


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 			snake_case__(							unittest.TestCase							):







						'''simple docstring'''




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




						def   lowercase_	(			self				,  __lowercase=0.99_99							)   ->      Dict:
										lowerCAmelCase_	: Union[str, Any]      			=			UNetaDConditionModel.from_pretrained(self.model_id				,  subfolder='''unet'''							)
										lowerCAmelCase_	: Tuple      			=			unet.to(__lowercase							)
										lowerCAmelCase_	: Optional[int]      			=			EMAModel(unet.parameters()				,  decay=__lowercase				,  model_cls=__lowercase				,  model_config=unet.config							)
										return unet, ema_unet




						def   lowercase_	(			self							)   ->      Tuple:
										lowerCAmelCase_	: Dict      			=			torch.randn(
										    self.batch_size				,  self.num_in_channels				,  self.latent_height				,  self.latent_width				,  generator=self.generator							).to(__lowercase							)
										lowerCAmelCase_	: Any      			=			torch.randint(0				,  1_0_0_0				,  size=(self.batch_size,)				,  generator=self.generator							).to(__lowercase							)
										lowerCAmelCase_	: List[str]      			=			torch.randn(
										    self.batch_size				,  self.prompt_length				,  self.text_encoder_hidden_dim				,  generator=self.generator							).to(__lowercase							)
										return noisy_latents, timesteps, encoder_hidden_states




						def   lowercase_	(			self				,  __lowercase							)   ->      int:
										lowerCAmelCase_	: Tuple      			=			{}
										for k, param in unet.state_dict().items():
														lowerCAmelCase_	: List[str]      			=			torch.randn_like(__lowercase							) + (param * torch.randn_like(__lowercase							))
														updated_state_dict.update({k: updated_param}							)
										unet.load_state_dict(__lowercase							)
										return unet




						def   lowercase_	(			self							)   ->      Union[str, Any]:
										lowerCAmelCase_	: List[Any]      			=			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   lowercase_	(			self							)   ->      Optional[int]:
										lowerCAmelCase_	: int      			=			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()							)
										lowerCAmelCase_	: Optional[Any]      			=			list(unet.parameters()							)
										for s_param, param in zip(ema_unet.shadow_params				,  __lowercase							):
														assert torch.allclose(__lowercase				,  __lowercase							)

										# 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				,  __lowercase							):
														assert torch.allclose(__lowercase				,  __lowercase							)




						def   lowercase_	(			self							)   ->      Any:
										lowerCAmelCase_	: Dict      			=			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.
										lowerCAmelCase_	: Union[str, Any]      			=			self.simulate_backprop(__lowercase							)

										# 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.
										lowerCAmelCase_	: List[Any]      			=			list(unet_pseudo_updated_step_one.parameters()							)
										for s_param, param in zip(ema_unet.shadow_params				,  __lowercase							):
														assert ~torch.allclose(__lowercase				,  __lowercase							)

										# 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				,  __lowercase							):
														assert ~torch.allclose(__lowercase				,  __lowercase							)




						def   lowercase_	(			self							)   ->      Any:
										# If we call EMA step after a backpropagation consecutively for two times,
										# the shadow params from those two steps should be different.
										lowerCAmelCase_	: Optional[int]      			=			self.get_models()

										# First backprop + EMA
										lowerCAmelCase_	: str      			=			self.simulate_backprop(__lowercase							)
										ema_unet.step(unet_step_one.parameters()							)
										lowerCAmelCase_	: Dict      			=			ema_unet.shadow_params

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

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




						def   lowercase_	(			self							)   ->      List[Any]:
										# 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.
										lowerCAmelCase_	: Union[str, Any]      			=			self.get_models(decay=0.0							)
										lowerCAmelCase_	: List[str]      			=			self.simulate_backprop(__lowercase							)
										ema_unet.step(unet_step_one.parameters()							)
										lowerCAmelCase_	: Tuple      			=			ema_unet.shadow_params

										lowerCAmelCase_	: Dict      			=			self.simulate_backprop(__lowercase							)
										ema_unet.step(unet_step_two.parameters()							)
										lowerCAmelCase_	: Dict      			=			ema_unet.shadow_params

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




						@skip_mps
						def   lowercase_	(			self							)   ->      Tuple:
										lowerCAmelCase_	: List[str]      			=			self.get_models()
										lowerCAmelCase_	: Dict      			=			self.get_dummy_inputs()

										with tempfile.TemporaryDirectory() as tmpdir:
														ema_unet.save_pretrained(__lowercase							)
														lowerCAmelCase_	: Any      			=			UNetaDConditionModel.from_pretrained(__lowercase				,  model_cls=__lowercase							)
														lowerCAmelCase_	: List[str]      			=			loaded_unet.to(unet.device							)

										# Since no EMA step has been performed the outputs should match.
										lowerCAmelCase_	: str      			=			unet(__lowercase				,  __lowercase				,  __lowercase							).sample
										lowerCAmelCase_	: List[Any]      			=			loaded_unet(__lowercase				,  __lowercase				,  __lowercase							).sample

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



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__":
						_UpperCAmelCase     :					Optional[int]       =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)

						_UpperCAmelCase       ,   _UpperCAmelCase     :					List[str]       =parser.parse_known_args()

						# overwrite batch size until we have tf_glue.py
						_UpperCAmelCase     :					Optional[Any]       =16
						_UpperCAmelCase     :					List[str]       =16

						# Set up logging
						_UpperCAmelCase     :					Optional[Any]       =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
						_UpperCAmelCase     :					List[str]       =TFAutoModelForSequenceClassification.from_pretrained(args.model_name_or_path)
						_UpperCAmelCase     :					Any       =AutoTokenizer.from_pretrained(args.model_name_or_path)

						# Load dataset
						_UpperCAmelCase       ,   _UpperCAmelCase     :					Union[str, Any]       =load_dataset("""imdb""", split=["""train""", """test"""])
						_UpperCAmelCase     :					int       =train_dataset.shuffle().select(range(5000))  # smaller the size for train dataset to 5k
						_UpperCAmelCase     :					str       =test_dataset.shuffle().select(range(500))  # smaller the size for test dataset to 500

						# Preprocess train dataset
						_UpperCAmelCase     :					str       =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"""])

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

						# Preprocess test dataset
						_UpperCAmelCase     :					List[Any]       =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"""])

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

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

						_UpperCAmelCase     :					Tuple       =time.time()
						_UpperCAmelCase     :					List[str]       =model.fit(tf_train_dataset, epochs=args.epochs, batch_size=args.per_device_train_batch_size)
						_UpperCAmelCase     :					Optional[int]       =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}""")