

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					(      __a :     Any      )     ->	str:

					"""simple docstring"""

					_a      :       List[str]   =						fname.split(os.path.sep      )[-1]
					return re.search(R'''^(.*)_\d+\.jpg$'''						,__a      ).groups()[0]

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







							def __init__(     self			,				_a			,				_a=None			,				_a=None			)       ->       Dict:
												_a      :       Optional[int]   =						file_names
												_a      :       List[str]   =						image_transform
												_a      :       List[Any]   =						label_to_id


							def __len__(     self			)       ->       Any:
												return len(self.file_names			)




							def __getitem__(     self			,				_a			)       ->       Tuple:
												_a      :       Union[str, Any]   =						self.file_names[idx]
												_a      :       Tuple   =						PIL.Image.open(_a			)
												_a      :       Dict   =						raw_image.convert('''RGB'''			)
												if self.image_transform is not None:
																	_a      :       Tuple   =						self.image_transform(_a			)
												_a      :       Dict   =						extract_label(_a			)
												if self.label_to_id is not None:
																	_a      :       Any   =						self.label_to_id[label]
												return {"image": image, "label": label}



def   __UpperCAmelCase					(      __a :     str						,__a :     int      )     ->	Union[str, Any]:

					"""simple docstring"""

					if args.with_tracking:
										_a      :       Tuple   =						Accelerator(
										    cpu=args.cpu						,mixed_precision=args.mixed_precision						,log_with='''all'''						,project_dir=args.project_dir      )
					else:
										_a      :       int   =						Accelerator(cpu=args.cpu						,mixed_precision=args.mixed_precision      )

					# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
					_a      :       List[Any]   =						config['''lr''']
					_a      :       Optional[Any]   =						int(config['''num_epochs''']      )
					_a      :       List[Any]   =						int(config['''seed''']      )
					_a      :       Optional[Any]   =						int(config['''batch_size''']      )
					_a      :       str   =						config['''image_size''']
					if not isinstance(__a						,(list, tuple)      ):
										_a      :       Optional[int]   =						(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":
															_a      :       List[str]   =						args.checkpointing_steps
										elif args.checkpointing_steps.isdigit():
															_a      :       int   =						int(args.checkpointing_steps      )
										else:
															raise ValueError(
															    F"""Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed."""      )
					else:
										_a      :       Optional[int]   =						None

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

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

					# Build the label correspondences
					_a      :       Dict   =						[extract_label(__a      ) for fname in file_names]
					_a      :       str   =						list(set(__a      )      )
					id_to_label.sort()
					_a      :       List[str]   =						{lbl: i for i, lbl in enumerate(__a      )}

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

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

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

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

					# Instantiate dataloaders.
					_a      :       Tuple   =						DataLoader(__a						,shuffle=__a						,batch_size=__a						,num_workers=4      )
					_a      :       Optional[Any]   =						DataLoader(__a						,shuffle=__a						,batch_size=__a						,num_workers=4      )

					# Instantiate the model (we build the model here so that the seed also control new weights initialization)
					_a      :       str   =						create_model('''resnet50d'''						,pretrained=__a						,num_classes=len(__a      )      )

					# 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).
					_a      :       int   =						model.to(accelerator.device      )

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

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

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

					# Instantiate learning rate scheduler
					_a      :       Optional[int]   =						OneCycleLR(optimizer=__a						,max_lr=__a						,epochs=__a						,steps_per_epoch=len(__a      )      )

					# 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.
					_a ,   _a ,   _a ,   _a ,   _a      :       Any   =						accelerator.prepare(
					    __a						,__a						,__a						,__a						,__a      )
					# We need to keep track of how many total steps we have iterated over
					_a      :       Optional[int]   =						0
					# We also need to keep track of the starting epoch so files are named properly
					_a      :       List[str]   =						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      )
															_a      :       Tuple   =						os.path.basename(args.resume_from_checkpoint      )
										else:
															# Get the most recent checkpoint
															_a      :       Optional[Any]   =						[f.name for f in os.scandir(os.getcwd()      ) if f.is_dir()]
															dirs.sort(key=os.path.getctime      )
															_a      :       List[Any]   =						dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
										# Extract `epoch_{i}` or `step_{i}`
										_a      :       Optional[int]   =						os.path.splitext(__a      )[0]

										if "epoch" in training_difference:
															_a      :       List[Any]   =						int(training_difference.replace('''epoch_'''						,''''''      )      ) + 1
															_a      :       str   =						None
										else:
															_a      :       str   =						int(training_difference.replace('''step_'''						,''''''      )      )
															_a      :       Any   =						resume_step // len(__a      )
															resume_step -= starting_epoch * len(__a      )

