
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


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

a											=							"""
    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   UpperCamelCase_(					__magic_name__      :	Any	,    __magic_name__      :	Tuple	,    __magic_name__      :	Tuple=8      ):


							"""simple docstring"""





							_lowerCAmelCase :int      	=						h // scale_factor**2
							if h % scale_factor**2 != 0:
														new_h += 1
							_lowerCAmelCase :Dict      	=						w // scale_factor**2
							if w % scale_factor**2 != 0:
														new_w += 1
							return new_h * scale_factor, new_w * scale_factor





class 	UpperCAmelCase_ (snake_case__							):
	"""simple docstring"""

	def __init__(     self:     List[Any]							,   _UpperCAmelCase:     MultilingualCLIP							,   _UpperCAmelCase:     XLMRobertaTokenizer							,   _UpperCAmelCase:     UNetaDConditionModel							,   _UpperCAmelCase:     Union[DDIMScheduler, DDPMScheduler]							,   _UpperCAmelCase:     VQModel							,   ):
								super().__init__()

								self.register_modules(
								    text_encoder=snake_case_							,   tokenizer=snake_case_							,   unet=snake_case_							,   scheduler=snake_case_							,   movq=snake_case_							,   )
								_lowerCAmelCase :List[Any]      	=						2 ** (len(self.movq.config.block_out_channels			) - 1)







	def       SCREAMING_SNAKE_CASE__     (     self:     List[str]							,   _UpperCAmelCase:     Dict							,   _UpperCAmelCase:     List[str]							,   _UpperCAmelCase:     Union[str, Any]							,   _UpperCAmelCase:     int							,   _UpperCAmelCase:     str							,   _UpperCAmelCase:     Any			):
								if latents is None:
															_lowerCAmelCase :Union[str, Any]      	=						randn_tensor(snake_case_							,   generator=snake_case_							,   device=snake_case_							,   dtype=snake_case_			)
								else:
															if latents.shape != shape:
																						raise ValueError(f"""Unexpected latents shape, got {latents.shape}, expected {shape}"""			)
															_lowerCAmelCase :Union[str, Any]      	=						latents.to(snake_case_			)

								_lowerCAmelCase :List[Any]      	=						latents * scheduler.init_noise_sigma
								return latents







	def       SCREAMING_SNAKE_CASE__     (     self:     Dict							,   _UpperCAmelCase:     Any							,   _UpperCAmelCase:     Union[str, Any]							,   _UpperCAmelCase:     Tuple							,   _UpperCAmelCase:     Dict							,   _UpperCAmelCase:     Tuple=None							,   ):
								_lowerCAmelCase :Any      	=						len(snake_case_			) if isinstance(snake_case_							,   snake_case_			) else 1
								# get prompt text embeddings
								_lowerCAmelCase :Tuple      	=						self.tokenizer(
								    snake_case_							,   padding='max_length'							,   truncation=snake_case_							,   max_length=77							,   return_attention_mask=snake_case_							,   add_special_tokens=snake_case_							,   return_tensors='pt'							,   )

								_lowerCAmelCase :Union[str, Any]      	=						text_inputs.input_ids
								_lowerCAmelCase :Optional[Any]      	=						self.tokenizer(snake_case_							,   padding='longest'							,   return_tensors='pt'			).input_ids

								if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(snake_case_							,   snake_case_			):
															_lowerCAmelCase :Tuple      	=						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}"""			)

								_lowerCAmelCase :Union[str, Any]      	=						text_input_ids.to(snake_case_			)
								_lowerCAmelCase :List[Any]      	=						text_inputs.attention_mask.to(snake_case_			)

								_lowerCAmelCase	,  _lowerCAmelCase :int      	=						self.text_encoder(
								    input_ids=snake_case_							,   attention_mask=snake_case_			)

								_lowerCAmelCase :Any      	=						prompt_embeds.repeat_interleave(snake_case_							,   dim=0			)
								_lowerCAmelCase :List[Any]      	=						text_encoder_hidden_states.repeat_interleave(snake_case_							,   dim=0			)
								_lowerCAmelCase :List[Any]      	=						text_mask.repeat_interleave(snake_case_							,   dim=0			)

