"""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,
)


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

_a      :		Dict           =   '\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     (			_lowerCamelCase						:       List[Any]			,_lowerCamelCase						:       Optional[int]			,_lowerCamelCase						:       Any=8	)       ->      int:
  _lowerCAmelCase						:       Union[str, Any]  			=     height // scale_factor**2
  if height % scale_factor**2 != 0:
    new_height += 1
  _lowerCAmelCase						:       Optional[Any]  			=     width // scale_factor**2
  if width % scale_factor**2 != 0:
    new_width += 1
  return new_height * scale_factor, new_width * scale_factor






class 						__A							( SCREAMING_SNAKE_CASE_		):






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

       self.register_modules(
           unet=a__				,     scheduler=a__				,     movq=a__				,     )
       _lowerCAmelCase						:       int  			=     2 ** (len(self.movq.config.block_out_channels  ) - 1)






     def     __A  (		self				,     a__				,     a__				,     a__				,     a__				,     a__				,     a__  ):
       if latents is None:
         _lowerCAmelCase						:       Dict  			=     randn_tensor(a__				,     generator=a__				,     device=a__				,     dtype=a__  )
       else:
         if latents.shape != shape:
           raise ValueError(F"Unexpected latents shape, got {latents.shape}, expected {shape}"  )
         _lowerCAmelCase						:       Optional[Any]  			=     latents.to(a__  )

       _lowerCAmelCase						:       List[str]  			=     latents * scheduler.init_noise_sigma
       return latents






     def     __A  (		self				,     a__=0  ):
       if is_accelerate_available():
         from accelerate import cpu_offload
       else:
         raise ImportError("""Please install accelerate via `pip install accelerate`"""  )

       _lowerCAmelCase						:       Tuple  			=     torch.device(F"cuda:{gpu_id}"  )

       _lowerCAmelCase						:       Optional[Any]  			=     [
           self.unet,
           self.movq,
       ]
       for cpu_offloaded_model in models:
         if cpu_offloaded_model is not None:
           cpu_offload(a__				,     a__  )






     def     __A  (		self				,     a__=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						:       List[str]  			=     torch.device(F"cuda:{gpu_id}"  )

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

       _lowerCAmelCase						:       Optional[int]  			=     None
       for cpu_offloaded_model in [self.unet, self.movq]:
         _lowerCAmelCase			,	_lowerCAmelCase						:       List[str]  			=     cpu_offload_with_hook(a__				,     a__				,     prev_module_hook=a__  )

       # We'll offload the last model manually.
       _lowerCAmelCase						:       str  			=     hook






     @property
     # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
     def     __A  (		self  ):
       if not hasattr(self.unet				,     """_hf_hook"""  ):
         return self.device
       for module in self.unet.modules():
         if (
             hasattr(a__				,     """_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(a__  )
     def __call__(		self				,     a__				,     a__				,     a__				,     a__ = 512				,     a__ = 512				,     a__ = 100				,     a__ = 4.0				,     a__ = 1				,     a__ = None				,     a__ = None				,     a__ = "pil"				,     a__ = True				,     ):
       _lowerCAmelCase						:       Optional[Any]  			=     self._execution_device

       _lowerCAmelCase						:       Dict  			=     guidance_scale > 1.0

       if isinstance(a__				,     a__  ):
         _lowerCAmelCase						:       List[str]  			=     torch.cat(a__				,     dim=0  )
       if isinstance(a__				,     a__  ):
         _lowerCAmelCase						:       Union[str, Any]  			=     torch.cat(a__				,     dim=0  )
       if isinstance(a__				,     a__  ):
         _lowerCAmelCase						:       str  			=     torch.cat(a__				,     dim=0  )

       _lowerCAmelCase						:       Optional[Any]  			=     image_embeds.shape[0] * num_images_per_prompt

       if do_classifier_free_guidance:
         _lowerCAmelCase						:       Optional[int]  			=     image_embeds.repeat_interleave(a__				,     dim=0  )
         _lowerCAmelCase						:       List[Any]  			=     negative_image_embeds.repeat_interleave(a__				,     dim=0  )
         _lowerCAmelCase						:       Optional[int]  			=     hint.repeat_interleave(a__				,     dim=0  )

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

       _lowerCAmelCase						:       List[str]  			=     self.movq.config.latent_channels

       _lowerCAmelCase			,	_lowerCAmelCase						:       Any  			=     downscale_height_and_width(a__				,     a__				,     self.movq_scale_factor  )

       # create initial latent
       _lowerCAmelCase						:       List[Any]  			=     self.prepare_latents(
           (batch_size, num_channels_latents, height, width)				,     image_embeds.dtype				,     a__				,     a__				,     a__				,     self.scheduler				,     )

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

         _lowerCAmelCase						:       Any  			=     {"""image_embeds""": image_embeds, """hint""": hint}
         _lowerCAmelCase						:       Optional[int]  			=     self.unet(
             sample=a__				,     timestep=a__				,     encoder_hidden_states=a__				,     added_cond_kwargs=a__				,     return_dict=a__				,     )[0]

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

         # compute the previous noisy sample x_t -> x_t-1
         _lowerCAmelCase						:       List[str]  			=     self.scheduler.step(
             a__				,     a__				,     a__				,     generator=a__				,     )[0]
       # post-processing
       _lowerCAmelCase						:       Optional[int]  			=     self.movq.decode(a__				,     force_not_quantize=a__  )["""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						:       List[str]  			=     image * 0.5 + 0.5
         _lowerCAmelCase						:       Tuple  			=     image.clamp(0				,     1  )
         _lowerCAmelCase						:       Optional[Any]  			=     image.cpu().permute(0				,     2				,     3				,     1  ).float().numpy()

       if output_type == "pil":
         _lowerCAmelCase						:       Union[str, Any]  			=     self.numpy_to_pil(a__  )

       if not return_dict:
         return (image,)

       return ImagePipelineOutput(images=a__  )





