'''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__   (				_lowerCAmelCase    :			List[Any])    ->		Dict:



						"""simple docstring"""

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




class 		SCREAMING_SNAKE_CASE_					(_UpperCAmelCase      ):



			"""simple docstring"""






			def __init__(							self				,			snake_case_				,			snake_case_=None				,			snake_case_=None					):
									snake_case :       List[str]				=    file_names
									snake_case :       Tuple				=    image_transform
									snake_case :       Union[str, Any]				=    label_to_id



			def __len__(							self					):
									return len(self.file_names					)






			def __getitem__(							self				,			snake_case_					):
									snake_case :       Dict				=    self.file_names[idx]
									snake_case :       List[Any]				=    PIL.Image.open(snake_case_					)
									snake_case :       Dict				=    raw_image.convert("RGB"					)
									if self.image_transform is not None:
															snake_case :       Any				=    self.image_transform(snake_case_					)
									snake_case :       Optional[Any]				=    extract_label(snake_case_					)
									if self.label_to_id is not None:
															snake_case :       List[Any]				=    self.label_to_id[label]
									return {"image": image, "label": label}







def        UpperCAmelCase__   (				_lowerCAmelCase    :			List[Any],      _lowerCAmelCase    :			Optional[int])    ->		Tuple:



						"""simple docstring"""

						snake_case :       str				=    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 :       Union[str, Any]				=    config["lr"]
						snake_case :       List[Any]				=    int(config["num_epochs"])
						snake_case :       Tuple				=    int(config["seed"])
						snake_case :       Optional[Any]				=    int(config["batch_size"])
						snake_case :       Tuple				=    config["image_size"]
						if not isinstance(_lowerCAmelCase,      (list, tuple)):
												snake_case :       List[str]				=    (image_size, image_size)

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

						# Build the label correspondences
						snake_case :       Tuple				=    [extract_label(_lowerCAmelCase) for fname in file_names]
						snake_case :       List[Any]				=    list(set(_lowerCAmelCase))
						id_to_label.sort()
						snake_case :       Optional[int]				=    {lbl: i for i, lbl in enumerate(_lowerCAmelCase)}

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

						# Split our filenames between train and validation
						snake_case :       Union[str, Any]				=    np.random.permutation(len(_lowerCAmelCase))
						snake_case :       int				=    int(0.8 * len(_lowerCAmelCase))
						snake_case :       Dict				=    random_perm[:cut]
						snake_case :       List[Any]				=    random_perm[cut:]

						# For training we use a simple RandomResizedCrop
						snake_case :       Optional[int]				=    Compose([RandomResizedCrop(_lowerCAmelCase,      scale=(0.5, 1.0)), ToTensor()])
						snake_case :       Dict				=    PetsDataset(
						    [file_names[i] for i in train_split],      image_transform=_lowerCAmelCase,      label_to_id=_lowerCAmelCase)

						# For evaluation, we use a deterministic Resize
						snake_case :       str				=    Compose([Resize(_lowerCAmelCase), ToTensor()])
						snake_case :       Union[str, Any]				=    PetsDataset([file_names[i] for i in eval_split],      image_transform=_lowerCAmelCase,      label_to_id=_lowerCAmelCase)

						# Instantiate dataloaders.
						snake_case :       Union[str, Any]				=    DataLoader(_lowerCAmelCase,      shuffle=_lowerCAmelCase,      batch_size=_lowerCAmelCase,      num_workers=4)
						snake_case :       Optional[Any]				=    DataLoader(_lowerCAmelCase,      shuffle=_lowerCAmelCase,      batch_size=_lowerCAmelCase,      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=_lowerCAmelCase,      num_classes=len(_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).
						snake_case :       Tuple				=    model.to(accelerator.device)

						# Freezing the base model
						for param in model.parameters():
												snake_case :       Optional[int]				=    False
						for param in model.get_classifier().parameters():
												snake_case :       Any				=    True

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

						# Instantiate optimizer
						snake_case :       Union[str, Any]				=    torch.optim.Adam(params=model.parameters(),      lr=lr / 25)

						# Instantiate learning rate scheduler
						snake_case :       int				=    OneCycleLR(optimizer=_lowerCAmelCase,      max_lr=_lowerCAmelCase,      epochs=_lowerCAmelCase,      steps_per_epoch=len(_lowerCAmelCase))

