



"""simple docstring"""

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

A_						:    Optional[Any]				=      '\n    Examples:\n        ```py\n        >>> from diffusers import KandinskyPipeline, KandinskyPriorPipeline\n        >>> import torch\n\n        >>> pipe_prior = KandinskyPriorPipeline.from_pretrained("kandinsky-community/Kandinsky-2-1-prior")\n        >>> pipe_prior.to("cuda")\n\n        >>> prompt = "red cat, 4k photo"\n        >>> out = pipe_prior(prompt)\n        >>> image_emb = out.image_embeds\n        >>> negative_image_emb = out.negative_image_embeds\n\n        >>> pipe = KandinskyPipeline.from_pretrained("kandinsky-community/kandinsky-2-1")\n        >>> pipe.to("cuda")\n\n        >>> image = pipe(\n        ...     prompt,\n        ...     image_embeds=image_emb,\n        ...     negative_image_embeds=negative_image_emb,\n        ...     height=768,\n        ...     width=768,\n        ...     num_inference_steps=100,\n        ... ).images\n\n        >>> image[0].save("cat.png")\n        ```\n'
def  __snake_case							(	__A      :						Union[str, Any]							,		__A      :						Optional[Any]							,		__A      :						Optional[int]=8     )     ->       int:






				'''simple docstring'''
				SCREAMING_SNAKE_CASE						:							List[str]							=     h // scale_factor**2
				if h % scale_factor**2 != 0:
								new_h += 1
				SCREAMING_SNAKE_CASE						:							Optional[int]							=     w // scale_factor**2
				if w % scale_factor**2 != 0:
								new_w += 1
				return new_h * scale_factor, new_w * scale_factor







class     lowerCAmelCase__						(	_lowerCamelCase ):


	'''simple docstring'''







	def __init__(      self				:       Dict     ,       _SCREAMING_SNAKE_CASE				:       MultilingualCLIP     ,       _SCREAMING_SNAKE_CASE				:       XLMRobertaTokenizer     ,       _SCREAMING_SNAKE_CASE				:       UNetaDConditionModel     ,       _SCREAMING_SNAKE_CASE				:       Union[DDIMScheduler, DDPMScheduler]     ,       _SCREAMING_SNAKE_CASE				:       VQModel     ,       )						->	Dict:





					"""simple docstring"""


					super().__init__()

					self.register_modules(
					    text_encoder=_SCREAMING_SNAKE_CASE     ,       tokenizer=_SCREAMING_SNAKE_CASE     ,       unet=_SCREAMING_SNAKE_CASE     ,       scheduler=_SCREAMING_SNAKE_CASE     ,       movq=_SCREAMING_SNAKE_CASE     ,       )
					SCREAMING_SNAKE_CASE						:							Optional[int]							=     2 ** (len(self.movq.config.block_out_channels    ) - 1)





	def 			_lowerCAmelCase     (      self				:       Optional[Any]     ,       _SCREAMING_SNAKE_CASE				:       Dict     ,       _SCREAMING_SNAKE_CASE				:       List[str]     ,       _SCREAMING_SNAKE_CASE				:       Any     ,       _SCREAMING_SNAKE_CASE				:       int     ,       _SCREAMING_SNAKE_CASE				:       List[str]     ,       _SCREAMING_SNAKE_CASE				:       Optional[Any]    )						->	Optional[Any]:





					"""simple docstring"""


					if latents is None:
									SCREAMING_SNAKE_CASE						:							Tuple							=     randn_tensor(_SCREAMING_SNAKE_CASE     ,       generator=_SCREAMING_SNAKE_CASE     ,       device=_SCREAMING_SNAKE_CASE     ,       dtype=_SCREAMING_SNAKE_CASE    )
					else:
									if latents.shape != shape:
													raise ValueError(f"""Unexpected latents shape, got {latents.shape}, expected {shape}"""    )
									SCREAMING_SNAKE_CASE						:							Optional[Any]							=     latents.to(_SCREAMING_SNAKE_CASE    )

					SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     latents * scheduler.init_noise_sigma
					return latents





	def 			_lowerCAmelCase     (      self				:       Optional[int]     ,       _SCREAMING_SNAKE_CASE				:       int     ,       _SCREAMING_SNAKE_CASE				:       Tuple     ,       _SCREAMING_SNAKE_CASE				:       str     ,       _SCREAMING_SNAKE_CASE				:       Union[str, Any]     ,       _SCREAMING_SNAKE_CASE				:       Optional[Any]=None     ,       )						->	Tuple:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							Optional[int]							=     len(_SCREAMING_SNAKE_CASE    ) if isinstance(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE    ) else 1
					# get prompt text embeddings
					SCREAMING_SNAKE_CASE						:							List[str]							=     self.tokenizer(
					    _SCREAMING_SNAKE_CASE     ,       padding='max_length'     ,       truncation=_SCREAMING_SNAKE_CASE     ,       max_length=77     ,       return_attention_mask=_SCREAMING_SNAKE_CASE     ,       add_special_tokens=_SCREAMING_SNAKE_CASE     ,       return_tensors='pt'     ,       )

					SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     text_inputs.input_ids
					SCREAMING_SNAKE_CASE						:							List[str]							=     self.tokenizer(_SCREAMING_SNAKE_CASE     ,       padding='longest'     ,       return_tensors='pt'    ).input_ids

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

					SCREAMING_SNAKE_CASE						:							Optional[Any]							=     text_input_ids.to(_SCREAMING_SNAKE_CASE    )
					SCREAMING_SNAKE_CASE						:							Any							=     text_inputs.attention_mask.to(_SCREAMING_SNAKE_CASE    )

					SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						:							Dict							=     self.text_encoder(
					    input_ids=_SCREAMING_SNAKE_CASE     ,       attention_mask=_SCREAMING_SNAKE_CASE    )

					SCREAMING_SNAKE_CASE						:							Dict							=     prompt_embeds.repeat_interleave(_SCREAMING_SNAKE_CASE     ,       dim=0    )
					SCREAMING_SNAKE_CASE						:							Optional[int]							=     text_encoder_hidden_states.repeat_interleave(_SCREAMING_SNAKE_CASE     ,       dim=0    )
					SCREAMING_SNAKE_CASE						:							Tuple							=     text_mask.repeat_interleave(_SCREAMING_SNAKE_CASE     ,       dim=0    )

					if do_classifier_free_guidance:
									SCREAMING_SNAKE_CASE						:							List[str]
									if negative_prompt is None:
													SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     [''] * batch_size
									elif type(_SCREAMING_SNAKE_CASE    ) is not type(_SCREAMING_SNAKE_CASE    ):
													raise TypeError(
													    f"""`negative_prompt` should be the same type to `prompt`, but got {type(_SCREAMING_SNAKE_CASE    )} !="""
													    f""" {type(_SCREAMING_SNAKE_CASE    )}."""    )
									elif isinstance(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE    ):
													SCREAMING_SNAKE_CASE						:							Any							=     [negative_prompt]
									elif batch_size != len(_SCREAMING_SNAKE_CASE    ):
													raise ValueError(
													    f"""`negative_prompt`: {negative_prompt} has batch size {len(_SCREAMING_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:
													SCREAMING_SNAKE_CASE						:							List[Any]							=     negative_prompt

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

									SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						:							Any							=     self.text_encoder(
									    input_ids=_SCREAMING_SNAKE_CASE     ,       attention_mask=_SCREAMING_SNAKE_CASE    )

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

									SCREAMING_SNAKE_CASE						:							List[Any]							=     negative_prompt_embeds.shape[1]
									SCREAMING_SNAKE_CASE						:							Optional[Any]							=     negative_prompt_embeds.repeat(1     ,       _SCREAMING_SNAKE_CASE    )
									SCREAMING_SNAKE_CASE						:							Tuple							=     negative_prompt_embeds.view(batch_size * num_images_per_prompt     ,       _SCREAMING_SNAKE_CASE    )

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

									SCREAMING_SNAKE_CASE						:							Dict							=     torch.cat([uncond_text_mask, text_mask]    )

					return prompt_embeds, text_encoder_hidden_states, text_mask





	def 			_lowerCAmelCase     (      self				:       List[Any]     ,       _SCREAMING_SNAKE_CASE				:       Any=0    )						->	Any:





					"""simple docstring"""


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

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

					SCREAMING_SNAKE_CASE						:							Dict							=     [
					    self.unet,
					    self.text_encoder,
					    self.movq,
					]
					for cpu_offloaded_model in models:
									if cpu_offloaded_model is not None:
													cpu_offload(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE    )





	def 			_lowerCAmelCase     (      self				:       Optional[int]     ,       _SCREAMING_SNAKE_CASE				:       Optional[int]=0    )						->	Optional[Any]:





					"""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.'    )

					SCREAMING_SNAKE_CASE						:							List[Any]							=     torch.device(f"""cuda:{gpu_id}"""    )

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

					SCREAMING_SNAKE_CASE						:							List[str]							=     None
					for cpu_offloaded_model in [self.text_encoder, self.unet, self.movq]:
									SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						:							Dict							=     cpu_offload_with_hook(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       prev_module_hook=_SCREAMING_SNAKE_CASE    )

					if self.safety_checker is not None:
									SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     cpu_offload_with_hook(self.safety_checker     ,       _SCREAMING_SNAKE_CASE     ,       prev_module_hook=_SCREAMING_SNAKE_CASE    )

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





	@property
	# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
	def 			_lowerCAmelCase     (      self				:       str    )						->	Any:





					"""simple docstring"""


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





					"""simple docstring"""


					if isinstance(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE    ):
									SCREAMING_SNAKE_CASE						:							List[str]							=     1
					elif isinstance(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE    ):
									SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     len(_SCREAMING_SNAKE_CASE    )
					else:
									raise ValueError(f"""`prompt` has to be of type `str` or `list` but is {type(_SCREAMING_SNAKE_CASE    )}"""    )

					SCREAMING_SNAKE_CASE						:							Optional[int]							=     self._execution_device