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

										_a      :       Any   =						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(__a      ),
															        '''epoch''': epoch,
															    }						,step=__a						,)
										if checkpointing_steps == "epoch":
															_a      :       str   =						F"""epoch_{epoch}"""
															if args.output_dir is not None:
																				_a      :       Optional[int]   =						os.path.join(args.output_dir						,__a      )
															accelerator.save_state(__a      )

					if args.with_tracking:
										accelerator.end_training()



def   __UpperCAmelCase					(      )     ->	List[Any]:

					"""simple docstring"""

					_a      :       List[Any]   =						argparse.ArgumentParser(description='''Simple example of training script.'''      )
					parser.add_argument('''--data_dir'''						,required=__a						,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=__a						,default=__a						,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=__a						,default=__a						,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=__a						,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=__a						,default=__a						,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=__a						,default='''logs'''						,help='''Location on where to store experiment tracking logs` and relevent project information'''						,)
					_a      :       str   =						parser.parse_args()
					_a      :       Optional[Any]   =						{'''lr''': 3E-2, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 64, '''image_size''': 224}
					training_function(__a						,__a      )


if __name__ == "__main__":
			main()






import argparse
import collections

import numpy as np
import torch
from flax import traverse_util
from tax import checkpoints

from transformers import MTaConfig, UMTaEncoderModel, UMTaForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()



def   __UpperCAmelCase					(      __a :     List[Any]						,__a :     Optional[Any]						,__a :     Optional[int]      )     ->	Dict:

					"""simple docstring"""

					return params[F"""{prefix}/{prefix}/relpos_bias/rel_embedding"""][:, i, :]



def   __UpperCAmelCase					(      __a :     List[Any]						,__a :     Optional[int]						,__a :     int						,__a :     List[str]="attention"      )     ->	List[str]:

					"""simple docstring"""

					_a      :       str   =						np.ascontiguousarray(params[F"""{prefix}/{prefix}/{layer_name}/key/kernel"""][:, i, :, :]      )
					_a      :       Tuple   =						k_tmp.reshape(k_tmp.shape[0]						,k_tmp.shape[1] * k_tmp.shape[2]      )
					_a      :       Any   =						np.ascontiguousarray(params[F"""{prefix}/{prefix}/{layer_name}/out/kernel"""][:, i, :, :]      )
					_a      :       Dict   =						o_tmp.reshape(o_tmp.shape[0] * o_tmp.shape[1]						,o_tmp.shape[2]      )
					_a      :       Union[str, Any]   =						np.ascontiguousarray(params[F"""{prefix}/{prefix}/{layer_name}/query/kernel"""][:, i, :, :]      )
					_a      :       Any   =						q_tmp.reshape(q_tmp.shape[0]						,q_tmp.shape[1] * q_tmp.shape[2]      )
					_a      :       Tuple   =						np.ascontiguousarray(params[F"""{prefix}/{prefix}/{layer_name}/value/kernel"""][:, i, :, :]      )
					_a      :       int   =						v_tmp.reshape(v_tmp.shape[0]						,v_tmp.shape[1] * v_tmp.shape[2]      )
					return k, o, q, v



def   __UpperCAmelCase					(      __a :     Union[str, Any]						,__a :     Union[str, Any]						,__a :     List[Any]						,__a :     Any=False      )     ->	Any:

					"""simple docstring"""

					if split_mlp_wi:
										_a      :       Union[str, Any]   =						params[F"""{prefix}/{prefix}/mlp/wi_0/kernel"""][:, i, :]
										_a      :       Union[str, Any]   =						params[F"""{prefix}/{prefix}/mlp/wi_1/kernel"""][:, i, :]
										_a      :       List[str]   =						(wi_a, wi_a)
					else:
										_a      :       List[str]   =						params[F"""{prefix}/{prefix}/mlp/wi/kernel"""][:, i, :]

					_a      :       Optional[int]   =						params[F"""{prefix}/{prefix}/mlp/wo/kernel"""][:, i, :]
					return wi, wo



def   __UpperCAmelCase					(      __a :     List[Any]						,__a :     Optional[Any]						,__a :     Union[str, Any]						,__a :     str      )     ->	List[str]:

