

"""simple docstring"""






from logging import getLogger
from typing import Any, Callable, List, Optional, Union

import numpy as np
import PIL
import torch

from ...schedulers import DDPMScheduler
from ..onnx_utils import ORT_TO_NP_TYPE, OnnxRuntimeModel
from ..pipeline_utils import ImagePipelineOutput
from . import StableDiffusionUpscalePipeline


__lowerCAmelCase				      =   getLogger(__name__)


__lowerCAmelCase				      =   4
__lowerCAmelCase				      =   7

__lowerCAmelCase				      =   {
    '''float16''': torch.floataa,
    '''float32''': torch.floataa,
}
def 		A_	(   __UpperCamelCase     :				Dict				):
		if isinstance(snake_case_       ,	torch.Tensor				):
				return image
		elif isinstance(snake_case_       ,	PIL.Image.Image				):
				lowercase										=							[image]

		if isinstance(image[0]       ,	PIL.Image.Image				):
				lowercase										=							image[0].size
				lowercase										=							(x - x % 64 for x in (w, h))  # resize to integer multiple of 32

				lowercase										=							[np.array(i.resize((w, h)				)				)[None, :] for i in image]
				lowercase										=							np.concatenate(snake_case_       ,	axis=0				)
				lowercase										=							np.array(snake_case_				).astype(np.floataa				) / 2_55.0
				lowercase										=							image.transpose(0       ,	3       ,	1       ,	2				)
				lowercase										=							2.0 * image - 1.0
				lowercase										=							torch.from_numpy(snake_case_				)
		elif isinstance(image[0]       ,	torch.Tensor				):
				lowercase										=							torch.cat(snake_case_       ,	dim=0				)

		return image



class 	_lowerCAmelCase    (      __snake_case       ):

							def __init__( self       :    Optional[int] ,		a       :    Optional[int] ,		a       :    Union[str, Any] ,		a       :    Dict ,		a       :    List[Any] ,		a       :    List[str] ,		a       :    List[str] ,		a       :    List[Any] = 350 ,		)   ->    int:

									"""simple docstring"""
									super().__init__(
									    vae=__SCREAMING_SNAKE_CASE ,		text_encoder=__SCREAMING_SNAKE_CASE ,		tokenizer=__SCREAMING_SNAKE_CASE ,		unet=__SCREAMING_SNAKE_CASE ,		low_res_scheduler=__SCREAMING_SNAKE_CASE ,		scheduler=__SCREAMING_SNAKE_CASE ,		safety_checker=__SCREAMING_SNAKE_CASE ,		feature_extractor=__SCREAMING_SNAKE_CASE ,		watermarker=__SCREAMING_SNAKE_CASE ,		max_noise_level=__SCREAMING_SNAKE_CASE ,		)

							def __call__( self       :    Union[str, Any] ,		a       :    Optional[int] ,		a       :    Any ,		a       :    int = 75 ,		a       :    Tuple = 9.0 ,		a       :    Dict = 20 ,		a       :    Optional[int] = None ,		a       :    Tuple = 1 ,		a       :    Tuple = 0.0 ,		a       :    int = None ,		a       :    Optional[Any] = None ,		a       :    str = None ,		a       :    Union[str, Any] = None ,		a       :    Union[str, Any] = "pil" ,		a       :    Union[str, Any] = True ,		a       :    str = None ,		a       :    Union[str, Any] = 1 ,		)   ->    Union[str, Any]:

									"""simple docstring"""
									self.check_inputs(__SCREAMING_SNAKE_CASE ,		__SCREAMING_SNAKE_CASE ,		__SCREAMING_SNAKE_CASE ,		__SCREAMING_SNAKE_CASE    )

									# 2. Define call parameters
									if prompt is not None and isinstance(__SCREAMING_SNAKE_CASE ,		__SCREAMING_SNAKE_CASE    ):
											lowercase										=							1
									elif prompt is not None and isinstance(__SCREAMING_SNAKE_CASE ,		__SCREAMING_SNAKE_CASE    ):
											lowercase										=							len(__SCREAMING_SNAKE_CASE    )
									else:
											lowercase										=							prompt_embeds.shape[0]

									lowercase										=							self._execution_device
									# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
									# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
									# corresponds to doing no classifier free guidance.
									lowercase										=							guidance_scale > 1.0

