


import argparse
import os
import pathlib

import fairseq
import torch
from fairseq.modules import TransformerSentenceEncoderLayer
from packaging import version

from transformers import (
    DataaVecTextConfig,
    DataaVecTextForMaskedLM,
    DataaVecTextForSequenceClassification,
    DataaVecTextModel,
)
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertSelfAttention,
    BertSelfOutput,
)

# IMPORTANT: In order for this script to run, please make sure to download the dictionary: `dict.txt` from wget https://dl.fbaipublicfiles.com/fairseq/models/roberta.large.tar.gz
# File copied from https://github.com/pytorch/fairseq/blob/main/examples/data2vec/models/data2vec_text.py
from transformers.utils import logging


if version.parse(fairseq.__version__) < version.parse('''0.9.0'''):
       raise Exception('''requires fairseq >= 0.9.0''')


logging.set_verbosity_info()
__lowerCamelCase   	=  logging.get_logger(__name__)

__lowerCamelCase   	=  '''Hello world! cécé herlolip'''



def 			_a      (    __UpperCamelCase	,	__UpperCamelCase	,	__UpperCamelCase			):
     a_      ,      a_     :  Dict				      =     os.path.split(__UpperCamelCase			)
     a_     :  str				      =     DataaVecTextModel.from_pretrained(
         __UpperCamelCase	,	checkpoint_file=__UpperCamelCase			)
     dataavec.eval()  # disable dropout
     a_     :  Tuple				      =     dataavec.models[0]
     a_     :  str				      =     dataavec_model.encoder.sentence_encoder
     a_     :  List[Any]				      =     DataaVecTextConfig(
         vocab_size=dataavec_sent_encoder.embed_tokens.num_embeddings	,	hidden_size=dataavec_model.args.encoder_embed_dim	,	num_hidden_layers=dataavec_model.args.encoder_layers	,	num_attention_heads=dataavec_model.args.encoder_attention_heads	,	intermediate_size=dataavec_model.args.encoder_ffn_embed_dim	,	max_position_embeddings=5_1_4	,	type_vocab_size=1	,	layer_norm_eps=1E-5	,	)
     if classification_head:
          a_     :  Any				      =     dataavec.model.classification_heads["""mnli"""].out_proj.weight.shape[0]
     print("""Our BERT config:"""	,	__UpperCamelCase			)

     a_     :  Union[str, Any]				      =     DataaVecTextForSequenceClassification(__UpperCamelCase			) if classification_head else DataaVecTextForMaskedLM(__UpperCamelCase			)
     model.eval()

     # Now let's copy all the weights.
     # Embeddings
     a_     :  List[str]				      =     dataavec_sent_encoder.embed_tokens.weight
     a_     :  Dict				      =     dataavec_sent_encoder.embed_positions.weight
     a_     :  Optional[Any]				      =     torch.zeros_like(
         model.dataavec_text.embeddings.token_type_embeddings.weight			)  # just zero them out b/c data2vec doesn't use them.
     a_     :  Tuple				      =     dataavec_sent_encoder.layernorm_embedding.weight
     a_     :  Any				      =     dataavec_sent_encoder.layernorm_embedding.bias

     for i in range(config.num_hidden_layers			):
          # Encoder: start of layer
          a_     :  BertLayer				      =     model.dataavec_text.encoder.layer[i]
          a_     :  TransformerSentenceEncoderLayer				      =     dataavec_sent_encoder.layers[i]

