






"""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():
  _UpperCamelCase     			=  True
  _UpperCamelCase     			=  '''p3dn.24xlarge'''
  import smdistributed.dataparallel.tensorflow as sdp
else:
  _UpperCamelCase     			=  False

def _a (		_snake_case      ,       _snake_case      ,       _snake_case      ,       _snake_case      ,       _snake_case      ,       _snake_case      ,       _snake_case=None							):







      """simple docstring"""






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

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

            UpperCAmelCase							=       tape.gradient(_snake_case      ,       model.trainable_variables							)
            opt.apply_gradients(zip(_snake_case      ,       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 _a (		_snake_case      ,       _snake_case      ,       _snake_case							):







      """simple docstring"""






      UpperCAmelCase			,					UpperCAmelCase							=       load_dataset("""imdb"""      ,       split=["""train""", """test"""]							)

      # Preprocess train dataset
      UpperCAmelCase							=       train_dataset.map(
          lambda _snake_case							: tokenizer(e["""text"""]      ,       truncation=_snake_case      ,       padding="""max_length"""							)      ,       batched=_snake_case							)
      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 _snake_case							: tokenizer(e["""text"""]      ,       truncation=_snake_case      ,       padding="""max_length"""							)      ,       batched=_snake_case							)
      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(_snake_case      ,       drop_remainder=_snake_case							)
      UpperCAmelCase							=       tf_test_dataset.batch(_snake_case      ,       drop_remainder=_snake_case							)

      return tf_train_dataset, tf_test_dataset


if __name__ == "__main__":
  _UpperCamelCase     			=  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=16)
  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"""])

  _UpperCamelCase     			=  parser.parse_known_args()

  # Set up logging
  _UpperCamelCase     			=  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()

    _UpperCamelCase     			=  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
  _UpperCamelCase     			=  TFAutoModelForSequenceClassification.from_pretrained(args.model_name_or_path)
  _UpperCamelCase     			=  AutoTokenizer.from_pretrained(args.model_name_or_path)

  # get datasets
  _UpperCamelCase     			=  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
  _UpperCamelCase     			=  tf.keras.optimizers.Adam(learning_rate=args.learning_rate)
  _UpperCamelCase     			=  tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
  _UpperCamelCase     			=  [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)
    _UpperCamelCase     			=  time.time()
    _UpperCamelCase     			=  fit(
        model,
        loss,
        optimizer,
        tf_train_dataset,
        args.epochs,
        args.per_device_train_batch_size,
        max_steps=args.max_steps,
    )
    _UpperCamelCase     			=  time.time() - start_train_time
    logger.info("""*** Train ***""")
    logger.info(F"""train_runtime = {end_train_time}""")

    _UpperCamelCase     			=  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):
    _UpperCamelCase     			=  model.evaluate(tf_test_dataset, batch_size=args.per_device_eval_batch_size, return_dict=True)
    logger.info("""*** Evaluate ***""")

    _UpperCamelCase     			=  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
