



'''simple docstring'''



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 							__UpperCAmelCase				(				UpperCamelCase__				:Optional[int]			)		->						str:
				snake_case__      :   Optional[int]      =    fname.split(os.path.sep			)[-1]
				return re.search(R'''^(.*)_\d+\.jpg$'''   ,				UpperCamelCase__			).groups()[0]






class 			_SCREAMING_SNAKE_CASE				(lowercase__      ):



			def __init__(     self		:						Tuple    ,			__UpperCamelCase		:						Tuple    ,			__UpperCamelCase		:						Any=None    ,			__UpperCamelCase		:						int=None			)      ->      List[str]:
							"""simple docstring"""





							snake_case__      :   Dict      =    file_names
							snake_case__      :   Tuple      =    image_transform
							snake_case__      :   Optional[Any]      =    label_to_id



			def __len__(     self		:						str			)      ->      Dict:
							"""simple docstring"""





							return len(self.file_names			)







			def __getitem__(     self		:						Any    ,			__UpperCamelCase		:						str			)      ->      Union[str, Any]:
							"""simple docstring"""





							snake_case__      :   str      =    self.file_names[idx]
							snake_case__      :   Tuple      =    PIL.Image.open(__UpperCamelCase			)
							snake_case__      :   Any      =    raw_image.convert('''RGB'''			)
							if self.image_transform is not None:
											snake_case__      :   int      =    self.image_transform(__UpperCamelCase			)
							snake_case__      :   Optional[int]      =    extract_label(__UpperCamelCase			)
							if self.label_to_id is not None:
											snake_case__      :   Optional[int]      =    self.label_to_id[label]
							return {"image": image, "label": label}



def 							__UpperCAmelCase				(				UpperCamelCase__				:List[Any]   ,				UpperCamelCase__				:Optional[Any]			)		->						int:
				# Initialize accelerator
				snake_case__      :   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
				snake_case__      :   Any      =    config['''lr''']
				snake_case__      :   Dict      =    int(config['''num_epochs''']			)
				snake_case__      :   str      =    int(config['''seed''']			)
				snake_case__      :   Union[str, Any]      =    int(config['''batch_size''']			)
				snake_case__      :   int      =    config['''image_size''']
				if not isinstance(UpperCamelCase__   ,				(list, tuple)			):
								snake_case__      :   List[str]      =    (image_size, image_size)

				# Grab all the image filenames
				snake_case__      :   str      =    [os.path.join(args.data_dir   ,				UpperCamelCase__			) for fname in os.listdir(args.data_dir			) if fname.endswith('''.jpg'''			)]

				# Build the label correspondences
				snake_case__      :   int      =    [extract_label(UpperCamelCase__			) for fname in file_names]
				snake_case__      :   Dict      =    list(set(UpperCamelCase__			)			)
				id_to_label.sort()
				snake_case__      :   Optional[int]      =    {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
				snake_case__      :   Optional[int]      =    np.random.permutation(len(UpperCamelCase__			)			)
				snake_case__      :   Tuple      =    int(0.8 * len(UpperCamelCase__			)			)
				snake_case__      :   Tuple      =    random_perm[:cut]
				snake_case__      :   int      =    random_perm[cut:]

				# For training we use a simple RandomResizedCrop
				snake_case__      :   Dict      =    Compose([RandomResizedCrop(UpperCamelCase__   ,				scale=(0.5, 1.0)			), ToTensor()]			)
				snake_case__      :   Union[str, Any]      =    PetsDataset(
				    [file_names[i] for i in train_split]   ,				image_transform=UpperCamelCase__   ,				label_to_id=UpperCamelCase__			)

				# For evaluation, we use a deterministic Resize
				snake_case__      :   Optional[Any]      =    Compose([Resize(UpperCamelCase__			), ToTensor()]			)
				snake_case__      :   Optional[int]      =    PetsDataset([file_names[i] for i in eval_split]   ,				image_transform=UpperCamelCase__   ,				label_to_id=UpperCamelCase__			)

				# Instantiate dataloaders.
				snake_case__      :   int      =    DataLoader(UpperCamelCase__   ,				shuffle=UpperCamelCase__   ,				batch_size=UpperCamelCase__   ,				num_workers=4			)
				snake_case__      :   Dict      =    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)
				snake_case__      :   Dict      =    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).
				snake_case__      :   Optional[Any]      =    model.to(accelerator.device			)

