


'''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


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

_UpperCAmelCase				: Any							    =				"""
    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 							__magic_name__(						lowerCamelCase,				lowerCamelCase,				lowerCamelCase=8):
						__lowerCAmelCase							       =				h // scale_factor**2
						if h % scale_factor**2 != 0:
												new_h += 1
						__lowerCAmelCase							       =				w // scale_factor**2
						if w % scale_factor**2 != 0:
												new_w += 1
						return new_h * scale_factor, new_w * scale_factor




class       a__				( __A      ):







				"""simple docstring"""



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

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




				def 					_snake_case  (self       ,			__lowercase       ,			__lowercase       ,			__lowercase       ,			__lowercase       ,			__lowercase       ,			__lowercase		):
										if latents is None:
																__lowerCAmelCase							       =				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}"""		)
																__lowerCAmelCase							       =				latents.to(__lowercase		)

										__lowerCAmelCase							       =				latents * scheduler.init_noise_sigma
										return latents




				def 					_snake_case  (self       ,			__lowercase       ,			__lowercase       ,			__lowercase       ,			__lowercase       ,			__lowercase=None       ,			):
										__lowerCAmelCase							       =				len(__lowercase		) if isinstance(__lowercase       ,			__lowercase		) else 1
										# get prompt text embeddings
										__lowerCAmelCase							       =				self.tokenizer(
										    __lowercase       ,			padding='''max_length'''       ,			truncation=__lowercase       ,			max_length=77       ,			return_attention_mask=__lowercase       ,			add_special_tokens=__lowercase       ,			return_tensors='''pt'''       ,			)

										__lowerCAmelCase							       =				text_inputs.input_ids
										__lowerCAmelCase							       =				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		):
																__lowerCAmelCase							       =				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							       =				text_input_ids.to(__lowercase		)
										__lowerCAmelCase							       =				text_inputs.attention_mask.to(__lowercase		)

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

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

										if do_classifier_free_guidance:
																__lowerCAmelCase							       =				42
																if negative_prompt is None:
																						__lowerCAmelCase							       =				[''''''] * 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		):
																						__lowerCAmelCase							       =				[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:
																						__lowerCAmelCase							       =				negative_prompt

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

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

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

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

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

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

										return prompt_embeds, text_encoder_hidden_states, text_mask




				def 					_snake_case  (self       ,			__lowercase=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.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       ,			__lowercase=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=__lowercase		)
																torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

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

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

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




				@property
				# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
				def 					_snake_case  (self		):
										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       ,			__lowercase       ,			__lowercase       ,			__lowercase       ,			__lowercase = None       ,			__lowercase = 5_12       ,			__lowercase = 5_12       ,			__lowercase = 1_00       ,			__lowercase = 4.0       ,			__lowercase = 1       ,			__lowercase = None       ,			__lowercase = None       ,			__lowercase = "pil"       ,			__lowercase = True       ,			):

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

										__lowerCAmelCase							       =				self._execution_device

										__lowerCAmelCase							       =				batch_size * num_images_per_prompt
										__lowerCAmelCase							       =				guidance_scale > 1.0

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

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

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

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

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

										__lowerCAmelCase							       =				self.unet.config.in_channels

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

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

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

										if not return_dict:
																return (image,)

										return ImagePipelineOutput(images=__lowercase		)





'''simple docstring'''

import sys

from .dependency_versions_table import deps
from .utils.versions import require_version, require_version_core


# define which module versions we always want to check at run time
# (usually the ones defined in `install_requires` in setup.py)
#
# order specific notes:
# - tqdm must be checked before tokenizers

_UpperCAmelCase				: Tuple							    =				"""python tqdm regex requests packaging filelock numpy tokenizers""".split()
if sys.version_info < (3, 7):
	pkgs_to_check_at_runtime.append("""dataclasses""")
if sys.version_info < (3, 8):
	pkgs_to_check_at_runtime.append("""importlib_metadata""")

for pkg in pkgs_to_check_at_runtime:
	if pkg in deps:
		if pkg == "tokenizers":
			# must be loaded here, or else tqdm check may fail
			from .utils import is_tokenizers_available

			if not is_tokenizers_available():
				continue  # not required, check version only if installed

		require_version_core(deps[pkg])
	else:
		raise ValueError(f"""can't find {pkg} in {deps.keys()}, check dependency_versions_table.py""")

def 							__magic_name__(						lowerCamelCase,				lowerCamelCase=None):
						require_version(deps[pkg],				lowerCamelCase)

