



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,
)


a__      :				Optional[int]   	=			logging.get_logger(__name__)  # pylint: disable=invalid-name

a__      :				Optional[int]   	=			"\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   _lowerCAmelCase   (						A__						,  A__						,  A__=8	):
      lowercase__     =  height // scale_factor**2
      if height % scale_factor**2 != 0:
            new_height += 1
      lowercase__     =  width // scale_factor**2
      if width % scale_factor**2 != 0:
            new_width += 1
      return new_height * scale_factor, new_width * scale_factor



class       UpperCAmelCase__(							lowerCamelCase	):

   '''simple docstring'''




   def __init__(						self  :			Tuple			,				lowerCAmelCase  :			UNetaDConditionModel			,				lowerCAmelCase  :			DDPMScheduler			,				lowerCAmelCase  :			VQModel			,				)	->				Any:




         """simple docstring"""

         super().__init__()

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


   def 			UpperCAmelCase		(						self  :			str			,				lowerCAmelCase  :			str			,				lowerCAmelCase  :			Union[str, Any]			,				lowerCAmelCase  :			Union[str, Any]			,				lowerCAmelCase  :			List[Any]			,				lowerCAmelCase  :			Optional[Any]			,				lowerCAmelCase  :			Tuple)	->				Dict:




         """simple docstring"""

         if latents is None:
               lowercase__     =  randn_tensor(lowerCAmelCase			,				generator=lowerCAmelCase			,				device=lowerCAmelCase			,				dtype=lowerCAmelCase)
         else:
               if latents.shape != shape:
                     raise ValueError(f'''Unexpected latents shape, got {latents.shape}, expected {shape}''')
               lowercase__     =  latents.to(lowerCAmelCase)

         lowercase__     =  latents * scheduler.init_noise_sigma
         return latents


   def 			UpperCAmelCase		(						self  :			List[Any]			,				lowerCAmelCase  :			List[str]=0)	->				Optional[int]:




         """simple docstring"""

         if is_accelerate_available():
               from accelerate import cpu_offload
         else:
               raise ImportError('Please install accelerate via `pip install accelerate`')

         lowercase__     =  torch.device(f'''cuda:{gpu_id}''')

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


   def 			UpperCAmelCase		(						self  :			Dict			,				lowerCAmelCase  :			List[str]=0)	->				Dict:




         """simple docstring"""

         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.')

         lowercase__     =  torch.device(f'''cuda:{gpu_id}''')

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

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

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


   @property
   # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
   def 			UpperCAmelCase		(						self  :			int)	->				Optional[Any]:




         """simple docstring"""

         if not hasattr(self.unet			,				'_hf_hook'):
               return self.device
         for module in self.unet.modules():
               if (
                   hasattr(lowerCAmelCase			,				'_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(lowerCAmelCase)
   def __call__(						self  :			Dict			,				lowerCAmelCase  :			Union[torch.FloatTensor, List[torch.FloatTensor]]			,				lowerCAmelCase  :			Union[torch.FloatTensor, List[torch.FloatTensor]]			,				lowerCAmelCase  :			torch.FloatTensor			,				lowerCAmelCase  :			int = 5_12			,				lowerCAmelCase  :			int = 5_12			,				lowerCAmelCase  :			int = 1_00			,				lowerCAmelCase  :			float = 4.0			,				lowerCAmelCase  :			int = 1			,				lowerCAmelCase  :			Optional[Union[torch.Generator, List[torch.Generator]]] = None			,				lowerCAmelCase  :			Optional[torch.FloatTensor] = None			,				lowerCAmelCase  :			Optional[str] = "pil"			,				lowerCAmelCase  :			bool = True			,				)	->				Dict:




         """simple docstring"""

         lowercase__     =  self._execution_device

         lowercase__     =  guidance_scale > 1.0

         if isinstance(lowerCAmelCase			,				lowerCAmelCase):
               lowercase__     =  torch.cat(lowerCAmelCase			,				dim=0)
         if isinstance(lowerCAmelCase			,				lowerCAmelCase):
               lowercase__     =  torch.cat(lowerCAmelCase			,				dim=0)
         if isinstance(lowerCAmelCase			,				lowerCAmelCase):
               lowercase__     =  torch.cat(lowerCAmelCase			,				dim=0)

         lowercase__     =  image_embeds.shape[0] * num_images_per_prompt

         if do_classifier_free_guidance:
               lowercase__     =  image_embeds.repeat_interleave(lowerCAmelCase			,				dim=0)
               lowercase__     =  negative_image_embeds.repeat_interleave(lowerCAmelCase			,				dim=0)
               lowercase__     =  hint.repeat_interleave(lowerCAmelCase			,				dim=0)

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

         lowercase__     =  self.movq.config.latent_channels

         lowercase__,	lowercase__     =  downscale_height_and_width(lowerCAmelCase			,				lowerCAmelCase			,				self.movq_scale_factor)

