

"""simple docstring"""


from json import JSONDecodeError  # Workaround for requests.exceptions.JSONDecodeError

import requests




def  _lowercase					(    __lowerCAmelCase = "isbn/0140328726"							)       ->				dict:
      SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	olid.strip().strip("""/"""							)  # Remove leading/trailing whitespace & slashes
      if new_olid.count("""/"""							) != 1:
            SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	F'''{olid} is not a valid Open Library olid'''
            raise ValueError(__lowerCAmelCase							)
      return requests.get(F'''https://openlibrary.org/{new_olid}.json'''							).json()




def  _lowercase					(    __lowerCAmelCase							)       ->				dict:
      SCREAMING_SNAKE_CASE__   :      Any      		=	{
          """title""": """Title""",
          """publish_date""": """Publish date""",
          """authors""": """Authors""",
          """number_of_pages""": """Number of pages:""",
          """first_sentence""": """First sentence""",
          """isbn_10""": """ISBN (10)""",
          """isbn_13""": """ISBN (13)""",
      }
      SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	{better_key: ol_book_data[key] for key, better_key in desired_keys.items()}
      SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	[
          get_openlibrary_data(author["""key"""]							)["""name"""] for author in data["""Authors"""]
      ]
      SCREAMING_SNAKE_CASE__   :      Dict      		=	data["""First sentence"""]["""value"""]
      for key, value in data.items():
            if isinstance(__lowerCAmelCase  ,					__lowerCAmelCase							):
                  SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	""", """.join(__lowerCAmelCase							)
      return data


if __name__ == "__main__":
  import doctest

  doctest.testmod()

  while True:
    a					:Any    = input("\nEnter the ISBN code to search (or 'quit' to stop): ").strip()
    if isbn.lower() in ("", "q", "quit", "exit", "stop"):
      break

    if len(isbn) not in (10, 13) or not isbn.isdigit():
      print(f'Sorry, {isbn} is not a valid ISBN.  Please, input a valid ISBN.')
      continue

    print(f'\nSearching Open Library for ISBN: {isbn}...\n')

    try:
      a					:Optional[Any]    = summarize_book(get_openlibrary_data(f'isbn/{isbn}'))
      print("\n".join(f'{key}: {value}' for key, value in book_summary.items()))
    except JSONDecodeError:  # Workaround for requests.exceptions.RequestException:
      print(f'Sorry, there are no results for ISBN: {isbn}.')







"""simple docstring"""


# Note: if you intend to run this script make sure you look under scripts/fsmt/
# to locate the appropriate script to do the work correctly. There is a set of scripts to:
# - download and prepare data and run the conversion script
# - perform eval to get the best hparam into the config
# - generate model_cards - useful if you have multiple models from the same paper

import argparse
import json
import os
import re
from collections import OrderedDict
from os.path import basename, dirname

import fairseq
import torch
from fairseq import hub_utils
from fairseq.data.dictionary import Dictionary

from transformers import FSMTConfig, FSMTForConditionalGeneration
from transformers.models.fsmt.tokenization_fsmt import VOCAB_FILES_NAMES
from transformers.tokenization_utils_base import TOKENIZER_CONFIG_FILE
from transformers.utils import WEIGHTS_NAME, logging


logging.set_verbosity_warning()

a					:str    = 2

# based on the results of a search on a range of `num_beams`, `length_penalty` and `early_stopping`
# values against wmt19 test data to obtain the best BLEU scores, we will use the following defaults:
#
# * `num_beams`: 5 (higher scores better, but requires more memory/is slower, can be adjusted by users)
# * `early_stopping`: `False` consistently scored better
# * `length_penalty` varied, so will assign the best one depending on the model
a					:int    = {
    # fairseq:
    "wmt19-ru-en": {"length_penalty": 1.1},
    "wmt19-en-ru": {"length_penalty": 1.15},
    "wmt19-en-de": {"length_penalty": 1.0},
    "wmt19-de-en": {"length_penalty": 1.1},
    # allenai:
    "wmt16-en-de-dist-12-1": {"length_penalty": 0.6},
    "wmt16-en-de-dist-6-1": {"length_penalty": 0.6},
    "wmt16-en-de-12-1": {"length_penalty": 0.8},
    "wmt19-de-en-6-6-base": {"length_penalty": 0.6},
    "wmt19-de-en-6-6-big": {"length_penalty": 0.6},
}

