


'''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


_UpperCAmelCase					:			Dict				     =					logging.get_logger(__name__)  # pylint: disable=invalid-name

def 				__magic_name__(    self,					lowerCamelCase,					lowerCamelCase,					lowerCamelCase,					lowerCamelCase):
						__lowerCAmelCase											=					image.to(device=lowerCamelCase)
						__lowerCAmelCase											=					image  # take image as image_embeddings
						__lowerCAmelCase											=					image_embeddings.unsqueeze(1)

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

						if do_classifier_free_guidance:
												__lowerCAmelCase											=					torch.zeros_like(lowerCamelCase)

												# 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
												__lowerCAmelCase											=					torch.cat([uncond_embeddings, image_embeddings])

						return image_embeddings




class    a__					(			__A   ):







				"""simple docstring"""



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

										__lowerCAmelCase											=					{'''image_encoder''': None} if decoder_pipe_kwargs is None else decoder_pipe_kwargs

										__lowerCAmelCase											=					decoder_pipe_kwargs.get('''torch_dtype'''							,      __lowercase		) or prior.dtype

										__lowerCAmelCase											=					StableDiffusionImageVariationPipeline.from_pretrained(
										    '''lambdalabs/sd-image-variations-diffusers'''							,      **__lowercase		)

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

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




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

																if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
																						__lowerCAmelCase											=					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}"""		)
																						__lowerCAmelCase											=					text_input_ids[:, : self.tokenizer.model_max_length]

																__lowerCAmelCase											=					self.text_encoder(text_input_ids.to(__lowercase		)		)

																__lowerCAmelCase											=					text_encoder_output.text_embeds
																__lowerCAmelCase											=					text_encoder_output.last_hidden_state

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

										__lowerCAmelCase											=					text_embeddings.repeat_interleave(__lowercase							,      dim=0		)
										__lowerCAmelCase											=					text_encoder_hidden_states.repeat_interleave(__lowercase							,      dim=0		)
										__lowerCAmelCase											=					text_mask.repeat_interleave(__lowercase							,      dim=0		)

										if do_classifier_free_guidance:
																__lowerCAmelCase											=					[''''''] * batch_size

																__lowerCAmelCase											=					self.tokenizer(
																    __lowercase							,      padding='''max_length'''							,      max_length=self.tokenizer.model_max_length							,      truncation=__lowercase							,      return_tensors='''pt'''							,      )
																__lowerCAmelCase											=					uncond_input.attention_mask.bool().to(__lowercase		)
																__lowerCAmelCase											=					self.text_encoder(uncond_input.input_ids.to(__lowercase		)		)

																__lowerCAmelCase											=					uncond_embeddings_text_encoder_output.text_embeds
																__lowerCAmelCase											=					uncond_embeddings_text_encoder_output.last_hidden_state

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

																__lowerCAmelCase											=					uncond_embeddings.shape[1]
																__lowerCAmelCase											=					uncond_embeddings.repeat(1							,      __lowercase		)
																__lowerCAmelCase											=					uncond_embeddings.view(batch_size * num_images_per_prompt							,      __lowercase		)

																__lowerCAmelCase											=					uncond_text_encoder_hidden_states.shape[1]
																__lowerCAmelCase											=					uncond_text_encoder_hidden_states.repeat(1							,      __lowercase							,      1		)
																__lowerCAmelCase											=					uncond_text_encoder_hidden_states.view(
																    batch_size * num_images_per_prompt							,      __lowercase							,      -1		)
																__lowerCAmelCase											=					uncond_text_mask.repeat_interleave(__lowercase							,      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
																__lowerCAmelCase											=					torch.cat([uncond_embeddings, text_embeddings]		)
																__lowerCAmelCase											=					torch.cat([uncond_text_encoder_hidden_states, text_encoder_hidden_states]		)

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

										return text_embeddings, text_encoder_hidden_states, text_mask




				@property
				def        _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(__lowercase							,      '''_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        _snake_case	(self							,      __lowercase							,      __lowercase							,      __lowercase							,      __lowercase							,      __lowercase							,      __lowercase		):
										if latents is None:
																__lowerCAmelCase											=					randn_tensor(__lowercase							,      generator=__lowercase							,      device=__lowercase							,      dtype=__lowercase		)
										else:
																if latents.shape != shape:
																						raise ValueError(F"""Unexpected latents shape, got {latents.shape}, expected {shape}"""		)
																__lowerCAmelCase											=					latents.to(__lowercase		)

