






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


lowerCamelCase			:							List[Any]       ='''block_records.npy'''


lowerCamelCase			:							Tuple       =logging.get_logger(__name__)







def 	SCREAMING_SNAKE_CASE							(      __lowerCAmelCase      ,					__lowerCAmelCase    )				->			np.ndarray:
     import tensorflow.compat.va as tf

     UpperCamelCase__    :     Union[str, Any]        =						tf.data.TFRecordDataset(__lowerCAmelCase      ,					buffer_size=512 * 1024 * 1024    )
     UpperCamelCase__    :     List[str]        =						blocks_dataset.batch(__lowerCAmelCase      ,					drop_remainder=__lowerCAmelCase    )
     UpperCamelCase__    :     Any        =						next(blocks_dataset.take(1    ).as_numpy_iterator()    )

     return np_record





class     __a						:







 def __init__(	self      :     str							,     SCREAMING_SNAKE_CASE      :     str							,     SCREAMING_SNAKE_CASE      :     Any							,     SCREAMING_SNAKE_CASE      :     Optional[int]=2							,     SCREAMING_SNAKE_CASE      :     Optional[Any]=10_00							,     SCREAMING_SNAKE_CASE      :     Optional[Any]=1_00							,     SCREAMING_SNAKE_CASE      :     int=10_00_00							,     ):


      '''simple docstring'''






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

      UpperCamelCase__    :     Any        =						Builder(db=SCREAMING_SNAKE_CASE							,     num_neighbors=SCREAMING_SNAKE_CASE							,     distance_measure="dot_product"		)
      UpperCamelCase__    :     List[str]        =						builder.tree(
          num_leaves=SCREAMING_SNAKE_CASE							,     num_leaves_to_search=SCREAMING_SNAKE_CASE							,     training_sample_size=SCREAMING_SNAKE_CASE		)
      UpperCamelCase__    :     Tuple        =						builder.score_ah(dimensions_per_block=SCREAMING_SNAKE_CASE		)

      UpperCamelCase__    :     Union[str, Any]        =						builder.build()







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


      '''simple docstring'''






      UpperCamelCase__       ,  UpperCamelCase__    :     Tuple        =						self.searcher.search_batched(question_projection.detach().cpu()		)
      return retrieved_block_ids.astype("int64"		)







class     __a						:







 def __init__(	self      :     Any							,     SCREAMING_SNAKE_CASE      :     Optional[int]							,     SCREAMING_SNAKE_CASE      :     List[Any]		):


      '''simple docstring'''






      super().__init__()
      UpperCamelCase__    :     str        =						block_records
      UpperCamelCase__    :     List[str]        =						tokenizer







 def __call__(	self      :     Optional[Any]							,     SCREAMING_SNAKE_CASE      :     List[Any]							,     SCREAMING_SNAKE_CASE      :     Any							,     SCREAMING_SNAKE_CASE      :     Optional[int]							,     SCREAMING_SNAKE_CASE      :     int=None							,     SCREAMING_SNAKE_CASE      :     Dict="pt"		):


      '''simple docstring'''






      UpperCamelCase__    :     Union[str, Any]        =						np.take(self.block_records							,     indices=SCREAMING_SNAKE_CASE							,     axis=0		)

      UpperCamelCase__    :     Dict        =						self.tokenizer.decode(question_input_ids[0]							,     skip_special_tokens=SCREAMING_SNAKE_CASE		)

      UpperCamelCase__    :     Dict        =						[]
      UpperCamelCase__    :     Tuple        =						[]
      for retrieved_block in retrieved_blocks:
           text.append(SCREAMING_SNAKE_CASE		)
           text_pair.append(retrieved_block.decode()		)

      UpperCamelCase__    :     List[str]        =						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		)
      UpperCamelCase__    :     Dict        =						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       __lowercase      (	cls      :     int							,     SCREAMING_SNAKE_CASE      :     Optional[Union[str, os.PathLike]]							,     *SCREAMING_SNAKE_CASE      :     Tuple							,     **SCREAMING_SNAKE_CASE      :     Union[str, Any]		):


      '''simple docstring'''






      if os.path.isdir(SCREAMING_SNAKE_CASE		):
           UpperCamelCase__    :     Optional[Any]        =						os.path.join(SCREAMING_SNAKE_CASE							,     _REALM_BLOCK_RECORDS_FILENAME		)
      else:
           UpperCamelCase__    :     List[str]        =						hf_hub_download(
               repo_id=SCREAMING_SNAKE_CASE							,     filename=_REALM_BLOCK_RECORDS_FILENAME							,     **SCREAMING_SNAKE_CASE		)
      UpperCamelCase__    :     Union[str, Any]        =						np.load(SCREAMING_SNAKE_CASE							,     allow_pickle=SCREAMING_SNAKE_CASE		)

