

from math import acos, sin
from typing import List, Tuple, Union

import numpy as np
import torch
from PIL import Image

from ...models import AutoencoderKL, UNetaDConditionModel
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import AudioPipelineOutput, BaseOutput, DiffusionPipeline, ImagePipelineOutput
from .mel import Mel


class 	A_		(	SCREAMING_SNAKE_CASE    ):
							_UpperCAmelCase   :  Optional[Any]										=				['''vqvae''']
							def __init__(		self		:      Tuple       ,SCREAMING_SNAKE_CASE__		:      AutoencoderKL       ,SCREAMING_SNAKE_CASE__		:      UNetaDConditionModel       ,SCREAMING_SNAKE_CASE__		:      Mel       ,SCREAMING_SNAKE_CASE__		:      Union[DDIMScheduler, DDPMScheduler]       ,):
								super().__init__()
								self.register_modules(unet=SCREAMING_SNAKE_CASE__       ,scheduler=SCREAMING_SNAKE_CASE__       ,mel=SCREAMING_SNAKE_CASE__       ,vqvae=SCREAMING_SNAKE_CASE__)
							def    lowerCAmelCase					(		self		:      Tuple):
								return 5_0 if isinstance(self.scheduler       ,SCREAMING_SNAKE_CASE__) else 1_0_0_0
							@torch.no_grad()
							def __call__(		self		:      Optional[int]       ,SCREAMING_SNAKE_CASE__		:      int = 1       ,SCREAMING_SNAKE_CASE__		:      str = None       ,SCREAMING_SNAKE_CASE__		:      np.ndarray = None       ,SCREAMING_SNAKE_CASE__		:      int = 0       ,SCREAMING_SNAKE_CASE__		:      int = 0       ,SCREAMING_SNAKE_CASE__		:      int = None       ,SCREAMING_SNAKE_CASE__		:      torch.Generator = None       ,SCREAMING_SNAKE_CASE__		:      float = 0       ,SCREAMING_SNAKE_CASE__		:      float = 0       ,SCREAMING_SNAKE_CASE__		:      torch.Generator = None       ,SCREAMING_SNAKE_CASE__		:      float = 0       ,SCREAMING_SNAKE_CASE__		:      torch.Tensor = None       ,SCREAMING_SNAKE_CASE__		:      torch.Tensor = None       ,SCREAMING_SNAKE_CASE__		:      List[Any]=True       ,):
								__lowerCamelCase    :    Union[str, Any] 	=			steps or self.get_default_steps()
								self.scheduler.set_timesteps(SCREAMING_SNAKE_CASE__)
								__lowerCamelCase    :    List[str] 	=			step_generator or generator
								# For backwards compatibility
								if type(self.unet.config.sample_size) == int:
									__lowerCamelCase    :    int 	=			(self.unet.config.sample_size, self.unet.config.sample_size)
								if noise is None:
									__lowerCamelCase    :    Tuple 	=			randn_tensor(
									    (
									        batch_size,
									        self.unet.config.in_channels,
									        self.unet.config.sample_size[0],
									        self.unet.config.sample_size[1],
									    )       ,generator=SCREAMING_SNAKE_CASE__       ,device=self.device       ,)
								__lowerCamelCase    :    Any 	=			noise
								__lowerCamelCase    :    Dict 	=			None

								if audio_file is not None or raw_audio is not None:
									self.mel.load_audio(SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__)
									__lowerCamelCase    :    int 	=			self.mel.audio_slice_to_image(SCREAMING_SNAKE_CASE__)
									__lowerCamelCase    :    Optional[Any] 	=			np.frombuffer(input_image.tobytes()       ,dtype='uint8').reshape(
									    (input_image.height, input_image.width))
									__lowerCamelCase    :    List[str] 	=			(input_image / 2_5_5) * 2 - 1
									__lowerCamelCase    :    Any 	=			torch.tensor(input_image[np.newaxis, :, :]       ,dtype=torch.float).to(self.device)

									if self.vqvae is not None:
										__lowerCamelCase    :    Optional[int] 	=			self.vqvae.encode(torch.unsqueeze(SCREAMING_SNAKE_CASE__       ,0)).latent_dist.sample(
										    generator=SCREAMING_SNAKE_CASE__)[0]
										__lowerCamelCase    :    Union[str, Any] 	=			self.vqvae.config.scaling_factor * input_images

									if start_step > 0:
										__lowerCamelCase    :    str 	=			self.scheduler.add_noise(SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__       ,self.scheduler.timesteps[start_step - 1])