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

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

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

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

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

         if output_type == "pil":
               lowercase__     =  self.numpy_to_pil(lowerCAmelCase)

         if not return_dict:
               return (image,)

         return ImagePipelineOutput(images=lowerCAmelCase)





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

import regex as re

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


a__      :				Optional[int]   	=			logging.get_logger(__name__)


a__      :				Dict   	=			{
    "vocab_file": "vocab.json",
    "merges_file": "merges.txt",
    "tokenizer_config_file": "tokenizer_config.json",
}

a__      :				str   	=			{
    "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"
        )
    },
}

a__      :				Any   	=			{"facebook/blenderbot_small-90M": 5_12}

def   _lowerCAmelCase   (						A__	):
      lowercase__     =  set()
      lowercase__     =  word[0]
      for char in word[1:]:
            pairs.add((prev_char, char)	)
            lowercase__     =  char

      lowercase__     =  set(A__	)
      return pairs



class       UpperCAmelCase__(							lowerCamelCase	):

   '''simple docstring'''




   A    :  List[str]       				=    VOCAB_FILES_NAMES
   A    :  str       				=    PRETRAINED_VOCAB_FILES_MAP
   A    :  int       				=    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   A    :  Tuple       				=    ["input_ids", "attention_mask"]


   def __init__(						self  :			Optional[int]			,				lowerCAmelCase  :			Dict			,				lowerCAmelCase  :			Dict			,				lowerCAmelCase  :			int="__start__"			,				lowerCAmelCase  :			Dict="__end__"			,				lowerCAmelCase  :			Any="__unk__"			,				lowerCAmelCase  :			str="__null__"			,				**lowerCAmelCase  :			Optional[Any]			,				)	->				List[str]:




         """simple docstring"""

         super().__init__(unk_token=lowerCAmelCase			,				bos_token=lowerCAmelCase			,				eos_token=lowerCAmelCase			,				pad_token=lowerCAmelCase			,				**lowerCAmelCase)

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


   @property
   def 			UpperCAmelCase		(						self  :			int)	->				int:




         """simple docstring"""

         return len(self.encoder)


   def 			UpperCAmelCase		(						self  :			str)	->				Dict:




         """simple docstring"""

         return dict(self.encoder			,				**self.added_tokens_encoder)


   def 			UpperCAmelCase		(						self  :			str			,				lowerCAmelCase  :			str)	->				str:




         """simple docstring"""

         if token in self.cache:
               return self.cache[token]
         lowercase__     =  re.sub('([.,!?()])'			,				R' \1'			,				lowerCAmelCase)
         lowercase__     =  re.sub('(\')'			,				R' \1 '			,				lowerCAmelCase)
         lowercase__     =  re.sub(R'\s{2,}'			,				' '			,				lowerCAmelCase)
         if "\n" in token:
               lowercase__     =  token.replace('\n'			,				' __newln__')

         lowercase__     =  token.split(' ')
         lowercase__     =  []
         for token in tokens:
               if not len(lowerCAmelCase):
                     continue

               lowercase__     =  token.lower()
               lowercase__     =  tuple(lowerCAmelCase)
               lowercase__     =  tuple(list(word[:-1]) + [word[-1] + '</w>'])
               lowercase__     =  get_pairs(lowerCAmelCase)

               if not pairs:
                     words.append(lowerCAmelCase)
                     continue

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

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

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

               lowercase__     =  word
               words.append(lowerCAmelCase)
         return " ".join(lowerCAmelCase)


   def 			UpperCAmelCase		(						self  :			List[str]			,				lowerCAmelCase  :			str)	->				List[str]:




         """simple docstring"""

         lowercase__     =  []

         lowercase__     =  re.findall(R'\S+\n?'			,				lowerCAmelCase)

         for token in words:
               split_tokens.extend(list(self.bpe(lowerCAmelCase).split(' ')))
         return split_tokens


   def 			UpperCAmelCase		(						self  :			int			,				lowerCAmelCase  :			str)	->				int:




         """simple docstring"""

         lowercase__     =  token.lower()
         return self.encoder.get(lowerCAmelCase			,				self.encoder.get(self.unk_token))


   def 			UpperCAmelCase		(						self  :			Optional[int]			,				lowerCAmelCase  :			int)	->				str:




         """simple docstring"""

         return self.decoder.get(lowerCAmelCase			,				self.unk_token)


   def 			UpperCAmelCase		(						self  :			Optional[Any]			,				lowerCAmelCase  :			List[str])	->				str:




         """simple docstring"""

         lowercase__     =  ' '.join(lowerCAmelCase).replace('@@ '			,				'').strip()
         return out_string



   def 			UpperCAmelCase		(						self  :			str			,				lowerCAmelCase  :			str			,				lowerCAmelCase  :			Optional[str] = None)	->				Tuple[str]:




         """simple docstring"""

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

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

         lowercase__     =  0
         with open(lowerCAmelCase			,				'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 lowerCAmelCase: 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!')
                           lowercase__     =  token_index
                     writer.write(' '.join(lowerCAmelCase) + '\n')
                     index += 1

         return vocab_file, merge_file
