




'''simple docstring'''



from typing import Optional

import torch
from PIL import Image
from tqdm.auto import tqdm
from transformers import CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, DDIMScheduler, DiffusionPipeline, UNetaDConditionModel
from diffusers.image_processor import VaeImageProcessor
from diffusers.utils import (
    deprecate,
)





class 			lowerCAmelCase      (       __lowerCAmelCase):


       def __init__(      self			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE = 0.93			,     __SCREAMING_SNAKE_CASE = True			,     )					->     str:



            '''simple docstring'''




            __snake_case			    =     mixing_coeff
            __snake_case			    =     leapfrog_steps

            super().__init__()
            self.register_modules(
                vae=__SCREAMING_SNAKE_CASE			,     text_encoder=__SCREAMING_SNAKE_CASE			,     tokenizer=__SCREAMING_SNAKE_CASE			,     unet=__SCREAMING_SNAKE_CASE			,     scheduler=__SCREAMING_SNAKE_CASE			,     )

            __snake_case			    =     2 ** (len(self.vae.config.block_out_channels     ) - 1)
            __snake_case			    =     VaeImageProcessor(vae_scale_factor=self.vae_scale_factor     )


       def 					lowerCAmelCase					(      self			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE = None			,     __SCREAMING_SNAKE_CASE = False     )					->     Optional[Any]:



            '''simple docstring'''




            __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			    =     self.text_encoder(text_inputs.input_ids.to(self.device     )     ).last_hidden_state

            __snake_case			    =     prompt_embeds.to(dtype=self.text_encoder.dtype			,     device=self.device     )

            if do_classifier_free_guidance:
                 __snake_case			    =     '''''' if negative_prompt is None else negative_prompt

                 __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			    =     self.text_encoder(uncond_input.input_ids.to(self.device     )     ).last_hidden_state

                 __snake_case			    =     torch.cat([negative_prompt_embeds, prompt_embeds]     )

            return prompt_embeds


       def 					lowerCAmelCase					(      self			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE     )					->     Dict:



            '''simple docstring'''




            __snake_case			    =     self.mixing_coeff * x + (1 - self.mixing_coeff) * y
            __snake_case			    =     self.mixing_coeff * y + (1 - self.mixing_coeff) * x

            return [x, y]


       def 					lowerCAmelCase					(      self			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE     )					->     Tuple:



            '''simple docstring'''




            __snake_case			    =     (y - (1 - self.mixing_coeff) * x) / self.mixing_coeff
            __snake_case			    =     (x - (1 - self.mixing_coeff) * y) / self.mixing_coeff

            return [x, y]


       def 					lowerCAmelCase					(      self			,     __SCREAMING_SNAKE_CASE     )					->     int:



            '''simple docstring'''




            __snake_case			    =     int(__SCREAMING_SNAKE_CASE     )

            __snake_case			    =     self.scheduler.alphas_cumprod[t] if t >= 0 else self.scheduler.final_alpha_cumprod

            return alpha_prod, 1 - alpha_prod


       def 					lowerCAmelCase					(      self			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     )					->     Any:



            '''simple docstring'''




            __snake_case			    =     timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

            __snake_case    ,			__snake_case			    =     self._get_alpha_and_beta(__SCREAMING_SNAKE_CASE     )
            __snake_case    ,			__snake_case			    =     self._get_alpha_and_beta(__SCREAMING_SNAKE_CASE     )

            __snake_case			    =     (alpha_prod_t_prev / alpha_prod_t) ** 0.5
            __snake_case			    =     -a_t * (beta_prod_t**0.5) + beta_prod_t_prev**0.5

            __snake_case			    =     (base - b_t * model_output) / a_t

            return model_input, next_model_input.to(base.dtype     )


       def 					lowerCAmelCase					(      self			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     )					->     Dict:



            '''simple docstring'''




            __snake_case			    =     timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

            __snake_case    ,			__snake_case			    =     self._get_alpha_and_beta(__SCREAMING_SNAKE_CASE     )
            __snake_case    ,			__snake_case			    =     self._get_alpha_and_beta(__SCREAMING_SNAKE_CASE     )

            __snake_case			    =     (alpha_prod_t_prev / alpha_prod_t) ** 0.5
            __snake_case			    =     -a_t * (beta_prod_t**0.5) + beta_prod_t_prev**0.5
            __snake_case			    =     a_t * base + b_t * model_output

            return model_input, next_model_input.to(base.dtype     )