					SCREAMING_SNAKE_CASE						:							str							=     batch_size * num_images_per_prompt
					SCREAMING_SNAKE_CASE						:							str							=     guidance_scale > 1.0

					SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						:							Optional[int]							=     self._encode_prompt(
					    _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE    )

					if isinstance(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE    ):
									SCREAMING_SNAKE_CASE						:							str							=     torch.cat(_SCREAMING_SNAKE_CASE     ,       dim=0    )
					if isinstance(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE    ):
									SCREAMING_SNAKE_CASE						:							Tuple							=     torch.cat(_SCREAMING_SNAKE_CASE     ,       dim=0    )

					if do_classifier_free_guidance:
									SCREAMING_SNAKE_CASE						:							int							=     image_embeds.repeat_interleave(_SCREAMING_SNAKE_CASE     ,       dim=0    )
									SCREAMING_SNAKE_CASE						:							str							=     negative_image_embeds.repeat_interleave(_SCREAMING_SNAKE_CASE     ,       dim=0    )

					SCREAMING_SNAKE_CASE						:							Tuple							=     torch.cat([negative_image_embeds, image_embeds]     ,       dim=0    ).to(
					    dtype=prompt_embeds.dtype     ,       device=_SCREAMING_SNAKE_CASE    )

					self.scheduler.set_timesteps(_SCREAMING_SNAKE_CASE     ,       device=_SCREAMING_SNAKE_CASE    )
					SCREAMING_SNAKE_CASE						:							List[Any]							=     self.scheduler.timesteps

					SCREAMING_SNAKE_CASE						:							str							=     self.unet.config.in_channels

					SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						:							int							=     get_new_h_w(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       self.movq_scale_factor    )

					# create initial latent
					SCREAMING_SNAKE_CASE						:							List[Any]							=     self.prepare_latents(
					    (batch_size, num_channels_latents, height, width)     ,       text_encoder_hidden_states.dtype     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       self.scheduler     ,       )

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

									SCREAMING_SNAKE_CASE						:							str							=     {'text_embeds': prompt_embeds, 'image_embeds': image_embeds}
									SCREAMING_SNAKE_CASE						:							Optional[int]							=     self.unet(
									    sample=_SCREAMING_SNAKE_CASE     ,       timestep=_SCREAMING_SNAKE_CASE     ,       encoder_hidden_states=_SCREAMING_SNAKE_CASE     ,       added_cond_kwargs=_SCREAMING_SNAKE_CASE     ,       return_dict=_SCREAMING_SNAKE_CASE     ,       )[0]

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

									# compute the previous noisy sample x_t -> x_t-1
									SCREAMING_SNAKE_CASE						:							int							=     self.scheduler.step(
									    _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       generator=_SCREAMING_SNAKE_CASE     ,       ).prev_sample
					# post-processing
					SCREAMING_SNAKE_CASE						:							List[Any]							=     self.movq.decode(_SCREAMING_SNAKE_CASE     ,       force_not_quantize=_SCREAMING_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"]:
									SCREAMING_SNAKE_CASE						:							List[str]							=     image * 0.5 + 0.5
									SCREAMING_SNAKE_CASE						:							List[str]							=     image.clamp(0     ,       1    )
									SCREAMING_SNAKE_CASE						:							Optional[Any]							=     image.cpu().permute(0     ,       2     ,       3     ,       1    ).float().numpy()

					if output_type == "pil":
									SCREAMING_SNAKE_CASE						:							Dict							=     self.numpy_to_pil(_SCREAMING_SNAKE_CASE    )

					if not return_dict:
									return (image,)

					return ImagePipelineOutput(images=_SCREAMING_SNAKE_CASE    )










"""simple docstring"""

import argparse
import json

from tqdm import tqdm
def  __snake_case							(	)     ->       Any:






				'''simple docstring'''
				SCREAMING_SNAKE_CASE						:							str							=     argparse.ArgumentParser()

				# Required parameters
				parser.add_argument(
				    '--src_path'							,		type=__A							,		default='biencoder-nq-dev.json'							,		help='Path to raw DPR training data'							,		)
				parser.add_argument(
				    '--evaluation_set'							,		type=__A							,		help='where to store parsed evaluation_set file'							,		)
				parser.add_argument(
				    '--gold_data_path'							,		type=__A							,		help='where to store parsed gold_data_path file'							,		)
				SCREAMING_SNAKE_CASE						:							Tuple							=     parser.parse_args()

				with open(args.src_path							,		'r'     ) as src_file, open(args.evaluation_set							,		'w'     ) as eval_file, open(
				    args.gold_data_path							,		'w'     ) as gold_file:
								SCREAMING_SNAKE_CASE						:							Tuple							=     json.load(__A     )
								for dpr_record in tqdm(__A     ):
												SCREAMING_SNAKE_CASE						:							Tuple							=     dpr_record['question']
												SCREAMING_SNAKE_CASE						:							Optional[int]							=     [context['title'] for context in dpr_record['positive_ctxs']]
												eval_file.write(question + '\n'     )
												gold_file.write('\t'.join(__A     ) + '\n'     )


if __name__ == "__main__":
							main()





