


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


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

__UpperCamelCase     		=							'''
    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_(					_A :Optional[int]					,  _A :Optional[int]					,  _A :Optional[Any]=8	)->		Tuple:
			UpperCamelCase__       =							h // scale_factor**2
			if h % scale_factor**2 != 0:
						new_h += 1
			UpperCamelCase__       =							w // scale_factor**2
			if w % scale_factor**2 != 0:
						new_w += 1
			return new_h * scale_factor, new_w * scale_factor







class 							lowerCamelCase__						(				UpperCAmelCase   ):






			"""simple docstring"""






			def __init__(				self   ,    snake_case   ,    snake_case   ,    snake_case   ,    snake_case   ,    snake_case   ,    ):

						'''simple docstring'''


						super().__init__()

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




			def 					snake_case__					(				self   ,    snake_case   ,    snake_case   ,    snake_case   ,    snake_case   ,    snake_case   ,    snake_case  ):

						'''simple docstring'''


						if latents is None:
									UpperCamelCase__       =							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}'''  )
									UpperCamelCase__       =							latents.to(lowercase__  )

						UpperCamelCase__       =							latents * scheduler.init_noise_sigma
						return latents




			def 					snake_case__					(				self   ,    snake_case   ,    snake_case   ,    snake_case   ,    snake_case   ,    snake_case=None   ,    ):

						'''simple docstring'''


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

						UpperCamelCase__       =							text_inputs.input_ids
						UpperCamelCase__       =							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__  ):
									UpperCamelCase__       =							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}'''  )

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

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

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

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

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

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

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

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

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

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

						return prompt_embeds, text_encoder_hidden_states, text_mask




			def 					snake_case__					(				self   ,    snake_case=0  ):

						'''simple docstring'''


						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.text_encoder,
						    self.movq,
						]
						for cpu_offloaded_model in models:
									if cpu_offloaded_model is not None:
												cpu_offload(lowercase__   ,    lowercase__  )




			def 					snake_case__					(				self   ,    snake_case=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."  )

						UpperCamelCase__       =							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)

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

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

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




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

						'''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   ,    snake_case   ,    snake_case   ,    snake_case   ,    snake_case = None   ,    snake_case = 512   ,    snake_case = 512   ,    snake_case = 100   ,    snake_case = 4.0   ,    snake_case = 1   ,    snake_case = None   ,    snake_case = None   ,    snake_case = "pil"   ,    snake_case = True   ,    ):

						'''simple docstring'''


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

						UpperCamelCase__       =							self._execution_device

						UpperCamelCase__       =							batch_size * num_images_per_prompt
						UpperCamelCase__       =							guidance_scale > 1.0

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

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

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

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

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

						UpperCamelCase__       =							self.unet.config.in_channels

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

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

									UpperCamelCase__       =							{"text_embeds": prompt_embeds, "image_embeds": image_embeds}
									UpperCamelCase__       =							self.unet(
									    sample=lowercase__   ,    timestep=lowercase__   ,    encoder_hidden_states=lowercase__   ,    added_cond_kwargs=lowercase__   ,    return_dict=lowercase__   ,    )[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(
									    lowercase__   ,    lowercase__   ,    lowercase__   ,    generator=lowercase__   ,    ).prev_sample
						# post-processing
						UpperCamelCase__       =							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"]:
									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(lowercase__  )

						if not return_dict:
									return (image,)

						return ImagePipelineOutput(images=lowercase__  )



import argparse
from collections import defaultdict

import yaml


_lowercase:	List[Any]       			=		'''docs/source/en/_toctree.yml'''






def _lowerCamelCase  (		snake_case  ):
							_lowerCAmelCase							=							defaultdict(snake_case  )
							_lowerCAmelCase							=							[]
							_lowerCAmelCase							=							[]
							for doc in doc_list:
														if "local" in doc:
																					counts[doc["local"]] += 1

														if doc["title"].lower() == "overview":
																					overview_doc.append({'local': doc['local'], 'title': doc['title']}  )
														else:
																					new_doc_list.append(snake_case  )

							_lowerCAmelCase							=							new_doc_list
							_lowerCAmelCase							=							[key for key, value in counts.items() if value > 1]

