




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

_lowerCamelCase      			=						'''
    Examples:
        ```py
        >>> from diffusers import KandinskyV22Img2ImgPipeline, KandinskyV22PriorPipeline
        >>> from diffusers.utils import load_image
        >>> import torch

        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(
        ...     "kandinsky-community/kandinsky-2-2-prior", torch_dtype=torch.float16
        ... )
        >>> pipe_prior.to("cuda")

        >>> prompt = "A red cartoon frog, 4k"
        >>> image_emb, zero_image_emb = pipe_prior(prompt, return_dict=False)

        >>> pipe = KandinskyV22Img2ImgPipeline.from_pretrained(
        ...     "kandinsky-community/kandinsky-2-2-decoder", torch_dtype=torch.float16
        ... )
        >>> pipe.to("cuda")

        >>> init_image = load_image(
        ...     "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
        ...     "/kandinsky/frog.png"
        ... )

        >>> image = pipe(
        ...     image=init_image,
        ...     image_embeds=image_emb,
        ...     negative_image_embeds=zero_image_emb,
        ...     height=768,
        ...     width=768,
        ...     num_inference_steps=100,
        ...     strength=0.2,
        ... ).images

        >>> image[0].save("red_frog.png")
        ```
'''




def  SCREAMING_SNAKE_CASE     (	__UpperCamelCase      :						List[Any]	,					__UpperCamelCase      :						int	,					__UpperCamelCase      :						Any=8				)  ->							Optional[Any]:
				UpperCAmelCase_										=					height // scale_factor**2
				if height % scale_factor**2 != 0:
								new_height += 1
				UpperCAmelCase_										=					width // scale_factor**2
				if width % scale_factor**2 != 0:
								new_width += 1
				return new_height * scale_factor, new_width * scale_factor




def  SCREAMING_SNAKE_CASE     (	__UpperCamelCase      :						List[Any]	,					__UpperCamelCase      :						Tuple=512	,					__UpperCamelCase      :						Union[str, Any]=512				)  ->							Union[str, Any]:
				UpperCAmelCase_										=					pil_image.resize((w, h)	,					resample=Image.BICUBIC	,					reducing_gap=1				)
				UpperCAmelCase_										=					np.array(pil_image.convert('''RGB'''				)				)
				UpperCAmelCase_										=					arr.astype(np.floataa				) / 127.5 - 1
				UpperCAmelCase_										=					np.transpose(__lowerCAmelCase	,					[2, 0, 1]				)
				UpperCAmelCase_										=					torch.from_numpy(__lowerCAmelCase				).unsqueeze(0				)
				return image



class       a					(       lowerCamelCase_      ):

					'''simple docstring'''





					def __init__(     self       :      List[Any] ,   __snake_case       :      UNetaDConditionModel ,   __snake_case       :      DDPMScheduler ,   __snake_case       :      VQModel ,   ):
									super().__init__()

									self.register_modules(
									    unet=__snake_case ,   scheduler=__snake_case ,   movq=__snake_case ,   )
									UpperCAmelCase_										=					2 ** (len(self.movq.config.block_out_channels    ) - 1)
					def   lowerCamelCase_       (     self       :      Optional[int] ,   __snake_case       :      Dict ,   __snake_case       :      Tuple ,   __snake_case       :      Union[str, Any]    ):
									# get the original timestep using init_timestep
									UpperCAmelCase_										=					min(int(num_inference_steps * strength    ) ,   __snake_case    )

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

									return timesteps, num_inference_steps - t_start
					def   lowerCamelCase_       (     self       :      Dict ,   __snake_case       :      List[str] ,   __snake_case       :      Union[str, Any] ,   __snake_case       :      int ,   __snake_case       :      Union[str, Any] ,   __snake_case       :      Tuple ,   __snake_case       :      int ,   __snake_case       :      Tuple=None    ):
									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    )}'    )

									UpperCAmelCase_										=					image.to(device=__snake_case ,   dtype=__snake_case    )

									UpperCAmelCase_										=					batch_size * num_images_per_prompt

									if image.shape[1] == 4:
													UpperCAmelCase_										=					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    ):
																	UpperCAmelCase_										=					[
																	    self.movq.encode(image[i : i + 1]    ).latent_dist.sample(generator[i]    ) for i in range(__snake_case    )
																	]
																	UpperCAmelCase_										=					torch.cat(__snake_case ,   dim=0    )
													else:
																	UpperCAmelCase_										=					self.movq.encode(__snake_case    ).latent_dist.sample(__snake_case    )

													UpperCAmelCase_										=					self.movq.config.scaling_factor * init_latents

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

									UpperCAmelCase_										=					init_latents.shape
									UpperCAmelCase_										=					randn_tensor(__snake_case ,   generator=__snake_case ,   device=__snake_case ,   dtype=__snake_case    )

									# get latents
									UpperCAmelCase_										=					self.scheduler.add_noise(__snake_case ,   __snake_case ,   __snake_case    )

									UpperCAmelCase_										=					init_latents

									return latents
					def   lowerCamelCase_       (     self       :      List[Any] ,   __snake_case       :      List[str]=0    ):
									if is_accelerate_available():
													from accelerate import cpu_offload
									else:
													raise ImportError('''Please install accelerate via `pip install accelerate`'''    )

									UpperCAmelCase_										=					torch.device(F'cuda:{gpu_id}'    )

