



'''simple docstring'''





import argparse
import json
import os
import re
import shutil

import torch

from transformers import BioGptConfig, BioGptForCausalLM
from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES
from transformers.tokenization_utils_base import TOKENIZER_CONFIG_FILE
from transformers.utils import WEIGHTS_NAME, logging


logging.set_verbosity_warning()

SCREAMING_SNAKE_CASE__          =  2


class 					snake_case			:



       def __init__(					self  ,*,  # begin keyword-only arguments
           UpperCAmelCase_="<s>"  ,UpperCAmelCase_="<pad>"  ,UpperCAmelCase_="</s>"  ,UpperCAmelCase_="<unk>"  ,UpperCAmelCase_=None  ,)	->				List[str]:
           lowercase__							,							lowercase__							,							lowercase__							,							lowercase__									=    bos, unk, pad, eos
           lowercase__									=    []
           lowercase__									=    []
           lowercase__									=    {}
           lowercase__									=    self.add_symbol(UpperCAmelCase_   )
           lowercase__									=    self.add_symbol(UpperCAmelCase_   )
           lowercase__									=    self.add_symbol(UpperCAmelCase_   )
           lowercase__									=    self.add_symbol(UpperCAmelCase_   )
           if extra_special_symbols:
               for s in extra_special_symbols:
                   self.add_symbol(UpperCAmelCase_   )
           lowercase__									=    len(self.symbols   )



       def __eq__(					self  ,UpperCAmelCase_   )	->				str:
           return self.indices == other.indices



       def __getitem__(					self  ,UpperCAmelCase_   )	->				str:
           if idx < len(self.symbols   ):
               return self.symbols[idx]
           return self.unk_word



       def __len__(					self   )	->				List[str]:
           return len(self.symbols   )



       def __contains__(					self  ,UpperCAmelCase_   )	->				Any:
           return sym in self.indices



       @classmethod
       def 				_a   (					cls  ,UpperCAmelCase_   )	->				Any:
           lowercase__									=    cls()
           d.add_from_file(UpperCAmelCase_   )
           return d



       def 				_a   (					self  ,UpperCAmelCase_  ,UpperCAmelCase_=1  ,UpperCAmelCase_=False   )	->				List[Any]:
           if word in self.indices and not overwrite:
               lowercase__									=    self.indices[word]
               lowercase__									=    self.count[idx] + n
               return idx
           else:
               lowercase__									=    len(self.symbols   )
               lowercase__									=    idx
               self.symbols.append(UpperCAmelCase_   )
               self.count.append(UpperCAmelCase_   )
               return idx



       def 				_a   (					self  ,UpperCAmelCase_   )	->				int:
           return 0







       def 				_a   (					self  ,UpperCAmelCase_   )	->				List[str]:
           if isinstance(UpperCAmelCase_  ,UpperCAmelCase_   ):
               try:
                   with open(UpperCAmelCase_  ,"r"  ,encoding="utf-8"   ) as fd:
                       self.add_from_file(UpperCAmelCase_   )
               except FileNotFoundError as fnfe:
                   raise fnfe
               except UnicodeError:
                   raise Exception("Incorrect encoding detected in {}, please rebuild the dataset".format(UpperCAmelCase_   )   )
               return

           lowercase__									=    f.readlines()
           lowercase__									=    self._load_meta(UpperCAmelCase_   )

           for line in lines[indices_start_line:]:
               try:
                   lowercase__							,							lowercase__									=    line.rstrip().rsplit(" "  ,1   )
                   if field == "#fairseq:overwrite":
                       lowercase__									=    True
                       lowercase__							,							lowercase__									=    line.rsplit(" "  ,1   )
                   else:
                       lowercase__									=    False
                   lowercase__									=    int(UpperCAmelCase_   )
                   lowercase__									=    line
                   if word in self and not overwrite:
                       raise RuntimeError(
                           "Duplicate word found when loading Dictionary: '{}'. "
                           "Duplicate words can overwrite earlier ones by adding the "
                           "#fairseq:overwrite flag at the end of the corresponding row "
                           "in the dictionary file. If using the Camembert model, please "
                           "download an updated copy of the model file.".format(UpperCAmelCase_   )   )
                   self.add_symbol(UpperCAmelCase_  ,n=UpperCAmelCase_  ,overwrite=UpperCAmelCase_   )
               except ValueError:
                   raise ValueError("Incorrect dictionary format, expected '<token> <cnt> [flags]'"   )





def 							lowerCamelCase	(					_snake_case	:    List[str]							):