							_lowerCAmelCase							=							[]
							for duplicate_key in duplicates:
														_lowerCAmelCase							=							list({doc['title'] for doc in doc_list if doc['local'] == duplicate_key}  )
														if len(snake_case  ) > 1:
																					raise ValueError(
																					    F'{duplicate_key} is present several times in the documentation table of content at '
																					    '`docs/source/en/_toctree.yml` with different *Title* values. Choose one of those and remove the '
																					    'others.'  )
														# Only add this once
														new_doc.append({'local': duplicate_key, 'title': titles[0]}  )

							# Add none duplicate-keys
							new_doc.extend([doc for doc in doc_list if 'local' not in counts or counts[doc['local']] == 1]  )
							_lowerCAmelCase							=							sorted(snake_case     , key=lambda snake_case  : s["title"].lower()  )

							# "overview" gets special treatment and is always first
							if len(snake_case  ) > 1:
														raise ValueError('{doc_list} has two \'overview\' docs which is not allowed.'  )

							overview_doc.extend(snake_case  )

							# Sort
							return overview_doc






def _lowerCamelCase  (		snake_case=False  ):
							with open(snake_case     , encoding='utf-8'  ) as f:
														_lowerCAmelCase							=							yaml.safe_load(f.read()  )

							# Get to the API doc
							_lowerCAmelCase							=							0
							while content[api_idx]["title"] != "API":
														api_idx += 1
							_lowerCAmelCase							=							content[api_idx]['sections']

							# Then to the model doc
							_lowerCAmelCase							=							0
							while api_doc[scheduler_idx]["title"] != "Schedulers":
														scheduler_idx += 1

							_lowerCAmelCase							=							api_doc[scheduler_idx]['sections']
							_lowerCAmelCase							=							clean_doc_toc(snake_case  )

							_lowerCAmelCase							=							False
							if new_scheduler_doc != scheduler_doc:
														_lowerCAmelCase							=							True
														if overwrite:
																					_lowerCAmelCase							=							new_scheduler_doc

							if diff:
														if overwrite:
																					_lowerCAmelCase							=							api_doc
																					with open(snake_case     , 'w'     , encoding='utf-8'  ) as f:
																												f.write(yaml.dump(snake_case     , allow_unicode=snake_case  )  )
														else:
																					raise ValueError(
																					    'The model doc part of the table of content is not properly sorted, run `make style` to fix this.'  )






def _lowerCamelCase  (		snake_case=False  ):
							with open(snake_case     , encoding='utf-8'  ) as f:
														_lowerCAmelCase							=							yaml.safe_load(f.read()  )

							# Get to the API doc
							_lowerCAmelCase							=							0
							while content[api_idx]["title"] != "API":
														api_idx += 1
							_lowerCAmelCase							=							content[api_idx]['sections']

							# Then to the model doc
							_lowerCAmelCase							=							0
							while api_doc[pipeline_idx]["title"] != "Pipelines":
														pipeline_idx += 1

							_lowerCAmelCase							=							False
							_lowerCAmelCase							=							api_doc[pipeline_idx]['sections']
							_lowerCAmelCase							=							[]

							# sort sub pipeline docs
							for pipeline_doc in pipeline_docs:
														if "section" in pipeline_doc:
																					_lowerCAmelCase							=							pipeline_doc['section']
																					_lowerCAmelCase							=							clean_doc_toc(snake_case  )
																					if overwrite:
																												_lowerCAmelCase							=							new_sub_pipeline_doc
														new_pipeline_docs.append(snake_case  )

							# sort overall pipeline doc
							_lowerCAmelCase							=							clean_doc_toc(snake_case  )

							if new_pipeline_docs != pipeline_docs:
														_lowerCAmelCase							=							True
														if overwrite:
																					_lowerCAmelCase							=							new_pipeline_docs

							if diff:
														if overwrite:
																					_lowerCAmelCase							=							api_doc
																					with open(snake_case     , 'w'     , encoding='utf-8'  ) as f:
																												f.write(yaml.dump(snake_case     , allow_unicode=snake_case  )  )
														else:
																					raise ValueError(
																					    'The model doc part of the table of content is not properly sorted, run `make style` to fix this.'  )


if __name__ == "__main__":
				_lowercase:	Tuple       			=		argparse.ArgumentParser()
				parser.add_argument('''--fix_and_overwrite''', action='''store_true''', help='''Whether to fix inconsistencies.''')
				_lowercase:	Optional[Any]       			=		parser.parse_args()

				check_scheduler_doc(args.fix_and_overwrite)
				check_pipeline_doc(args.fix_and_overwrite)