									__lowerCamelCase    :    Union[str, Any] 	=			(
									    self.unet.config.sample_size[1] * self.mel.get_sample_rate() / self.mel.x_res / self.mel.hop_length
									)
									__lowerCamelCase    :    List[str] 	=			int(mask_start_secs * pixels_per_second)
									__lowerCamelCase    :    List[str] 	=			int(mask_end_secs * pixels_per_second)
									__lowerCamelCase    :    Tuple 	=			self.scheduler.add_noise(SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__       ,torch.tensor(self.scheduler.timesteps[start_step:]))

								for step, t in enumerate(self.progress_bar(self.scheduler.timesteps[start_step:])):
									if isinstance(self.unet       ,SCREAMING_SNAKE_CASE__):
										__lowerCamelCase    :    Any 	=			self.unet(SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__)['sample']
									else:
										__lowerCamelCase    :    Dict 	=			self.unet(SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__)['sample']

									if isinstance(self.scheduler       ,SCREAMING_SNAKE_CASE__):
										__lowerCamelCase    :    Dict 	=			self.scheduler.step(
										    model_output=SCREAMING_SNAKE_CASE__       ,timestep=SCREAMING_SNAKE_CASE__       ,sample=SCREAMING_SNAKE_CASE__       ,eta=SCREAMING_SNAKE_CASE__       ,generator=SCREAMING_SNAKE_CASE__       ,)['prev_sample']
									else:
										__lowerCamelCase    :    Any 	=			self.scheduler.step(
										    model_output=SCREAMING_SNAKE_CASE__       ,timestep=SCREAMING_SNAKE_CASE__       ,sample=SCREAMING_SNAKE_CASE__       ,generator=SCREAMING_SNAKE_CASE__       ,)['prev_sample']

									if mask is not None:
										if mask_start > 0:
											__lowerCamelCase    :    Dict 	=			mask[:, step, :, :mask_start]
										if mask_end > 0:
											__lowerCamelCase    :    Optional[int] 	=			mask[:, step, :, -mask_end:]

								if self.vqvae is not None:
									# 0.18215 was scaling factor used in training to ensure unit variance
									__lowerCamelCase    :    Dict 	=			1 / self.vqvae.config.scaling_factor * images
									__lowerCamelCase    :    Dict 	=			self.vqvae.decode(SCREAMING_SNAKE_CASE__)['sample']

								__lowerCamelCase    :    Union[str, Any] 	=			(images / 2 + 0.5).clamp(0       ,1)
								__lowerCamelCase    :    str 	=			images.cpu().permute(0       ,2       ,3       ,1).numpy()
								__lowerCamelCase    :    List[Any] 	=			(images * 2_5_5).round().astype('uint8')
								__lowerCamelCase    :    Union[str, Any] 	=			list(
								    (Image.fromarray(_[:, :, 0]) for _ in images)
								    if images.shape[3] == 1
								    else (Image.fromarray(SCREAMING_SNAKE_CASE__       ,mode='RGB').convert('L') for _ in images))

