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

a=  '''
    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  _UpperCamelCase (     _a	:	Optional[Any]				,     _a	:	List[Any]				,     _a	:	List[str]=8			):




    """simple docstring"""



    __UpperCamelCase	:	Any		    =  height // scale_factor**2
    if height % scale_factor**2 != 0:
        new_height += 1
    __UpperCamelCase	:	List[Any]		    =  width // scale_factor**2
    if width % scale_factor**2 != 0:
        new_width += 1
    return new_height * scale_factor, new_width * scale_factor






class 			__lowercase		(						_lowerCamelCase						):







     """simple docstring"""






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

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







     def lowerCAmelCase     (		self     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase    ):
         if latents is None:
             __UpperCamelCase	:	Tuple		    =  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}"""    )
             __UpperCamelCase	:	List[str]		    =  latents.to(_lowerCamelCase    )

         __UpperCamelCase	:	Any		    =  latents * scheduler.init_noise_sigma
         return latents







     def lowerCAmelCase     (		self     ,			_lowerCamelCase=0    ):
         if is_accelerate_available():
             from accelerate import cpu_offload
         else:
             raise ImportError('Please install accelerate via `pip install accelerate`'    )

         __UpperCamelCase	:	Union[str, Any]		    =  torch.device(f"""cuda:{gpu_id}"""    )

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







     def lowerCAmelCase     (		self     ,			_lowerCamelCase=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.'    )

         __UpperCamelCase	:	Optional[int]		    =  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)

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

         # We'll offload the last model manually.
         __UpperCamelCase	:	List[Any]		    =  hook







     @property
     # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
     def lowerCAmelCase     (		self    ):
         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     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase = 5_1_2     ,			_lowerCamelCase = 5_1_2     ,			_lowerCamelCase = 1_0_0     ,			_lowerCamelCase = 4.0     ,			_lowerCamelCase = 1     ,			_lowerCamelCase = None     ,			_lowerCamelCase = None     ,			_lowerCamelCase = "pil"     ,			_lowerCamelCase = True     ,			):
         __UpperCamelCase	:	Dict		    =  self._execution_device

         __UpperCamelCase	:	Optional[int]		    =  guidance_scale > 1.0

         if isinstance(_lowerCamelCase     ,			_lowerCamelCase    ):
             __UpperCamelCase	:	List[Any]		    =  torch.cat(_lowerCamelCase     ,			dim=0    )
         if isinstance(_lowerCamelCase     ,			_lowerCamelCase    ):
             __UpperCamelCase	:	Tuple		    =  torch.cat(_lowerCamelCase     ,			dim=0    )
         if isinstance(_lowerCamelCase     ,			_lowerCamelCase    ):
             __UpperCamelCase	:	Optional[int]		    =  torch.cat(_lowerCamelCase     ,			dim=0    )

         __UpperCamelCase	:	Dict		    =  image_embeds.shape[0] * num_images_per_prompt

         if do_classifier_free_guidance:
             __UpperCamelCase	:	Optional[Any]		    =  image_embeds.repeat_interleave(_lowerCamelCase     ,			dim=0    )
             __UpperCamelCase	:	Dict		    =  negative_image_embeds.repeat_interleave(_lowerCamelCase     ,			dim=0    )
             __UpperCamelCase	:	Optional[Any]		    =  hint.repeat_interleave(_lowerCamelCase     ,			dim=0    )

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

         __UpperCamelCase	:	int		    =  self.movq.config.latent_channels

         __UpperCamelCase	:	Any		    =  downscale_height_and_width(_lowerCamelCase     ,			_lowerCamelCase     ,			self.movq_scale_factor    )

         # create initial latent
         __UpperCamelCase	:	str		    =  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
             __UpperCamelCase	:	str		    =  torch.cat([latents] * 2    ) if do_classifier_free_guidance else latents

             __UpperCamelCase	:	str		    =  {'image_embeds': image_embeds, 'hint': hint}
             __UpperCamelCase	:	Union[str, Any]		    =  self.unet(
                 sample=_lowerCamelCase     ,			timestep=_lowerCamelCase     ,			encoder_hidden_states=_lowerCamelCase     ,			added_cond_kwargs=_lowerCamelCase     ,			return_dict=_lowerCamelCase     ,			)[0]

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

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

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

         if not return_dict:
             return (image,)

         return ImagePipelineOutput(images=_lowerCamelCase    )




'''simple docstring'''
import json
import os
import unittest

from transformers import OpenAIGPTTokenizer, OpenAIGPTTokenizerFast
from transformers.models.openai.tokenization_openai import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_spacy, require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin






@require_tokenizers
class 			__lowercase		(						_lowerCamelCase			,							unittest.TestCase						):







     """simple docstring"""






