



"""simple docstring"""
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()

_lowerCAmelCase       :Optional[Any]	=   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
_lowerCAmelCase       :Optional[Any]	=   {
    # 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
_lowerCAmelCase       :Optional[Any]	=   {}
for m in ["wmt19-ru-en", "wmt19-en-ru", "wmt19-en-de", "wmt19-de-en"]:
  _lowerCAmelCase       :Optional[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",
]:
  _lowerCAmelCase       :List[str]	=   'allenai'





def 					lowerCamelCase_		(UpperCamelCase__						:						int				):
    # (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}
    _UpperCAmelCase				:    Tuple											=       dict((re.sub(r'''@@$'''	,			''''''	,			A__				), v) if k.endswith('''@@'''				) else (re.sub(r'''$'''	,			'''</w>'''	,			A__				), v) for k, v in d.items()				)
    _UpperCAmelCase				:    str											=       '''<s> <pad> </s> <unk>'''.split()
    # restore the special tokens
    for k in keep_keys:
        del da[F'{k}</w>']
        _UpperCAmelCase				:    List[Any]											=       d[k]  # restore
    return da





def 					lowerCamelCase_		(UpperCamelCase__						:						List[str]	,			UpperCamelCase__						:						Dict				):
    # prep
    assert os.path.exists(A__				)
    os.makedirs(A__	,			exist_ok=A__				)
    print(F'Writing results to {pytorch_dump_folder_path}'				)

    # handle various types of models

    _UpperCAmelCase				:    Union[str, Any]											=       basename(A__				)
    _UpperCAmelCase				:    Any											=       dirname(A__				)

    _UpperCAmelCase				:    Union[str, Any]											=       fairseq.model_parallel.models.transformer.ModelParallelTransformerModel
    _UpperCAmelCase				:    Any											=       cls.hub_models()
    _UpperCAmelCase				:    Any											=       {'''bpe''': '''fastbpe''', '''tokenizer''': '''moses'''}
    _UpperCAmelCase				:    List[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}'				)
    _UpperCAmelCase				:    List[str]											=       hub_utils.from_pretrained(
        A__	,			A__	,			A__	,			archive_map=A__	,			**A__				)

    _UpperCAmelCase				:    Tuple											=       vars(chkpt['''args''']['''model''']				)

    _UpperCAmelCase				:    str											=       args['''source_lang''']
    _UpperCAmelCase				:    str											=       args['''target_lang''']

    _UpperCAmelCase				:    List[Any]											=       dirname(A__				)
    _UpperCAmelCase				:    str											=       basename(A__				)

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

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

    # 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
    _UpperCAmelCase				:    Any											=       True
    for k in src_vocab.keys():
        if not k.islower():
            _UpperCAmelCase				:    List[str]											=       False
            break

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

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

    # model config
    _UpperCAmelCase				:    Any											=       os.path.join(A__	,			'''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"]}'

    _UpperCAmelCase				:    List[Any]											=       {
        '''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
    _UpperCAmelCase				:    int											=       5
    _UpperCAmelCase				:    List[Any]											=       False
    if model_dir in best_score_hparams and "length_penalty" in best_score_hparams[model_dir]:
        _UpperCAmelCase				:    List[Any]											=       best_score_hparams[model_dir]['''length_penalty''']
    else:
        _UpperCAmelCase				:    Optional[int]											=       1.0

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

    # tokenizer config
    _UpperCAmelCase				:    Any											=       os.path.join(A__	,			A__				)

    _UpperCAmelCase				:    str											=       {
        '''langs''': [src_lang, tgt_lang],
        '''model_max_length''': 1024,
        '''do_lower_case''': do_lower_case,
    }

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

    # model
    _UpperCAmelCase				:    str											=       chkpt['''models'''][0]
    _UpperCAmelCase				:    Optional[int]											=       model.state_dict()

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

    # remove unneeded keys
    _UpperCAmelCase				:    int											=       [
        '''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(A__	,			A__				)

    _UpperCAmelCase				:    str											=       FSMTConfig.from_pretrained(A__				)
    _UpperCAmelCase				:    Optional[int]											=       FSMTForConditionalGeneration(A__				)

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

    # save
    _UpperCAmelCase				:    Optional[Any]											=       os.path.join(A__	,			A__				)
    print(F'Generating {pytorch_weights_dump_path}'				)
    torch.save(A__	,			A__				)

    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__":
  _lowerCAmelCase       :str	=   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.'
  )
  _lowerCAmelCase       :Dict	=   parser.parse_args()
  convert_fsmt_checkpoint_to_pytorch(args.fsmt_checkpoint_path, args.pytorch_dump_folder_path)




'''simple docstring'''
import requests

lowercase__											=''  # <-- Put your OpenWeatherMap appid here!
lowercase__											='https://api.openweathermap.org/data/2.5/'






def      UpperCamelCase_				(      A__ = "Chicago"  ,      A__ = APPID						):
	return requests.get(URL_BASE + """weather"""  ,      params=locals()						).json()






def      UpperCamelCase_				(      A__ = "Kolkata, India"  ,      A__ = APPID						):
	return requests.get(URL_BASE + """forecast"""  ,      params=locals()						).json()






def      UpperCamelCase_				(      A__ = 55.68  ,      A__ = 12.57  ,      A__ = APPID						):
	return requests.get(URL_BASE + """onecall"""  ,      params=locals()						).json()


if __name__ == "__main__":
		from pprint import pprint

		while True:
				lowercase__											=input('Enter a location:').strip()
				if location:
						pprint(current_weather(location))
				else:
						break
