
from typing import List, Optional, Union

import torch
from transformers import (
    XLMRobertaTokenizer,
)

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


_lowerCamelCase	:    Union[str, Any]          =      logging.get_logger(__name__)  # pylint: disable=invalid-name

_lowerCamelCase	:    Any          =      '''
    Examples:
        ```py
        >>> from diffusers import KandinskyPipeline, KandinskyPriorPipeline
        >>> import torch

        >>> pipe_prior = KandinskyPriorPipeline.from_pretrained("kandinsky-community/Kandinsky-2-1-prior")
        >>> pipe_prior.to("cuda")

        >>> prompt = "red cat, 4k photo"
        >>> out = pipe_prior(prompt)
        >>> image_emb = out.image_embeds
        >>> negative_image_emb = out.negative_image_embeds

        >>> pipe = KandinskyPipeline.from_pretrained("kandinsky-community/kandinsky-2-1")
        >>> pipe.to("cuda")

        >>> image = pipe(
        ...     prompt,
        ...     image_embeds=image_emb,
        ...     negative_image_embeds=negative_image_emb,
        ...     height=768,
        ...     width=768,
        ...     num_inference_steps=100,
        ... ).images

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






def 	a_		(	__lowercase       :     Union[str, Any]	, __lowercase       :     Optional[Any]	, __lowercase       :     Tuple=8			)			->  Union[str, Any]:
			_snake_case          =      h // scale_factor**2
			if h % scale_factor**2 != 0:
						new_h += 1
			_snake_case          =      w // scale_factor**2
			if w % scale_factor**2 != 0:
						new_w += 1
			return new_h * scale_factor, new_w * scale_factor




class        SCREAMING_SNAKE_CASE__					(     UpperCAmelCase ):





	'''simple docstring'''



	def __init__(  self     :   Any      ,  lowercase     :   MultilingualCLIP      ,  lowercase     :   XLMRobertaTokenizer      ,  lowercase     :   UNetaDConditionModel      ,  lowercase     :   Union[DDIMScheduler, DDPMScheduler]      ,  lowercase     :   VQModel      ,  ):


				'''simple docstring'''


				super().__init__()

				self.register_modules(
				    text_encoder=lowercase      ,  tokenizer=lowercase      ,  unet=lowercase      ,  scheduler=lowercase      ,  movq=lowercase      ,  )
				_snake_case          =      2 ** (len(self.movq.config.block_out_channels     ) - 1)



	def 			A							(  self     :   Union[str, Any]      ,  lowercase     :   Union[str, Any]      ,  lowercase     :   Optional[int]      ,  lowercase     :   Union[str, Any]      ,  lowercase     :   Optional[int]      ,  lowercase     :   Dict      ,  lowercase     :   Union[str, Any]     ):


				'''simple docstring'''


				if latents is None:
							_snake_case          =      randn_tensor(lowercase      ,  generator=lowercase      ,  device=lowercase      ,  dtype=lowercase     )
				else:
							if latents.shape != shape:
										raise ValueError(f'''Unexpected latents shape, got {latents.shape}, expected {shape}'''     )
							_snake_case          =      latents.to(lowercase     )

				_snake_case          =      latents * scheduler.init_noise_sigma
				return latents



	def 			A							(  self     :   Union[str, Any]      ,  lowercase     :   Tuple      ,  lowercase     :   int      ,  lowercase     :   List[Any]      ,  lowercase     :   Optional[int]      ,  lowercase     :   Optional[int]=None      ,  ):


				'''simple docstring'''


				_snake_case          =      len(lowercase     ) if isinstance(lowercase      ,  lowercase     ) else 1
				# get prompt text embeddings
				_snake_case          =      self.tokenizer(
				    lowercase      ,  padding='max_length'      ,  truncation=lowercase      ,  max_length=77      ,  return_attention_mask=lowercase      ,  add_special_tokens=lowercase      ,  return_tensors='pt'      ,  )

				_snake_case          =      text_inputs.input_ids
				_snake_case          =      self.tokenizer(lowercase      ,  padding='longest'      ,  return_tensors='pt'     ).input_ids

				if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(lowercase      ,  lowercase     ):
							_snake_case          =      self.tokenizer.batch_decode(untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]     )
							logger.warning(
							    'The following part of your input was truncated because CLIP can only handle sequences up to'
							    f''' {self.tokenizer.model_max_length} tokens: {removed_text}'''     )

				_snake_case          =      text_input_ids.to(lowercase     )
				_snake_case          =      text_inputs.attention_mask.to(lowercase     )

				_snake_case     , _snake_case          =      self.text_encoder(
				    input_ids=lowercase      ,  attention_mask=lowercase     )

