





'''simple docstring'''







import json
import os
from typing import Dict, List, Optional, Tuple

import regex as re

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


lowerCAmelCase_					:    int      	=			logging.get_logger(__name__)


lowerCAmelCase_					:    str      	=			{
    'vocab_file': 'vocab.json',
    'merges_file': 'merges.txt',
    'tokenizer_config_file': 'tokenizer_config.json',
}

lowerCAmelCase_					:    List[Any]      	=			{
    'vocab_file': {
        'facebook/blenderbot_small-90M': 'https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/vocab.json'
    },
    'merges_file': {
        'facebook/blenderbot_small-90M': 'https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/merges.txt'
    },
    'tokenizer_config_file': {
        'facebook/blenderbot_small-90M': (
            'https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/tokenizer_config.json'
        )
    },
}

lowerCAmelCase_					:    str      	=			{'facebook/blenderbot_small-90M': 5_12}






def  _lowerCamelCase				( lowercase						:      List[Any]						)	->	List[str]:
				_a								= set()
				_a								= word[0]
				for char in word[1:]:
								pairs.add((prev_char, char)						)
								_a								= char

				_a								= set(lowercase						)
				return pairs



class 				__SCREAMING_SNAKE_CASE  (lowerCamelCase_      ):






				"""simple docstring"""







				__a            =VOCAB_FILES_NAMES
				__a            =PRETRAINED_VOCAB_FILES_MAP
				__a            =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				__a            =['input_ids', 'attention_mask']




				def __init__(  self     :					List[Any]					,				__a     :					List[Any]					,				__a     :					List[Any]					,				__a     :					Optional[int]="__start__"					,				__a     :					Union[str, Any]="__end__"					,				__a     :					Any="__unk__"					,				__a     :					Union[str, Any]="__null__"					,				**__a     :					Tuple					,				):
								super().__init__(unk_token=__a					,				bos_token=__a					,				eos_token=__a					,				pad_token=__a					,				**__a					)

								with open(__a					,				encoding="utf-8"					) as vocab_handle:
												_a								= json.load(__a					)
								_a								= {v: k for k, v in self.encoder.items()}
								with open(__a					,				encoding="utf-8"					) as merges_handle:
												_a								= merges_handle.read().split("\n"					)[1:-1]
								_a								= [tuple(merge.split()					) for merge in merges]
								_a								= dict(zip(__a					,				range(len(__a					)					)					)					)
								_a								= {}




				@property
				def   UpperCamelCase__   (  self     :					Any					):
								return len(self.encoder					)




				def   UpperCamelCase__   (  self     :					Dict					):
								return dict(self.encoder					,				**self.added_tokens_encoder					)




				def   UpperCamelCase__   (  self     :					Tuple					,				__a     :					str					):
								if token in self.cache:
												return self.cache[token]
								_a								= re.sub("([.,!?()])"					,				r" \1"					,				__a					)
								_a								= re.sub("(')"					,				r" \1 "					,				__a					)
								_a								= re.sub(r"\s{2,}"					,				" "					,				__a					)
								if "\n" in token:
												_a								= token.replace("\n"					,				" __newln__"					)

								_a								= token.split(" "					)
								_a								= []
								for token in tokens:
												if not len(__a					):
																continue

												_a								= token.lower()
												_a								= tuple(__a					)
												_a								= tuple(list(word[:-1]					) + [word[-1] + "</w>"]					)
												_a								= get_pairs(__a					)

												if not pairs:
																words.append(__a					)
																continue

												while True:
																_a								= min(__a					,				key=lambda __a					: self.bpe_ranks.get(__a					,				float("inf"					)					)					)
																if bigram not in self.bpe_ranks:
																				break
																_a	,					_a								= bigram
																_a								= []
																_a								= 0

																while i < len(__a					):
																				try:
																								_a								= word.index(__a					,				__a					)
																								new_word.extend(word[i:j]					)
																								_a								= j
																				except ValueError:
																								new_word.extend(word[i:]					)
																								break

																				if word[i] == first and i < len(__a					) - 1 and word[i + 1] == second:
																								new_word.append(first + second					)
																								i += 2
																				else:
																								new_word.append(word[i]					)
																								i += 1
																_a								= tuple(__a					)
																_a								= new_word
																if len(__a					) == 1:
																				break
																else:
																				_a								= get_pairs(__a					)
												_a								= "@@ ".join(__a					)
												_a								= word[:-4]

