




"""simple docstring"""
from typing import List, Optional, Union

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

from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)


SCREAMING_SNAKE_CASE__   =					logging.get_logger(__name__)  # pylint: disable=invalid-name

SCREAMING_SNAKE_CASE__   =					"\n    Examples:\n        ```py\n        >>> from diffusers import KandinskyV22Img2ImgPipeline, KandinskyV22PriorPipeline\n        >>> from diffusers.utils import load_image\n        >>> import torch\n\n        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(\n        ...     \"kandinsky-community/kandinsky-2-2-prior\", torch_dtype=torch.float16\n        ... )\n        >>> pipe_prior.to(\"cuda\")\n\n        >>> prompt = \"A red cartoon frog, 4k\"\n        >>> image_emb, zero_image_emb = pipe_prior(prompt, return_dict=False)\n\n        >>> pipe = KandinskyV22Img2ImgPipeline.from_pretrained(\n        ...     \"kandinsky-community/kandinsky-2-2-decoder\", torch_dtype=torch.float16\n        ... )\n        >>> pipe.to(\"cuda\")\n\n        >>> init_image = load_image(\n        ...     \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main\"\n        ...     \"/kandinsky/frog.png\"\n        ... )\n\n        >>> image = pipe(\n        ...     image=init_image,\n        ...     image_embeds=image_emb,\n        ...     negative_image_embeds=zero_image_emb,\n        ...     height=768,\n        ...     width=768,\n        ...     num_inference_steps=100,\n        ...     strength=0.2,\n        ... ).images\n\n        >>> image[0].save(\"red_frog.png\")\n        ```\n"





def 				lowerCAmelCase__      (     _UpperCamelCase    :			Tuple	,     _UpperCamelCase    :			Tuple	,     _UpperCamelCase    :			List[Any]=8   )   ->			List[Any]:





						"""simple docstring"""






						snake_case        =     height // scale_factor**2
						if height % scale_factor**2 != 0:
												new_height += 1
						snake_case        =     width // scale_factor**2
						if width % scale_factor**2 != 0:
												new_width += 1
						return new_height * scale_factor, new_width * scale_factor





def 				lowerCAmelCase__      (     _UpperCamelCase    :			str	,     _UpperCamelCase    :			str=5_1_2	,     _UpperCamelCase    :			List[Any]=5_1_2   )   ->			List[str]:





						"""simple docstring"""






						snake_case        =     pil_image.resize((w, h)	,     resample=Image.BICUBIC	,     reducing_gap=1   )
						snake_case        =     np.array(pil_image.convert('RGB'   )   )
						snake_case        =     arr.astype(np.floataa   ) / 1_2_7.5 - 1
						snake_case        =     np.transpose(_UpperCamelCase	,     [2, 0, 1]   )
						snake_case        =     torch.from_numpy(_UpperCamelCase   ).unsqueeze(0   )
						return image



class 					lowerCAmelCase_      (     lowerCAmelCase   ):



				"""simple docstring"""

				def __init__(			self						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      ):






										"""simple docstring"""

										super().__init__()

										self.register_modules(
										    unet=lowerCAmelCase						,      scheduler=lowerCAmelCase						,      movq=lowerCAmelCase						,      )
										snake_case        =     2 ** (len(self.movq.config.block_out_channels      ) - 1)

				def 						snake_case	(			self						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase      ):






										"""simple docstring"""

										snake_case        =     min(int(num_inference_steps * strength      )						,      lowerCAmelCase      )

										snake_case        =     max(num_inference_steps - init_timestep						,      0      )
										snake_case        =     self.scheduler.timesteps[t_start:]

										return timesteps, num_inference_steps - t_start

				def 						snake_case	(			self						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase=None      ):






