





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       : List[str]						    = logging.get_logger(__name__)

__A       : int						    = "Hello world! cécé herlolip"





def 							__lowerCAmelCase(					_SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE							)       ->   int:




    """simple docstring"""




    _A, _A    =       os.path.split(_SCREAMING_SNAKE_CASE							)
    _A    =       DataaVecTextModel.from_pretrained(
        _SCREAMING_SNAKE_CASE       ,    checkpoint_file=_SCREAMING_SNAKE_CASE							)
    dataavec.eval()  # disable dropout
    _A    =       dataavec.models[0]
    _A    =       dataavec_model.encoder.sentence_encoder
    _A    =       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:
        _A    =       dataavec.model.classification_heads['mnli'].out_proj.weight.shape[0]
    print('Our BERT config:'       ,    _SCREAMING_SNAKE_CASE							)

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

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

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

        # self attention
        _A    =       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    =       dataavec_layer.self_attn.q_proj.weight
        _A    =       dataavec_layer.self_attn.q_proj.bias
        _A    =       dataavec_layer.self_attn.k_proj.weight
        _A    =       dataavec_layer.self_attn.k_proj.bias
        _A    =       dataavec_layer.self_attn.v_proj.weight
        _A    =       dataavec_layer.self_attn.v_proj.bias

        # self-attention output
        _A    =       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    =       dataavec_layer.self_attn.out_proj.weight
        _A    =       dataavec_layer.self_attn.out_proj.bias
        _A    =       dataavec_layer.self_attn_layer_norm.weight
        _A    =       dataavec_layer.self_attn_layer_norm.bias

        # intermediate
        _A    =       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    =       dataavec_layer.fca.weight
        _A    =       dataavec_layer.fca.bias

        # output
        _A    =       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    =       dataavec_layer.fca.weight
        _A    =       dataavec_layer.fca.bias
        _A    =       dataavec_layer.final_layer_norm.weight
        _A    =       dataavec_layer.final_layer_norm.bias
        # end of layer

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

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

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

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


if __name__ == "__main__":
 __A       : str						    = 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       : int						    = parser.parse_args()
 convert_dataavec_checkpoint_to_pytorch(
     args.checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
 )












from sklearn.metrics import accuracy_score

import datasets


__A       : Union[str, Any]						    = "\nAccuracy is the proportion of correct predictions among the total number of cases processed. It can be computed with:\nAccuracy = (TP + TN) / (TP + TN + FP + FN)\n Where:\nTP: True positive\nTN: True negative\nFP: False positive\nFN: False negative\n"


__A       : List[Any]						    = "\nArgs:\n    predictions (`list` of `int`): Predicted labels.\n    references (`list` of `int`): Ground truth labels.\n    normalize (`boolean`): If set to False, returns the number of correctly classified samples. Otherwise, returns the fraction of correctly classified samples. Defaults to True.\n    sample_weight (`list` of `float`): Sample weights Defaults to None.\n\nReturns:\n    accuracy (`float` or `int`): Accuracy score. Minimum possible value is 0. Maximum possible value is 1.0, or the number of examples input, if `normalize` is set to `True`.. A higher score means higher accuracy.\n\nExamples:\n\n    Example 1-A simple example\n        >>> accuracy_metric = datasets.load_metric(\"accuracy\")\n        >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0])\n        >>> print(results)\n        {'accuracy': 0.5}\n\n    Example 2-The same as Example 1, except with `normalize` set to `False`.\n        >>> accuracy_metric = datasets.load_metric(\"accuracy\")\n        >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0], normalize=False)\n        >>> print(results)\n        {'accuracy': 3.0}\n\n    Example 3-The same as Example 1, except with `sample_weight` set.\n        >>> accuracy_metric = datasets.load_metric(\"accuracy\")\n        >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0], sample_weight=[0.5, 2, 0.7, 0.5, 9, 0.4])\n        >>> print(results)\n        {'accuracy': 0.8778625954198473}\n"


__A       : List[str]						    = "\n@article{scikit-learn,\n  title={Scikit-learn: Machine Learning in {P}ython},\n  author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.\n         and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.\n         and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and\n         Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},\n  journal={Journal of Machine Learning Research},\n  volume={12},\n  pages={2825--2830},\n  year={2011}\n}\n"







@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION ,   _KWARGS_DESCRIPTION  )
class     lowerCamelCase(	datasets.Metric  ):
     '''simple docstring'''


     def 					lowerCAmelCase__		(						self       ):
         return datasets.MetricInfo(
             description=_DESCRIPTION							,    citation=_CITATION							,    inputs_description=_KWARGS_DESCRIPTION							,    features=datasets.Features(
                 {
                     'predictions': datasets.Sequence(datasets.Value('int32'       )       ),
                     'references': datasets.Sequence(datasets.Value('int32'       )       ),
                 }
                 if self.config_name == 'multilabel'
                 else {
                     'predictions': datasets.Value('int32'       ),
                     'references': datasets.Value('int32'       ),
                 }       )							,    reference_urls=['https://scikit-learn.org/stable/modules/generated/sklearn.metrics.accuracy_score.html']							,    )




     def 					lowerCAmelCase__		(						self							,    snake_case_							,    snake_case_							,    snake_case_=True							,    snake_case_=None       ):
         return {
             "accuracy": float(
                 accuracy_score(snake_case_							,    snake_case_							,    normalize=snake_case_							,    sample_weight=snake_case_       )       )
         }





