




from typing import Dict, List, Optional, Tuple, Union

import torch

from ...models import AutoencoderKL, TransformeraDModel
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput







class a (     __SCREAMING_SNAKE_CASE  ):
						"""simple docstring"""

						def __init__( self :     Any				,							lowerCamelCase__ :     TransformeraDModel				,							lowerCamelCase__ :     AutoencoderKL				,							lowerCamelCase__ :     KarrasDiffusionSchedulers				,							lowerCamelCase__ :     Optional[Dict[int, str]] = None				,							)    ->						Dict:
													"""simple docstring"""


													super().__init__()
													self.register_modules(transformer=lowerCamelCase__				,							vae=lowerCamelCase__				,							scheduler=lowerCamelCase__				)

													# create a imagenet -> id dictionary for easier use
													__lowercase      =    {}
													if idalabel is not None:
																				for key, value in idalabel.items():
																											for label in value.split(''','''				):
																																		__lowercase      =    int(lowerCamelCase__				)
																				__lowercase      =    dict(sorted(self.labels.items()				)				)






						def     UpperCAmelCase_      ( self :     List[str]				,							lowerCamelCase__ :     Union[str, List[str]]				)    ->						List[int]:
													"""simple docstring"""


													if not isinstance(lowerCamelCase__				,							lowerCamelCase__				):
																				__lowercase      =    list(lowerCamelCase__				)

													for l in label:
																				if l not in self.labels:
																											raise ValueError(
																											    f'{l} does not exist. Please make sure to select one of the following labels: \n {self.labels}.'				)

													return [self.labels[l] for l in label]






						@torch.no_grad()
						def __call__( self :     str				,							lowerCamelCase__ :     List[int]				,							lowerCamelCase__ :     float = 4.0				,							lowerCamelCase__ :     Optional[Union[torch.Generator, List[torch.Generator]]] = None				,							lowerCamelCase__ :     int = 50				,							lowerCamelCase__ :     Optional[str] = "pil"				,							lowerCamelCase__ :     bool = True				,							)    ->						Union[ImagePipelineOutput, Tuple]:
													"""simple docstring"""


													__lowercase      =    len(lowerCamelCase__				)
													__lowercase      =    self.transformer.config.sample_size
													__lowercase      =    self.transformer.config.in_channels

													__lowercase      =    randn_tensor(
													    shape=(batch_size, latent_channels, latent_size, latent_size)				,							generator=lowerCamelCase__				,							device=self.device				,							dtype=self.transformer.dtype				,							)
													__lowercase      =    torch.cat([latents] * 2				) if guidance_scale > 1 else latents

													__lowercase      =    torch.tensor(lowerCamelCase__				,							device=self.device				).reshape(-1				)
													__lowercase      =    torch.tensor([1_000] * batch_size				,							device=self.device				)
													__lowercase      =    torch.cat([class_labels, class_null]				,							0				) if guidance_scale > 1 else class_labels

													# set step values
													self.scheduler.set_timesteps(lowerCamelCase__				)

													for t in self.progress_bar(self.scheduler.timesteps				):
																				if guidance_scale > 1:
																											__lowercase      =    latent_model_input[: len(lowerCamelCase__				) // 2]
																											__lowercase      =    torch.cat([half, half]				,							dim=0				)
																				__lowercase      =    self.scheduler.scale_model_input(lowerCamelCase__				,							lowerCamelCase__				)

																				__lowercase      =    t
																				if not torch.is_tensor(lowerCamelCase__				):
																											# TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
																											# This would be a good case for the `match` statement (Python 3.10+)
																											__lowercase      =    latent_model_input.device.type == '''mps'''
																											if isinstance(lowerCamelCase__				,							lowerCamelCase__				):
																																		__lowercase      =    torch.floataa if is_mps else torch.floataa
																											else:
																																		__lowercase      =    torch.intaa if is_mps else torch.intaa
																											__lowercase      =    torch.tensor([timesteps]				,							dtype=lowerCamelCase__				,							device=latent_model_input.device				)
																				elif len(timesteps.shape				) == 0:
																											__lowercase      =    timesteps[None].to(latent_model_input.device				)
																				# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
																				__lowercase      =    timesteps.expand(latent_model_input.shape[0]				)
																				# predict noise model_output
																				__lowercase      =    self.transformer(
																				    lowerCamelCase__				,							timestep=lowerCamelCase__				,							class_labels=lowerCamelCase__				).sample