# this remaps the different models to their organization names
a					:Dict    = {}
for m in ["wmt19-ru-en", "wmt19-en-ru", "wmt19-en-de", "wmt19-de-en"]:
  a					:List[Any]    = "facebook"
for m in [
    "wmt16-en-de-dist-12-1",
    "wmt16-en-de-dist-6-1",
    "wmt16-en-de-12-1",
    "wmt19-de-en-6-6-base",
    "wmt19-de-en-6-6-big",
]:
  a					:str    = "allenai"




def  _lowercase					(    __lowerCAmelCase							)       ->				Any:
      # (1) remove word breaking symbol, (2) add word ending symbol where the word is not broken up,
      # e.g.: d = {'le@@': 5, 'tt@@': 6, 'er': 7} => {'le': 5, 'tt': 6, 'er</w>': 7}
      SCREAMING_SNAKE_CASE__   :      str      		=	dict((re.sub(r"""@@$"""  ,					""""""  ,					__lowerCAmelCase							), v) if k.endswith("""@@"""							) else (re.sub(r"""$"""  ,					"""</w>"""  ,					__lowerCAmelCase							), v) for k, v in d.items()							)
      SCREAMING_SNAKE_CASE__   :      Tuple      		=	"""<s> <pad> </s> <unk>""".split()
      # restore the special tokens
      for k in keep_keys:
            del da[F'''{k}</w>''']
            SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	d[k]  # restore
      return da




def  _lowercase					(    __lowerCAmelCase  ,					__lowerCAmelCase							)       ->				Optional[int]:
      # prep
      assert os.path.exists(__lowerCAmelCase							)
      os.makedirs(__lowerCAmelCase  ,					exist_ok=__lowerCAmelCase							)
      print(F'''Writing results to {pytorch_dump_folder_path}'''							)

      # handle various types of models

      SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	basename(__lowerCAmelCase							)
      SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	dirname(__lowerCAmelCase							)

      SCREAMING_SNAKE_CASE__   :      int      		=	fairseq.model_parallel.models.transformer.ModelParallelTransformerModel
      SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	cls.hub_models()
      SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	{"""bpe""": """fastbpe""", """tokenizer""": """moses"""}
      SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	"""."""
      # note: since the model dump is old, fairseq has upgraded its model some
      # time later, and it does a whole lot of rewrites and splits on the saved
      # weights, therefore we can't use torch.load() directly on the model file.
      # see: upgrade_state_dict(state_dict) in fairseq_model.py
      print(F'''using checkpoint {checkpoint_file}'''							)
      SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	hub_utils.from_pretrained(
          __lowerCAmelCase  ,					__lowerCAmelCase  ,					__lowerCAmelCase  ,					archive_map=__lowerCAmelCase  ,					**__lowerCAmelCase							)

      SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	vars(chkpt["""args"""]["""model"""]							)

      SCREAMING_SNAKE_CASE__   :      Any      		=	args["""source_lang"""]
      SCREAMING_SNAKE_CASE__   :      Any      		=	args["""target_lang"""]

      SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	dirname(__lowerCAmelCase							)
      SCREAMING_SNAKE_CASE__   :      Tuple      		=	basename(__lowerCAmelCase							)

      # dicts
      SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	os.path.join(__lowerCAmelCase  ,					F'''dict.{src_lang}.txt'''							)
      SCREAMING_SNAKE_CASE__   :      Any      		=	os.path.join(__lowerCAmelCase  ,					F'''dict.{tgt_lang}.txt'''							)

      SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	Dictionary.load(__lowerCAmelCase							)
      SCREAMING_SNAKE_CASE__   :      List[str]      		=	rewrite_dict_keys(src_dict.indices							)
      SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	len(__lowerCAmelCase							)
      SCREAMING_SNAKE_CASE__   :      int      		=	os.path.join(__lowerCAmelCase  ,					"""vocab-src.json"""							)
      print(F'''Generating {src_vocab_file} of {src_vocab_size} of {src_lang} records'''							)
      with open(__lowerCAmelCase  ,					"""w"""  ,					encoding="""utf-8"""							) as f:
            f.write(json.dumps(__lowerCAmelCase  ,					ensure_ascii=__lowerCAmelCase  ,					indent=__lowerCAmelCase							)							)

      # detect whether this is a do_lower_case situation, which can be derived by checking whether we
      # have at least one uppercase letter in the source vocab
      SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	True
      for k in src_vocab.keys():
            if not k.islower():
                  SCREAMING_SNAKE_CASE__   :      Tuple      		=	False
                  break

      SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	Dictionary.load(__lowerCAmelCase							)
      SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	rewrite_dict_keys(tgt_dict.indices							)
      SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	len(__lowerCAmelCase							)
      SCREAMING_SNAKE_CASE__   :      List[str]      		=	os.path.join(__lowerCAmelCase  ,					"""vocab-tgt.json"""							)
      print(F'''Generating {tgt_vocab_file} of {tgt_vocab_size} of {tgt_lang} records'''							)
      with open(__lowerCAmelCase  ,					"""w"""  ,					encoding="""utf-8"""							) as f:
            f.write(json.dumps(__lowerCAmelCase  ,					ensure_ascii=__lowerCAmelCase  ,					indent=__lowerCAmelCase							)							)

      # merges_file (bpecodes)
      SCREAMING_SNAKE_CASE__   :      List[str]      		=	os.path.join(__lowerCAmelCase  ,					VOCAB_FILES_NAMES["""merges_file"""]							)
      for fn in ["bpecodes", "code"]:  # older fairseq called the merges file "code"
            SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	os.path.join(__lowerCAmelCase  ,					__lowerCAmelCase							)
            if os.path.exists(__lowerCAmelCase							):
                  break
      with open(__lowerCAmelCase  ,					encoding="""utf-8"""							) as fin:
            SCREAMING_SNAKE_CASE__   :      Any      		=	fin.read()
      SCREAMING_SNAKE_CASE__   :      Tuple      		=	re.sub(r""" \d+$"""  ,					""""""  ,					__lowerCAmelCase  ,					0  ,					re.M							)  # remove frequency number
      print(F'''Generating {merges_file}'''							)
      with open(__lowerCAmelCase  ,					"""w"""  ,					encoding="""utf-8"""							) as fout:
            fout.write(__lowerCAmelCase							)

      # model config
      SCREAMING_SNAKE_CASE__   :      Dict      		=	os.path.join(__lowerCAmelCase  ,					"""config.json"""							)

      # validate bpe/tokenizer config, as currently it's hardcoded to moses+fastbpe -
      # may have to modify the tokenizer if a different type is used by a future model
      assert args["bpe"] == "fastbpe", F'''need to extend tokenizer to support bpe={args['bpe']}'''
      assert args["tokenizer"] == "moses", F'''need to extend tokenizer to support bpe={args['tokenizer']}'''