				_snake_case          =      prompt_embeds.repeat_interleave(lowercase      ,  dim=0     )
				_snake_case          =      text_encoder_hidden_states.repeat_interleave(lowercase      ,  dim=0     )
				_snake_case          =      text_mask.repeat_interleave(lowercase      ,  dim=0     )

				if do_classifier_free_guidance:
							_snake_case          =      42
							if negative_prompt is None:
										_snake_case          =      [''] * batch_size
							elif type(lowercase     ) is not type(lowercase     ):
										raise TypeError(
										    f'''`negative_prompt` should be the same type to `prompt`, but got {type(lowercase     )} !='''
										    f''' {type(lowercase     )}.'''     )
							elif isinstance(lowercase      ,  lowercase     ):
										_snake_case          =      [negative_prompt]
							elif batch_size != len(lowercase     ):
										raise ValueError(
										    f'''`negative_prompt`: {negative_prompt} has batch size {len(lowercase     )}, but `prompt`:'''
										    f''' {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches'''
										    ' the batch size of `prompt`.'     )
							else:
										_snake_case          =      negative_prompt

							_snake_case          =      self.tokenizer(
							    lowercase      ,  padding='max_length'      ,  max_length=77      ,  truncation=lowercase      ,  return_attention_mask=lowercase      ,  add_special_tokens=lowercase      ,  return_tensors='pt'      ,  )
							_snake_case          =      uncond_input.input_ids.to(lowercase     )
							_snake_case          =      uncond_input.attention_mask.to(lowercase     )

							_snake_case     , _snake_case          =      self.text_encoder(
							    input_ids=lowercase      ,  attention_mask=lowercase     )

							# duplicate unconditional embeddings for each generation per prompt, using mps friendly method

							_snake_case          =      negative_prompt_embeds.shape[1]
							_snake_case          =      negative_prompt_embeds.repeat(1      ,  lowercase     )
							_snake_case          =      negative_prompt_embeds.view(batch_size * num_images_per_prompt      ,  lowercase     )

							_snake_case          =      uncond_text_encoder_hidden_states.shape[1]
							_snake_case          =      uncond_text_encoder_hidden_states.repeat(1      ,  lowercase      ,  1     )
							_snake_case          =      uncond_text_encoder_hidden_states.view(
							    batch_size * num_images_per_prompt      ,  lowercase      ,  -1     )
							_snake_case          =      uncond_text_mask.repeat_interleave(lowercase      ,  dim=0     )

							# done duplicates

							# For classifier free guidance, we need to do two forward passes.
							# Here we concatenate the unconditional and text embeddings into a single batch
							# to avoid doing two forward passes
							_snake_case          =      torch.cat([negative_prompt_embeds, prompt_embeds]     )
							_snake_case          =      torch.cat([uncond_text_encoder_hidden_states, text_encoder_hidden_states]     )

							_snake_case          =      torch.cat([uncond_text_mask, text_mask]     )

				return prompt_embeds, text_encoder_hidden_states, text_mask



	def 			A							(  self     :   Dict      ,  lowercase     :   List[str]=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.text_encoder,
				    self.movq,
				]
				for cpu_offloaded_model in models:
							if cpu_offloaded_model is not None:
										cpu_offload(lowercase      ,  lowercase     )



	def 			A							(  self     :   str      ,  lowercase     :   str=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=lowercase     )
							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.text_encoder, self.unet, self.movq]:
							_snake_case     , _snake_case          =      cpu_offload_with_hook(lowercase      ,  lowercase      ,  prev_module_hook=lowercase     )

				if self.safety_checker is not None:
							_snake_case     , _snake_case          =      cpu_offload_with_hook(self.safety_checker      ,  lowercase      ,  prev_module_hook=lowercase     )

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



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


				'''simple docstring'''


				if not hasattr(self.unet      ,  '_hf_hook'     ):
							return self.device
				for module in self.unet.modules():
							if (
							    hasattr(lowercase      ,  '_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(lowercase     )
	def __call__(  self     :   Dict      ,  lowercase     :   Union[str, List[str]]      ,  lowercase     :   Union[torch.FloatTensor, List[torch.FloatTensor]]      ,  lowercase     :   Union[torch.FloatTensor, List[torch.FloatTensor]]      ,  lowercase     :   Optional[Union[str, List[str]]] = None      ,  lowercase     :   int = 512      ,  lowercase     :   int = 512      ,  lowercase     :   int = 100      ,  lowercase     :   float = 4.0      ,  lowercase     :   int = 1      ,  lowercase     :   Optional[Union[torch.Generator, List[torch.Generator]]] = None      ,  lowercase     :   Optional[torch.FloatTensor] = None      ,  lowercase     :   Optional[str] = "pil"      ,  lowercase     :   bool = True      ,  ):


				'''simple docstring'''


				if isinstance(lowercase      ,  lowercase     ):
							_snake_case          =      1
				elif isinstance(lowercase      ,  lowercase     ):
							_snake_case          =      len(lowercase     )
				else:
							raise ValueError(f'''`prompt` has to be of type `str` or `list` but is {type(lowercase     )}'''     )