																				# perform guidance
																				if guidance_scale > 1:
																											__lowercase     ,     __lowercase      =    noise_pred[:, :latent_channels], noise_pred[:, latent_channels:]
																											__lowercase     ,     __lowercase      =    torch.split(lowerCamelCase__				,							len(lowerCamelCase__				) // 2				,							dim=0				)

																											__lowercase      =    uncond_eps + guidance_scale * (cond_eps - uncond_eps)
																											__lowercase      =    torch.cat([half_eps, half_eps]				,							dim=0				)

																											__lowercase      =    torch.cat([eps, rest]				,							dim=1				)

																				# learned sigma
																				if self.transformer.config.out_channels // 2 == latent_channels:
																											__lowercase     ,     __lowercase      =    torch.split(lowerCamelCase__				,							lowerCamelCase__				,							dim=1				)
																				else:
																											__lowercase      =    noise_pred

																				# compute previous image: x_t -> x_t-1
																				__lowercase      =    self.scheduler.step(lowerCamelCase__				,							lowerCamelCase__				,							lowerCamelCase__				).prev_sample

													if guidance_scale > 1:
																				__lowercase     ,     __lowercase      =    latent_model_input.chunk(2				,							dim=0				)
													else:
																				__lowercase      =    latent_model_input

													__lowercase      =    1 / self.vae.config.scaling_factor * latents
													__lowercase      =    self.vae.decode(lowerCamelCase__				).sample

													__lowercase      =    (samples / 2 + 0.5).clamp(0				,							1				)

													# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
													__lowercase      =    samples.cpu().permute(0				,							2				,							3				,							1				).float().numpy()

													if output_type == "pil":
																				__lowercase      =    self.numpy_to_pil(lowerCamelCase__				)

													if not return_dict:
																				return (samples,)

													return ImagePipelineOutput(images=lowerCamelCase__				)












import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available


if is_vision_available():
			from PIL import Image

			from transformers import AutoProcessor, BlipaProcessor, BlipImageProcessor, GPTaTokenizer, PreTrainedTokenizerFast







@require_vision
class a (     unittest.TestCase  ):
						"""simple docstring"""

						def     UpperCAmelCase_      ( self :     List[str]				)    ->						Tuple:
													"""simple docstring"""


													__lowercase      =    tempfile.mkdtemp()

													__lowercase      =    BlipImageProcessor()
													__lowercase      =    GPTaTokenizer.from_pretrained('''hf-internal-testing/tiny-random-GPT2Model'''				)

													__lowercase      =    BlipaProcessor(lowerCamelCase__				,							lowerCamelCase__				)

													processor.save_pretrained(self.tmpdirname				)






						def     UpperCAmelCase_      ( self :     Optional[int]				,							**lowerCamelCase__ :     Any				)    ->						str:
													"""simple docstring"""


													return AutoProcessor.from_pretrained(self.tmpdirname				,							**lowerCamelCase__				).tokenizer






						def     UpperCAmelCase_      ( self :     Dict				,							**lowerCamelCase__ :     List[str]				)    ->						Dict:
													"""simple docstring"""


													return AutoProcessor.from_pretrained(self.tmpdirname				,							**lowerCamelCase__				).image_processor






						def     UpperCAmelCase_      ( self :     List[str]				)    ->						str:
													"""simple docstring"""


													shutil.rmtree(self.tmpdirname				)






						def     UpperCAmelCase_      ( self :     Any				)    ->						Any:
													"""simple docstring"""


													__lowercase      =    [np.random.randint(255				,							size=(3, 30, 400)				,							dtype=np.uinta				)]

													__lowercase      =    [Image.fromarray(np.moveaxis(lowerCamelCase__				,							0				,							-1				)				) for x in image_inputs]

													return image_inputs






						def     UpperCAmelCase_      ( self :     List[Any]				)    ->						List[str]:
													"""simple docstring"""


													__lowercase      =    BlipaProcessor(tokenizer=self.get_tokenizer()				,							image_processor=self.get_image_processor()				)
													processor.save_pretrained(self.tmpdirname				)

