






"""simple docstring"""







import argparse
import logging
import os
import sys
import time

import tensorflow as tf
from datasets import load_dataset
from tqdm import tqdm

from transformers import AutoTokenizer, TFAutoModelForSequenceClassification
from transformers.utils import is_sagemaker_dp_enabled


if os.environ.get('''SDP_ENABLED''') or is_sagemaker_dp_enabled():
 lowerCAmelCase_		:			Optional[Any]				   =					True
 lowerCAmelCase_		:			List[str]				   =					'''p3dn.24xlarge'''
 import smdistributed.dataparallel.tensorflow as sdp
else:
 lowerCAmelCase_		:			List[str]				   =					False






def 					_lowerCAmelCase				(					lowerCAmelCase		,					lowerCAmelCase		,					lowerCAmelCase		,					lowerCAmelCase		,					lowerCAmelCase		,					lowerCAmelCase		,					lowerCAmelCase=None						):







       '''simple docstring'''
       UpperCAmelCase					=	tqdm(lowerCAmelCase						)
       for i, batch in enumerate(lowerCAmelCase						):
              with tf.GradientTape() as tape:
                     UpperCAmelCase			,				UpperCAmelCase					=	batch
                     UpperCAmelCase					=	model(lowerCAmelCase						)
                     UpperCAmelCase					=	loss(lowerCAmelCase		,					outputs.logits						)

              if SDP_ENABLED:
                     UpperCAmelCase					=	sdp.DistributedGradientTape(lowerCAmelCase		,					sparse_as_dense=lowerCAmelCase						)

              UpperCAmelCase					=	tape.gradient(lowerCAmelCase		,					model.trainable_variables						)
              opt.apply_gradients(zip(lowerCAmelCase		,					model.trainable_variables						)						)

              pbar.set_description(F'''Loss: {loss_value:.4f}'''						)

              if SDP_ENABLED and i == 0:
                     sdp.broadcast_variables(model.variables		,					root_rank=0						)
                     sdp.broadcast_variables(opt.variables()		,					root_rank=0						)

              if max_steps and i >= max_steps:
                     break

       UpperCAmelCase					=	{"""loss""": loss_value.numpy()}
       return train_results






def 					_lowerCAmelCase				(					lowerCAmelCase		,					lowerCAmelCase		,					lowerCAmelCase						):







       '''simple docstring'''
       # Load dataset
       UpperCAmelCase			,				UpperCAmelCase					=	load_dataset("""imdb"""		,					split=["""train""", """test"""]						)

       # Preprocess train dataset
       UpperCAmelCase					=	train_dataset.map(
           lambda lowerCAmelCase						: tokenizer(e["""text"""]		,					truncation=lowerCAmelCase		,					padding="""max_length"""						)		,					batched=lowerCAmelCase						)
       train_dataset.set_format(type="""tensorflow"""		,					columns=["""input_ids""", """attention_mask""", """label"""]						)

       UpperCAmelCase					=	{
           x: train_dataset[x].to_tensor(default_value=0		,					shape=[None, tokenizer.model_max_length]						)
           for x in ["""input_ids""", """attention_mask"""]
       }
       UpperCAmelCase					=	tf.data.Dataset.from_tensor_slices((train_features, train_dataset["""label"""])						)

       # Preprocess test dataset
       UpperCAmelCase					=	test_dataset.map(
           lambda lowerCAmelCase						: tokenizer(e["""text"""]		,					truncation=lowerCAmelCase		,					padding="""max_length"""						)		,					batched=lowerCAmelCase						)
       test_dataset.set_format(type="""tensorflow"""		,					columns=["""input_ids""", """attention_mask""", """label"""]						)

