

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__":
       lowerCamelCase 	=   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)

       lowerCamelCase	,			lowerCamelCase 	=   parser.parse_known_args()

       # overwrite batch size until we have tf_glue.py
       lowerCamelCase 	=   16
       lowerCamelCase 	=   16

       # Set up logging
       lowerCamelCase 	=   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
       lowerCamelCase 	=   TFAutoModelForSequenceClassification.from_pretrained(args.model_name_or_path)
       lowerCamelCase 	=   AutoTokenizer.from_pretrained(args.model_name_or_path)

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

       # Preprocess train dataset
       lowerCamelCase 	=   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'''])

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

       # Preprocess test dataset
       lowerCamelCase 	=   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'''])

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

       # fine optimizer and loss
       lowerCamelCase 	=   tf.keras.optimizers.Adam(learning_rate=args.learning_rate)
       lowerCamelCase 	=   tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
       lowerCamelCase 	=   [tf.keras.metrics.SparseCategoricalAccuracy()]
       model.compile(optimizer=optimizer, loss=loss, metrics=metrics)

       lowerCamelCase 	=   time.time()
       lowerCamelCase 	=   model.fit(tf_train_dataset, epochs=args.epochs, batch_size=args.per_device_train_batch_size)
       lowerCamelCase 	=   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}""")

"""simple docstring"""

import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionDualGuidedPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device


__lowercase    =				False






@nightly
@require_torch_gpu
class 		_lowercase (						unittest.TestCase      ):



						"""simple docstring"""




						def   UpperCAmelCase_ (				self							:    List[str]				)				->				List[Any]:
													'''simple docstring'''



													super().tearDown()
													gc.collect()
													torch.cuda.empty_cache()





						def   UpperCAmelCase_ (				self							:    List[Any]				)				->				List[Any]:
													'''simple docstring'''



													__UpperCamelCase							=VersatileDiffusionDualGuidedPipeline.from_pretrained('''shi-labs/versatile-diffusion'''				)
													# remove text_unet
													pipe.remove_unused_weights()
													pipe.to(UpperCamelCase__				)
													pipe.set_progress_bar_config(disable=UpperCamelCase__				)

													__UpperCamelCase							=load_image(
													    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'''				)

													__UpperCamelCase							=torch.manual_seed(0				)
													__UpperCamelCase							=pipe(
													    prompt='''first prompt'''     , image=UpperCamelCase__     , text_to_image_strength=0.75     , generator=UpperCamelCase__     , guidance_scale=7.5     , num_inference_steps=2     , output_type='''numpy'''     , ).images

													with tempfile.TemporaryDirectory() as tmpdirname:
																				pipe.save_pretrained(UpperCamelCase__				)
																				__UpperCamelCase							=VersatileDiffusionDualGuidedPipeline.from_pretrained(UpperCamelCase__				)

													pipe.to(UpperCamelCase__				)
													pipe.set_progress_bar_config(disable=UpperCamelCase__				)

													__UpperCamelCase							=generator.manual_seed(0				)
													__UpperCamelCase							=pipe(
													    prompt='''first prompt'''     , image=UpperCamelCase__     , text_to_image_strength=0.75     , generator=UpperCamelCase__     , guidance_scale=7.5     , num_inference_steps=2     , output_type='''numpy'''     , ).images

													assert np.abs(image - new_image				).sum() < 1E-5, "Models don't have the same forward pass"





						def   UpperCAmelCase_ (				self							:    Optional[int]				)				->				Optional[int]:
													'''simple docstring'''



													__UpperCamelCase							=VersatileDiffusionDualGuidedPipeline.from_pretrained('''shi-labs/versatile-diffusion'''				)
													pipe.remove_unused_weights()
													pipe.to(UpperCamelCase__				)
													pipe.set_progress_bar_config(disable=UpperCamelCase__				)

													__UpperCamelCase							='''cyberpunk 2077'''
													__UpperCamelCase							=load_image(
													    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'''				)
													__UpperCamelCase							=torch.manual_seed(0				)
													__UpperCamelCase							=pipe(
													    prompt=UpperCamelCase__     , image=UpperCamelCase__     , text_to_image_strength=0.75     , generator=UpperCamelCase__     , guidance_scale=7.5     , num_inference_steps=50     , output_type='''numpy'''     , ).images

													__UpperCamelCase							=image[0, 253:256, 253:256, -1]

													assert image.shape == (1, 512, 512, 3)
													__UpperCamelCase							=np.array([0.07_87, 0.08_49, 0.08_26, 0.08_12, 0.08_07, 0.07_95, 0.08_18, 0.07_98, 0.07_79]				)

													assert np.abs(image_slice.flatten() - expected_slice				).max() < 1E-2



