




'''simple docstring'''







import types
from typing import List, Optional, Tuple, Union

import torch
from transformers import CLIPTextModelWithProjection, CLIPTokenizer
from transformers.models.clip.modeling_clip import CLIPTextModelOutput

from diffusers.models import PriorTransformer
from diffusers.pipelines import DiffusionPipeline, StableDiffusionImageVariationPipeline
from diffusers.schedulers import UnCLIPScheduler
from diffusers.utils import logging, randn_tensor


_lowerCamelCase							:						Any								=  logging.get_logger(__name__)  # pylint: disable=invalid-name






def 		_lowercase		(	self						,							lowercase_						,							lowercase_						,							lowercase_						,							lowercase_  ):



		'''simple docstring'''







		_snake_case	       =       image.to(device=lowercase_  )
		_snake_case	       =       image  # take image as image_embeddings
		_snake_case	       =       image_embeddings.unsqueeze(1  )

		# duplicate image embeddings for each generation per prompt, using mps friendly method
		_snake_case       ,    _snake_case       ,    _snake_case	       =       image_embeddings.shape
		_snake_case	       =       image_embeddings.repeat(1						,							lowercase_						,							1  )
		_snake_case	       =       image_embeddings.view(bs_embed * num_images_per_prompt						,							lowercase_						,							-1  )

		if do_classifier_free_guidance:
				_snake_case	       =       torch.zeros_like(lowercase_  )

				# For classifier free guidance, we need to do two forward passes.
				# Here we concatenate the unconditional and text embeddings into a single batch
				# to avoid doing two forward passes
				_snake_case	       =       torch.cat([uncond_embeddings, image_embeddings]  )

		return image_embeddings





class      lowerCamelCase_							(       UpperCAmelCase_							):




					'''simple docstring'''






					def __init__(    self,  __UpperCamelCase,  __UpperCamelCase,  __UpperCamelCase,  __UpperCamelCase,  __UpperCamelCase = None,  ):
							super().__init__()

							_snake_case	       =       {"image_encoder": None} if decoder_pipe_kwargs is None else decoder_pipe_kwargs

							_snake_case	       =       decoder_pipe_kwargs.get("torch_dtype",  __UpperCamelCase ) or prior.dtype

							_snake_case	       =       StableDiffusionImageVariationPipeline.from_pretrained(
							    "lambdalabs/sd-image-variations-diffusers",  **__UpperCamelCase )

							# replace `_encode_image` method
							_snake_case	       =       types.MethodType(_encode_image,  self.decoder_pipe )

							self.register_modules(
							    prior=__UpperCamelCase,  tokenizer=__UpperCamelCase,  text_encoder=__UpperCamelCase,  prior_scheduler=__UpperCamelCase,  )






					def 				__SCREAMING_SNAKE_CASE	(    self,  __UpperCamelCase,  __UpperCamelCase,  __UpperCamelCase,  __UpperCamelCase,  __UpperCamelCase = None,  __UpperCamelCase = None,  ):
							if text_model_output is None:
									_snake_case	       =       len(__UpperCamelCase ) if isinstance(__UpperCamelCase,  __UpperCamelCase ) else 1
									# get prompt text embeddings
									_snake_case	       =       self.tokenizer(
									    __UpperCamelCase,  padding="max_length",  max_length=self.tokenizer.model_max_length,  return_tensors="pt",  )
									_snake_case	       =       text_inputs.input_ids
									_snake_case	       =       text_inputs.attention_mask.bool().to(__UpperCamelCase )

									if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
											_snake_case	       =       self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :] )
											logger.warning(
											    "The following part of your input was truncated because CLIP can only handle sequences up to"
											    f''' {self.tokenizer.model_max_length} tokens: {removed_text}''' )
											_snake_case	       =       text_input_ids[:, : self.tokenizer.model_max_length]

									_snake_case	       =       self.text_encoder(text_input_ids.to(__UpperCamelCase ) )

									_snake_case	       =       text_encoder_output.text_embeds
									_snake_case	       =       text_encoder_output.last_hidden_state

							else:
									_snake_case	       =       text_model_output[0].shape[0]
									_snake_case       ,    _snake_case	       =       text_model_output[0], text_model_output[1]
									_snake_case	       =       text_attention_mask

							_snake_case	       =       text_embeddings.repeat_interleave(__UpperCamelCase,  dim=0 )
							_snake_case	       =       text_encoder_hidden_states.repeat_interleave(__UpperCamelCase,  dim=0 )
							_snake_case	       =       text_mask.repeat_interleave(__UpperCamelCase,  dim=0 )

							if do_classifier_free_guidance:
									_snake_case	       =       [""] * batch_size