					"""simple docstring"""

					return params[F"""{prefix}/{prefix}/{layer_name}/scale"""][:, i]



def   __UpperCAmelCase					(      __a :     dict						,*, __a :     int						,__a :     bool						,__a :     bool = False      )     ->	Any:

					"""simple docstring"""

					_a      :       Dict   =						traverse_util.flatten_dict(variables['''target''']      )
					_a      :       Any   =						{'''/'''.join(__a      ): v for k, v in old.items()}

					# v1.1 models have a gated GeLU with wi_0 and wi_1 instead of wi
					_a      :       Optional[int]   =						'''encoder/encoder/mlp/wi_0/kernel''' in old
					print('''Split MLP:'''						,__a      )

					_a      :       Tuple   =						collections.OrderedDict()

					# Shared embeddings.
					_a      :       Any   =						old['''token_embedder/embedding''']

					# Encoder.
					for i in range(__a      ):
										# Block i, layer 0 (Self Attention).
										_a      :       Optional[Any]   =						tax_layer_norm_lookup(__a						,__a						,'''encoder'''						,'''pre_attention_layer_norm'''      )
										_a ,   _a ,   _a ,   _a      :       List[str]   =						tax_attention_lookup(__a						,__a						,'''encoder'''						,'''attention'''      )
										_a      :       List[str]   =						layer_norm
										_a      :       Optional[Any]   =						k.T
										_a      :       str   =						o.T
										_a      :       List[Any]   =						q.T
										_a      :       Tuple   =						v.T

										# Block i, layer 1 (MLP).
										_a      :       str   =						tax_layer_norm_lookup(__a						,__a						,'''encoder'''						,'''pre_mlp_layer_norm'''      )
										_a ,   _a      :       Any   =						tax_mlp_lookup(__a						,__a						,'''encoder'''						,__a      )
										_a      :       str   =						layer_norm
										if split_mlp_wi:
															_a      :       List[Any]   =						wi[0].T
															_a      :       Any   =						wi[1].T
										else:
															_a      :       Any   =						wi.T
										_a      :       Optional[Any]   =						wo.T
										if scalable_attention:
															# convert the rel_embedding of each layer
															_a      :       Dict   =						tax_relpos_bias_lookup(
															    __a						,__a						,'''encoder'''      ).T

					_a      :       List[str]   =						old['''encoder/encoder_norm/scale''']

					if not scalable_attention:
										_a      :       List[Any]   =						tax_relpos_bias_lookup(
										    __a						,0						,'''encoder'''      ).T
										_a      :       Optional[Any]   =						tax_relpos_bias_lookup(
										    __a						,0						,'''decoder'''      ).T

					if not is_encoder_only:
										# Decoder.
										for i in range(__a      ):
															# Block i, layer 0 (Self Attention).
															_a      :       Union[str, Any]   =						tax_layer_norm_lookup(__a						,__a						,'''decoder'''						,'''pre_self_attention_layer_norm'''      )
															_a ,   _a ,   _a ,   _a      :       Optional[Any]   =						tax_attention_lookup(__a						,__a						,'''decoder'''						,'''self_attention'''      )
															_a      :       Optional[Any]   =						layer_norm
															_a      :       Dict   =						k.T
															_a      :       str   =						o.T
															_a      :       str   =						q.T
															_a      :       List[str]   =						v.T

															# Block i, layer 1 (Cross Attention).
															_a      :       Any   =						tax_layer_norm_lookup(__a						,__a						,'''decoder'''						,'''pre_cross_attention_layer_norm'''      )
															_a ,   _a ,   _a ,   _a      :       str   =						tax_attention_lookup(__a						,__a						,'''decoder'''						,'''encoder_decoder_attention'''      )
															_a      :       Optional[Any]   =						layer_norm
															_a      :       Optional[int]   =						k.T
															_a      :       Dict   =						o.T
															_a      :       str   =						q.T
															_a      :       int   =						v.T

															# Block i, layer 2 (MLP).
															_a      :       Optional[int]   =						tax_layer_norm_lookup(__a						,__a						,'''decoder'''						,'''pre_mlp_layer_norm'''      )
															_a ,   _a      :       Tuple   =						tax_mlp_lookup(__a						,__a						,'''decoder'''						,__a      )
															_a      :       Optional[Any]   =						layer_norm
															if split_mlp_wi:
																				_a      :       List[str]   =						wi[0].T
																				_a      :       List[Any]   =						wi[1].T
															else:
																				_a      :       Dict   =						wi.T
															_a      :       str   =						wo.T

															if scalable_attention:
																				# convert the rel_embedding of each layer
																				_a      :       Tuple   =						tax_relpos_bias_lookup(__a						,__a						,'''decoder'''      ).T