       @torch.no_grad()
       def 					lowerCAmelCase					(      self			,     __SCREAMING_SNAKE_CASE     )					->     Union[str, Any]:



            '''simple docstring'''




            __snake_case			    =     1 / self.vae.config.scaling_factor * latents
            __snake_case			    =     self.vae.decode(__SCREAMING_SNAKE_CASE     ).sample
            __snake_case			    =     (image / 2 + 0.5).clamp(0			,     1     )
            return image


       @torch.no_grad()
       def 					lowerCAmelCase					(      self			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE = None			,     )					->     List[Any]:



            '''simple docstring'''




            __snake_case			    =     guidance_scale > 1.0

            __snake_case			    =     image.to(device=self.device			,     dtype=text_embeds.dtype     )
            __snake_case			    =     self.vae.encode(__SCREAMING_SNAKE_CASE     ).latent_dist.sample(__SCREAMING_SNAKE_CASE     )

            __snake_case			    =     self.vae.config.scaling_factor * latent

            __snake_case			    =     [latent.clone(), latent.clone()]

            for i, t in tqdm(enumerate(__SCREAMING_SNAKE_CASE     )			,     total=len(__SCREAMING_SNAKE_CASE     )     ):
                 __snake_case			    =     self.noise_mixing_layer(x=coupled_latents[0]			,     y=coupled_latents[1]     )

                 # j - model_input index, k - base index
                 for j in range(2     ):
                      __snake_case			    =     j ^ 1

                      if self.leapfrog_steps:
                           if i % 2 == 0:
                                __snake_case    ,			__snake_case			    =     j, k

                      __snake_case			    =     coupled_latents[j]
                      __snake_case			    =     coupled_latents[k]

                      __snake_case			    =     torch.cat([model_input] * 2     ) if do_classifier_free_guidance else model_input

                      __snake_case			    =     self.unet(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     encoder_hidden_states=__SCREAMING_SNAKE_CASE     ).sample

                      if do_classifier_free_guidance:
                           __snake_case    ,			__snake_case			    =     noise_pred.chunk(2     )
                           __snake_case			    =     noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

                      __snake_case    ,			__snake_case			    =     self.noise_step(
                          base=__SCREAMING_SNAKE_CASE			,     model_input=__SCREAMING_SNAKE_CASE			,     model_output=__SCREAMING_SNAKE_CASE			,     timestep=__SCREAMING_SNAKE_CASE			,     )

                      __snake_case			    =     model_input

            return coupled_latents




       @torch.no_grad()
       def __call__(      self			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE = 3.0			,     __SCREAMING_SNAKE_CASE = 50			,     __SCREAMING_SNAKE_CASE = 0.8			,     __SCREAMING_SNAKE_CASE = None			,     __SCREAMING_SNAKE_CASE = None			,     __SCREAMING_SNAKE_CASE = "pil"			,     )					->     Tuple:



            '''simple docstring'''




            __snake_case			    =     guidance_scale > 1.0

            __snake_case			    =     self.image_processor.preprocess(__SCREAMING_SNAKE_CASE     )

            __snake_case			    =     self._encode_prompt(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE     )
            __snake_case			    =     self._encode_prompt(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE     )

            self.scheduler.set_timesteps(__SCREAMING_SNAKE_CASE			,     self.device     )

            __snake_case			    =     num_inference_steps - int(num_inference_steps * strength     )
            __snake_case			    =     self.scheduler.timesteps[t_limit:]
            __snake_case			    =     fwd_timesteps.flip(0     )

            __snake_case			    =     self.prepare_latents(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE     )

            for i, t in tqdm(enumerate(__SCREAMING_SNAKE_CASE     )			,     total=len(__SCREAMING_SNAKE_CASE     )     ):
                 # j - model_input index, k - base index
                 for k in range(2     ):
                      __snake_case			    =     k ^ 1

                      if self.leapfrog_steps:
                           if i % 2 == 1:
                                __snake_case    ,			__snake_case			    =     j, k

                      __snake_case			    =     coupled_latents[j]
                      __snake_case			    =     coupled_latents[k]

                      __snake_case			    =     torch.cat([model_input] * 2     ) if do_classifier_free_guidance else model_input

                      __snake_case			    =     self.unet(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     encoder_hidden_states=__SCREAMING_SNAKE_CASE     ).sample

                      if do_classifier_free_guidance:
                           __snake_case    ,			__snake_case			    =     noise_pred.chunk(2     )
                           __snake_case			    =     noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