       UpperCAmelCase					=	{
           x: test_dataset[x].to_tensor(default_value=0		,					shape=[None, tokenizer.model_max_length]						)
           for x in ["""input_ids""", """attention_mask"""]
       }
       UpperCAmelCase					=	tf.data.Dataset.from_tensor_slices((test_features, test_dataset["""label"""])						)

       if SDP_ENABLED:
              UpperCAmelCase					=	tf_train_dataset.shard(sdp.size()		,					sdp.rank()						)
              UpperCAmelCase					=	tf_test_dataset.shard(sdp.size()		,					sdp.rank()						)
       UpperCAmelCase					=	tf_train_dataset.batch(lowerCAmelCase		,					drop_remainder=lowerCAmelCase						)
       UpperCAmelCase					=	tf_test_dataset.batch(lowerCAmelCase		,					drop_remainder=lowerCAmelCase						)

       return tf_train_dataset, tf_test_dataset


if __name__ == "__main__":
 lowerCAmelCase_		:			Dict				   =					argparse.ArgumentParser()

 # Hyperparameters sent by the client are passed as command-line arguments to the script.
 parser.add_argument('''--epochs''', type=int, default=3)
 parser.add_argument('''--per_device_train_batch_size''', type=int, default=1_6)
 parser.add_argument('''--per_device_eval_batch_size''', type=int, default=8)
 parser.add_argument('''--model_name_or_path''', type=str)
 parser.add_argument('''--learning_rate''', type=str, default=5E-5)
 parser.add_argument('''--do_train''', type=bool, default=True)
 parser.add_argument('''--do_eval''', type=bool, default=True)
 parser.add_argument('''--output_dir''', type=str)
 parser.add_argument('''--max_steps''', type=int, default=None)

 # Data, model, and output directories
 parser.add_argument('''--output_data_dir''', type=str, default=os.environ['''SM_OUTPUT_DATA_DIR'''])
 parser.add_argument('''--model_dir''', type=str, default=os.environ['''SM_MODEL_DIR'''])
 parser.add_argument('''--n_gpus''', type=str, default=os.environ['''SM_NUM_GPUS'''])

 lowerCAmelCase_   ,    lowerCAmelCase_		:			Optional[int]				   =					parser.parse_known_args()

 # Set up logging
 lowerCAmelCase_		:			List[Any]				   =					logging.getLogger(__name__)

 logging.basicConfig(
     level=logging.getLevelName('''INFO'''),
     handlers=[logging.StreamHandler(sys.stdout)],
     format='''%(asctime)s - %(name)s - %(levelname)s - %(message)s''',
 )

 if SDP_ENABLED:
  sdp.init()

  lowerCAmelCase_		:			Dict				   =					tf.config.experimental.list_physical_devices('''GPU''')
  for gpu in gpus:
   tf.config.experimental.set_memory_growth(gpu, True)
  if gpus:
   tf.config.experimental.set_visible_devices(gpus[sdp.local_rank()], '''GPU''')

    # Load model and tokenizer
 lowerCAmelCase_		:			Any				   =					TFAutoModelForSequenceClassification.from_pretrained(args.model_name_or_path)
 lowerCAmelCase_		:			Optional[Any]				   =					AutoTokenizer.from_pretrained(args.model_name_or_path)

 # get datasets
 lowerCAmelCase_   ,    lowerCAmelCase_		:			Dict				   =					get_datasets(
     tokenizer=tokenizer,
     train_batch_size=args.per_device_train_batch_size,
     eval_batch_size=args.per_device_eval_batch_size,
 )

 # fine optimizer and loss
 lowerCAmelCase_		:			Optional[int]				   =					tf.keras.optimizers.Adam(learning_rate=args.learning_rate)
 lowerCAmelCase_		:			Union[str, Any]				   =					tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
 lowerCAmelCase_		:			Optional[int]				   =					[tf.keras.metrics.SparseCategoricalAccuracy()]
 model.compile(optimizer=optimizer, loss=loss, metrics=metrics)

 # Training
 if args.do_train:
  # train_results = model.fit(tf_train_dataset, epochs=args.epochs, batch_size=args.train_batch_size)
  lowerCAmelCase_		:			Optional[Any]				   =					time.time()
  lowerCAmelCase_		:			int				   =					fit(
      model,
      loss,
      optimizer,
      tf_train_dataset,
      args.epochs,
      args.per_device_train_batch_size,
      max_steps=args.max_steps,
  )
  lowerCAmelCase_		:			Optional[Any]				   =					time.time() - start_train_time
  logger.info('''*** Train ***''')
  logger.info(F'train_runtime = {end_train_time}')

  lowerCAmelCase_		:			Tuple				   =					os.path.join(args.output_dir, '''train_results.txt''')

  if not SDP_ENABLED or sdp.rank() == 0:
   with open(output_eval_file, '''w''') as writer:
    logger.info('''***** Train results *****''')
    logger.info(train_results)
    for key, value in train_results.items():
     logger.info(F'  {key} = {value}')
     writer.write(F'{key} = {value}\n')

