

'''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


UpperCAmelCase							=					getLogger(__name__)


UpperCAmelCase							=					4
UpperCAmelCase							=					7

UpperCAmelCase							=					{
    '''float16''': torch.floataa,
    '''float32''': torch.floataa,
}

def 			__UpperCamelCase							(		lowercase__  :					Optional[Any]	):






     '''simple docstring'''





     if isinstance(_lowerCAmelCase,   torch.Tensor	):
          return image
     elif isinstance(_lowerCAmelCase,   PIL.Image.Image	):
          __lowercase     						=[image]

     if isinstance(image[0],   PIL.Image.Image	):
          __lowercase						,       __lowercase     						=image[0].size
          __lowercase						,       __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(_lowerCAmelCase,   axis=0	)
          __lowercase     						=np.array(_lowerCAmelCase	).astype(np.floataa	) / 255.0
          __lowercase     						=image.transpose(0,   3,   1,   2	)
          __lowercase     						=2.0 * image - 1.0
          __lowercase     						=torch.from_numpy(_lowerCAmelCase	)
     elif isinstance(image[0],   torch.Tensor	):
          __lowercase     						=torch.cat(_lowerCAmelCase,   dim=0	)

     return image







class 					lowerCAmelCase							( A__				):







       def __init__(							self     :	str  , __lowercase     :	Dict  , __lowercase     :	List[str]  , __lowercase     :	List[Any]  , __lowercase     :	Optional[int]  , __lowercase     :	List[Any]  , __lowercase     :	Optional[Any]  , __lowercase     :	List[str] = 350  , ):







            """simple docstring"""







            super().__init__(
                vae=snake_case_  , text_encoder=snake_case_  , tokenizer=snake_case_  , unet=snake_case_  , low_res_scheduler=snake_case_  , scheduler=snake_case_  , safety_checker=snake_case_  , feature_extractor=snake_case_  , watermarker=snake_case_  , max_noise_level=snake_case_  , )







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







            """simple docstring"""







            self.check_inputs(snake_case_  , snake_case_  , snake_case_  , snake_case_ )

            # 2. Define call parameters
            if prompt is not None and isinstance(snake_case_  , snake_case_ ):
                 __lowercase     						=1
            elif prompt is not None and isinstance(snake_case_  , snake_case_ ):
                 __lowercase     						=len(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(
                snake_case_  , snake_case_  , snake_case_  , snake_case_  , snake_case_  , prompt_embeds=snake_case_  , negative_prompt_embeds=snake_case_  , )

            __lowercase     						=ORT_TO_PT_TYPE[str(text_embeddings.dtype )]

            # 4. Preprocess image
            __lowercase     						=preprocess(snake_case_ )
            __lowercase     						=image.cpu()

            # 5. set timesteps
            self.scheduler.set_timesteps(snake_case_  , device=snake_case_ )
            __lowercase     						=self.scheduler.timesteps

            # 5. Add noise to image
            __lowercase     						=torch.tensor([noise_level]  , dtype=torch.long  , device=snake_case_ )
            __lowercase     						=torch.randn(image.shape  , generator=snake_case_  , device=snake_case_  , dtype=snake_case_ )
            __lowercase     						=self.low_res_scheduler.add_noise(snake_case_  , snake_case_  , 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						,       __lowercase     						=image.shape[2:]
            __lowercase     						=self.prepare_latents(
                batch_size * num_images_per_prompt  , snake_case_  , snake_case_  , snake_case_  , snake_case_  , snake_case_  , snake_case_  , 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(snake_case_  , 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(snake_case_ ) - num_inference_steps * self.scheduler.order
            with self.progress_bar(total=snake_case_ ) as progress_bar:
                 for i, t in enumerate(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(snake_case_  , snake_case_ )
                      __lowercase     						=np.concatenate([latent_model_input, image]  , axis=1 )

