




import argparse
from pathlib import Path

import fairseq
import torch
from fairseq.models.xmod import XMODModel as FairseqXmodModel
from packaging import version

from transformers import XmodConfig, XmodForMaskedLM, XmodForSequenceClassification
from transformers.utils import logging


if version.parse(fairseq.__version__) < version.parse('0.12.2'):
   raise Exception('requires fairseq >= 0.12.2')
if version.parse(fairseq.__version__) > version.parse('2'):
   raise Exception('requires fairseq < v2')

logging.set_verbosity_info()
lowerCamelCase__			:       Optional[Any]         =							logging.get_logger(__name__)

lowerCamelCase__			:       Optional[Any]         =							'Hello, World!'
lowerCamelCase__			:       List[Any]         =							'en_XX'

def  UpperCAmelCase_							(				__UpperCAmelCase						:     str      ,      __UpperCAmelCase						:     str      ,      __UpperCAmelCase						:     bool							) ->			Optional[Any]:
      SCREAMING_SNAKE_CASE_     						=      Path('data_bin'							)
      SCREAMING_SNAKE_CASE_     						=      FairseqXmodModel.from_pretrained(
          model_name_or_path=str(Path(__UpperCAmelCase							).parent							)      ,      checkpoint_file=Path(__UpperCAmelCase							).name      ,      _name='xmod_base'      ,      arch='xmod_base'      ,      task='multilingual_masked_lm'      ,      data_name_or_path=str(__UpperCAmelCase							)      ,      bpe='sentencepiece'      ,      sentencepiece_model=str(Path(__UpperCAmelCase							).parent / 'sentencepiece.bpe.model'							)      ,      src_dict=str(data_dir / 'dict.txt'							)      ,      )
      xmod.eval()  # disable dropout
      print(__UpperCAmelCase							)

      SCREAMING_SNAKE_CASE_     						=      xmod.model.encoder.sentence_encoder
      SCREAMING_SNAKE_CASE_     						=      XmodConfig(
          vocab_size=xmod_sent_encoder.embed_tokens.num_embeddings      ,      hidden_size=xmod.cfg.model.encoder_embed_dim      ,      num_hidden_layers=xmod.cfg.model.encoder_layers      ,      num_attention_heads=xmod.cfg.model.encoder_attention_heads      ,      intermediate_size=xmod.cfg.model.encoder_ffn_embed_dim      ,      max_position_embeddings=5_14      ,      type_vocab_size=1      ,      layer_norm_eps=1E-5      ,      pre_norm=xmod.cfg.model.encoder_normalize_before      ,      adapter_reduction_factor=getattr(xmod.cfg.model      ,      'bottleneck'      ,      2							)      ,      adapter_layer_norm=xmod.cfg.model.adapter_layer_norm      ,      adapter_reuse_layer_norm=xmod.cfg.model.adapter_reuse_layer_norm      ,      ln_before_adapter=xmod.cfg.model.ln_before_adapter      ,      languages=xmod.cfg.model.languages      ,      )
      if classification_head:
            SCREAMING_SNAKE_CASE_     						=      xmod.model.classification_heads['mnli'].out_proj.weight.shape[0]

      print('Our X-MOD config:'      ,      __UpperCAmelCase							)

      SCREAMING_SNAKE_CASE_     						=      XmodForSequenceClassification(__UpperCAmelCase							) if classification_head else XmodForMaskedLM(__UpperCAmelCase							)
      model.eval()

      # Now let's copy all the weights.
      # Embeddings
      SCREAMING_SNAKE_CASE_     						=      xmod_sent_encoder.embed_tokens.weight
      SCREAMING_SNAKE_CASE_     						=      xmod_sent_encoder.embed_positions.weight
      SCREAMING_SNAKE_CASE_     						=      torch.zeros_like(
          model.roberta.embeddings.token_type_embeddings.weight							)  # just zero them out b/c xmod doesn't use them.