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

															_lowerCAmelCase :Optional[Any]      	=						self.tokenizer(
															    snake_case_							,   padding='max_length'							,   max_length=77							,   truncation=snake_case_							,   return_attention_mask=snake_case_							,   add_special_tokens=snake_case_							,   return_tensors='pt'							,   )
															_lowerCAmelCase :Any      	=						uncond_input.input_ids.to(snake_case_			)
															_lowerCAmelCase :Any      	=						uncond_input.attention_mask.to(snake_case_			)

															_lowerCAmelCase	,  _lowerCAmelCase :Tuple      	=						self.text_encoder(
															    input_ids=snake_case_							,   attention_mask=snake_case_			)

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

															_lowerCAmelCase :Union[str, Any]      	=						negative_prompt_embeds.shape[1]
															_lowerCAmelCase :Optional[Any]      	=						negative_prompt_embeds.repeat(1							,   snake_case_			)
															_lowerCAmelCase :Optional[int]      	=						negative_prompt_embeds.view(batch_size * num_images_per_prompt							,   snake_case_			)

															_lowerCAmelCase :Optional[Any]      	=						uncond_text_encoder_hidden_states.shape[1]
															_lowerCAmelCase :List[str]      	=						uncond_text_encoder_hidden_states.repeat(1							,   snake_case_							,   1			)
															_lowerCAmelCase :List[Any]      	=						uncond_text_encoder_hidden_states.view(
															    batch_size * num_images_per_prompt							,   snake_case_							,   -1			)
															_lowerCAmelCase :Tuple      	=						uncond_text_mask.repeat_interleave(snake_case_							,   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
															_lowerCAmelCase :List[Any]      	=						torch.cat([negative_prompt_embeds, prompt_embeds]			)
															_lowerCAmelCase :List[Any]      	=						torch.cat([uncond_text_encoder_hidden_states, text_encoder_hidden_states]			)

															_lowerCAmelCase :List[str]      	=						torch.cat([uncond_text_mask, text_mask]			)

								return prompt_embeds, text_encoder_hidden_states, text_mask







	def       SCREAMING_SNAKE_CASE__     (     self:     Tuple							,   _UpperCAmelCase:     Optional[Any]=0			):
								if is_accelerate_available():
															from accelerate import cpu_offload
								else:
															raise ImportError('Please install accelerate via `pip install accelerate`'			)

								_lowerCAmelCase :int      	=						torch.device(f"""cuda:{gpu_id}"""			)

								_lowerCAmelCase :List[Any]      	=						[
								    self.unet,
								    self.text_encoder,
								    self.movq,
								]
								for cpu_offloaded_model in models:
															if cpu_offloaded_model is not None:
																						cpu_offload(snake_case_							,   snake_case_			)







	def       SCREAMING_SNAKE_CASE__     (     self:     Dict							,   _UpperCAmelCase:     Optional[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.'			)

								_lowerCAmelCase :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)

								_lowerCAmelCase :int      	=						None
								for cpu_offloaded_model in [self.text_encoder, self.unet, self.movq]:
															_lowerCAmelCase	,  _lowerCAmelCase :List[str]      	=						cpu_offload_with_hook(snake_case_							,   snake_case_							,   prev_module_hook=snake_case_			)

								if self.safety_checker is not None:
															_lowerCAmelCase	,  _lowerCAmelCase :Optional[int]      	=						cpu_offload_with_hook(self.safety_checker							,   snake_case_							,   prev_module_hook=snake_case_			)

								# We'll offload the last model manually.
								_lowerCAmelCase :Optional[Any]      	=						hook







	@property
	# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
	def       SCREAMING_SNAKE_CASE__     (     self:     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:     Union[str, Any]							,   _UpperCAmelCase:     Union[str, List[str]]							,   _UpperCAmelCase:     Union[torch.FloatTensor, List[torch.FloatTensor]]							,   _UpperCAmelCase:     Union[torch.FloatTensor, List[torch.FloatTensor]]							,   _UpperCAmelCase:     Optional[Union[str, List[str]]] = None							,   _UpperCAmelCase:     int = 512							,   _UpperCAmelCase:     int = 512							,   _UpperCAmelCase:     int = 100							,   _UpperCAmelCase:     float = 4.0							,   _UpperCAmelCase:     int = 1							,   _UpperCAmelCase:     Optional[Union[torch.Generator, List[torch.Generator]]] = None							,   _UpperCAmelCase:     Optional[torch.FloatTensor] = None							,   _UpperCAmelCase:     Optional[str] = "pil"							,   _UpperCAmelCase:     bool = True							,   ):

