






import argparse
import json
import os

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, DistributedType
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler


__UpperCAmelCase     							=						16
__UpperCAmelCase     							=						32




def 					lowercase__				(					__snake_case   :							Accelerator					,	__snake_case   :							int = 16					,	__snake_case   :							str = "bert-base-cased"  ):


     '''simple docstring'''


     UpperCAmelCase_   :				List[Any]						=						AutoTokenizer.from_pretrained(__snake_case  )
     UpperCAmelCase_   :				Union[str, Any]						=						load_dataset('glue'					,	'mrpc'  )

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

     # Apply the method we just defined to all the examples in all the splits of the dataset
     UpperCAmelCase_   :				str						=						datasets.map(
         __snake_case					,	batched=__snake_case					,	remove_columns=['idx', 'sentence1', 'sentence2']					,	load_from_cache_file=__snake_case  )

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

     def collate_fn(__snake_case   :							str  ):
          # On TPU it's best to pad everything to the same length or training will be very slow.
          if accelerator.distributed_type == DistributedType.TPU:
               return tokenizer.pad(__snake_case					,	padding='max_length'					,	max_length=128					,	return_tensors='pt'  )
          return tokenizer.pad(__snake_case					,	padding='longest'					,	return_tensors='pt'  )

     # Instantiate dataloaders.
     UpperCAmelCase_   :				Any						=						DataLoader(
         tokenized_datasets['train']					,	shuffle=__snake_case					,	collate_fn=__snake_case					,	batch_size=__snake_case  )
     UpperCAmelCase_   :				Optional[Any]						=						DataLoader(
         tokenized_datasets['validation']					,	shuffle=__snake_case					,	collate_fn=__snake_case					,	batch_size=__snake_case  )

     return train_dataloader, eval_dataloader




def 					lowercase__				(					__snake_case   :							int					,	__snake_case   :							str  ):


     '''simple docstring'''


     UpperCAmelCase_   :				List[str]						=						Accelerator()

     # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
     UpperCAmelCase_   :				List[Any]						=						config['lr']
     UpperCAmelCase_   :				Dict						=						int(config['num_epochs']  )
     UpperCAmelCase_   :				List[Any]						=						int(config['seed']  )
     UpperCAmelCase_   :				List[Any]						=						int(config['batch_size']  )
     UpperCAmelCase_   :				Optional[Any]						=						args.model_name_or_path

     set_seed(__snake_case  )
     UpperCAmelCase_   :				Optional[Any]						=						get_dataloaders(__snake_case					,	__snake_case					,	__snake_case  )

     # Instantiate the model (we build the model here so that the seed also control new weights initialization)
     UpperCAmelCase_   :				Dict						=						AutoModelForSequenceClassification.from_pretrained(__snake_case					,	return_dict=__snake_case  )

     # Instantiate optimizer
     UpperCAmelCase_   :				List[Any]						=						(
         AdamW
         if accelerator.state.deepspeed_plugin is None
         or 'optimizer' not in accelerator.state.deepspeed_plugin.deepspeed_config
         else DummyOptim
     )
     UpperCAmelCase_   :				List[str]						=						optimizer_cls(params=model.parameters()					,	lr=__snake_case  )

     if accelerator.state.deepspeed_plugin is not None:
          UpperCAmelCase_   :				str						=						accelerator.state.deepspeed_plugin.deepspeed_config[
              'gradient_accumulation_steps'
          ]
     else:
          UpperCAmelCase_   :				Dict						=						1
     UpperCAmelCase_   :				Union[str, Any]						=						(len(__snake_case  ) * num_epochs) // gradient_accumulation_steps

     # Instantiate scheduler
     if (
         accelerator.state.deepspeed_plugin is None
         or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
     ):
          UpperCAmelCase_   :				Tuple						=						get_linear_schedule_with_warmup(
              optimizer=__snake_case					,	num_warmup_steps=0					,	num_training_steps=__snake_case					,	)
     else:
          UpperCAmelCase_   :				Union[str, Any]						=						DummyScheduler(__snake_case					,	total_num_steps=__snake_case					,	warmup_num_steps=0  )