    '''simple docstring'''


    lowercase__									=    dict((re.sub(R"@@$"  ,""  ,_snake_case							), v) if k.endswith("@@"							) else (re.sub(R"$"  ,"</w>"  ,_snake_case							), v) for k, v in d.items()							)
    lowercase__									=    "<s> <pad> </s> <unk>".split()
    # restore the special tokens
    for k in keep_keys:
        del da[f'''{k}</w>''']
        lowercase__									=    d[k]  # restore
    return da




def 							lowerCamelCase	(					_snake_case	:    str  ,_snake_case	:    Tuple							):





    '''simple docstring'''


    if not os.path.exists(_snake_case							):
        raise ValueError(f'''path {biogpt_checkpoint_path} does not exist!'''							)
    os.makedirs(_snake_case  ,exist_ok=_snake_case							)
    print(f'''Writing results to {pytorch_dump_folder_path}'''							)

    # handle various types of models

    lowercase__									=    os.path.join(_snake_case  ,"checkpoint.pt"							)
    if not os.path.isfile(_snake_case							):
        raise ValueError(f'''path to the file {checkpoint_file} does not exist!'''							)
    lowercase__									=    torch.load(_snake_case  ,map_location="cpu"							)

    lowercase__									=    chkpt["cfg"]["model"]

    # dicts
    lowercase__									=    os.path.join(_snake_case  ,"dict.txt"							)
    if not os.path.isfile(_snake_case							):
        raise ValueError(f'''path to the file {dict_file} does not exist!'''							)
    lowercase__									=    Dictionary.load(_snake_case							)
    lowercase__									=    rewrite_dict_keys(src_dict.indices							)
    lowercase__									=    len(_snake_case							)
    lowercase__									=    os.path.join(_snake_case  ,VOCAB_FILES_NAMES["vocab_file"]							)
    print(f'''Generating {src_vocab_file} of {src_vocab_size} records'''							)
    with open(_snake_case  ,"w"  ,encoding="utf-8"							) as f:
        f.write(json.dumps(_snake_case  ,ensure_ascii=_snake_case  ,indent=_snake_case							)							)

    # merges_file (bpecodes)
    lowercase__									=    os.path.join(_snake_case  ,"bpecodes"							)
    if not os.path.isfile(_snake_case							):
        raise ValueError(f'''path to the file {bpecodes_file} does not exist!'''							)

    lowercase__									=    os.path.join(_snake_case  ,VOCAB_FILES_NAMES["merges_file"]							)
    shutil.copyfile(_snake_case  ,_snake_case							)

    # model config
    lowercase__									=    os.path.join(_snake_case  ,"config.json"							)

    lowercase__									=    {
        "activation_dropout": args["activation_dropout"],
        "architectures": ["BioGptForCausalLM"],
        "attention_probs_dropout_prob": args["attention_dropout"],
        "bos_token_id": 0,
        "eos_token_id": 2,
        "hidden_act": args["activation_fn"],
        "hidden_dropout_prob": args["dropout"],
        "hidden_size": args["decoder_embed_dim"],
        "initializer_range": 0.02,
        "intermediate_size": args["decoder_ffn_embed_dim"],
        "layer_norm_eps": 1e-1_2,
        "layerdrop": args["decoder_layerdrop"],
        "max_position_embeddings": args["max_target_positions"],
        "model_type": "biogpt",
        "num_attention_heads": args["decoder_attention_heads"],
        "num_hidden_layers": args["decoder_layers"],
        "pad_token_id": 1,
        "scale_embedding": not args["no_scale_embedding"],
        "tie_word_embeddings": args["share_decoder_input_output_embed"],
        "vocab_size": src_vocab_size,
    }

    # good hparam defaults to start with

    print(f'''Generating {biogpt_model_config_file}'''							)
    with open(_snake_case  ,"w"  ,encoding="utf-8"							) as f:
        f.write(json.dumps(_snake_case  ,ensure_ascii=_snake_case  ,indent=_snake_case							)							)

    # tokenizer config
    lowercase__									=    os.path.join(_snake_case  ,_snake_case							)

    lowercase__									=    {
        "bos_token": "<s>",
        "eos_token": "</s>",
        "model_max_length": 1_024,
        "pad_token": "<pad>",
        "special_tokens_map_file": None,
        "tokenizer_class": "BioGptTokenizer",
        "unk_token": "<unk>",
    }

    print(f'''Generating {biogpt_tokenizer_config_file}'''							)
    with open(_snake_case  ,"w"  ,encoding="utf-8"							) as f:
        f.write(json.dumps(_snake_case  ,ensure_ascii=_snake_case  ,indent=_snake_case							)							)

