


'''simple docstring'''

from typing import List, Union

from ..utils import (
    add_end_docstrings,
    is_tf_available,
    is_torch_available,
    is_vision_available,
    logging,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
	from PIL import Image

	from ..image_utils import load_image

if is_tf_available():
	from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_VISION_2_SEQ_MAPPING

if is_torch_available():
	import torch

	from ..models.auto.modeling_auto import MODEL_FOR_VISION_2_SEQ_MAPPING

_UpperCAmelCase				: str							    =				logging.get_logger(__name__)


@add_end_docstrings(__lowercase      )
class       a__				( __lowercase      ):







				"""simple docstring"""



				def __init__(self       ,			*__lowercase       ,			**__lowercase		):
										super().__init__(*__A       ,			**__A		)
										requires_backends(self       ,			'''vision'''		)
										self.check_model_type(
										    TF_MODEL_FOR_VISION_2_SEQ_MAPPING if self.framework == '''tf''' else MODEL_FOR_VISION_2_SEQ_MAPPING		)




				def 					_snake_case  (self       ,			__lowercase=None       ,			__lowercase=None       ,			__lowercase=None		):
										__lowerCAmelCase							       =				{}
										__lowerCAmelCase							       =				{}

										if prompt is not None:
																__lowerCAmelCase							       =				prompt

										if generate_kwargs is not None:
																__lowerCAmelCase							       =				generate_kwargs
										if max_new_tokens is not None:
																if "generate_kwargs" not in forward_kwargs:
																						__lowerCAmelCase							       =				{}
																if "max_new_tokens" in forward_kwargs["generate_kwargs"]:
																						raise ValueError(
																						    '''\'max_new_tokens\' is defined twice, once in \'generate_kwargs\' and once as a direct parameter,'''
																						    ''' please use only one'''		)
																__lowerCAmelCase							       =				max_new_tokens
										return preprocess_params, forward_kwargs, {}




				def __call__(self       ,			__lowercase       ,			**__lowercase		):
										return super().__call__(__A       ,			**__A		)




				def 					_snake_case  (self       ,			__lowercase       ,			__lowercase=None		):
										__lowerCAmelCase							       =				load_image(__A		)

										if prompt is not None:
																if not isinstance(__A       ,			__A		):
																						raise ValueError(
																						    F"""Received an invalid text input, got - {type(__A		)} - but expected a single string. """
																						    '''Note also that one single text can be provided for conditional image to text generation.'''		)

																__lowerCAmelCase							       =				self.model.config.model_type

																if model_type == "git":
																						__lowerCAmelCase							       =				self.image_processor(images=__A       ,			return_tensors=self.framework		)
																						__lowerCAmelCase							       =				self.tokenizer(text=__A       ,			add_special_tokens=__A		).input_ids
																						__lowerCAmelCase							       =				[self.tokenizer.cls_token_id] + input_ids
																						__lowerCAmelCase							       =				torch.tensor(__A		).unsqueeze(0		)
																						model_inputs.update({'''input_ids''': input_ids}		)

																elif model_type == "pix2struct":
																						__lowerCAmelCase							       =				self.image_processor(images=__A       ,			header_text=__A       ,			return_tensors=self.framework		)

																elif model_type != "vision-encoder-decoder":
																						# vision-encoder-decoder does not support conditional generation
																						__lowerCAmelCase							       =				self.image_processor(images=__A       ,			return_tensors=self.framework		)
																						__lowerCAmelCase							       =				self.tokenizer(__A       ,			return_tensors=self.framework		)
																						model_inputs.update(__A		)

																else:
																						raise ValueError(F"""Model type {model_type} does not support conditional text generation"""		)

										else:
																__lowerCAmelCase							       =				self.image_processor(images=__A       ,			return_tensors=self.framework		)

										if self.model.config.model_type == "git" and prompt is None:
																__lowerCAmelCase							       =				None

										return model_inputs




				def 					_snake_case  (self       ,			__lowercase       ,			__lowercase=None		):
										# Git model sets `model_inputs["input_ids"] = None` in `preprocess` (when `prompt=None`). In batch model, the
										# pipeline will group them into a list of `None`, which fail `_forward`. Avoid this by checking it first.
										if (
										    "input_ids" in model_inputs
										    and isinstance(model_inputs['''input_ids''']       ,			__A		)
										    and all(x is None for x in model_inputs['''input_ids''']		)
										):
																__lowerCAmelCase							       =				None

										if generate_kwargs is None:
																__lowerCAmelCase							       =				{}
										# FIXME: We need to pop here due to a difference in how `generation.py` and `generation.tf_utils.py`
										#  parse inputs. In the Tensorflow version, `generate` raises an error if we don't use `input_ids` whereas
										#  the PyTorch version matches it with `self.model.main_input_name` or `self.model.encoder.main_input_name`
										#  in the `_prepare_model_inputs` method.
										__lowerCAmelCase							       =				model_inputs.pop(self.model.main_input_name		)
										__lowerCAmelCase							       =				self.model.generate(__A       ,			**__A       ,			**__A		)
										return model_outputs




				def 					_snake_case  (self       ,			__lowercase		):
										__lowerCAmelCase							       =				[]
										for output_ids in model_outputs:
																__lowerCAmelCase							       =				{
																    '''generated_text''': self.tokenizer.decode(
																        __A       ,			skip_special_tokens=__A       ,			)
																}
																records.append(__A		)
										return records





'''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				: Tuple							    =				transforms.Compose(
    [
        transforms.Resize((2_5_6, 2_5_6)),
        transforms.ToTensor(),
        transforms.Normalize([0.5], [0.5]),
    ]
)

def 							__magic_name__(						lowerCamelCase):
						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       a__				( __A      ):







				"""simple docstring"""



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

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

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




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




				def 					_snake_case  (self       ,			__lowercase       ,			__lowercase       ,			__lowercase		):
										# get the original timestep using init_timestep
										__lowerCAmelCase							       =				min(int(num_inference_steps * strength		)       ,			__lowercase		)

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

										return timesteps, num_inference_steps - t_start




				def 					_snake_case  (self       ,			__lowercase       ,			__lowercase       ,			__lowercase       ,			__lowercase       ,			__lowercase       ,			__lowercase=None		):
										if not isinstance(__lowercase       ,			(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(__lowercase		)}"""		)

										__lowerCAmelCase							       =				image.to(device=__lowercase       ,			dtype=__lowercase		)

										if isinstance(__lowercase       ,			__lowercase		) and len(__lowercase		) != batch_size:
																raise ValueError(
																    F"""You have passed a list of generators of length {len(__lowercase		)}, 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(__lowercase       ,			generator=__lowercase       ,			device=__lowercase       ,			dtype=__lowercase		)

										# get latents
										print('''add noise to latents at timestep'''       ,			__lowercase		)
										__lowerCAmelCase							       =				self.scheduler.add_noise(__lowercase       ,			__lowercase       ,			__lowercase		)
										__lowerCAmelCase							       =				init_latents

										return latents




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

										# 2. Preprocess image
										__lowerCAmelCase							       =				preprocess(__lowercase		)

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

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

										# 5. Denoising loop
										for t in self.progress_bar(__lowercase		):
																# 1. predict noise model_output
																__lowerCAmelCase							       =				self.unet(__lowercase       ,			__lowercase		).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(
																    __lowercase       ,			__lowercase       ,			__lowercase       ,			eta=__lowercase       ,			use_clipped_model_output=__lowercase       ,			generator=__lowercase       ,			).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(__lowercase		)

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

										return ImagePipelineOutput(images=__lowercase		)