						# 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 :       str				=    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`.
																		snake_case :       Tuple				=    {k: v.to(accelerator.device) for k, v in batch.items()}
																		snake_case :       List[str]				=    (batch["image"] - mean) / std
																		snake_case :       Dict				=    model(_lowerCAmelCase)
																		snake_case :       Dict				=    torch.nn.functional.cross_entropy(_lowerCAmelCase,      batch["label"])
																		accelerator.backward(_lowerCAmelCase)
																		optimizer.step()
																		lr_scheduler.step()
																		optimizer.zero_grad()

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

												snake_case :       Any				=    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__   (				)    ->		int:



						"""simple docstring"""

						snake_case :       Optional[Any]				=    argparse.ArgumentParser(description="Simple example of training script.")
						parser.add_argument("--data_dir",      required=_lowerCAmelCase,      help="The data folder on disk.")
						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(
						    "--checkpointing_steps",      type=_lowerCAmelCase,      default=_lowerCAmelCase,      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 :       Any				=    parser.parse_args()
						snake_case :       Any				=    {"lr": 3E-2, "num_epochs": 3, "seed": 42, "batch_size": 64, "image_size": 224}
						training_function(_lowerCAmelCase,      _lowerCAmelCase)


if __name__ == "__main__":
						main()






'''simple docstring'''

import argparse
import collections
from pathlib import Path

import torch
from torch.serialization import default_restore_location

from transformers import BertConfig, DPRConfig, DPRContextEncoder, DPRQuestionEncoder, DPRReader


A_	   = collections.namedtuple(
    "CheckpointState", ["model_dict", "optimizer_dict", "scheduler_dict", "offset", "epoch", "encoder_params"]
)


def        UpperCAmelCase__   (				_lowerCAmelCase    :			str)    ->		CheckpointState:



						"""simple docstring"""

						print(f"""Reading saved model from {model_file}""")
						snake_case :       Optional[Any]				=    torch.load(_lowerCAmelCase,      map_location=lambda _lowerCAmelCase,      _lowerCAmelCase: default_restore_location(_lowerCAmelCase,      "cpu"))
						return CheckpointState(**_lowerCAmelCase)




class 		SCREAMING_SNAKE_CASE_					:



			"""simple docstring"""






			def __init__(							self				,			snake_case_					):
									snake_case :       Union[str, Any]				=    src_file



			def   lowerCamelCase (							self					):
									raise NotImplementedError






			@staticmethod
			def   lowerCamelCase (							snake_case_				,			*snake_case_				,			**snake_case_					):
									if comp_type.startswith("c"					):
															return DPRContextEncoderState(*snake_case_				,			**snake_case_					)
									if comp_type.startswith("q"					):
															return DPRQuestionEncoderState(*snake_case_				,			**snake_case_					)
									if comp_type.startswith("r"					):
															return DPRReaderState(*snake_case_				,			**snake_case_					)
									else:
															raise ValueError("Component type must be either 'ctx_encoder', 'question_encoder' or 'reader'."					)







class 		SCREAMING_SNAKE_CASE_					(_UpperCAmelCase      ):



			"""simple docstring"""






			def   lowerCamelCase (							self					):
									snake_case :       List[Any]				=    DPRContextEncoder(DPRConfig(**BertConfig.get_config_dict("bert-base-uncased"					)[0]					)					)
									print(F"""Loading DPR biencoder from {self.src_file}"""					)
									snake_case :       List[Any]				=    load_states_from_checkpoint(self.src_file					)
									snake_case				,					snake_case :       List[str]				=    model.ctx_encoder, "ctx_model."
									# Fix changes from https://github.com/huggingface/transformers/commit/614fef1691edb806de976756d4948ecbcd0c0ca3
									snake_case :       List[str]				=    {"bert_model.embeddings.position_ids": model.ctx_encoder.bert_model.embeddings.position_ids}
									for key, value in saved_state.model_dict.items():
															if key.startswith(snake_case_					):
																					snake_case :       str				=    key[len(snake_case_					) :]
																					if not key.startswith("encode_proj."					):
																											snake_case :       int				=    "bert_model." + key
																					snake_case :       Union[str, Any]				=    value
									encoder.load_state_dict(snake_case_					)
									return model







class 		SCREAMING_SNAKE_CASE_					(_UpperCAmelCase      ):