									# 3. Encode input prompt
									lowercase										=							self._encode_prompt(
									    __SCREAMING_SNAKE_CASE ,		__SCREAMING_SNAKE_CASE ,		__SCREAMING_SNAKE_CASE ,		__SCREAMING_SNAKE_CASE ,		__SCREAMING_SNAKE_CASE ,		prompt_embeds=__SCREAMING_SNAKE_CASE ,		negative_prompt_embeds=__SCREAMING_SNAKE_CASE ,		)

									lowercase										=							ORT_TO_PT_TYPE[str(text_embeddings.dtype    )]

									# 4. Preprocess image
									lowercase										=							preprocess(__SCREAMING_SNAKE_CASE    )
									lowercase										=							image.cpu()

									# 5. set timesteps
									self.scheduler.set_timesteps(__SCREAMING_SNAKE_CASE ,		device=__SCREAMING_SNAKE_CASE    )
									lowercase										=							self.scheduler.timesteps

									# 5. Add noise to image
									lowercase										=							torch.tensor([noise_level] ,		dtype=torch.long ,		device=__SCREAMING_SNAKE_CASE    )
									lowercase										=							torch.randn(image.shape ,		generator=__SCREAMING_SNAKE_CASE ,		device=__SCREAMING_SNAKE_CASE ,		dtype=__SCREAMING_SNAKE_CASE    )
									lowercase										=							self.low_res_scheduler.add_noise(__SCREAMING_SNAKE_CASE ,		__SCREAMING_SNAKE_CASE ,		__SCREAMING_SNAKE_CASE    )

									lowercase										=							2 if do_classifier_free_guidance else 1
									lowercase										=							np.concatenate([image] * batch_multiplier * num_images_per_prompt    )
									lowercase										=							np.concatenate([noise_level] * image.shape[0]    )

									# 6. Prepare latent variables
									lowercase										=							image.shape[2:]
									lowercase										=							self.prepare_latents(
									    batch_size * num_images_per_prompt ,		__SCREAMING_SNAKE_CASE ,		__SCREAMING_SNAKE_CASE ,		__SCREAMING_SNAKE_CASE ,		__SCREAMING_SNAKE_CASE ,		__SCREAMING_SNAKE_CASE ,		__SCREAMING_SNAKE_CASE ,		__SCREAMING_SNAKE_CASE ,		)

									# 7. Check that sizes of image and latents match
									lowercase										=							image.shape[1]
									if NUM_LATENT_CHANNELS + num_channels_image != NUM_UNET_INPUT_CHANNELS:
											raise ValueError(
											    '''Incorrect configuration settings! The config of `pipeline.unet` expects'''
											    f""" {NUM_UNET_INPUT_CHANNELS} but received `num_channels_latents`: {NUM_LATENT_CHANNELS} +"""
											    f""" `num_channels_image`: {num_channels_image} """
											    f""" = {NUM_LATENT_CHANNELS+num_channels_image}. Please verify the config of"""
											    ''' `pipeline.unet` or your `image` input.'''    )

									# 8. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
									lowercase										=							self.prepare_extra_step_kwargs(__SCREAMING_SNAKE_CASE ,		__SCREAMING_SNAKE_CASE    )

									lowercase										=							next(
									    (input.type for input in self.unet.model.get_inputs() if input.name == '''timestep''') ,		'''tensor(float)'''    )
									lowercase										=							ORT_TO_NP_TYPE[timestep_dtype]

									# 9. Denoising loop
									lowercase										=							len(__SCREAMING_SNAKE_CASE    ) - num_inference_steps * self.scheduler.order
									with self.progress_bar(total=__SCREAMING_SNAKE_CASE    ) as progress_bar:
											for i, t in enumerate(__SCREAMING_SNAKE_CASE    ):
													# expand the latents if we are doing classifier free guidance
													lowercase										=							np.concatenate([latents] * 2    ) if do_classifier_free_guidance else latents