    # Evaluation
 if args.do_eval and (not SDP_ENABLED or sdp.rank() == 0):
  lowerCAmelCase_		:			Any				   =					model.evaluate(tf_test_dataset, batch_size=args.per_device_eval_batch_size, return_dict=True)
  logger.info('''*** Evaluate ***''')

  lowerCAmelCase_		:			Any				   =					os.path.join(args.output_dir, '''eval_results.txt''')

  with open(output_eval_file, '''w''') as writer:
   logger.info('''***** Eval results *****''')
   logger.info(result)
   for key, value in result.items():
    logger.info(F'  {key} = {value}')
    writer.write(F'{key} = {value}\n')

    # Save result
 if SDP_ENABLED:
  if sdp.rank() == 0:
   model.save_pretrained(args.output_dir)
   tokenizer.save_pretrained(args.output_dir)
 else:
  model.save_pretrained(args.output_dir)
  tokenizer.save_pretrained(args.output_dir)








"""simple docstring"""







from typing import Optional

import torch
from PIL import Image
from tqdm.auto import tqdm
from transformers import CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, DDIMScheduler, DiffusionPipeline, UNetaDConditionModel
from diffusers.image_processor import VaeImageProcessor
from diffusers.utils import (
    deprecate,
)





class      UpperCamelCase_       (				a_     ):



  def __init__(					self       ,						snake_case__       ,						snake_case__       ,						snake_case__       ,						snake_case__       ,						snake_case__       ,						snake_case__ = 0.93       ,						snake_case__ = True       ,						)	->	Optional[Any]:



         """simple docstring"""





         UpperCAmelCase					=	mixing_coeff
         UpperCAmelCase					=	leapfrog_steps

         super().__init__()
         self.register_modules(
             vae=snake_case__       ,						text_encoder=snake_case__       ,						tokenizer=snake_case__       ,						unet=snake_case__       ,						scheduler=snake_case__       ,						)

         UpperCAmelCase					=	2 ** (len(self.vae.config.block_out_channels ) - 1)
         UpperCAmelCase					=	VaeImageProcessor(vae_scale_factor=self.vae_scale_factor )



  def 						UpperCamelCase_ (					self       ,						snake_case__       ,						snake_case__ = None       ,						snake_case__ = False )	->	Dict:



         """simple docstring"""





         UpperCAmelCase					=	self.tokenizer(
             snake_case__       ,						padding="""max_length"""       ,						max_length=self.tokenizer.model_max_length       ,						truncation=snake_case__       ,						return_tensors="""pt"""       ,						)

         UpperCAmelCase					=	self.text_encoder(text_inputs.input_ids.to(self.device ) ).last_hidden_state

         UpperCAmelCase					=	prompt_embeds.to(dtype=self.text_encoder.dtype       ,						device=self.device )

         if do_classifier_free_guidance:
                UpperCAmelCase					=	"""""" if negative_prompt is None else negative_prompt

                UpperCAmelCase					=	self.tokenizer(
                    snake_case__       ,						padding="""max_length"""       ,						max_length=self.tokenizer.model_max_length       ,						truncation=snake_case__       ,						return_tensors="""pt"""       ,						)

                UpperCAmelCase					=	self.text_encoder(uncond_input.input_ids.to(self.device ) ).last_hidden_state

                UpperCAmelCase					=	torch.cat([negative_prompt_embeds, prompt_embeds] )

         return prompt_embeds



  def 						UpperCamelCase_ (					self       ,						snake_case__       ,						snake_case__ )	->	List[Any]:



         """simple docstring"""