										__lowerCAmelCase											=					latents * scheduler.init_noise_sigma
										return latents




				def        _snake_case	(self							,      __lowercase = None		):
										self.decoder_pipe.to(__lowercase		)
										super().to(__lowercase		)




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

										__lowerCAmelCase											=					self._execution_device

										__lowerCAmelCase											=					batch_size * num_images_per_prompt

										__lowerCAmelCase											=					prior_guidance_scale > 1.0 or decoder_guidance_scale > 1.0

										__lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase											=					self._encode_prompt(
										    __lowercase							,      __lowercase							,      __lowercase							,      __lowercase							,      __lowercase							,      __lowercase		)

										# prior

										self.prior_scheduler.set_timesteps(__lowercase							,      device=__lowercase		)
										__lowerCAmelCase											=					self.prior_scheduler.timesteps

										__lowerCAmelCase											=					self.prior.config.embedding_dim

										__lowerCAmelCase											=					self.prepare_latents(
										    (batch_size, embedding_dim)							,      text_embeddings.dtype							,      __lowercase							,      __lowercase							,      __lowercase							,      self.prior_scheduler							,      )

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

																__lowerCAmelCase											=					self.prior(
																    __lowercase							,      timestep=__lowercase							,      proj_embedding=__lowercase							,      encoder_hidden_states=__lowercase							,      attention_mask=__lowercase							,      ).predicted_image_embedding

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

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

																__lowerCAmelCase											=					self.prior_scheduler.step(
																    __lowercase							,      timestep=__lowercase							,      sample=__lowercase							,      generator=__lowercase							,      prev_timestep=__lowercase							,      ).prev_sample

										__lowerCAmelCase											=					self.prior.post_process_latents(__lowercase		)

										__lowerCAmelCase											=					prior_latents

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





'''simple docstring'''

import argparse
import os

# New Code #
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator
from accelerate.utils import find_executable_batch_size


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to combine both the gradient accumulation
# and automatic batch size finder utilities of Accelerate to perfrom
# automatic gradient accumulation
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################

_UpperCAmelCase					:			List[Any]				     =					3_2

def 				__magic_name__(    lowerCamelCase,					lowerCamelCase = 1_6):
						__lowerCAmelCase											=					AutoTokenizer.from_pretrained('''bert-base-cased''')
						__lowerCAmelCase											=					load_dataset('''glue''',					'''mrpc''')

						def tokenize_function(lowerCamelCase):
												# max_length=None => use the model max length (it's actually the default)
												__lowerCAmelCase											=					tokenizer(examples['''sentence1'''],					examples['''sentence2'''],					truncation=lowerCamelCase,					max_length=lowerCamelCase)
												return outputs

						# Apply the method we just defined to all the examples in all the splits of the dataset
						# starting with the main process first:
						with accelerator.main_process_first():
												__lowerCAmelCase											=					datasets.map(
												    lowerCamelCase,					batched=lowerCamelCase,					remove_columns=['''idx''', '''sentence1''', '''sentence2'''],					)

						# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
						# transformers library
						__lowerCAmelCase											=					tokenized_datasets.rename_column('''label''',					'''labels''')

						def collate_fn(lowerCamelCase):
												# When using mixed precision we want round multiples of 8/16
												if accelerator.mixed_precision == "fp8":
																		__lowerCAmelCase											=					1_6
												elif accelerator.mixed_precision != "no":
																		__lowerCAmelCase											=					8
												else:
																		__lowerCAmelCase											=					None

												return tokenizer.pad(
												    lowerCamelCase,					padding='''longest''',					pad_to_multiple_of=lowerCamelCase,					return_tensors='''pt''',					)

						# Instantiate dataloaders.
						__lowerCAmelCase											=					DataLoader(
						    tokenized_datasets['''train'''],					shuffle=lowerCamelCase,					collate_fn=lowerCamelCase,					batch_size=lowerCamelCase)
						__lowerCAmelCase											=					DataLoader(
						    tokenized_datasets['''validation'''],					shuffle=lowerCamelCase,					collate_fn=lowerCamelCase,					batch_size=lowerCamelCase)

						return train_dataloader, eval_dataloader


# For testing only
if os.environ.get("""TESTING_MOCKED_DATALOADERS""", None) == "1":
	from accelerate.test_utils.training import mocked_dataloaders

