
'''simple docstring'''
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  							= '\n    Examples:\n        ```py\n        >>> import torch\n        >>> import numpy as np\n\n        >>> from diffusers import KandinskyV22PriorEmb2EmbPipeline, KandinskyV22ControlnetImg2ImgPipeline\n        >>> from transformers import pipeline\n        >>> from diffusers.utils import load_image\n\n\n        >>> def make_hint(image, depth_estimator):\n        ...     image = depth_estimator(image)["depth"]\n        ...     image = np.array(image)\n        ...     image = image[:, :, None]\n        ...     image = np.concatenate([image, image, image], axis=2)\n        ...     detected_map = torch.from_numpy(image).float() / 255.0\n        ...     hint = detected_map.permute(2, 0, 1)\n        ...     return hint\n\n\n        >>> depth_estimator = pipeline("depth-estimation")\n\n        >>> pipe_prior = KandinskyV22PriorEmb2EmbPipeline.from_pretrained(\n        ...     "kandinsky-community/kandinsky-2-2-prior", torch_dtype=torch.float16\n        ... )\n        >>> pipe_prior = pipe_prior.to("cuda")\n\n        >>> pipe = KandinskyV22ControlnetImg2ImgPipeline.from_pretrained(\n        ...     "kandinsky-community/kandinsky-2-2-controlnet-depth", torch_dtype=torch.float16\n        ... )\n        >>> pipe = pipe.to("cuda")\n\n        >>> img = load_image(\n        ...     "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"\n        ...     "/kandinsky/cat.png"\n        ... ).resize((768, 768))\n\n\n        >>> hint = make_hint(img, depth_estimator).unsqueeze(0).half().to("cuda")\n\n        >>> prompt = "A robot, 4k photo"\n        >>> negative_prior_prompt = "lowres, text, error, cropped, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, out of frame, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck, username, watermark, signature"\n\n        >>> generator = torch.Generator(device="cuda").manual_seed(43)\n\n        >>> img_emb = pipe_prior(prompt=prompt, image=img, strength=0.85, generator=generator)\n        >>> negative_emb = pipe_prior(prompt=negative_prior_prompt, image=img, strength=1, generator=generator)\n\n        >>> images = pipe(\n        ...     image=img,\n        ...     strength=0.5,\n        ...     image_embeds=img_emb.image_embeds,\n        ...     negative_image_embeds=negative_emb.image_embeds,\n        ...     hint=hint,\n        ...     num_inference_steps=50,\n        ...     generator=generator,\n        ...     height=768,\n        ...     width=768,\n        ... ).images\n\n        >>> images[0].save("robot_cat.png")\n        ```\n'






def       __SCREAMING_SNAKE_CASE   (						_SCREAMING_SNAKE_CASE							,      _SCREAMING_SNAKE_CASE							,      _SCREAMING_SNAKE_CASE=8   )     ->   int:
							_snake_case     =						height // scale_factor**2
							if height % scale_factor**2 != 0:
														new_height += 1
							_snake_case     =						width // scale_factor**2
							if width % scale_factor**2 != 0:
														new_width += 1
							return new_height * scale_factor, new_width * scale_factor






def       __SCREAMING_SNAKE_CASE   (						_SCREAMING_SNAKE_CASE							,      _SCREAMING_SNAKE_CASE=512							,      _SCREAMING_SNAKE_CASE=512   )     ->   Any:
							_snake_case     =						pil_image.resize((w, h)							,      resample=Image.BICUBIC							,      reducing_gap=1   )
							_snake_case     =						np.array(pil_image.convert("""RGB"""   )   )
							_snake_case     =						arr.astype(np.floataa   ) / 127.5 - 1
							_snake_case     =						np.transpose(_SCREAMING_SNAKE_CASE							,      [2, 0, 1]   )
							_snake_case     =						torch.from_numpy(_SCREAMING_SNAKE_CASE   ).unsqueeze(0   )
							return image


class     _lowerCAmelCase					(					__snake_case   ):







							'''simple docstring'''

							def __init__(self						,						UpperCAmelCase						,						UpperCAmelCase						,						UpperCAmelCase						,						)  ->     Tuple:
														super().__init__()

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

							def  lowercase							(self						,						UpperCAmelCase						,						UpperCAmelCase						,						UpperCAmelCase						)  ->     Optional[int]:
														# get the original timestep using init_timestep
														_snake_case     =						min(int(num_inference_steps * strength						)						,						UpperCAmelCase						)

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

														return timesteps, num_inference_steps - t_start

							def  lowercase							(self						,						UpperCAmelCase						,						UpperCAmelCase						,						UpperCAmelCase						,						UpperCAmelCase						,						UpperCAmelCase						,						UpperCAmelCase						,						UpperCAmelCase=None						)  ->     Tuple:
														if not isinstance(UpperCAmelCase						,						(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(UpperCAmelCase						)}"""						)