         UpperCAmelCase					=	self.mixing_coeff * x + (1 - self.mixing_coeff) * y
         UpperCAmelCase					=	self.mixing_coeff * y + (1 - self.mixing_coeff) * x

         return [x, y]



  def 						UpperCamelCase_ (					self       ,						snake_case__       ,						snake_case__ )	->	Dict:



         """simple docstring"""





         UpperCAmelCase					=	(y - (1 - self.mixing_coeff) * x) / self.mixing_coeff
         UpperCAmelCase					=	(x - (1 - self.mixing_coeff) * y) / self.mixing_coeff

         return [x, y]



  def 						UpperCamelCase_ (					self       ,						snake_case__ )	->	Dict:



         """simple docstring"""





         UpperCAmelCase					=	int(snake_case__ )

         UpperCAmelCase					=	self.scheduler.alphas_cumprod[t] if t >= 0 else self.scheduler.final_alpha_cumprod

         return alpha_prod, 1 - alpha_prod



  def 						UpperCamelCase_ (					self       ,						snake_case__       ,						snake_case__       ,						snake_case__       ,						snake_case__       ,						)	->	int:



         """simple docstring"""





         UpperCAmelCase					=	timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

         UpperCAmelCase			,				UpperCAmelCase					=	self._get_alpha_and_beta(snake_case__ )
         UpperCAmelCase			,				UpperCAmelCase					=	self._get_alpha_and_beta(snake_case__ )

         UpperCAmelCase					=	(alpha_prod_t_prev / alpha_prod_t) ** 0.5
         UpperCAmelCase					=	-a_t * (beta_prod_t**0.5) + beta_prod_t_prev**0.5

         UpperCAmelCase					=	(base - b_t * model_output) / a_t

         return model_input, next_model_input.to(base.dtype )



  def 						UpperCamelCase_ (					self       ,						snake_case__       ,						snake_case__       ,						snake_case__       ,						snake_case__       ,						)	->	str:



         """simple docstring"""





         UpperCAmelCase					=	timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

         UpperCAmelCase			,				UpperCAmelCase					=	self._get_alpha_and_beta(snake_case__ )
         UpperCAmelCase			,				UpperCAmelCase					=	self._get_alpha_and_beta(snake_case__ )

         UpperCAmelCase					=	(alpha_prod_t_prev / alpha_prod_t) ** 0.5
         UpperCAmelCase					=	-a_t * (beta_prod_t**0.5) + beta_prod_t_prev**0.5
         UpperCAmelCase					=	a_t * base + b_t * model_output

         return model_input, next_model_input.to(base.dtype )



  @torch.no_grad()
  def 						UpperCamelCase_ (					self       ,						snake_case__ )	->	str:



         """simple docstring"""





         UpperCAmelCase					=	1 / self.vae.config.scaling_factor * latents
         UpperCAmelCase					=	self.vae.decode(snake_case__ ).sample
         UpperCAmelCase					=	(image / 2 + 0.5).clamp(0       ,						1 )
         return image



  @torch.no_grad()
  def 						UpperCamelCase_ (					self       ,						snake_case__       ,						snake_case__       ,						snake_case__       ,						snake_case__       ,						snake_case__ = None       ,						)	->	List[Any]:



         """simple docstring"""





         UpperCAmelCase					=	guidance_scale > 1.0

         UpperCAmelCase					=	image.to(device=self.device       ,						dtype=text_embeds.dtype )
         UpperCAmelCase					=	self.vae.encode(snake_case__ ).latent_dist.sample(snake_case__ )

         UpperCAmelCase					=	self.vae.config.scaling_factor * latent

         UpperCAmelCase					=	[latent.clone(), latent.clone()]

         for i, t in tqdm(enumerate(snake_case__ )       ,						total=len(snake_case__ ) ):
                UpperCAmelCase					=	self.noise_mixing_layer(x=coupled_latents[0]       ,						y=coupled_latents[1] )

                # j - model_input index, k - base index
                for j in range(2 ):
                       UpperCAmelCase					=	j ^ 1

                       if self.leapfrog_steps:
                              if i % 2 == 0:
                                     UpperCAmelCase			,				UpperCAmelCase					=	j, k

                       UpperCAmelCase					=	coupled_latents[j]
                       UpperCAmelCase					=	coupled_latents[k]