									_snake_case	       =       self.tokenizer(
									    __UpperCamelCase,  padding="max_length",  max_length=self.tokenizer.model_max_length,  truncation=__UpperCamelCase,  return_tensors="pt",  )
									_snake_case	       =       uncond_input.attention_mask.bool().to(__UpperCamelCase )
									_snake_case	       =       self.text_encoder(uncond_input.input_ids.to(__UpperCamelCase ) )

									_snake_case	       =       uncond_embeddings_text_encoder_output.text_embeds
									_snake_case	       =       uncond_embeddings_text_encoder_output.last_hidden_state

									# duplicate unconditional embeddings for each generation per prompt, using mps friendly method

									_snake_case	       =       uncond_embeddings.shape[1]
									_snake_case	       =       uncond_embeddings.repeat(1,  __UpperCamelCase )
									_snake_case	       =       uncond_embeddings.view(batch_size * num_images_per_prompt,  __UpperCamelCase )

									_snake_case	       =       uncond_text_encoder_hidden_states.shape[1]
									_snake_case	       =       uncond_text_encoder_hidden_states.repeat(1,  __UpperCamelCase,  1 )
									_snake_case	       =       uncond_text_encoder_hidden_states.view(
									    batch_size * num_images_per_prompt,  __UpperCamelCase,  -1 )
									_snake_case	       =       uncond_text_mask.repeat_interleave(__UpperCamelCase,  dim=0 )

									# done duplicates

									# For classifier free guidance, we need to do two forward passes.
									# Here we concatenate the unconditional and text embeddings into a single batch
									# to avoid doing two forward passes
									_snake_case	       =       torch.cat([uncond_embeddings, text_embeddings] )
									_snake_case	       =       torch.cat([uncond_text_encoder_hidden_states, text_encoder_hidden_states] )

									_snake_case	       =       torch.cat([uncond_text_mask, text_mask] )

							return text_embeddings, text_encoder_hidden_states, text_mask






					@property
					def 				__SCREAMING_SNAKE_CASE	(    self ):
							if self.device != torch.device("meta" ) or not hasattr(self.prior,  "_hf_hook" ):
									return self.device
							for module in self.prior.modules():
									if (
									    hasattr(__UpperCamelCase,  "_hf_hook" )
									    and hasattr(module._hf_hook,  "execution_device" )
									    and module._hf_hook.execution_device is not None
									):
											return torch.device(module._hf_hook.execution_device )
							return self.device






					def 				__SCREAMING_SNAKE_CASE	(    self,  __UpperCamelCase,  __UpperCamelCase,  __UpperCamelCase,  __UpperCamelCase,  __UpperCamelCase,  __UpperCamelCase ):
							if latents is None:
									_snake_case	       =       randn_tensor(__UpperCamelCase,  generator=__UpperCamelCase,  device=__UpperCamelCase,  dtype=__UpperCamelCase )
							else:
									if latents.shape != shape:
											raise ValueError(f'''Unexpected latents shape, got {latents.shape}, expected {shape}''' )
									_snake_case	       =       latents.to(__UpperCamelCase )

							_snake_case	       =       latents * scheduler.init_noise_sigma
							return latents






					def 				__SCREAMING_SNAKE_CASE	(    self,  __UpperCamelCase = None ):
							self.decoder_pipe.to(__UpperCamelCase )
							super().to(__UpperCamelCase )






					@torch.no_grad()
					def __call__(    self,  __UpperCamelCase = None,  __UpperCamelCase = None,  __UpperCamelCase = None,  __UpperCamelCase = 1,  __UpperCamelCase = 25,  __UpperCamelCase = None,  __UpperCamelCase = None,  __UpperCamelCase = None,  __UpperCamelCase = None,  __UpperCamelCase = 4.0,  __UpperCamelCase = 8.0,  __UpperCamelCase = 50,  __UpperCamelCase = 1,  __UpperCamelCase = 0.0,  __UpperCamelCase = "pil",  __UpperCamelCase = True,  ):
							if prompt is not None:
									if isinstance(__UpperCamelCase,  __UpperCamelCase ):
											_snake_case	       =       1
									elif isinstance(__UpperCamelCase,  __UpperCamelCase ):
											_snake_case	       =       len(__UpperCamelCase )
									else:
											raise ValueError(f'''`prompt` has to be of type `str` or `list` but is {type(__UpperCamelCase )}''' )
							else:
									_snake_case	       =       text_model_output[0].shape[0]

							_snake_case	       =       self._execution_device

							_snake_case	       =       batch_size * num_images_per_prompt

							_snake_case	       =       prior_guidance_scale > 1.0 or decoder_guidance_scale > 1.0

							_snake_case       ,    _snake_case       ,    _snake_case	       =       self._encode_prompt(
							    __UpperCamelCase,  __UpperCamelCase,  __UpperCamelCase,  __UpperCamelCase,  __UpperCamelCase,  __UpperCamelCase )