          # self attention
          a_     :  BertSelfAttention				      =     layer.attention.self
          assert dataavec_layer.self_attn.k_proj.weight.data.shape == torch.Size(
              (config.hidden_size, config.hidden_size)			), (
              "Shape for data2vec_layer.self_attn.k_proj.weight.data should be"
              F''' {torch.Size((config.hidden_size, config.hidden_size)			)}'''
          )
          assert dataavec_layer.self_attn.q_proj.weight.data.shape == torch.Size(
              (config.hidden_size, config.hidden_size)			), (
              "Shape for data2vec_layer.self_attn.q_proj.weight.data should be"
              F''' {torch.Size((config.hidden_size, config.hidden_size)			)}'''
          )
          assert dataavec_layer.self_attn.v_proj.weight.data.shape == torch.Size(
              (config.hidden_size, config.hidden_size)			), (
              "Shape for data2vec_layer.self_attn.v_proj.weight.data should be"
              F''' {torch.Size((config.hidden_size, config.hidden_size)			)}'''
          )

          a_     :  str				      =     dataavec_layer.self_attn.q_proj.weight
          a_     :  Optional[int]				      =     dataavec_layer.self_attn.q_proj.bias
          a_     :  Optional[int]				      =     dataavec_layer.self_attn.k_proj.weight
          a_     :  int				      =     dataavec_layer.self_attn.k_proj.bias
          a_     :  Any				      =     dataavec_layer.self_attn.v_proj.weight
          a_     :  List[str]				      =     dataavec_layer.self_attn.v_proj.bias

          # self-attention output
          a_     :  BertSelfOutput				      =     layer.attention.output
          assert (
              self_output.dense.weight.shape == dataavec_layer.self_attn.out_proj.weight.shape
          ), F'''Shape for self_output.dense.weight should be {dataavec_layer.self_attn.out_proj.weight.shape}'''
          a_     :  List[Any]				      =     dataavec_layer.self_attn.out_proj.weight
          a_     :  List[str]				      =     dataavec_layer.self_attn.out_proj.bias
          a_     :  str				      =     dataavec_layer.self_attn_layer_norm.weight
          a_     :  int				      =     dataavec_layer.self_attn_layer_norm.bias

          # intermediate
          a_     :  BertIntermediate				      =     layer.intermediate
          assert (
              intermediate.dense.weight.shape == dataavec_layer.fca.weight.shape
          ), F'''Shape for intermediate.dense.weight should be {dataavec_layer.fca.weight.shape}'''
          a_     :  Union[str, Any]				      =     dataavec_layer.fca.weight
          a_     :  List[str]				      =     dataavec_layer.fca.bias

          # output
          a_     :  BertOutput				      =     layer.output
          assert (
              bert_output.dense.weight.shape == dataavec_layer.fca.weight.shape
          ), F'''Shape for bert_output.dense.weight should be {dataavec_layer.fca.weight.shape}'''
          a_     :  Optional[Any]				      =     dataavec_layer.fca.weight
          a_     :  int				      =     dataavec_layer.fca.bias
          a_     :  Dict				      =     dataavec_layer.final_layer_norm.weight
          a_     :  List[Any]				      =     dataavec_layer.final_layer_norm.bias
          # end of layer

     if classification_head:
          a_     :  List[str]				      =     dataavec.model.classification_heads["""mnli"""].dense.weight
          a_     :  Optional[Any]				      =     dataavec.model.classification_heads["""mnli"""].dense.bias
          a_     :  str				      =     dataavec.model.classification_heads["""mnli"""].out_proj.weight
          a_     :  Dict				      =     dataavec.model.classification_heads["""mnli"""].out_proj.bias
     else:
          # LM Head
          a_     :  Optional[Any]				      =     dataavec_model.encoder.lm_head.dense.weight
          a_     :  Any				      =     dataavec_model.encoder.lm_head.dense.bias
          a_     :  Any				      =     dataavec_model.encoder.lm_head.layer_norm.weight
          a_     :  int				      =     dataavec_model.encoder.lm_head.layer_norm.bias
          a_     :  Union[str, Any]				      =     dataavec_model.encoder.lm_head.weight
          a_     :  Optional[Any]				      =     dataavec_model.encoder.lm_head.bias