     # 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.
     UpperCAmelCase_   :				Union[str, Any]						=						accelerator.prepare(
         __snake_case					,	__snake_case					,	__snake_case					,	__snake_case					,	__snake_case  )

     # We need to keep track of how many total steps we have iterated over
     UpperCAmelCase_   :				Any						=						0
     # We also need to keep track of the stating epoch so files are named properly
     UpperCAmelCase_   :				Union[str, Any]						=						0

     # Now we train the model
     UpperCAmelCase_   :				str						=						evaluate.load('glue'					,	'mrpc'  )
     UpperCAmelCase_   :				Optional[Any]						=						0
     UpperCAmelCase_   :				List[Any]						=						{}
     for epoch in range(__snake_case					,	__snake_case  ):
          model.train()
          for step, batch in enumerate(__snake_case  ):
               UpperCAmelCase_   :				Union[str, Any]						=						model(**__snake_case  )
               UpperCAmelCase_   :				int						=						outputs.loss
               UpperCAmelCase_   :				Any						=						loss / gradient_accumulation_steps
               accelerator.backward(__snake_case  )
               if step % gradient_accumulation_steps == 0:
                    optimizer.step()
                    lr_scheduler.step()
                    optimizer.zero_grad()

               overall_step += 1

          model.eval()
          UpperCAmelCase_   :				Optional[int]						=						0
          for step, batch in enumerate(__snake_case  ):
               # We could avoid this line since we set the accelerator with `device_placement=True`.
               batch.to(accelerator.device  )
               with torch.no_grad():
                    UpperCAmelCase_   :				Any						=						model(**__snake_case  )
               UpperCAmelCase_   :				Optional[Any]						=						outputs.logits.argmax(dim=-1  )
               # It is slightly faster to call this once, than multiple times
               UpperCAmelCase_   :				Optional[Any]						=						accelerator.gather(
                   (predictions, batch['labels'])  )  # If we are in a multiprocess environment, the last batch has duplicates
               if accelerator.use_distributed:
                    if step == len(__snake_case  ) - 1:
                         UpperCAmelCase_   :				Tuple						=						predictions[: len(eval_dataloader.dataset  ) - samples_seen]
                         UpperCAmelCase_   :				List[Any]						=						references[: len(eval_dataloader.dataset  ) - samples_seen]
                    else:
                         samples_seen += references.shape[0]
               metric.add_batch(
                   predictions=__snake_case					,	references=__snake_case					,	)

          UpperCAmelCase_   :				Union[str, Any]						=						metric.compute()
          # Use accelerator.print to print only on the main process.
          accelerator.print(F"epoch {epoch}:"					,	__snake_case  )
          UpperCAmelCase_   :				Optional[Any]						=						eval_metric['accuracy']

          if best_performance < eval_metric["accuracy"]:
               UpperCAmelCase_   :				str						=						eval_metric['accuracy']

     if args.performance_lower_bound is not None:
          assert (
              args.performance_lower_bound <= best_performance
          ), F"Best performance metric {best_performance} is lower than the lower bound {args.performance_lower_bound}"

     accelerator.wait_for_everyone()
     if accelerator.is_main_process:
          with open(os.path.join(args.output_dir					,	'all_results.json'  )					,	'w'  ) as f:
               json.dump(__snake_case					,	__snake_case  )




def 					lowercase__				(					):


     '''simple docstring'''


     UpperCAmelCase_   :				List[Any]						=						argparse.ArgumentParser(description='Simple example of training script tracking peak GPU memory usage.'  )
     parser.add_argument(
         '--model_name_or_path'					,	type=__snake_case					,	default='bert-base-cased'					,	help='Path to pretrained model or model identifier from huggingface.co/models.'					,	required=__snake_case					,	)
     parser.add_argument(
         '--output_dir'					,	type=__snake_case					,	default='.'					,	help='Optional save directory where all checkpoint folders will be stored. Default is the current working directory.'					,	)
     parser.add_argument(
         '--performance_lower_bound'					,	type=__snake_case					,	default=__snake_case					,	help='Optional lower bound for the performance metric. If set, the training will throw error when the performance metric drops below this value.'					,	)
     parser.add_argument(
         '--num_epochs'					,	type=__snake_case					,	default=3					,	help='Number of train epochs.'					,	)
     UpperCAmelCase_   :				Dict						=						parser.parse_args()
     UpperCAmelCase_   :				str						=						{'lr': 2E-5, 'num_epochs': args.num_epochs, 'seed': 42, 'batch_size': 16}
     training_function(__snake_case					,	__snake_case  )


if __name__ == "__main__":
  main()