								__lowerCamelCase    :    str 	=			[self.mel.image_to_audio(SCREAMING_SNAKE_CASE__) for _ in images]
								if not return_dict:
									return images, (self.mel.get_sample_rate(), audios)

								return BaseOutput(**AudioPipelineOutput(np.array(SCREAMING_SNAKE_CASE__)[:, np.newaxis, :])       ,**ImagePipelineOutput(SCREAMING_SNAKE_CASE__))
							@torch.no_grad()
							def    lowerCAmelCase					(		self		:      int       ,SCREAMING_SNAKE_CASE__		:      List[Image.Image]       ,SCREAMING_SNAKE_CASE__		:      int = 5_0):
								assert isinstance(self.scheduler       ,SCREAMING_SNAKE_CASE__)
								self.scheduler.set_timesteps(SCREAMING_SNAKE_CASE__)
								__lowerCamelCase    :    Union[str, Any] 	=			np.array(
								    [np.frombuffer(image.tobytes()       ,dtype='uint8').reshape((1, image.height, image.width)) for image in images])
								__lowerCamelCase    :    List[Any] 	=			(sample / 2_5_5) * 2 - 1
								__lowerCamelCase    :    Any 	=			torch.Tensor(SCREAMING_SNAKE_CASE__).to(self.device)

								for t in self.progress_bar(torch.flip(self.scheduler.timesteps       ,(0,))):
									__lowerCamelCase    :    Any 	=			t - self.scheduler.config.num_train_timesteps // self.scheduler.num_inference_steps
									__lowerCamelCase    :    List[str] 	=			self.scheduler.alphas_cumprod[t]
									__lowerCamelCase    :    Union[str, Any] 	=			(
									    self.scheduler.alphas_cumprod[prev_timestep]
									    if prev_timestep >= 0
									    else self.scheduler.final_alpha_cumprod
									)
									__lowerCamelCase    :    int 	=			1 - alpha_prod_t
									__lowerCamelCase    :    Tuple 	=			self.unet(SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__)['sample']
									__lowerCamelCase    :    Union[str, Any] 	=			(1 - alpha_prod_t_prev) ** 0.5 * model_output
									__lowerCamelCase    :    Any 	=			(sample - pred_sample_direction) * alpha_prod_t_prev ** (-0.5)
									__lowerCamelCase    :    List[Any] 	=			sample * alpha_prod_t ** 0.5 + beta_prod_t ** 0.5 * model_output

								return sample


							@staticmethod
							def    lowerCAmelCase					(		SCREAMING_SNAKE_CASE__		:      torch.Tensor       ,SCREAMING_SNAKE_CASE__		:      torch.Tensor       ,SCREAMING_SNAKE_CASE__		:      float):
								__lowerCamelCase    :    Any 	=			acos(torch.dot(torch.flatten(SCREAMING_SNAKE_CASE__)       ,torch.flatten(SCREAMING_SNAKE_CASE__)) / torch.norm(SCREAMING_SNAKE_CASE__) / torch.norm(SCREAMING_SNAKE_CASE__))
								return sin((1 - alpha) * theta) * xa / sin(SCREAMING_SNAKE_CASE__) + sin(alpha * theta) * xa / sin(SCREAMING_SNAKE_CASE__)







import math
import os
from copy import deepcopy

import datasets
import evaluate
import torch
import transformers
from datasets import load_dataset
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer

from accelerate import Accelerator
from accelerate.test_utils import RegressionDataset, RegressionModel
from accelerate.utils import is_tpu_available, set_seed


a		="""true"""






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__     ,			lowerCamelCase__=8_2     ,			lowerCamelCase__=1_6      )  ->					List[Any]:
	set_seed(4_2      )
	__lowerCamelCase    :    Tuple 	=			RegressionModel()
	__lowerCamelCase    :    str 	=			deepcopy(lowerCamelCase__      )
	__lowerCamelCase    :    Optional[int] 	=			RegressionDataset(length=lowerCamelCase__      )
	__lowerCamelCase    :    Union[str, Any] 	=			DataLoader(lowerCamelCase__     ,			batch_size=lowerCamelCase__      )
	model.to(accelerator.device      )
	__lowerCamelCase	,			__lowerCamelCase    :    Tuple 	=			accelerator.prepare(lowerCamelCase__     ,			lowerCamelCase__      )
	return model, ddp_model, dataloader






