

'''simple docstring'''





import tensorflow as tf

from ...tf_utils import shape_list





class     snake_case_			(				tf.keras.layers.Layer							):


							"""simple docstring"""





							def __init__( self					,						_A					,						_A					,						_A					,						_A					,						_A=1					,						_A=False					,						**_A							):
												super().__init__(**_A							)

												__lowerCAmelCase					=							vocab_size
												__lowerCAmelCase					=							d_embed
												__lowerCAmelCase					=							d_proj

												__lowerCAmelCase					=							cutoffs + [vocab_size]
												__lowerCAmelCase					=							[0] + self.cutoffs
												__lowerCAmelCase					=							div_val

												__lowerCAmelCase					=							self.cutoffs[0]
												__lowerCAmelCase					=							len(self.cutoffs							) - 1
												__lowerCAmelCase					=							self.shortlist_size + self.n_clusters
												__lowerCAmelCase					=							keep_order

												__lowerCAmelCase					=							[]
												__lowerCAmelCase					=							[]




							def        A__  ( self					,						_A							):
												if self.n_clusters > 0:
																	__lowerCAmelCase					=							self.add_weight(
																	    shape=(self.n_clusters, self.d_embed)					,						initializer='zeros'					,						trainable=_A					,						name='cluster_weight'							)
																	__lowerCAmelCase					=							self.add_weight(
																	    shape=(self.n_clusters,)					,						initializer='zeros'					,						trainable=_A					,						name='cluster_bias'							)

												if self.div_val == 1:
																	for i in range(len(self.cutoffs							)							):
																						if self.d_proj != self.d_embed:
																											__lowerCAmelCase					=							self.add_weight(
																											    shape=(self.d_embed, self.d_proj)					,						initializer='zeros'					,						trainable=_A					,						name=F"""out_projs_._{i}"""					,						)
																											self.out_projs.append(_A							)
																						else:
																											self.out_projs.append(_A							)
																						__lowerCAmelCase					=							self.add_weight(
																						    shape=(self.vocab_size, self.d_embed)					,						initializer='zeros'					,						trainable=_A					,						name=F"""out_layers_._{i}_._weight"""					,						)
																						__lowerCAmelCase					=							self.add_weight(
																						    shape=(self.vocab_size,)					,						initializer='zeros'					,						trainable=_A					,						name=F"""out_layers_._{i}_._bias"""					,						)
																						self.out_layers.append((weight, bias)							)
												else:
																	for i in range(len(self.cutoffs							)							):
																						__lowerCAmelCase,	__lowerCAmelCase					=							self.cutoff_ends[i], self.cutoff_ends[i + 1]
																						__lowerCAmelCase					=							self.d_embed // (self.div_val**i)

																						__lowerCAmelCase					=							self.add_weight(
																						    shape=(d_emb_i, self.d_proj)					,						initializer='zeros'					,						trainable=_A					,						name=F"""out_projs_._{i}"""							)
																						self.out_projs.append(_A							)
																						__lowerCAmelCase					=							self.add_weight(
																						    shape=(r_idx - l_idx, d_emb_i)					,						initializer='zeros'					,						trainable=_A					,						name=F"""out_layers_._{i}_._weight"""					,						)
																						__lowerCAmelCase					=							self.add_weight(
																						    shape=(r_idx - l_idx,)					,						initializer='zeros'					,						trainable=_A					,						name=F"""out_layers_._{i}_._bias"""					,						)
																						self.out_layers.append((weight, bias)							)
												super().build(_A							)




							@staticmethod
							def        A__  ( _A					,						_A					,						_A					,						_A=None							):
												__lowerCAmelCase					=							x
												if proj is not None:
																	__lowerCAmelCase					=							tf.einsum('ibd,ed->ibe'					,						_A					,						_A							)
												return tf.einsum('ibd,nd->ibn'					,						_A					,						_A							) + b




