

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__										=      logging.get_logger(__name__)

A__										=      "Hello world! cécé herlolip"





def 				__a							(       __A			,							__A			,							__A						):
    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=514			,							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__":
  A__										=      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__										=      parser.parse_args()
  convert_dataavec_checkpoint_to_pytorch(
      args.checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
  )





from typing import Optional

from ..utils.logging import get_logger
from .audio_classificiation import AudioClassification
from .automatic_speech_recognition import AutomaticSpeechRecognition
from .base import TaskTemplate
from .image_classification import ImageClassification
from .language_modeling import LanguageModeling
from .question_answering import QuestionAnsweringExtractive
from .summarization import Summarization
from .text_classification import TextClassification


A__										=      [
    "AutomaticSpeechRecognition",
    "AudioClassification",
    "ImageClassification",
    "LanguageModeling",
    "QuestionAnsweringExtractive",
    "Summarization",
    "TaskTemplate",
    "TextClassification",
]

A__										=      get_logger(__name__)


A__										=      {
    AutomaticSpeechRecognition.task: AutomaticSpeechRecognition,
    AudioClassification.task: AudioClassification,
    ImageClassification.task: ImageClassification,
    LanguageModeling.task: LanguageModeling,
    QuestionAnsweringExtractive.task: QuestionAnsweringExtractive,
    Summarization.task: Summarization,
    TextClassification.task: TextClassification,
}





def 				__a							(       __A						):
    SCREAMING_SNAKE_CASE__         =    task_template_dict.get("task"						)
    if task_name is None:
        logger.warning(f"Couldn't find template for task '{task_name}'. Available templates: {list(__A						)}"						)
        return None
    SCREAMING_SNAKE_CASE__         =    NAMEaTEMPLATE.get(__A						)
    return template.from_dict(__A						)


