
"""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()
a		:					Union[str, Any]    	=					logging.get_logger(__name__)

a		:					List[Any]    	=					'Hello world! cécé herlolip'



def 						__magic_name__		(				UpperCamelCase    :		str       ,		UpperCamelCase    :		str       ,		UpperCamelCase    :		bool    )    ->	Optional[int]:
    a__  ,    a__    = os.path.split(lowercase__    )
    a__    = DataaVecTextModel.from_pretrained(
        lowercase__       ,		checkpoint_file=lowercase__    )
    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:'       ,		lowercase__    )

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

    a__    = model(lowercase__    )[0]
    if classification_head:
        a__    = dataavec.model.classification_heads['mnli'](dataavec.extract_features(lowercase__    )    )
    else:
        a__    = dataavec_model(lowercase__    )[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(lowercase__       ,		lowercase__       ,		atol=1E-3    )
    print('Do both models output the same tensors?'       ,		'🔥' if success else '💩'    )
    if not success:
        raise Exception('Something went wRoNg'    )

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


if __name__ == "__main__":
    a		:					Union[str, 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.'
    )
    a		:					Optional[int]    	=					parser.parse_args()
    convert_dataavec_checkpoint_to_pytorch(
        args.checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
    )




"""simple docstring"""




import math







class 						lowercase:



     def __init__(       self    ,							__SCREAMING_SNAKE_CASE      )		->    Optional[int]:







         """simple docstring"""



         a__    = len(__SCREAMING_SNAKE_CASE      )
         a__    = [0] * (
             4 * self.N
         )  # approximate the overall size of segment tree with array N
         self.build(1    ,							0    ,							self.N - 1      )



     def  lowercase__     (       self    ,							__SCREAMING_SNAKE_CASE      )		->    Optional[Any]:







         """simple docstring"""



         return idx * 2



     def  lowercase__     (       self    ,							__SCREAMING_SNAKE_CASE      )		->    Optional[int]:







         """simple docstring"""



         return idx * 2 + 1



     def  lowercase__     (       self    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE      )		->    List[str]:  # noqa: E741







         """simple docstring"""



         if l == r:
             a__    = A[l]
         else:
             a__    = (l + r) // 2
             self.build(self.left(__SCREAMING_SNAKE_CASE      )    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE      )
             self.build(self.right(__SCREAMING_SNAKE_CASE      )    ,							mid + 1    ,							__SCREAMING_SNAKE_CASE      )
             a__    = max(self.st[self.left(__SCREAMING_SNAKE_CASE      )]    ,							self.st[self.right(__SCREAMING_SNAKE_CASE      )]      )



     def  lowercase__     (       self    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE      )		->    int:







         """simple docstring"""



         return self.update_recursive(1    ,							0    ,							self.N - 1    ,							a - 1    ,							b - 1    ,							__SCREAMING_SNAKE_CASE      )



     def  lowercase__     (       self    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE      )		->    Optional[int]:  # noqa: E741







         """simple docstring"""



         if r < a or l > b:
             return True
         if l == r:
             a__    = val
             return True
         a__    = (l + r) // 2
         self.update_recursive(self.left(__SCREAMING_SNAKE_CASE      )    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE      )
         self.update_recursive(self.right(__SCREAMING_SNAKE_CASE      )    ,							mid + 1    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE      )
         a__    = max(self.st[self.left(__SCREAMING_SNAKE_CASE      )]    ,							self.st[self.right(__SCREAMING_SNAKE_CASE      )]      )
         return True



     def  lowercase__     (       self    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE      )		->    List[str]:







         """simple docstring"""



         return self.query_recursive(1    ,							0    ,							self.N - 1    ,							a - 1    ,							b - 1      )



     def  lowercase__     (       self    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE      )		->    Any:  # noqa: E741







         """simple docstring"""



         if r < a or l > b:
             return -math.inf
         if l >= a and r <= b:
             return self.st[idx]
         a__    = (l + r) // 2
         a__    = self.query_recursive(self.left(__SCREAMING_SNAKE_CASE      )    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE      )
         a__    = self.query_recursive(self.right(__SCREAMING_SNAKE_CASE      )    ,							mid + 1    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE      )
         return max(__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE      )




     def  lowercase__     (       self      )		->    List[Any]:







         """simple docstring"""



         a__    = []
         for i in range(1    ,							N + 1      ):
             show_list += [self.query(__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE      )]
         print(__SCREAMING_SNAKE_CASE      )


if __name__ == "__main__":
    a		:					Union[str, Any]    	=					[1, 2, -4, 7, 3, -5, 6, 11, -20, 9, 14, 15, 5, 2, -8]
    a		:					Dict    	=					15
    a		:					Tuple    	=					SegmentTree(A)
    print(segt.query(4, 6))
    print(segt.query(7, 11))
    print(segt.query(7, 12))
    segt.update(1, 3, 1_11)
    print(segt.query(1, 15))
    segt.update(7, 8, 2_35)
    segt.show_data()