      SCREAMING_SNAKE_CASE_     						=      xmod_sent_encoder.layernorm_embedding.weight
      SCREAMING_SNAKE_CASE_     						=      xmod_sent_encoder.layernorm_embedding.bias

      for i in range(config.num_hidden_layers							):
            # Encoder: start of layer
            SCREAMING_SNAKE_CASE_     						=      model.roberta.encoder.layer[i]
            SCREAMING_SNAKE_CASE_     						=      xmod_sent_encoder.layers[i]

            # self attention
            SCREAMING_SNAKE_CASE_     						=      layer.attention.self
            if not (
                xmod_layer.self_attn.k_proj.weight.data.shape
                == xmod_layer.self_attn.q_proj.weight.data.shape
                == xmod_layer.self_attn.v_proj.weight.data.shape
                == torch.Size((config.hidden_size, config.hidden_size)							)
            ):
                  raise AssertionError('Dimensions of self-attention weights do not match.'							)

            SCREAMING_SNAKE_CASE_     						=      xmod_layer.self_attn.q_proj.weight
            SCREAMING_SNAKE_CASE_     						=      xmod_layer.self_attn.q_proj.bias
            SCREAMING_SNAKE_CASE_     						=      xmod_layer.self_attn.k_proj.weight
            SCREAMING_SNAKE_CASE_     						=      xmod_layer.self_attn.k_proj.bias
            SCREAMING_SNAKE_CASE_     						=      xmod_layer.self_attn.v_proj.weight
            SCREAMING_SNAKE_CASE_     						=      xmod_layer.self_attn.v_proj.bias

            # self-attention output
            SCREAMING_SNAKE_CASE_     						=      layer.attention.output
            if self_output.dense.weight.shape != xmod_layer.self_attn.out_proj.weight.shape:
                  raise AssertionError('Dimensions of self-attention output weights do not match.'							)
            SCREAMING_SNAKE_CASE_     						=      xmod_layer.self_attn.out_proj.weight
            SCREAMING_SNAKE_CASE_     						=      xmod_layer.self_attn.out_proj.bias
            SCREAMING_SNAKE_CASE_     						=      xmod_layer.self_attn_layer_norm.weight
            SCREAMING_SNAKE_CASE_     						=      xmod_layer.self_attn_layer_norm.bias

            # intermediate
            SCREAMING_SNAKE_CASE_     						=      layer.intermediate
            if intermediate.dense.weight.shape != xmod_layer.fca.weight.shape:
                  raise AssertionError('Dimensions of intermediate weights do not match.'							)
            SCREAMING_SNAKE_CASE_     						=      xmod_layer.fca.weight
            SCREAMING_SNAKE_CASE_     						=      xmod_layer.fca.bias

            # output
            SCREAMING_SNAKE_CASE_     						=      layer.output
            if bert_output.dense.weight.shape != xmod_layer.fca.weight.shape:
                  raise AssertionError('Dimensions of feed-forward weights do not match.'							)
            SCREAMING_SNAKE_CASE_     						=      xmod_layer.fca.weight
            SCREAMING_SNAKE_CASE_     						=      xmod_layer.fca.bias
            SCREAMING_SNAKE_CASE_     						=      xmod_layer.final_layer_norm.weight
            SCREAMING_SNAKE_CASE_     						=      xmod_layer.final_layer_norm.bias
            if bert_output.adapter_layer_norm is not None:
                  SCREAMING_SNAKE_CASE_     						=      xmod_layer.adapter_layer_norm.weight
                  SCREAMING_SNAKE_CASE_     						=      xmod_layer.adapter_layer_norm.bias

            if sorted(bert_output.adapter_modules.keys()							) != sorted(xmod_layer.adapter_modules.keys()							):
                  raise AssertionError('Lists of language adapters do not match.'							)
            for lang_code, adapter in xmod_layer.adapter_modules.items():
                  SCREAMING_SNAKE_CASE_     						=      bert_output.adapter_modules[lang_code]
                  SCREAMING_SNAKE_CASE_     						=      xmod_layer.adapter_modules[lang_code]
                  SCREAMING_SNAKE_CASE_     						=      from_adapter.fca.weight
                  SCREAMING_SNAKE_CASE_     						=      from_adapter.fca.bias
                  SCREAMING_SNAKE_CASE_     						=      from_adapter.fca.weight
                  SCREAMING_SNAKE_CASE_     						=      from_adapter.fca.bias

