


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,
)


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

UpperCAmelCase_      : Optional[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        SCREAMING_SNAKE_CASE_						(			__A :			str  ,    __A :			List[Any]  ,    __A :			int=8				)	->		Optional[Any]:

       """simple docstring"""

       a_					:		int							=  height // scale_factor**2
       if height % scale_factor**2 != 0:
              new_height += 1
       a_					:		Optional[int]							=  width // scale_factor**2
       if width % scale_factor**2 != 0:
              new_width += 1
       return new_height * scale_factor, new_width * scale_factor
class  SCREAMING_SNAKE_CASE__  (				lowercase__			):
      def __init__(       self				:       List[Any]     ,							SCREAMING_SNAKE_CASE__				:       UNetaDConditionModel     ,							SCREAMING_SNAKE_CASE__				:       DDPMScheduler     ,							SCREAMING_SNAKE_CASE__				:       VQModel     ,							)		->			Optional[int]:
             super().__init__()

             self.register_modules(
                 unet=SCREAMING_SNAKE_CASE__     ,							scheduler=SCREAMING_SNAKE_CASE__     ,							movq=SCREAMING_SNAKE_CASE__     ,							)
             a_					:		Optional[int]							=  2 ** (len(self.movq.config.block_out_channels     ) - 1)
      def        SCREAMING_SNAKE_CASE							(       self				:       Any     ,							SCREAMING_SNAKE_CASE__				:       int     ,							SCREAMING_SNAKE_CASE__				:       int     ,							SCREAMING_SNAKE_CASE__				:       int     ,							SCREAMING_SNAKE_CASE__				:       str     ,							SCREAMING_SNAKE_CASE__				:       int     ,							SCREAMING_SNAKE_CASE__				:       Optional[int]     )		->			Optional[Any]:
             if latents is None:
                    a_					:		Tuple							=  randn_tensor(SCREAMING_SNAKE_CASE__     ,							generator=SCREAMING_SNAKE_CASE__     ,							device=SCREAMING_SNAKE_CASE__     ,							dtype=SCREAMING_SNAKE_CASE__     )
             else:
                    if latents.shape != shape:
                           raise ValueError(F"""Unexpected latents shape, got {latents.shape}, expected {shape}"""     )
                    a_					:		Tuple							=  latents.to(SCREAMING_SNAKE_CASE__     )

             a_					:		int							=  latents * scheduler.init_noise_sigma
             return latents
      def        SCREAMING_SNAKE_CASE							(       self				:       str     ,							SCREAMING_SNAKE_CASE__				:       Optional[Any]=0     )		->			Any:
             if is_accelerate_available():
                    from accelerate import cpu_offload
             else:
                    raise ImportError('Please install accelerate via `pip install accelerate`'     )

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

             a_					:		Tuple							=  [
                 self.unet,
                 self.movq,
             ]
             for cpu_offloaded_model in models:
                    if cpu_offloaded_model is not None:
                           cpu_offload(SCREAMING_SNAKE_CASE__     ,							SCREAMING_SNAKE_CASE__     )
      def        SCREAMING_SNAKE_CASE							(       self				:       Tuple     ,							SCREAMING_SNAKE_CASE__				:       Union[str, Any]=0     )		->			List[str]:
             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_					:		Any							=  torch.device(F"""cuda:{gpu_id}"""     )

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

             a_					:		Any							=  None
             for cpu_offloaded_model in [self.unet, self.movq]:
                    a_							,		a_					:		Optional[int]							=  cpu_offload_with_hook(SCREAMING_SNAKE_CASE__     ,							SCREAMING_SNAKE_CASE__     ,							prev_module_hook=SCREAMING_SNAKE_CASE__     )