							@staticmethod
							def        A__  ( _A					,						_A							):
												__lowerCAmelCase					=							shape_list(_A							)
												__lowerCAmelCase					=							tf.range(lp_size[0]					,						dtype=target.dtype							)
												__lowerCAmelCase					=							tf.stack([r, target]					,						1							)
												return tf.gather_nd(_A					,						_A							)




							def        A__  ( self					,						_A					,						_A					,						_A=True					,						_A=False							):
												__lowerCAmelCase					=							0
												if self.n_clusters == 0:
																	__lowerCAmelCase					=							self._logit(_A					,						self.out_layers[0][0]					,						self.out_layers[0][1]					,						self.out_projs[0]							)
																	if target is not None:
																						__lowerCAmelCase					=							tf.nn.sparse_softmax_cross_entropy_with_logits(labels=_A					,						logits=_A							)
																	__lowerCAmelCase					=							tf.nn.log_softmax(_A					,						axis=-1							)
												else:
																	__lowerCAmelCase					=							shape_list(_A							)
																	__lowerCAmelCase					=							[]
																	__lowerCAmelCase					=							tf.zeros(hidden_sizes[:2]							)
																	for i in range(len(self.cutoffs							)							):
																						__lowerCAmelCase,	__lowerCAmelCase					=							self.cutoff_ends[i], self.cutoff_ends[i + 1]
																						if target is not None:
																											__lowerCAmelCase					=							(target >= l_idx) & (target < r_idx)
																											__lowerCAmelCase					=							tf.where(_A							)
																											__lowerCAmelCase					=							tf.boolean_mask(_A					,						_A							) - l_idx

																						if self.div_val == 1:
																											__lowerCAmelCase					=							self.out_layers[0][0][l_idx:r_idx]
																											__lowerCAmelCase					=							self.out_layers[0][1][l_idx:r_idx]
																						else:
																											__lowerCAmelCase					=							self.out_layers[i][0]
																											__lowerCAmelCase					=							self.out_layers[i][1]

																						if i == 0:
																											__lowerCAmelCase					=							tf.concat([cur_W, self.cluster_weight]					,						0							)
																											__lowerCAmelCase					=							tf.concat([cur_b, self.cluster_bias]					,						0							)

																											__lowerCAmelCase					=							self._logit(_A					,						_A					,						_A					,						self.out_projs[0]							)
																											__lowerCAmelCase					=							tf.nn.log_softmax(_A							)
																											out.append(head_logprob[..., : self.cutoffs[0]]							)
																											if target is not None:
																																__lowerCAmelCase					=							tf.boolean_mask(_A					,						_A							)
																																__lowerCAmelCase					=							self._gather_logprob(_A					,						_A							)
																						else:
																											__lowerCAmelCase					=							self._logit(_A					,						_A					,						_A					,						self.out_projs[i]							)
																											__lowerCAmelCase					=							tf.nn.log_softmax(_A							)
																											__lowerCAmelCase					=							self.cutoffs[0] + i - 1  # No probability for the head cluster
																											__lowerCAmelCase					=							head_logprob[..., cluster_prob_idx, None] + tail_logprob
																											out.append(_A							)
																											if target is not None:
																																__lowerCAmelCase					=							tf.boolean_mask(_A					,						_A							)
																																__lowerCAmelCase					=							tf.boolean_mask(_A					,						_A							)
																																__lowerCAmelCase					=							self._gather_logprob(_A					,						_A							)
																																cur_logprob += cur_head_logprob[:, self.cutoff_ends[1] + i - 1]
																						if target is not None:
																											loss += tf.scatter_nd(_A					,						-cur_logprob					,						shape_list(_A							)							)
																	__lowerCAmelCase					=							tf.concat(_A					,						axis=-1							)

												if target is not None:
																	if return_mean:
																						__lowerCAmelCase					=							tf.reduce_mean(_A							)
																	# Add the training-time loss value to the layer using `self.add_loss()`.
																	self.add_loss(_A							)

																	# Log the loss as a metric (we could log arbitrary metrics,
																	# including different metrics for training and inference.
																	self.add_metric(_A					,						name=self.name					,						aggregation='mean' if return_mean else ''							)

												return out









from typing import List, Optional, Union

import numpy as np
import PIL
import torch
from PIL import Image

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


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

lowerCamelCase      				=     '''
    Examples:
        ```py
        >>> from diffusers import KandinskyV22Img2ImgPipeline, KandinskyV22PriorPipeline
        >>> from diffusers.utils import load_image
        >>> import torch

        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(
        ...     "kandinsky-community/kandinsky-2-2-prior", torch_dtype=torch.float16
        ... )
        >>> pipe_prior.to("cuda")

        >>> prompt = "A red cartoon frog, 4k"
        >>> image_emb, zero_image_emb = pipe_prior(prompt, return_dict=False)

        >>> pipe = KandinskyV22Img2ImgPipeline.from_pretrained(
        ...     "kandinsky-community/kandinsky-2-2-decoder", torch_dtype=torch.float16
        ... )
        >>> pipe.to("cuda")

        >>> init_image = load_image(
        ...     "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
        ...     "/kandinsky/frog.png"
        ... )