										"""simple docstring"""

										if not isinstance(lowerCAmelCase						,      (torch.Tensor, PIL.Image.Image, list)      ):
																raise ValueError(
																    F"""`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(lowerCAmelCase      )}"""      )

										snake_case        =     image.to(device=lowerCAmelCase						,      dtype=lowerCAmelCase      )

										snake_case        =     batch_size * num_images_per_prompt

										if image.shape[1] == 4:
																snake_case        =     image

										else:
																if isinstance(lowerCAmelCase						,      lowerCAmelCase      ) and len(lowerCAmelCase      ) != batch_size:
																						raise ValueError(
																						    F"""You have passed a list of generators of length {len(lowerCAmelCase      )}, but requested an effective batch"""
																						    F""" size of {batch_size}. Make sure the batch size matches the length of the generators."""      )

																elif isinstance(lowerCAmelCase						,      lowerCAmelCase      ):
																						snake_case        =     [
																						    self.movq.encode(image[i : i + 1]      ).latent_dist.sample(generator[i]      ) for i in range(lowerCAmelCase      )
																						]
																						snake_case        =     torch.cat(lowerCAmelCase						,      dim=0      )
																else:
																						snake_case        =     self.movq.encode(lowerCAmelCase      ).latent_dist.sample(lowerCAmelCase      )

																snake_case        =     self.movq.config.scaling_factor * init_latents

										snake_case        =     torch.cat([init_latents]						,      dim=0      )

										snake_case        =     init_latents.shape
										snake_case        =     randn_tensor(lowerCAmelCase						,      generator=lowerCAmelCase						,      device=lowerCAmelCase						,      dtype=lowerCAmelCase      )

										# get latents
										snake_case        =     self.scheduler.add_noise(lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase      )

										snake_case        =     init_latents

										return latents

				def 						snake_case	(			self						,      lowerCAmelCase=0      ):






										"""simple docstring"""

										if is_accelerate_available():
																from accelerate import cpu_offload
										else:
																raise ImportError('Please install accelerate via `pip install accelerate`'      )

										snake_case        =     torch.device(F"""cuda:{gpu_id}"""      )

										snake_case        =     [
										    self.unet,
										    self.movq,
										]
										for cpu_offloaded_model in models:
																if cpu_offloaded_model is not None:
																						cpu_offload(lowerCAmelCase						,      lowerCAmelCase      )

				def 						snake_case	(			self						,      lowerCAmelCase=0      ):






										"""simple docstring"""

										if is_accelerate_available() and is_accelerate_version('>='						,      '0.17.0.dev0'      ):
																from accelerate import cpu_offload_with_hook
										else:
																raise ImportError('`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.'      )

										snake_case        =     torch.device(F"""cuda:{gpu_id}"""      )

										if self.device.type != "cpu":
																self.to('cpu'						,      silence_dtype_warnings=lowerCAmelCase      )
																torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

										snake_case        =     None
										for cpu_offloaded_model in [self.unet, self.movq]:
																snake_case   ,snake_case        =     cpu_offload_with_hook(lowerCAmelCase						,      lowerCAmelCase						,      prev_module_hook=lowerCAmelCase      )

										# We'll offload the last model manually.
										snake_case        =     hook

				@property
				# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
				def 						snake_case	(			self      ):






										"""simple docstring"""

										if not hasattr(self.unet						,      '_hf_hook'      ):
																return self.device
										for module in self.unet.modules():
																if (
																    hasattr(lowerCAmelCase						,      '_hf_hook'      )
																    and hasattr(module._hf_hook						,      'execution_device'      )
																    and module._hf_hook.execution_device is not None
																):
																						return torch.device(module._hf_hook.execution_device      )
										return self.device

				@torch.no_grad()
				@replace_example_docstring(lowerCAmelCase      )
				def __call__(			self						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase = 5_12						,      lowerCAmelCase = 5_12						,      lowerCAmelCase = 1_00						,      lowerCAmelCase = 4.0						,      lowerCAmelCase = 0.3						,      lowerCAmelCase = 1						,      lowerCAmelCase = None						,      lowerCAmelCase = "pil"						,      lowerCAmelCase = True						,      ):






