


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		   =			logging.get_logger(__name__)

lowerCamelCase		   =			'Hello, World!'
lowerCamelCase		   =			'en_XX'




def       a_ (       SCREAMING_SNAKE_CASE__   : str						,     SCREAMING_SNAKE_CASE__   : str						,     SCREAMING_SNAKE_CASE__   : bool	):
       '''simple docstring'''





       _lowerCamelCase			:						List[Any]            =Path('data_bin'	)
       _lowerCamelCase			:						Optional[int]            =FairseqXmodModel.from_pretrained(
           model_name_or_path=str(Path(SCREAMING_SNAKE_CASE__	).parent	)						,     checkpoint_file=Path(SCREAMING_SNAKE_CASE__	).name						,     _name='xmod_base'						,     arch='xmod_base'						,     task='multilingual_masked_lm'						,     data_name_or_path=str(SCREAMING_SNAKE_CASE__	)						,     bpe='sentencepiece'						,     sentencepiece_model=str(Path(SCREAMING_SNAKE_CASE__	).parent / 'sentencepiece.bpe.model'	)						,     src_dict=str(data_dir / 'dict.txt'	)						,     )
       xmod.eval()  # disable dropout
       print(SCREAMING_SNAKE_CASE__	)

       _lowerCamelCase			:						int            =xmod.model.encoder.sentence_encoder
       _lowerCamelCase			:						Any            =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=514						,     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:
              _lowerCamelCase			:						Optional[Any]            =xmod.model.classification_heads['mnli'].out_proj.weight.shape[0]

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

       _lowerCamelCase			:						int            =XmodForSequenceClassification(SCREAMING_SNAKE_CASE__	) if classification_head else XmodForMaskedLM(SCREAMING_SNAKE_CASE__	)
       model.eval()

       # Now let's copy all the weights.
       # Embeddings
       _lowerCamelCase			:						Optional[Any]            =xmod_sent_encoder.embed_tokens.weight
       _lowerCamelCase			:						List[Any]            =xmod_sent_encoder.embed_positions.weight
       _lowerCamelCase			:						int            =torch.zeros_like(
           model.roberta.embeddings.token_type_embeddings.weight	)  # just zero them out b/c xmod doesn't use them.

       _lowerCamelCase			:						Optional[Any]            =xmod_sent_encoder.layernorm_embedding.weight
       _lowerCamelCase			:						Dict            =xmod_sent_encoder.layernorm_embedding.bias

       for i in range(config.num_hidden_layers	):
              # Encoder: start of layer
              _lowerCamelCase			:						Optional[Any]            =model.roberta.encoder.layer[i]
              _lowerCamelCase			:						List[str]            =xmod_sent_encoder.layers[i]

              # self attention
              _lowerCamelCase			:						Any            =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.'	)

              _lowerCamelCase			:						List[str]            =xmod_layer.self_attn.q_proj.weight
              _lowerCamelCase			:						Tuple            =xmod_layer.self_attn.q_proj.bias
              _lowerCamelCase			:						Optional[int]            =xmod_layer.self_attn.k_proj.weight
              _lowerCamelCase			:						int            =xmod_layer.self_attn.k_proj.bias
              _lowerCamelCase			:						List[str]            =xmod_layer.self_attn.v_proj.weight
              _lowerCamelCase			:						str            =xmod_layer.self_attn.v_proj.bias

              # self-attention output
              _lowerCamelCase			:						List[Any]            =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.'	)
              _lowerCamelCase			:						Optional[Any]            =xmod_layer.self_attn.out_proj.weight
              _lowerCamelCase			:						Optional[int]            =xmod_layer.self_attn.out_proj.bias
              _lowerCamelCase			:						Union[str, Any]            =xmod_layer.self_attn_layer_norm.weight
              _lowerCamelCase			:						Optional[int]            =xmod_layer.self_attn_layer_norm.bias

              # intermediate
              _lowerCamelCase			:						Optional[Any]            =layer.intermediate
              if intermediate.dense.weight.shape != xmod_layer.fca.weight.shape:
                     raise AssertionError('Dimensions of intermediate weights do not match.'	)
              _lowerCamelCase			:						List[str]            =xmod_layer.fca.weight
              _lowerCamelCase			:						Union[str, Any]            =xmod_layer.fca.bias