     # Let's check that we get the same results.
     a_     :  torch.Tensor				      =     dataavec.encode(__UpperCamelCase			).unsqueeze(0			)  # batch of size 1

     a_     :  str				      =     model(__UpperCamelCase			)[0]
     if classification_head:
          a_     :  List[Any]				      =     dataavec.model.classification_heads["""mnli"""](dataavec.extract_features(__UpperCamelCase			)			)
     else:
          a_     :  int				      =     dataavec_model(__UpperCamelCase			)[0]
     print(our_output.shape	,	their_output.shape			)
     a_     :  Tuple				      =     torch.max(torch.abs(our_output - their_output			)			).item()
     print(F'''max_absolute_diff = {max_absolute_diff}'''			)  # ~ 1e-7
     a_     :  str				      =     torch.allclose(__UpperCamelCase	,	__UpperCamelCase	,	atol=1E-3			)
     print("""Do both models output the same tensors?"""	,	"""🔥""" if success else """💩"""			)
     if not success:
          raise Exception("""Something went wRoNg"""			)

     pathlib.Path(__UpperCamelCase			).mkdir(parents=__UpperCamelCase	,	exist_ok=__UpperCamelCase			)
     print(F'''Saving model to {pytorch_dump_folder_path}'''			)
     model.save_pretrained(__UpperCamelCase			)


if __name__ == "__main__":
       __lowerCamelCase   	=  argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           '''--checkpoint_path''', default=None, type=str, required=True, help='''Path the official PyTorch dump.'''
       )
       parser.add_argument(
           '''--pytorch_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
       )
       parser.add_argument(
           '''--classification_head''', action='''store_true''', help='''Whether to convert a final classification head.'''
       )
       __lowerCamelCase   	=  parser.parse_args()
       convert_dataavec_checkpoint_to_pytorch(
           args.checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
       )






"""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 							_lowerCAmelCase   (    UpperCAmelCase  :				Union[str, Any]   ,				UpperCAmelCase  :				Tuple   ,				UpperCAmelCase  :				Tuple=0					):


					'''simple docstring'''






					if name is None:
										UpperCamelCase__		:     Any								=None
					else:
										UpperCamelCase__		:     str								='''.''' * max(0   ,				spaces - 2					) + '''# {:''' + str(50 - spaces					) + '''s}'''
										UpperCamelCase__		:     int								=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 							_lowerCAmelCase   (    UpperCAmelCase  :				List[str]   ,				UpperCAmelCase  :				Dict   ,				UpperCAmelCase  :				Union[str, Any]   ,				UpperCAmelCase  :				List[str]   ,				UpperCAmelCase  :				List[Any]					):


					'''simple docstring'''






					UpperCamelCase__		:     Any								=param.size()
					if checkpoint_version == 1.0:
										# version 1.0 stores [num_heads * hidden_size * num_splits, :]
										UpperCamelCase__		:     Tuple								=(num_heads, hidden_size, num_splits) + input_shape[1:]
										UpperCamelCase__		:     List[Any]								=param.view(*UpperCAmelCase					)
										UpperCamelCase__		:     List[Any]								=param.transpose(0   ,				2					)
										UpperCamelCase__		:     int								=param.transpose(1   ,				2					).contiguous()
					elif checkpoint_version >= 2.0:
										# other versions store [num_heads * num_splits * hidden_size, :]
										UpperCamelCase__		:     int								=(num_heads, num_splits, hidden_size) + input_shape[1:]
										UpperCamelCase__		:     Any								=param.view(*UpperCAmelCase					)
										UpperCamelCase__		:     Any								=param.transpose(0   ,				1					).contiguous()
					UpperCamelCase__		:     List[str]								=param.view(*UpperCAmelCase					)
					return param



def 							_lowerCAmelCase   (    UpperCAmelCase  :				Optional[int]   ,				UpperCAmelCase  :				Tuple   ,				UpperCAmelCase  :				int					):


					'''simple docstring'''






					UpperCamelCase__		:     int								={}

					# old versions did not store training args
					UpperCamelCase__		:     Optional[int]								=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))