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

import torch
from transformers import CLIPTextModel, CLIPTokenizer

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin, TransformeraDModel, VQModel
from ...schedulers import VQDiffusionScheduler
from ...utils import logging
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


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



class   lowerCamelCase				(_snake_case			,  _snake_case      ):







		'''simple docstring'''



		@register_to_config
		def __init__(							self    ,		_UpperCamelCase    ,		_UpperCamelCase = None    ,		_UpperCamelCase = None       )     ->							int:
							super().__init__()

							UpperCAmelCase_   :				str						=						learnable

							if self.learnable:
												assert hidden_size is not None, "learnable=True requires `hidden_size` to be set"
												assert length is not None, "learnable=True requires `length` to be set"

												UpperCAmelCase_   :				Optional[Any]						=						torch.zeros(_UpperCamelCase    ,		_UpperCamelCase       )
							else:
												UpperCAmelCase_   :				Any						=						None

							UpperCAmelCase_   :				Any						=						torch.nn.Parameter(_UpperCamelCase       )





class   lowerCamelCase				(_snake_case      ):







		'''simple docstring'''



		_snake_case   :   VQModel
		_snake_case   :   CLIPTextModel
		_snake_case   :   CLIPTokenizer
		_snake_case   :   TransformeraDModel
		_snake_case   :   LearnedClassifierFreeSamplingEmbeddings
		_snake_case   :   VQDiffusionScheduler



		def __init__(							self    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		)     ->							List[Any]:
							super().__init__()

							self.register_modules(
							    vqvae=_UpperCamelCase    ,		transformer=_UpperCamelCase    ,		text_encoder=_UpperCamelCase    ,		tokenizer=_UpperCamelCase    ,		scheduler=_UpperCamelCase    ,		learned_classifier_free_sampling_embeddings=_UpperCamelCase    ,		)



		def  __UpperCAmelCase      (							self    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase       )     ->							List[str]:
							UpperCAmelCase_   :				Union[str, Any]						=						len(_UpperCamelCase       ) if isinstance(_UpperCamelCase    ,		_UpperCamelCase       ) else 1

							# get prompt text embeddings
							UpperCAmelCase_   :				str						=						self.tokenizer(
							    _UpperCamelCase    ,		padding='max_length'    ,		max_length=self.tokenizer.model_max_length    ,		return_tensors='pt'    ,		)
							UpperCAmelCase_   :				Optional[Any]						=						text_inputs.input_ids

							if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
												UpperCAmelCase_   :				List[Any]						=						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}"       )
												UpperCAmelCase_   :				str						=						text_input_ids[:, : self.tokenizer.model_max_length]
							UpperCAmelCase_   :				str						=						self.text_encoder(text_input_ids.to(self.device       )       )[0]

							# NOTE: This additional step of normalizing the text embeddings is from VQ-Diffusion.
							# While CLIP does normalize the pooled output of the text transformer when combining
							# the image and text embeddings, CLIP does not directly normalize the last hidden state.
							#
							# CLIP normalizing the pooled output.
							# https://github.com/huggingface/transformers/blob/d92e22d1f28324f513f3080e5c47c071a3916721/src/transformers/models/clip/modeling_clip.py#L1052-L1053
							UpperCAmelCase_   :				Dict						=						prompt_embeds / prompt_embeds.norm(dim=-1    ,		keepdim=_UpperCamelCase       )

							# duplicate text embeddings for each generation per prompt
							UpperCAmelCase_   :				Dict						=						prompt_embeds.repeat_interleave(_UpperCamelCase    ,		dim=0       )

							if do_classifier_free_guidance:
												if self.learned_classifier_free_sampling_embeddings.learnable:
																	UpperCAmelCase_   :				List[str]						=						self.learned_classifier_free_sampling_embeddings.embeddings
																	UpperCAmelCase_   :				List[str]						=						negative_prompt_embeds.unsqueeze(0       ).repeat(_UpperCamelCase    ,		1    ,		1       )
												else:
																	UpperCAmelCase_   :				List[Any]						=						[''] * batch_size

