






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()
a_					= logging.get_logger(__name__)

a_					= """Hello, World!"""
a_					= """en_XX"""





def  _a(  UpperCamelCase__				:						str,	UpperCamelCase__				:						str,	UpperCamelCase__				:						bool							):




      '''simple docstring'''






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

      SCREAMING_SNAKE_CASE__     :				Optional[int]									=xmod.model.encoder.sentence_encoder
      SCREAMING_SNAKE_CASE__     :				Optional[int]									=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_1_4,	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__     :				List[str]									=xmod.model.classification_heads['''mnli'''].out_proj.weight.shape[0]

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

      SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=XmodForSequenceClassification(_lowerCamelCase							) if classification_head else XmodForMaskedLM(_lowerCamelCase							)
      model.eval()

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

      SCREAMING_SNAKE_CASE__     :				Optional[Any]									=xmod_sent_encoder.layernorm_embedding.weight
      SCREAMING_SNAKE_CASE__     :				Optional[Any]									=xmod_sent_encoder.layernorm_embedding.bias

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

            # self attention
            SCREAMING_SNAKE_CASE__     :				List[str]									=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__     :				Optional[Any]									=xmod_layer.self_attn.q_proj.weight
            SCREAMING_SNAKE_CASE__     :				List[str]									=xmod_layer.self_attn.q_proj.bias
            SCREAMING_SNAKE_CASE__     :				int									=xmod_layer.self_attn.k_proj.weight
            SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=xmod_layer.self_attn.k_proj.bias
            SCREAMING_SNAKE_CASE__     :				str									=xmod_layer.self_attn.v_proj.weight
            SCREAMING_SNAKE_CASE__     :				Tuple									=xmod_layer.self_attn.v_proj.bias

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

            # intermediate
            SCREAMING_SNAKE_CASE__     :				Optional[int]									=layer.intermediate
            if intermediate.dense.weight.shape != xmod_layer.fca.weight.shape:
                  raise AssertionError('''Dimensions of intermediate weights do not match.'''							)
            SCREAMING_SNAKE_CASE__     :				Tuple									=xmod_layer.fca.weight
            SCREAMING_SNAKE_CASE__     :				Tuple									=xmod_layer.fca.bias

            # output
            SCREAMING_SNAKE_CASE__     :				str									=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__     :				List[str]									=xmod_layer.fca.weight
            SCREAMING_SNAKE_CASE__     :				str									=xmod_layer.fca.bias
            SCREAMING_SNAKE_CASE__     :				Any									=xmod_layer.final_layer_norm.weight
            SCREAMING_SNAKE_CASE__     :				Tuple									=xmod_layer.final_layer_norm.bias
            if bert_output.adapter_layer_norm is not None:
                  SCREAMING_SNAKE_CASE__     :				List[Any]									=xmod_layer.adapter_layer_norm.weight
                  SCREAMING_SNAKE_CASE__     :				List[Any]									=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__     :				Optional[Any]									=bert_output.adapter_modules[lang_code]
                  SCREAMING_SNAKE_CASE__     :				int									=xmod_layer.adapter_modules[lang_code]
                  SCREAMING_SNAKE_CASE__     :				List[str]									=from_adapter.fca.weight
                  SCREAMING_SNAKE_CASE__     :				List[str]									=from_adapter.fca.bias
                  SCREAMING_SNAKE_CASE__     :				int									=from_adapter.fca.weight
                  SCREAMING_SNAKE_CASE__     :				Optional[int]									=from_adapter.fca.bias

            # end of layer

      if xmod_sent_encoder.layer_norm is not None:
            SCREAMING_SNAKE_CASE__     :				List[Any]									=xmod_sent_encoder.layer_norm.weight
            SCREAMING_SNAKE_CASE__     :				Optional[Any]									=xmod_sent_encoder.layer_norm.bias

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

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

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

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


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







'''simple docstring'''
from math import isqrt





def  _a(  UpperCamelCase__				:						int							):




      '''simple docstring'''






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

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





def  _a(  UpperCamelCase__				:						int = 1_0**8							):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__     :				Tuple									=calculate_prime_numbers(max_number // 2							)

      SCREAMING_SNAKE_CASE__     :				int									=0
      SCREAMING_SNAKE_CASE__     :				int									=0
      SCREAMING_SNAKE_CASE__     :				Optional[int]									=len(UpperCamelCase__							) - 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() = }''')