												_a								= word
												words.append(__a					)
								return " ".join(__a					)




				def   UpperCamelCase__   (  self     :					Union[str, Any]					,				__a     :					str					):
								_a								= []

								_a								= re.findall(r"\S+\n?"					,				__a					)

								for token in words:
												split_tokens.extend(list(self.bpe(__a					).split(" "					)					)					)
								return split_tokens




				def   UpperCamelCase__   (  self     :					Optional[int]					,				__a     :					str					):
								_a								= token.lower()
								return self.encoder.get(__a					,				self.encoder.get(self.unk_token					)					)




				def   UpperCamelCase__   (  self     :					Optional[int]					,				__a     :					int					):
								return self.decoder.get(__a					,				self.unk_token					)




				def   UpperCamelCase__   (  self     :					Tuple					,				__a     :					List[str]					):
								_a								= " ".join(__a					).replace("@@ "					,				""					).strip()
								return out_string




				def   UpperCamelCase__   (  self     :					Optional[int]					,				__a     :					str					,				__a     :					Optional[str] = None					):
								if not os.path.isdir(__a					):
												logger.error(f'Vocabulary path ({save_directory}) should be a directory'					)
												return
								_a								= os.path.join(
								    __a					,				(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]					)
								_a								= os.path.join(
								    __a					,				(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"]					)

								with open(__a					,				"w"					,				encoding="utf-8"					) as f:
												f.write(json.dumps(self.encoder					,				indent=2					,				sort_keys=__a					,				ensure_ascii=__a					) + "\n"					)

								_a								= 0
								with open(__a					,				"w"					,				encoding="utf-8"					) as writer:
												writer.write("#version: 0.2\n"					)
												for bpe_tokens, token_index in sorted(self.bpe_ranks.items()					,				key=lambda __a					: kv[1]					):
																if index != token_index:
																				logger.warning(
																				    f'Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'
																				    " Please check that the tokenizer is not corrupted!"					)
																				_a								= token_index
																writer.write(" ".join(__a					) + "\n"					)
																index += 1

								return vocab_file, merge_file







"""simple docstring"""
from typing import List, Optional, Union

import torch

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,
)


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

_snake_case     :		List[Any]       						= '\n    Examples:\n        ```py\n        >>> import torch\n        >>> import numpy as np\n\n        >>> from diffusers import KandinskyV22PriorPipeline, KandinskyV22ControlnetPipeline\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 = KandinskyV22PriorPipeline.from_pretrained(\n        ...     "kandinsky-community/kandinsky-2-2-prior", torch_dtype=torch.float16\n        ... )\n        >>> pipe_prior = pipe_prior.to("cuda")\n\n        >>> pipe = KandinskyV22ControlnetPipeline.from_pretrained(\n        ...     "kandinsky-community/kandinsky-2-2-controlnet-depth", torch_dtype=torch.float16\n        ... )\n        >>> pipe = pipe.to("cuda")\n\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        >>> 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        >>> image_emb, zero_image_emb = pipe_prior(\n        ...     prompt=prompt, negative_prompt=negative_prior_prompt, generator=generator\n        ... ).to_tuple()\n\n        >>> images = pipe(\n        ...     image_embeds=image_emb,\n        ...     negative_image_embeds=zero_image_emb,\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 					A__				(			UpperCamelCase					,  UpperCamelCase					,  UpperCamelCase=8       ):
							A		=  height // scale_factor**2
							if height % scale_factor**2 != 0:
														new_height += 1
							A		=  width // scale_factor**2
							if width % scale_factor**2 != 0:
														new_width += 1
							return new_height * scale_factor, new_width * scale_factor






class 					_UpperCAmelCase   (	lowercase_ ):


					def __init__(					self			:Any     ,       __UpperCamelCase			:UNetaDConditionModel     ,       __UpperCamelCase			:DDPMScheduler     ,       __UpperCamelCase			:VQModel     ,       ):
												super().__init__()

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


					def 	lowerCamelCase       (					self			:Union[str, Any]     ,       __UpperCamelCase			:Tuple     ,       __UpperCamelCase			:Dict     ,       __UpperCamelCase			:Dict     ,       __UpperCamelCase			:List[str]     ,       __UpperCamelCase			:Optional[int]     ,       __UpperCamelCase			:List[str]		):
												if latents is None:
																			A		=  randn_tensor(__UpperCamelCase     ,       generator=__UpperCamelCase     ,       device=__UpperCamelCase     ,       dtype=__UpperCamelCase		)
												else:
																			if latents.shape != shape:
																										raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {shape}"		)
																			A		=  latents.to(__UpperCamelCase		)