																	UpperCAmelCase_   :				List[Any]						=						text_input_ids.shape[-1]
																	UpperCAmelCase_   :				Dict						=						self.tokenizer(
																	    _UpperCamelCase    ,		padding='max_length'    ,		max_length=_UpperCamelCase    ,		truncation=_UpperCamelCase    ,		return_tensors='pt'    ,		)
																	UpperCAmelCase_   :				Tuple						=						self.text_encoder(uncond_input.input_ids.to(self.device       )       )[0]
																	# See comment for normalizing text embeddings
																	UpperCAmelCase_   :				Dict						=						negative_prompt_embeds / negative_prompt_embeds.norm(dim=-1    ,		keepdim=_UpperCamelCase       )

												# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
												UpperCAmelCase_   :				List[Any]						=						negative_prompt_embeds.shape[1]
												UpperCAmelCase_   :				Dict						=						negative_prompt_embeds.repeat(1    ,		_UpperCamelCase    ,		1       )
												UpperCAmelCase_   :				Any						=						negative_prompt_embeds.view(batch_size * num_images_per_prompt    ,		_UpperCamelCase    ,		-1       )

												# 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
												UpperCAmelCase_   :				Union[str, Any]						=						torch.cat([negative_prompt_embeds, prompt_embeds]       )

							return prompt_embeds



		@torch.no_grad()
		def __call__(							self    ,		_UpperCamelCase    ,		_UpperCamelCase = 1_0_0    ,		_UpperCamelCase = 5.0    ,		_UpperCamelCase = 1.0    ,		_UpperCamelCase = 1    ,		_UpperCamelCase = None    ,		_UpperCamelCase = None    ,		_UpperCamelCase = "pil"    ,		_UpperCamelCase = True    ,		_UpperCamelCase = None    ,		_UpperCamelCase = 1    ,		)     ->							Union[ImagePipelineOutput, Tuple]:
							if isinstance(_UpperCamelCase    ,		_UpperCamelCase       ):
												UpperCAmelCase_   :				Any						=						1
							elif isinstance(_UpperCamelCase    ,		_UpperCamelCase       ):
												UpperCAmelCase_   :				Tuple						=						len(_UpperCamelCase       )
							else:
												raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(_UpperCamelCase       )}"       )

							UpperCAmelCase_   :				Union[str, Any]						=						batch_size * num_images_per_prompt

							UpperCAmelCase_   :				Optional[int]						=						guidance_scale > 1.0

							UpperCAmelCase_   :				Any						=						self._encode_prompt(_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase       )

							if (callback_steps is None) or (
							    callback_steps is not None and (not isinstance(_UpperCamelCase    ,		_UpperCamelCase       ) or callback_steps <= 0)
							):
												raise ValueError(
												    f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
												    f" {type(_UpperCamelCase       )}."       )

							# get the initial completely masked latents unless the user supplied it

							UpperCAmelCase_   :				Optional[int]						=						(batch_size, self.transformer.num_latent_pixels)
							if latents is None:
												UpperCAmelCase_   :				Tuple						=						self.transformer.num_vector_embeds - 1
												UpperCAmelCase_   :				List[Any]						=						torch.full(_UpperCamelCase    ,		_UpperCamelCase       ).to(self.device       )
							else:
												if latents.shape != latents_shape:
																	raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {latents_shape}"       )
												if (latents < 0).any() or (latents >= self.transformer.num_vector_embeds).any():
																	raise ValueError(
																	    'Unexpected latents value(s). All latents be valid embedding indices i.e. in the range 0,'
																	    f" {self.transformer.num_vector_embeds - 1} (inclusive)."       )
												UpperCAmelCase_   :				Any						=						latents.to(self.device       )

