





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


__A       : Tuple						    = "block_records.npy"


__A       : Union[str, Any]						    = logging.get_logger(__name__)





def 							__lowerCAmelCase(					_SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE							)       ->   np.ndarray:




    """simple docstring"""




    import tensorflow.compat.va as tf

    _A    =       tf.data.TFRecordDataset(_SCREAMING_SNAKE_CASE       ,    buffer_size=512 * 1_024 * 1_024							)
    _A    =       blocks_dataset.batch(_SCREAMING_SNAKE_CASE       ,    drop_remainder=_SCREAMING_SNAKE_CASE							)
    _A    =       next(blocks_dataset.take(1							).as_numpy_iterator()							)

    return np_record







class     lowerCamelCase:
     '''simple docstring'''


     def __init__(						self							,    snake_case_							,    snake_case_							,    snake_case_=2							,    snake_case_=1000							,    snake_case_=100							,    snake_case_=10_0000							,    ):
         from scann.scann_ops.py.scann_ops_pybind import builder as Builder

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

         _A    =       builder.build()




     def 					lowerCAmelCase__		(						self							,    snake_case_       ):
         _A, _A    =       self.searcher.search_batched(question_projection.detach().cpu()       )
         return retrieved_block_ids.astype('int64'       )







class     lowerCamelCase:
     '''simple docstring'''


     def __init__(						self							,    snake_case_							,    snake_case_       ):
         super().__init__()
         _A    =       block_records
         _A    =       tokenizer




     def __call__(						self							,    snake_case_							,    snake_case_							,    snake_case_							,    snake_case_=None							,    snake_case_="pt"       ):
         _A    =       np.take(self.block_records							,    indices=snake_case_							,    axis=0       )

         _A    =       self.tokenizer.decode(question_input_ids[0]							,    skip_special_tokens=snake_case_       )

         _A    =       []
         _A    =       []
         for retrieved_block in retrieved_blocks:
             text.append(snake_case_       )
             text_pair.append(retrieved_block.decode()       )

         _A    =       self.tokenizer(
             snake_case_							,    snake_case_							,    padding=snake_case_							,    truncation=snake_case_							,    return_special_tokens_mask=snake_case_							,    max_length=snake_case_       )
         _A    =       concat_inputs.convert_to_tensors(snake_case_       )

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




     @classmethod
     def 					lowerCAmelCase__		(						cls							,    snake_case_							,    *snake_case_							,    **snake_case_       ):
         if os.path.isdir(snake_case_       ):
             _A    =       os.path.join(snake_case_							,    _REALM_BLOCK_RECORDS_FILENAME       )
         else:
             _A    =       hf_hub_download(
                 repo_id=snake_case_							,    filename=_REALM_BLOCK_RECORDS_FILENAME							,    **snake_case_       )
         _A    =       np.load(snake_case_							,    allow_pickle=snake_case_       )

         _A    =       AutoTokenizer.from_pretrained(snake_case_							,    *snake_case_							,    **snake_case_       )

         return cls(snake_case_							,    snake_case_       )




     def 					lowerCAmelCase__		(						self							,    snake_case_       ):
         # save block records
         np.save(os.path.join(snake_case_							,    _REALM_BLOCK_RECORDS_FILENAME       )							,    self.block_records       )
         # save tokenizer
         self.tokenizer.save_pretrained(snake_case_       )




     def 					lowerCAmelCase__		(						self							,    snake_case_							,    snake_case_       ):
         _A    =       []
         _A    =       []
         _A    =       []
         _A    =       0

         for input_id in concat_inputs.input_ids:
             _A    =       input_id.tolist()
             # Check answers between two [SEP] tokens
             _A    =       input_id_list.index(self.tokenizer.sep_token_id       )
             _A    =       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							,    snake_case_       ):
                     if answer[0] == input_id_list[idx]:
                         if input_id_list[idx : idx + len(snake_case_       )] == answer:
                             start_pos[-1].append(snake_case_       )
                             end_pos[-1].append(idx + len(snake_case_       ) - 1       )

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

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












import unittest

import numpy as np
import PIL
import torch

from diffusers.image_processor import VaeImageProcessor







class     lowerCamelCase(	unittest.TestCase  ):
     '''simple docstring'''


     @property
     def 					lowerCAmelCase__		(						self       ):
         _A    =       1
         _A    =       3
         _A    =       8
         _A    =       8