													# concat latents, mask, masked_image_latents in the channel dimension
													lowercase										=							self.scheduler.scale_model_input(__SCREAMING_SNAKE_CASE ,		__SCREAMING_SNAKE_CASE    )
													lowercase										=							np.concatenate([latent_model_input, image] ,		axis=1    )

													# timestep to tensor
													lowercase										=							np.array([t] ,		dtype=__SCREAMING_SNAKE_CASE    )

													# predict the noise residual
													lowercase										=							self.unet(
													    sample=__SCREAMING_SNAKE_CASE ,		timestep=__SCREAMING_SNAKE_CASE ,		encoder_hidden_states=__SCREAMING_SNAKE_CASE ,		class_labels=noise_level.astype(np.intaa    ) ,		)[0]

													# perform guidance
													if do_classifier_free_guidance:
															lowercase										=							np.split(__SCREAMING_SNAKE_CASE ,		2    )
													lowercase										=							noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

													# compute the previous noisy sample x_t -> x_t-1
													lowercase										=							self.scheduler.step(
													    torch.from_numpy(__SCREAMING_SNAKE_CASE    ) ,		__SCREAMING_SNAKE_CASE ,		__SCREAMING_SNAKE_CASE ,		**__SCREAMING_SNAKE_CASE    ).prev_sample

													# call the callback, if provided
													if i == len(__SCREAMING_SNAKE_CASE    ) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
															progress_bar.update()
															if callback is not None and i % callback_steps == 0:
																	callback(__SCREAMING_SNAKE_CASE ,		__SCREAMING_SNAKE_CASE ,		__SCREAMING_SNAKE_CASE    )

																	# 10. Post-processing
									lowercase										=							self.decode_latents(latents.float()    )

									# 11. Convert to PIL
									if output_type == "pil":
											lowercase										=							self.numpy_to_pil(__SCREAMING_SNAKE_CASE    )

									if not return_dict:
											return (image,)

									return ImagePipelineOutput(images=__SCREAMING_SNAKE_CASE    )

							def 					_lowerCAmelCase  ( self       :    Dict ,		a       :    Optional[int]    )   ->    Tuple:

									"""simple docstring"""
									lowercase										=							1 / 0.08_333 * latents
									lowercase										=							self.vae(latent_sample=__SCREAMING_SNAKE_CASE    )[0]
									lowercase										=							np.clip(image / 2 + 0.5 ,		0 ,		1    )
									lowercase										=							image.transpose((0, 2, 3, 1)    )
									return image

							def 					_lowerCAmelCase  ( self       :    Optional[Any] ,		a       :    Tuple ,		a       :    str ,		a       :    str ,		a       :    Any ,		a       :    Union[str, Any] ,		a       :    str = None ,		a       :    Union[str, Any] = None ,		)   ->    str:

									"""simple docstring"""
									if prompt is not None and isinstance(__SCREAMING_SNAKE_CASE ,		__SCREAMING_SNAKE_CASE    ):
											lowercase										=							1
									elif prompt is not None and isinstance(__SCREAMING_SNAKE_CASE ,		__SCREAMING_SNAKE_CASE    ):
											lowercase										=							len(__SCREAMING_SNAKE_CASE    )
									else:
											lowercase										=							prompt_embeds.shape[0]

									if prompt_embeds is None:
											lowercase										=							self.tokenizer(
											    __SCREAMING_SNAKE_CASE ,		padding='''max_length''' ,		max_length=self.tokenizer.model_max_length ,		truncation=__SCREAMING_SNAKE_CASE ,		return_tensors='''pt''' ,		)
											lowercase										=							text_inputs.input_ids
											lowercase										=							self.tokenizer(__SCREAMING_SNAKE_CASE ,		padding='''longest''' ,		return_tensors='''pt'''    ).input_ids

											if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(
											    __SCREAMING_SNAKE_CASE ,		__SCREAMING_SNAKE_CASE    ):
													lowercase										=							self.tokenizer.batch_decode(
													    untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]    )
													logger.warning(
													    '''The following part of your input was truncated because CLIP can only handle sequences up to'''
													    f""" {self.tokenizer.model_max_length} tokens: {removed_text}"""    )