     SCREAMING_SNAKE_CASE__		   =						OpenAIGPTTokenizer
     SCREAMING_SNAKE_CASE__		   =						OpenAIGPTTokenizerFast
     SCREAMING_SNAKE_CASE__		   =						True
     SCREAMING_SNAKE_CASE__		   =						False







     def lowerCAmelCase     (		self    ):
         super().setUp()

         # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
         __UpperCamelCase	:	List[Any]		    =  [
             'l',
             'o',
             'w',
             'e',
             'r',
             's',
             't',
             'i',
             'd',
             'n',
             'w</w>',
             'r</w>',
             't</w>',
             'lo',
             'low',
             'er</w>',
             'low</w>',
             'lowest</w>',
             'newer</w>',
             'wider</w>',
             '<unk>',
         ]
         __UpperCamelCase	:	List[str]		    =  dict(zip(_lowerCamelCase     ,			range(len(_lowerCamelCase    )    )    )    )
         __UpperCamelCase	:	Union[str, Any]		    =  ['#version: 0.2', 'l o', 'lo w', 'e r</w>', '']

         __UpperCamelCase	:	Optional[int]		    =  os.path.join(self.tmpdirname     ,			VOCAB_FILES_NAMES['vocab_file']    )
         __UpperCamelCase	:	Tuple		    =  os.path.join(self.tmpdirname     ,			VOCAB_FILES_NAMES['merges_file']    )
         with open(self.vocab_file     ,			'w'    ) as fp:
             fp.write(json.dumps(_lowerCamelCase    )    )
         with open(self.merges_file     ,			'w'    ) as fp:
             fp.write('\n'.join(_lowerCamelCase    )    )







     def lowerCAmelCase     (		self     ,			_lowerCamelCase    ):
         return "lower newer", "lower newer"







     def lowerCAmelCase     (		self    ):
         __UpperCamelCase	:	Dict		    =  OpenAIGPTTokenizer(self.vocab_file     ,			self.merges_file    )

         __UpperCamelCase	:	List[Any]		    =  'lower'
         __UpperCamelCase	:	Union[str, Any]		    =  ['low', 'er</w>']
         __UpperCamelCase	:	Tuple		    =  tokenizer.tokenize(_lowerCamelCase    )
         self.assertListEqual(_lowerCamelCase     ,			_lowerCamelCase    )

         __UpperCamelCase	:	List[str]		    =  tokens + ['<unk>']
         __UpperCamelCase	:	Optional[Any]		    =  [1_4, 1_5, 2_0]
         self.assertListEqual(tokenizer.convert_tokens_to_ids(_lowerCamelCase    )     ,			_lowerCamelCase    )







     def lowerCAmelCase     (		self     ,			_lowerCamelCase=1_5    ):
         for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
             with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""    ):
                 __UpperCamelCase	:	Dict		    =  self.rust_tokenizer_class.from_pretrained(_lowerCamelCase     ,			**_lowerCamelCase    )

                 # Simple input
                 __UpperCamelCase	:	int		    =  'This is a simple input'
                 __UpperCamelCase	:	Any		    =  ['This is a simple input 1', 'This is a simple input 2']
                 __UpperCamelCase	:	Union[str, Any]		    =  ('This is a simple input', 'This is a pair')
                 __UpperCamelCase	:	Any		    =  [
                     ('This is a simple input 1', 'This is a simple input 2'),
                     ('This is a simple pair 1', 'This is a simple pair 2'),
                 ]

                 # Simple input tests
                 self.assertRaises(_lowerCamelCase     ,			tokenizer_r.encode     ,			_lowerCamelCase     ,			max_length=_lowerCamelCase     ,			padding='max_length'    )

                 # Simple input
                 self.assertRaises(_lowerCamelCase     ,			tokenizer_r.encode_plus     ,			_lowerCamelCase     ,			max_length=_lowerCamelCase     ,			padding='max_length'    )

                 # Simple input
                 self.assertRaises(
                     _lowerCamelCase     ,			tokenizer_r.batch_encode_plus     ,			_lowerCamelCase     ,			max_length=_lowerCamelCase     ,			padding='max_length'     ,			)

                 # Pair input
                 self.assertRaises(_lowerCamelCase     ,			tokenizer_r.encode     ,			_lowerCamelCase     ,			max_length=_lowerCamelCase     ,			padding='max_length'    )

                 # Pair input
                 self.assertRaises(_lowerCamelCase     ,			tokenizer_r.encode_plus     ,			_lowerCamelCase     ,			max_length=_lowerCamelCase     ,			padding='max_length'    )

                 # Pair input
                 self.assertRaises(
                     _lowerCamelCase     ,			tokenizer_r.batch_encode_plus     ,			_lowerCamelCase     ,			max_length=_lowerCamelCase     ,			padding='max_length'     ,			)







     def lowerCAmelCase     (		self    ):
         pass






@require_ftfy
@require_spacy
@require_tokenizers
class 			__lowercase		(						_lowerCamelCase						):







     """simple docstring"""






     pass



