



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_    :  Dict						  =		logging.get_logger(__name__)

a_    :  str						  =		'Hello, World!'
a_    :  Any						  =		'en_XX'

def      __lowercase(						UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__			):


      """simple docstring"""



      lowerCamelCase             =		Path("data_bin"			)
      lowerCamelCase             =		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__			)

      lowerCamelCase             =		xmod.model.encoder.sentence_encoder
      lowerCamelCase             =		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             =		xmod.model.classification_heads["mnli"].out_proj.weight.shape[0]

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

      lowerCamelCase             =		XmodForSequenceClassification(UpperCAmelCase__			) if classification_head else XmodForMaskedLM(UpperCAmelCase__			)
      model.eval()

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

      lowerCamelCase             =		xmod_sent_encoder.layernorm_embedding.weight
      lowerCamelCase             =		xmod_sent_encoder.layernorm_embedding.bias

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

            # self attention
            lowerCamelCase             =		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             =		xmod_layer.self_attn.q_proj.weight
            lowerCamelCase             =		xmod_layer.self_attn.q_proj.bias
            lowerCamelCase             =		xmod_layer.self_attn.k_proj.weight
            lowerCamelCase             =		xmod_layer.self_attn.k_proj.bias
            lowerCamelCase             =		xmod_layer.self_attn.v_proj.weight
            lowerCamelCase             =		xmod_layer.self_attn.v_proj.bias

            # self-attention output
            lowerCamelCase             =		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             =		xmod_layer.self_attn.out_proj.weight
            lowerCamelCase             =		xmod_layer.self_attn.out_proj.bias
            lowerCamelCase             =		xmod_layer.self_attn_layer_norm.weight
            lowerCamelCase             =		xmod_layer.self_attn_layer_norm.bias

            # intermediate
            lowerCamelCase             =		layer.intermediate
            if intermediate.dense.weight.shape != xmod_layer.fca.weight.shape:
                  raise AssertionError("Dimensions of intermediate weights do not match."			)
            lowerCamelCase             =		xmod_layer.fca.weight
            lowerCamelCase             =		xmod_layer.fca.bias

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

            # end of layer

      if xmod_sent_encoder.layer_norm is not None:
            lowerCamelCase             =		xmod_sent_encoder.layer_norm.weight
            lowerCamelCase             =		xmod_sent_encoder.layer_norm.bias

      if classification_head:
            lowerCamelCase             =		xmod.model.classification_heads["mnli"].dense.weight
            lowerCamelCase             =		xmod.model.classification_heads["mnli"].dense.bias
            lowerCamelCase             =		xmod.model.classification_heads["mnli"].out_proj.weight
            lowerCamelCase             =		xmod.model.classification_heads["mnli"].out_proj.bias
      else:
            # LM Head
            lowerCamelCase             =		xmod.model.encoder.lm_head.dense.weight
            lowerCamelCase             =		xmod.model.encoder.lm_head.dense.bias
            lowerCamelCase             =		xmod.model.encoder.lm_head.layer_norm.weight
            lowerCamelCase             =		xmod.model.encoder.lm_head.layer_norm.bias
            lowerCamelCase             =		xmod.model.encoder.lm_head.weight
            lowerCamelCase             =		xmod.model.encoder.lm_head.bias

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

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




import argparse
import os
import re

import numpy as np
import PIL
import torch
from timm import create_model
from torch.optim.lr_scheduler import OneCycleLR
from torch.utils.data import DataLoader, Dataset
from torchvision.transforms import Compose, RandomResizedCrop, Resize, ToTensor

from accelerate import Accelerator

def      __lowercase(						UpperCAmelCase__			):


      """simple docstring"""



      lowerCamelCase             =		fname.split(os.path.sep			)[-1]
      return re.search(r"^(.*)_\d+\.jpg$" , UpperCAmelCase__			).groups()[0]





class  lowerCamelCase__	(    UpperCAmelCase_):
     """simple docstring"""



     def __init__(self					,      __a					,      __a=None					,      __a=None     ):






           '''simple docstring'''



           lowerCamelCase             =		file_names
           lowerCamelCase             =		image_transform
           lowerCamelCase             =		label_to_id







     def __len__(self     ):






