






'''simple docstring'''







from typing import List, Optional, Tuple, Union

import PIL
import torch
from torchvision import transforms

from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from diffusers.schedulers import DDIMScheduler
from diffusers.utils import randn_tensor


UpperCamelCase     :   Union[str, Any]	      =		transforms.Compose(
    [
        transforms.Resize((2_56, 2_56)),
        transforms.ToTensor(),
        transforms.Normalize([0.5], [0.5]),
    ]
)
def     A__      (					__lowerCAmelCase		:      Optional[int]    ):
						if isinstance(__lowerCAmelCase ,     torch.Tensor    ):
												return image
						elif isinstance(__lowerCAmelCase ,     PIL.Image.Image    ):
												lowerCamelCase__								=	[image]

						lowerCamelCase__								=	[trans(img.convert("""RGB"""    )    ) for img in image]
						lowerCamelCase__								=	torch.stack(__lowerCAmelCase    )
						return image





class 		UpperCamelCase__     (a      ):




		'''simple docstring'''





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

								# make sure scheduler can always be converted to DDIM
								lowerCamelCase__								=	DDIMScheduler.from_config(scheduler.config							)

								self.register_modules(unet=_lowerCAmelCase ,scheduler=_lowerCAmelCase							)






		def       UpperCamelCase_       (   self ,_lowerCAmelCase							):
								if strength < 0 or strength > 1:
														raise ValueError(F'''The value of strength should in [0.0, 1.0] but is {strength}'''							)






		def       UpperCamelCase_       (   self ,_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase							):
								# get the original timestep using init_timestep
								lowerCamelCase__								=	min(int(num_inference_steps * strength							) ,_lowerCAmelCase							)

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

								return timesteps, num_inference_steps - t_start






		def       UpperCamelCase_       (   self ,_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase=None							):
								if not isinstance(_lowerCAmelCase ,(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(_lowerCAmelCase							)}'''							)

								lowerCamelCase__								=	image.to(device=_lowerCAmelCase ,dtype=_lowerCAmelCase							)

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

								lowerCamelCase__								=	init_latents.shape
								lowerCamelCase__								=	randn_tensor(_lowerCAmelCase ,generator=_lowerCAmelCase ,device=_lowerCAmelCase ,dtype=_lowerCAmelCase							)

								# get latents
								print("""add noise to latents at timestep""" ,_lowerCAmelCase							)
								lowerCamelCase__								=	self.scheduler.add_noise(_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase							)
								lowerCamelCase__								=	init_latents

								return latents






		@torch.no_grad()
		def __call__(   self ,_lowerCAmelCase = None ,_lowerCAmelCase = 0.8 ,_lowerCAmelCase = 1 ,_lowerCAmelCase = None ,_lowerCAmelCase = 0.0 ,_lowerCAmelCase = 50 ,_lowerCAmelCase = None ,_lowerCAmelCase = "pil" ,_lowerCAmelCase = True ,):
								self.check_inputs(_lowerCAmelCase							)

								# 2. Preprocess image
								lowerCamelCase__								=	preprocess(_lowerCAmelCase							)

								# 3. set timesteps
								self.scheduler.set_timesteps(_lowerCAmelCase ,device=self.device							)
								lowerCamelCase__							,      lowerCamelCase__								=	self.get_timesteps(_lowerCAmelCase ,_lowerCAmelCase ,self.device							)
								lowerCamelCase__								=	timesteps[:1].repeat(_lowerCAmelCase							)

								# 4. Prepare latent variables
								lowerCamelCase__								=	self.prepare_latents(_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ,self.unet.dtype ,self.device ,_lowerCAmelCase							)
								lowerCamelCase__								=	latents

								# 5. Denoising loop
								for t in self.progress_bar(_lowerCAmelCase							):
														# 1. predict noise model_output
														lowerCamelCase__								=	self.unet(_lowerCAmelCase ,_lowerCAmelCase							).sample

														# 2. predict previous mean of image x_t-1 and add variance depending on eta
														# eta corresponds to η in paper and should be between [0, 1]
														# do x_t -> x_t-1
														lowerCamelCase__								=	self.scheduler.step(
														    _lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ,eta=_lowerCAmelCase ,use_clipped_model_output=_lowerCAmelCase ,generator=_lowerCAmelCase ,).prev_sample

								lowerCamelCase__								=	(image / 2 + 0.5).clamp(0 ,1							)
								lowerCamelCase__								=	image.cpu().permute(0 ,2 ,3 ,1							).numpy()
								if output_type == "pil":
														lowerCamelCase__								=	self.numpy_to_pil(_lowerCAmelCase							)

								if not return_dict:
														return (image, latent_timestep.item())

								return ImagePipelineOutput(images=_lowerCAmelCase							)


