



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


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

lowercase_								=     """
    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 __lowerCAmelCase (     SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_=8  ):
			lowercase__					  =				h // scale_factor**2
			if h % scale_factor**2 != 0:
						new_h += 1
			lowercase__					  =				w // scale_factor**2
			if w % scale_factor**2 != 0:
						new_w += 1
			return new_h * scale_factor, new_w * scale_factor




class 				_snake_case			(					lowercase__):




				def __init__(						self					:						Tuple,					__lowercase					:						MultilingualCLIP,					__lowercase					:						XLMRobertaTokenizer,					__lowercase					:						UNetaDConditionModel,					__lowercase					:						Union[DDIMScheduler, DDPMScheduler],					__lowercase					:						VQModel,					):
							super().__init__()

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




				def 							A__      (						self					:						Optional[Any],					__lowercase					:						List[str],					__lowercase					:						Tuple,					__lowercase					:						Dict,					__lowercase					:						Dict,					__lowercase					:						Any,					__lowercase					:						List[Any]						):
							if latents is None:
										lowercase__					  =				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}'''						)
										lowercase__					  =				latents.to(__lowercase						)

							lowercase__					  =				latents * scheduler.init_noise_sigma
							return latents




				def 							A__      (						self					:						Tuple,					__lowercase					:						List[Any],					__lowercase					:						str,					__lowercase					:						int,					__lowercase					:						List[str],					__lowercase					:						Union[str, Any]=None,					):
							lowercase__					  =				len(__lowercase						) if isinstance(__lowercase,					__lowercase						) else 1
							# get prompt text embeddings
							lowercase__					  =				self.tokenizer(
							    __lowercase,					padding="max_length",					truncation=__lowercase,					max_length=77,					return_attention_mask=__lowercase,					add_special_tokens=__lowercase,					return_tensors="pt",					)

							lowercase__					  =				text_inputs.input_ids
							lowercase__					  =				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						):
										lowercase__					  =				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}'''						)

							lowercase__					  =				text_input_ids.to(__lowercase						)
							lowercase__					  =				text_inputs.attention_mask.to(__lowercase						)

							lowercase__  ,       lowercase__					  =				self.text_encoder(
							    input_ids=__lowercase,					attention_mask=__lowercase						)

							lowercase__					  =				prompt_embeds.repeat_interleave(__lowercase,					dim=0						)
							lowercase__					  =				text_encoder_hidden_states.repeat_interleave(__lowercase,					dim=0						)
							lowercase__					  =				text_mask.repeat_interleave(__lowercase,					dim=0						)

							if do_classifier_free_guidance:
										lowercase__					  =				42
										if negative_prompt is None:
													lowercase__					  =				[""] * 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						):
													lowercase__					  =				[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:
													lowercase__					  =				negative_prompt

										lowercase__					  =				self.tokenizer(
										    __lowercase,					padding="max_length",					max_length=77,					truncation=__lowercase,					return_attention_mask=__lowercase,					add_special_tokens=__lowercase,					return_tensors="pt",					)
										lowercase__					  =				uncond_input.input_ids.to(__lowercase						)
										lowercase__					  =				uncond_input.attention_mask.to(__lowercase						)

										lowercase__  ,       lowercase__					  =				self.text_encoder(
										    input_ids=__lowercase,					attention_mask=__lowercase						)

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

										lowercase__					  =				negative_prompt_embeds.shape[1]
										lowercase__					  =				negative_prompt_embeds.repeat(1,					__lowercase						)
										lowercase__					  =				negative_prompt_embeds.view(batch_size * num_images_per_prompt,					__lowercase						)

										lowercase__					  =				uncond_text_encoder_hidden_states.shape[1]
										lowercase__					  =				uncond_text_encoder_hidden_states.repeat(1,					__lowercase,					1						)
										lowercase__					  =				uncond_text_encoder_hidden_states.view(
										    batch_size * num_images_per_prompt,					__lowercase,					-1						)
										lowercase__					  =				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
										lowercase__					  =				torch.cat([negative_prompt_embeds, prompt_embeds]						)
										lowercase__					  =				torch.cat([uncond_text_encoder_hidden_states, text_encoder_hidden_states]						)

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

							return prompt_embeds, text_encoder_hidden_states, text_mask




				def 							A__      (						self					:						Optional[int],					__lowercase					:						str=0						):
							if is_accelerate_available():
										from accelerate import cpu_offload
							else:
										raise ImportError("Please install accelerate via `pip install accelerate`"						)