										_a      :       Tuple   =						old['''decoder/decoder_norm/scale''']

										# LM Head (only in v1.1 checkpoints, in v1.0 embeddings are used instead)
										if "decoder/logits_dense/kernel" in old:
															_a      :       Any   =						old['''decoder/logits_dense/kernel'''].T

					return new



def   __UpperCAmelCase					(      __a :     Dict						,__a :     bool      )     ->	Tuple:

					"""simple docstring"""

					_a      :       Tuple   =						collections.OrderedDict([(k, torch.from_numpy(v.copy()      )) for (k, v) in converted_params.items()]      )

					# Add what is missing.
					if "encoder.embed_tokens.weight" not in state_dict:
										_a      :       Any   =						state_dict['''shared.weight''']

					if not is_encoder_only:
										if "decoder.embed_tokens.weight" not in state_dict:
															_a      :       Optional[int]   =						state_dict['''shared.weight''']

										if "lm_head.weight" not in state_dict:  # For old 1.0 models.
															print('''Using shared word embeddings as lm_head.'''      )
															_a      :       str   =						state_dict['''shared.weight''']

					return state_dict



def   __UpperCAmelCase					(      __a :     List[str]						,__a :     Union[str, Any]						,__a :     Dict						,__a :     Union[str, Any]						,__a :     List[Any]      )     ->	int:

					"""simple docstring"""

					_a      :       List[str]   =						checkpoints.load_tax_checkpoint(__a      )
					_a      :       str   =						convert_tax_to_pytorch(
					    __a						,num_layers=config.num_layers						,is_encoder_only=__a						,scalable_attention=__a      )
					_a      :       str   =						make_state_dict(__a						,__a      )
					model.load_state_dict(__a						,strict=__a      )



def   __UpperCAmelCase					(      __a :     List[Any]						,__a :     Any						,__a :     Union[str, Any]						,__a :     bool = False						,__a :     bool = False						,)     ->	Optional[Any]:

					"""simple docstring"""

					_a      :       List[str]   =						MTaConfig.from_json_file(__a      )
					print(F"""Building PyTorch model from configuration: {config}"""      )
					# Non-v1.1 checkpoints could also use T5Model, but this works for all.
					# The v1.0 checkpoints will simply have an LM head that is the word embeddings.
					if is_encoder_only:
										_a      :       Any   =						UMTaEncoderModel(__a      )
					else:
										_a      :       Tuple   =						UMTaForConditionalGeneration(__a      )

					# Load weights from tf checkpoint
					load_tax_weights_in_ta(__a						,__a						,__a						,__a						,__a      )

					# Save pytorch-model
					print(F"""Save PyTorch model to {pytorch_dump_path}"""      )
					model.save_pretrained(__a      )

					# Verify that we can load the checkpoint.
					model.from_pretrained(__a      )
					print('''Done'''      )


if __name__ == "__main__":
			a__												=						argparse.ArgumentParser(description='''Converts a native T5X checkpoint into a PyTorch checkpoint.''')
			# Required parameters
			parser.add_argument(
			    '''--t5x_checkpoint_path''', default=None, type=str, required=True, help='''Path to the T5X checkpoint.'''
			)
			parser.add_argument(
			    '''--config_file''',
			    default=None,
			    type=str,
			    required=True,
			    help='''The config json file corresponding to the pre-trained T5 model.\nThis specifies the model architecture.''',
			)
			parser.add_argument(
			    '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
			)
			parser.add_argument(
			    '''--is_encoder_only''', action='''store_true''', help='''Check if the model is encoder-decoder model''', default=False
			)
			parser.add_argument(
			    '''--scalable_attention''',
			    action='''store_true''',
			    help='''Whether the model uses scaled attention (umt5 model)''',
			    default=False,
			)
			a__												=						parser.parse_args()
			convert_tax_checkpoint_to_pytorch(
			    args.tax_checkpoint_path,
			    args.config_file,
			    args.pytorch_dump_path,
			    args.is_encoder_only,
			    args.scalable_attention,
			)