           '''simple docstring'''



           return len(self.file_names     )







     def __getitem__(self					,      __a     ):






           '''simple docstring'''



           lowerCamelCase             =		self.file_names[idx]
           lowerCamelCase             =		PIL.Image.open(__a     )
           lowerCamelCase             =		raw_image.convert("RGB"     )
           if self.image_transform is not None:
                 lowerCamelCase             =		self.image_transform(__a     )
           lowerCamelCase             =		extract_label(__a     )
           if self.label_to_id is not None:
                 lowerCamelCase             =		self.label_to_id[label]
           return {"image": image, "label": label}



def      __lowercase(						UpperCAmelCase__ , UpperCAmelCase__			):


      """simple docstring"""



      if args.with_tracking:
            lowerCamelCase             =		Accelerator(
                cpu=args.cpu , mixed_precision=args.mixed_precision , log_with="all" , project_dir=args.project_dir			)
      else:
            lowerCamelCase             =		Accelerator(cpu=args.cpu , mixed_precision=args.mixed_precision			)

      # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
      lowerCamelCase             =		config["lr"]
      lowerCamelCase             =		int(config["num_epochs"]			)
      lowerCamelCase             =		int(config["seed"]			)
      lowerCamelCase             =		int(config["batch_size"]			)
      lowerCamelCase             =		config["image_size"]
      if not isinstance(UpperCAmelCase__ , (list, tuple)			):
            lowerCamelCase             =		(image_size, image_size)

      # Parse out whether we are saving every epoch or after a certain number of batches
      if hasattr(args.checkpointing_steps , "isdigit"			):
            if args.checkpointing_steps == "epoch":
                  lowerCamelCase             =		args.checkpointing_steps
            elif args.checkpointing_steps.isdigit():
                  lowerCamelCase             =		int(args.checkpointing_steps			)
            else:
                  raise ValueError(
                      F"""Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed."""			)
      else:
            lowerCamelCase             =		None

      # We need to initialize the trackers we use, and also store our configuration
      if args.with_tracking:
            lowerCamelCase             =		os.path.split(UpperCAmelCase__			)[-1].split("."			)[0]
            accelerator.init_trackers(UpperCAmelCase__ , UpperCAmelCase__			)

      # Grab all the image filenames
      lowerCamelCase             =		[os.path.join(args.data_dir , UpperCAmelCase__			) for fname in os.listdir(args.data_dir			) if fname.endswith(".jpg"			)]

      # Build the label correspondences
      lowerCamelCase             =		[extract_label(UpperCAmelCase__			) for fname in file_names]
      lowerCamelCase             =		list(set(UpperCAmelCase__			)			)
      id_to_label.sort()
      lowerCamelCase             =		{lbl: i for i, lbl in enumerate(UpperCAmelCase__			)}

      # Set the seed before splitting the data.
      np.random.seed(UpperCAmelCase__			)
      torch.manual_seed(UpperCAmelCase__			)
      torch.cuda.manual_seed_all(UpperCAmelCase__			)

      # Split our filenames between train and validation
      lowerCamelCase             =		np.random.permutation(len(UpperCAmelCase__			)			)
      lowerCamelCase             =		int(0.8 * len(UpperCAmelCase__			)			)
      lowerCamelCase             =		random_perm[:cut]
      lowerCamelCase             =		random_perm[cut:]

      # For training we use a simple RandomResizedCrop
      lowerCamelCase             =		Compose([RandomResizedCrop(UpperCAmelCase__ , scale=(0.5, 1.0)			), ToTensor()]			)
      lowerCamelCase             =		PetsDataset(
          [file_names[i] for i in train_split] , image_transform=UpperCAmelCase__ , label_to_id=UpperCAmelCase__			)

      # For evaluation, we use a deterministic Resize
      lowerCamelCase             =		Compose([Resize(UpperCAmelCase__			), ToTensor()]			)
      lowerCamelCase             =		PetsDataset([file_names[i] for i in eval_split] , image_transform=UpperCAmelCase__ , label_to_id=UpperCAmelCase__			)

