





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,
)


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

__lowerCAmelCase :str     =    '\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    A				(							UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase=8				):
     _snake_case       :					Union[str, Any]				       =   height // scale_factor**2
     if height % scale_factor**2 != 0:
          new_height += 1
     _snake_case       :					Dict				       =   width // scale_factor**2
     if width % scale_factor**2 != 0:
          new_width += 1
     return new_height * scale_factor, new_width * scale_factor







def    A				(							UpperCAmelCase  ,  UpperCAmelCase=512  ,  UpperCAmelCase=512				):
     _snake_case       :					int				       =   pil_image.resize((w, h)  ,  resample=Image.BICUBIC  ,  reducing_gap=1				)
     _snake_case       :					List[Any]				       =   np.array(pil_image.convert("RGB"				)				)
     _snake_case       :					Dict				       =   arr.astype(np.floataa				) / 127.5 - 1
     _snake_case       :					Optional[Any]				       =   np.transpose(UpperCAmelCase  ,  [2, 0, 1]				)
     _snake_case       :					Optional[Any]				       =   torch.from_numpy(UpperCAmelCase				).unsqueeze(0				)
     return image






class        _a(    __A    ):







  def __init__(    self ,						__snake_case ,						__snake_case ,						__snake_case ,						)      ->      str:


       '''simple docstring'''

       super().__init__()

       self.register_modules(
           unet=__snake_case ,						scheduler=__snake_case ,						movq=__snake_case ,						)
       _snake_case       :					Optional[int]				       =   2 ** (len(self.movq.config.block_out_channels				) - 1)







  def        lowercase       (    self ,						__snake_case ,						__snake_case ,						__snake_case				)      ->      Optional[Any]:


       '''simple docstring'''

       _snake_case       :					Dict				       =   min(int(num_inference_steps * strength				) ,						__snake_case				)

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

       return timesteps, num_inference_steps - t_start







  def        lowercase       (    self ,						__snake_case ,						__snake_case ,						__snake_case ,						__snake_case ,						__snake_case ,						__snake_case ,						__snake_case=None				)      ->      Optional[Any]:


       '''simple docstring'''

       if not isinstance(__snake_case ,						(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(__snake_case				)}"""				)

       _snake_case       :					Optional[Any]				       =   image.to(device=__snake_case ,						dtype=__snake_case				)

       _snake_case       :					str				       =   batch_size * num_images_per_prompt

       if image.shape[1] == 4:
            _snake_case       :					Optional[Any]				       =   image

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

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

            _snake_case       :					Tuple				       =   self.movq.config.scaling_factor * init_latents

       _snake_case       :					List[str]				       =   torch.cat([init_latents] ,						dim=0				)

       _snake_case       :					str				       =   init_latents.shape
       _snake_case       :					Dict				       =   randn_tensor(__snake_case ,						generator=__snake_case ,						device=__snake_case ,						dtype=__snake_case				)

       # get latents
       _snake_case       :					List[str]				       =   self.scheduler.add_noise(__snake_case ,						__snake_case ,						__snake_case				)

       _snake_case       :					Any				       =   init_latents

       return latents







  def        lowercase       (    self ,						__snake_case=0				)      ->      str:


       '''simple docstring'''

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

       _snake_case       :					Dict				       =   torch.device(f"""cuda:{gpu_id}"""				)

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







  def        lowercase       (    self ,						__snake_case=0				)      ->      str:


       '''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       :					str				       =   torch.device(f"""cuda:{gpu_id}"""				)

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

       _snake_case       :					List[str]				       =   None
       for cpu_offloaded_model in [self.unet, self.movq]:
            _snake_case       :					Any				       =   cpu_offload_with_hook(__snake_case ,						__snake_case ,						prev_module_hook=__snake_case				)

       # We'll offload the last model manually.
       _snake_case       :					int				       =   hook







  @property
  # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
  def        lowercase       (    self				)      ->      Union[str, Any]:


       '''simple docstring'''

       if not hasattr(self.unet ,						"_hf_hook"				):
            return self.device
       for module in self.unet.modules():
            if (
                hasattr(__snake_case ,						"_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(__snake_case				)
  def __call__(    self ,						__snake_case ,						__snake_case ,						__snake_case ,						__snake_case = 5_1_2 ,						__snake_case = 5_1_2 ,						__snake_case = 1_0_0 ,						__snake_case = 4.0 ,						__snake_case = 0.3 ,						__snake_case = 1 ,						__snake_case = None ,						__snake_case = "pil" ,						__snake_case = True ,						)      ->      str:


       '''simple docstring'''