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__     ,			lowerCamelCase__=False      )  ->					List[Any]:
	__lowerCamelCase    :    Tuple 	=			AutoTokenizer.from_pretrained('hf-internal-testing/mrpc-bert-base-cased'      )
	__lowerCamelCase    :    Any 	=			load_dataset('glue'     ,			'mrpc'     ,			split='validation'      )

	def tokenize_function(lowerCamelCase__      ):
		__lowerCamelCase    :    Union[str, Any] 	=			tokenizer(examples['sentence1']     ,			examples['sentence2']     ,			truncation=lowerCamelCase__     ,			max_length=lowerCamelCase__      )
		return outputs

	with accelerator.main_process_first():
		__lowerCamelCase    :    Union[str, Any] 	=			dataset.map(
		    lowerCamelCase__     ,			batched=lowerCamelCase__     ,			remove_columns=['idx', 'sentence1', 'sentence2']     ,			)

	__lowerCamelCase    :    Tuple 	=			tokenized_datasets.rename_column('label'     ,			'labels'      )

	def collate_fn(lowerCamelCase__      ):
		if use_longest:
			return tokenizer.pad(lowerCamelCase__     ,			padding='longest'     ,			return_tensors='pt'      )
		return tokenizer.pad(lowerCamelCase__     ,			padding='max_length'     ,			max_length=1_2_8     ,			return_tensors='pt'      )

	return DataLoader(lowerCamelCase__     ,			shuffle=lowerCamelCase__     ,			collate_fn=lowerCamelCase__     ,			batch_size=1_6      )






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__     ,			lowerCamelCase__      )  ->					Optional[int]:
	__lowerCamelCase    :    Optional[int] 	=			Accelerator(dispatch_batches=lowerCamelCase__     ,			split_batches=lowerCamelCase__      )
	__lowerCamelCase    :    Union[str, Any] 	=			get_dataloader(lowerCamelCase__     ,			not dispatch_batches      )
	__lowerCamelCase    :    Union[str, Any] 	=			AutoModelForSequenceClassification.from_pretrained(
	    'hf-internal-testing/mrpc-bert-base-cased'     ,			return_dict=lowerCamelCase__      )
	__lowerCamelCase	,			__lowerCamelCase    :    Tuple 	=			accelerator.prepare(lowerCamelCase__     ,			lowerCamelCase__      )
	return {"ddp": [ddp_model, ddp_dataloader, "cuda:0"], "no": [model, dataloader, accelerator.device]}, accelerator






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__     ,			lowerCamelCase__     ,			lowerCamelCase__      )  ->					List[Any]:
	__lowerCamelCase    :    str 	=			[]
	for batch in dataloader:
		__lowerCamelCase	,			__lowerCamelCase    :    Union[str, Any] 	=			batch.values()
		with torch.no_grad():
			__lowerCamelCase    :    Tuple 	=			model(lowerCamelCase__      )
			__lowerCamelCase	,			__lowerCamelCase    :    Optional[Any] 	=			accelerator.gather_for_metrics((logit, target)      )
			logits_and_targets.append((logit, target)      )
	__lowerCamelCase	,			__lowerCamelCase    :    Dict 	=			[], []
	for logit, targ in logits_and_targets:
		logits.append(lowerCamelCase__      )
		targs.append(lowerCamelCase__      )
	__lowerCamelCase	,			__lowerCamelCase    :    Union[str, Any] 	=			torch.cat(lowerCamelCase__      ), torch.cat(lowerCamelCase__      )
	return logits, targs






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__     ,			lowerCamelCase__=8_2     ,			lowerCamelCase__=False     ,			lowerCamelCase__=False     ,			lowerCamelCase__=1_6      )  ->					Dict:
	__lowerCamelCase	,			__lowerCamelCase	,			__lowerCamelCase    :    Optional[Any] 	=			get_basic_setup(lowerCamelCase__     ,			lowerCamelCase__     ,			lowerCamelCase__      )
	__lowerCamelCase	,			__lowerCamelCase    :    Dict 	=			generate_predictions(lowerCamelCase__     ,			lowerCamelCase__     ,			lowerCamelCase__      )
	assert (
	    len(lowerCamelCase__      ) == num_samples
	), F"Unexpected number of inputs:\n    Expected: {num_samples}\n    Actual: {len(lowerCamelCase__      )}"