      SCREAMING_SNAKE_CASE__   :      str      		=	{
          """architectures""": ["""FSMTForConditionalGeneration"""],
          """model_type""": """fsmt""",
          """activation_dropout""": args["""activation_dropout"""],
          """activation_function""": """relu""",
          """attention_dropout""": args["""attention_dropout"""],
          """d_model""": args["""decoder_embed_dim"""],
          """dropout""": args["""dropout"""],
          """init_std""": 0.02,
          """max_position_embeddings""": args["""max_source_positions"""],
          """num_hidden_layers""": args["""encoder_layers"""],
          """src_vocab_size""": src_vocab_size,
          """tgt_vocab_size""": tgt_vocab_size,
          """langs""": [src_lang, tgt_lang],
          """encoder_attention_heads""": args["""encoder_attention_heads"""],
          """encoder_ffn_dim""": args["""encoder_ffn_embed_dim"""],
          """encoder_layerdrop""": args["""encoder_layerdrop"""],
          """encoder_layers""": args["""encoder_layers"""],
          """decoder_attention_heads""": args["""decoder_attention_heads"""],
          """decoder_ffn_dim""": args["""decoder_ffn_embed_dim"""],
          """decoder_layerdrop""": args["""decoder_layerdrop"""],
          """decoder_layers""": args["""decoder_layers"""],
          """bos_token_id""": 0,
          """pad_token_id""": 1,
          """eos_token_id""": 2,
          """is_encoder_decoder""": True,
          """scale_embedding""": not args["""no_scale_embedding"""],
          """tie_word_embeddings""": args["""share_all_embeddings"""],
      }

      # good hparam defaults to start with
      SCREAMING_SNAKE_CASE__   :      Tuple      		=	5
      SCREAMING_SNAKE_CASE__   :      str      		=	False
      if model_dir in best_score_hparams and "length_penalty" in best_score_hparams[model_dir]:
            SCREAMING_SNAKE_CASE__   :      Tuple      		=	best_score_hparams[model_dir]["""length_penalty"""]
      else:
            SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	1.0

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

      # tokenizer config
      SCREAMING_SNAKE_CASE__   :      Tuple      		=	os.path.join(__lowerCAmelCase  ,					__lowerCAmelCase							)

      SCREAMING_SNAKE_CASE__   :      Tuple      		=	{
          """langs""": [src_lang, tgt_lang],
          """model_max_length""": 1024,
          """do_lower_case""": do_lower_case,
      }

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

      # model
      SCREAMING_SNAKE_CASE__   :      Dict      		=	chkpt["""models"""][0]
      SCREAMING_SNAKE_CASE__   :      int      		=	model.state_dict()

      # rename keys to start with 'model.'
      SCREAMING_SNAKE_CASE__   :      Tuple      		=	OrderedDict(("""model.""" + k, v) for k, v in model_state_dict.items()							)

      # remove unneeded keys
      SCREAMING_SNAKE_CASE__   :      str      		=	[
          """model.model""",
          """model.encoder.version""",
          """model.decoder.version""",
          """model.encoder_embed_tokens.weight""",
          """model.decoder_embed_tokens.weight""",
          """model.encoder.embed_positions._float_tensor""",
          """model.decoder.embed_positions._float_tensor""",
      ]
      for k in ignore_keys:
            model_state_dict.pop(__lowerCAmelCase  ,					__lowerCAmelCase							)

      SCREAMING_SNAKE_CASE__   :      Dict      		=	FSMTConfig.from_pretrained(__lowerCAmelCase							)
      SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	FSMTForConditionalGeneration(__lowerCAmelCase							)

      # check that it loads ok
      model_new.load_state_dict(__lowerCAmelCase  ,					strict=__lowerCAmelCase							)

      # save
      SCREAMING_SNAKE_CASE__   :      int      		=	os.path.join(__lowerCAmelCase  ,					__lowerCAmelCase							)
      print(F'''Generating {pytorch_weights_dump_path}'''							)
      torch.save(__lowerCAmelCase  ,					__lowerCAmelCase							)

      print("""Conversion is done!"""							)
      print("""\nLast step is to upload the files to s3"""							)
      print(F'''cd {data_root}'''							)
      print(F'''transformers-cli upload {model_dir}'''							)


if __name__ == "__main__":
  a					:Optional[Any]    = argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      "--fsmt_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."
  )
  a					:List[str]    = parser.parse_args()
  convert_fsmt_checkpoint_to_pytorch(args.fsmt_checkpoint_path, args.pytorch_dump_folder_path)




