



'''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__    :   List[str]           =					logging.get_logger(__name__)  # pylint: disable=invalid-name

A__    :   str           =					'''
    Examples:
        ```py
        >>> import torch
        >>> import numpy as np

        >>> from diffusers import KandinskyV22PriorPipeline, KandinskyV22ControlnetPipeline
        >>> from transformers import pipeline
        >>> from diffusers.utils import load_image


        >>> def make_hint(image, depth_estimator):
        ...     image = depth_estimator(image)["depth"]
        ...     image = np.array(image)
        ...     image = image[:, :, None]
        ...     image = np.concatenate([image, image, image], axis=2)
        ...     detected_map = torch.from_numpy(image).float() / 255.0
        ...     hint = detected_map.permute(2, 0, 1)
        ...     return hint


        >>> depth_estimator = pipeline("depth-estimation")

        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(
        ...     "kandinsky-community/kandinsky-2-2-prior", torch_dtype=torch.float16
        ... )
        >>> pipe_prior = pipe_prior.to("cuda")

        >>> pipe = KandinskyV22ControlnetPipeline.from_pretrained(
        ...     "kandinsky-community/kandinsky-2-2-controlnet-depth", torch_dtype=torch.float16
        ... )
        >>> pipe = pipe.to("cuda")


        >>> img = load_image(
        ...     "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
        ...     "/kandinsky/cat.png"
        ... ).resize((768, 768))

        >>> hint = make_hint(img, depth_estimator).unsqueeze(0).half().to("cuda")

        >>> prompt = "A robot, 4k photo"
        >>> 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"

        >>> generator = torch.Generator(device="cuda").manual_seed(43)

        >>> image_emb, zero_image_emb = pipe_prior(
        ...     prompt=prompt, negative_prompt=negative_prior_prompt, generator=generator
        ... ).to_tuple()

        >>> images = pipe(
        ...     image_embeds=image_emb,
        ...     negative_image_embeds=zero_image_emb,
        ...     hint=hint,
        ...     num_inference_steps=50,
        ...     generator=generator,
        ...     height=768,
        ...     width=768,
        ... ).images

        >>> images[0].save("robot_cat.png")
        ```
'''





def   a_   (		_UpperCAmelCase    :      Optional[int] ,_UpperCAmelCase    :      Optional[Any] ,_UpperCAmelCase    :      str=8				)    ->  List[Any]:
      __snake_case :   Any													=    height // scale_factor**2
      if height % scale_factor**2 != 0:
            new_height += 1
      __snake_case :   Tuple													=    width // scale_factor**2
      if width % scale_factor**2 != 0:
            new_width += 1
      return new_height * scale_factor, new_width * scale_factor





class  snake_case__						(    SCREAMING_SNAKE_CASE_				):
 def __init__(   self						:							Any      ,     __a						:							UNetaDConditionModel      ,     __a						:							DDPMScheduler      ,     __a						:							VQModel      ,     )      ->   List[str]:







       '''simple docstring'''
       super().__init__()

       self.register_modules(
           unet=__a      ,     scheduler=__a      ,     movq=__a      ,     )
       __snake_case :   Optional[Any]													=    2 ** (len(self.movq.config.block_out_channels  ) - 1)
 def A_   (   self						:							int      ,     __a						:							int      ,     __a						:							Tuple      ,     __a						:							int      ,     __a						:							Optional[Any]      ,     __a						:							List[str]      ,     __a						:							Optional[int]  )      ->   List[Any]:







       '''simple docstring'''
       if latents is None:
             __snake_case :   Union[str, Any]													=    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}'''  )
             __snake_case :   Optional[int]													=    latents.to(__a  )

       __snake_case :   Tuple													=    latents * scheduler.init_noise_sigma
       return latents
 def A_   (   self						:							Optional[int]      ,     __a						:							List[Any]=0  )      ->   Any:







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

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

       __snake_case :   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						:							str      ,     __a						:							Optional[Any]=0  )      ->   Optional[int]:







       '''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.'  )

       __snake_case :   Union[str, Any]													=    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)

       __snake_case :   str													=    None
       for cpu_offloaded_model in [self.unet, self.movq]:
             __snake_case							,     __snake_case :   Dict													=    cpu_offload_with_hook(__a      ,     __a      ,     prev_module_hook=__a  )

       # We'll offload the last model manually.
       __snake_case :   Optional[int]													=    hook
 @property
 # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
 def A_   (   self						:							Any  )      ->   Optional[Any]:







       '''simple docstring'''
       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						:							str      ,     __a						:							Union[torch.FloatTensor, List[torch.FloatTensor]]      ,     __a						:							Union[torch.FloatTensor, List[torch.FloatTensor]]      ,     __a						:							torch.FloatTensor      ,     __a						:							int = 512      ,     __a						:							int = 512      ,     __a						:							int = 100      ,     __a						:							float = 4.0      ,     __a						:							int = 1      ,     __a						:							Optional[Union[torch.Generator, List[torch.Generator]]] = None      ,     __a						:							Optional[torch.FloatTensor] = None      ,     __a						:							Optional[str] = "pil"      ,     __a						:							bool = True      ,     )      ->   Optional[int]:







       '''simple docstring'''
       __snake_case :   Union[str, Any]													=    self._execution_device

       __snake_case :   Any													=    guidance_scale > 1.0

       if isinstance(__a      ,     __a  ):
             __snake_case :   Tuple													=    torch.cat(__a      ,     dim=0  )
       if isinstance(__a      ,     __a  ):
             __snake_case :   Optional[Any]													=    torch.cat(__a      ,     dim=0  )
       if isinstance(__a      ,     __a  ):
             __snake_case :   Union[str, Any]													=    torch.cat(__a      ,     dim=0  )