				# Freezing the base model
				for param in model.parameters():
								snake_case__      :   List[Any]      =    False
				for param in model.get_classifier().parameters():
								snake_case__      :   str      =    True

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

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

				# Instantiate learning rate scheduler
				snake_case__      :   List[str]      =    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.
				snake_case__  ,      snake_case__  ,      snake_case__  ,      snake_case__  ,      snake_case__      :   int      =    accelerator.prepare(
				    UpperCamelCase__   ,				UpperCamelCase__   ,				UpperCamelCase__   ,				UpperCamelCase__   ,				UpperCamelCase__			)

				# Now we train the model
				for epoch in range(UpperCamelCase__			):
								model.train()
								for step, batch in enumerate(UpperCamelCase__			):
												# We could avoid this line since we set the accelerator with `device_placement=True`.
												snake_case__      :   Optional[int]      =    {k: v.to(accelerator.device			) for k, v in batch.items()}
												snake_case__      :   Optional[Any]      =    (batch['''image'''] - mean) / std
												snake_case__      :   Optional[Any]      =    model(UpperCamelCase__			)
												snake_case__      :   str      =    torch.nn.functional.cross_entropy(UpperCamelCase__   ,				batch['''label''']			)
												accelerator.backward(UpperCamelCase__			)
												optimizer.step()
												lr_scheduler.step()
												optimizer.zero_grad()

								model.eval()
								snake_case__      :   List[Any]      =    0
								snake_case__      :   List[Any]      =    0
								for _, batch in enumerate(UpperCamelCase__			):
												# We could avoid this line since we set the accelerator with `device_placement=True`.
												snake_case__      :   int      =    {k: v.to(accelerator.device			) for k, v in batch.items()}
												snake_case__      :   int      =    (batch['''image'''] - mean) / std
												with torch.no_grad():
																snake_case__      :   Optional[int]      =    model(UpperCamelCase__			)
												snake_case__      :   Dict      =    outputs.argmax(dim=-1			)
												snake_case__  ,      snake_case__      :   Tuple      =    accelerator.gather_for_metrics((predictions, batch['''label'''])			)
												snake_case__      :   Tuple      =    predictions == references
												num_elems += accurate_preds.shape[0]
												accurate += accurate_preds.long().sum()

								snake_case__      :   str      =    accurate.item() / num_elems
								# Use accelerator.print to print only on the main process.
								accelerator.print(F'''epoch {epoch}: {100 * eval_metric:.2f}'''			)


def 							__UpperCAmelCase				(				)		->						Optional[int]:
				snake_case__      :   List[str]      =    argparse.ArgumentParser(description='''Simple example of training script.'''			)
				parser.add_argument('''--data_dir'''   ,				required=UpperCamelCase__   ,				help='''The data folder on disk.'''			)
				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(
				    '''--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('''--cpu'''   ,				action='''store_true'''   ,				help='''If passed, will train on the CPU.'''			)
				snake_case__      :   Union[str, Any]      =    parser.parse_args()
				snake_case__      :   str      =    {'''lr''': 3e-2, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 64, '''image_size''': 224}
				training_function(UpperCamelCase__   ,				UpperCamelCase__			)


if __name__ == "__main__":
				main()









'''simple docstring'''



import argparse
import os
import pathlib

import fairseq
import torch
from fairseq.modules import TransformerSentenceEncoderLayer
from packaging import version

from transformers import (
    DataaVecTextConfig,
    DataaVecTextForMaskedLM,
    DataaVecTextForSequenceClassification,
    DataaVecTextModel,
)
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertSelfAttention,
    BertSelfOutput,
)

# IMPORTANT: In order for this script to run, please make sure to download the dictionary: `dict.txt` from wget https://dl.fbaipublicfiles.com/fairseq/models/roberta.large.tar.gz
# File copied from https://github.com/pytorch/fairseq/blob/main/examples/data2vec/models/data2vec_text.py
from transformers.utils import logging


if version.parse(fairseq.__version__) < version.parse("0.9.0"):
				raise Exception("requires fairseq >= 0.9.0")


logging.set_verbosity_info()
_lowercase       :						Tuple						=logging.get_logger(__name__)