	_UpperCAmelCase					:			Optional[int]				     =					mocked_dataloaders  # noqa: F811

def 				__magic_name__(    lowerCamelCase,					lowerCamelCase):
						# For testing only
						if os.environ.get('''TESTING_MOCKED_DATALOADERS''',					lowerCamelCase) == "1":
												__lowerCAmelCase											=					2
						# Initialize accelerator
						__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											=					evaluate.load('''glue''',					'''mrpc''')

						# New Code #
						# We use the `find_executable_batch_size` decorator, passing in the desired observed batch size
						# to train on. If a CUDA OOM error occurs, it will retry this loop cutting the batch size in
						# half each time. From this, we can calculate the number of gradient accumulation steps needed
						# and modify the Accelerator object as a result
						@find_executable_batch_size(starting_batch_size=int(lowerCamelCase))
						def inner_training_loop(lowerCamelCase):
												# Since we need to modify the outside accelerator object, we need to bring it
												# to the local scope
												nonlocal accelerator

												# We can calculate the number of gradient accumulation steps based on the current
												# batch size vs the starting batch size
												__lowerCAmelCase											=					observed_batch_size // batch_size

												# And then set it in the Accelerator directly:
												__lowerCAmelCase											=					num_gradient_accumulation_steps

												# Next we need to free all of the stored model references in the Accelerator each time
												accelerator.free_memory()

												# And set the seed so our results are reproducable each reset
												set_seed(lowerCamelCase)

												# Instantiate the model (we build the model here so that the seed also control new weights initialization)
												__lowerCAmelCase											=					AutoModelForSequenceClassification.from_pretrained('''bert-base-cased''',					return_dict=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).
												__lowerCAmelCase											=					model.to(accelerator.device)

												# Instantiate optimizer
												__lowerCAmelCase											=					AdamW(params=model.parameters(),					lr=lowerCamelCase)
												__lowerCAmelCase							,       __lowerCAmelCase											=					get_dataloaders(lowerCamelCase,					lowerCamelCase)

												# Instantiate scheduler
												__lowerCAmelCase											=					get_linear_schedule_with_warmup(
												    optimizer=lowerCamelCase,					num_warmup_steps=1_0_0,					num_training_steps=(len(lowerCamelCase) * num_epochs),					)

												# 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(
												    lowerCamelCase,					lowerCamelCase,					lowerCamelCase,					lowerCamelCase,					lowerCamelCase)

												# Now we train the model
												for epoch in range(lowerCamelCase):
																		model.train()
																		for step, batch in enumerate(lowerCamelCase):
																								# And perform gradient accumulation
																								with accelerator.accumulate(lowerCamelCase):
																														# We could avoid this line since we set the accelerator with `device_placement=True`.
																														batch.to(accelerator.device)
																														__lowerCAmelCase											=					model(**lowerCamelCase)
																														__lowerCAmelCase											=					outputs.loss
																														accelerator.backward(lowerCamelCase)
																														optimizer.step()
																														lr_scheduler.step()
																														optimizer.zero_grad()

																		model.eval()
																		for step, batch in enumerate(lowerCamelCase):
																								# We could avoid this line since we set the accelerator with `device_placement=True`.
																								batch.to(accelerator.device)
																								with torch.no_grad():
																														__lowerCAmelCase											=					model(**lowerCamelCase)
																								__lowerCAmelCase											=					outputs.logits.argmax(dim=-1)
																								__lowerCAmelCase							,       __lowerCAmelCase											=					accelerator.gather_for_metrics((predictions, batch['''labels''']))
																								metric.add_batch(
																								    predictions=lowerCamelCase,					references=lowerCamelCase,					)

																		__lowerCAmelCase											=					metric.compute()
																		# Use accelerator.print to print only on the main process.
																		accelerator.print(F"""epoch {epoch}:""",					lowerCamelCase)

    # New Code #
    # And call it at the end with no arguments
    # Note: You could also refactor this outside of your training loop function
						inner_training_loop()

def 				__magic_name__(    ):
						__lowerCAmelCase											=					argparse.ArgumentParser(description='''Simple example of training script.''')
						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('''--cpu''',					action='''store_true''',					help='''If passed, will train on the CPU.''')
						__lowerCAmelCase											=					parser.parse_args()
						# New Code #
						# We modify the starting batch size to be an observed batch size of 256, to guarentee an initial CUDA OOM
						__lowerCAmelCase											=					{'''lr''': 2E-5, '''num_epochs''': 3, '''seed''': 4_2, '''batch_size''': 2_5_6}
						training_function(lowerCamelCase,					lowerCamelCase)


if __name__ == "__main__":
	main()

