

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

A_       						=							"Hello, World!"
A_       						=							"en_XX"





def    A_  ( snake_case      ,       snake_case      ,       snake_case ):
		SCREAMING_SNAKE_CASE:int						      =					Path("data_bin" )
		SCREAMING_SNAKE_CASE:int						      =					FairseqXmodModel.from_pretrained(
		    model_name_or_path=str(Path(snake_case ).parent )      ,       checkpoint_file=Path(snake_case ).name      ,       _name="xmod_base"      ,       arch="xmod_base"      ,       task="multilingual_masked_lm"      ,       data_name_or_path=str(snake_case )      ,       bpe="sentencepiece"      ,       sentencepiece_model=str(Path(snake_case ).parent / "sentencepiece.bpe.model" )      ,       src_dict=str(data_dir / "dict.txt" )      ,       )
		xmod.eval()  # disable dropout
		print(snake_case )

		SCREAMING_SNAKE_CASE:Tuple						      =					xmod.model.encoder.sentence_encoder
		SCREAMING_SNAKE_CASE:Tuple						      =					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:
				SCREAMING_SNAKE_CASE:Tuple						      =					xmod.model.classification_heads["mnli"].out_proj.weight.shape[0]

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

		SCREAMING_SNAKE_CASE:List[Any]						      =					XmodForSequenceClassification(snake_case ) if classification_head else XmodForMaskedLM(snake_case )
		model.eval()

		# Now let's copy all the weights.
		# Embeddings
		SCREAMING_SNAKE_CASE:Optional[int]						      =					xmod_sent_encoder.embed_tokens.weight
		SCREAMING_SNAKE_CASE:Optional[int]						      =					xmod_sent_encoder.embed_positions.weight
		SCREAMING_SNAKE_CASE:Union[str, 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:Tuple						      =					xmod_sent_encoder.layernorm_embedding.weight
		SCREAMING_SNAKE_CASE:List[str]						      =					xmod_sent_encoder.layernorm_embedding.bias

		for i in range(config.num_hidden_layers ):
				# Encoder: start of layer
				SCREAMING_SNAKE_CASE:Tuple						      =					model.roberta.encoder.layer[i]
				SCREAMING_SNAKE_CASE:Union[str, Any]						      =					xmod_sent_encoder.layers[i]

				# self attention
				SCREAMING_SNAKE_CASE:Union[str, 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." )

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

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

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

				# end of layer

		if xmod_sent_encoder.layer_norm is not None:
				SCREAMING_SNAKE_CASE:List[str]						      =					xmod_sent_encoder.layer_norm.weight
				SCREAMING_SNAKE_CASE:str						      =					xmod_sent_encoder.layer_norm.bias

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

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

		SCREAMING_SNAKE_CASE:Optional[int]						      =					model(snake_case )[0]
		if classification_head:
				SCREAMING_SNAKE_CASE:Tuple						      =					xmod.model.classification_heads["mnli"](xmod.extract_features(snake_case ) )
		else:
				SCREAMING_SNAKE_CASE:Tuple						      =					xmod.model(snake_case      ,       lang_id=[SAMPLE_LANGUAGE] )[0]
		print(our_output.shape      ,       their_output.shape )
		SCREAMING_SNAKE_CASE:Tuple						      =					torch.max(torch.abs(our_output - their_output ) ).item()
		print(F'''max_absolute_diff = {max_absolute_diff}''' )  # ~ 1e-7
		SCREAMING_SNAKE_CASE:Tuple						      =					torch.allclose(snake_case      ,       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(snake_case ).mkdir(parents=snake_case      ,       exist_ok=snake_case )
		print(F'''Saving model to {pytorch_dump_folder_path}''' )
		model.save_pretrained(snake_case )


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'''

import math
import os
from copy import deepcopy

import datasets
import evaluate
import torch
import transformers
from datasets import load_dataset
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer

from accelerate import Accelerator
from accelerate.test_utils import RegressionDataset, RegressionModel
from accelerate.utils import is_tpu_available, set_seed


