
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_				:							Union[str, Any]       =    getLogger(__name__)


lowerCamelCase_				:							Union[str, Any]       =    4
lowerCamelCase_				:							Tuple       =    7

lowerCamelCase_				:							str       =    {
    "float16": torch.floataa,
    "float32": torch.floataa,
}
def        __lowercase(							__snake_case				:		Optional[Any]					)       ->   Any:
       if isinstance(__snake_case    ,torch.Tensor					):
              return image
       elif isinstance(__snake_case    ,PIL.Image.Image					):
              __snake_case						       =   [image]

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

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

       return image







class  _lowerCamelCase			(lowerCamelCase					):

     def __init__(  self							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_ = 350							,       ):
            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							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_ = 75							,       SCREAMING_SNAKE_CASE_ = 9.0							,       SCREAMING_SNAKE_CASE_ = 20							,       SCREAMING_SNAKE_CASE_ = None							,       SCREAMING_SNAKE_CASE_ = 1							,       SCREAMING_SNAKE_CASE_ = 0.0							,       SCREAMING_SNAKE_CASE_ = None							,       SCREAMING_SNAKE_CASE_ = None							,       SCREAMING_SNAKE_CASE_ = None							,       SCREAMING_SNAKE_CASE_ = None							,       SCREAMING_SNAKE_CASE_ = "pil"							,       SCREAMING_SNAKE_CASE_ = True							,       SCREAMING_SNAKE_CASE_ = None							,       SCREAMING_SNAKE_CASE_ = 1							,       ):
            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_    ):
                   __snake_case						       =   1
            elif prompt is not None and isinstance(SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_    ):
                   __snake_case						       =   len(SCREAMING_SNAKE_CASE_    )
            else:
                   __snake_case						       =   prompt_embeds.shape[0]

            __snake_case						       =   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.
            __snake_case						       =   guidance_scale > 1.0

            # 3. Encode input prompt
            __snake_case						       =   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_							,       )

            __snake_case						       =   ORT_TO_PT_TYPE[str(text_embeddings.dtype    )]

            # 4. Preprocess image
            __snake_case						       =   preprocess(SCREAMING_SNAKE_CASE_    )
            __snake_case						       =   image.cpu()

            # 5. set timesteps
            self.scheduler.set_timesteps(SCREAMING_SNAKE_CASE_							,       device=SCREAMING_SNAKE_CASE_    )
            __snake_case						       =   self.scheduler.timesteps

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

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

            # 6. Prepare latent variables
            __snake_case       ,       __snake_case						       =   image.shape[2:]
            __snake_case						       =   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
            __snake_case						       =   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
            __snake_case						       =   self.prepare_extra_step_kwargs(SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_    )

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

            # 9. Denoising loop
            __snake_case						       =   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
                          __snake_case						       =   np.concatenate([latents] * 2    ) if do_classifier_free_guidance else latents

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

                          # timestep to tensor
                          __snake_case						       =   np.array([t]							,       dtype=SCREAMING_SNAKE_CASE_    )

                          # predict the noise residual
                          __snake_case						       =   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:
                                 __snake_case       ,       __snake_case						       =   np.split(SCREAMING_SNAKE_CASE_							,       2    )
                          __snake_case						       =   noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

                          # compute the previous noisy sample x_t -> x_t-1
                          __snake_case						       =   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
            __snake_case						       =   self.decode_latents(latents.float()    )

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

            if not return_dict:
                   return (image,)

            return ImagePipelineOutput(images=SCREAMING_SNAKE_CASE_    )

     def 						__lowerCamelCase				(  self							,       SCREAMING_SNAKE_CASE_    ):
            __snake_case						       =   1 / 0.0_8_3_3_3 * latents
            __snake_case						       =   self.vae(latent_sample=SCREAMING_SNAKE_CASE_    )[0]
            __snake_case						       =   np.clip(image / 2 + 0.5							,       0							,       1    )
            __snake_case						       =   image.transpose((0, 2, 3, 1)    )
            return image

     def 						__lowerCamelCase				(  self							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_ = None							,       SCREAMING_SNAKE_CASE_ = None							,       ):
            if prompt is not None and isinstance(SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_    ):
                   __snake_case						       =   1
            elif prompt is not None and isinstance(SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_    ):
                   __snake_case						       =   len(SCREAMING_SNAKE_CASE_    )
            else:
                   __snake_case						       =   prompt_embeds.shape[0]

            if prompt_embeds is None:
                   __snake_case						       =   self.tokenizer(
                       SCREAMING_SNAKE_CASE_							,       padding='max_length'							,       max_length=self.tokenizer.model_max_length							,       truncation=SCREAMING_SNAKE_CASE_							,       return_tensors='pt'							,       )
                   __snake_case						       =   text_inputs.input_ids
                   __snake_case						       =   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_    ):
                          __snake_case						       =   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
                   __snake_case						       =   self.text_encoder(
                       input_ids=text_input_ids.int().to(SCREAMING_SNAKE_CASE_    )							,       )
                   __snake_case						       =   prompt_embeds[0]

            __snake_case       ,       __snake_case       ,       __snake_case						       =   prompt_embeds.shape
            # duplicate text embeddings for each generation per prompt, using mps friendly method
            __snake_case						       =   prompt_embeds.repeat(1							,       SCREAMING_SNAKE_CASE_    )
            __snake_case						       =   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:
                   __snake_case						       =   42
                   if negative_prompt is None:
                          __snake_case						       =   [''] * 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_    ):
                          __snake_case						       =   [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:
                          __snake_case						       =   negative_prompt

                   __snake_case						       =   text_input_ids.shape[-1]
                   __snake_case						       =   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