                      __snake_case    ,			__snake_case			    =     self.denoise_step(
                          base=__SCREAMING_SNAKE_CASE			,     model_input=__SCREAMING_SNAKE_CASE			,     model_output=__SCREAMING_SNAKE_CASE			,     timestep=__SCREAMING_SNAKE_CASE			,     )

                      __snake_case			    =     model_input

                 __snake_case			    =     self.denoise_mixing_layer(x=coupled_latents[0]			,     y=coupled_latents[1]     )

            # either one is fine
            __snake_case			    =     coupled_latents[0]

            if output_type not in ["latent", "pt", "np", "pil"]:
                 __snake_case			    =     (
                     F'''the output_type {output_type} is outdated. Please make sure to set it to one of these instead: '''
                     '''`pil`, `np`, `pt`, `latent`'''
                 )
                 deprecate('''Unsupported output_type'''			,     '''1.0.0'''			,     __SCREAMING_SNAKE_CASE			,     standard_warn=__SCREAMING_SNAKE_CASE     )
                 __snake_case			    =     '''np'''

            if output_type == "latent":
                 __snake_case			    =     final_latent
            else:
                 __snake_case			    =     self.decode_latents(__SCREAMING_SNAKE_CASE     )
                 __snake_case			    =     self.image_processor.postprocess(__SCREAMING_SNAKE_CASE			,     output_type=__SCREAMING_SNAKE_CASE     )

            return image












'''simple docstring'''



import os
from typing import Optional, Union

import numpy as np
from huggingface_hub import hf_hub_download

from ... import AutoTokenizer
from ...utils import logging


UpperCAmelCase_				:     Optional[int]     			=						'''block_records.npy'''


UpperCAmelCase_				:     Any     			=						logging.get_logger(__name__)







def 	_UpperCamelCase       (_lowerCamelCase							:   str      ,		_lowerCamelCase							:   int		)->      np.ndarray:




     '''simple docstring'''





     import tensorflow.compat.va as tf

     __snake_case			    =     tf.data.TFRecordDataset(_lowerCamelCase      ,		buffer_size=5_12 * 10_24 * 10_24		)
     __snake_case			    =     blocks_dataset.batch(_lowerCamelCase      ,		drop_remainder=_lowerCamelCase		)
     __snake_case			    =     next(blocks_dataset.take(1		).as_numpy_iterator()		)

     return np_record





class 			lowerCAmelCase      :


       def __init__(      self			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE=2			,     __SCREAMING_SNAKE_CASE=1000			,     __SCREAMING_SNAKE_CASE=100			,     __SCREAMING_SNAKE_CASE=10_0000			,     )					->     Optional[int]:



            '''simple docstring'''




            from scann.scann_ops.py.scann_ops_pybind import builder as Builder

            __snake_case			    =     Builder(db=__SCREAMING_SNAKE_CASE			,     num_neighbors=__SCREAMING_SNAKE_CASE			,     distance_measure='''dot_product'''     )
            __snake_case			    =     builder.tree(
                num_leaves=__SCREAMING_SNAKE_CASE			,     num_leaves_to_search=__SCREAMING_SNAKE_CASE			,     training_sample_size=__SCREAMING_SNAKE_CASE     )
            __snake_case			    =     builder.score_ah(dimensions_per_block=__SCREAMING_SNAKE_CASE     )

            __snake_case			    =     builder.build()




       def 					lowerCAmelCase					(      self			,     __SCREAMING_SNAKE_CASE     )					->     Any:



            '''simple docstring'''




            __snake_case    ,			__snake_case			    =     self.searcher.search_batched(question_projection.detach().cpu()     )
            return retrieved_block_ids.astype('''int64'''     )





class 			lowerCAmelCase      :


       def __init__(      self			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE     )					->     List[Any]:



            '''simple docstring'''




            super().__init__()
            __snake_case			    =     block_records
            __snake_case			    =     tokenizer


       def __call__(      self			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE=None			,     __SCREAMING_SNAKE_CASE="pt"     )					->     Union[str, Any]:



            '''simple docstring'''




            __snake_case			    =     np.take(self.block_records			,     indices=__SCREAMING_SNAKE_CASE			,     axis=0     )

            __snake_case			    =     self.tokenizer.decode(question_input_ids[0]			,     skip_special_tokens=__SCREAMING_SNAKE_CASE     )