A_       						=							"true"





def    A_  ( snake_case      ,       snake_case=82      ,       snake_case=16 ):
  set_seed(42 )
  SCREAMING_SNAKE_CASE:Optional[int]						      =					RegressionModel()
  SCREAMING_SNAKE_CASE:int						      =					deepcopy(snake_case )
  SCREAMING_SNAKE_CASE:List[str]						      =					RegressionDataset(length=snake_case )
  SCREAMING_SNAKE_CASE:int						      =					DataLoader(snake_case      ,       batch_size=snake_case )
  model.to(accelerator.device )
  SCREAMING_SNAKE_CASE      ,    SCREAMING_SNAKE_CASE:Dict						      =					accelerator.prepare(snake_case      ,       snake_case )
  return model, ddp_model, dataloader





def    A_  ( snake_case      ,       snake_case=False ):
  SCREAMING_SNAKE_CASE:List[Any]						      =					AutoTokenizer.from_pretrained("hf-internal-testing/mrpc-bert-base-cased" )
  SCREAMING_SNAKE_CASE:Dict						      =					load_dataset("glue"      ,       "mrpc"      ,       split="validation" )

  def tokenize_function(snake_case ):
    SCREAMING_SNAKE_CASE:Optional[int]						      =					tokenizer(examples["sentence1"]      ,       examples["sentence2"]      ,       truncation=snake_case      ,       max_length=snake_case )
    return outputs

  with accelerator.main_process_first():
    SCREAMING_SNAKE_CASE:int						      =					dataset.map(
        snake_case      ,       batched=snake_case      ,       remove_columns=["idx", "sentence1", "sentence2"]      ,       )

  SCREAMING_SNAKE_CASE:Union[str, Any]						      =					tokenized_datasets.rename_column("label"      ,       "labels" )

  def collate_fn(snake_case ):
    if use_longest:
      return tokenizer.pad(snake_case      ,       padding="longest"      ,       return_tensors="pt" )
    return tokenizer.pad(snake_case      ,       padding="max_length"      ,       max_length=128      ,       return_tensors="pt" )

  return DataLoader(snake_case      ,       shuffle=snake_case      ,       collate_fn=snake_case      ,       batch_size=16 )





def    A_  ( snake_case      ,       snake_case ):
  SCREAMING_SNAKE_CASE:Optional[int]						      =					Accelerator(dispatch_batches=snake_case      ,       split_batches=snake_case )
  SCREAMING_SNAKE_CASE:List[Any]						      =					get_dataloader(snake_case      ,       not dispatch_batches )
  SCREAMING_SNAKE_CASE:Tuple						      =					AutoModelForSequenceClassification.from_pretrained(
      "hf-internal-testing/mrpc-bert-base-cased"      ,       return_dict=snake_case )
  SCREAMING_SNAKE_CASE      ,    SCREAMING_SNAKE_CASE:List[str]						      =					accelerator.prepare(snake_case      ,       snake_case )
  return {"ddp": [ddp_model, ddp_dataloader, "cuda:0"], "no": [model, dataloader, accelerator.device]}, accelerator





def    A_  ( snake_case      ,       snake_case      ,       snake_case ):
  SCREAMING_SNAKE_CASE:str						      =					[]
  for batch in dataloader:
    SCREAMING_SNAKE_CASE      ,    SCREAMING_SNAKE_CASE:Tuple						      =					batch.values()
    with torch.no_grad():
      SCREAMING_SNAKE_CASE:int						      =					model(snake_case )
      SCREAMING_SNAKE_CASE      ,    SCREAMING_SNAKE_CASE:List[str]						      =					accelerator.gather_for_metrics((logit, target) )
      logits_and_targets.append((logit, target) )
  SCREAMING_SNAKE_CASE      ,    SCREAMING_SNAKE_CASE:Union[str, Any]						      =					[], []
  for logit, targ in logits_and_targets:
    logits.append(snake_case )
    targs.append(snake_case )
  SCREAMING_SNAKE_CASE      ,    SCREAMING_SNAKE_CASE:Optional[Any]						      =					torch.cat(snake_case ), torch.cat(snake_case )
  return logits, targs





def    A_  ( snake_case      ,       snake_case=82      ,       snake_case=False      ,       snake_case=False      ,       snake_case=16 ):
  SCREAMING_SNAKE_CASE      ,    SCREAMING_SNAKE_CASE      ,    SCREAMING_SNAKE_CASE:int						      =					get_basic_setup(snake_case      ,       snake_case      ,       snake_case )
  SCREAMING_SNAKE_CASE      ,    SCREAMING_SNAKE_CASE:Optional[Any]						      =					generate_predictions(snake_case      ,       snake_case      ,       snake_case )
  assert (
      len(snake_case ) == num_samples
  ), F'''Unexpected number of inputs:\n    Expected: {num_samples}\n    Actual: {len(snake_case )}'''