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__ = False     ,			lowerCamelCase__ = False      )  ->					Dict:
	__lowerCamelCase    :    Dict 	=			evaluate.load('glue'     ,			'mrpc'      )
	__lowerCamelCase	,			__lowerCamelCase    :    Optional[int] 	=			get_mrpc_setup(lowerCamelCase__     ,			lowerCamelCase__      )
	# First do baseline
	__lowerCamelCase	,			__lowerCamelCase	,			__lowerCamelCase    :    int 	=			setup['no']
	model.to(lowerCamelCase__      )
	model.eval()
	for batch in dataloader:
		batch.to(lowerCamelCase__      )
		with torch.inference_mode():
			__lowerCamelCase    :    Dict 	=			model(**lowerCamelCase__      )
		__lowerCamelCase    :    Any 	=			outputs.logits.argmax(dim=-1      )
		metric.add_batch(predictions=lowerCamelCase__     ,			references=batch['labels']      )
	__lowerCamelCase    :    str 	=			metric.compute()

	# Then do distributed
	__lowerCamelCase	,			__lowerCamelCase	,			__lowerCamelCase    :    Union[str, Any] 	=			setup['ddp']
	model.eval()
	for batch in dataloader:
		with torch.inference_mode():
			__lowerCamelCase    :    List[str] 	=			model(**lowerCamelCase__      )
		__lowerCamelCase    :    List[Any] 	=			outputs.logits.argmax(dim=-1      )
		__lowerCamelCase    :    List[str] 	=			batch['labels']
		__lowerCamelCase	,			__lowerCamelCase    :    Union[str, Any] 	=			accelerator.gather_for_metrics((preds, references)      )
		metric.add_batch(predictions=lowerCamelCase__     ,			references=lowerCamelCase__      )
	__lowerCamelCase    :    Dict 	=			metric.compute()

	for key in "accuracy f1".split():
		assert math.isclose(
		    baseline[key]     ,			distributed[key]      ), F"Baseline and Distributed are not the same for key {key}:\n\tBaseline: {baseline[key]}\n\tDistributed: {distributed[key]}\n"






def        SCREAMING_SNAKE_CASE__	(  )  ->					List[str]:
	__lowerCamelCase    :    int 	=			Accelerator(split_batches=lowerCamelCase__     ,			dispatch_batches=lowerCamelCase__      )
	if accelerator.is_local_main_process:
		datasets.utils.logging.set_verbosity_warning()
		transformers.utils.logging.set_verbosity_warning()
	else:
		datasets.utils.logging.set_verbosity_error()
		transformers.utils.logging.set_verbosity_error()
	# These are a bit slower so they should only be ran on the GPU or TPU
	if torch.cuda.is_available() or is_tpu_available():
		if accelerator.is_local_main_process:
			print('**Testing gather_for_metrics**'      )
		for split_batches in [True, False]:
			for dispatch_batches in [True, False]:
				if accelerator.is_local_main_process:
					print(F"With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`"      )
				test_mrpc(lowerCamelCase__     ,			lowerCamelCase__      )
				accelerator.state._reset_state()
	if accelerator.is_local_main_process:
		print('**Test torch metrics**'      )
	for split_batches in [True, False]:
		for dispatch_batches in [True, False]:
			__lowerCamelCase    :    Optional[Any] 	=			Accelerator(split_batches=lowerCamelCase__     ,			dispatch_batches=lowerCamelCase__      )
			if accelerator.is_local_main_process:
				print(F"With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`, length=99"      )
			test_torch_metrics(lowerCamelCase__     ,			9_9      )
			accelerator.state._reset_state()
	if accelerator.is_local_main_process:
		print('**Test last batch is not dropped when perfectly divisible**'      )
	__lowerCamelCase    :    Dict 	=			Accelerator()
	test_torch_metrics(lowerCamelCase__     ,			5_1_2      )
	accelerator.state._reset_state()






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__      )  ->					Optional[Any]:
	# For xla_spawn (TPUs)
	main()


if __name__ == "__main__":
					main()




