
"""simple docstring"""


from typing import List, Optional, Union

import numpy as np
import PIL
import torch
from PIL import Image

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,
)


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

_lowercase				 =					'''
    Examples:
        ```py
        >>> from diffusers import KandinskyV22Img2ImgPipeline, KandinskyV22PriorPipeline
        >>> from diffusers.utils import load_image
        >>> import torch

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

        >>> prompt = "A red cartoon frog, 4k"
        >>> image_emb, zero_image_emb = pipe_prior(prompt, return_dict=False)

        >>> pipe = KandinskyV22Img2ImgPipeline.from_pretrained(
        ...     "kandinsky-community/kandinsky-2-2-decoder", torch_dtype=torch.float16
        ... )
        >>> pipe.to("cuda")

        >>> init_image = load_image(
        ...     "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
        ...     "/kandinsky/frog.png"
        ... )

        >>> image = pipe(
        ...     image=init_image,
        ...     image_embeds=image_emb,
        ...     negative_image_embeds=zero_image_emb,
        ...     height=768,
        ...     width=768,
        ...     num_inference_steps=100,
        ...     strength=0.2,
        ... ).images

        >>> image[0].save("red_frog.png")
        ```
'''






def    _snake_case					(		snake_case__ :  Union[str, Any]						,   snake_case__ :  Union[str, Any]						,   snake_case__ :  str=8  ):
	A     = height // scale_factor**2
	if height % scale_factor**2 != 0:
		new_height += 1
	A     = width // scale_factor**2
	if width % scale_factor**2 != 0:
		new_width += 1
	return new_height * scale_factor, new_width * scale_factor






def    _snake_case					(		snake_case__ :  List[Any]						,   snake_case__ :  Optional[int]=512						,   snake_case__ :  Tuple=512  ):
	A     = pil_image.resize((w, h)						,   resample=Image.BICUBIC						,   reducing_gap=1  )
	A     = np.array(pil_image.convert('RGB'  )  )
	A     = arr.astype(np.floataa  ) / 127.5 - 1
	A     = np.transpose(snake_case__						,   [2, 0, 1]  )
	A     = torch.from_numpy(snake_case__  ).unsqueeze(0  )
	return image







class 					lowerCAmelCase_					(   _lowercase     ):



						'''simple docstring'''


						def __init__(			self  :   Any	,A_  :   UNetaDConditionModel	,A_  :   DDPMScheduler	,A_  :   VQModel	,)  -> Union[str, Any]:
							super().__init__()

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


						def     _SCREAMING_SNAKE_CASE   (			self  :   Optional[int]	,A_  :   int	,A_  :   Tuple	,A_  :   Tuple   )  -> List[Any]:
							# get the original timestep using init_timestep
							A     = min(int(num_inference_steps * strength   )	,A_   )

							A     = max(num_inference_steps - init_timestep	,0   )
							A     = self.scheduler.timesteps[t_start:]

							return timesteps, num_inference_steps - t_start


						def     _SCREAMING_SNAKE_CASE   (			self  :   str	,A_  :   Optional[int]	,A_  :   Tuple	,A_  :   Optional[int]	,A_  :   str	,A_  :   Union[str, Any]	,A_  :   Optional[Any]	,A_  :   Union[str, Any]=None   )  -> List[Any]:
							if not isinstance(A_	,(torch.Tensor, PIL.Image.Image, list)   ):
								raise ValueError(
								    F'`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(A_   )}'   )

							A     = image.to(device=A_	,dtype=A_   )

							A     = batch_size * num_images_per_prompt

							if image.shape[1] == 4:
								A     = image

							else:
								if isinstance(A_	,A_   ) and len(A_   ) != batch_size:
									raise ValueError(
									    F'You have passed a list of generators of length {len(A_   )}, but requested an effective batch'
									    F' size of {batch_size}. Make sure the batch size matches the length of the generators.'   )

								elif isinstance(A_	,A_   ):
									A     = [
									    self.movq.encode(image[i : i + 1]   ).latent_dist.sample(generator[i]   ) for i in range(A_   )
									]
									A     = torch.cat(A_	,dim=0   )
								else:
									A     = self.movq.encode(A_   ).latent_dist.sample(A_   )

								A     = self.movq.config.scaling_factor * init_latents

							A     = torch.cat([init_latents]	,dim=0   )

							A     = init_latents.shape
							A     = randn_tensor(A_	,generator=A_	,device=A_	,dtype=A_   )

							# get latents
							A     = self.scheduler.add_noise(A_	,A_	,A_   )

							A     = init_latents

							return latents


						def     _SCREAMING_SNAKE_CASE   (			self  :   Dict	,A_  :   str=0   )  -> Optional[Any]:
							if is_accelerate_available():
								from accelerate import cpu_offload
							else:
								raise ImportError('Please install accelerate via `pip install accelerate`'   )

							A     = torch.device(F'cuda:{gpu_id}'   )

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


						def     _SCREAMING_SNAKE_CASE   (			self  :   Optional[int]	,A_  :   Tuple=0   )  -> List[Any]:
							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     = 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)

