


'''simple docstring'''







import tempfile
import unittest

import torch

from diffusers import UNetaDConditionModel
from diffusers.training_utils import EMAModel
from diffusers.utils.testing_utils import enable_full_determinism, skip_mps, torch_device


enable_full_determinism()







class A__	(						unittest.TestCase				):
						__UpperCamelCase	:  Optional[Any]     	=							"hf-internal-testing/tiny-stable-diffusion-pipe"
						__UpperCamelCase	:  Dict     	=							1
						__UpperCamelCase	:  Optional[Any]     	=							77
						__UpperCamelCase	:  int     	=							32
						__UpperCamelCase	:  List[str]     	=							4
						__UpperCamelCase	:  Any     	=							64
						__UpperCamelCase	:  List[str]     	=							torch.manual_seed(0				)






						def __UpperCAmelCase	(			self      :str					,						SCREAMING_SNAKE_CASE      :Tuple=0.9_999     )	-> Optional[Any]:




													'''simple docstring'''
													_a   :			List[Any]					 =UNetaDConditionModel.from_pretrained(self.model_id					,						subfolder="""unet"""     )
													_a   :			Optional[Any]					 =unet.to(SCREAMING_SNAKE_CASE     )
													_a   :			Tuple					 =EMAModel(unet.parameters()					,						decay=SCREAMING_SNAKE_CASE					,						model_cls=SCREAMING_SNAKE_CASE					,						model_config=unet.config     )
													return unet, ema_unet






						def __UpperCAmelCase	(			self      :str     )	-> Any:




													'''simple docstring'''
													_a   :			int					 =torch.randn(
													    self.batch_size					,						self.num_in_channels					,						self.latent_height					,						self.latent_width					,						generator=self.generator     ).to(SCREAMING_SNAKE_CASE     )
													_a   :			Optional[int]					 =torch.randint(0					,						1_0_0_0					,						size=(self.batch_size,)					,						generator=self.generator     ).to(SCREAMING_SNAKE_CASE     )
													_a   :			List[str]					 =torch.randn(
													    self.batch_size					,						self.prompt_length					,						self.text_encoder_hidden_dim					,						generator=self.generator     ).to(SCREAMING_SNAKE_CASE     )
													return noisy_latents, timesteps, encoder_hidden_states






						def __UpperCAmelCase	(			self      :List[Any]					,						SCREAMING_SNAKE_CASE      :Any     )	-> Optional[int]:




													'''simple docstring'''
													_a   :			str					 ={}
													for k, param in unet.state_dict().items():
																				_a   :			List[str]					 =torch.randn_like(SCREAMING_SNAKE_CASE     ) + (param * torch.randn_like(SCREAMING_SNAKE_CASE     ))
																				updated_state_dict.update({k: updated_param}     )
													unet.load_state_dict(SCREAMING_SNAKE_CASE     )
													return unet






						def __UpperCAmelCase	(			self      :Any     )	-> str:




													'''simple docstring'''
													_a   :			str					 =self.get_models()
													# Take the first (hypothetical) EMA step.
													ema_unet.step(unet.parameters()     )
													assert ema_unet.optimization_step == 1

													# Take two more.
													for _ in range(2     ):
																				ema_unet.step(unet.parameters()     )
													assert ema_unet.optimization_step == 3






						def __UpperCAmelCase	(			self      :Union[str, Any]     )	-> List[Any]:




													'''simple docstring'''
													_a   :			Union[str, Any]					 =self.get_models()
													# Since the `unet` is not being updated (i.e., backprop'd)
													# there won't be any difference between the `params` of `unet`
													# and `ema_unet` even if we call `ema_unet.step(unet.parameters())`.
													ema_unet.step(unet.parameters()     )
													_a   :			List[str]					 =list(unet.parameters()     )
													for s_param, param in zip(ema_unet.shadow_params					,						SCREAMING_SNAKE_CASE     ):
																				assert torch.allclose(SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE     )

													# The above holds true even if we call `ema.step()` multiple times since
													# `unet` params are still not being updated.
													for _ in range(4     ):
																				ema_unet.step(unet.parameters()     )
													for s_param, param in zip(ema_unet.shadow_params					,						SCREAMING_SNAKE_CASE     ):
																				assert torch.allclose(SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE     )






						def __UpperCAmelCase	(			self      :List[Any]     )	-> Any:




													'''simple docstring'''
													_a   :			str					 =self.get_models()
													# Here we simulate the parameter updates for `unet`. Since there might
													# be some parameters which are initialized to zero we take extra care to
													# initialize their values to something non-zero before the multiplication.
													_a   :			Tuple					 =self.simulate_backprop(SCREAMING_SNAKE_CASE     )

													# Take the EMA step.
													ema_unet.step(unet_pseudo_updated_step_one.parameters()     )

													# Now the EMA'd parameters won't be equal to the original model parameters.
													_a   :			Dict					 =list(unet_pseudo_updated_step_one.parameters()     )
													for s_param, param in zip(ema_unet.shadow_params					,						SCREAMING_SNAKE_CASE     ):
																				assert ~torch.allclose(SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE     )

													# Ensure this is the case when we take multiple EMA steps.
													for _ in range(4     ):
																				ema_unet.step(unet.parameters()     )
													for s_param, param in zip(ema_unet.shadow_params					,						SCREAMING_SNAKE_CASE     ):
																				assert ~torch.allclose(SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE     )






						def __UpperCAmelCase	(			self      :List[Any]     )	-> List[Any]:




													'''simple docstring'''
													# If we call EMA step after a backpropagation consecutively for two times,
													# the shadow params from those two steps should be different.
													_a   :			Dict					 =self.get_models()

													# First backprop + EMA
													_a   :			List[Any]					 =self.simulate_backprop(SCREAMING_SNAKE_CASE     )
													ema_unet.step(unet_step_one.parameters()     )
													_a   :			List[str]					 =ema_unet.shadow_params

													# Second backprop + EMA
													_a   :			Union[str, Any]					 =self.simulate_backprop(SCREAMING_SNAKE_CASE     )
													ema_unet.step(unet_step_two.parameters()     )
													_a   :			Tuple					 =ema_unet.shadow_params

													for step_one, step_two in zip(SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE     ):
																				assert ~torch.allclose(SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE     )






						def __UpperCAmelCase	(			self      :Optional[Any]     )	-> Union[str, Any]:




													'''simple docstring'''
													# If there's no decay even if there are backprops, EMA steps
													# won't take any effect i.e., the shadow params would remain the
													# same.
													_a   :			Union[str, Any]					 =self.get_models(decay=0.0     )
													_a   :			str					 =self.simulate_backprop(SCREAMING_SNAKE_CASE     )
													ema_unet.step(unet_step_one.parameters()     )
													_a   :			str					 =ema_unet.shadow_params

													_a   :			Any					 =self.simulate_backprop(SCREAMING_SNAKE_CASE     )
													ema_unet.step(unet_step_two.parameters()     )
													_a   :			Optional[int]					 =ema_unet.shadow_params

													for step_one, step_two in zip(SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE     ):
																				assert torch.allclose(SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE     )






						@skip_mps
						def __UpperCAmelCase	(			self      :Optional[Any]     )	-> Any:




													'''simple docstring'''
													_a   :			Tuple					 =self.get_models()
													_a   :			Optional[Any]					 =self.get_dummy_inputs()

													with tempfile.TemporaryDirectory() as tmpdir:
																				ema_unet.save_pretrained(SCREAMING_SNAKE_CASE     )
																				_a   :			Optional[int]					 =UNetaDConditionModel.from_pretrained(SCREAMING_SNAKE_CASE					,						model_cls=SCREAMING_SNAKE_CASE     )
																				_a   :			Any					 =loaded_unet.to(unet.device     )

													# Since no EMA step has been performed the outputs should match.
													_a   :			Any					 =unet(SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE     ).sample
													_a   :			Any					 =loaded_unet(SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE     ).sample

													assert torch.allclose(SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE					,						atol=1e-4     )






'''simple docstring'''







import argparse
import logging
import sys
import time

import tensorflow as tf
from datasets import load_dataset

from transformers import AutoTokenizer, TFAutoModelForSequenceClassification


if __name__ == "__main__":
   A__:   Optional[Any]    =	argparse.ArgumentParser()

   # Hyperparameters sent by the client are passed as command-line arguments to the script.
   parser.add_argument('''--epochs''', type=int, default=1)
   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)

   A__   ,	A__:   List[Any]    =	parser.parse_known_args()

   # overwrite batch size until we have tf_glue.py
   A__:   Tuple    =	16
   A__:   List[str]    =	16

   # Set up logging
   A__:   str    =	logging.getLogger(__name__)

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

   # Load model and tokenizer
   A__:   int    =	TFAutoModelForSequenceClassification.from_pretrained(args.model_name_or_path)
   A__:   Any    =	AutoTokenizer.from_pretrained(args.model_name_or_path)

   # Load dataset
   A__   ,	A__:   Optional[int]    =	load_dataset('''imdb''', split=['''train''', '''test'''])
   A__:   int    =	train_dataset.shuffle().select(range(5000))  # smaller the size for train dataset to 5k
   A__:   int    =	test_dataset.shuffle().select(range(500))  # smaller the size for test dataset to 500

   # Preprocess train dataset
   A__:   Optional[int]    =	train_dataset.map(
       lambda e: tokenizer(e['''text'''], truncation=True, padding='''max_length'''), batched=True
   )
   train_dataset.set_format(type='''tensorflow''', columns=['''input_ids''', '''attention_mask''', '''label'''])

   A__:   List[Any]    =	{
       x: train_dataset[x].to_tensor(default_value=0, shape=[None, tokenizer.model_max_length])
       for x in ['''input_ids''', '''attention_mask''']
   }
   A__:   str    =	tf.data.Dataset.from_tensor_slices((train_features, train_dataset['''label'''])).batch(
       args.per_device_train_batch_size
   )

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

   A__:   Tuple    =	{
       x: test_dataset[x].to_tensor(default_value=0, shape=[None, tokenizer.model_max_length])
       for x in ['''input_ids''', '''attention_mask''']
   }
   A__:   Any    =	tf.data.Dataset.from_tensor_slices((test_features, test_dataset['''label'''])).batch(
       args.per_device_eval_batch_size
   )

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

   A__:   str    =	time.time()
   A__:   Dict    =	model.fit(tf_train_dataset, epochs=args.epochs, batch_size=args.per_device_train_batch_size)
   A__:   Tuple    =	time.time() - start_train_time

   logger.info('''*** Train ***''')
   logger.info(F"train_runtime = {end_train_time}")
   for key, value in train_results.history.items():
      logger.info(F"  {key} = {value}")