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

							lowercase__					  =				[
							    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					:						Union[str, Any]=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."						)

							lowercase__					  =				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)

							lowercase__					  =				None
							for cpu_offloaded_model in [self.text_encoder, self.unet, self.movq]:
										lowercase__  ,       lowercase__					  =				cpu_offload_with_hook(__lowercase,					__lowercase,					prev_module_hook=__lowercase						)

							if self.safety_checker is not None:
										lowercase__  ,       lowercase__					  =				cpu_offload_with_hook(self.safety_checker,					__lowercase,					prev_module_hook=__lowercase						)

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




				@property
				# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
				def 							A__      (						self					:						List[Any]						):
							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,					):

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

							lowercase__					  =				self._execution_device

							lowercase__					  =				batch_size * num_images_per_prompt
							lowercase__					  =				guidance_scale > 1.0

							lowercase__  ,       lowercase__  ,       lowercase__					  =				self._encode_prompt(
							    __lowercase,					__lowercase,					__lowercase,					__lowercase,					__lowercase						)

							if isinstance(__lowercase,					__lowercase						):
										lowercase__					  =				torch.cat(__lowercase,					dim=0						)
							if isinstance(__lowercase,					__lowercase						):
										lowercase__					  =				torch.cat(__lowercase,					dim=0						)

							if do_classifier_free_guidance:
										lowercase__					  =				image_embeds.repeat_interleave(__lowercase,					dim=0						)
										lowercase__					  =				negative_image_embeds.repeat_interleave(__lowercase,					dim=0						)

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

							self.scheduler.set_timesteps(__lowercase,					device=__lowercase						)
							lowercase__					  =				self.scheduler.timesteps

							lowercase__					  =				self.unet.config.in_channels

							lowercase__  ,       lowercase__					  =				get_new_h_w(__lowercase,					__lowercase,					self.movq_scale_factor						)

							# create initial latent
							lowercase__					  =				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
										lowercase__					  =				torch.cat([latents] * 2						) if do_classifier_free_guidance else latents

										lowercase__					  =				{"text_embeds": prompt_embeds, "image_embeds": image_embeds}
										lowercase__					  =				self.unet(
										    sample=__lowercase,					timestep=__lowercase,					encoder_hidden_states=__lowercase,					added_cond_kwargs=__lowercase,					return_dict=__lowercase,					)[0]

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

										# compute the previous noisy sample x_t -> x_t-1
										lowercase__					  =				self.scheduler.step(
										    __lowercase,					__lowercase,					__lowercase,					generator=__lowercase,					).prev_sample
							# post-processing
							lowercase__					  =				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"]:
										lowercase__					  =				image * 0.5 + 0.5
										lowercase__					  =				image.clamp(0,					1						)
										lowercase__					  =				image.cpu().permute(0,					2,					3,					1						).float().numpy()

							if output_type == "pil":
										lowercase__					  =				self.numpy_to_pil(__lowercase						)

							if not return_dict:
										return (image,)

							return ImagePipelineOutput(images=__lowercase						)









def __lowerCAmelCase (     SCREAMING_SNAKE_CASE_  ):
			if upper_limit < 0:
						raise ValueError("Limit for the Catalan sequence must be ≥ 0"  )

			lowercase__					  =				[0] * (upper_limit + 1)

			# Base case: C(0) = C(1) = 1
			lowercase__					  =				1
			if upper_limit > 0:
						lowercase__					  =				1

			# Recurrence relation: C(i) = sum(C(j).C(i-j-1)), from j = 0 to i
			for i in range(2						,  upper_limit + 1  ):
						for j in range(SCREAMING_SNAKE_CASE_  ):
									catalan_list[i] += catalan_list[j] * catalan_list[i - j - 1]

			return catalan_list


if __name__ == "__main__":
							print("""\n********* Catalan Numbers Using Dynamic Programming ************\n""")
							print("""\n*** Enter -1 at any time to quit ***""")
							print("""\nEnter the upper limit (≥ 0) for the Catalan number sequence: """, end="""""")
							try:
														while True:
																					lowercase_								=     int(input().strip())
																					if N < 0:
																												print("""\n********* Goodbye!! ************""")
																												break
																					else:
																												print(F'The Catalan numbers from 0 through {N} are:')
																												print(catalan_numbers(N))
																												print("""Try another upper limit for the sequence: """, end="""""")
							except (NameError, ValueError):
														print("""\n********* Invalid input, goodbye! ************\n""")

							import doctest

							doctest.testmod()




