






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()
_UpperCAmelCase					:						Optional[int]         =     logging.get_logger(__name__)

_UpperCAmelCase					:						Union[str, Any]         =     'Hello world! cécé herlolip'






def  UpperCAmelCase__						(    lowerCamelCase	:			str			,lowerCamelCase	:			str			,lowerCamelCase	:			bool    ):
   lowerCamelCase				,       lowerCamelCase									=os.path.split(lowerCamelCase    )
   lowerCamelCase									=DataaVecTextModel.from_pretrained(
       lowerCamelCase			,checkpoint_file=lowerCamelCase    )
   dataavec.eval()  # disable dropout
   lowerCamelCase									=dataavec.models[0]
   lowerCamelCase									=dataavec_model.encoder.sentence_encoder
   lowerCamelCase									=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:
      lowerCamelCase									=dataavec.model.classification_heads["""mnli"""].out_proj.weight.shape[0]
   print("""Our BERT config:"""			,lowerCamelCase    )

   lowerCamelCase									=DataaVecTextForSequenceClassification(lowerCamelCase    ) if classification_head else DataaVecTextForMaskedLM(lowerCamelCase    )
   model.eval()

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

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

      # self attention
      lowerCamelCase									=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)    )}'''
      )

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

      # self-attention output
      lowerCamelCase									=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}'''
      lowerCamelCase									=dataavec_layer.self_attn.out_proj.weight
      lowerCamelCase									=dataavec_layer.self_attn.out_proj.bias
      lowerCamelCase									=dataavec_layer.self_attn_layer_norm.weight
      lowerCamelCase									=dataavec_layer.self_attn_layer_norm.bias

      # intermediate
      lowerCamelCase									=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}'''
      lowerCamelCase									=dataavec_layer.fca.weight
      lowerCamelCase									=dataavec_layer.fca.bias

      # output
      lowerCamelCase									=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}'''
      lowerCamelCase									=dataavec_layer.fca.weight
      lowerCamelCase									=dataavec_layer.fca.bias
      lowerCamelCase									=dataavec_layer.final_layer_norm.weight
      lowerCamelCase									=dataavec_layer.final_layer_norm.bias
      # end of layer

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

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

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

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


if __name__ == "__main__":
   _UpperCAmelCase					:						List[Any]         =     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.'
   )
   _UpperCAmelCase					:						int         =     parser.parse_args()
   convert_dataavec_checkpoint_to_pytorch(
       args.checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
   )







def  UpperCAmelCase__						(    lowerCamelCase	:			list    ):
   lowerCamelCase									=1.3
   lowerCamelCase									=len(lowerCamelCase    )
   lowerCamelCase									=False

   while not completed:
      # Update the gap value for a next comb
      lowerCamelCase									=int(gap / shrink_factor    )
      if gap <= 1:
         lowerCamelCase									=True

      lowerCamelCase									=0
      while index + gap < len(lowerCamelCase    ):
         if data[index] > data[index + gap]:
            # Swap values
            lowerCamelCase				,       lowerCamelCase									=data[index + gap], data[index]
            lowerCamelCase									=False
         index += 1

   return data


if __name__ == "__main__":
   import doctest

   doctest.testmod()

   _UpperCAmelCase					:						List[Any]         =     input('Enter numbers separated by a comma:\n').strip()
   _UpperCAmelCase					:						Optional[Any]         =     [int(item) for item in user_input.split(',')]
   print(comb_sort(unsorted))