
'''simple docstring'''


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()
__SCREAMING_SNAKE_CASE  =			logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE  =			"Hello world! cécé herlolip"
def 				__magic_name__(					UpperCAmelCase__   :					str       ,							UpperCAmelCase__   :					str       ,							UpperCAmelCase__   :					bool  ):
						UpperCamelCase_    :  Tuple				    =    os.path.split(UpperCAmelCase__  )
						UpperCamelCase_    :  Union[str, Any]				    =    DataaVecTextModel.from_pretrained(
						    UpperCAmelCase__       ,							checkpoint_file=UpperCAmelCase__  )
						dataavec.eval()  # disable dropout
						UpperCamelCase_    :  Optional[Any]				    =    dataavec.models[0]
						UpperCamelCase_    :  Optional[int]				    =    dataavec_model.encoder.sentence_encoder
						UpperCamelCase_    :  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=514       ,							type_vocab_size=1       ,							layer_norm_eps=1E-5       ,							)
						if classification_head:
												UpperCamelCase_    :  int				    =    dataavec.model.classification_heads["""mnli"""].out_proj.weight.shape[0]
						print("""Our BERT config:"""       ,							UpperCAmelCase__  )

						UpperCamelCase_    :  Optional[Any]				    =    DataaVecTextForSequenceClassification(UpperCAmelCase__  ) if classification_head else DataaVecTextForMaskedLM(UpperCAmelCase__  )
						model.eval()

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

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

												# self attention
												UpperCamelCase_    :  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)  )}"""
												)

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

												# self-attention output
												UpperCamelCase_    :  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}"""
												UpperCamelCase_    :  Tuple				    =    dataavec_layer.self_attn.out_proj.weight
												UpperCamelCase_    :  List[str]				    =    dataavec_layer.self_attn.out_proj.bias
												UpperCamelCase_    :  Union[str, Any]				    =    dataavec_layer.self_attn_layer_norm.weight
												UpperCamelCase_    :  int				    =    dataavec_layer.self_attn_layer_norm.bias

												# intermediate
												UpperCamelCase_    :  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}"""
												UpperCamelCase_    :  Tuple				    =    dataavec_layer.fca.weight
												UpperCamelCase_    :  List[Any]				    =    dataavec_layer.fca.bias

												# output
												UpperCamelCase_    :  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}"""
												UpperCamelCase_    :  Optional[Any]				    =    dataavec_layer.fca.weight
												UpperCamelCase_    :  Optional[int]				    =    dataavec_layer.fca.bias
												UpperCamelCase_    :  Union[str, Any]				    =    dataavec_layer.final_layer_norm.weight
												UpperCamelCase_    :  Union[str, Any]				    =    dataavec_layer.final_layer_norm.bias
												# end of layer

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

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

						UpperCamelCase_    :  str				    =    model(UpperCAmelCase__  )[0]
						if classification_head:
												UpperCamelCase_    :  List[str]				    =    dataavec.model.classification_heads["""mnli"""](dataavec.extract_features(UpperCAmelCase__  )  )
						else:
												UpperCamelCase_    :  Optional[int]				    =    dataavec_model(UpperCAmelCase__  )[0]
						print(our_output.shape       ,							their_output.shape  )
						UpperCamelCase_    :  Optional[int]				    =    torch.max(torch.abs(our_output - their_output  )  ).item()
						print(f"""max_absolute_diff = {max_absolute_diff}"""  )  # ~ 1e-7
						UpperCamelCase_    :  List[Any]				    =    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__":
	__SCREAMING_SNAKE_CASE  =			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."
	)
	__SCREAMING_SNAKE_CASE  =			parser.parse_args()
	convert_dataavec_checkpoint_to_pytorch(
	    args.checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
	)


'''simple docstring'''


import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_mpnet import MPNetTokenizer


__SCREAMING_SNAKE_CASE  =			logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE  =			{"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}

__SCREAMING_SNAKE_CASE  =			{
    "vocab_file": {
        "microsoft/mpnet-base": "https://huggingface.co/microsoft/mpnet-base/resolve/main/vocab.txt",
    },
    "tokenizer_file": {
        "microsoft/mpnet-base": "https://huggingface.co/microsoft/mpnet-base/resolve/main/tokenizer.json",
    },
}

