






'''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()
UpperCamelCase__			=      logging.get_logger(__name__)

UpperCamelCase__			=      'Hello, World!'
UpperCamelCase__			=      'en_XX'




def 		__SCREAMING_SNAKE_CASE       (	_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase				):


   """simple docstring"""




   lowercase_						:     Optional[Any]							     =  Path("data_bin"				)
   lowercase_						:     Optional[Any]							     =  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				)

   lowercase_						:     List[str]							     =  xmod.model.encoder.sentence_encoder
   lowercase_						:     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=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:
      lowercase_						:     Dict							     =  xmod.model.classification_heads["mnli"].out_proj.weight.shape[0]

   print("Our X-MOD config:"    ,		_UpperCamelCase				)

   lowercase_						:     int							     =  XmodForSequenceClassification(_UpperCamelCase				) if classification_head else XmodForMaskedLM(_UpperCamelCase				)
   model.eval()

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

   lowercase_						:     int							     =  xmod_sent_encoder.layernorm_embedding.weight
   lowercase_						:     List[Any]							     =  xmod_sent_encoder.layernorm_embedding.bias

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

      # self attention
      lowercase_						:     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."				)

      lowercase_						:     int							     =  xmod_layer.self_attn.q_proj.weight
      lowercase_						:     Any							     =  xmod_layer.self_attn.q_proj.bias
      lowercase_						:     str							     =  xmod_layer.self_attn.k_proj.weight
      lowercase_						:     Union[str, Any]							     =  xmod_layer.self_attn.k_proj.bias
      lowercase_						:     Any							     =  xmod_layer.self_attn.v_proj.weight
      lowercase_						:     Tuple							     =  xmod_layer.self_attn.v_proj.bias

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

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

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

      # end of layer

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

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

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

   lowercase_						:     Optional[Any]							     =  model(_UpperCamelCase				)[0]
   if classification_head:
      lowercase_						:     str							     =  xmod.model.classification_heads["mnli"](xmod.extract_features(_UpperCamelCase				)				)
   else:
      lowercase_						:     str							     =  xmod.model(_UpperCamelCase    ,		lang_id=[SAMPLE_LANGUAGE]				)[0]
   print(our_output.shape    ,		their_output.shape				)
   lowercase_						:     Optional[int]							     =  torch.max(torch.abs(our_output - their_output				)				).item()
   print(F"""max_absolute_diff = {max_absolute_diff}"""				)  # ~ 1e-7
   lowercase_						:     int							     =  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__":
 UpperCamelCase__			=      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.'
 )
 UpperCamelCase__			=      parser.parse_args()
 convert_xmod_checkpoint_to_pytorch(
     args.xmod_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
 )












'''simple docstring'''
import logging

import torch
from accelerate import Accelerator
from arguments import EvaluationArguments
from datasets import load_dataset
from torch.utils.data import IterableDataset
from torch.utils.data.dataloader import DataLoader

from transformers import AutoModelForCausalLM, AutoTokenizer, HfArgumentParser, set_seed


class 							_UpperCAmelCase    (		snake_case      ):




    def __init__(      self	:							Optional[Any]	,  a	:							Dict	,  a	:							int	,  a	:							Any=1_0_2_4	,  a	:							Tuple=1_0_2_4	,  a	:							Optional[int]=3.6      ):






       '''simple docstring'''






       lowercase_						:     List[str]							     =  tokenizer
       lowercase_						:     Union[str, Any]							     =  tokenizer.bos_token_id
       lowercase_						:     Union[str, Any]							     =  dataset
       lowercase_						:     Optional[Any]							     =  seq_length
       lowercase_						:     Any							     =  seq_length * chars_per_token * num_of_sequences





    def __iter__(      self	:							int      ):






       '''simple docstring'''






       lowercase_						:     Optional[int]							     =  iter(self.dataset      )
       lowercase_						:     Dict							     =  True
       while more_examples:
          lowercase_     ,		lowercase_						:     Optional[int]							     =  [], 0
          while True:
             if buffer_len >= self.input_characters:
                break
             try:
                buffer.append(next(a      )["content"]      )
                buffer_len += len(buffer[-1]      )
             except StopIteration:
                lowercase_						:     int							     =  False
                break
          lowercase_						:     Any							     =  tokenizer(a	,  truncation=a      )["input_ids"]
          lowercase_						:     List[str]							     =  []
          for tokenized_input in tokenized_inputs:
             all_token_ids.extend(tokenized_input + [self.concat_token_id]      )
          for i in range(0	,  len(a      )	,  self.seq_length      ):
             lowercase_						:     List[Any]							     =  all_token_ids[i : i + self.seq_length]
             if len(a      ) == self.seq_length:
                yield torch.tensor(a      )




def 		__SCREAMING_SNAKE_CASE       (	_UpperCamelCase				):


   """simple docstring"""




   lowercase_						:     Union[str, Any]							     =  {"streaming": True}
   lowercase_						:     List[Any]							     =  load_dataset(args.dataset_name    ,		split="train"    ,		**_UpperCamelCase				)
   lowercase_						:     int							     =  ConstantLengthDataset(_UpperCamelCase    ,		_UpperCamelCase    ,		seq_length=args.seq_length				)
   lowercase_						:     Dict							     =  DataLoader(_UpperCamelCase    ,		batch_size=args.batch_size				)
   return eval_dataloader




def 		__SCREAMING_SNAKE_CASE       (	_UpperCamelCase				):


   """simple docstring"""




   model.eval()
   lowercase_						:     Optional[Any]							     =  []
   for step, batch in enumerate(_UpperCamelCase				):
      with torch.no_grad():
         lowercase_						:     str							     =  model(_UpperCamelCase    ,		labels=_UpperCamelCase				)
      lowercase_						:     List[Any]							     =  outputs.loss.repeat(args.batch_size				)
      losses.append(accelerator.gather(_UpperCamelCase				)				)

      if args.max_eval_steps > 0 and step >= args.max_eval_steps:
         break
   lowercase_						:     Optional[int]							     =  torch.mean(torch.cat(_UpperCamelCase				)				)
   try:
      lowercase_						:     Dict							     =  torch.exp(_UpperCamelCase				)
   except OverflowError:
      lowercase_						:     Dict							     =  float("inf"				)
   return loss.item(), perplexity.item()


# Setup Accelerator
UpperCamelCase__			=      Accelerator()

# Parse configuration
UpperCamelCase__			=      HfArgumentParser(EvaluationArguments)
UpperCamelCase__			=      parser.parse_args()
set_seed(args.seed)

# Logging
UpperCamelCase__			=      logging.getLogger(__name__)
logging.basicConfig(
    format='%(asctime)s - %(levelname)s - %(name)s - %(message)s', datefmt='%m/%d/%Y %H:%M:%S', level=logging.INFO
)

# Load model and tokenizer
UpperCamelCase__			=      AutoModelForCausalLM.from_pretrained(args.model_ckpt)
UpperCamelCase__			=      AutoTokenizer.from_pretrained(args.model_ckpt)

# Load dataset and dataloader
UpperCamelCase__			=      create_dataloader(args)

# Prepare everything with our `accelerator`.
UpperCamelCase__,  UpperCamelCase__			=      accelerator.prepare(model, eval_dataloader)

# Evaluate and save the last checkpoint
logger.info('Evaluating and saving model after training')
UpperCamelCase__,  UpperCamelCase__			=      evaluate(args)





logger.info(f"""loss/eval: {eval_loss}, perplexity: {perplexity}""")




