



"""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					:List[Any]  =      getLogger(__name__)


_lowerCAmelCase					:List[Any]  =      4
_lowerCAmelCase					:List[str]  =      7

_lowerCAmelCase					:Any  =      {
    "float16": torch.floataa,
    "float32": torch.floataa,
}





def 				lowerCamelCase_		(UpperCamelCase__	:	str							):







    if isinstance(UpperCamelCase__  , torch.Tensor							):
        return image
    elif isinstance(UpperCamelCase__  , PIL.Image.Image							):
        _UpperCAmelCase							:						Tuple       							=					[image]

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

        _UpperCAmelCase							:						List[str]       							=					[np.array(i.resize((w, h)							)							)[None, :] for i in image]
        _UpperCAmelCase							:						Dict       							=					np.concatenate(UpperCamelCase__  , axis=0							)
        _UpperCAmelCase							:						str       							=					np.array(UpperCamelCase__							).astype(np.floataa							) / 255.0
        _UpperCAmelCase							:						List[Any]       							=					image.transpose(0  , 3  , 1  , 2							)
        _UpperCAmelCase							:						Optional[Any]       							=					2.0 * image - 1.0
        _UpperCAmelCase							:						Optional[Any]       							=					torch.from_numpy(UpperCamelCase__							)
    elif isinstance(image[0]  , torch.Tensor							):
        _UpperCAmelCase							:						Union[str, Any]       							=					torch.cat(UpperCamelCase__  , dim=0							)

    return image


class 							_UpperCAmelCase							(				SCREAMING_SNAKE_CASE__   ):





     '''simple docstring'''



     def __init__(      self ,			A ,			A ,			A ,			A ,			A ,			A ,			A = 3_5_0 ,			)							->      int:
         super().__init__(
             vae=A ,			text_encoder=A ,			tokenizer=A ,			unet=A ,			low_res_scheduler=A ,			scheduler=A ,			safety_checker=A ,			feature_extractor=A ,			watermarker=A ,			max_noise_level=A ,			)





     def __call__(      self ,			A ,			A ,			A = 7_5 ,			A = 9.0 ,			A = 2_0 ,			A = None ,			A = 1 ,			A = 0.0 ,			A = None ,			A = None ,			A = None ,			A = None ,			A = "pil" ,			A = True ,			A = None ,			A = 1 ,			)							->      List[Any]:
         self.check_inputs(A ,			A ,			A ,			A	)

         # 2. Define call parameters
         if prompt is not None and isinstance(A ,			A	):
             _UpperCAmelCase							:						Dict       							=					1
         elif prompt is not None and isinstance(A ,			A	):
             _UpperCAmelCase							:						List[str]       							=					len(A	)
         else:
             _UpperCAmelCase							:						Dict       							=					prompt_embeds.shape[0]

         _UpperCAmelCase							:						str       							=					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.
         _UpperCAmelCase							:						Optional[Any]       							=					guidance_scale > 1.0

         # 3. Encode input prompt
         _UpperCAmelCase							:						Union[str, Any]       							=					self._encode_prompt(
             A ,			A ,			A ,			A ,			A ,			prompt_embeds=A ,			negative_prompt_embeds=A ,			)

         _UpperCAmelCase							:						str       							=					ORT_TO_PT_TYPE[str(text_embeddings.dtype	)]

         # 4. Preprocess image
         _UpperCAmelCase							:						Union[str, Any]       							=					preprocess(A	)
         _UpperCAmelCase							:						Union[str, Any]       							=					image.cpu()

         # 5. set timesteps
         self.scheduler.set_timesteps(A ,			device=A	)
         _UpperCAmelCase							:						Optional[Any]       							=					self.scheduler.timesteps

         # 5. Add noise to image
         _UpperCAmelCase							:						Any       							=					torch.tensor([noise_level] ,			dtype=torch.long ,			device=A	)
         _UpperCAmelCase							:						Union[str, Any]       							=					torch.randn(image.shape ,			generator=A ,			device=A ,			dtype=A	)
         _UpperCAmelCase							:						Optional[int]       							=					self.low_res_scheduler.add_noise(A ,			A ,			A	)

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

         # 6. Prepare latent variables
         _UpperCAmelCase							,       _UpperCAmelCase							:						str       							=					image.shape[2:]
         _UpperCAmelCase							:						Optional[int]       							=					self.prepare_latents(
             batch_size * num_images_per_prompt ,			A ,			A ,			A ,			A ,			A ,			A ,			A ,			)

