __lowercase     :	Optional[int]  , __lowercase     :	str=-1  , ):







            """simple docstring"""







            if checkpoint_batch_size < 0 or input_ids.shape[0] < checkpoint_batch_size:
                 return sent_encoder(__lowercase  , attention_mask=__lowercase )[1]
            else:
                 # prepare implicit variables
                 __lowercase     						=input_ids.device
                 __lowercase     						=input_ids.size()
                 __lowercase     						=torch.zeros(__lowercase  , dtype=torch.long  , device=__lowercase )
                 __lowercase     						=[None] * sent_encoder.config.num_hidden_layers
                 __lowercase     						=sent_encoder.get_extended_attention_mask(
                     __lowercase  , __lowercase )

                 # define function for checkpointing
                 def partial_encode(*__lowercase     :	int ):
                      __lowercase     						=sent_encoder.encoder(
                          inputs[0]  , attention_mask=inputs[1]  , head_mask=__lowercase  , )
                      __lowercase     						=encoder_outputs[0]
                      __lowercase     						=sent_encoder.pooler(__lowercase )
                      return pooled_output

                 # run embedding layer on everything at once
                 __lowercase     						=sent_encoder.embeddings(
                     input_ids=__lowercase  , position_ids=__lowercase  , token_type_ids=__lowercase  , inputs_embeds=__lowercase )
                 # run encoding and pooling on one mini-batch at a time
                 __lowercase     						=[]
                 for b in range(math.ceil(input_ids.shape[0] / checkpoint_batch_size ) ):
                      __lowercase     						=embedding_output[b * checkpoint_batch_size : (b + 1) * checkpoint_batch_size]
                      __lowercase     						=extended_attention_mask[b * checkpoint_batch_size : (b + 1) * checkpoint_batch_size]
                      __lowercase     						=checkpoint.checkpoint(__lowercase  , __lowercase  , __lowercase )
                      pooled_output_list.append(__lowercase )
                 return torch.cat(__lowercase  , dim=0 )







       def      snake_case				(							self     :	List[Any]  , __lowercase     :	Any  , __lowercase     :	Any=None  , __lowercase     :	Optional[int]=-1  , ):







            """simple docstring"""







            __lowercase     						=self.embed_sentences_checkpointed(
                __lowercase  , __lowercase  , self.bert_query  , __lowercase  , )
            return self.project_query(__lowercase )







       def      snake_case				(							self     :	Dict  , __lowercase     :	Tuple  , __lowercase     :	Optional[int]=None  , __lowercase     :	Any=-1  , ):







            """simple docstring"""







            __lowercase     						=self.embed_sentences_checkpointed(
                __lowercase  , __lowercase  , self.bert_query if self.bert_doc is None else self.bert_doc  , __lowercase  , )
            return self.project_doc(__lowercase )







       def      snake_case				(							self     :	int  , __lowercase     :	torch.LongTensor  , __lowercase     :	Optional[torch.FloatTensor]  , __lowercase     :	torch.LongTensor  , __lowercase     :	Optional[torch.FloatTensor]  , __lowercase     :	int = -1  , ):







            """simple docstring"""







            __lowercase     						=input_ids_query.device
            __lowercase     						=self.embed_questions(__lowercase  , __lowercase  , __lowercase )
            __lowercase     						=self.embed_answers(__lowercase  , __lowercase  , __lowercase )
            __lowercase     						=torch.mm(__lowercase  , a_reps.t() )
            __lowercase     						=self.ce_loss(__lowercase  , torch.arange(compare_scores.shape[1] ).to(__lowercase ) )
            __lowercase     						=self.ce_loss(compare_scores.t()  , torch.arange(compare_scores.shape[0] ).to(__lowercase ) )
            __lowercase     						=(loss_qa + loss_aq) / 2
            return loss