__SCREAMING_SNAKE_CASE  =			{
    "microsoft/mpnet-base": 512,
}

__SCREAMING_SNAKE_CASE  =			{
    "microsoft/mpnet-base": {"do_lower_case": True},
}




class 						__snake_case (						_a  ):
		a_        =				VOCAB_FILES_NAMES
		a_        =				PRETRAINED_VOCAB_FILES_MAP
		a_        =				PRETRAINED_INIT_CONFIGURATION
		a_        =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		a_        =				MPNetTokenizer
		a_        =				["input_ids", "attention_mask"]




		def __init__(     self				:Tuple ,						_SCREAMING_SNAKE_CASE				:Dict=None ,						_SCREAMING_SNAKE_CASE				:List[Any]=None ,						_SCREAMING_SNAKE_CASE				:Optional[Any]=True ,						_SCREAMING_SNAKE_CASE				:Any="<s>" ,						_SCREAMING_SNAKE_CASE				:int="</s>" ,						_SCREAMING_SNAKE_CASE				:Optional[Any]="</s>" ,						_SCREAMING_SNAKE_CASE				:Any="<s>" ,						_SCREAMING_SNAKE_CASE				:Optional[int]="[UNK]" ,						_SCREAMING_SNAKE_CASE				:List[Any]="<pad>" ,						_SCREAMING_SNAKE_CASE				:int="<mask>" ,						_SCREAMING_SNAKE_CASE				:Union[str, Any]=True ,						_SCREAMING_SNAKE_CASE				:Union[str, Any]=None ,						**_SCREAMING_SNAKE_CASE				:Optional[int] ,						)				-> Optional[Any]:






								'''simple docstring'''






								super().__init__(
								    _SCREAMING_SNAKE_CASE ,						tokenizer_file=_SCREAMING_SNAKE_CASE ,						do_lower_case=_SCREAMING_SNAKE_CASE ,						bos_token=_SCREAMING_SNAKE_CASE ,						eos_token=_SCREAMING_SNAKE_CASE ,						sep_token=_SCREAMING_SNAKE_CASE ,						cls_token=_SCREAMING_SNAKE_CASE ,						unk_token=_SCREAMING_SNAKE_CASE ,						pad_token=_SCREAMING_SNAKE_CASE ,						mask_token=_SCREAMING_SNAKE_CASE ,						tokenize_chinese_chars=_SCREAMING_SNAKE_CASE ,						strip_accents=_SCREAMING_SNAKE_CASE ,						**_SCREAMING_SNAKE_CASE ,						)

								UpperCamelCase_    :  Dict				    =    json.loads(self.backend_tokenizer.normalizer.__getstate__())
								if (
								    pre_tok_state.get("""lowercase""" ,						_SCREAMING_SNAKE_CASE) != do_lower_case
								    or pre_tok_state.get("""strip_accents""" ,						_SCREAMING_SNAKE_CASE) != strip_accents
								):
														UpperCamelCase_    :  Any				    =    getattr(_SCREAMING_SNAKE_CASE ,						pre_tok_state.pop("""type"""))
														UpperCamelCase_    :  Optional[int]				    =    do_lower_case
														UpperCamelCase_    :  str				    =    strip_accents
														UpperCamelCase_    :  str				    =    pre_tok_class(**_SCREAMING_SNAKE_CASE)

								UpperCamelCase_    :  List[str]				    =    do_lower_case




		@property
		def   _snake_case  (     self				:Optional[int])				-> str:






								'''simple docstring'''






								if self._mask_token is None:
														if self.verbose:
																				logger.error("""Using mask_token, but it is not set yet.""")
														return None
								return str(self._mask_token)




		@mask_token.setter
		def   _snake_case  (     self				:Optional[Any] ,						_SCREAMING_SNAKE_CASE				:List[Any])				-> int:






								'''simple docstring'''






								UpperCamelCase_    :  Tuple				    =    AddedToken(_SCREAMING_SNAKE_CASE ,						lstrip=_SCREAMING_SNAKE_CASE ,						rstrip=_SCREAMING_SNAKE_CASE) if isinstance(_SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE) else value
								UpperCamelCase_    :  int				    =    value




		def   _snake_case  (     self				:str ,						_SCREAMING_SNAKE_CASE				:int ,						_SCREAMING_SNAKE_CASE				:Any=None)				-> str:






								'''simple docstring'''






								UpperCamelCase_    :  Tuple				    =    [self.bos_token_id] + token_ids_a + [self.eos_token_id]
								if token_ids_a is None:
														return output

								return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]




		def   _snake_case  (     self				:Optional[Any] ,						_SCREAMING_SNAKE_CASE				:List[int] ,						_SCREAMING_SNAKE_CASE				:Optional[List[int]] = None)				-> List[int]:






								'''simple docstring'''






								UpperCamelCase_    :  Optional[Any]				    =    [self.sep_token_id]
								UpperCamelCase_    :  Dict				    =    [self.cls_token_id]

								if token_ids_a is None:
														return len(cls + token_ids_a + sep) * [0]
								return len(cls + token_ids_a + sep + sep + token_ids_a + sep) * [0]





		def   _snake_case  (     self				:List[str] ,						_SCREAMING_SNAKE_CASE				:str ,						_SCREAMING_SNAKE_CASE				:Optional[str] = None)				-> Tuple[str]:






								'''simple docstring'''






								UpperCamelCase_    :  List[str]				    =    self._tokenizer.model.save(_SCREAMING_SNAKE_CASE ,						name=_SCREAMING_SNAKE_CASE)
								return tuple(_SCREAMING_SNAKE_CASE)