							# prior

							self.prior_scheduler.set_timesteps(__UpperCamelCase,  device=__UpperCamelCase )
							_snake_case	       =       self.prior_scheduler.timesteps

							_snake_case	       =       self.prior.config.embedding_dim

							_snake_case	       =       self.prepare_latents(
							    (batch_size, embedding_dim),  text_embeddings.dtype,  __UpperCamelCase,  __UpperCamelCase,  __UpperCamelCase,  self.prior_scheduler,  )

							for i, t in enumerate(self.progress_bar(__UpperCamelCase ) ):
									# expand the latents if we are doing classifier free guidance
									_snake_case	       =       torch.cat([prior_latents] * 2 ) if do_classifier_free_guidance else prior_latents

									_snake_case	       =       self.prior(
									    __UpperCamelCase,  timestep=__UpperCamelCase,  proj_embedding=__UpperCamelCase,  encoder_hidden_states=__UpperCamelCase,  attention_mask=__UpperCamelCase,  ).predicted_image_embedding

									if do_classifier_free_guidance:
											_snake_case       ,    _snake_case	       =       predicted_image_embedding.chunk(2 )
											_snake_case	       =       predicted_image_embedding_uncond + prior_guidance_scale * (
											    predicted_image_embedding_text - predicted_image_embedding_uncond
											)

									if i + 1 == prior_timesteps_tensor.shape[0]:
											_snake_case	       =       None
									else:
											_snake_case	       =       prior_timesteps_tensor[i + 1]

									_snake_case	       =       self.prior_scheduler.step(
									    __UpperCamelCase,  timestep=__UpperCamelCase,  sample=__UpperCamelCase,  generator=__UpperCamelCase,  prev_timestep=__UpperCamelCase,  ).prev_sample

							_snake_case	       =       self.prior.post_process_latents(__UpperCamelCase )

							_snake_case	       =       prior_latents

							_snake_case	       =       self.decoder_pipe(
							    image=__UpperCamelCase,  height=__UpperCamelCase,  width=__UpperCamelCase,  num_inference_steps=__UpperCamelCase,  guidance_scale=__UpperCamelCase,  generator=__UpperCamelCase,  output_type=__UpperCamelCase,  return_dict=__UpperCamelCase,  num_images_per_prompt=__UpperCamelCase,  eta=__UpperCamelCase,  )
							return output











'''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 		_lowercase		(	lowercase_  ):



		'''simple docstring'''







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





class      lowerCamelCase_							(       UpperCAmelCase_							):




					'''simple docstring'''






					def __init__(    self,  __UpperCamelCase,  __UpperCamelCase=None,  __UpperCamelCase=None ):
							_snake_case	       =       file_names
							_snake_case	       =       image_transform
							_snake_case	       =       label_to_id






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






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






def 		_lowercase		(	lowercase_						,							lowercase_  ):



		'''simple docstring'''







		_snake_case	       =       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	       =       config["lr"]
		_snake_case	       =       int(config["num_epochs"]  )
		_snake_case	       =       int(config["seed"]  )
		_snake_case	       =       int(config["batch_size"]  )
		_snake_case	       =       config["image_size"]
		if not isinstance(lowercase_						,							(list, tuple)  ):
				_snake_case	       =       (image_size, image_size)

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

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

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

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

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

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

		# Instantiate dataloaders.
		_snake_case	       =       DataLoader(lowercase_						,							shuffle=lowercase_						,							batch_size=lowercase_						,							num_workers=4  )
		_snake_case	       =       DataLoader(lowercase_						,							shuffle=lowercase_						,							batch_size=lowercase_						,							num_workers=4  )

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

		# 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	       =       model.to(accelerator.device  )

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

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

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

		# Instantiate learning rate scheduler
		_snake_case	       =       OneCycleLR(optimizer=lowercase_						,							max_lr=lowercase_						,							epochs=lowercase_						,							steps_per_epoch=len(lowercase_  )  )

		# 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	       =       accelerator.prepare(
		    lowercase_						,							lowercase_						,							lowercase_						,							lowercase_						,							lowercase_  )

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

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

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






def 		_lowercase		(	):



		'''simple docstring'''







		_snake_case	       =       argparse.ArgumentParser(description="Simple example of training script."  )
		parser.add_argument("--data_dir"						,							required=lowercase_						,							help="The data folder on disk."  )
		parser.add_argument(
		    "--mixed_precision"						,							type=lowercase_						,							default=lowercase_						,							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=lowercase_						,							default=lowercase_						,							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	       =       parser.parse_args()
		_snake_case	       =       {"lr": 3e-2, "num_epochs": 3, "seed": 42, "batch_size": 64, "image_size": 224}
		training_function(lowercase_						,							lowercase_  )


if __name__ == "__main__":
						main()