                      # timestep to tensor
                      __lowercase     						=np.array([t]  , dtype=snake_case_ )

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

                      # perform guidance
                      if do_classifier_free_guidance:
                           __lowercase						,       __lowercase     						=np.split(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(snake_case_ )  , snake_case_  , snake_case_  , **snake_case_ ).prev_sample

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

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

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

            if not return_dict:
                 return (image,)

            return ImagePipelineOutput(images=snake_case_ )







       def      snake_case				(							self     :	Union[str, Any]  , __lowercase     :	Union[str, Any] ):







            """simple docstring"""







            __lowercase     						=1 / 0.0_8_3_3_3 * latents
            __lowercase     						=self.vae(latent_sample=snake_case_ )[0]
            __lowercase     						=np.clip(image / 2 + 0.5  , 0  , 1 )
            __lowercase     						=image.transpose((0, 2, 3, 1) )
            return image







       def      snake_case				(							self     :	int  , __lowercase     :	Optional[Any]  , __lowercase     :	Any  , __lowercase     :	int  , __lowercase     :	Tuple  , __lowercase     :	List[str]  , __lowercase     :	List[str] = None  , __lowercase     :	str = None  , ):







            """simple docstring"""







            if prompt is not None and isinstance(snake_case_  , snake_case_ ):
                 __lowercase     						=1
            elif prompt is not None and isinstance(snake_case_  , snake_case_ ):
                 __lowercase     						=len(snake_case_ )
            else:
                 __lowercase     						=prompt_embeds.shape[0]

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

                 if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(
                     snake_case_  , 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(snake_case_ )  , )
                 __lowercase     						=prompt_embeds[0]

            __lowercase						,       __lowercase						,       __lowercase     						=prompt_embeds.shape
            # duplicate text embeddings for each generation per prompt, using mps friendly method
            __lowercase     						=prompt_embeds.repeat(1  , snake_case_ )
            __lowercase     						=prompt_embeds.reshape(bs_embed * num_images_per_prompt  , 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(snake_case_ ) is not type(snake_case_ ):
                      raise TypeError(
                          f'''`negative_prompt` should be the same type to `prompt`, but got {type(snake_case_ )} !='''
                          f''' {type(snake_case_ )}.''' )
                 elif isinstance(snake_case_  , snake_case_ ):
                      __lowercase     						=[negative_prompt]
                 elif batch_size != len(snake_case_ ):
                      raise ValueError(
                          f'''`negative_prompt`: {negative_prompt} has batch size {len(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(
                     snake_case_  , padding='max_length'  , max_length=snake_case_  , truncation=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(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  , snake_case_ )
                 __lowercase     						=uncond_embeddings.reshape(batch_size * num_images_per_prompt  , 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, is_vision_available


UpperCAmelCase							=					{
    '''configuration_videomae''': ['''VIDEOMAE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''VideoMAEConfig'''],
}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCAmelCase							=					[
           '''VIDEOMAE_PRETRAINED_MODEL_ARCHIVE_LIST''',
           '''VideoMAEForPreTraining''',
           '''VideoMAEModel''',
           '''VideoMAEPreTrainedModel''',
           '''VideoMAEForVideoClassification''',
       ]

try:
       if not is_vision_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCAmelCase							=					['''VideoMAEFeatureExtractor''']
       UpperCAmelCase							=					['''VideoMAEImageProcessor''']

if TYPE_CHECKING:
       from .configuration_videomae import VIDEOMAE_PRETRAINED_CONFIG_ARCHIVE_MAP, VideoMAEConfig

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_videomae import (
                  VIDEOMAE_PRETRAINED_MODEL_ARCHIVE_LIST,
                  VideoMAEForPreTraining,
                  VideoMAEForVideoClassification,
                  VideoMAEModel,
                  VideoMAEPreTrainedModel,
              )

       try:
              if not is_vision_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .feature_extraction_videomae import VideoMAEFeatureExtractor
              from .image_processing_videomae import VideoMAEImageProcessor

else:
       import sys

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


