





import argparse

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType


########################################################################
# This is a fully working simple example to use Accelerate
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################


__A             =       16
__A             =       32





def   lowerCAmelCase_				( __a			,					__a = 16	)  ->	Union[str, Any]:







		"""simple docstring"""





		lowerCamelCase__:   Tuple    						=AutoTokenizer.from_pretrained("bert-base-cased"	)
		lowerCamelCase__:   Tuple    						=load_dataset("glue"			,					"mrpc"	)

		def tokenize_function(__a	):
				# max_length=None => use the model max length (it's actually the default)
				lowerCamelCase__:   Optional[int]    						=tokenizer(examples["sentence1"]			,					examples["sentence2"]			,					truncation=lowerCAmelCase_			,					max_length=lowerCAmelCase_	)
				return outputs

		# Apply the method we just defined to all the examples in all the splits of the dataset
		# starting with the main process first:
		with accelerator.main_process_first():
				lowerCamelCase__:   Tuple    						=datasets.map(
				    lowerCAmelCase_			,					batched=lowerCAmelCase_			,					remove_columns=["idx", "sentence1", "sentence2"]			,					)

		# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
		# transformers library
		lowerCamelCase__:   List[Any]    						=tokenized_datasets.rename_column("label"			,					"labels"	)

		def collate_fn(__a	):
				# On TPU it's best to pad everything to the same length or training will be very slow.
				lowerCamelCase__:   Tuple    						=128 if accelerator.distributed_type == DistributedType.TPU else None
				# When using mixed precision we want round multiples of 8/16
				if accelerator.mixed_precision == "fp8":
						lowerCamelCase__:   Optional[int]    						=16
				elif accelerator.mixed_precision != "no":
						lowerCamelCase__:   Optional[int]    						=8
				else:
						lowerCamelCase__:   Optional[int]    						=None

				return tokenizer.pad(
				    lowerCAmelCase_			,					padding="longest"			,					max_length=lowerCAmelCase_			,					pad_to_multiple_of=lowerCAmelCase_			,					return_tensors="pt"			,					)

		# Instantiate dataloaders.
		lowerCamelCase__:   str    						=DataLoader(
		    tokenized_datasets["train"]			,					shuffle=lowerCAmelCase_			,					collate_fn=lowerCAmelCase_			,					batch_size=lowerCAmelCase_			,					drop_last=lowerCAmelCase_	)
		lowerCamelCase__:   int    						=DataLoader(
		    tokenized_datasets["validation"]			,					shuffle=lowerCAmelCase_			,					collate_fn=lowerCAmelCase_			,					batch_size=lowerCAmelCase_			,					drop_last=(accelerator.mixed_precision == "fp8")			,					)

		return train_dataloader, eval_dataloader





def   lowerCAmelCase_				( __a			,					__a	)  ->	Optional[int]:







		"""simple docstring"""





		lowerCamelCase__:   Union[str, Any]    						=Accelerator(cpu=args.cpu			,					mixed_precision=args.mixed_precision	)
		# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
		lowerCamelCase__:   Union[str, Any]    						=config["lr"]
		lowerCamelCase__:   Any    						=int(config["num_epochs"]	)
		lowerCamelCase__:   int    						=int(config["seed"]	)
		lowerCamelCase__:   List[Any]    						=int(config["batch_size"]	)

		lowerCamelCase__:   str    						=evaluate.load("glue"			,					"mrpc"	)

		# If the batch size is too big we use gradient accumulation
		lowerCamelCase__:   str    						=1
		if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
				lowerCamelCase__:   str    						=batch_size // MAX_GPU_BATCH_SIZE
				lowerCamelCase__:   Any    						=MAX_GPU_BATCH_SIZE

		set_seed(lowerCAmelCase_	)
		lowerCamelCase__:   List[Any]    						=get_dataloaders(lowerCAmelCase_			,					lowerCAmelCase_	)
		# Instantiate the model (we build the model here so that the seed also control new weights initialization)
		lowerCamelCase__:   Union[str, Any]    						=AutoModelForSequenceClassification.from_pretrained("bert-base-cased"			,					return_dict=lowerCAmelCase_	)

		# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
		# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
		# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
		lowerCamelCase__:   Union[str, Any]    						=model.to(accelerator.device	)
		# Instantiate optimizer
		lowerCamelCase__:   str    						=AdamW(params=model.parameters()			,					lr=lowerCAmelCase_	)

		# Instantiate scheduler
		lowerCamelCase__:   Tuple    						=get_linear_schedule_with_warmup(
		    optimizer=lowerCAmelCase_			,					num_warmup_steps=100			,					num_training_steps=(len(lowerCAmelCase_	) * num_epochs) // gradient_accumulation_steps			,					)

		# Prepare everything
		# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
		# prepare method.

		lowerCamelCase__:   int    						=accelerator.prepare(
		    lowerCAmelCase_			,					lowerCAmelCase_			,					lowerCAmelCase_			,					lowerCAmelCase_			,					lowerCAmelCase_	)

