




import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyInpaintPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()
class 						UpperCAmelCase  (							UpperCAmelCase__			,  unittest.TestCase     ):







						'''simple docstring'''





						lowerCAmelCase_							=  KandinskyInpaintPipeline
						lowerCAmelCase_							=  ['''prompt''', '''image_embeds''', '''negative_image_embeds''', '''image''', '''mask_image''']
						lowerCAmelCase_							=  [
						    '''prompt''',
						    '''negative_prompt''',
						    '''image_embeds''',
						    '''negative_image_embeds''',
						    '''image''',
						    '''mask_image''',
						]
						lowerCAmelCase_							=  [
						    '''generator''',
						    '''height''',
						    '''width''',
						    '''latents''',
						    '''guidance_scale''',
						    '''negative_prompt''',
						    '''num_inference_steps''',
						    '''return_dict''',
						    '''guidance_scale''',
						    '''num_images_per_prompt''',
						    '''output_type''',
						    '''return_dict''',
						]
						lowerCAmelCase_							=  False






						@property
						def 			snake_case__		(		self				:	List[str]		):






							"""simple docstring"""






							return 32






						@property
						def 			snake_case__		(		self				:	Tuple		):






							"""simple docstring"""






							return 32






						@property
						def 			snake_case__		(		self				:	str		):






							"""simple docstring"""






							return self.time_input_dim






						@property
						def 			snake_case__		(		self				:	Tuple		):






							"""simple docstring"""






							return self.time_input_dim * 4






						@property
						def 			snake_case__		(		self				:	int		):






							"""simple docstring"""






							return 1_00






						@property
						def 			snake_case__		(		self				:	Optional[Any]		):






							"""simple docstring"""






							snake_case_						       =	XLMRobertaTokenizerFast.from_pretrained("YiYiXu/tiny-random-mclip-base"		)
							return tokenizer






						@property
						def 			snake_case__		(		self				:	int		):






							"""simple docstring"""






							torch.manual_seed(0		)
							snake_case_						       =	MCLIPConfig(
							    numDims=self.cross_attention_dim			,      transformerDimensions=self.text_embedder_hidden_size			,      hidden_size=self.text_embedder_hidden_size			,      intermediate_size=37			,      num_attention_heads=4			,      num_hidden_layers=5			,      vocab_size=10_05			,      )

							snake_case_						       =	MultilingualCLIP(__lowercase		)
							snake_case_						       =	text_encoder.eval()

							return text_encoder






						@property
						def 			snake_case__		(		self				:	List[Any]		):






							"""simple docstring"""






							torch.manual_seed(0		)

							snake_case_						       =	{
							    "in_channels": 9,
							    # Out channels is double in channels because predicts mean and variance
							    "out_channels": 8,
							    "addition_embed_type": "text_image",
							    "down_block_types": ("ResnetDownsampleBlock2D", "SimpleCrossAttnDownBlock2D"),
							    "up_block_types": ("SimpleCrossAttnUpBlock2D", "ResnetUpsampleBlock2D"),
							    "mid_block_type": "UNetMidBlock2DSimpleCrossAttn",
							    "block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
							    "layers_per_block": 1,
							    "encoder_hid_dim": self.text_embedder_hidden_size,
							    "encoder_hid_dim_type": "text_image_proj",
							    "cross_attention_dim": self.cross_attention_dim,
							    "attention_head_dim": 4,
							    "resnet_time_scale_shift": "scale_shift",
							    "class_embed_type": None,
							}

							snake_case_						       =	UNetaDConditionModel(**__lowercase		)
							return model






						@property
						def 			snake_case__		(		self				:	List[str]		):






							"""simple docstring"""






							return {
							    "block_out_channels": [32, 64],
							    "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
							    "in_channels": 3,
							    "latent_channels": 4,
							    "layers_per_block": 1,
							    "norm_num_groups": 8,
							    "norm_type": "spatial",
							    "num_vq_embeddings": 12,
							    "out_channels": 3,
							    "up_block_types": [
							        "AttnUpDecoderBlock2D",
							        "UpDecoderBlock2D",
							    ],
							    "vq_embed_dim": 4,
							}






						@property
						def 			snake_case__		(		self				:	Dict		):






							"""simple docstring"""






							torch.manual_seed(0		)
							snake_case_						       =	VQModel(**self.dummy_movq_kwargs		)
							return model






						def 			snake_case__		(		self				:	Optional[int]		):