											# no positional arguments to text_encoder
											lowercase										=							self.text_encoder(
											    input_ids=text_input_ids.int().to(__SCREAMING_SNAKE_CASE    ) ,		)
											lowercase										=							prompt_embeds[0]

									lowercase										=							prompt_embeds.shape
									# duplicate text embeddings for each generation per prompt, using mps friendly method
									lowercase										=							prompt_embeds.repeat(1 ,		__SCREAMING_SNAKE_CASE    )
									lowercase										=							prompt_embeds.reshape(bs_embed * num_images_per_prompt ,		__SCREAMING_SNAKE_CASE ,		-1    )

									# get unconditional embeddings for classifier free guidance
									if do_classifier_free_guidance and negative_prompt_embeds is None:
											lowercase										=							42
											if negative_prompt is None:
													lowercase										=							[''] * batch_size
											elif type(__SCREAMING_SNAKE_CASE    ) is not type(__SCREAMING_SNAKE_CASE    ):
													raise TypeError(
													    f"""`negative_prompt` should be the same type to `prompt`, but got {type(__SCREAMING_SNAKE_CASE    )} !="""
													    f""" {type(__SCREAMING_SNAKE_CASE    )}."""    )
											elif isinstance(__SCREAMING_SNAKE_CASE ,		__SCREAMING_SNAKE_CASE    ):
													lowercase										=							[negative_prompt]
											elif batch_size != len(__SCREAMING_SNAKE_CASE    ):
													raise ValueError(
													    f"""`negative_prompt`: {negative_prompt} has batch size {len(__SCREAMING_SNAKE_CASE    )}, but `prompt`:"""
													    f""" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"""
													    ''' the batch size of `prompt`.'''    )
											else:
													lowercase										=							negative_prompt

											lowercase										=							text_input_ids.shape[-1]
											lowercase										=							self.tokenizer(
											    __SCREAMING_SNAKE_CASE ,		padding='''max_length''' ,		max_length=__SCREAMING_SNAKE_CASE ,		truncation=__SCREAMING_SNAKE_CASE ,		return_tensors='''pt''' ,		)

											# if hasattr(uncond_input, "attention_mask"):
											#     attention_mask = uncond_input.attention_mask.to(device)
											# else:
											#     attention_mask = None

											lowercase										=							self.text_encoder(
											    input_ids=uncond_input.input_ids.int().to(__SCREAMING_SNAKE_CASE    ) ,		)
											lowercase										=							uncond_embeddings[0]

									if do_classifier_free_guidance:
											lowercase										=							uncond_embeddings.shape[1]
											# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
											lowercase										=							uncond_embeddings.repeat(1 ,		__SCREAMING_SNAKE_CASE    )
											lowercase										=							uncond_embeddings.reshape(batch_size * num_images_per_prompt ,		__SCREAMING_SNAKE_CASE ,		-1    )

											# For classifier free guidance, we need to do two forward passes.
											# Here we concatenate the unconditional and text embeddings into a single batch
											# to avoid doing two forward passes
											lowercase										=							np.concatenate([uncond_embeddings, prompt_embeds]    )

									return prompt_embeds

'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__UpperCAmelCase												=   {'configuration_sew_d': ['SEW_D_PRETRAINED_CONFIG_ARCHIVE_MAP', 'SEWDConfig']}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __UpperCAmelCase												=   [
       'SEW_D_PRETRAINED_MODEL_ARCHIVE_LIST',
       'SEWDForCTC',
       'SEWDForSequenceClassification',
       'SEWDModel',
       'SEWDPreTrainedModel',
   ]

if TYPE_CHECKING:
   from .configuration_sew_d import SEW_D_PRETRAINED_CONFIG_ARCHIVE_MAP, SEWDConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_sew_d import (
          SEW_D_PRETRAINED_MODEL_ARCHIVE_LIST,
          SEWDForCTC,
          SEWDForSequenceClassification,
          SEWDModel,
          SEWDPreTrainedModel,
      )


else:
   import sys

   __UpperCAmelCase												=   _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