      UpperCamelCase__    :     List[Any]        =						AutoTokenizer.from_pretrained(SCREAMING_SNAKE_CASE							,     *SCREAMING_SNAKE_CASE							,     **SCREAMING_SNAKE_CASE		)

      return cls(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE		)







 def       __lowercase      (	self      :     int							,     SCREAMING_SNAKE_CASE      :     Tuple		):


      '''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       __lowercase      (	self      :     int							,     SCREAMING_SNAKE_CASE      :     List[str]							,     SCREAMING_SNAKE_CASE      :     Any		):


      '''simple docstring'''






      UpperCamelCase__    :     Optional[int]        =						[]
      UpperCamelCase__    :     Optional[int]        =						[]
      UpperCamelCase__    :     Any        =						[]
      UpperCamelCase__    :     int        =						0

      for input_id in concat_inputs.input_ids:
           UpperCamelCase__    :     List[str]        =						input_id.tolist()
           # Check answers between two [SEP] tokens
           UpperCamelCase__    :     Optional[int]        =						input_id_list.index(self.tokenizer.sep_token_id		)
           UpperCamelCase__    :     str        =						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:
                     UpperCamelCase__    :     str        =						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:
                UpperCamelCase__    :     Dict        =						[-1] * (max_answers - len(start_pos_		))
                start_pos_ += padded
                end_pos_ += padded
      return has_answers, start_pos, end_pos







import unittest

from transformers import (
    MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
    TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
    PreTrainedTokenizer,
    is_vision_available,
)
from transformers.pipelines import ImageClassificationPipeline, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_torch_or_tf,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_vision_available():
 from PIL import Image
else:





 class     __a						:







  @staticmethod
  def       __lowercase      (	*SCREAMING_SNAKE_CASE      :     Optional[Any]							,     **SCREAMING_SNAKE_CASE      :     Union[str, Any]		):


       '''simple docstring'''






       pass





@is_pipeline_test
@require_torch_or_tf
@require_vision
class     __a						(					unittest.TestCase ):
 _lowerCAmelCase : Tuple 				=				MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
 _lowerCAmelCase : str 				=				TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING







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


      '''simple docstring'''






      UpperCamelCase__    :     Dict        =						ImageClassificationPipeline(model=SCREAMING_SNAKE_CASE							,     image_processor=SCREAMING_SNAKE_CASE							,     top_k=2		)
      UpperCamelCase__    :     Union[str, Any]        =						[
          Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"		),
          "http://images.cocodataset.org/val2017/000000039769.jpg",
      ]
      return image_classifier, examples







 def       __lowercase      (	self      :     Tuple							,     SCREAMING_SNAKE_CASE      :     Tuple							,     SCREAMING_SNAKE_CASE      :     Tuple		):


      '''simple docstring'''






      UpperCamelCase__    :     Optional[Any]        =						image_classifier("./tests/fixtures/tests_samples/COCO/000000039769.png"		)

      self.assertEqual(
          SCREAMING_SNAKE_CASE							,     [
              {"score": ANY(SCREAMING_SNAKE_CASE		), "label": ANY(SCREAMING_SNAKE_CASE		)},
              {"score": ANY(SCREAMING_SNAKE_CASE		), "label": ANY(SCREAMING_SNAKE_CASE		)},
          ]							,     )

      import datasets

      UpperCamelCase__    :     int        =						datasets.load_dataset("hf-internal-testing/fixtures_image_utils"							,     "image"							,     split="test"		)