												A		=  latents * scheduler.init_noise_sigma
												return latents


					def 	lowerCamelCase       (					self			:Tuple     ,       __UpperCamelCase			:Any=0		):
												if is_accelerate_available():
																			from accelerate import cpu_offload
												else:
																			raise ImportError("Please install accelerate via `pip install accelerate`"		)

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

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


					def 	lowerCamelCase       (					self			:Dict     ,       __UpperCamelCase			:int=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."		)

												A		=  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)

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

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


					@property
					# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
					def 	lowerCamelCase       (					self			:str		):
												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			:List[Any]     ,       __UpperCamelCase			:Union[torch.FloatTensor, List[torch.FloatTensor]]     ,       __UpperCamelCase			:Union[torch.FloatTensor, List[torch.FloatTensor]]     ,       __UpperCamelCase			:torch.FloatTensor     ,       __UpperCamelCase			:int = 5_12     ,       __UpperCamelCase			:int = 5_12     ,       __UpperCamelCase			:int = 1_00     ,       __UpperCamelCase			:float = 4.0     ,       __UpperCamelCase			:int = 1     ,       __UpperCamelCase			:Optional[Union[torch.Generator, List[torch.Generator]]] = None     ,       __UpperCamelCase			:Optional[torch.FloatTensor] = None     ,       __UpperCamelCase			:Optional[str] = "pil"     ,       __UpperCamelCase			:bool = True     ,       ):
												A		=  self._execution_device

												A		=  guidance_scale > 1.0

												if isinstance(__UpperCamelCase     ,       __UpperCamelCase		):
																			A		=  torch.cat(__UpperCamelCase     ,       dim=0		)
												if isinstance(__UpperCamelCase     ,       __UpperCamelCase		):
																			A		=  torch.cat(__UpperCamelCase     ,       dim=0		)
												if isinstance(__UpperCamelCase     ,       __UpperCamelCase		):
																			A		=  torch.cat(__UpperCamelCase     ,       dim=0		)

												A		=  image_embeds.shape[0] * num_images_per_prompt

												if do_classifier_free_guidance:
																			A		=  image_embeds.repeat_interleave(__UpperCamelCase     ,       dim=0		)
																			A		=  negative_image_embeds.repeat_interleave(__UpperCamelCase     ,       dim=0		)
																			A		=  hint.repeat_interleave(__UpperCamelCase     ,       dim=0		)

												A		=  torch.cat([negative_image_embeds, image_embeds]     ,       dim=0		).to(dtype=self.unet.dtype     ,       device=__UpperCamelCase		)
												A		=  torch.cat([hint, hint]     ,       dim=0		).to(dtype=self.unet.dtype     ,       device=__UpperCamelCase		)
												self.scheduler.set_timesteps(__UpperCamelCase     ,       device=__UpperCamelCase		)
												A		=  self.scheduler.timesteps

												A		=  self.movq.config.latent_channels

												A,	A		=  downscale_height_and_width(__UpperCamelCase     ,       __UpperCamelCase     ,       self.movq_scale_factor		)

												# create initial latent
												A		=  self.prepare_latents(
												    (batch_size, num_channels_latents, height, width)     ,       image_embeds.dtype     ,       __UpperCamelCase     ,       __UpperCamelCase     ,       __UpperCamelCase     ,       self.scheduler     ,       )

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

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

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

																			# compute the previous noisy sample x_t -> x_t-1
																			A		=  self.scheduler.step(
																			    __UpperCamelCase     ,       __UpperCamelCase     ,       __UpperCamelCase     ,       generator=__UpperCamelCase     ,       )[0]
												# post-processing
												A		=  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"]:
																			A		=  image * 0.5 + 0.5
																			A		=  image.clamp(0     ,       1		)
																			A		=  image.cpu().permute(0     ,       2     ,       3     ,       1		).float().numpy()

												if output_type == "pil":
																			A		=  self.numpy_to_pil(__UpperCamelCase		)

												if not return_dict:
																			return (image,)

												return ImagePipelineOutput(images=__UpperCamelCase		)