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

								_lowerCAmelCase :List[str]      	=						self._execution_device

								_lowerCAmelCase :Tuple      	=						batch_size * num_images_per_prompt
								_lowerCAmelCase :int      	=						guidance_scale > 1.0

								_lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase :List[str]      	=						self._encode_prompt(
								    snake_case_							,   snake_case_							,   snake_case_							,   snake_case_							,   snake_case_			)

								if isinstance(snake_case_							,   snake_case_			):
															_lowerCAmelCase :str      	=						torch.cat(snake_case_							,   dim=0			)
								if isinstance(snake_case_							,   snake_case_			):
															_lowerCAmelCase :Optional[int]      	=						torch.cat(snake_case_							,   dim=0			)

								if do_classifier_free_guidance:
															_lowerCAmelCase :Union[str, Any]      	=						image_embeds.repeat_interleave(snake_case_							,   dim=0			)
															_lowerCAmelCase :Tuple      	=						negative_image_embeds.repeat_interleave(snake_case_							,   dim=0			)

								_lowerCAmelCase :Optional[Any]      	=						torch.cat([negative_image_embeds, image_embeds]							,   dim=0			).to(
								    dtype=prompt_embeds.dtype							,   device=snake_case_			)

								self.scheduler.set_timesteps(snake_case_							,   device=snake_case_			)
								_lowerCAmelCase :Union[str, Any]      	=						self.scheduler.timesteps

								_lowerCAmelCase :Dict      	=						self.unet.config.in_channels

								_lowerCAmelCase	,  _lowerCAmelCase :Tuple      	=						get_new_h_w(snake_case_							,   snake_case_							,   self.movq_scale_factor			)

								# create initial latent
								_lowerCAmelCase :Any      	=						self.prepare_latents(
								    (batch_size, num_channels_latents, height, width)							,   text_encoder_hidden_states.dtype							,   snake_case_							,   snake_case_							,   snake_case_							,   self.scheduler							,   )

								for i, t in enumerate(self.progress_bar(snake_case_			)			):
															# expand the latents if we are doing classifier free guidance
															_lowerCAmelCase :Optional[Any]      	=						torch.cat([latents] * 2			) if do_classifier_free_guidance else latents

															_lowerCAmelCase :Tuple      	=						{'text_embeds': prompt_embeds, 'image_embeds': image_embeds}
															_lowerCAmelCase :str      	=						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:
																						_lowerCAmelCase	,  _lowerCAmelCase :List[Any]      	=						noise_pred.split(latents.shape[1]							,   dim=1			)
																						_lowerCAmelCase	,  _lowerCAmelCase :Tuple      	=						noise_pred.chunk(2			)
																						_lowerCAmelCase	,  _lowerCAmelCase :List[str]      	=						variance_pred.chunk(2			)
																						_lowerCAmelCase :int      	=						noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
																						_lowerCAmelCase :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"]
															):
																						_lowerCAmelCase	,  _lowerCAmelCase :Optional[Any]      	=						noise_pred.split(latents.shape[1]							,   dim=1			)