              # output
              _lowerCamelCase			:						Any            =layer.output
              if bert_output.dense.weight.shape != xmod_layer.fca.weight.shape:
                     raise AssertionError('Dimensions of feed-forward weights do not match.'	)
              _lowerCamelCase			:						List[str]            =xmod_layer.fca.weight
              _lowerCamelCase			:						Dict            =xmod_layer.fca.bias
              _lowerCamelCase			:						Tuple            =xmod_layer.final_layer_norm.weight
              _lowerCamelCase			:						str            =xmod_layer.final_layer_norm.bias
              if bert_output.adapter_layer_norm is not None:
                     _lowerCamelCase			:						Union[str, Any]            =xmod_layer.adapter_layer_norm.weight
                     _lowerCamelCase			:						List[str]            =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():
                     _lowerCamelCase			:						Dict            =bert_output.adapter_modules[lang_code]
                     _lowerCamelCase			:						List[Any]            =xmod_layer.adapter_modules[lang_code]
                     _lowerCamelCase			:						Tuple            =from_adapter.fca.weight
                     _lowerCamelCase			:						Optional[int]            =from_adapter.fca.bias
                     _lowerCamelCase			:						int            =from_adapter.fca.weight
                     _lowerCamelCase			:						Optional[int]            =from_adapter.fca.bias

              # end of layer

       if xmod_sent_encoder.layer_norm is not None:
              _lowerCamelCase			:						Union[str, Any]            =xmod_sent_encoder.layer_norm.weight
              _lowerCamelCase			:						Union[str, Any]            =xmod_sent_encoder.layer_norm.bias

       if classification_head:
              _lowerCamelCase			:						Union[str, Any]            =xmod.model.classification_heads['mnli'].dense.weight
              _lowerCamelCase			:						Any            =xmod.model.classification_heads['mnli'].dense.bias
              _lowerCamelCase			:						List[str]            =xmod.model.classification_heads['mnli'].out_proj.weight
              _lowerCamelCase			:						Union[str, Any]            =xmod.model.classification_heads['mnli'].out_proj.bias
       else:
              # LM Head
              _lowerCamelCase			:						List[Any]            =xmod.model.encoder.lm_head.dense.weight
              _lowerCamelCase			:						Union[str, Any]            =xmod.model.encoder.lm_head.dense.bias
              _lowerCamelCase			:						Union[str, Any]            =xmod.model.encoder.lm_head.layer_norm.weight
              _lowerCamelCase			:						Optional[Any]            =xmod.model.encoder.lm_head.layer_norm.bias
              _lowerCamelCase			:						Optional[int]            =xmod.model.encoder.lm_head.weight
              _lowerCamelCase			:						Dict            =xmod.model.encoder.lm_head.bias

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

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

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


if __name__ == "__main__":
 lowerCamelCase		   =			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		   =			parser.parse_args()
 convert_xmod_checkpoint_to_pytorch(
     args.xmod_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
 )






from math import isqrt




def       a_ (       SCREAMING_SNAKE_CASE__   : int	):
       '''simple docstring'''





       _lowerCamelCase			:						Optional[int]            =[True] * max_number
       for i in range(2						,     isqrt(max_number - 1	) + 1	):
              if is_prime[i]:
                     for j in range(i**2						,     SCREAMING_SNAKE_CASE__						,     SCREAMING_SNAKE_CASE__	):
                            _lowerCamelCase			:						str            =False

       return [i for i in range(2						,     SCREAMING_SNAKE_CASE__	) if is_prime[i]]




def       a_ (       SCREAMING_SNAKE_CASE__   : int = 10**8	):
       '''simple docstring'''





       _lowerCamelCase			:						Union[str, Any]            =calculate_prime_numbers(max_number // 2	)

       _lowerCamelCase			:						Dict            =0
       _lowerCamelCase			:						int            =0
       _lowerCamelCase			:						Optional[Any]            =len(SCREAMING_SNAKE_CASE__	) - 1
       while left <= right:
              while prime_numbers[left] * prime_numbers[right] >= max_number:
                     right -= 1
              semiprimes_count += right - left + 1
              left += 1

       return semiprimes_count


if __name__ == "__main__":
 print(F"""{solution() = }""")