def    A_  ( snake_case = False      ,       snake_case = False ):
  SCREAMING_SNAKE_CASE:List[Any]						      =					evaluate.load("glue"      ,       "mrpc" )
  SCREAMING_SNAKE_CASE      ,    SCREAMING_SNAKE_CASE:Dict						      =					get_mrpc_setup(snake_case      ,       snake_case )
  # First do baseline
  SCREAMING_SNAKE_CASE      ,    SCREAMING_SNAKE_CASE      ,    SCREAMING_SNAKE_CASE:Tuple						      =					setup["no"]
  model.to(snake_case )
  model.eval()
  for batch in dataloader:
    batch.to(snake_case )
    with torch.inference_mode():
      SCREAMING_SNAKE_CASE:Dict						      =					model(**snake_case )
    SCREAMING_SNAKE_CASE:Any						      =					outputs.logits.argmax(dim=-1 )
    metric.add_batch(predictions=snake_case      ,       references=batch["labels"] )
  SCREAMING_SNAKE_CASE:Union[str, Any]						      =					metric.compute()

  # Then do distributed
  SCREAMING_SNAKE_CASE      ,    SCREAMING_SNAKE_CASE      ,    SCREAMING_SNAKE_CASE:Tuple						      =					setup["ddp"]
  model.eval()
  for batch in dataloader:
    with torch.inference_mode():
      SCREAMING_SNAKE_CASE:Any						      =					model(**snake_case )
    SCREAMING_SNAKE_CASE:Optional[Any]						      =					outputs.logits.argmax(dim=-1 )
    SCREAMING_SNAKE_CASE:List[str]						      =					batch["labels"]
    SCREAMING_SNAKE_CASE      ,    SCREAMING_SNAKE_CASE:int						      =					accelerator.gather_for_metrics((preds, references) )
    metric.add_batch(predictions=snake_case      ,       references=snake_case )
  SCREAMING_SNAKE_CASE:Any						      =					metric.compute()

  for key in "accuracy f1".split():
    assert math.isclose(
        baseline[key]      ,       distributed[key] ), F'''Baseline and Distributed are not the same for key {key}:\n\tBaseline: {baseline[key]}\n\tDistributed: {distributed[key]}\n'''





def    A_  ( ):
  SCREAMING_SNAKE_CASE:Optional[int]						      =					Accelerator(split_batches=snake_case      ,       dispatch_batches=snake_case )
  if accelerator.is_local_main_process:
    datasets.utils.logging.set_verbosity_warning()
    transformers.utils.logging.set_verbosity_warning()
  else:
    datasets.utils.logging.set_verbosity_error()
    transformers.utils.logging.set_verbosity_error()
  # These are a bit slower so they should only be ran on the GPU or TPU
  if torch.cuda.is_available() or is_tpu_available():
    if accelerator.is_local_main_process:
      print("**Testing gather_for_metrics**" )
    for split_batches in [True, False]:
      for dispatch_batches in [True, False]:
        if accelerator.is_local_main_process:
          print(F'''With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`''' )
        test_mrpc(snake_case      ,       snake_case )
        accelerator.state._reset_state()
  if accelerator.is_local_main_process:
    print("**Test torch metrics**" )
  for split_batches in [True, False]:
    for dispatch_batches in [True, False]:
      SCREAMING_SNAKE_CASE:Optional[Any]						      =					Accelerator(split_batches=snake_case      ,       dispatch_batches=snake_case )
      if accelerator.is_local_main_process:
        print(F'''With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`, length=99''' )
      test_torch_metrics(snake_case      ,       99 )
      accelerator.state._reset_state()
  if accelerator.is_local_main_process:
    print("**Test last batch is not dropped when perfectly divisible**" )
  SCREAMING_SNAKE_CASE:List[str]						      =					Accelerator()
  test_torch_metrics(snake_case      ,       512 )
  accelerator.state._reset_state()





def    A_  ( snake_case ):
  # For xla_spawn (TPUs)
  main()


if __name__ == "__main__":
    main()






