




'''simple docstring'''




import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPImageProcessor, CLIPVisionConfig

from diffusers import AutoencoderKL, PaintByExamplePipeline, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.paint_by_example import PaintByExampleImageEncoder
from diffusers.utils import floats_tensor, load_image, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import IMAGE_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, IMAGE_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()






class     UpperCamelCase_     (lowercase__,      unittest.TestCase     ):







						"""simple docstring"""


						_lowerCAmelCase    						=					PaintByExamplePipeline
						_lowerCAmelCase    						=					IMAGE_GUIDED_IMAGE_INPAINTING_PARAMS
						_lowerCAmelCase    						=					IMAGE_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
						_lowerCAmelCase    						=					frozenset([]     )  # TO_DO: update the image_prams once refactored VaeImageProcessor.preprocess







						def       _a    (     self      :       List[str]    ):





											"""simple docstring"""







											torch.manual_seed(0    )
											A_ :     Optional[Any] =							UNetaDConditionModel(
											    block_out_channels=(32, 64)							, layers_per_block=2							, sample_size=32							, in_channels=9							, out_channels=4							, down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')							, up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')							, cross_attention_dim=32							, )
											A_ :     Tuple =							PNDMScheduler(skip_prk_steps=_A    )
											torch.manual_seed(0    )
											A_ :     Optional[int] =							AutoencoderKL(
											    block_out_channels=[32, 64]							, in_channels=3							, out_channels=3							, down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']							, up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']							, latent_channels=4							, )
											torch.manual_seed(0    )
											A_ :     Tuple =							CLIPVisionConfig(
											    hidden_size=32							, projection_dim=32							, intermediate_size=37							, layer_norm_eps=1E-05							, num_attention_heads=4							, num_hidden_layers=5							, image_size=32							, patch_size=4							, )
											A_ :     int =							PaintByExampleImageEncoder(_A							, proj_size=32    )
											A_ :     Optional[int] =							CLIPImageProcessor(crop_size=32							, size=32    )

											A_ :     Optional[int] =							{
											    'unet': unet,
											    'scheduler': scheduler,
											    'vae': vae,
											    'image_encoder': image_encoder,
											    'safety_checker': None,
											    'feature_extractor': feature_extractor,
											}
											return components







						def       _a    (     self      :       Any							, _lowerCamelCase      :       Tuple    ):





											"""simple docstring"""







											A_ :     Any =							np.array(image.convert('''RGB'''    )    )
											A_ :     List[Any] =							image[None].transpose(0							, 3							, 1							, 2    )
											A_ :     Any =							torch.from_numpy(_A    ).to(dtype=torch.floataa    ) / 1_27.5 - 1.0
											return image







						def       _a    (     self      :       List[str]							, _lowerCamelCase      :       List[str]="cpu"							, _lowerCamelCase      :       Tuple=0    ):





											"""simple docstring"""







											A_ :     Optional[Any] =							floats_tensor((1, 3, 32, 32)							, rng=random.Random(_A    )    ).to(_A    )
											A_ :     Tuple =							image.cpu().permute(0							, 2							, 3							, 1    )[0]
											A_ :     int =							Image.fromarray(np.uinta(_A    )    ).convert('''RGB'''    ).resize((64, 64)    )
											A_ :     str =							Image.fromarray(np.uinta(image + 4    )    ).convert('''RGB'''    ).resize((64, 64)    )
											A_ :     Dict =							Image.fromarray(np.uinta(_A    )    ).convert('''RGB'''    ).resize((32, 32)    )

											if str(_A    ).startswith('''mps'''    ):
																A_ :     List[str] =							torch.manual_seed(_A    )
											else:
																A_ :     List[Any] =							torch.Generator(device=_A    ).manual_seed(_A    )
											A_ :     Union[str, Any] =							{
											    'example_image': example_image,
											    'image': init_image,
											    'mask_image': mask_image,
											    'generator': generator,
											    'num_inference_steps': 2,
											    'guidance_scale': 6.0,
											    'output_type': 'numpy',
											}
											return inputs







						def       _a    (     self      :       str    ):





											"""simple docstring"""







											A_ :     Optional[int] =							self.get_dummy_components()

											# make sure here that pndm scheduler skips prk
											A_ :     str =							PaintByExamplePipeline(**_A    )
											A_ :     Optional[int] =							pipe.to('''cpu'''    )
											pipe.set_progress_bar_config(disable=_A    )