             # We'll offload the last model manually.
             a_					:		Union[str, Any]							=  hook
      @property
      # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
      def        SCREAMING_SNAKE_CASE							(       self				:       Union[str, Any]     )		->			List[str]:
             if not hasattr(self.unet     ,							'_hf_hook'     ):
                    return self.device
             for module in self.unet.modules():
                    if (
                        hasattr(SCREAMING_SNAKE_CASE__     ,							'_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(SCREAMING_SNAKE_CASE__     )
      def __call__(       self				:       List[Any]     ,							SCREAMING_SNAKE_CASE__				:       Union[torch.FloatTensor, List[torch.FloatTensor]]     ,							SCREAMING_SNAKE_CASE__				:       Union[torch.FloatTensor, List[torch.FloatTensor]]     ,							SCREAMING_SNAKE_CASE__				:       torch.FloatTensor     ,							SCREAMING_SNAKE_CASE__				:       int = 5_1_2     ,							SCREAMING_SNAKE_CASE__				:       int = 5_1_2     ,							SCREAMING_SNAKE_CASE__				:       int = 1_0_0     ,							SCREAMING_SNAKE_CASE__				:       float = 4.0     ,							SCREAMING_SNAKE_CASE__				:       int = 1     ,							SCREAMING_SNAKE_CASE__				:       Optional[Union[torch.Generator, List[torch.Generator]]] = None     ,							SCREAMING_SNAKE_CASE__				:       Optional[torch.FloatTensor] = None     ,							SCREAMING_SNAKE_CASE__				:       Optional[str] = "pil"     ,							SCREAMING_SNAKE_CASE__				:       bool = True     ,							)		->			Optional[int]:
             a_					:		Union[str, Any]							=  self._execution_device

             a_					:		int							=  guidance_scale > 1.0

             if isinstance(SCREAMING_SNAKE_CASE__     ,							SCREAMING_SNAKE_CASE__     ):
                    a_					:		List[Any]							=  torch.cat(SCREAMING_SNAKE_CASE__     ,							dim=0     )
             if isinstance(SCREAMING_SNAKE_CASE__     ,							SCREAMING_SNAKE_CASE__     ):
                    a_					:		str							=  torch.cat(SCREAMING_SNAKE_CASE__     ,							dim=0     )
             if isinstance(SCREAMING_SNAKE_CASE__     ,							SCREAMING_SNAKE_CASE__     ):
                    a_					:		List[str]							=  torch.cat(SCREAMING_SNAKE_CASE__     ,							dim=0     )

             a_					:		Union[str, Any]							=  image_embeds.shape[0] * num_images_per_prompt

             if do_classifier_free_guidance:
                    a_					:		int							=  image_embeds.repeat_interleave(SCREAMING_SNAKE_CASE__     ,							dim=0     )
                    a_					:		Optional[Any]							=  negative_image_embeds.repeat_interleave(SCREAMING_SNAKE_CASE__     ,							dim=0     )
                    a_					:		Optional[Any]							=  hint.repeat_interleave(SCREAMING_SNAKE_CASE__     ,							dim=0     )

             a_					:		Union[str, Any]							=  torch.cat([negative_image_embeds, image_embeds]     ,							dim=0     ).to(dtype=self.unet.dtype     ,							device=SCREAMING_SNAKE_CASE__     )
             a_					:		Tuple							=  torch.cat([hint, hint]     ,							dim=0     ).to(dtype=self.unet.dtype     ,							device=SCREAMING_SNAKE_CASE__     )
             self.scheduler.set_timesteps(SCREAMING_SNAKE_CASE__     ,							device=SCREAMING_SNAKE_CASE__     )
             a_					:		List[str]							=  self.scheduler.timesteps

             a_					:		str							=  self.movq.config.latent_channels

             a_							,		a_					:		Optional[Any]							=  downscale_height_and_width(SCREAMING_SNAKE_CASE__     ,							SCREAMING_SNAKE_CASE__     ,							self.movq_scale_factor     )

             # create initial latent
             a_					:		Tuple							=  self.prepare_latents(
                 (batch_size, num_channels_latents, height, width)     ,							image_embeds.dtype     ,							SCREAMING_SNAKE_CASE__     ,							SCREAMING_SNAKE_CASE__     ,							SCREAMING_SNAKE_CASE__     ,							self.scheduler     ,							)

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

                    a_					:		Dict							=  {'image_embeds': image_embeds, 'hint': hint}
                    a_					:		List[Any]							=  self.unet(
                        sample=SCREAMING_SNAKE_CASE__     ,							timestep=SCREAMING_SNAKE_CASE__     ,							encoder_hidden_states=SCREAMING_SNAKE_CASE__     ,							added_cond_kwargs=SCREAMING_SNAKE_CASE__     ,							return_dict=SCREAMING_SNAKE_CASE__     ,							)[0]

                    if do_classifier_free_guidance:
                           a_							,		a_					:		str							=  noise_pred.split(latents.shape[1]     ,							dim=1     )
                           a_							,		a_					:		Optional[Any]							=  noise_pred.chunk(2     )
                           a_							,		a_					:		Optional[Any]							=  variance_pred.chunk(2     )
                           a_					:		List[str]							=  noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
                           a_					:		Optional[Any]							=  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_					:		str							=  noise_pred.split(latents.shape[1]     ,							dim=1     )