                   __snake_case						       =   self.text_encoder(
                       input_ids=uncond_input.input_ids.int().to(SCREAMING_SNAKE_CASE_    )							,       )
                   __snake_case						       =   uncond_embeddings[0]

            if do_classifier_free_guidance:
                   __snake_case						       =   uncond_embeddings.shape[1]
                   # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
                   __snake_case						       =   uncond_embeddings.repeat(1							,       SCREAMING_SNAKE_CASE_    )
                   __snake_case						       =   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
                   __snake_case						       =   np.concatenate([uncond_embeddings, prompt_embeds]    )

            return prompt_embeds


from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


if TYPE_CHECKING:
 from ... import FeatureExtractionMixin, PreTrainedTokenizerBase, TensorType


lowerCamelCase_				:							Any       =    logging.get_logger(__name__)

lowerCamelCase_				:							Optional[Any]       =    {
    "microsoft/deberta-base": "https://huggingface.co/microsoft/deberta-base/resolve/main/config.json",
    "microsoft/deberta-large": "https://huggingface.co/microsoft/deberta-large/resolve/main/config.json",
    "microsoft/deberta-xlarge": "https://huggingface.co/microsoft/deberta-xlarge/resolve/main/config.json",
    "microsoft/deberta-base-mnli": "https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/config.json",
    "microsoft/deberta-large-mnli": "https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/config.json",
    "microsoft/deberta-xlarge-mnli": "https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/config.json",
}







class  _lowerCamelCase			(lowerCamelCase					):
     lowercase__				  =			"""deberta"""

     def __init__(  self							,       SCREAMING_SNAKE_CASE_=50_265							,       SCREAMING_SNAKE_CASE_=768							,       SCREAMING_SNAKE_CASE_=12							,       SCREAMING_SNAKE_CASE_=12							,       SCREAMING_SNAKE_CASE_=3_072							,       SCREAMING_SNAKE_CASE_="gelu"							,       SCREAMING_SNAKE_CASE_=0.1							,       SCREAMING_SNAKE_CASE_=0.1							,       SCREAMING_SNAKE_CASE_=512							,       SCREAMING_SNAKE_CASE_=0							,       SCREAMING_SNAKE_CASE_=0.0_2							,       SCREAMING_SNAKE_CASE_=1E-7							,       SCREAMING_SNAKE_CASE_=False							,       SCREAMING_SNAKE_CASE_=-1							,       SCREAMING_SNAKE_CASE_=0							,       SCREAMING_SNAKE_CASE_=True							,       SCREAMING_SNAKE_CASE_=None							,       SCREAMING_SNAKE_CASE_=0							,       SCREAMING_SNAKE_CASE_="gelu"							,       **SCREAMING_SNAKE_CASE_							,       ):
            super().__init__(**SCREAMING_SNAKE_CASE_    )

            __snake_case						       =   hidden_size
            __snake_case						       =   num_hidden_layers
            __snake_case						       =   num_attention_heads
            __snake_case						       =   intermediate_size
            __snake_case						       =   hidden_act
            __snake_case						       =   hidden_dropout_prob
            __snake_case						       =   attention_probs_dropout_prob
            __snake_case						       =   max_position_embeddings
            __snake_case						       =   type_vocab_size
            __snake_case						       =   initializer_range
            __snake_case						       =   relative_attention
            __snake_case						       =   max_relative_positions
            __snake_case						       =   pad_token_id
            __snake_case						       =   position_biased_input

            # Backwards compatibility
            if type(SCREAMING_SNAKE_CASE_    ) == str:
                   __snake_case						       =   [x.strip() for x in pos_att_type.lower().split('|'    )]

            __snake_case						       =   pos_att_type
            __snake_case						       =   vocab_size
            __snake_case						       =   layer_norm_eps

            __snake_case						       =   kwargs.get('pooler_hidden_size'							,       SCREAMING_SNAKE_CASE_    )
            __snake_case						       =   pooler_dropout
            __snake_case						       =   pooler_hidden_act







class  _lowerCamelCase			(lowerCamelCase					):

     @property
     def 						__lowerCamelCase				(  self    ):
            if self.task == "multiple-choice":
                   __snake_case						       =   {0: 'batch', 1: 'choice', 2: 'sequence'}
            else:
                   __snake_case						       =   {0: 'batch', 1: 'sequence'}
            if self._config.type_vocab_size > 0:
                   return OrderedDict(
                       [('input_ids', dynamic_axis), ('attention_mask', dynamic_axis), ('token_type_ids', dynamic_axis)]    )
            else:
                   return OrderedDict([('input_ids', dynamic_axis), ('attention_mask', dynamic_axis)]    )

     @property
     def 						__lowerCamelCase				(  self    ):
            return 12

     def 						__lowerCamelCase				(  self							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_ = -1							,       SCREAMING_SNAKE_CASE_ = -1							,       SCREAMING_SNAKE_CASE_ = -1							,       SCREAMING_SNAKE_CASE_ = False							,       SCREAMING_SNAKE_CASE_ = None							,       SCREAMING_SNAKE_CASE_ = 3							,       SCREAMING_SNAKE_CASE_ = 40							,       SCREAMING_SNAKE_CASE_ = 40							,       SCREAMING_SNAKE_CASE_ = None							,       ):
            __snake_case						       =   super().generate_dummy_inputs(preprocessor=SCREAMING_SNAKE_CASE_							,       framework=SCREAMING_SNAKE_CASE_    )
            if self._config.type_vocab_size == 0 and "token_type_ids" in dummy_inputs:
                   del dummy_inputs["token_type_ids"]
            return dummy_inputs