														_snake_case     =						image.to(device=UpperCAmelCase						,						dtype=UpperCAmelCase						)

														_snake_case     =						batch_size * num_images_per_prompt

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

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

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

																					_snake_case     =						self.movq.config.scaling_factor * init_latents

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

														_snake_case     =						init_latents.shape
														_snake_case     =						randn_tensor(UpperCAmelCase						,						generator=UpperCAmelCase						,						device=UpperCAmelCase						,						dtype=UpperCAmelCase						)

														# get latents
														_snake_case     =						self.scheduler.add_noise(UpperCAmelCase						,						UpperCAmelCase						,						UpperCAmelCase						)

														_snake_case     =						init_latents

														return latents

							def  lowercase							(self						,						UpperCAmelCase=0						)  ->     List[str]:
														if is_accelerate_available():
																					from accelerate import cpu_offload
														else:
																					raise ImportError("""Please install accelerate via `pip install accelerate`"""						)

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

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

							def  lowercase							(self						,						UpperCAmelCase=0						)  ->     Tuple:
														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."""						)

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

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

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

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

							@property
							# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
							def  lowercase							(self						)  ->     Dict:
														if not hasattr(self.unet						,						"""_hf_hook"""						):
																					return self.device
														for module in self.unet.modules():
																					if (
																					    hasattr(UpperCAmelCase						,						"""_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(UpperCAmelCase						)
							def __call__(self						,						UpperCAmelCase						,						UpperCAmelCase						,						UpperCAmelCase						,						UpperCAmelCase						,						UpperCAmelCase = 512						,						UpperCAmelCase = 512						,						UpperCAmelCase = 100						,						UpperCAmelCase = 4.0						,						UpperCAmelCase = 0.3						,						UpperCAmelCase = 1						,						UpperCAmelCase = None						,						UpperCAmelCase = "pil"						,						UpperCAmelCase = True						,						)  ->     Dict:
														_snake_case     =						self._execution_device

														_snake_case     =						guidance_scale > 1.0

														if isinstance(UpperCAmelCase						,						UpperCAmelCase						):
																					_snake_case     =						torch.cat(UpperCAmelCase						,						dim=0						)
														if isinstance(UpperCAmelCase						,						UpperCAmelCase						):
																					_snake_case     =						torch.cat(UpperCAmelCase						,						dim=0						)
														if isinstance(UpperCAmelCase						,						UpperCAmelCase						):
																					_snake_case     =						torch.cat(UpperCAmelCase						,						dim=0						)

														_snake_case     =						image_embeds.shape[0]

														if do_classifier_free_guidance:
																					_snake_case     =						image_embeds.repeat_interleave(UpperCAmelCase						,						dim=0						)
																					_snake_case     =						negative_image_embeds.repeat_interleave(UpperCAmelCase						,						dim=0						)
																					_snake_case     =						hint.repeat_interleave(UpperCAmelCase						,						dim=0						)

														_snake_case     =						torch.cat([negative_image_embeds, image_embeds]						,						dim=0						).to(dtype=self.unet.dtype						,						device=UpperCAmelCase						)
														_snake_case     =						torch.cat([hint, hint]						,						dim=0						).to(dtype=self.unet.dtype						,						device=UpperCAmelCase						)

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

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

														_snake_case     =						self.movq.encode(UpperCAmelCase						)["""latents"""]
														_snake_case     =						latents.repeat_interleave(UpperCAmelCase						,						dim=0						)
														self.scheduler.set_timesteps(UpperCAmelCase						,						device=UpperCAmelCase						)
														_snake_case,							_snake_case     =						self.get_timesteps(UpperCAmelCase						,						UpperCAmelCase						,						UpperCAmelCase						)
														_snake_case     =						timesteps[:1].repeat(batch_size * num_images_per_prompt						)
														_snake_case,							_snake_case     =						downscale_height_and_width(UpperCAmelCase						,						UpperCAmelCase						,						self.movq_scale_factor						)
														_snake_case     =						self.prepare_latents(
														    UpperCAmelCase						,						UpperCAmelCase						,						UpperCAmelCase						,						UpperCAmelCase						,						image_embeds.dtype						,						UpperCAmelCase						,						UpperCAmelCase						)
														for i, t in enumerate(self.progress_bar(UpperCAmelCase						)						):
																					# expand the latents if we are doing classifier free guidance
																					_snake_case     =						torch.cat([latents] * 2						) if do_classifier_free_guidance else latents

																					_snake_case     =						{"""image_embeds""": image_embeds, """hint""": hint}
																					_snake_case     =						self.unet(
																					    sample=UpperCAmelCase						,						timestep=UpperCAmelCase						,						encoder_hidden_states=UpperCAmelCase						,						added_cond_kwargs=UpperCAmelCase						,						return_dict=UpperCAmelCase						,						)[0]