        >>> image = pipe(
        ...     image=init_image,
        ...     image_embeds=image_emb,
        ...     negative_image_embeds=zero_image_emb,
        ...     height=768,
        ...     width=768,
        ...     num_inference_steps=100,
        ...     strength=0.2,
        ... ).images

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


def        __lowercase    (  UpperCAmelCase__		,					UpperCAmelCase__		,					UpperCAmelCase__=8      ):

					"""simple docstring"""






					__lowerCAmelCase					=							height // scale_factor**2
					if height % scale_factor**2 != 0:
										new_height += 1
					__lowerCAmelCase					=							width // scale_factor**2
					if width % scale_factor**2 != 0:
										new_width += 1
					return new_height * scale_factor, new_width * scale_factor


def        __lowercase    (  UpperCAmelCase__		,					UpperCAmelCase__=512		,					UpperCAmelCase__=512      ):

					"""simple docstring"""






					__lowerCAmelCase					=							pil_image.resize((w, h)		,					resample=Image.BICUBIC		,					reducing_gap=1      )
					__lowerCAmelCase					=							np.array(pil_image.convert('RGB'      )      )
					__lowerCAmelCase					=							arr.astype(np.floataa      ) / 127.5 - 1
					__lowerCAmelCase					=							np.transpose(UpperCAmelCase__		,					[2, 0, 1]      )
					__lowerCAmelCase					=							torch.from_numpy(UpperCAmelCase__      ).unsqueeze(0      )
					return image





class     snake_case_			(				_a							):


							"""simple docstring"""





							def __init__( self					,						_A					,						_A					,						_A					,						):
												super().__init__()

												self.register_modules(
												    unet=_A					,						scheduler=_A					,						movq=_A					,						)
												__lowerCAmelCase					=							2 ** (len(self.movq.config.block_out_channels							) - 1)




							def        A__  ( self					,						_A					,						_A					,						_A							):
												# get the original timestep using init_timestep
												__lowerCAmelCase					=							min(int(num_inference_steps * strength							)					,						_A							)

												__lowerCAmelCase					=							max(num_inference_steps - init_timestep					,						0							)
												__lowerCAmelCase					=							self.scheduler.timesteps[t_start:]

												return timesteps, num_inference_steps - t_start




							def        A__  ( self					,						_A					,						_A					,						_A					,						_A					,						_A					,						_A					,						_A=None							):
												if not isinstance(_A					,						(torch.Tensor, PIL.Image.Image, list)							):
																	raise ValueError(
																	    F"""`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(_A							)}"""							)

												__lowerCAmelCase					=							image.to(device=_A					,						dtype=_A							)

												__lowerCAmelCase					=							batch_size * num_images_per_prompt

												if image.shape[1] == 4:
																	__lowerCAmelCase					=							image

												else:
																	if isinstance(_A					,						_A							) and len(_A							) != batch_size:
																						raise ValueError(
																						    F"""You have passed a list of generators of length {len(_A							)}, but requested an effective batch"""
																						    F""" size of {batch_size}. Make sure the batch size matches the length of the generators."""							)

																	elif isinstance(_A					,						_A							):
																						__lowerCAmelCase					=							[
																						    self.movq.encode(image[i : i + 1]							).latent_dist.sample(generator[i]							) for i in range(_A							)
																						]
																						__lowerCAmelCase					=							torch.cat(_A					,						dim=0							)
																	else:
																						__lowerCAmelCase					=							self.movq.encode(_A							).latent_dist.sample(_A							)

																	__lowerCAmelCase					=							self.movq.config.scaling_factor * init_latents

												__lowerCAmelCase					=							torch.cat([init_latents]					,						dim=0							)

												__lowerCAmelCase					=							init_latents.shape
												__lowerCAmelCase					=							randn_tensor(_A					,						generator=_A					,						device=_A					,						dtype=_A							)

												# get latents
												__lowerCAmelCase					=							self.scheduler.add_noise(_A					,						_A					,						_A							)

												__lowerCAmelCase					=							init_latents

												return latents




							def        A__  ( self					,						_A=0							):
												if is_accelerate_available():
																	from accelerate import cpu_offload
												else:
																	raise ImportError('Please install accelerate via `pip install accelerate`'							)

												__lowerCAmelCase					=							torch.device(F"""cuda:{gpu_id}"""							)

												__lowerCAmelCase					=							[
												    self.unet,
												    self.movq,
												]
												for cpu_offloaded_model in models:
																	if cpu_offloaded_model is not None:
																						cpu_offload(_A					,						_A							)




							def        A__  ( self					,						_A=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					=							torch.device(F"""cuda:{gpu_id}"""							)