                    # compute the previous noisy sample x_t -> x_t-1
                    a_					:		Optional[int]							=  self.scheduler.step(
                        SCREAMING_SNAKE_CASE__     ,							SCREAMING_SNAKE_CASE__     ,							SCREAMING_SNAKE_CASE__     ,							generator=SCREAMING_SNAKE_CASE__     ,							)[0]
             # post-processing
             a_					:		Dict							=  self.movq.decode(SCREAMING_SNAKE_CASE__     ,							force_not_quantize=SCREAMING_SNAKE_CASE__     )['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_					:		str							=  image * 0.5 + 0.5
                    a_					:		str							=  image.clamp(0     ,							1     )
                    a_					:		Union[str, Any]							=  image.cpu().permute(0     ,							2     ,							3     ,							1     ).float().numpy()

             if output_type == "pil":
                    a_					:		List[Any]							=  self.numpy_to_pil(SCREAMING_SNAKE_CASE__     )

             if not return_dict:
                    return (image,)

             return ImagePipelineOutput(images=SCREAMING_SNAKE_CASE__     )




import os
import re
from shutil import copyfile
from typing import List, Optional, Tuple

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


snake_case :		Dict							      =  logging.get_logger(__name__)

snake_case :		Union[str, Any]							      =  {
    "vocab_file": "vocab.txt",
    "merges_file": "bpe.codes",
}

snake_case :		Dict							      =  {
    "vocab_file": {
        "vinai/phobert-base": "https://huggingface.co/vinai/phobert-base/resolve/main/vocab.txt",
        "vinai/phobert-large": "https://huggingface.co/vinai/phobert-large/resolve/main/vocab.txt",
    },
    "merges_file": {
        "vinai/phobert-base": "https://huggingface.co/vinai/phobert-base/resolve/main/bpe.codes",
        "vinai/phobert-large": "https://huggingface.co/vinai/phobert-large/resolve/main/bpe.codes",
    },
}

snake_case :		Union[str, Any]							      =  {
    "vinai/phobert-base": 256,
    "vinai/phobert-large": 256,
}






def   lowerCAmelCase_						(					_snake_case      :  str   )				->		Union[str, Any]:







	'''simple docstring'''


	__magic_name__		:		List[str]	   =     set()
	__magic_name__		:		Any	   =     word[0]
	for char in word[1:]:
		pairs.add((prev_char, char)   )
		__magic_name__		:		int	   =     char

	__magic_name__		:		List[str]	   =     set(_snake_case   )
	return pairs





class       _snake_case					(     snake_case				):
			UpperCamelCase__          =    VOCAB_FILES_NAMES
			UpperCamelCase__          =    PRETRAINED_VOCAB_FILES_MAP
			UpperCamelCase__          =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES







			def __init__(				self						,   _a						,   _a						,   _a="<s>"						,   _a="</s>"						,   _a="</s>"						,   _a="<s>"						,   _a="<unk>"						,   _a="<pad>"						,   _a="<mask>"						,   **_a						,   ):
				super().__init__(
				    bos_token=_a						,   eos_token=_a						,   unk_token=_a						,   sep_token=_a						,   cls_token=_a						,   pad_token=_a						,   mask_token=_a						,   **_a						,   )

				__magic_name__		:		Dict	   =     vocab_file
				__magic_name__		:		Tuple	   =     merges_file

				__magic_name__		:		List[Any]	   =     {}
				__magic_name__		:		List[Any]	   =     0
				__magic_name__		:		Tuple	   =     1
				__magic_name__		:		int	   =     2
				__magic_name__		:		Union[str, Any]	   =     3

				self.add_from_file(_a		)

				__magic_name__		:		Optional[int]	   =     {v: k for k, v in self.encoder.items()}

				with open(_a						,   encoding="utf-8"		) as merges_handle:
					__magic_name__		:		List[str]	   =     merges_handle.read().split("\n"		)[:-1]
				__magic_name__		:		Union[str, Any]	   =     [tuple(merge.split()[:-1]		) for merge in merges]
				__magic_name__		:		Union[str, Any]	   =     dict(zip(_a						,   range(len(_a		)		)		)		)
				__magic_name__		:		Optional[int]	   =     {}







			def 			SCREAMING_SNAKE_CASE    (				self						,   _a						,   _a = None		):

