


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"""




from .hash_table import HashTable
from .number_theory.prime_numbers import is_prime, next_prime




class 					_a		( _lowerCAmelCase							):






				def __init__(					self						:       Tuple,					*lowerCAmelCase__						:       Tuple,					**lowerCAmelCase__						:       Optional[int]				)      -> Union[str, Any]:




										'''simple docstring'''



										super().__init__(*lowerCAmelCase__,					**lowerCAmelCase__				)






				def 							snake_case      (					self						:       Any,					lowerCAmelCase__						:       Dict,					lowerCAmelCase__						:       Tuple				)      -> Optional[Any]:




										'''simple docstring'''



										_UpperCamelCase     :   Tuple      =       (
										    next_prime(value % self.size_table				)
										    if not is_prime(value % self.size_table				)
										    else value % self.size_table
										)  # gt = bigger than
										return next_prime_gt - (data % next_prime_gt)






				def 							snake_case      (					self						:       int,					lowerCAmelCase__						:       Any,					lowerCAmelCase__						:       List[Any],					lowerCAmelCase__						:       Tuple				)      -> Any:




										'''simple docstring'''



										return (increment * self.__hash_function_a(lowerCAmelCase__,					lowerCAmelCase__				)) % self.size_table






				def 							snake_case      (					self						:       Dict,					lowerCAmelCase__						:       str,					lowerCAmelCase__						:       Optional[Any]=None				)      -> Union[str, Any]:




										'''simple docstring'''



										_UpperCamelCase     :   Tuple      =       1
										_UpperCamelCase     :   Dict      =       self.hash_function(lowerCAmelCase__				)

										while self.values[new_key] is not None and self.values[new_key] != key:
																_UpperCamelCase     :   Optional[int]      =       (
																    self.__hash_double_function(lowerCAmelCase__,					lowerCAmelCase__,					lowerCAmelCase__				)
																    if self.balanced_factor() >= self.lim_charge
																    else None
																)
																if new_key is None:
																						break
																else:
																						i += 1

										return new_key