                       UpperCAmelCase					=	torch.cat([model_input] * 2 ) if do_classifier_free_guidance else model_input

                       UpperCAmelCase					=	self.unet(snake_case__       ,						snake_case__       ,						encoder_hidden_states=snake_case__ ).sample

                       if do_classifier_free_guidance:
                              UpperCAmelCase			,				UpperCAmelCase					=	noise_pred.chunk(2 )
                              UpperCAmelCase					=	noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

                       UpperCAmelCase			,				UpperCAmelCase					=	self.noise_step(
                           base=snake_case__       ,						model_input=snake_case__       ,						model_output=snake_case__       ,						timestep=snake_case__       ,						)

                       UpperCAmelCase					=	model_input

         return coupled_latents



  @torch.no_grad()
  def __call__(					self       ,						snake_case__       ,						snake_case__       ,						snake_case__       ,						snake_case__ = 3.0       ,						snake_case__ = 50       ,						snake_case__ = 0.8       ,						snake_case__ = None       ,						snake_case__ = None       ,						snake_case__ = "pil"       ,						)	->	str:



         """simple docstring"""





         UpperCAmelCase					=	guidance_scale > 1.0

         UpperCAmelCase					=	self.image_processor.preprocess(snake_case__ )

         UpperCAmelCase					=	self._encode_prompt(snake_case__       ,						snake_case__       ,						snake_case__ )
         UpperCAmelCase					=	self._encode_prompt(snake_case__       ,						snake_case__       ,						snake_case__ )

         self.scheduler.set_timesteps(snake_case__       ,						self.device )

         UpperCAmelCase					=	num_inference_steps - int(num_inference_steps * strength )
         UpperCAmelCase					=	self.scheduler.timesteps[t_limit:]
         UpperCAmelCase					=	fwd_timesteps.flip(0 )

         UpperCAmelCase					=	self.prepare_latents(snake_case__       ,						snake_case__       ,						snake_case__       ,						snake_case__       ,						snake_case__ )

         for i, t in tqdm(enumerate(snake_case__ )       ,						total=len(snake_case__ ) ):
                # j - model_input index, k - base index
                for k in range(2 ):
                       UpperCAmelCase					=	k ^ 1

                       if self.leapfrog_steps:
                              if i % 2 == 1:
                                     UpperCAmelCase			,				UpperCAmelCase					=	j, k

                       UpperCAmelCase					=	coupled_latents[j]
                       UpperCAmelCase					=	coupled_latents[k]

                       UpperCAmelCase					=	torch.cat([model_input] * 2 ) if do_classifier_free_guidance else model_input

                       UpperCAmelCase					=	self.unet(snake_case__       ,						snake_case__       ,						encoder_hidden_states=snake_case__ ).sample

                       if do_classifier_free_guidance:
                              UpperCAmelCase			,				UpperCAmelCase					=	noise_pred.chunk(2 )
                              UpperCAmelCase					=	noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

                       UpperCAmelCase			,				UpperCAmelCase					=	self.denoise_step(
                           base=snake_case__       ,						model_input=snake_case__       ,						model_output=snake_case__       ,						timestep=snake_case__       ,						)

                       UpperCAmelCase					=	model_input

                UpperCAmelCase					=	self.denoise_mixing_layer(x=coupled_latents[0]       ,						y=coupled_latents[1] )

         # either one is fine
         UpperCAmelCase					=	coupled_latents[0]

         if output_type not in ["latent", "pt", "np", "pil"]:
                UpperCAmelCase					=	(
                    f'''the output_type {output_type} is outdated. Please make sure to set it to one of these instead: '''
                    """`pil`, `np`, `pt`, `latent`"""
                )
                deprecate("""Unsupported output_type"""       ,						"""1.0.0"""       ,						snake_case__       ,						standard_warn=snake_case__ )
                UpperCAmelCase					=	"""np"""

         if output_type == "latent":
                UpperCAmelCase					=	final_latent
         else:
                UpperCAmelCase					=	self.decode_latents(snake_case__ )
                UpperCAmelCase					=	self.image_processor.postprocess(snake_case__       ,						output_type=snake_case__ )

         return image