         # 7. Check that sizes of image and latents match
         _UpperCAmelCase							:						int       							=					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
         _UpperCAmelCase							:						Dict       							=					self.prepare_extra_step_kwargs(A ,			A	)

         _UpperCAmelCase							:						Dict       							=					next(
             (input.type for input in self.unet.model.get_inputs() if input.name == '''timestep''') ,			'''tensor(float)'''	)
         _UpperCAmelCase							:						Union[str, Any]       							=					ORT_TO_NP_TYPE[timestep_dtype]

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

                 # concat latents, mask, masked_image_latents in the channel dimension
                 _UpperCAmelCase							:						Tuple       							=					self.scheduler.scale_model_input(A ,			A	)
                 _UpperCAmelCase							:						int       							=					np.concatenate([latent_model_input, image] ,			axis=1	)

                 # timestep to tensor
                 _UpperCAmelCase							:						Union[str, Any]       							=					np.array([t] ,			dtype=A	)

                 # predict the noise residual
                 _UpperCAmelCase							:						Optional[int]       							=					self.unet(
                     sample=A ,			timestep=A ,			encoder_hidden_states=A ,			class_labels=noise_level.astype(np.intaa	) ,			)[0]

                 # perform guidance
                 if do_classifier_free_guidance:
                     _UpperCAmelCase							,       _UpperCAmelCase							:						Tuple       							=					np.split(A ,			2	)
                 _UpperCAmelCase							:						Union[str, Any]       							=					noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

                 # compute the previous noisy sample x_t -> x_t-1
                 _UpperCAmelCase							:						Union[str, Any]       							=					self.scheduler.step(
                     torch.from_numpy(A	) ,			A ,			A ,			**A	).prev_sample

                 # call the callback, if provided
                 if i == len(A	) - 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(A ,			A ,			A	)

        # 10. Post-processing
         _UpperCAmelCase							:						Dict       							=					self.decode_latents(latents.float()	)

         # 11. Convert to PIL
         if output_type == "pil":
             _UpperCAmelCase							:						Any       							=					self.numpy_to_pil(A	)

         if not return_dict:
             return (image,)

         return ImagePipelineOutput(images=A	)





     def 				__lowerCAmelCase			(      self ,			A	)							->      Any:
         _UpperCAmelCase							:						Union[str, Any]       							=					1 / 0.08_333 * latents
         _UpperCAmelCase							:						Tuple       							=					self.vae(latent_sample=A	)[0]
         _UpperCAmelCase							:						List[str]       							=					np.clip(image / 2 + 0.5 ,			0 ,			1	)
         _UpperCAmelCase							:						int       							=					image.transpose((0, 2, 3, 1)	)
         return image





     def 				__lowerCAmelCase			(      self ,			A ,			A ,			A ,			A ,			A ,			A = None ,			A = None ,			)							->      Optional[int]:



         if prompt is not None and isinstance(A ,			A	):
             _UpperCAmelCase							:						Tuple       							=					1
         elif prompt is not None and isinstance(A ,			A	):
             _UpperCAmelCase							:						Tuple       							=					len(A	)
         else:
             _UpperCAmelCase							:						List[str]       							=					prompt_embeds.shape[0]

         if prompt_embeds is None:
             _UpperCAmelCase							:						List[Any]       							=					self.tokenizer(
                 A ,			padding='''max_length''' ,			max_length=self.tokenizer.model_max_length ,			truncation=A ,			return_tensors='''pt''' ,			)
             _UpperCAmelCase							:						str       							=					text_inputs.input_ids
             _UpperCAmelCase							:						int       							=					self.tokenizer(A ,			padding='''longest''' ,			return_tensors='''pt'''	).input_ids

             if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(
                 A ,			A	):
                 _UpperCAmelCase							:						Optional[int]       							=					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
             _UpperCAmelCase							:						Any       							=					self.text_encoder(
                 input_ids=text_input_ids.int().to(A	) ,			)
             _UpperCAmelCase							:						Union[str, Any]       							=					prompt_embeds[0]

         _UpperCAmelCase							,       _UpperCAmelCase							,       _UpperCAmelCase							:						Dict       							=					prompt_embeds.shape
         # duplicate text embeddings for each generation per prompt, using mps friendly method
         _UpperCAmelCase							:						Union[str, Any]       							=					prompt_embeds.repeat(1 ,			A	)
         _UpperCAmelCase							:						Any       							=					prompt_embeds.reshape(bs_embed * num_images_per_prompt ,			A ,			-1	)