										"""simple docstring"""

										snake_case        =     self._execution_device

										snake_case        =     guidance_scale > 1.0

										if isinstance(lowerCAmelCase						,      lowerCAmelCase      ):
																snake_case        =     torch.cat(lowerCAmelCase						,      dim=0      )
										snake_case        =     image_embeds.shape[0]
										if isinstance(lowerCAmelCase						,      lowerCAmelCase      ):
																snake_case        =     torch.cat(lowerCAmelCase						,      dim=0      )

										if do_classifier_free_guidance:
																snake_case        =     image_embeds.repeat_interleave(lowerCAmelCase						,      dim=0      )
																snake_case        =     negative_image_embeds.repeat_interleave(lowerCAmelCase						,      dim=0      )

										snake_case        =     torch.cat([negative_image_embeds, image_embeds]						,      dim=0      ).to(dtype=self.unet.dtype						,      device=lowerCAmelCase      )

										if not isinstance(lowerCAmelCase						,      lowerCAmelCase      ):
																snake_case        =     [image]
										if not all(isinstance(lowerCAmelCase						,      (PIL.Image.Image, torch.Tensor)      ) for i in image      ):
																raise ValueError(
																    F"""Input is in incorrect format: {[type(lowerCAmelCase      ) for i in image]}. Currently, we only support  PIL image and pytorch tensor"""      )

										snake_case        =     torch.cat([prepare_image(lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase      ) for i in image]						,      dim=0      )
										snake_case        =     image.to(dtype=image_embeds.dtype						,      device=lowerCAmelCase      )

										snake_case        =     self.movq.encode(lowerCAmelCase      )['latents']
										snake_case        =     latents.repeat_interleave(lowerCAmelCase						,      dim=0      )
										self.scheduler.set_timesteps(lowerCAmelCase						,      device=lowerCAmelCase      )
										snake_case   ,snake_case        =     self.get_timesteps(lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase      )
										snake_case        =     timesteps[:1].repeat(batch_size * num_images_per_prompt      )
										snake_case   ,snake_case        =     downscale_height_and_width(lowerCAmelCase						,      lowerCAmelCase						,      self.movq_scale_factor      )
										snake_case        =     self.prepare_latents(
										    lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      image_embeds.dtype						,      lowerCAmelCase						,      lowerCAmelCase      )
										for i, t in enumerate(self.progress_bar(lowerCAmelCase      )      ):
																# expand the latents if we are doing classifier free guidance
																snake_case        =     torch.cat([latents] * 2      ) if do_classifier_free_guidance else latents

																snake_case        =     {'image_embeds': image_embeds}
																snake_case        =     self.unet(
																    sample=lowerCAmelCase						,      timestep=lowerCAmelCase						,      encoder_hidden_states=lowerCAmelCase						,      added_cond_kwargs=lowerCAmelCase						,      return_dict=lowerCAmelCase						,      )[0]

																if do_classifier_free_guidance:
																						snake_case   ,snake_case        =     noise_pred.split(latents.shape[1]						,      dim=1      )
																						snake_case   ,snake_case        =     noise_pred.chunk(2      )
																						snake_case   ,snake_case        =     variance_pred.chunk(2      )
																						snake_case        =     noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
																						snake_case        =     torch.cat([noise_pred, variance_pred_text]						,      dim=1      )

																if not (
																    hasattr(self.scheduler.config						,      'variance_type'      )
																    and self.scheduler.config.variance_type in ["learned", "learned_range"]
																):
																						snake_case   ,snake_case        =     noise_pred.split(latents.shape[1]						,      dim=1      )

																# compute the previous noisy sample x_t -> x_t-1
																snake_case        =     self.scheduler.step(
																    lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      generator=lowerCAmelCase						,      )[0]