							"""simple docstring"""






							snake_case_						       =	self.dummy_text_encoder
							snake_case_						       =	self.dummy_tokenizer
							snake_case_						       =	self.dummy_unet
							snake_case_						       =	self.dummy_movq

							snake_case_						       =	DDIMScheduler(
							    num_train_timesteps=10_00			,      beta_schedule="linear"			,      beta_start=0.0_0085			,      beta_end=0.012			,      clip_sample=__lowercase			,      set_alpha_to_one=__lowercase			,      steps_offset=1			,      prediction_type="epsilon"			,      thresholding=__lowercase			,      )

							snake_case_						       =	{
							    "text_encoder": text_encoder,
							    "tokenizer": tokenizer,
							    "unet": unet,
							    "scheduler": scheduler,
							    "movq": movq,
							}

							return components






						def 			snake_case__		(		self				:	List[Any]			,      __lowercase				:	int			,      __lowercase				:	Any=0		):






							"""simple docstring"""






							snake_case_						       =	floats_tensor((1, self.cross_attention_dim)			,      rng=random.Random(__lowercase		)		).to(__lowercase		)
							snake_case_						       =	floats_tensor((1, self.cross_attention_dim)			,      rng=random.Random(seed + 1		)		).to(__lowercase		)
							# create init_image
							snake_case_						       =	floats_tensor((1, 3, 64, 64)			,      rng=random.Random(__lowercase		)		).to(__lowercase		)
							snake_case_						       =	image.cpu().permute(0			,      2			,      3			,      1		)[0]
							snake_case_						       =	Image.fromarray(np.uinta(__lowercase		)		).convert("RGB"		).resize((2_56, 2_56)		)
							# create mask
							snake_case_						       =	np.ones((64, 64)			,      dtype=np.floataa		)
							snake_case_						       =	0

							if str(__lowercase		).startswith("mps"		):
								snake_case_						       =	torch.manual_seed(__lowercase		)
							else:
								snake_case_						       =	torch.Generator(device=__lowercase		).manual_seed(__lowercase		)
							snake_case_						       =	{
							    "prompt": "horse",
							    "image": init_image,
							    "mask_image": mask,
							    "image_embeds": image_embeds,
							    "negative_image_embeds": negative_image_embeds,
							    "generator": generator,
							    "height": 64,
							    "width": 64,
							    "num_inference_steps": 2,
							    "guidance_scale": 4.0,
							    "output_type": "np",
							}
							return inputs






						def 			snake_case__		(		self				:	Any		):






							"""simple docstring"""






							snake_case_						       =	"cpu"

							snake_case_						       =	self.get_dummy_components()

							snake_case_						       =	self.pipeline_class(**__lowercase		)
							snake_case_						       =	pipe.to(__lowercase		)

							pipe.set_progress_bar_config(disable=__lowercase		)

							snake_case_						       =	pipe(**self.get_dummy_inputs(__lowercase		)		)
							snake_case_						       =	output.images

							snake_case_						       =	pipe(
							    **self.get_dummy_inputs(__lowercase		)			,      return_dict=__lowercase			,      )[0]

							snake_case_						       =	image[0, -3:, -3:, -1]
							snake_case_						       =	image_from_tuple[0, -3:, -3:, -1]

							print(f"image.shape {image.shape}"		)

							assert image.shape == (1, 64, 64, 3)

							snake_case_						       =	np.array(
							    [0.832_6919, 0.7379_0467, 0.2091_8581, 0.930_9612, 0.551_1791, 0.4371_3328, 0.551_3321, 0.4992_2934, 0.5949_7786]		)

							assert (
							    np.abs(image_slice.flatten() - expected_slice		).max() < 1E-2
							), f" expected_slice {expected_slice}, but got {image_slice.flatten()}"
							assert (
							    np.abs(image_from_tuple_slice.flatten() - expected_slice		).max() < 1E-2
							), f" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"






						def 			snake_case__		(		self				:	Dict		):






							"""simple docstring"""






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







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







						'''simple docstring'''





						def 			snake_case__		(		self				:	Tuple		):






							"""simple docstring"""






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






						def 			snake_case__		(		self				:	int		):