				if token_ids_a is None:
					return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
				__magic_name__		:		Optional[Any]	   =     [self.cls_token_id]
				__magic_name__		:		Union[str, Any]	   =     [self.sep_token_id]
				return cls + token_ids_a + sep + sep + token_ids_a + sep







			def 			SCREAMING_SNAKE_CASE    (				self						,   _a						,   _a = None						,   _a = False		):

				if already_has_special_tokens:
					return super().get_special_tokens_mask(
					    token_ids_a=_a						,   token_ids_a=_a						,   already_has_special_tokens=_a		)

				if token_ids_a is None:
					return [1] + ([0] * len(_a		)) + [1]
				return [1] + ([0] * len(_a		)) + [1, 1] + ([0] * len(_a		)) + [1]







			def 			SCREAMING_SNAKE_CASE    (				self						,   _a						,   _a = None		):
				__magic_name__		:		Optional[Any]	   =     [self.sep_token_id]
				__magic_name__		:		Tuple	   =     [self.cls_token_id]

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







			@property
			def 			SCREAMING_SNAKE_CASE    (				self		):
				return len(self.encoder		)







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







			def 			SCREAMING_SNAKE_CASE    (				self						,   _a		):
				if token in self.cache:
					return self.cache[token]
				__magic_name__		:		List[Any]	   =     tuple(_a		)
				__magic_name__		:		List[Any]	   =     tuple(list(word[:-1]		) + [word[-1] + "</w>"]		)
				__magic_name__		:		Any	   =     get_pairs(_a		)

				if not pairs:
					return token

				while True:
					__magic_name__		:		str	   =     min(_a						,   key=lambda _a		: self.bpe_ranks.get(_a						,   float("inf"		)		)		)
					if bigram not in self.bpe_ranks:
						break
					__magic_name__   ,    __magic_name__		:		List[str]	   =     bigram
					__magic_name__		:		List[str]	   =     []
					__magic_name__		:		List[str]	   =     0
					while i < len(_a		):
						try:
							__magic_name__		:		Any	   =     word.index(_a						,   _a		)
						except ValueError:
							new_word.extend(word[i:]		)
							break
						else:
							new_word.extend(word[i:j]		)
							__magic_name__		:		Tuple	   =     j

						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
					__magic_name__		:		Union[str, Any]	   =     tuple(_a		)
					__magic_name__		:		Optional[int]	   =     new_word
					if len(_a		) == 1:
						break
					else:
						__magic_name__		:		List[Any]	   =     get_pairs(_a		)
				__magic_name__		:		Optional[int]	   =     "@@ ".join(_a		)
				__magic_name__		:		Tuple	   =     word[:-4]
				__magic_name__		:		str	   =     word
				return word







			def 			SCREAMING_SNAKE_CASE    (				self						,   _a		):
				__magic_name__		:		Optional[Any]	   =     []

				__magic_name__		:		Dict	   =     re.findall(r"\S+\n?"						,   _a		)

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







			def 			SCREAMING_SNAKE_CASE    (				self						,   _a		):
				return self.encoder.get(_a						,   self.encoder.get(self.unk_token		)		)







			def 			SCREAMING_SNAKE_CASE    (				self						,   _a		):
				return self.decoder.get(_a						,   self.unk_token		)







			def 			SCREAMING_SNAKE_CASE    (				self						,   _a		):
				__magic_name__		:		Tuple	   =     " ".join(_a		).replace("@@ "						,   ""		).strip()
				return out_string







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

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

				if os.path.abspath(self.merges_file		) != os.path.abspath(_a		):
					copyfile(self.merges_file						,   _a		)

				return out_vocab_file, out_merge_file







			def 			SCREAMING_SNAKE_CASE    (				self						,   _a		):
				if isinstance(_a						,   _a		):
					try:
						with open(_a						,   "r"						,   encoding="utf-8"		) as fd:
							self.add_from_file(_a		)
					except FileNotFoundError as fnfe:
						raise fnfe
					except UnicodeError:
						raise Exception(f'''Incorrect encoding detected in {f}, please rebuild the dataset'''		)
					return

				__magic_name__		:		List[Any]	   =     f.readlines()
				for lineTmp in lines:
					__magic_name__		:		Optional[Any]	   =     lineTmp.strip()
					__magic_name__		:		Union[str, Any]	   =     line.rfind(" "		)
					if idx == -1:
						raise ValueError("Incorrect dictionary format, expected '<token> <cnt>'"		)
					__magic_name__		:		Optional[int]	   =     line[:idx]
					__magic_name__		:		Dict	   =     len(self.encoder		)