_lowercase       :						Optional[int]						="Hello world! cécé herlolip"


def 							__UpperCAmelCase				(				UpperCamelCase__				:str   ,				UpperCamelCase__				:str   ,				UpperCamelCase__				:bool			)		->						int:
				snake_case__  ,      snake_case__      :   int      =    os.path.split(UpperCamelCase__			)
				snake_case__      :   List[Any]      =    DataaVecTextModel.from_pretrained(
				    UpperCamelCase__   ,				checkpoint_file=UpperCamelCase__			)
				dataavec.eval()  # disable dropout
				snake_case__      :   Dict      =    dataavec.models[0]
				snake_case__      :   int      =    dataavec_model.encoder.sentence_encoder
				snake_case__      :   List[Any]      =    DataaVecTextConfig(
				    vocab_size=dataavec_sent_encoder.embed_tokens.num_embeddings   ,				hidden_size=dataavec_model.args.encoder_embed_dim   ,				num_hidden_layers=dataavec_model.args.encoder_layers   ,				num_attention_heads=dataavec_model.args.encoder_attention_heads   ,				intermediate_size=dataavec_model.args.encoder_ffn_embed_dim   ,				max_position_embeddings=514   ,				type_vocab_size=1   ,				layer_norm_eps=1e-5   ,				)
				if classification_head:
								snake_case__      :   int      =    dataavec.model.classification_heads['''mnli'''].out_proj.weight.shape[0]
				print('''Our BERT config:'''   ,				UpperCamelCase__			)

				snake_case__      :   Optional[int]      =    DataaVecTextForSequenceClassification(UpperCamelCase__			) if classification_head else DataaVecTextForMaskedLM(UpperCamelCase__			)
				model.eval()

				# Now let's copy all the weights.
				# Embeddings
				snake_case__      :   Optional[Any]      =    dataavec_sent_encoder.embed_tokens.weight
				snake_case__      :   Union[str, Any]      =    dataavec_sent_encoder.embed_positions.weight
				snake_case__      :   Any      =    torch.zeros_like(
				    model.dataavec_text.embeddings.token_type_embeddings.weight			)  # just zero them out b/c data2vec doesn't use them.
				snake_case__      :   Tuple      =    dataavec_sent_encoder.layernorm_embedding.weight
				snake_case__      :   Dict      =    dataavec_sent_encoder.layernorm_embedding.bias

				for i in range(config.num_hidden_layers			):
								# Encoder: start of layer
								snake_case__      :   BertLayer      =    model.dataavec_text.encoder.layer[i]
								snake_case__      :   TransformerSentenceEncoderLayer      =    dataavec_sent_encoder.layers[i]

								# self attention
								snake_case__      :   BertSelfAttention      =    layer.attention.self
								assert dataavec_layer.self_attn.k_proj.weight.data.shape == torch.Size(
								    (config.hidden_size, config.hidden_size)			), (
								    "Shape for data2vec_layer.self_attn.k_proj.weight.data should be"
								    F''' {torch.Size((config.hidden_size, config.hidden_size)			)}'''
								)
								assert dataavec_layer.self_attn.q_proj.weight.data.shape == torch.Size(
								    (config.hidden_size, config.hidden_size)			), (
								    "Shape for data2vec_layer.self_attn.q_proj.weight.data should be"
								    F''' {torch.Size((config.hidden_size, config.hidden_size)			)}'''
								)
								assert dataavec_layer.self_attn.v_proj.weight.data.shape == torch.Size(
								    (config.hidden_size, config.hidden_size)			), (
								    "Shape for data2vec_layer.self_attn.v_proj.weight.data should be"
								    F''' {torch.Size((config.hidden_size, config.hidden_size)			)}'''
								)

								snake_case__      :   List[Any]      =    dataavec_layer.self_attn.q_proj.weight
								snake_case__      :   List[str]      =    dataavec_layer.self_attn.q_proj.bias
								snake_case__      :   str      =    dataavec_layer.self_attn.k_proj.weight
								snake_case__      :   List[Any]      =    dataavec_layer.self_attn.k_proj.bias
								snake_case__      :   Optional[Any]      =    dataavec_layer.self_attn.v_proj.weight
								snake_case__      :   int      =    dataavec_layer.self_attn.v_proj.bias