       _snake_case       :					Dict				       =   self._execution_device

       _snake_case       :					str				       =   guidance_scale > 1.0

       if isinstance(__snake_case ,						__snake_case				):
            _snake_case       :					Optional[Any]				       =   torch.cat(__snake_case ,						dim=0				)
       _snake_case       :					int				       =   image_embeds.shape[0]
       if isinstance(__snake_case ,						__snake_case				):
            _snake_case       :					int				       =   torch.cat(__snake_case ,						dim=0				)

       if do_classifier_free_guidance:
            _snake_case       :					List[str]				       =   image_embeds.repeat_interleave(__snake_case ,						dim=0				)
            _snake_case       :					Union[str, Any]				       =   negative_image_embeds.repeat_interleave(__snake_case ,						dim=0				)

       _snake_case       :					Union[str, Any]				       =   torch.cat([negative_image_embeds, image_embeds] ,						dim=0				).to(dtype=self.unet.dtype ,						device=__snake_case				)

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

       _snake_case       :					List[str]				       =   torch.cat([prepare_image(__snake_case ,						__snake_case ,						__snake_case				) for i in image] ,						dim=0				)
       _snake_case       :					str				       =   image.to(dtype=image_embeds.dtype ,						device=__snake_case				)

       _snake_case       :					Dict				       =   self.movq.encode(__snake_case				)["latents"]
       _snake_case       :					Optional[Any]				       =   latents.repeat_interleave(__snake_case ,						dim=0				)
       self.scheduler.set_timesteps(__snake_case ,						device=__snake_case				)
       _snake_case       :					Union[str, Any]				       =   self.get_timesteps(__snake_case ,						__snake_case ,						__snake_case				)
       _snake_case       :					int				       =   timesteps[:1].repeat(batch_size * num_images_per_prompt				)
       _snake_case       :					List[Any]				       =   downscale_height_and_width(__snake_case ,						__snake_case ,						self.movq_scale_factor				)
       _snake_case       :					str				       =   self.prepare_latents(
           __snake_case ,						__snake_case ,						__snake_case ,						__snake_case ,						image_embeds.dtype ,						__snake_case ,						__snake_case				)
       for i, t in enumerate(self.progress_bar(__snake_case				)				):
            # expand the latents if we are doing classifier free guidance
            _snake_case       :					List[Any]				       =   torch.cat([latents] * 2				) if do_classifier_free_guidance else latents

            _snake_case       :					List[Any]				       =   {"image_embeds": image_embeds}
            _snake_case       :					Dict				       =   self.unet(
                sample=__snake_case ,						timestep=__snake_case ,						encoder_hidden_states=__snake_case ,						added_cond_kwargs=__snake_case ,						return_dict=__snake_case ,						)[0]

            if do_classifier_free_guidance:
                 _snake_case       :					Dict				       =   noise_pred.split(latents.shape[1] ,						dim=1				)
                 _snake_case       :					Optional[Any]				       =   noise_pred.chunk(2				)
                 _snake_case       :					Optional[Any]				       =   variance_pred.chunk(2				)
                 _snake_case       :					Optional[int]				       =   noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
                 _snake_case       :					Dict				       =   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       :					Dict				       =   noise_pred.split(latents.shape[1] ,						dim=1				)

            # compute the previous noisy sample x_t -> x_t-1
            _snake_case       :					Optional[Any]				       =   self.scheduler.step(
                __snake_case ,						__snake_case ,						__snake_case ,						generator=__snake_case ,						)[0]

       # post-processing
       _snake_case       :					List[str]				       =   self.movq.decode(__snake_case ,						force_not_quantize=__snake_case				)["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       :					Optional[int]				       =   image * 0.5 + 0.5
            _snake_case       :					Tuple				       =   image.clamp(0 ,						1				)
            _snake_case       :					Optional[Any]				       =   image.cpu().permute(0 ,						2 ,						3 ,						1				).float().numpy()

       if output_type == "pil":
            _snake_case       :					List[Any]				       =   self.numpy_to_pil(__snake_case				)

       if not return_dict:
            return (image,)

       return ImagePipelineOutput(images=__snake_case				)






import os
import time
import warnings
from dataclasses import dataclass, field
from enum import Enum
from typing import List, Optional, Union

import torch
from filelock import FileLock
from torch.utils.data import Dataset

from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import logging
from ..processors.glue import glue_convert_examples_to_features, glue_output_modes, glue_processors
from ..processors.utils import InputFeatures


__lowerCAmelCase :Tuple     =    logging.get_logger(__name__)






@dataclass
class        _a:
		lowerCamelCase__     :str   				=		field(metadata={'help': 'The name of the task to train on: ' + ', '.join(glue_processors.keys()    )}    )
		lowerCamelCase__     :str   				=		field(
		    metadata={'help': 'The input data dir. Should contain the .tsv files (or other data files) for the task.'}    )
		lowerCamelCase__     :int   				=		field(
		    default=128	,   metadata={
		        'help': (
		            'The maximum total input sequence length after tokenization. Sequences longer '
		            'than this will be truncated, sequences shorter will be padded.'
		        )
		    }	,   )
		lowerCamelCase__     :bool   				=		field(
		    default=__A	,   metadata={'help': 'Overwrite the cached training and evaluation sets'}    )







		def        lowercase       (    self				)      ->      Optional[Any]:


							'''simple docstring'''

							_snake_case       :					Dict				       =   self.task_name.lower()






class        _a(    __A    ):
		lowerCamelCase__     :Optional[Any]   				=		'train'
		lowerCamelCase__     :List[str]   				=		'dev'
		lowerCamelCase__     :Any   				=		'test'






class        _a(    __A    ):
		lowerCamelCase__     :GlueDataTrainingArguments
		lowerCamelCase__     :str
		lowerCamelCase__     :List[InputFeatures]







		def __init__(    self ,						__snake_case ,						__snake_case ,						__snake_case = None ,						__snake_case = Split.train ,						__snake_case = None ,						)      ->      Any:


							'''simple docstring'''

							warnings.warn(
							    "This dataset will be removed from the library soon, preprocessing should be handled with the 🤗 Datasets "
							    "library. You can have a look at this example script for pointers: "
							    "https://github.com/huggingface/transformers/blob/main/examples/pytorch/text-classification/run_glue.py" ,						__snake_case ,						)
							_snake_case       :					Optional[Any]				       =   args
							_snake_case       :					Optional[int]				       =   glue_processors[args.task_name]()
							_snake_case       :					int				       =   glue_output_modes[args.task_name]
							if isinstance(__snake_case ,						__snake_case				):
												try:
																	_snake_case       :					Tuple				       =   Split[mode]
												except KeyError:
																	raise KeyError("mode is not a valid split name"				)
        # Load data features from cache or dataset file
							_snake_case       :					List[str]				       =   os.path.join(
							    cache_dir if cache_dir is not None else args.data_dir ,						f"""cached_{mode.value}_{tokenizer.__class__.__name__}_{args.max_seq_length}_{args.task_name}""" ,						)
							_snake_case       :					List[Any]				       =   self.processor.get_labels()
							if args.task_name in ["mnli", "mnli-mm"] and tokenizer.__class__.__name__ in (
							    "RobertaTokenizer",
							    "RobertaTokenizerFast",
							    "XLMRobertaTokenizer",
							    "BartTokenizer",
							    "BartTokenizerFast",
							):
												# HACK(label indices are swapped in RoBERTa pretrained model)
												_snake_case       , _snake_case       :					int				       =   label_list[2], label_list[1]
							_snake_case       :					str				       =   label_list

							# Make sure only the first process in distributed training processes the dataset,
							# and the others will use the cache.
							_snake_case       :					Optional[Any]				       =   cached_features_file + ".lock"
							with FileLock(__snake_case				):
												if os.path.exists(__snake_case				) and not args.overwrite_cache:
																	_snake_case       :					List[str]				       =   time.time()
																	_snake_case       :					str				       =   torch.load(__snake_case				)
																	logger.info(
																	    f"""Loading features from cached file {cached_features_file} [took %.3f s]""" ,						time.time() - start				)
												else:
																	logger.info(f"""Creating features from dataset file at {args.data_dir}"""				)

																	if mode == Split.dev:
																						_snake_case       :					List[Any]				       =   self.processor.get_dev_examples(args.data_dir				)
																	elif mode == Split.test:
																						_snake_case       :					List[Any]				       =   self.processor.get_test_examples(args.data_dir				)
																	else:
																						_snake_case       :					str				       =   self.processor.get_train_examples(args.data_dir				)
																	if limit_length is not None:
																						_snake_case       :					Tuple				       =   examples[:limit_length]
																	_snake_case       :					List[str]				       =   glue_convert_examples_to_features(
																	    __snake_case ,						__snake_case ,						max_length=args.max_seq_length ,						label_list=__snake_case ,						output_mode=self.output_mode ,						)
																	_snake_case       :					Optional[Any]				       =   time.time()
																	torch.save(self.features ,						__snake_case				)
																	# ^ This seems to take a lot of time so I want to investigate why and how we can improve.
																	logger.info(
																	    f"""Saving features into cached file {cached_features_file} [took {time.time() - start:.3f} s]"""				)







		def __len__(    self				)      ->      List[str]:


							'''simple docstring'''

							return len(self.features				)







		def __getitem__(    self ,						__snake_case				)      ->      InputFeatures:


							'''simple docstring'''

							return self.features[i]







		def        lowercase       (    self				)      ->      Optional[Any]:


							'''simple docstring'''

							return self.label_list