							# set timesteps
							self.scheduler.set_timesteps(_UpperCamelCase    ,		device=self.device       )

							UpperCAmelCase_   :				List[str]						=						self.scheduler.timesteps.to(self.device       )

							UpperCAmelCase_   :				Union[str, Any]						=						latents

							for i, t in enumerate(self.progress_bar(_UpperCamelCase       )       ):
												# expand the sample if we are doing classifier free guidance
												UpperCAmelCase_   :				Union[str, Any]						=						torch.cat([sample] * 2       ) if do_classifier_free_guidance else sample

												# predict the un-noised image
												# model_output == `log_p_x_0`
												UpperCAmelCase_   :				Dict						=						self.transformer(_UpperCamelCase    ,		encoder_hidden_states=_UpperCamelCase    ,		timestep=_UpperCamelCase       ).sample

												if do_classifier_free_guidance:
																	UpperCAmelCase_	,  UpperCAmelCase_   :				Optional[Any]						=						model_output.chunk(2       )
																	UpperCAmelCase_   :				Optional[int]						=						model_output_uncond + guidance_scale * (model_output_text - model_output_uncond)
																	model_output -= torch.logsumexp(_UpperCamelCase    ,		dim=1    ,		keepdim=_UpperCamelCase       )

												UpperCAmelCase_   :				str						=						self.truncate(_UpperCamelCase    ,		_UpperCamelCase       )

												# remove `log(0)`'s (`-inf`s)
												UpperCAmelCase_   :				Optional[int]						=						model_output.clamp(-7_0       )

												# compute the previous noisy sample x_t -> x_t-1
												UpperCAmelCase_   :				Union[str, Any]						=						self.scheduler.step(_UpperCamelCase    ,		timestep=_UpperCamelCase    ,		sample=_UpperCamelCase    ,		generator=_UpperCamelCase       ).prev_sample

												# call the callback, if provided
												if callback is not None and i % callback_steps == 0:
																	callback(_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase       )

							UpperCAmelCase_   :				str						=						self.vqvae.config.vq_embed_dim
							UpperCAmelCase_   :				Optional[int]						=						(batch_size, self.transformer.height, self.transformer.width, embedding_channels)
							UpperCAmelCase_   :				int						=						self.vqvae.quantize.get_codebook_entry(_UpperCamelCase    ,		shape=_UpperCamelCase       )
							UpperCAmelCase_   :				Dict						=						self.vqvae.decode(_UpperCamelCase    ,		force_not_quantize=_UpperCamelCase       ).sample

							UpperCAmelCase_   :				List[str]						=						(image / 2 + 0.5).clamp(0    ,		1       )
							UpperCAmelCase_   :				Any						=						image.cpu().permute(0    ,		2    ,		3    ,		1       ).numpy()

							if output_type == "pil":
												UpperCAmelCase_   :				int						=						self.numpy_to_pil(_UpperCamelCase       )

							if not return_dict:
												return (image,)

							return ImagePipelineOutput(images=_UpperCamelCase       )



		def  __UpperCAmelCase      (							self    ,		_UpperCamelCase    ,		_UpperCamelCase       )     ->							torch.FloatTensor:
							UpperCAmelCase_	,  UpperCAmelCase_   :				int						=						torch.sort(_UpperCamelCase    ,		1    ,		descending=_UpperCamelCase       )
							UpperCAmelCase_   :				Dict						=						torch.exp(_UpperCamelCase       )
							UpperCAmelCase_   :				int						=						sorted_p_x_0.cumsum(dim=1       ) < truncation_rate

							# Ensure that at least the largest probability is not zeroed out
							UpperCAmelCase_   :				Tuple						=						torch.full_like(keep_mask[:, 0:1, :]    ,		_UpperCamelCase       )
							UpperCAmelCase_   :				List[str]						=						torch.cat((all_true, keep_mask)    ,		dim=1       )
							UpperCAmelCase_   :				int						=						keep_mask[:, :-1, :]

							UpperCAmelCase_   :				Any						=						keep_mask.gather(1    ,		indices.argsort(1       )       )

							UpperCAmelCase_   :				str						=						log_p_x_0.clone()

							UpperCAmelCase_   :				Any						=						-torch.inf  # -inf = log(0)

							return rv