							"""simple docstring"""






							snake_case_						       =	load_numpy(
							    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
							    "/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy"		)

							snake_case_						       =	load_image(
							    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/kandinsky/cat.png"		)
							snake_case_						       =	np.ones((7_68, 7_68)			,      dtype=np.floataa		)
							snake_case_						       =	0

							snake_case_						       =	"a hat"

							snake_case_						       =	KandinskyPriorPipeline.from_pretrained(
							    "kandinsky-community/kandinsky-2-1-prior"			,      torch_dtype=torch.floataa		)
							pipe_prior.to(__lowercase		)

							snake_case_						       =	KandinskyInpaintPipeline.from_pretrained(
							    "kandinsky-community/kandinsky-2-1-inpaint"			,      torch_dtype=torch.floataa		)
							snake_case_						       =	pipeline.to(__lowercase		)
							pipeline.set_progress_bar_config(disable=__lowercase		)

							snake_case_						       =	torch.Generator(device="cpu"		).manual_seed(0		)
							snake_case_       , snake_case_						       =	pipe_prior(
							    __lowercase			,      generator=__lowercase			,      num_inference_steps=5			,      negative_prompt=""			,      ).to_tuple()

							snake_case_						       =	pipeline(
							    __lowercase			,      image=__lowercase			,      mask_image=__lowercase			,      image_embeds=__lowercase			,      negative_image_embeds=__lowercase			,      generator=__lowercase			,      num_inference_steps=1_00			,      height=7_68			,      width=7_68			,      output_type="np"			,      )

							snake_case_						       =	output.images[0]

							assert image.shape == (7_68, 7_68, 3)

							assert_mean_pixel_difference(__lowercase			,      __lowercase		)






import argparse
import json
import os

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler


lowercase__							:	Dict											=		16
lowercase__							:	str											=		32


def 		lowerCamelCase__       (						_A	,      _A = 16	,      _A = "bert-base-cased"				):







	'''simple docstring'''





	snake_case_						       =	AutoTokenizer.from_pretrained(_A				)
	snake_case_						       =	load_dataset("glue"	,      "mrpc"				)

	def tokenize_function(_A				):
		# max_length=None => use the model max length (it's actually the default)
		snake_case_						       =	tokenizer(examples["sentence1"]	,      examples["sentence2"]	,      truncation=_A	,      max_length=_A				)
		return outputs

	# Apply the method we just defined to all the examples in all the splits of the dataset
	snake_case_						       =	datasets.map(
	    _A	,      batched=_A	,      remove_columns=["idx", "sentence1", "sentence2"]	,      load_from_cache_file=_A				)

	# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
	# transformers library
	snake_case_						       =	tokenized_datasets.rename_column("label"	,      "labels"				)

	def collate_fn(_A				):
		# On TPU it's best to pad everything to the same length or training will be very slow.
		if accelerator.distributed_type == DistributedType.TPU:
			return tokenizer.pad(_A	,      padding="max_length"	,      max_length=128	,      return_tensors="pt"				)
		return tokenizer.pad(_A	,      padding="longest"	,      return_tensors="pt"				)

	# Instantiate dataloaders.
	snake_case_						       =	DataLoader(
	    tokenized_datasets["train"]	,      shuffle=_A	,      collate_fn=_A	,      batch_size=_A				)
	snake_case_						       =	DataLoader(
	    tokenized_datasets["validation"]	,      shuffle=_A	,      collate_fn=_A	,      batch_size=_A				)

	return train_dataloader, eval_dataloader


def 		lowerCamelCase__       (						_A	,      _A				):







	'''simple docstring'''





	snake_case_						       =	Accelerator()

	# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
	snake_case_						       =	config["lr"]
	snake_case_						       =	int(config["num_epochs"]				)
	snake_case_						       =	int(config["seed"]				)
	snake_case_						       =	int(config["batch_size"]				)
	snake_case_						       =	args.model_name_or_path

	set_seed(_A				)
	snake_case_       , snake_case_						       =	get_dataloaders(_A	,      _A	,      _A				)

	# Instantiate the model (we build the model here so that the seed also control new weights initialization)
	snake_case_						       =	AutoModelForSequenceClassification.from_pretrained(_A	,      return_dict=_A				)

	# Instantiate optimizer
	snake_case_						       =	(
	    AdamW
	    if accelerator.state.deepspeed_plugin is None
	    or "optimizer" not in accelerator.state.deepspeed_plugin.deepspeed_config
	    else DummyOptim
	)
	snake_case_						       =	optimizer_cls(params=model.parameters()	,      lr=_A				)

	if accelerator.state.deepspeed_plugin is not None:
		snake_case_						       =	accelerator.state.deepspeed_plugin.deepspeed_config[
		    "gradient_accumulation_steps"
		]
	else:
		snake_case_						       =	1
	snake_case_						       =	(len(_A				) * num_epochs) // gradient_accumulation_steps

