




'''simple docstring'''





import argparse
import pathlib

import fairseq
import torch
from fairseq.models.roberta import RobertaModel as FairseqRobertaModel
from fairseq.modules import TransformerSentenceEncoderLayer
from packaging import version

from transformers import RobertaConfig, RobertaForMaskedLM, RobertaForSequenceClassification
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertSelfAttention,
    BertSelfOutput,
)
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[str]   			=			'''Hello world! cécé herlolip'''





def UpperCAmelCase						(					UpperCAmelCase_      ,  UpperCAmelCase_      ,  UpperCAmelCase_	):
    _a							:       List[Any]       					=			FairseqRobertaModel.from_pretrained(UpperCAmelCase_	)
    roberta.eval()  # disable dropout
    _a							:       str       					=			roberta.model.encoder.sentence_encoder
    _a							:       Optional[int]       					=			RobertaConfig(
        vocab_size=roberta_sent_encoder.embed_tokens.num_embeddings      ,  hidden_size=roberta.args.encoder_embed_dim      ,  num_hidden_layers=roberta.args.encoder_layers      ,  num_attention_heads=roberta.args.encoder_attention_heads      ,  intermediate_size=roberta.args.encoder_ffn_embed_dim      ,  max_position_embeddings=514      ,  type_vocab_size=1      ,  layer_norm_eps=1e-5      ,  )
    if classification_head:
        _a							:       int       					=			roberta.model.classification_heads["""mnli"""].out_proj.weight.shape[0]
    print("""Our BERT config:"""      ,  UpperCAmelCase_	)

    _a							:       Union[str, Any]       					=			RobertaForSequenceClassification(UpperCAmelCase_	) if classification_head else RobertaForMaskedLM(UpperCAmelCase_	)
    model.eval()

    # Now let's copy all the weights.
    # Embeddings
    _a							:       Dict       					=			roberta_sent_encoder.embed_tokens.weight
    _a							:       str       					=			roberta_sent_encoder.embed_positions.weight
    _a							:       Dict       					=			torch.zeros_like(
        model.roberta.embeddings.token_type_embeddings.weight	)  # just zero them out b/c RoBERTa doesn't use them.
    _a							:       Optional[int]       					=			roberta_sent_encoder.emb_layer_norm.weight
    _a							:       Union[str, Any]       					=			roberta_sent_encoder.emb_layer_norm.bias

    for i in range(config.num_hidden_layers	):
        # Encoder: start of layer
        _a							:       BertLayer       					=			model.roberta.encoder.layer[i]
        _a							:       TransformerSentenceEncoderLayer       					=			roberta_sent_encoder.layers[i]

        # self attention
        _a							:       BertSelfAttention       					=			layer.attention.self
        assert (
            roberta_layer.self_attn.k_proj.weight.data.shape
            == roberta_layer.self_attn.q_proj.weight.data.shape
            == roberta_layer.self_attn.v_proj.weight.data.shape
            == torch.Size((config.hidden_size, config.hidden_size)	)
        )

        _a							:       Optional[Any]       					=			roberta_layer.self_attn.q_proj.weight
        _a							:       Optional[int]       					=			roberta_layer.self_attn.q_proj.bias
        _a							:       int       					=			roberta_layer.self_attn.k_proj.weight
        _a							:       Tuple       					=			roberta_layer.self_attn.k_proj.bias
        _a							:       List[str]       					=			roberta_layer.self_attn.v_proj.weight
        _a							:       Optional[int]       					=			roberta_layer.self_attn.v_proj.bias

        # self-attention output
        _a							:       BertSelfOutput       					=			layer.attention.output
        assert self_output.dense.weight.shape == roberta_layer.self_attn.out_proj.weight.shape
        _a							:       Optional[Any]       					=			roberta_layer.self_attn.out_proj.weight
        _a							:       Optional[int]       					=			roberta_layer.self_attn.out_proj.bias
        _a							:       Union[str, Any]       					=			roberta_layer.self_attn_layer_norm.weight
        _a							:       Optional[Any]       					=			roberta_layer.self_attn_layer_norm.bias

        # intermediate
        _a							:       BertIntermediate       					=			layer.intermediate
        assert intermediate.dense.weight.shape == roberta_layer.fca.weight.shape
        _a							:       Union[str, Any]       					=			roberta_layer.fca.weight
        _a							:       List[str]       					=			roberta_layer.fca.bias

        # output
        _a							:       BertOutput       					=			layer.output
        assert bert_output.dense.weight.shape == roberta_layer.fca.weight.shape
        _a							:       List[Any]       					=			roberta_layer.fca.weight
        _a							:       Tuple       					=			roberta_layer.fca.bias
        _a							:       Any       					=			roberta_layer.final_layer_norm.weight
        _a							:       List[Any]       					=			roberta_layer.final_layer_norm.bias
        # end of layer

    if classification_head:
        _a							:       Any       					=			roberta.model.classification_heads["""mnli"""].dense.weight
        _a							:       str       					=			roberta.model.classification_heads["""mnli"""].dense.bias
        _a							:       Optional[int]       					=			roberta.model.classification_heads["""mnli"""].out_proj.weight
        _a							:       Optional[int]       					=			roberta.model.classification_heads["""mnli"""].out_proj.bias
    else:
        # LM Head
        _a							:       Tuple       					=			roberta.model.encoder.lm_head.dense.weight
        _a							:       Tuple       					=			roberta.model.encoder.lm_head.dense.bias
        _a							:       Union[str, Any]       					=			roberta.model.encoder.lm_head.layer_norm.weight
        _a							:       str       					=			roberta.model.encoder.lm_head.layer_norm.bias
        _a							:       List[Any]       					=			roberta.model.encoder.lm_head.weight
        _a							:       int       					=			roberta.model.encoder.lm_head.bias

    # Let's check that we get the same results.
    _a							:       torch.Tensor       					=			roberta.encode(UpperCAmelCase_	).unsqueeze(0	)  # batch of size 1

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

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


if __name__ == "__main__":
  a		:  str   			=			argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      '''--roberta_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_roberta_checkpoint_to_pytorch(
      args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
  )








'''simple docstring'''





from math import exp





def UpperCAmelCase						(					UpperCAmelCase_	):
    return 8 * x - 2 * exp(-x	)





def UpperCAmelCase						(					UpperCAmelCase_      ,  UpperCAmelCase_      ,  UpperCAmelCase_	):
    _a							:       Optional[int]       					=			lower_bound
    _a							:       str       					=			upper_bound
    for _ in range(0      ,  UpperCAmelCase_	):
        _a					,   _a							:       List[str]       					=			xa, xa - (f(UpperCAmelCase_	) * (xa - xa)) / (f(UpperCAmelCase_	) - f(UpperCAmelCase_	))
    return xa


if __name__ == "__main__":
  print(F"""Example: {secant_method(1, 3, 2)}""")


