




'''simple docstring'''



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 			SCREAMING_SNAKE_CASE_			(							__A    :			str	,					__A    :			str	,					__A    :			bool  )			->			Dict:
   _SCREAMING_SNAKE_CASE	=				Path("data_bin"  )
   _SCREAMING_SNAKE_CASE	=				FairseqXmodModel.from_pretrained(
       model_name_or_path=str(Path(__A  ).parent  )	,					checkpoint_file=Path(__A  ).name	,					_name="xmod_base"	,					arch="xmod_base"	,					task="multilingual_masked_lm"	,					data_name_or_path=str(__A  )	,					bpe="sentencepiece"	,					sentencepiece_model=str(Path(__A  ).parent / "sentencepiece.bpe.model"  )	,					src_dict=str(data_dir / "dict.txt"  )	,					)
   xmod.eval()  # disable dropout
   print(__A  )

   _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:"	,					__A  )

   _SCREAMING_SNAKE_CASE	=				XmodForSequenceClassification(__A  ) if classification_head else XmodForMaskedLM(__A  )
   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(__A  ).unsqueeze(0  )  # batch of size 1
   model.roberta.set_default_language(__A  )

   _SCREAMING_SNAKE_CASE	=				model(__A  )[0]
   if classification_head:
      _SCREAMING_SNAKE_CASE	=				xmod.model.classification_heads["mnli"](xmod.extract_features(__A  )  )
   else:
      _SCREAMING_SNAKE_CASE	=				xmod.model(__A	,					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(__A	,					__A	,					atol=1e-3  )
   print("Do both models output the same tensors?"	,					"🔥" if success else "💩"  )
   if not success:
      raise Exception("Something went wRoNg"  )

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


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
 )







'''simple docstring'''



from collections.abc import Sequence
def 			SCREAMING_SNAKE_CASE_			(							__A    :			Sequence[int] | None = None  )			->			int:
   if nums is None or not nums:
      raise ValueError("Input sequence should not be empty"  )

   _SCREAMING_SNAKE_CASE	=				nums[0]
   for i in range(1	,					len(__A  )  ):
      _SCREAMING_SNAKE_CASE	=				nums[i]
      _SCREAMING_SNAKE_CASE	=				max(__A	,					ans + num	,					__A  )

   return ans


if __name__ == "__main__":
 import doctest

 doctest.testmod()

 # Try on a sample input from the user
 lowerCamelCase_			=						int(input('Enter number of elements : ').strip())
 lowerCamelCase_			=						list(map(int, input('\nEnter the numbers : ').strip().split()))[:n]
 print(max_subsequence_sum(array))