       __snake_case :   int													=    image_embeds.shape[0] * num_images_per_prompt

       if do_classifier_free_guidance:
             __snake_case :   int													=    image_embeds.repeat_interleave(__a      ,     dim=0  )
             __snake_case :   Union[str, Any]													=    negative_image_embeds.repeat_interleave(__a      ,     dim=0  )
             __snake_case :   List[Any]													=    hint.repeat_interleave(__a      ,     dim=0  )

       __snake_case :   Optional[Any]													=    torch.cat([negative_image_embeds, image_embeds]      ,     dim=0  ).to(dtype=self.unet.dtype      ,     device=__a  )
       __snake_case :   str													=    torch.cat([hint, hint]      ,     dim=0  ).to(dtype=self.unet.dtype      ,     device=__a  )
       self.scheduler.set_timesteps(__a      ,     device=__a  )
       __snake_case :   Tuple													=    self.scheduler.timesteps

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

       __snake_case							,     __snake_case :   List[Any]													=    downscale_height_and_width(__a      ,     __a      ,     self.movq_scale_factor  )

       # create initial latent
       __snake_case :   Dict													=    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
             __snake_case :   Any													=    torch.cat([latents] * 2  ) if do_classifier_free_guidance else latents

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

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

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

       if output_type == "pil":
             __snake_case :   List[str]													=    self.numpy_to_pil(__a  )

       if not return_dict:
             return (image,)

       return ImagePipelineOutput(images=__a  )








'''simple docstring'''





import json
import os
import unittest

from transformers.models.blenderbot_small.tokenization_blenderbot_small import (
    VOCAB_FILES_NAMES,
    BlenderbotSmallTokenizer,
)

from ...test_tokenization_common import TokenizerTesterMixin



class  snake_case__						(    SCREAMING_SNAKE_CASE_  ,     unittest.TestCase				):
 A__				= BlenderbotSmallTokenizer
 A__				= False
 def A_   (   self						:							Any  )      ->   str:







       '''simple docstring'''
       super().setUp()

       __snake_case :   Any													=    ['__start__', 'adapt', 'act', 'ap@@', 'te', '__end__', '__unk__']
       __snake_case :   str													=    dict(zip(__a      ,     range(len(__a  )  )  )  )

       __snake_case :   List[Any]													=    ['#version: 0.2', 'a p', 't e</w>', 'ap t</w>', 'a d', 'ad apt</w>', 'a c', 'ac t</w>', '']
       __snake_case :   Tuple													=    {'unk_token': '__unk__', 'bos_token': '__start__', 'eos_token': '__end__'}

       __snake_case :   str													=    os.path.join(self.tmpdirname      ,     VOCAB_FILES_NAMES['vocab_file']  )
       __snake_case :   Union[str, Any]													=    os.path.join(self.tmpdirname      ,     VOCAB_FILES_NAMES['merges_file']  )
       with open(self.vocab_file      ,     'w'      ,     encoding='utf-8'  ) as fp:
             fp.write(json.dumps(__a  ) + '\n'  )
       with open(self.merges_file      ,     'w'      ,     encoding='utf-8'  ) as fp:
             fp.write('\n'.join(__a  )  )
 def A_   (   self						:							Tuple      ,     **__a						:							Optional[int]  )      ->   List[str]:







       '''simple docstring'''
       kwargs.update(self.special_tokens_map  )
       return BlenderbotSmallTokenizer.from_pretrained(self.tmpdirname      ,     **__a  )
 def A_   (   self						:							int      ,     __a						:							int  )      ->   List[Any]:







       '''simple docstring'''
       __snake_case :   List[str]													=    'adapt act apte'
       __snake_case :   List[str]													=    'adapt act apte'
       return input_text, output_text
 def A_   (   self						:							Dict  )      ->   str:







       '''simple docstring'''
       __snake_case :   Dict													=    BlenderbotSmallTokenizer(self.vocab_file      ,     self.merges_file      ,     **self.special_tokens_map  )
       __snake_case :   List[str]													=    'adapt act apte'
       __snake_case :   List[Any]													=    ['adapt', 'act', 'ap@@', 'te']
       __snake_case :   Optional[int]													=    tokenizer.tokenize(__a  )
       self.assertListEqual(__a      ,     __a  )

       __snake_case :   List[str]													=    [tokenizer.bos_token] + tokens + [tokenizer.eos_token]

       __snake_case :   List[str]													=    [0, 1, 2, 3, 4, 5]
       self.assertListEqual(tokenizer.convert_tokens_to_ids(__a  )      ,     __a  )
 def A_   (   self						:							int  )      ->   str:







       '''simple docstring'''
       __snake_case :   int													=    BlenderbotSmallTokenizer.from_pretrained('facebook/blenderbot-90M'  )
       assert tok('sam'  ).input_ids == [1384]
       __snake_case :   List[str]													=    'I am a small frog.'
       __snake_case :   List[Any]													=    tok([src_text]      ,     padding=__a      ,     truncation=__a  )['input_ids']
       __snake_case :   int													=    tok.batch_decode(__a      ,     skip_special_tokens=__a      ,     clean_up_tokenization_spaces=__a  )[0]
       assert src_text != decoded  # I wish it did!
       assert decoded == "i am a small frog ."





 def A_   (   self						:							List[str]  )      ->   Union[str, Any]:







       '''simple docstring'''
       __snake_case :   Tuple													=    BlenderbotSmallTokenizer.from_pretrained('facebook/blenderbot-90M'  )
       __snake_case :   Optional[Any]													=    'I am a small frog .'
       __snake_case :   List[str]													=    '.'
       __snake_case :   List[str]													=    tok(__a  )['input_ids']
       __snake_case :   str													=    tok(__a  )['input_ids']

       assert encoded[-1] == encoded_dot[0]