         _A    =       torch.rand((batch_size, num_channels, height, width)       )

         return sample




     def 					lowerCAmelCase__		(						self							,    snake_case_       ):
         if isinstance(image[0]							,    PIL.Image.Image       ):
             return np.stack([np.array(snake_case_       ) for i in image]							,    axis=0       )
         elif isinstance(snake_case_							,    torch.Tensor       ):
             return image.cpu().numpy().transpose(0							,    2							,    3							,    1       )
         return image




     def 					lowerCAmelCase__		(						self       ):
         _A    =       VaeImageProcessor(do_resize=snake_case_							,    do_normalize=snake_case_       )

         _A    =       self.dummy_sample
         _A    =       self.to_np(snake_case_       )

         for output_type in ["pt", "np", "pil"]:
             _A    =       image_processor.postprocess(
                 image_processor.preprocess(snake_case_       )							,    output_type=snake_case_							,    )
             _A    =       self.to_np(snake_case_       )
             _A    =       (input_np * 255).round() if output_type == 'pil' else input_np
             assert (
                 np.abs(in_np - out_np       ).max() < 1E-6
             ), F"decoded output does not match input for output_type {output_type}"




     def 					lowerCAmelCase__		(						self       ):
         _A    =       VaeImageProcessor(do_resize=snake_case_							,    do_normalize=snake_case_       )
         _A    =       self.dummy_sample.cpu().numpy().transpose(0							,    2							,    3							,    1       )

         for output_type in ["pt", "np", "pil"]:
             _A    =       image_processor.postprocess(image_processor.preprocess(snake_case_       )							,    output_type=snake_case_       )

             _A    =       self.to_np(snake_case_       )
             _A    =       (input_np * 255).round() if output_type == 'pil' else input_np
             assert (
                 np.abs(in_np - out_np       ).max() < 1E-6
             ), F"decoded output does not match input for output_type {output_type}"




     def 					lowerCAmelCase__		(						self       ):
         _A    =       VaeImageProcessor(do_resize=snake_case_							,    do_normalize=snake_case_       )

         _A    =       self.dummy_sample.cpu().numpy().transpose(0							,    2							,    3							,    1       )
         _A    =       image_processor.numpy_to_pil(snake_case_       )

         for output_type in ["pt", "np", "pil"]:
             _A    =       image_processor.postprocess(image_processor.preprocess(snake_case_       )							,    output_type=snake_case_       )
             for i, o in zip(snake_case_							,    snake_case_       ):
                 _A    =       np.array(snake_case_       )
                 _A    =       self.to_np(snake_case_       ) if output_type == 'pil' else (self.to_np(snake_case_       ) * 255).round()
                 assert (
                     np.abs(in_np - out_np       ).max() < 1E-6
                 ), F"decoded output does not match input for output_type {output_type}"




     def 					lowerCAmelCase__		(						self       ):
         _A    =       VaeImageProcessor(do_resize=snake_case_							,    do_normalize=snake_case_       )

         _A    =       self.dummy_sample
         _A    =       input_pt_ad.squeeze(0       )

         _A    =       image_processor.postprocess(
             image_processor.preprocess(snake_case_       )							,    output_type='np'							,    )
         _A    =       image_processor.postprocess(
             image_processor.preprocess(snake_case_       )							,    output_type='np'							,    )

         _A    =       self.to_np(self.dummy_sample       )
         _A    =       input_np_ad.squeeze(0       )

         _A    =       image_processor.postprocess(
             image_processor.preprocess(snake_case_       )							,    output_type='np'							,    )
         _A    =       image_processor.postprocess(
             image_processor.preprocess(snake_case_       )							,    output_type='np'							,    )

         assert np.abs(out_pt_ad - out_pt_ad       ).max() < 1E-6
         assert np.abs(out_np_ad - out_np_ad       ).max() < 1E-6




     def 					lowerCAmelCase__		(						self       ):
         _A    =       VaeImageProcessor(do_resize=snake_case_							,    do_normalize=snake_case_       )

         _A    =       self.dummy_sample
         _A    =       list(snake_case_       )

         _A    =       image_processor.postprocess(
             image_processor.preprocess(snake_case_       )							,    output_type='np'							,    )

         _A    =       image_processor.postprocess(
             image_processor.preprocess(snake_case_       )							,    output_type='np'							,    )

         _A    =       self.to_np(self.dummy_sample       )
         list(snake_case_       )

         _A    =       image_processor.postprocess(
             image_processor.preprocess(snake_case_       )							,    output_type='np'							,    )

         _A    =       image_processor.postprocess(
             image_processor.preprocess(snake_case_       )							,    output_type='np'							,    )

         assert np.abs(out_pt_ad - out_pt_list       ).max() < 1E-6
         assert np.abs(out_np_ad - out_np_list       ).max() < 1E-6