      # Instantiate dataloaders.
      lowerCamelCase             =		DataLoader(UpperCAmelCase__ , shuffle=UpperCAmelCase__ , batch_size=UpperCAmelCase__ , num_workers=4			)
      lowerCamelCase             =		DataLoader(UpperCAmelCase__ , shuffle=UpperCAmelCase__ , batch_size=UpperCAmelCase__ , num_workers=4			)

      # Instantiate the model (we build the model here so that the seed also control new weights initialization)
      lowerCamelCase             =		create_model("resnet50d" , pretrained=UpperCAmelCase__ , num_classes=len(UpperCAmelCase__			)			)

      # 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             =		model.to(accelerator.device			)

      # Freezing the base model
      for param in model.parameters():
            lowerCamelCase             =		False
      for param in model.get_classifier().parameters():
            lowerCamelCase             =		True

      # We normalize the batches of images to be a bit faster.
      lowerCamelCase             =		torch.tensor(model.default_cfg["mean"]			)[None, :, None, None].to(accelerator.device			)
      lowerCamelCase             =		torch.tensor(model.default_cfg["std"]			)[None, :, None, None].to(accelerator.device			)

      # Instantiate optimizer
      lowerCamelCase             =		torch.optim.Adam(params=model.parameters() , lr=lr / 25			)

      # Instantiate learning rate scheduler
      lowerCamelCase             =		OneCycleLR(optimizer=UpperCAmelCase__ , max_lr=UpperCAmelCase__ , epochs=UpperCAmelCase__ , steps_per_epoch=len(UpperCAmelCase__			)			)

      # 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      ,				lowerCamelCase      ,				lowerCamelCase      ,				lowerCamelCase      ,				lowerCamelCase             =		accelerator.prepare(
          UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__			)
      # We need to keep track of how many total steps we have iterated over
      lowerCamelCase             =		0
      # We also need to keep track of the starting epoch so files are named properly
      lowerCamelCase             =		0

      # Potentially load in the weights and states from a previous save
      if args.resume_from_checkpoint:
            if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
                  accelerator.print(F"""Resumed from checkpoint: {args.resume_from_checkpoint}"""			)
                  accelerator.load_state(args.resume_from_checkpoint			)
                  lowerCamelCase             =		os.path.basename(args.resume_from_checkpoint			)
            else:
                  # Get the most recent checkpoint
                  lowerCamelCase             =		[f.name for f in os.scandir(os.getcwd()			) if f.is_dir()]
                  dirs.sort(key=os.path.getctime			)
                  lowerCamelCase             =		dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
            # Extract `epoch_{i}` or `step_{i}`
            lowerCamelCase             =		os.path.splitext(UpperCAmelCase__			)[0]

            if "epoch" in training_difference:
                  lowerCamelCase             =		int(training_difference.replace("epoch_" , ""			)			) + 1
                  lowerCamelCase             =		None
            else:
                  lowerCamelCase             =		int(training_difference.replace("step_" , ""			)			)
                  lowerCamelCase             =		resume_step // len(UpperCAmelCase__			)
                  resume_step -= starting_epoch * len(UpperCAmelCase__			)