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

																					# compute the previous noisy sample x_t -> x_t-1

																					_snake_case     =						self.scheduler.step(
																					    UpperCAmelCase						,						UpperCAmelCase						,						UpperCAmelCase						,						generator=UpperCAmelCase						,						)[0]

														# post-processing
														_snake_case     =						self.movq.decode(UpperCAmelCase						,						force_not_quantize=UpperCAmelCase						)["""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"]:
																					_snake_case     =						image * 0.5 + 0.5
																					_snake_case     =						image.clamp(0						,						1						)
																					_snake_case     =						image.cpu().permute(0						,						2						,						3						,						1						).float().numpy()

														if output_type == "pil":
																					_snake_case     =						self.numpy_to_pil(UpperCAmelCase						)

														if not return_dict:
																					return (image,)

														return ImagePipelineOutput(images=UpperCAmelCase						)

'''simple docstring'''
import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
				from .tokenization_xglm import XGLMTokenizer
else:
				__lowerCAmelCase  							= None


__lowerCAmelCase  							= logging.get_logger(__name__)

__lowerCAmelCase  							= {'vocab_file': 'sentencepiece.bpe.model', 'tokenizer_file': 'tokenizer.json'}

__lowerCAmelCase  							= {
    'vocab_file': {
        'facebook/xglm-564M': 'https://huggingface.co/facebook/xglm-564M/resolve/main/sentencepiece.bpe.model',
    },
    'tokenizer_file': {
        'facebook/xglm-564M': 'https://huggingface.co/facebook/xglm-564M/resolve/main/tokenizer.json',
    },
}

__lowerCAmelCase  							= {
    'facebook/xglm-564M': 2_048,
}


class     _lowerCAmelCase					(					__snake_case   ):







							'''simple docstring'''

							lowerCAmelCase_      		=		VOCAB_FILES_NAMES
							lowerCAmelCase_      		=		PRETRAINED_VOCAB_FILES_MAP
							lowerCAmelCase_      		=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							lowerCAmelCase_      		=		["input_ids", "attention_mask"]
							lowerCAmelCase_      		=		XGLMTokenizer

							def __init__(self						,						UpperCAmelCase=None						,						UpperCAmelCase=None						,						UpperCAmelCase="<s>"						,						UpperCAmelCase="</s>"						,						UpperCAmelCase="</s>"						,						UpperCAmelCase="<s>"						,						UpperCAmelCase="<unk>"						,						UpperCAmelCase="<pad>"						,						**UpperCAmelCase						,						)  ->     List[Any]:
														# Compatibility with the original tokenizer
														_snake_case     =						7
														_snake_case     =						[f"""<madeupword{i}>""" for i in range(self.num_madeup_words						)]

														_snake_case     =						kwargs.get("""additional_special_tokens"""						,						[]						)
														kwargs["additional_special_tokens"] += [
														    word for word in madeup_words if word not in kwargs["additional_special_tokens"]
														]

														super().__init__(
														    UpperCAmelCase						,						tokenizer_file=UpperCAmelCase						,						bos_token=UpperCAmelCase						,						eos_token=UpperCAmelCase						,						sep_token=UpperCAmelCase						,						cls_token=UpperCAmelCase						,						unk_token=UpperCAmelCase						,						pad_token=UpperCAmelCase						,						**UpperCAmelCase						,						)

														_snake_case     =						vocab_file
														_snake_case     =						False if not self.vocab_file else True

							def  lowercase							(self						,						UpperCAmelCase						,						UpperCAmelCase = None						)  ->     List[int]:

														if token_ids_a is None:
																					return [self.sep_token_id] + token_ids_a
														_snake_case     =						[self.sep_token_id]
														return sep + token_ids_a + sep + sep + token_ids_a

							def  lowercase							(self						,						UpperCAmelCase						,						UpperCAmelCase = None						)  ->     List[int]:
														_snake_case     =						[self.sep_token_id]

														if token_ids_a is None:
																					return len(sep + token_ids_a						) * [0]
														return len(sep + token_ids_a + sep + sep + token_ids_a						) * [0]





							def  lowercase							(self						,						UpperCAmelCase						,						UpperCAmelCase = None						)  ->     Tuple[str]:
														if not self.can_save_slow_tokenizer:
																					raise ValueError(
																					    """Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
																					    """tokenizer."""						)

														if not os.path.isdir(UpperCAmelCase						):
																					logger.error(f"""Vocabulary path ({save_directory}) should be a directory."""						)
																					return
														_snake_case     =						os.path.join(
														    UpperCAmelCase						,						(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]						)

														if os.path.abspath(self.vocab_file						) != os.path.abspath(UpperCAmelCase						):
																					copyfile(self.vocab_file						,						UpperCAmelCase						)

														return (out_vocab_file,)