




'''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()
lowerCamelCase_	 =		logging.get_logger(__name__)

lowerCamelCase_	 =		'Hello world! cécé herlolip'
def 				SCREAMING_SNAKE_CASE_				(							__A       :				str ,  __A       :				str ,  __A       :				bool						)				->				int:
   _SCREAMING_SNAKE_CASE, _SCREAMING_SNAKE_CASE =   os.path.split(__A						)
   _SCREAMING_SNAKE_CASE =   DataaVecTextModel.from_pretrained(
       __A ,  checkpoint_file=__A						)
   dataavec.eval()  # disable dropout
   _SCREAMING_SNAKE_CASE =   dataavec.models[0]
   _SCREAMING_SNAKE_CASE =   dataavec_model.encoder.sentence_encoder
   _SCREAMING_SNAKE_CASE =   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_14 ,  type_vocab_size=1 ,  layer_norm_eps=1e-5 ,  )
   if classification_head:
      _SCREAMING_SNAKE_CASE =   dataavec.model.classification_heads["mnli"].out_proj.weight.shape[0]
   print("Our BERT config:" ,  __A						)

   _SCREAMING_SNAKE_CASE =   DataaVecTextForSequenceClassification(__A						) if classification_head else DataaVecTextForMaskedLM(__A						)
   model.eval()

   # Now let's copy all the weights.
   # Embeddings
   _SCREAMING_SNAKE_CASE =   dataavec_sent_encoder.embed_tokens.weight
   _SCREAMING_SNAKE_CASE =   dataavec_sent_encoder.embed_positions.weight
   _SCREAMING_SNAKE_CASE =   torch.zeros_like(
       model.dataavec_text.embeddings.token_type_embeddings.weight						)  # just zero them out b/c data2vec doesn't use them.
   _SCREAMING_SNAKE_CASE =   dataavec_sent_encoder.layernorm_embedding.weight
   _SCREAMING_SNAKE_CASE =   dataavec_sent_encoder.layernorm_embedding.bias

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

      # self attention
      _SCREAMING_SNAKE_CASE =   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)						)}"""
      )

      _SCREAMING_SNAKE_CASE =   dataavec_layer.self_attn.q_proj.weight
      _SCREAMING_SNAKE_CASE =   dataavec_layer.self_attn.q_proj.bias
      _SCREAMING_SNAKE_CASE =   dataavec_layer.self_attn.k_proj.weight
      _SCREAMING_SNAKE_CASE =   dataavec_layer.self_attn.k_proj.bias
      _SCREAMING_SNAKE_CASE =   dataavec_layer.self_attn.v_proj.weight
      _SCREAMING_SNAKE_CASE =   dataavec_layer.self_attn.v_proj.bias

      # self-attention output
      _SCREAMING_SNAKE_CASE =   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}"""
      _SCREAMING_SNAKE_CASE =   dataavec_layer.self_attn.out_proj.weight
      _SCREAMING_SNAKE_CASE =   dataavec_layer.self_attn.out_proj.bias
      _SCREAMING_SNAKE_CASE =   dataavec_layer.self_attn_layer_norm.weight
      _SCREAMING_SNAKE_CASE =   dataavec_layer.self_attn_layer_norm.bias

      # intermediate
      _SCREAMING_SNAKE_CASE =   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}"""
      _SCREAMING_SNAKE_CASE =   dataavec_layer.fca.weight
      _SCREAMING_SNAKE_CASE =   dataavec_layer.fca.bias

      # output
      _SCREAMING_SNAKE_CASE =   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}"""
      _SCREAMING_SNAKE_CASE =   dataavec_layer.fca.weight
      _SCREAMING_SNAKE_CASE =   dataavec_layer.fca.bias
      _SCREAMING_SNAKE_CASE =   dataavec_layer.final_layer_norm.weight
      _SCREAMING_SNAKE_CASE =   dataavec_layer.final_layer_norm.bias
      # end of layer

   if classification_head:
      _SCREAMING_SNAKE_CASE =   dataavec.model.classification_heads["mnli"].dense.weight
      _SCREAMING_SNAKE_CASE =   dataavec.model.classification_heads["mnli"].dense.bias
      _SCREAMING_SNAKE_CASE =   dataavec.model.classification_heads["mnli"].out_proj.weight
      _SCREAMING_SNAKE_CASE =   dataavec.model.classification_heads["mnli"].out_proj.bias
   else:
      # LM Head
      _SCREAMING_SNAKE_CASE =   dataavec_model.encoder.lm_head.dense.weight
      _SCREAMING_SNAKE_CASE =   dataavec_model.encoder.lm_head.dense.bias
      _SCREAMING_SNAKE_CASE =   dataavec_model.encoder.lm_head.layer_norm.weight
      _SCREAMING_SNAKE_CASE =   dataavec_model.encoder.lm_head.layer_norm.bias
      _SCREAMING_SNAKE_CASE =   dataavec_model.encoder.lm_head.weight
      _SCREAMING_SNAKE_CASE =   dataavec_model.encoder.lm_head.bias

   # Let's check that we get the same results.
   _SCREAMING_SNAKE_CASE =   dataavec.encode(__A						).unsqueeze(0						)  # batch of size 1

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

   pathlib.Path(__A						).mkdir(parents=__A ,  exist_ok=__A						)
   print(f"""Saving model to {pytorch_dump_folder_path}"""						)
   model.save_pretrained(__A						)


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'''



def 				SCREAMING_SNAKE_CASE_				(							__A       :				List[Any] ,  __A       :				Dict						)				->				Union[str, Any]:
   if n == 0:
      return 1

   elif n % 2 == 1:
      return binary_exponentiation(__A ,  n - 1						) * a

   else:
      _SCREAMING_SNAKE_CASE =   binary_exponentiation(__A ,  n / 2						)
      return b * b


if __name__ == "__main__":
 try:
  lowerCamelCase_	 =		int(input('Enter Base : ').strip())
  lowerCamelCase_	 =		int(input('Enter Power : ').strip())
 except ValueError:
  print('Invalid literal for integer')

 lowerCamelCase_	 =		binary_exponentiation(BASE, POWER)
 print(f'''{BASE}^({POWER}) : {RESULT}''')