    # Now we train the model
      for epoch in range(UpperCAmelCase__ , UpperCAmelCase__			):
            model.train()
            if args.with_tracking:
                  lowerCamelCase             =		0
            if args.resume_from_checkpoint and epoch == starting_epoch and resume_step is not None:
                  # We need to skip steps until we reach the resumed step
                  lowerCamelCase             =		accelerator.skip_first_batches(UpperCAmelCase__ , UpperCAmelCase__			)
                  overall_step += resume_step
            else:
                  # After the first iteration though, we need to go back to the original dataloader
                  lowerCamelCase             =		train_dataloader
            for batch in active_dataloader:
                  # We could avoid this line since we set the accelerator with `device_placement=True`.
                  lowerCamelCase             =		{k: v.to(accelerator.device			) for k, v in batch.items()}
                  lowerCamelCase             =		(batch["image"] - mean) / std
                  lowerCamelCase             =		model(UpperCAmelCase__			)
                  lowerCamelCase             =		torch.nn.functional.cross_entropy(UpperCAmelCase__ , batch["label"]			)
                  # We keep track of the loss at each epoch
                  if args.with_tracking:
                        total_loss += loss.detach().float()
                  accelerator.backward(UpperCAmelCase__			)
                  optimizer.step()
                  lr_scheduler.step()
                  optimizer.zero_grad()
                  overall_step += 1
                  if isinstance(UpperCAmelCase__ , UpperCAmelCase__			):
                        lowerCamelCase             =		F"""step_{overall_step}"""
                        if overall_step % checkpointing_steps == 0:
                              if args.output_dir is not None:
                                    lowerCamelCase             =		os.path.join(args.output_dir , UpperCAmelCase__			)
                              accelerator.save_state(UpperCAmelCase__			)
            model.eval()
            lowerCamelCase             =		0
            lowerCamelCase             =		0
            for step, batch in enumerate(UpperCAmelCase__			):
                  # We could avoid this line since we set the accelerator with `device_placement=True`.
                  lowerCamelCase             =		{k: v.to(accelerator.device			) for k, v in batch.items()}
                  lowerCamelCase             =		(batch["image"] - mean) / std
                  with torch.no_grad():
                        lowerCamelCase             =		model(UpperCAmelCase__			)
                  lowerCamelCase             =		outputs.argmax(dim=-1			)
                  lowerCamelCase      ,				lowerCamelCase             =		accelerator.gather_for_metrics((predictions, batch["label"])			)
                  lowerCamelCase             =		predictions == references
                  num_elems += accurate_preds.shape[0]
                  accurate += accurate_preds.long().sum()

            lowerCamelCase             =		accurate.item() / num_elems
            # Use accelerator.print to print only on the main process.
            accelerator.print(F"""epoch {epoch}: {100 * eval_metric:.2f}"""			)
            if args.with_tracking:
                  accelerator.log(
                      {
                          "accuracy": 100 * eval_metric,
                          "train_loss": total_loss.item() / len(UpperCAmelCase__			),
                          "epoch": epoch,
                      } , step=UpperCAmelCase__ , )
            if checkpointing_steps == "epoch":
                  lowerCamelCase             =		F"""epoch_{epoch}"""
                  if args.output_dir is not None:
                        lowerCamelCase             =		os.path.join(args.output_dir , UpperCAmelCase__			)
                  accelerator.save_state(UpperCAmelCase__			)

      if args.with_tracking:
            accelerator.end_training()

def      __lowercase(						):


      """simple docstring"""



      lowerCamelCase             =		argparse.ArgumentParser(description="Simple example of training script."			)
      parser.add_argument("--data_dir" , required=UpperCAmelCase__ , help="The data folder on disk."			)
      parser.add_argument("--fp16" , action="store_true" , help="If passed, will use FP16 training."			)
      parser.add_argument(
          "--mixed_precision" , type=UpperCAmelCase__ , default=UpperCAmelCase__ , 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."			)
      parser.add_argument(
          "--checkpointing_steps" , type=UpperCAmelCase__ , default=UpperCAmelCase__ , help="Whether the various states should be saved at the end of every n steps, or 'epoch' for each epoch." , )
      parser.add_argument(
          "--output_dir" , type=UpperCAmelCase__ , default="." , help="Optional save directory where all checkpoint folders will be stored. Default is the current working directory." , )
      parser.add_argument(
          "--resume_from_checkpoint" , type=UpperCAmelCase__ , default=UpperCAmelCase__ , help="If the training should continue from a checkpoint folder." , )
      parser.add_argument(
          "--with_tracking" , action="store_true" , help="Whether to load in all available experiment trackers from the environment and use them for logging." , )
      parser.add_argument(
          "--project_dir" , type=UpperCAmelCase__ , default="logs" , help="Location on where to store experiment tracking logs` and relevent project information" , )
      lowerCamelCase             =		parser.parse_args()
      lowerCamelCase             =		{"lr": 3E-2, "num_epochs": 3, "seed": 42, "batch_size": 64, "image_size": 224}
      training_function(UpperCAmelCase__ , UpperCAmelCase__			)


if __name__ == "__main__":
     main()