															# compute the previous noisy sample x_t -> x_t-1
															_lowerCAmelCase :Any      	=						self.scheduler.step(
															    snake_case_							,   snake_case_							,   snake_case_							,   generator=snake_case_							,   ).prev_sample
								# post-processing
								_lowerCAmelCase :Any      	=						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"]:
															_lowerCAmelCase :Union[str, Any]      	=						image * 0.5 + 0.5
															_lowerCAmelCase :Tuple      	=						image.clamp(0							,   1			)
															_lowerCAmelCase :int      	=						image.cpu().permute(0							,   2							,   3							,   1			).float().numpy()

								if output_type == "pil":
															_lowerCAmelCase :Optional[int]      	=						self.numpy_to_pil(snake_case_			)

								if not return_dict:
															return (image,)

								return ImagePipelineOutput(images=snake_case_			)




from __future__ import annotations






class     __a			:



	def __init__(    self				:     Optional[int]					,						snake_case_				:     int = 0)->				List[str]:
						__lowerCAmelCase						 =key



	def 		UpperCamelCase				(    self				:     Any					,						snake_case_				:     str					,						snake_case_				:     int)->				list[str]:
						assert isinstance(snake_case_					,						snake_case_) and isinstance(snake_case_					,						snake_case_)

						__lowerCAmelCase						 =key or self.__key or 1

						# make sure key is an appropriate size
						key %= 2_55

						return [chr(ord(snake_case_) ^ key) for ch in content]



	def 		UpperCamelCase				(    self				:     Tuple					,						snake_case_				:     str					,						snake_case_				:     int)->				list[str]:
						assert isinstance(snake_case_					,						snake_case_) and isinstance(snake_case_					,						snake_case_)

						__lowerCAmelCase						 =key or self.__key or 1

						# make sure key is an appropriate size
						key %= 2_55

						return [chr(ord(snake_case_) ^ key) for ch in content]



	def 		UpperCamelCase				(    self				:     str					,						snake_case_				:     str					,						snake_case_				:     int = 0)->				str:
						assert isinstance(snake_case_					,						snake_case_) and isinstance(snake_case_					,						snake_case_)

						__lowerCAmelCase						 =key or self.__key or 1

						# make sure key can be any size
						while key > 2_55:
											key -= 2_55

						# This will be returned
						__lowerCAmelCase						 =""""""

						for ch in content:
											ans += chr(ord(snake_case_) ^ key)

						return ans



	def 		UpperCamelCase				(    self				:     Tuple					,						snake_case_				:     str					,						snake_case_				:     int = 0)->				str:
						assert isinstance(snake_case_					,						snake_case_) and isinstance(snake_case_					,						snake_case_)

						__lowerCAmelCase						 =key or self.__key or 1

						# make sure key can be any size
						while key > 2_55:
											key -= 2_55

						# This will be returned
						__lowerCAmelCase						 =""""""

						for ch in content:
											ans += chr(ord(snake_case_) ^ key)

						return ans



	def 		UpperCamelCase				(    self				:     int					,						snake_case_				:     str					,						snake_case_				:     int = 0)->				bool:
						assert isinstance(snake_case_					,						snake_case_) and isinstance(snake_case_					,						snake_case_)

						try:
											with open(snake_case_) as fin, open("""encrypt.out"""					,						"""w+""") as fout:
																# actual encrypt-process
																for line in fin:
																					fout.write(self.encrypt_string(snake_case_					,						snake_case_))

						except OSError:
											return False

						return True



	def 		UpperCamelCase				(    self				:     Tuple					,						snake_case_				:     str					,						snake_case_				:     int)->				bool:
						assert isinstance(snake_case_					,						snake_case_) and isinstance(snake_case_					,						snake_case_)

						try:
											with open(snake_case_) as fin, open("""decrypt.out"""					,						"""w+""") as fout:
																# actual encrypt-process
																for line in fin:
																					fout.write(self.decrypt_string(snake_case_					,						snake_case_))

						except OSError:
											return False

						return True


# Tests
# crypt = XORCipher()
# key = 67

# # test encrypt
# print(crypt.encrypt("hallo welt",key))
# # test decrypt
# print(crypt.decrypt(crypt.encrypt("hallo welt",key), key))

# # test encrypt_string
# print(crypt.encrypt_string("hallo welt",key))

# # test decrypt_string
# print(crypt.decrypt_string(crypt.encrypt_string("hallo welt",key),key))

# if (crypt.encrypt_file("test.txt",key)):
#       print("encrypt successful")
# else:
#       print("encrypt unsuccessful")

# if (crypt.decrypt_file("encrypt.out",key)):
#       print("decrypt successful")
# else:
#       print("decrypt unsuccessful")