            # end of layer

      if xmod_sent_encoder.layer_norm is not None:
            SCREAMING_SNAKE_CASE_     						=      xmod_sent_encoder.layer_norm.weight
            SCREAMING_SNAKE_CASE_     						=      xmod_sent_encoder.layer_norm.bias

      if classification_head:
            SCREAMING_SNAKE_CASE_     						=      xmod.model.classification_heads['mnli'].dense.weight
            SCREAMING_SNAKE_CASE_     						=      xmod.model.classification_heads['mnli'].dense.bias
            SCREAMING_SNAKE_CASE_     						=      xmod.model.classification_heads['mnli'].out_proj.weight
            SCREAMING_SNAKE_CASE_     						=      xmod.model.classification_heads['mnli'].out_proj.bias
      else:
            # LM Head
            SCREAMING_SNAKE_CASE_     						=      xmod.model.encoder.lm_head.dense.weight
            SCREAMING_SNAKE_CASE_     						=      xmod.model.encoder.lm_head.dense.bias
            SCREAMING_SNAKE_CASE_     						=      xmod.model.encoder.lm_head.layer_norm.weight
            SCREAMING_SNAKE_CASE_     						=      xmod.model.encoder.lm_head.layer_norm.bias
            SCREAMING_SNAKE_CASE_     						=      xmod.model.encoder.lm_head.weight
            SCREAMING_SNAKE_CASE_     						=      xmod.model.encoder.lm_head.bias

      # Let's check that we get the same results.
      SCREAMING_SNAKE_CASE_     						=      xmod.encode(__UpperCAmelCase							).unsqueeze(0							)  # batch of size 1
      model.roberta.set_default_language(__UpperCAmelCase							)

      SCREAMING_SNAKE_CASE_     						=      model(__UpperCAmelCase							)[0]
      if classification_head:
            SCREAMING_SNAKE_CASE_     						=      xmod.model.classification_heads['mnli'](xmod.extract_features(__UpperCAmelCase							)							)
      else:
            SCREAMING_SNAKE_CASE_     						=      xmod.model(__UpperCAmelCase      ,      lang_id=[SAMPLE_LANGUAGE]							)[0]
      print(our_output.shape      ,      their_output.shape							)
      SCREAMING_SNAKE_CASE_     						=      torch.max(torch.abs(our_output - their_output							)							).item()
      print(f"max_absolute_diff = {max_absolute_diff}"							)  # ~ 1e-7
      SCREAMING_SNAKE_CASE_     						=      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'							)

      Path(__UpperCAmelCase							).mkdir(parents=__UpperCAmelCase      ,      exist_ok=__UpperCAmelCase							)
      print(f"Saving model to {pytorch_dump_folder_path}"							)
      model.save_pretrained(__UpperCAmelCase							)


if __name__ == "__main__":
   lowerCamelCase__			:       List[Any]         =							argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       '--xmod_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.'
   )
   lowerCamelCase__			:       Dict         =							parser.parse_args()
   convert_xmod_checkpoint_to_pytorch(
       args.xmod_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
   )





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

import requests

def  UpperCAmelCase_							(				__UpperCAmelCase						:     str = "isbn/0140328726"							) ->			dict:
      SCREAMING_SNAKE_CASE_     						=      olid.strip().strip('/'							)  # Remove leading/trailing whitespace & slashes
      if new_olid.count('/'							) != 1:
            SCREAMING_SNAKE_CASE_     						=      f"{olid} is not a valid Open Library olid"
            raise ValueError(__UpperCAmelCase							)
      return requests.get(f"https://openlibrary.org/{new_olid}.json"							).json()

def  UpperCAmelCase_							(				__UpperCAmelCase						:     dict							) ->			dict:
      SCREAMING_SNAKE_CASE_     						=      {
          '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_     						=      {better_key: ol_book_data[key] for key, better_key in desired_keys.items()}
      SCREAMING_SNAKE_CASE_     						=      [
          get_openlibrary_data(author['key']							)['name'] for author in data['Authors']
      ]
      SCREAMING_SNAKE_CASE_     						=      data['First sentence']['value']
      for key, value in data.items():
            if isinstance(__UpperCAmelCase      ,      __UpperCAmelCase							):
                  SCREAMING_SNAKE_CASE_     						=      ', '.join(__UpperCAmelCase							)
      return data


if __name__ == "__main__":
   import doctest

   doctest.testmod()

   while True:
      lowerCamelCase__			:       Optional[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:
         lowerCamelCase__			:       Union[str, 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}.''')