         # get unconditional embeddings for classifier free guidance
         if do_classifier_free_guidance and negative_prompt_embeds is None:
             _UpperCAmelCase							:						Any       							=					4_2
             if negative_prompt is None:
                 _UpperCAmelCase							:						Any       							=					[''''''] * batch_size
             elif type(A	) is not type(A	):
                 raise TypeError(
                     f'`negative_prompt` should be the same type to `prompt`, but got {type(A	)} !='
                     f' {type(A	)}.'	)
             elif isinstance(A ,			A	):
                 _UpperCAmelCase							:						Any       							=					[negative_prompt]
             elif batch_size != len(A	):
                 raise ValueError(
                     f'`negative_prompt`: {negative_prompt} has batch size {len(A	)}, but `prompt`:'
                     f' {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches'
                     ''' the batch size of `prompt`.'''	)
             else:
                 _UpperCAmelCase							:						Any       							=					negative_prompt

             _UpperCAmelCase							:						Dict       							=					text_input_ids.shape[-1]
             _UpperCAmelCase							:						str       							=					self.tokenizer(
                 A ,			padding='''max_length''' ,			max_length=A ,			truncation=A ,			return_tensors='''pt''' ,			)

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

             _UpperCAmelCase							:						Optional[int]       							=					self.text_encoder(
                 input_ids=uncond_input.input_ids.int().to(A	) ,			)
             _UpperCAmelCase							:						int       							=					uncond_embeddings[0]

         if do_classifier_free_guidance:
             _UpperCAmelCase							:						Tuple       							=					uncond_embeddings.shape[1]
             # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
             _UpperCAmelCase							:						int       							=					uncond_embeddings.repeat(1 ,			A	)
             _UpperCAmelCase							:						List[Any]       							=					uncond_embeddings.reshape(batch_size * num_images_per_prompt ,			A ,			-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
             _UpperCAmelCase							:						Union[str, Any]       							=					np.concatenate([uncond_embeddings, prompt_embeds]	)

         return prompt_embeds







"""simple docstring"""
import os

from apache_beam.io.filesystems import FileSystems
from apache_beam.pipeline import Pipeline

from .logging import get_logger


_lowerCAmelCase					:Optional[int]  =      2 << 20  # 2mb
_lowerCAmelCase					:Union[str, Any]  =      get_logger(__name__)
class 							_UpperCAmelCase							(				a   ):





     '''simple docstring'''



     def 				__lowerCAmelCase			(      self	)							->      Any:
         _UpperCAmelCase							:						str       							=					self._options.get_all_options().get('''runner'''	)
         return runner in [None, "DirectRunner", "PortableRunner"]





def 				lowerCamelCase_		(UpperCamelCase__	:	List[str]  , UpperCamelCase__	:	str  , UpperCamelCase__	:	Dict=False							):
    _UpperCAmelCase							:						Union[str, Any]       							=					FileSystems
    if fs.exists(UpperCamelCase__							):
        if force_upload:
            logger.info(F'Remote path already exist: {remote_file_path}. Overwriting it as force_upload=True.'							)
        else:
            logger.info(F'Remote path already exist: {remote_file_path}. Skipping it as force_upload=False.'							)
            return
    with fs.create(UpperCamelCase__							) as remote_file:
        with open(UpperCamelCase__  , '''rb'''							) as local_file:
            _UpperCAmelCase							:						Optional[Any]       							=					local_file.read(UpperCamelCase__							)
            while chunk:
                remote_file.write(UpperCamelCase__							)
                _UpperCAmelCase							:						List[Any]       							=					local_file.read(UpperCamelCase__							)





def 				lowerCamelCase_		(UpperCamelCase__	:	int  , UpperCamelCase__	:	Dict  , UpperCamelCase__	:	str=False							):
    _UpperCAmelCase							:						int       							=					FileSystems
    if os.path.exists(UpperCamelCase__							):
        if force_download:
            logger.info(F'Local path already exist: {remote_file_path}. Overwriting it as force_upload=True.'							)
        else:
            logger.info(F'Local path already exist: {remote_file_path}. Skipping it as force_upload=False.'							)
            return
    with fs.open(UpperCamelCase__							) as remote_file:
        with open(UpperCamelCase__  , '''wb'''							) as local_file:
            _UpperCAmelCase							:						List[str]       							=					remote_file.read(UpperCamelCase__							)
            while chunk:
                local_file.write(UpperCamelCase__							)
                _UpperCAmelCase							:						List[Any]       							=					remote_file.read(UpperCamelCase__							)