				_snake_case          =      self._execution_device

				_snake_case          =      batch_size * num_images_per_prompt
				_snake_case          =      guidance_scale > 1.0

				_snake_case     , _snake_case     , _snake_case          =      self._encode_prompt(
				    lowercase      ,  lowercase      ,  lowercase      ,  lowercase      ,  lowercase     )

				if isinstance(lowercase      ,  lowercase     ):
							_snake_case          =      torch.cat(lowercase      ,  dim=0     )
				if isinstance(lowercase      ,  lowercase     ):
							_snake_case          =      torch.cat(lowercase      ,  dim=0     )

				if do_classifier_free_guidance:
							_snake_case          =      image_embeds.repeat_interleave(lowercase      ,  dim=0     )
							_snake_case          =      negative_image_embeds.repeat_interleave(lowercase      ,  dim=0     )

				_snake_case          =      torch.cat([negative_image_embeds, image_embeds]      ,  dim=0     ).to(
				    dtype=prompt_embeds.dtype      ,  device=lowercase     )

				self.scheduler.set_timesteps(lowercase      ,  device=lowercase     )
				_snake_case          =      self.scheduler.timesteps

				_snake_case          =      self.unet.config.in_channels

				_snake_case     , _snake_case          =      get_new_h_w(lowercase      ,  lowercase      ,  self.movq_scale_factor     )

				# create initial latent
				_snake_case          =      self.prepare_latents(
				    (batch_size, num_channels_latents, height, width)      ,  text_encoder_hidden_states.dtype      ,  lowercase      ,  lowercase      ,  lowercase      ,  self.scheduler      ,  )

				for i, t in enumerate(self.progress_bar(lowercase     )     ):
							# 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          =      {'text_embeds': prompt_embeds, 'image_embeds': image_embeds}
							_snake_case          =      self.unet(
							    sample=lowercase      ,  timestep=lowercase      ,  encoder_hidden_states=lowercase      ,  added_cond_kwargs=lowercase      ,  return_dict=lowercase      ,  )[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(
							    lowercase      ,  lowercase      ,  lowercase      ,  generator=lowercase      ,  ).prev_sample
				# post-processing
				_snake_case          =      self.movq.decode(lowercase      ,  force_not_quantize=lowercase     )['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(lowercase     )

				if not return_dict:
							return (image,)

				return ImagePipelineOutput(images=lowercase     )

from __future__ import annotations

_lowerCamelCase	:    Optional[Any]          =      [-10, -5, 0, 5, 5.1, 11, 13, 21, 3, 4, -21, -10, -5, -1, 0]
_lowerCamelCase	:    Optional[int]          =      [-5, 0, 5, 5.1, 11, 13, 21, -1, 4, -1, -10, -5, -1, 0, -1]






def 	a_		(	__lowercase       :     list[float]			)			->  list[float]:
			_snake_case          =      []
			_snake_case          =      len(__lowercase			)

			for i in range(__lowercase			):
						_snake_case          =      -1
						for j in range(i + 1	, __lowercase			):
									if arr[i] < arr[j]:
												_snake_case          =      arr[j]
												break
						result.append(__lowercase			)
			return result






def 	a_		(	__lowercase       :     list[float]			)			->  list[float]:
			_snake_case          =      []
			for i, outer in enumerate(__lowercase			):
						_snake_case          =      -1
						for inner in arr[i + 1 :]:
									if outer < inner:
												_snake_case          =      inner
												break
						result.append(__lowercase			)
			return result






def 	a_		(	__lowercase       :     list[float]			)			->  list[float]:
			_snake_case          =      len(__lowercase			)
			_snake_case          =      []
			_snake_case          =      [-1] * arr_size

			for index in reversed(range(__lowercase			)			):
						if stack:
									while stack[-1] <= arr[index]:
												stack.pop()
												if not stack:
															break
						if stack:
									_snake_case          =      stack[-1]
						stack.append(arr[index]			)
			return result


if __name__ == "__main__":
			from doctest import testmod
			from timeit import timeit

			testmod()
			print(next_greatest_element_slow(arr))
			print(next_greatest_element_fast(arr))
			print(next_greatest_element(arr))

			_lowerCamelCase	:    Union[str, Any]          =      (
			    '''from __main__ import arr, next_greatest_element_slow, '''
			    '''next_greatest_element_fast, next_greatest_element'''
			)
			print(
			    '''next_greatest_element_slow():''',
			    timeit('''next_greatest_element_slow(arr)''', setup=setup),
			)
			print(
			    '''next_greatest_element_fast():''',
			    timeit('''next_greatest_element_fast(arr)''', setup=setup),
			)
			print(
			    '''     next_greatest_element():''',
			    timeit('''next_greatest_element(arr)''', setup=setup),
			)