    # model
    lowercase__									=    chkpt["model"]

    # remove unneeded keys
    lowercase__									=    [
        "decoder.version",
    ]
    for k in ignore_keys:
        model_state_dict.pop(_snake_case  ,_snake_case							)

    lowercase__									=    list(model_state_dict.keys()							)
    for layer_name in layer_names:
        if layer_name.endswith("output_projection.weight"							):
            lowercase__									=    model_state_dict.pop(_snake_case							)
        else:
            lowercase__									=    model_state_dict.pop(_snake_case							)

    lowercase__									=    BioGptConfig.from_pretrained(_snake_case							)
    lowercase__									=    BioGptForCausalLM(_snake_case							)

    # check that it loads ok
    model_new.load_state_dict(_snake_case							)

    # save
    lowercase__									=    os.path.join(_snake_case  ,_snake_case							)
    print(f'''Generating {pytorch_weights_dump_path}'''							)
    torch.save(_snake_case  ,_snake_case							)

    print("Conversion is done!"							)


if __name__ == "__main__":
 SCREAMING_SNAKE_CASE__          =  argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     "--biogpt_checkpoint_path",
     default=None,
     type=str,
     required=True,
     help=(
         "Path to the official PyTorch checkpoint file which is expected to reside in the dump dir with dicts,"
         " bpecodes, etc."
     ),
 )
 parser.add_argument(
     "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
 )
 SCREAMING_SNAKE_CASE__          =  parser.parse_args()
 convert_biogpt_checkpoint_to_pytorch(args.biogpt_checkpoint_path, args.pytorch_dump_folder_path)









'''simple docstring'''





def 							lowerCamelCase	(					):





    '''simple docstring'''


    return [list(range(1_000 - i  ,-1_000 - i  ,-1							)							) for i in range(1_000							)]


SCREAMING_SNAKE_CASE__          =  generate_large_matrix()
SCREAMING_SNAKE_CASE__          =  (
    [[4, 3, 2, -1], [3, 2, 1, -1], [1, 1, -1, -2], [-1, -1, -2, -3]],
    [[3, 2], [1, 0]],
    [[7, 7, 6]],
    [[7, 7, 6], [-1, -2, -3]],
    grid,
)




def 							lowerCamelCase	(					_snake_case	:    list[list[int]]							):





    '''simple docstring'''


    assert all(row == sorted(_snake_case  ,reverse=_snake_case							) for row in grid							)
    assert all(list(_snake_case							) == sorted(_snake_case  ,reverse=_snake_case							) for col in zip(*_snake_case							)							)




def 							lowerCamelCase	(					_snake_case	:    list[int]							):





    '''simple docstring'''


    lowercase__									=    0
    lowercase__									=    len(_snake_case							) - 1

    # Edge cases such as no values or all numbers are negative.
    if not array or array[0] < 0:
        return 0

    while right + 1 > left:
        lowercase__									=    (left + right) // 2
        lowercase__									=    array[mid]

        # Num must be negative and the index must be greater than or equal to 0.
        if num < 0 and array[mid - 1] >= 0:
            return mid

        if num >= 0:
            lowercase__									=    mid + 1
        else:
            lowercase__									=    mid - 1
    # No negative numbers so return the last index of the array + 1 which is the length.
    return len(_snake_case							)




def 							lowerCamelCase	(					_snake_case	:    list[list[int]]							):





    '''simple docstring'''


    lowercase__									=    0
    lowercase__									=    len(grid[0]							)

    for i in range(len(_snake_case							)							):
        lowercase__									=    find_negative_index(grid[i][:bound]							)
        total += bound
    return (len(_snake_case							) * len(grid[0]							)) - total




def 							lowerCamelCase	(					_snake_case	:    list[list[int]]							):





    '''simple docstring'''


    return len([number for row in grid for number in row if number < 0]							)




def 							lowerCamelCase	(					_snake_case	:    list[list[int]]							):





    '''simple docstring'''


    lowercase__									=    0
    for row in grid:
        for i, number in enumerate(_snake_case							):
            if number < 0:
                total += len(_snake_case							) - i
                break
    return total




def 							lowerCamelCase	(					):





    '''simple docstring'''


    from timeit import timeit

    print("Running benchmarks"							)
    lowercase__									=    (
        "from __main__ import count_negatives_binary_search, "
        "count_negatives_brute_force, count_negatives_brute_force_with_break, grid"
    )
    for func in (
        "count_negatives_binary_search",  # took 0.7727 seconds
        "count_negatives_brute_force_with_break",  # took 4.6505 seconds
        "count_negatives_brute_force",  # took 12.8160 seconds
    ):
        lowercase__									=    timeit(f'''{func}(grid=grid)'''  ,setup=_snake_case  ,number=500							)
        print(f'''{func}() took {time:0.4f} seconds'''							)


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 benchmark()