									UpperCAmelCase_										=					[
									    self.unet,
									    self.movq,
									]
									for cpu_offloaded_model in models:
													if cpu_offloaded_model is not None:
																	cpu_offload(__snake_case ,   __snake_case    )
					def   lowerCamelCase_       (     self       :      List[str] ,   __snake_case       :      Optional[int]=0    ):
									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.'''    )

									UpperCAmelCase_										=					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)

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

									# We'll offload the last model manually.
									UpperCAmelCase_										=					hook
					@property
					# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
					def   lowerCamelCase_       (     self       :      Optional[Any]    ):
									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       :      str ,   __snake_case       :      Union[torch.FloatTensor, List[torch.FloatTensor]] ,   __snake_case       :      Union[torch.FloatTensor, PIL.Image.Image, List[torch.FloatTensor], List[PIL.Image.Image]] ,   __snake_case       :      Union[torch.FloatTensor, List[torch.FloatTensor]] ,   __snake_case       :      int = 5_12 ,   __snake_case       :      int = 5_12 ,   __snake_case       :      int = 1_00 ,   __snake_case       :      float = 4.0 ,   __snake_case       :      float = 0.3 ,   __snake_case       :      int = 1 ,   __snake_case       :      Optional[Union[torch.Generator, List[torch.Generator]]] = None ,   __snake_case       :      Optional[str] = "pil" ,   __snake_case       :      bool = True ,   ):
									UpperCAmelCase_										=					self._execution_device

									UpperCAmelCase_										=					guidance_scale > 1.0

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

									if do_classifier_free_guidance:
													UpperCAmelCase_										=					image_embeds.repeat_interleave(__snake_case ,   dim=0    )
													UpperCAmelCase_										=					negative_image_embeds.repeat_interleave(__snake_case ,   dim=0    )

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

									if not isinstance(__snake_case ,   __snake_case    ):
													UpperCAmelCase_										=					[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'    )

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

									UpperCAmelCase_										=					self.movq.encode(__snake_case    )['''latents''']
									UpperCAmelCase_										=					latents.repeat_interleave(__snake_case ,   dim=0    )
									self.scheduler.set_timesteps(__snake_case ,   device=__snake_case    )
									UpperCAmelCase_			,							UpperCAmelCase_										=					self.get_timesteps(__snake_case ,   __snake_case ,   __snake_case    )
									UpperCAmelCase_										=					timesteps[:1].repeat(batch_size * num_images_per_prompt    )
									UpperCAmelCase_			,							UpperCAmelCase_										=					downscale_height_and_width(__snake_case ,   __snake_case ,   self.movq_scale_factor    )
									UpperCAmelCase_										=					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
													UpperCAmelCase_										=					torch.cat([latents] * 2    ) if do_classifier_free_guidance else latents

													UpperCAmelCase_										=					{'''image_embeds''': image_embeds}
													UpperCAmelCase_										=					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:
																	UpperCAmelCase_			,							UpperCAmelCase_										=					noise_pred.split(latents.shape[1] ,   dim=1    )
																	UpperCAmelCase_			,							UpperCAmelCase_										=					noise_pred.chunk(2    )
																	UpperCAmelCase_			,							UpperCAmelCase_										=					variance_pred.chunk(2    )
																	UpperCAmelCase_										=					noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
																	UpperCAmelCase_										=					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"]
													):
																	UpperCAmelCase_			,							UpperCAmelCase_										=					noise_pred.split(latents.shape[1] ,   dim=1    )

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

									# post-processing
									UpperCAmelCase_										=					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"]:
													UpperCAmelCase_										=					image * 0.5 + 0.5
													UpperCAmelCase_										=					image.clamp(0 ,   1    )
													UpperCAmelCase_										=					image.cpu().permute(0 ,   2 ,   3 ,   1    ).float().numpy()

									if output_type == "pil":
													UpperCAmelCase_										=					self.numpy_to_pil(__snake_case    )

									if not return_dict:
													return (image,)

									return ImagePipelineOutput(images=__snake_case    )











import argparse
import logging
import pickle
from collections import Counter


logging.basicConfig(
    format='%(asctime)s - %(levelname)s - %(name)s - %(message)s', datefmt='%m/%d/%Y %H:%M:%S', level=logging.INFO
)
_lowerCamelCase      			=						logging.getLogger(__name__)

if __name__ == "__main__":
 _lowerCamelCase      			=						argparse.ArgumentParser(
     description='Token Counts for smoothing the masking probabilities in MLM (cf XLM/word2vec)'
 )
 parser.add_argument(
     '--data_file', type=str, default='data/dump.bert-base-uncased.pickle', help='The binarized dataset.'
 )
 parser.add_argument(
     '--token_counts_dump', type=str, default='data/token_counts.bert-base-uncased.pickle', help='The dump file.'
 )
 parser.add_argument('--vocab_size', default=3_05_22, type=int)
 _lowerCamelCase      			=						parser.parse_args()

 logger.info(F"Loading data from {args.data_file}")
 with open(args.data_file, 'rb') as fp:
  _lowerCamelCase      			=						pickle.load(fp)

 logger.info('Counting occurrences for MLM.')
 _lowerCamelCase      			=						Counter()
 for tk_ids in data:
  counter.update(tk_ids)
 _lowerCamelCase      			=						[0] * args.vocab_size
 for k, v in counter.items():
  _lowerCamelCase      			=						v

 logger.info(F"Dump to {args.token_counts_dump}")
 with open(args.token_counts_dump, 'wb') as handle:
  pickle.dump(counts, handle, protocol=pickle.HIGHEST_PROTOCOL)