'''simple docstring'''






import unittest

from transformers import (
    MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    TextaTextGenerationPipeline,
    pipeline,
)
from transformers.testing_utils import is_pipeline_test, require_tf, require_torch
from transformers.utils import is_torch_available

from .test_pipelines_common import ANY


if is_torch_available():
   import torch







@is_pipeline_test
class lowercase_       (		unittest.TestCase			):







   """simple docstring"""

   __lowerCAmelCase         =   MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
   __lowerCAmelCase         =   TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
   def 	__UpperCAmelCase   (      self      :			Any, UpperCamelCase__      :			str, UpperCamelCase__      :			Optional[int], UpperCamelCase__      :			Any     )  ->			List[str]:
        _A    		=  TextaTextGenerationPipeline(model=UpperCamelCase__, tokenizer=UpperCamelCase__     )
        return generator, ["Something to write", "Something else"]
   def 	__UpperCAmelCase   (      self      :			Any, UpperCamelCase__      :			Tuple, UpperCamelCase__      :			Optional[Any]     )  ->			List[str]:
        _A    		=  generator('Something there'     )
        self.assertEqual(UpperCamelCase__, [{'generated_text': ANY(UpperCamelCase__     )}]     )
        # These are encoder decoder, they don't just append to incoming string
        self.assertFalse(outputs[0]['generated_text'].startswith('Something there'     )     )

        _A    		=  generator(['This is great !', 'Something else'], num_return_sequences=2, do_sample=UpperCamelCase__     )
        self.assertEqual(
            UpperCamelCase__, [
                [{'generated_text': ANY(UpperCamelCase__     )}, {'generated_text': ANY(UpperCamelCase__     )}],
                [{'generated_text': ANY(UpperCamelCase__     )}, {'generated_text': ANY(UpperCamelCase__     )}],
            ], )

        _A    		=  generator(
            ['This is great !', 'Something else'], num_return_sequences=2, batch_size=2, do_sample=UpperCamelCase__     )
        self.assertEqual(
            UpperCamelCase__, [
                [{'generated_text': ANY(UpperCamelCase__     )}, {'generated_text': ANY(UpperCamelCase__     )}],
                [{'generated_text': ANY(UpperCamelCase__     )}, {'generated_text': ANY(UpperCamelCase__     )}],
            ], )

        with self.assertRaises(UpperCamelCase__     ):
             generator(4     )
   @require_torch
   def 	__UpperCAmelCase   (      self      :			Union[str, Any]     )  ->			Optional[Any]:
        _A    		=  pipeline('text2text-generation', model='patrickvonplaten/t5-tiny-random', framework='pt'     )
        # do_sample=False necessary for reproducibility
        _A    		=  generator('Something there', do_sample=UpperCamelCase__     )
        self.assertEqual(UpperCamelCase__, [{'generated_text': ''}]     )

        _A    		=  3
        _A    		=  generator(
            'Something there', num_return_sequences=UpperCamelCase__, num_beams=UpperCamelCase__, )
        _A    		=  [
            {'generated_text': 'Beide Beide Beide Beide Beide Beide Beide Beide Beide'},
            {'generated_text': 'Beide Beide Beide Beide Beide Beide Beide Beide'},
            {'generated_text': ''},
        ]
        self.assertEqual(UpperCamelCase__, UpperCamelCase__     )

        _A    		=  generator('This is a test', do_sample=UpperCamelCase__, num_return_sequences=2, return_tensors=UpperCamelCase__     )
        self.assertEqual(
            UpperCamelCase__, [
                {'generated_token_ids': ANY(torch.Tensor     )},
                {'generated_token_ids': ANY(torch.Tensor     )},
            ], )
        _A    		=  generator.model.config.eos_token_id
        _A    		=  '<pad>'
        _A    		=  generator(
            ['This is a test', 'This is a second test'], do_sample=UpperCamelCase__, num_return_sequences=2, batch_size=2, return_tensors=UpperCamelCase__, )
        self.assertEqual(
            UpperCamelCase__, [
                [
                    {'generated_token_ids': ANY(torch.Tensor     )},
                    {'generated_token_ids': ANY(torch.Tensor     )},
                ],
                [
                    {'generated_token_ids': ANY(torch.Tensor     )},
                    {'generated_token_ids': ANY(torch.Tensor     )},
                ],
            ], )

   @require_tf
   def 	__UpperCAmelCase   (      self      :			Optional[Any]     )  ->			Optional[int]:
        _A    		=  pipeline('text2text-generation', model='patrickvonplaten/t5-tiny-random', framework='tf'     )
        # do_sample=False necessary for reproducibility
        _A    		=  generator('Something there', do_sample=UpperCamelCase__     )
        self.assertEqual(UpperCamelCase__, [{'generated_text': ''}]     )