			"""simple docstring"""






			def   lowerCamelCase (							self					):
									snake_case :       Any				=    DPRQuestionEncoder(DPRConfig(**BertConfig.get_config_dict("bert-base-uncased"					)[0]					)					)
									print(F"""Loading DPR biencoder from {self.src_file}"""					)
									snake_case :       Dict				=    load_states_from_checkpoint(self.src_file					)
									snake_case				,					snake_case :       Tuple				=    model.question_encoder, "question_model."
									# Fix changes from https://github.com/huggingface/transformers/commit/614fef1691edb806de976756d4948ecbcd0c0ca3
									snake_case :       Dict				=    {"bert_model.embeddings.position_ids": model.question_encoder.bert_model.embeddings.position_ids}
									for key, value in saved_state.model_dict.items():
															if key.startswith(snake_case_					):
																					snake_case :       Optional[int]				=    key[len(snake_case_					) :]
																					if not key.startswith("encode_proj."					):
																											snake_case :       List[str]				=    "bert_model." + key
																					snake_case :       Union[str, Any]				=    value
									encoder.load_state_dict(snake_case_					)
									return model







class 		SCREAMING_SNAKE_CASE_					(_UpperCAmelCase      ):



			"""simple docstring"""






			def   lowerCamelCase (							self					):
									snake_case :       List[Any]				=    DPRReader(DPRConfig(**BertConfig.get_config_dict("bert-base-uncased"					)[0]					)					)
									print(F"""Loading DPR reader from {self.src_file}"""					)
									snake_case :       List[str]				=    load_states_from_checkpoint(self.src_file					)
									# Fix changes from https://github.com/huggingface/transformers/commit/614fef1691edb806de976756d4948ecbcd0c0ca3
									snake_case :       List[str]				=    {
									    "encoder.bert_model.embeddings.position_ids": model.span_predictor.encoder.bert_model.embeddings.position_ids
									}
									for key, value in saved_state.model_dict.items():
															if key.startswith("encoder."					) and not key.startswith("encoder.encode_proj"					):
																					snake_case :       List[Any]				=    "encoder.bert_model." + key[len("encoder."					) :]
															snake_case :       Union[str, Any]				=    value
									model.span_predictor.load_state_dict(snake_case_					)
									return model







def        UpperCAmelCase__   (				_lowerCAmelCase    :			str,      _lowerCAmelCase    :			Path,      _lowerCAmelCase    :			Path)    ->		List[Any]:



						"""simple docstring"""

						snake_case :       Any				=    Path(_lowerCAmelCase)
						dest_dir.mkdir(exist_ok=_lowerCAmelCase)

						snake_case :       Tuple				=    DPRState.from_type(_lowerCAmelCase,      src_file=_lowerCAmelCase)
						snake_case :       Optional[int]				=    dpr_state.load_dpr_model()
						model.save_pretrained(_lowerCAmelCase)
						model.from_pretrained(_lowerCAmelCase)  # sanity check


if __name__ == "__main__":
						A_	   = argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    "--type", type=str, help="Type of the component to convert: 'ctx_encoder', 'question_encoder' or 'reader'."
						)
						parser.add_argument(
						    "--src",
						    type=str,
						    help=(
						        "Path to the dpr checkpoint file. They can be downloaded from the official DPR repo"
						        " https://github.com/facebookresearch/DPR. Note that in the official repo, both encoders are stored in the"
						        " 'retriever' checkpoints."
						    ),
						)
						parser.add_argument("--dest", type=str, default=None, help="Path to the output PyTorch model directory.")
						A_	   = parser.parse_args()

						A_	   = Path(args.src)
						A_	   = f"converted-{src_file.name}" if args.dest is None else args.dest
						A_	   = Path(dest_dir)
						assert src_file.exists()
						assert (
						    args.type is not None
						), "Please specify the component type of the DPR model to convert: 'ctx_encoder', 'question_encoder' or 'reader'."
						convert(args.type, src_file, dest_dir)