													__lowercase      =    self.get_tokenizer(bos_token='''(BOS)'''				,							eos_token='''(EOS)'''				)
													__lowercase      =    self.get_image_processor(do_normalize=lowerCamelCase__				,							padding_value=1.0				)

													__lowercase      =    BlipaProcessor.from_pretrained(
													    self.tmpdirname				,							bos_token='''(BOS)'''				,							eos_token='''(EOS)'''				,							do_normalize=lowerCamelCase__				,							padding_value=1.0				)

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

													self.assertEqual(processor.image_processor.to_json_string()				,							image_processor_add_kwargs.to_json_string()				)
													self.assertIsInstance(processor.image_processor				,							lowerCamelCase__				)






						def     UpperCAmelCase_      ( self :     List[Any]				)    ->						Any:
													"""simple docstring"""


													__lowercase      =    self.get_image_processor()
													__lowercase      =    self.get_tokenizer()

													__lowercase      =    BlipaProcessor(tokenizer=lowerCamelCase__				,							image_processor=lowerCamelCase__				)

													__lowercase      =    self.prepare_image_inputs()

													__lowercase      =    image_processor(lowerCamelCase__				,							return_tensors='''np'''				)
													__lowercase      =    processor(images=lowerCamelCase__				,							return_tensors='''np'''				)

													for key in input_feat_extract.keys():
																				self.assertAlmostEqual(input_feat_extract[key].sum()				,							input_processor[key].sum()				,							delta=1e-2				)






						def     UpperCAmelCase_      ( self :     Tuple				)    ->						Tuple:
													"""simple docstring"""


													__lowercase      =    self.get_image_processor()
													__lowercase      =    self.get_tokenizer()

													__lowercase      =    BlipaProcessor(tokenizer=lowerCamelCase__				,							image_processor=lowerCamelCase__				)

													__lowercase      =    '''lower newer'''

													__lowercase      =    processor(text=lowerCamelCase__				)

													__lowercase      =    tokenizer(lowerCamelCase__				,							return_token_type_ids=lowerCamelCase__				)

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






						def     UpperCAmelCase_      ( self :     Dict				)    ->						Tuple:
													"""simple docstring"""


													__lowercase      =    self.get_image_processor()
													__lowercase      =    self.get_tokenizer()

													__lowercase      =    BlipaProcessor(tokenizer=lowerCamelCase__				,							image_processor=lowerCamelCase__				)

													__lowercase      =    '''lower newer'''
													__lowercase      =    self.prepare_image_inputs()

													__lowercase      =    processor(text=lowerCamelCase__				,							images=lowerCamelCase__				)

													self.assertListEqual(list(inputs.keys()				)				,							['''pixel_values''', '''input_ids''', '''attention_mask''']				)

													# test if it raises when no input is passed
													with pytest.raises(lowerCamelCase__				):
																				processor()






						def     UpperCAmelCase_      ( self :     Optional[Any]				)    ->						Any:
													"""simple docstring"""


													__lowercase      =    self.get_image_processor()
													__lowercase      =    self.get_tokenizer()

													__lowercase      =    BlipaProcessor(tokenizer=lowerCamelCase__				,							image_processor=lowerCamelCase__				)

													__lowercase      =    [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

													__lowercase      =    processor.batch_decode(lowerCamelCase__				)
													__lowercase      =    tokenizer.batch_decode(lowerCamelCase__				)

													self.assertListEqual(lowerCamelCase__				,							lowerCamelCase__				)






						def     UpperCAmelCase_      ( self :     List[str]				)    ->						Any:
													"""simple docstring"""


													__lowercase      =    self.get_image_processor()
													__lowercase      =    self.get_tokenizer()

													__lowercase      =    BlipaProcessor(tokenizer=lowerCamelCase__				,							image_processor=lowerCamelCase__				)

													__lowercase      =    '''lower newer'''
													__lowercase      =    self.prepare_image_inputs()

													__lowercase      =    processor(text=lowerCamelCase__				,							images=lowerCamelCase__				)

													# For now the processor supports only ['pixel_values', 'input_ids', 'attention_mask']
													self.assertListEqual(list(inputs.keys()				)				,							['''pixel_values''', '''input_ids''', '''attention_mask''']				)