	# Instantiate scheduler
	if (
	    accelerator.state.deepspeed_plugin is None
	    or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
	):
		snake_case_						       =	get_linear_schedule_with_warmup(
		    optimizer=_A	,      num_warmup_steps=0	,      num_training_steps=_A	,      )
	else:
		snake_case_						       =	DummyScheduler(_A	,      total_num_steps=_A	,      warmup_num_steps=0				)

	# Prepare everything
	# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
	# prepare method.
	snake_case_       , snake_case_       , snake_case_       , snake_case_       , snake_case_						       =	accelerator.prepare(
	    _A	,      _A	,      _A	,      _A	,      _A				)

	# We need to keep track of how many total steps we have iterated over
	snake_case_						       =	0
	# We also need to keep track of the stating epoch so files are named properly
	snake_case_						       =	0

	# Now we train the model
	snake_case_						       =	evaluate.load("glue"	,      "mrpc"				)
	snake_case_						       =	0
	snake_case_						       =	{}
	for epoch in range(_A	,      _A				):
		model.train()
		for step, batch in enumerate(_A				):
			snake_case_						       =	model(**_A				)
			snake_case_						       =	outputs.loss
			snake_case_						       =	loss / gradient_accumulation_steps
			accelerator.backward(_A				)
			if step % gradient_accumulation_steps == 0:
				optimizer.step()
				lr_scheduler.step()
				optimizer.zero_grad()

			overall_step += 1

		model.eval()
		snake_case_						       =	0
		for step, batch in enumerate(_A				):
			# We could avoid this line since we set the accelerator with `device_placement=True`.
			batch.to(accelerator.device				)
			with torch.no_grad():
				snake_case_						       =	model(**_A				)
			snake_case_						       =	outputs.logits.argmax(dim=-1				)
			# It is slightly faster to call this once, than multiple times
			snake_case_       , snake_case_						       =	accelerator.gather(
			    (predictions, batch["labels"])				)  # If we are in a multiprocess environment, the last batch has duplicates
			if accelerator.use_distributed:
				if step == len(_A				) - 1:
					snake_case_						       =	predictions[: len(eval_dataloader.dataset				) - samples_seen]
					snake_case_						       =	references[: len(eval_dataloader.dataset				) - samples_seen]
				else:
					samples_seen += references.shape[0]
			metric.add_batch(
			    predictions=_A	,      references=_A	,      )

		snake_case_						       =	metric.compute()
		# Use accelerator.print to print only on the main process.
		accelerator.print(f"epoch {epoch}:"	,      _A				)
		snake_case_						       =	eval_metric["accuracy"]

		if best_performance < eval_metric["accuracy"]:
			snake_case_						       =	eval_metric["accuracy"]

	if args.performance_lower_bound is not None:
		assert (
		    args.performance_lower_bound <= best_performance
		), f"Best performance metric {best_performance} is lower than the lower bound {args.performance_lower_bound}"

	accelerator.wait_for_everyone()
	if accelerator.is_main_process:
		with open(os.path.join(args.output_dir	,      "all_results.json"				)	,      "w"				) as f:
			json.dump(_A	,      _A				)


def 		lowerCamelCase__       (						):







	'''simple docstring'''





	snake_case_						       =	argparse.ArgumentParser(description="Simple example of training script tracking peak GPU memory usage."				)
	parser.add_argument(
	    "--model_name_or_path"	,      type=_A	,      default="bert-base-cased"	,      help="Path to pretrained model or model identifier from huggingface.co/models."	,      required=_A	,      )
	parser.add_argument(
	    "--output_dir"	,      type=_A	,      default="."	,      help="Optional save directory where all checkpoint folders will be stored. Default is the current working directory."	,      )
	parser.add_argument(
	    "--performance_lower_bound"	,      type=_A	,      default=_A	,      help="Optional lower bound for the performance metric. If set, the training will throw error when the performance metric drops below this value."	,      )
	parser.add_argument(
	    "--num_epochs"	,      type=_A	,      default=3	,      help="Number of train epochs."	,      )
	snake_case_						       =	parser.parse_args()
	snake_case_						       =	{"lr": 2E-5, "num_epochs": args.num_epochs, "seed": 42, "batch_size": 16}
	training_function(_A	,      _A				)


if __name__ == "__main__":
		main()