								# self-attention output
								snake_case__      :   BertSelfOutput      =    layer.attention.output
								assert (
								    self_output.dense.weight.shape == dataavec_layer.self_attn.out_proj.weight.shape
								), F'''Shape for self_output.dense.weight should be {dataavec_layer.self_attn.out_proj.weight.shape}'''
								snake_case__      :   Optional[int]      =    dataavec_layer.self_attn.out_proj.weight
								snake_case__      :   str      =    dataavec_layer.self_attn.out_proj.bias
								snake_case__      :   int      =    dataavec_layer.self_attn_layer_norm.weight
								snake_case__      :   List[Any]      =    dataavec_layer.self_attn_layer_norm.bias

								# intermediate
								snake_case__      :   BertIntermediate      =    layer.intermediate
								assert (
								    intermediate.dense.weight.shape == dataavec_layer.fca.weight.shape
								), F'''Shape for intermediate.dense.weight should be {dataavec_layer.fca.weight.shape}'''
								snake_case__      :   List[str]      =    dataavec_layer.fca.weight
								snake_case__      :   str      =    dataavec_layer.fca.bias

								# output
								snake_case__      :   BertOutput      =    layer.output
								assert (
								    bert_output.dense.weight.shape == dataavec_layer.fca.weight.shape
								), F'''Shape for bert_output.dense.weight should be {dataavec_layer.fca.weight.shape}'''
								snake_case__      :   List[str]      =    dataavec_layer.fca.weight
								snake_case__      :   List[Any]      =    dataavec_layer.fca.bias
								snake_case__      :   str      =    dataavec_layer.final_layer_norm.weight
								snake_case__      :   List[Any]      =    dataavec_layer.final_layer_norm.bias
								# end of layer

				if classification_head:
								snake_case__      :   Any      =    dataavec.model.classification_heads['''mnli'''].dense.weight
								snake_case__      :   List[Any]      =    dataavec.model.classification_heads['''mnli'''].dense.bias
								snake_case__      :   List[Any]      =    dataavec.model.classification_heads['''mnli'''].out_proj.weight
								snake_case__      :   List[Any]      =    dataavec.model.classification_heads['''mnli'''].out_proj.bias
				else:
								# LM Head
								snake_case__      :   str      =    dataavec_model.encoder.lm_head.dense.weight
								snake_case__      :   str      =    dataavec_model.encoder.lm_head.dense.bias
								snake_case__      :   Union[str, Any]      =    dataavec_model.encoder.lm_head.layer_norm.weight
								snake_case__      :   Tuple      =    dataavec_model.encoder.lm_head.layer_norm.bias
								snake_case__      :   Tuple      =    dataavec_model.encoder.lm_head.weight
								snake_case__      :   List[str]      =    dataavec_model.encoder.lm_head.bias

				# Let's check that we get the same results.
				snake_case__      :   torch.Tensor      =    dataavec.encode(UpperCamelCase__			).unsqueeze(0			)  # batch of size 1

				snake_case__      :   str      =    model(UpperCamelCase__			)[0]
				if classification_head:
								snake_case__      :   List[str]      =    dataavec.model.classification_heads['''mnli'''](dataavec.extract_features(UpperCamelCase__			)			)
				else:
								snake_case__      :   Tuple      =    dataavec_model(UpperCamelCase__			)[0]
				print(our_output.shape   ,				their_output.shape			)
				snake_case__      :   Dict      =    torch.max(torch.abs(our_output - their_output			)			).item()
				print(F'''max_absolute_diff = {max_absolute_diff}'''			)  # ~ 1e-7
				snake_case__      :   Tuple      =    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'''			)

				pathlib.Path(UpperCamelCase__			).mkdir(parents=UpperCamelCase__   ,				exist_ok=UpperCamelCase__			)
				print(F'''Saving model to {pytorch_dump_folder_path}'''			)
				model.save_pretrained(UpperCamelCase__			)


if __name__ == "__main__":
				_lowercase       :						Any						=argparse.ArgumentParser()
				# Required parameters
				parser.add_argument(
				    "--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."
				)
				_lowercase       :						Tuple						=parser.parse_args()
				convert_dataavec_checkpoint_to_pytorch(
				    args.checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
				)