										UpperCamelCase__		:     List[str]								=ds_args.tokenizer_type
										UpperCamelCase__		:     str								=ds_args.padded_vocab_size
										UpperCamelCase__		:     List[str]								=ds_args.max_position_embeddings
										UpperCamelCase__		:     Any								=ds_args.hidden_size
										UpperCamelCase__		:     Optional[Any]								=ds_args.num_layers
										UpperCamelCase__		:     Optional[Any]								=ds_args.num_attention_heads
										UpperCamelCase__		:     Optional[Any]								=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__		:     List[str]								=config.num_attention_heads
					# The hidden_size per head.
					UpperCamelCase__		:     Union[str, Any]								=config.hidden_size // heads
					# Megatron-LM checkpoint version
					if "checkpoint_version" in input_state_dict.keys():
										UpperCamelCase__		:     Optional[Any]								=input_state_dict['''checkpoint_version''']
					else:
										UpperCamelCase__		:     int								=0.0

					# The model.
					UpperCamelCase__		:     Optional[int]								=input_state_dict['''model''']
					# The language model.
					UpperCamelCase__		:     int								=model['''language_model''']
					# The embeddings.
					UpperCamelCase__		:     Any								=lm['''embedding''']

					# The word embeddings.
					UpperCamelCase__		:     Union[str, Any]								=embeddings['''word_embeddings''']['''weight''']
					# Truncate the embedding table to vocab_size rows.
					UpperCamelCase__		:     Optional[Any]								=word_embeddings[: config.vocab_size, :]
					# Store the word embeddings.
					UpperCamelCase__		:     List[str]								=word_embeddings

					# The position embeddings.
					UpperCamelCase__		:     Dict								=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__		:     List[str]								=pos_embeddings

					# The token-type embeddings.
					UpperCamelCase__		:     str								=embeddings['''tokentype_embeddings''']['''weight''']
					# Store the position embeddings.
					UpperCamelCase__		:     List[str]								=tokentype_embeddings

					# The transformer.
					UpperCamelCase__		:     Optional[int]								=lm['''transformer'''] if '''transformer''' in lm.keys() else lm['''encoder''']

					# The regex to extract layer names.
					UpperCamelCase__		:     int								=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__		:     Tuple								=None

					# Extract the layers.
					for key, val in transformer.items():
										# Match the name.
										UpperCamelCase__		:     Union[str, Any]								=layer_re.match(UpperCAmelCase					)

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

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

										# The name of the layer.
										UpperCamelCase__		:     str								=F'''bert.encoder.layer.{layer_idx}'''

										# For layernorm(s), simply store the layer norm.
										if op_name.endswith('''layernorm'''					):
															UpperCamelCase__		:     int								='''attention.ln''' if op_name.startswith('''input'''					) else '''ln'''
															UpperCamelCase__		:     Tuple								=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__		:     Any								=fix_query_key_value_ordering(UpperCAmelCase   ,				UpperCAmelCase   ,				3   ,				UpperCAmelCase   ,				UpperCAmelCase					)
															# Store the tensor as we need the bias as well to interleave QKV and biases.
															UpperCamelCase__		:     Tuple								=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__		:     Tuple								=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__		:     str								=attention_qkv_weight[2 * config.hidden_size : 3 * config.hidden_size, :]

															UpperCamelCase__		:     int								=fix_query_key_value_ordering(UpperCAmelCase   ,				UpperCAmelCase   ,				3   ,				UpperCAmelCase   ,				UpperCAmelCase					)
															# Split the bias.
															UpperCamelCase__		:     Any								=out_val[0 * config.hidden_size : 1 * config.hidden_size]
															UpperCamelCase__		:     Any								=out_val[1 * config.hidden_size : 2 * config.hidden_size]
															UpperCamelCase__		:     List[Any]								=out_val[2 * config.hidden_size : 3 * config.hidden_size]