"""simple docstring"""




import os
import shutil
import tempfile
import unittest

import numpy as np

from transformers import AutoTokenizer, BarkProcessor
from transformers.testing_utils import require_torch, slow
@require_torch
class 						__A							( unittest.TestCase		):






     def     __A  (		self  ):
       _lowerCAmelCase						:       Optional[int]  			=     """ylacombe/bark-small"""
       _lowerCAmelCase						:       Optional[Any]  			=     tempfile.mkdtemp()
       _lowerCAmelCase						:       int  			=     """en_speaker_1"""
       _lowerCAmelCase						:       List[Any]  			=     """This is a test string"""
       _lowerCAmelCase						:       Any  			=     """speaker_embeddings_path.json"""
       _lowerCAmelCase						:       List[Any]  			=     """speaker_embeddings"""






     def     __A  (		self				,     **a__  ):
       return AutoTokenizer.from_pretrained(self.checkpoint				,     **a__  )






     def     __A  (		self  ):
       shutil.rmtree(self.tmpdirname  )






     def     __A  (		self  ):
       _lowerCAmelCase						:       List[Any]  			=     self.get_tokenizer()

       _lowerCAmelCase						:       int  			=     BarkProcessor(tokenizer=a__  )

       processor.save_pretrained(self.tmpdirname  )
       _lowerCAmelCase						:       str  			=     BarkProcessor.from_pretrained(self.tmpdirname  )

       self.assertEqual(processor.tokenizer.get_vocab()				,     tokenizer.get_vocab()  )






     @slow
     def     __A  (		self  ):
       _lowerCAmelCase						:       Optional[int]  			=     BarkProcessor.from_pretrained(
           pretrained_processor_name_or_path=self.checkpoint				,     speaker_embeddings_dict_path=self.speaker_embeddings_dict_path				,     )
       processor.save_pretrained(
           self.tmpdirname				,     speaker_embeddings_dict_path=self.speaker_embeddings_dict_path				,     speaker_embeddings_directory=self.speaker_embeddings_directory				,     )

       _lowerCAmelCase						:       Tuple  			=     self.get_tokenizer(bos_token="""(BOS)"""				,     eos_token="""(EOS)"""  )

       _lowerCAmelCase						:       List[Any]  			=     BarkProcessor.from_pretrained(
           self.tmpdirname				,     self.speaker_embeddings_dict_path				,     bos_token="""(BOS)"""				,     eos_token="""(EOS)"""				,     )

       self.assertEqual(processor.tokenizer.get_vocab()				,     tokenizer_add_kwargs.get_vocab()  )






     def     __A  (		self  ):
       _lowerCAmelCase						:       List[str]  			=     BarkProcessor.from_pretrained(
           pretrained_processor_name_or_path=self.checkpoint				,     speaker_embeddings_dict_path=self.speaker_embeddings_dict_path				,     )

       _lowerCAmelCase						:       Union[str, Any]  			=     35
       _lowerCAmelCase						:       Union[str, Any]  			=     2
       _lowerCAmelCase						:       Optional[int]  			=     8

       _lowerCAmelCase						:       Dict  			=     {
           """semantic_prompt""": np.ones(a__  ),
           """coarse_prompt""": np.ones((nb_codebooks_coarse, seq_len)  ),
           """fine_prompt""": np.ones((nb_codebooks_total, seq_len)  ),
       }

       # test providing already loaded voice_preset
       _lowerCAmelCase						:       Dict  			=     processor(text=self.input_string				,     voice_preset=a__  )

       _lowerCAmelCase						:       Tuple  			=     inputs["""history_prompt"""]
       for key in voice_preset:
         self.assertListEqual(voice_preset[key].tolist()				,     processed_voice_preset.get(a__				,     np.array([]  )  ).tolist()  )

       # test loading voice preset from npz file
       _lowerCAmelCase						:       List[Any]  			=     os.path.join(self.tmpdirname				,     """file.npz"""  )
       np.savez(a__				,     **a__  )
       _lowerCAmelCase						:       List[Any]  			=     processor(text=self.input_string				,     voice_preset=a__  )
       _lowerCAmelCase						:       Optional[int]  			=     inputs["""history_prompt"""]

       for key in voice_preset:
         self.assertListEqual(voice_preset[key].tolist()				,     processed_voice_preset.get(a__				,     np.array([]  )  ).tolist()  )

       # test loading voice preset from the hub
       _lowerCAmelCase						:       str  			=     processor(text=self.input_string				,     voice_preset=self.voice_preset  )






     def     __A  (		self  ):
       _lowerCAmelCase						:       int  			=     self.get_tokenizer()

       _lowerCAmelCase						:       List[Any]  			=     BarkProcessor(tokenizer=a__  )

       _lowerCAmelCase						:       Dict  			=     processor(text=self.input_string  )

       _lowerCAmelCase						:       Tuple  			=     tokenizer(
           self.input_string				,     padding="""max_length"""				,     max_length=256				,     add_special_tokens=a__				,     return_attention_mask=a__				,     return_token_type_ids=a__				,     )

       for key in encoded_tok.keys():
         self.assertListEqual(encoded_tok[key]				,     encoded_processor[key].squeeze().tolist()  )