		# Now we train the model
		for epoch in range(lowerCAmelCase_	):
				model.train()
				for step, batch in enumerate(lowerCAmelCase_	):
						# We could avoid this line since we set the accelerator with `device_placement=True`.
						batch.to(accelerator.device	)
						lowerCamelCase__:   Any    						=model(**lowerCAmelCase_	)
						lowerCamelCase__:   Tuple    						=outputs.loss
						lowerCamelCase__:   int    						=loss / gradient_accumulation_steps
						accelerator.backward(lowerCAmelCase_	)
						if step % gradient_accumulation_steps == 0:
								optimizer.step()
								lr_scheduler.step()
								optimizer.zero_grad()

				model.eval()
				for step, batch in enumerate(lowerCAmelCase_	):
						# We could avoid this line since we set the accelerator with `device_placement=True`.
						batch.to(accelerator.device	)
						with torch.no_grad():
								lowerCamelCase__:   str    						=model(**lowerCAmelCase_	)
						lowerCamelCase__:   str    						=outputs.logits.argmax(dim=-1	)
						lowerCamelCase__:   Any    						=accelerator.gather_for_metrics((predictions, batch["labels"])	)
						metric.add_batch(
						    predictions=lowerCAmelCase_			,					references=lowerCAmelCase_			,					)

				lowerCamelCase__:   List[str]    						=metric.compute()
				# Use accelerator.print to print only on the main process.
				accelerator.print(F"""epoch {epoch}:"""			,					lowerCAmelCase_	)





def   lowerCAmelCase_				( )  ->	Tuple:







		"""simple docstring"""





		lowerCamelCase__:   int    						=argparse.ArgumentParser(description="Simple example of training script."	)
		parser.add_argument(
		    "--mixed_precision"			,					type=lowerCAmelCase_			,					default=lowerCAmelCase_			,					choices=["no", "fp16", "bf16", "fp8"]			,					help="Whether to use mixed precision. Choose"
		    "between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."
		    "and an Nvidia Ampere GPU."			,					)
		parser.add_argument("--cpu"			,					action="store_true"			,					help="If passed, will train on the CPU."	)
		lowerCamelCase__:   str    						=parser.parse_args()
		lowerCamelCase__:   Tuple    						={"lr": 2e-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
		training_function(lowerCAmelCase_			,					lowerCAmelCase_	)


if __name__ == "__main__":
					main()








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   lowerCAmelCase_				( __a			,					__a			,					__a	)  ->	List[Any]:







  """simple docstring"""





  lowerCamelCase__:   Any    						=Path("data_bin"	)
  lowerCamelCase__:   int    						=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	)

  lowerCamelCase__:   Optional[int]    						=xmod.model.encoder.sentence_encoder
  lowerCamelCase__:   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:
    lowerCamelCase__:   Optional[Any]    						=xmod.model.classification_heads["mnli"].out_proj.weight.shape[0]

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

  lowerCamelCase__:   Tuple    						=XmodForSequenceClassification(__a	) if classification_head else XmodForMaskedLM(__a	)
  model.eval()

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

  lowerCamelCase__:   List[Any]    						=xmod_sent_encoder.layernorm_embedding.weight
  lowerCamelCase__:   Union[str, Any]    						=xmod_sent_encoder.layernorm_embedding.bias

  for i in range(config.num_hidden_layers	):
    # Encoder: start of layer
    lowerCamelCase__:   List[Any]    						=model.roberta.encoder.layer[i]
    lowerCamelCase__:   Union[str, Any]    						=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__:   Any    						=xmod_layer.self_attn.q_proj.bias
    lowerCamelCase__:   Any    						=xmod_layer.self_attn.k_proj.weight
    lowerCamelCase__:   Tuple    						=xmod_layer.self_attn.k_proj.bias
    lowerCamelCase__:   Optional[int]    						=xmod_layer.self_attn.v_proj.weight
    lowerCamelCase__:   List[str]    						=xmod_layer.self_attn.v_proj.bias

    # self-attention output
    lowerCamelCase__:   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."	)
    lowerCamelCase__:   Dict    						=xmod_layer.self_attn.out_proj.weight
    lowerCamelCase__:   Optional[Any]    						=xmod_layer.self_attn.out_proj.bias
    lowerCamelCase__:   List[Any]    						=xmod_layer.self_attn_layer_norm.weight
    lowerCamelCase__:   Dict    						=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__:   int    						=xmod_layer.fca.weight
    lowerCamelCase__:   List[str]    						=xmod_layer.fca.bias

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

    # end of layer

  if xmod_sent_encoder.layer_norm is not None:
    lowerCamelCase__:   Tuple    						=xmod_sent_encoder.layer_norm.weight
    lowerCamelCase__:   Dict    						=xmod_sent_encoder.layer_norm.bias

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

  # Let's check that we get the same results.
  lowerCamelCase__:   List[str]    						=xmod.encode(__a	).unsqueeze(0	)  # batch of size 1
  model.roberta.set_default_language(__a	)

  lowerCamelCase__:   List[Any]    						=model(__a	)[0]
  if classification_head:
    lowerCamelCase__:   Union[str, Any]    						=xmod.model.classification_heads["mnli"](xmod.extract_features(__a	)	)
  else:
    lowerCamelCase__:   Dict    						=xmod.model(__a			,					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__:   Tuple    						=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__":
     __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
     )