            __snake_case			    =     []
            __snake_case			    =     []
            for retrieved_block in retrieved_blocks:
                 text.append(__SCREAMING_SNAKE_CASE     )
                 text_pair.append(retrieved_block.decode()     )

            __snake_case			    =     self.tokenizer(
                __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     padding=__SCREAMING_SNAKE_CASE			,     truncation=__SCREAMING_SNAKE_CASE			,     return_special_tokens_mask=__SCREAMING_SNAKE_CASE			,     max_length=__SCREAMING_SNAKE_CASE     )
            __snake_case			    =     concat_inputs.convert_to_tensors(__SCREAMING_SNAKE_CASE     )

            if answer_ids is not None:
                 return self.block_has_answer(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE     ) + (concat_inputs_tensors,)
            else:
                 return (None, None, None, concat_inputs_tensors)


       @classmethod
       def 					lowerCAmelCase					(      cls			,     __SCREAMING_SNAKE_CASE			,     *__SCREAMING_SNAKE_CASE			,     **__SCREAMING_SNAKE_CASE     )					->     Optional[int]:



            '''simple docstring'''




            if os.path.isdir(__SCREAMING_SNAKE_CASE     ):
                 __snake_case			    =     os.path.join(__SCREAMING_SNAKE_CASE			,     _REALM_BLOCK_RECORDS_FILENAME     )
            else:
                 __snake_case			    =     hf_hub_download(
                     repo_id=__SCREAMING_SNAKE_CASE			,     filename=_REALM_BLOCK_RECORDS_FILENAME			,     **__SCREAMING_SNAKE_CASE     )
            __snake_case			    =     np.load(__SCREAMING_SNAKE_CASE			,     allow_pickle=__SCREAMING_SNAKE_CASE     )

            __snake_case			    =     AutoTokenizer.from_pretrained(__SCREAMING_SNAKE_CASE			,     *__SCREAMING_SNAKE_CASE			,     **__SCREAMING_SNAKE_CASE     )

            return cls(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE     )


       def 					lowerCAmelCase					(      self			,     __SCREAMING_SNAKE_CASE     )					->     Any:



            '''simple docstring'''




            np.save(os.path.join(__SCREAMING_SNAKE_CASE			,     _REALM_BLOCK_RECORDS_FILENAME     )			,     self.block_records     )
            # save tokenizer
            self.tokenizer.save_pretrained(__SCREAMING_SNAKE_CASE     )




       def 					lowerCAmelCase					(      self			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE     )					->     Union[str, Any]:



            '''simple docstring'''




            __snake_case			    =     []
            __snake_case			    =     []
            __snake_case			    =     []
            __snake_case			    =     0

            for input_id in concat_inputs.input_ids:
                 __snake_case			    =     input_id.tolist()
                 # Check answers between two [SEP] tokens
                 __snake_case			    =     input_id_list.index(self.tokenizer.sep_token_id     )
                 __snake_case			    =     first_sep_idx + 1 + input_id_list[first_sep_idx + 1 :].index(self.tokenizer.sep_token_id     )

                 start_pos.append([]     )
                 end_pos.append([]     )
                 for answer in answer_ids:
                      for idx in range(first_sep_idx + 1			,     __SCREAMING_SNAKE_CASE     ):
                           if answer[0] == input_id_list[idx]:
                                if input_id_list[idx : idx + len(__SCREAMING_SNAKE_CASE     )] == answer:
                                     start_pos[-1].append(__SCREAMING_SNAKE_CASE     )
                                     end_pos[-1].append(idx + len(__SCREAMING_SNAKE_CASE     ) - 1     )

                 if len(start_pos[-1]     ) == 0:
                      has_answers.append(__SCREAMING_SNAKE_CASE     )
                 else:
                      has_answers.append(__SCREAMING_SNAKE_CASE     )
                      if len(start_pos[-1]     ) > max_answers:
                           __snake_case			    =     len(start_pos[-1]     )

        # Pad -1 to max_answers
            for start_pos_, end_pos_ in zip(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE     ):
                 if len(start_pos_     ) < max_answers:
                      __snake_case			    =     [-1] * (max_answers - len(start_pos_     ))
                      start_pos_ += padded
                      end_pos_ += padded
            return has_answers, start_pos, end_pos