															# Store.
															UpperCamelCase__		:     int								=q
															UpperCamelCase__		:     Dict								=q_bias
															UpperCamelCase__		:     List[Any]								=k
															UpperCamelCase__		:     Optional[int]								=k_bias
															UpperCamelCase__		:     Tuple								=v
															UpperCamelCase__		:     Optional[Any]								=v_bias

															# Clear the stored tensor.
															UpperCamelCase__		:     Optional[Any]								=None

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

    # The final layernorm.
					UpperCamelCase__		:     Dict								=transformer['''final_layernorm.weight''']
					UpperCamelCase__		:     List[str]								=transformer['''final_layernorm.bias''']

					# The pooler.
					UpperCamelCase__		:     str								=lm['''pooler''']

					# Store the matrix and the bias.
					UpperCamelCase__		:     Optional[Any]								=pooler['''dense.weight''']
					UpperCamelCase__		:     Tuple								=pooler['''dense.bias''']

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

					# The transform matrix.
					UpperCamelCase__		:     Optional[Any]								=lm_head['''dense.weight''']
					UpperCamelCase__		:     Union[str, Any]								=lm_head['''dense.bias''']

					# The transform LN.
					UpperCamelCase__		:     Optional[int]								=lm_head['''layernorm.weight''']
					UpperCamelCase__		:     Dict								=lm_head['''layernorm.bias''']

					# For the decoder, we replicate the weights.
					UpperCamelCase__		:     Any								=word_embeddings
					UpperCamelCase__		:     Optional[Any]								=lm_head['''bias''']

					# The classifier from Megatron (for MLNI).
					UpperCamelCase__		:     Union[str, Any]								=model['''binary_head''']

					# Store the classifier.
					UpperCamelCase__		:     Union[str, Any]								=binary_head['''weight''']
					UpperCamelCase__		:     int								=binary_head['''bias''']

					# It should be done!
					return output_state_dict



def 							_lowerCAmelCase   (    ):


					'''simple docstring'''






					UpperCamelCase__		:     Dict								=argparse.ArgumentParser()
					parser.add_argument('''--print-checkpoint-structure'''   ,				action='''store_true'''					)
					parser.add_argument('''path_to_checkpoint'''   ,				type=UpperCAmelCase   ,				help='''Path to the ZIP file containing the checkpoint'''					)
					parser.add_argument(
					    '''--config_file'''   ,				default=''''''   ,				type=UpperCAmelCase   ,				help='''An optional config json file describing the pre-trained model.'''   ,				)
					UpperCamelCase__		:     Dict								=parser.parse_args()

					# Extract the basename.
					UpperCamelCase__		:     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:
																				UpperCamelCase__		:     Optional[int]								=torch.load(UpperCAmelCase   ,				map_location='''cpu'''					)
					else:
										UpperCamelCase__		:     Optional[Any]								=torch.load(args.path_to_checkpoint   ,				map_location='''cpu'''					)

					if args.config_file == "":
										# Default config of megatron-bert 345m
										UpperCamelCase__		:     Union[str, Any]								=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__		:     Tuple								=input_state_dict['''model''']['''lm_head''']['''bias'''].numel()
					else:
										UpperCamelCase__		:     Any								=MegatronBertConfig.from_json_file(args.config_file					)

					# Convert.
					print('''Converting'''					)
					UpperCamelCase__		:     str								=convert_megatron_checkpoint(UpperCAmelCase   ,				UpperCAmelCase   ,				UpperCAmelCase					)

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

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

					# Store the state_dict to file.
					UpperCamelCase__		:     Union[str, Any]								=os.path.join(UpperCAmelCase   ,				'''pytorch_model.bin'''					)
					print(F'''Saving checkpoint to "{output_checkpoint_file}"'''					)
					torch.save(UpperCAmelCase   ,				UpperCAmelCase					)


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

if __name__ == "__main__":
				main()

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