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

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


						@property
						# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
						def     _SCREAMING_SNAKE_CASE   (			self  :   List[Any]   )  -> str:
							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  :   Optional[Any]	,A_  :   Union[torch.FloatTensor, List[torch.FloatTensor]]	,A_  :   Union[torch.FloatTensor, PIL.Image.Image, List[torch.FloatTensor], List[PIL.Image.Image]]	,A_  :   Union[torch.FloatTensor, List[torch.FloatTensor]]	,A_  :   int = 512	,A_  :   int = 512	,A_  :   int = 100	,A_  :   float = 4.0	,A_  :   float = 0.3	,A_  :   int = 1	,A_  :   Optional[Union[torch.Generator, List[torch.Generator]]] = None	,A_  :   Optional[str] = "pil"	,A_  :   bool = True	,)  -> List[Any]:
							A     = self._execution_device

							A     = guidance_scale > 1.0

							if isinstance(A_	,A_   ):
								A     = torch.cat(A_	,dim=0   )
							A     = image_embeds.shape[0]
							if isinstance(A_	,A_   ):
								A     = torch.cat(A_	,dim=0   )

							if do_classifier_free_guidance:
								A     = image_embeds.repeat_interleave(A_	,dim=0   )
								A     = negative_image_embeds.repeat_interleave(A_	,dim=0   )

							A     = torch.cat([negative_image_embeds, image_embeds]	,dim=0   ).to(dtype=self.unet.dtype	,device=A_   )

							if not isinstance(A_	,A_   ):
								A     = [image]
							if not all(isinstance(A_	,(PIL.Image.Image, torch.Tensor)   ) for i in image   ):
								raise ValueError(
								    F'Input is in incorrect format: {[type(A_   ) for i in image]}. Currently, we only support  PIL image and pytorch tensor'   )

							A     = torch.cat([prepare_image(A_	,A_	,A_   ) for i in image]	,dim=0   )
							A     = image.to(dtype=image_embeds.dtype	,device=A_   )

							A     = self.movq.encode(A_   )['latents']
							A     = latents.repeat_interleave(A_	,dim=0   )
							self.scheduler.set_timesteps(A_	,device=A_   )
							A ,   A     = self.get_timesteps(A_	,A_	,A_   )
							A     = timesteps[:1].repeat(batch_size * num_images_per_prompt   )
							A ,   A     = downscale_height_and_width(A_	,A_	,self.movq_scale_factor   )
							A     = self.prepare_latents(
							    A_	,A_	,A_	,A_	,image_embeds.dtype	,A_	,A_   )
							for i, t in enumerate(self.progress_bar(A_   )   ):
								# expand the latents if we are doing classifier free guidance
								A     = torch.cat([latents] * 2   ) if do_classifier_free_guidance else latents

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

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

								# compute the previous noisy sample x_t -> x_t-1
								A     = self.scheduler.step(
								    A_	,A_	,A_	,generator=A_	,)[0]

							# post-processing
							A     = 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"]:
								A     = image * 0.5 + 0.5
								A     = image.clamp(0	,1   )
								A     = image.cpu().permute(0	,2	,3	,1   ).float().numpy()

							if output_type == "pil":
								A     = 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.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES, BioGptTokenizer
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin







class 					lowerCAmelCase_					(   _lowercase		,       unittest.TestCase     ):



						'''simple docstring'''


						_lowerCamelCase:     List[str]   =     BioGptTokenizer
						_lowerCamelCase:     Tuple   =     False


						def     _SCREAMING_SNAKE_CASE   (			self  :   List[str]   )  -> Optional[int]:
							super().setUp()

							# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
							A     = [
							    '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>',
							]
							A     = dict(zip(A_	,range(len(A_   )   )   )   )
							A     = ['l o 123', 'lo w 1456', 'e r</w> 1789', '']

							A     = os.path.join(self.tmpdirname	,VOCAB_FILES_NAMES['vocab_file']   )
							A     = os.path.join(self.tmpdirname	,VOCAB_FILES_NAMES['merges_file']   )
							with open(self.vocab_file	,'w'   ) as fp:
								fp.write(json.dumps(A_   )   )
							with open(self.merges_file	,'w'   ) as fp:
								fp.write('\n'.join(A_   )   )


						def     _SCREAMING_SNAKE_CASE   (			self  :   Dict	,A_  :   Tuple   )  -> int:
							A     = 'lower newer'
							A     = 'lower newer'
							return input_text, output_text


						def     _SCREAMING_SNAKE_CASE   (			self  :   List[Any]   )  -> Any:
							A     = BioGptTokenizer(self.vocab_file	,self.merges_file   )

							A     = 'lower'
							A     = ['low', 'er</w>']
							A     = tokenizer.tokenize(A_   )
							self.assertListEqual(A_	,A_   )

							A     = tokens + ['<unk>']
							A     = [14, 15, 20]
							self.assertListEqual(tokenizer.convert_tokens_to_ids(A_   )	,A_   )




						@slow
						def     _SCREAMING_SNAKE_CASE   (			self  :   Union[str, Any]   )  -> Optional[Any]:
							A     = BioGptTokenizer.from_pretrained('microsoft/biogpt'   )

							A     = tokenizer.encode('sequence builders'	,add_special_tokens=A_   )
							A     = tokenizer.encode('multi-sequence build'	,add_special_tokens=A_   )

							A     = tokenizer.build_inputs_with_special_tokens(A_   )
							A     = tokenizer.build_inputs_with_special_tokens(A_	,A_   )

							self.assertTrue(encoded_sentence == [2] + text   )
							self.assertTrue(encoded_pair == [2] + text + [2] + text_a   )