										# post-processing
										snake_case        =     self.movq.decode(lowerCAmelCase						,      force_not_quantize=lowerCAmelCase      )['sample']

										if output_type not in ["pt", "np", "pil"]:
																raise ValueError(F"""Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}"""      )

										if output_type in ["np", "pil"]:
																snake_case        =     image * 0.5 + 0.5
																snake_case        =     image.clamp(0						,      1      )
																snake_case        =     image.cpu().permute(0						,      2						,      3						,      1      ).float().numpy()

										if output_type == "pil":
																snake_case        =     self.numpy_to_pil(lowerCAmelCase      )

										if not return_dict:
																return (image,)

										return ImagePipelineOutput(images=lowerCAmelCase      )



"""simple docstring"""
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


SCREAMING_SNAKE_CASE__   =					"true"





def 				lowerCAmelCase__      (     _UpperCamelCase    :			Optional[int]	,     _UpperCamelCase    :			Dict=8_2	,     _UpperCamelCase    :			int=1_6   )   ->			str:





						"""simple docstring"""






						set_seed(4_2   )
						snake_case        =     RegressionModel()
						snake_case        =     deepcopy(_UpperCamelCase   )
						snake_case        =     RegressionDataset(length=_UpperCamelCase   )
						snake_case        =     DataLoader(_UpperCamelCase	,     batch_size=_UpperCamelCase   )
						model.to(accelerator.device   )
						snake_case   ,snake_case        =     accelerator.prepare(_UpperCamelCase	,     _UpperCamelCase   )
						return model, ddp_model, dataloader





def 				lowerCAmelCase__      (     _UpperCamelCase    :			Accelerator	,     _UpperCamelCase    :			Optional[Any]=False   )   ->			List[str]:





						"""simple docstring"""






						snake_case        =     AutoTokenizer.from_pretrained('hf-internal-testing/mrpc-bert-base-cased'   )
						snake_case        =     load_dataset('glue'	,     'mrpc'	,     split='validation'   )

						def tokenize_function(_UpperCamelCase    :			Optional[Any]   ):
												snake_case        =     tokenizer(examples['sentence1']	,     examples['sentence2']	,     truncation=_UpperCamelCase	,     max_length=_UpperCamelCase   )
												return outputs

						with accelerator.main_process_first():
												snake_case        =     dataset.map(
												    _UpperCamelCase	,     batched=_UpperCamelCase	,     remove_columns=['idx', 'sentence1', 'sentence2']	,     )

						snake_case        =     tokenized_datasets.rename_column('label'	,     'labels'   )

						def collate_fn(_UpperCamelCase    :			Optional[Any]   ):
												if use_longest:
																		return tokenizer.pad(_UpperCamelCase	,     padding='longest'	,     return_tensors='pt'   )
												return tokenizer.pad(_UpperCamelCase	,     padding='max_length'	,     max_length=1_2_8	,     return_tensors='pt'   )

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





def 				lowerCAmelCase__      (     _UpperCamelCase    :			str	,     _UpperCamelCase    :			Any   )   ->			List[Any]:





						"""simple docstring"""






						snake_case        =     Accelerator(dispatch_batches=_UpperCamelCase	,     split_batches=_UpperCamelCase   )
						snake_case        =     get_dataloader(_UpperCamelCase	,     not dispatch_batches   )
						snake_case        =     AutoModelForSequenceClassification.from_pretrained(
						    'hf-internal-testing/mrpc-bert-base-cased'	,     return_dict=_UpperCamelCase   )
						snake_case   ,snake_case        =     accelerator.prepare(_UpperCamelCase	,     _UpperCamelCase   )
						return {"ddp": [ddp_model, ddp_dataloader, "cuda:0"], "no": [model, dataloader, accelerator.device]}, accelerator





def 				lowerCAmelCase__      (     _UpperCamelCase    :			int	,     _UpperCamelCase    :			Any	,     _UpperCamelCase    :			Optional[Any]   )   ->			Dict:





						"""simple docstring"""






						snake_case        =     []
						for batch in dataloader:
												snake_case   ,snake_case        =     batch.values()
												with torch.no_grad():
																		snake_case        =     model(_UpperCamelCase   )
																		snake_case   ,snake_case        =     accelerator.gather_for_metrics((logit, target)   )
																		logits_and_targets.append((logit, target)   )
						snake_case   ,snake_case        =     [], []
						for logit, targ in logits_and_targets:
												logits.append(_UpperCamelCase   )
												targs.append(_UpperCamelCase   )
						snake_case   ,snake_case        =     torch.cat(_UpperCamelCase   ), torch.cat(_UpperCamelCase   )
						return logits, targs





def 				lowerCAmelCase__      (     _UpperCamelCase    :			Accelerator	,     _UpperCamelCase    :			Tuple=8_2	,     _UpperCamelCase    :			Optional[Any]=False	,     _UpperCamelCase    :			int=False	,     _UpperCamelCase    :			List[str]=1_6   )   ->			Optional[Any]:





						"""simple docstring"""






						snake_case   ,snake_case   ,snake_case        =     get_basic_setup(_UpperCamelCase	,     _UpperCamelCase	,     _UpperCamelCase   )
						snake_case   ,snake_case        =     generate_predictions(_UpperCamelCase	,     _UpperCamelCase	,     _UpperCamelCase   )
						assert (
						    len(_UpperCamelCase   ) == num_samples
						), f"""Unexpected number of inputs:\n    Expected: {num_samples}\n    Actual: {len(_UpperCamelCase   )}"""





def 				lowerCAmelCase__      (     _UpperCamelCase    :			bool = False	,     _UpperCamelCase    :			bool = False   )   ->			Tuple:





						"""simple docstring"""






						snake_case        =     evaluate.load('glue'	,     'mrpc'   )
						snake_case   ,snake_case        =     get_mrpc_setup(_UpperCamelCase	,     _UpperCamelCase   )
						# First do baseline
						snake_case   ,snake_case   ,snake_case        =     setup['no']
						model.to(_UpperCamelCase   )
						model.eval()
						for batch in dataloader:
												batch.to(_UpperCamelCase   )
												with torch.inference_mode():
																		snake_case        =     model(**_UpperCamelCase   )
												snake_case        =     outputs.logits.argmax(dim=-1   )
												metric.add_batch(predictions=_UpperCamelCase	,     references=batch['labels']   )
						snake_case        =     metric.compute()

						# Then do distributed
						snake_case   ,snake_case   ,snake_case        =     setup['ddp']
						model.eval()
						for batch in dataloader:
												with torch.inference_mode():
																		snake_case        =     model(**_UpperCamelCase   )
												snake_case        =     outputs.logits.argmax(dim=-1   )
												snake_case        =     batch['labels']
												snake_case   ,snake_case        =     accelerator.gather_for_metrics((preds, references)   )
												metric.add_batch(predictions=_UpperCamelCase	,     references=_UpperCamelCase   )
						snake_case        =     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 				lowerCAmelCase__      (     )   ->			Tuple:





						"""simple docstring"""






						snake_case        =     Accelerator(split_batches=_UpperCamelCase	,     dispatch_batches=_UpperCamelCase   )
						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(_UpperCamelCase	,     _UpperCamelCase   )
																								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]:
																		snake_case        =     Accelerator(split_batches=_UpperCamelCase	,     dispatch_batches=_UpperCamelCase   )
																		if accelerator.is_local_main_process:
																								print(f"""With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`, length=99"""   )
																		test_torch_metrics(_UpperCamelCase	,     9_9   )
																		accelerator.state._reset_state()
						if accelerator.is_local_main_process:
												print('**Test last batch is not dropped when perfectly divisible**'   )
						snake_case        =     Accelerator()
						test_torch_metrics(_UpperCamelCase	,     5_1_2   )
						accelerator.state._reset_state()





def 				lowerCAmelCase__      (     _UpperCamelCase    :			Tuple   )   ->			str:





						"""simple docstring"""






						main()


if __name__ == "__main__":
					main()