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

												__lowerCAmelCase					=							None
												for cpu_offloaded_model in [self.unet, self.movq]:
																	__lowerCAmelCase,	__lowerCAmelCase					=							cpu_offload_with_hook(_A					,						_A					,						prev_module_hook=_A							)

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




							@property
							# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
							def        A__  ( self							):
												if not hasattr(self.unet					,						'_hf_hook'							):
																	return self.device
												for module in self.unet.modules():
																	if (
																	    hasattr(_A					,						'_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(_A							)
							def __call__( self					,						_A					,						_A					,						_A					,						_A = 5_1_2					,						_A = 5_1_2					,						_A = 1_0_0					,						_A = 4.0					,						_A = 0.3					,						_A = 1					,						_A = None					,						_A = "pil"					,						_A = True					,						):
												__lowerCAmelCase					=							self._execution_device

												__lowerCAmelCase					=							guidance_scale > 1.0

												if isinstance(_A					,						_A							):
																	__lowerCAmelCase					=							torch.cat(_A					,						dim=0							)
												__lowerCAmelCase					=							image_embeds.shape[0]
												if isinstance(_A					,						_A							):
																	__lowerCAmelCase					=							torch.cat(_A					,						dim=0							)

												if do_classifier_free_guidance:
																	__lowerCAmelCase					=							image_embeds.repeat_interleave(_A					,						dim=0							)
																	__lowerCAmelCase					=							negative_image_embeds.repeat_interleave(_A					,						dim=0							)

												__lowerCAmelCase					=							torch.cat([negative_image_embeds, image_embeds]					,						dim=0							).to(dtype=self.unet.dtype					,						device=_A							)

												if not isinstance(_A					,						_A							):
																	__lowerCAmelCase					=							[image]
												if not all(isinstance(_A					,						(PIL.Image.Image, torch.Tensor)							) for i in image							):
																	raise ValueError(
																	    F"""Input is in incorrect format: {[type(_A							) for i in image]}. Currently, we only support  PIL image and pytorch tensor"""							)

												__lowerCAmelCase					=							torch.cat([prepare_image(_A					,						_A					,						_A							) for i in image]					,						dim=0							)
												__lowerCAmelCase					=							image.to(dtype=image_embeds.dtype					,						device=_A							)

												__lowerCAmelCase					=							self.movq.encode(_A							)['latents']
												__lowerCAmelCase					=							latents.repeat_interleave(_A					,						dim=0							)
												self.scheduler.set_timesteps(_A					,						device=_A							)
												__lowerCAmelCase,	__lowerCAmelCase					=							self.get_timesteps(_A					,						_A					,						_A							)
												__lowerCAmelCase					=							timesteps[:1].repeat(batch_size * num_images_per_prompt							)
												__lowerCAmelCase,	__lowerCAmelCase					=							downscale_height_and_width(_A					,						_A					,						self.movq_scale_factor							)
												__lowerCAmelCase					=							self.prepare_latents(
												    _A					,						_A					,						_A					,						_A					,						image_embeds.dtype					,						_A					,						_A							)
												for i, t in enumerate(self.progress_bar(_A							)							):
																	# expand the latents if we are doing classifier free guidance
																	__lowerCAmelCase					=							torch.cat([latents] * 2							) if do_classifier_free_guidance else latents

																	__lowerCAmelCase					=							{'image_embeds': image_embeds}
																	__lowerCAmelCase					=							self.unet(
																	    sample=_A					,						timestep=_A					,						encoder_hidden_states=_A					,						added_cond_kwargs=_A					,						return_dict=_A					,						)[0]

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

																	# compute the previous noisy sample x_t -> x_t-1
																	__lowerCAmelCase					=							self.scheduler.step(
																	    _A					,						_A					,						_A					,						generator=_A					,						)[0]

												# post-processing
												__lowerCAmelCase					=							self.movq.decode(_A					,						force_not_quantize=_A							)['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					=							image * 0.5 + 0.5
																	__lowerCAmelCase					=							image.clamp(0					,						1							)
																	__lowerCAmelCase					=							image.cpu().permute(0					,						2					,						3					,						1							).float().numpy()

												if output_type == "pil":
																	__lowerCAmelCase					=							self.numpy_to_pil(_A							)

												if not return_dict:
																	return (image,)

												return ImagePipelineOutput(images=_A							)