											A_ :     Optional[int] =							self.get_dummy_inputs()
											A_ :     str =							pipe(**_A    )
											A_ :     List[Any] =							output.images

											A_ :     str =							image[0, -3:, -3:, -1]

											assert image.shape == (1, 64, 64, 3)
											A_ :     Tuple =							np.array([0.46_86, 0.56_87, 0.40_07, 0.52_18, 0.57_41, 0.44_82, 0.49_40, 0.46_29, 0.45_03]    )

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







						def       _a    (     self      :       List[Any]    ):





											"""simple docstring"""







											A_ :     Union[str, Any] =							'cpu'
											A_ :     List[str] =							self.get_dummy_inputs()
											inputs.pop('''mask_image'''    )
											A_ :     int =							self.convert_to_pt(inputs.pop('''image'''    )    )
											A_ :     Optional[Any] =							image.clamp(0							, 1    ) / 2

											# make sure here that pndm scheduler skips prk
											A_ :     Union[str, Any] =							PaintByExamplePipeline(**self.get_dummy_components()    )
											A_ :     List[str] =							pipe.to(_A    )
											pipe.set_progress_bar_config(disable=_A    )

											A_ :     Tuple =							pipe(image=_A							, mask_image=mask_image[:, 0]							, **_A    )
											A_ :     Dict =							output.images

											A_ :     Dict =							image.cpu().permute(0							, 2							, 3							, 1    )[0]
											A_ :     Optional[Any] =							mask_image.cpu().permute(0							, 2							, 3							, 1    )[0]

											A_ :     Tuple =							Image.fromarray(np.uinta(_A    )    ).convert('''RGB'''    )
											A_ :     str =							Image.fromarray(np.uinta(_A    )    ).convert('''RGB'''    )

											A_ :     Union[str, Any] =							pipe(**self.get_dummy_inputs()    )
											A_ :     List[str] =							output.images

											assert out_a.shape == (1, 64, 64, 3)
											assert np.abs(out_a.flatten() - out_a.flatten()    ).max() < 5E-2







						def       _a    (     self      :       Optional[int]    ):





											"""simple docstring"""







											super().test_inference_batch_single_identical(expected_max_diff=3E-3    )






@slow
@require_torch_gpu
class     UpperCamelCase_     (unittest.TestCase     ):







						"""simple docstring"""


						def       _a    (     self      :       int    ):





											"""simple docstring"""







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







						def       _a    (     self      :       Optional[int]    ):





											"""simple docstring"""







											A_ :     Optional[Any] =							load_image(
											    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
											    '''/paint_by_example/dog_in_bucket.png'''    )
											A_ :     List[Any] =							load_image(
											    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
											    '''/paint_by_example/mask.png'''    )
											A_ :     Union[str, Any] =							load_image(
											    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
											    '''/paint_by_example/panda.jpg'''    )

											A_ :     Union[str, Any] =							PaintByExamplePipeline.from_pretrained('''Fantasy-Studio/Paint-by-Example'''    )
											A_ :     Tuple =							pipe.to(_A    )
											pipe.set_progress_bar_config(disable=_A    )

											A_ :     Optional[Any] =							torch.manual_seed(321    )
											A_ :     Dict =							pipe(
											    image=_A							, mask_image=_A							, example_image=_A							, generator=_A							, guidance_scale=5.0							, num_inference_steps=50							, output_type='''np'''							, )

											A_ :     Dict =							output.images

											A_ :     Optional[int] =							image[0, -3:, -3:, -1]

											assert image.shape == (1, 512, 512, 3)
											A_ :     str =							np.array([0.48_34, 0.48_11, 0.48_74, 0.51_22, 0.50_81, 0.51_44, 0.52_91, 0.52_90, 0.53_74]    )

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









'''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__":
							snake_case__          =						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)

							snake_case__       ,						snake_case__          =						parser.parse_known_args()

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

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

							# Load dataset
							snake_case__       ,						snake_case__          =						load_dataset("""imdb""", split=["""train""", """test"""])
							snake_case__          =						train_dataset.shuffle().select(range(50_00))  # smaller the size for train dataset to 5k
							snake_case__          =						test_dataset.shuffle().select(range(5_00))  # smaller the size for test dataset to 500

							# Preprocess train dataset
							snake_case__          =						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"""])

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

							# Preprocess test dataset
							snake_case__          =						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"""])

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

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

							snake_case__          =						time.time()
							snake_case__          =						model.fit(tf_train_dataset, epochs=args.epochs, batch_size=args.per_device_train_batch_size)
							snake_case__          =						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}')



