





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()
_A				:							Optional[Any]							= logging.get_logger(__name__)

_A				:							List[Any]							= """Hello world! cécé herlolip"""


def        __snake_case       (						lowerCAmelCase_   ,       lowerCAmelCase_   ,       lowerCAmelCase_  )							->	Union[str, Any]:
    SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__       							= os.path.split(lowerCAmelCase_  )
    SCREAMING_SNAKE_CASE__       							= DataaVecTextModel.from_pretrained(
        lowerCAmelCase_   ,       checkpoint_file=lowerCAmelCase_  )
    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_1_4   ,       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:'''   ,       lowerCAmelCase_  )

    SCREAMING_SNAKE_CASE__       							= DataaVecTextForSequenceClassification(lowerCAmelCase_  ) if classification_head else DataaVecTextForMaskedLM(lowerCAmelCase_  )
    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(lowerCAmelCase_  ).unsqueeze(0  )  # batch of size 1

    SCREAMING_SNAKE_CASE__       							= model(lowerCAmelCase_  )[0]
    if classification_head:
        SCREAMING_SNAKE_CASE__       							= dataavec.model.classification_heads['''mnli'''](dataavec.extract_features(lowerCAmelCase_  )  )
    else:
        SCREAMING_SNAKE_CASE__       							= dataavec_model(lowerCAmelCase_  )[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(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__":
  _A				:							Optional[int]							= 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."""
  )
  _A				:							Dict							= parser.parse_args()
  convert_dataavec_checkpoint_to_pytorch(
      args.checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
  )







import numpy as np

_A				:							List[Any]							= [
    ["""a""", """b""", """c""", """d""", """e"""],
    ["""f""", """g""", """h""", """i""", """k"""],
    ["""l""", """m""", """n""", """o""", """p"""],
    ["""q""", """r""", """s""", """t""", """u"""],
    ["""v""", """w""", """x""", """y""", """z"""],
]



class 				__snake_case  :


    '''simple docstring'''







    def __init__(					self    ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__       							= np.array(A_    )



    def    lowercase_				(					self      ,							A_    ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__       							= np.where(letter == self.SQUARE    )
        SCREAMING_SNAKE_CASE__       							= np.concatenate([indexa + 1, indexa + 1]    )
        return indexes



    def    lowercase_				(					self      ,							A_      ,							A_    ):





        '''simple docstring'''

        return self.SQUARE[indexa - 1, indexa - 1]



    def    lowercase_				(					self      ,							A_    ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__       							= message.lower()
        SCREAMING_SNAKE_CASE__       							= message.replace('''j'''      ,							'''i'''    )

        SCREAMING_SNAKE_CASE__       							= ''''''
        for letter_index in range(len(A_    )    ):
            if message[letter_index] != " ":
                SCREAMING_SNAKE_CASE__       							= self.letter_to_numbers(message[letter_index]    )
                SCREAMING_SNAKE_CASE__       							= encoded_message + str(numbers[0]    ) + str(numbers[1]    )
            elif message[letter_index] == " ":
                SCREAMING_SNAKE_CASE__       							= encoded_message + ''' '''

        return encoded_message




    def    lowercase_				(					self      ,							A_    ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__       							= message.replace(''' '''      ,							'''  '''    )
        SCREAMING_SNAKE_CASE__       							= ''''''
        for numbers_index in range(int(len(A_    ) / 2    )    ):
            if message[numbers_index * 2] != " ":
                SCREAMING_SNAKE_CASE__       							= message[numbers_index * 2]
                SCREAMING_SNAKE_CASE__       							= message[numbers_index * 2 + 1]

                SCREAMING_SNAKE_CASE__       							= self.numbers_to_letter(int(A_    )      ,							int(A_    )    )
                SCREAMING_SNAKE_CASE__       							= decoded_message + letter
            elif message[numbers_index * 2] == " ":
                SCREAMING_SNAKE_CASE__       							= decoded_message + ''' '''

        return decoded_message