      # Accepts URL + PIL.Image + lists
      UpperCamelCase__    :     Tuple        =						image_classifier(
          [
              Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"		),
              "http://images.cocodataset.org/val2017/000000039769.jpg",
              # RGBA
              dataset[0]["file"],
              # LA
              dataset[1]["file"],
              # L
              dataset[2]["file"],
          ]		)
      self.assertEqual(
          SCREAMING_SNAKE_CASE							,     [
              [
                  {"score": ANY(SCREAMING_SNAKE_CASE		), "label": ANY(SCREAMING_SNAKE_CASE		)},
                  {"score": ANY(SCREAMING_SNAKE_CASE		), "label": ANY(SCREAMING_SNAKE_CASE		)},
              ],
              [
                  {"score": ANY(SCREAMING_SNAKE_CASE		), "label": ANY(SCREAMING_SNAKE_CASE		)},
                  {"score": ANY(SCREAMING_SNAKE_CASE		), "label": ANY(SCREAMING_SNAKE_CASE		)},
              ],
              [
                  {"score": ANY(SCREAMING_SNAKE_CASE		), "label": ANY(SCREAMING_SNAKE_CASE		)},
                  {"score": ANY(SCREAMING_SNAKE_CASE		), "label": ANY(SCREAMING_SNAKE_CASE		)},
              ],
              [
                  {"score": ANY(SCREAMING_SNAKE_CASE		), "label": ANY(SCREAMING_SNAKE_CASE		)},
                  {"score": ANY(SCREAMING_SNAKE_CASE		), "label": ANY(SCREAMING_SNAKE_CASE		)},
              ],
              [
                  {"score": ANY(SCREAMING_SNAKE_CASE		), "label": ANY(SCREAMING_SNAKE_CASE		)},
                  {"score": ANY(SCREAMING_SNAKE_CASE		), "label": ANY(SCREAMING_SNAKE_CASE		)},
              ],
          ]							,     )







 @require_torch
 def       __lowercase      (	self      :     List[str]		):


      '''simple docstring'''






      UpperCamelCase__    :     List[Any]        =						"hf-internal-testing/tiny-random-vit"
      UpperCamelCase__    :     List[str]        =						pipeline("image-classification"							,     model=SCREAMING_SNAKE_CASE		)

      UpperCamelCase__    :     List[str]        =						image_classifier("http://images.cocodataset.org/val2017/000000039769.jpg"		)
      self.assertEqual(
          nested_simplify(SCREAMING_SNAKE_CASE							,     decimals=4		)							,     [{"label": "LABEL_1", "score": 0.5_7_4}, {"label": "LABEL_0", "score": 0.4_2_6}]							,     )

      UpperCamelCase__    :     Optional[Any]        =						image_classifier(
          [
              "http://images.cocodataset.org/val2017/000000039769.jpg",
              "http://images.cocodataset.org/val2017/000000039769.jpg",
          ]							,     top_k=2							,     )
      self.assertEqual(
          nested_simplify(SCREAMING_SNAKE_CASE							,     decimals=4		)							,     [
              [{"label": "LABEL_1", "score": 0.5_7_4}, {"label": "LABEL_0", "score": 0.4_2_6}],
              [{"label": "LABEL_1", "score": 0.5_7_4}, {"label": "LABEL_0", "score": 0.4_2_6}],
          ]							,     )







 @require_tf
 def       __lowercase      (	self      :     int		):


      '''simple docstring'''






      UpperCamelCase__    :     str        =						"hf-internal-testing/tiny-random-vit"
      UpperCamelCase__    :     int        =						pipeline("image-classification"							,     model=SCREAMING_SNAKE_CASE							,     framework="tf"		)

      UpperCamelCase__    :     List[Any]        =						image_classifier("http://images.cocodataset.org/val2017/000000039769.jpg"		)
      self.assertEqual(
          nested_simplify(SCREAMING_SNAKE_CASE							,     decimals=4		)							,     [{"label": "LABEL_1", "score": 0.5_7_4}, {"label": "LABEL_0", "score": 0.4_2_6}]							,     )

      UpperCamelCase__    :     int        =						image_classifier(
          [
              "http://images.cocodataset.org/val2017/000000039769.jpg",
              "http://images.cocodataset.org/val2017/000000039769.jpg",
          ]							,     top_k=2							,     )
      self.assertEqual(
          nested_simplify(SCREAMING_SNAKE_CASE							,     decimals=4		)							,     [
              [{"label": "LABEL_1", "score": 0.5_7_4}, {"label": "LABEL_0", "score": 0.4_2_6}],
              [{"label": "LABEL_1", "score": 0.5_7_4}, {"label": "LABEL_0", "score": 0.4_2_6}],
          ]							,     )







 def       __lowercase      (	self      :     Optional[int]		):


      '''simple docstring'''






      UpperCamelCase__    :     Any        =						PreTrainedTokenizer()

      # Assert that the pipeline can be initialized with a feature extractor that is not in any mapping
      UpperCamelCase__    :     str        =						pipeline(
          "image-classification"							,     model="hf-internal-testing/tiny-random-vit"							,     tokenizer=SCREAMING_SNAKE_CASE		)

      self.assertIs(image_classifier.tokenizer							,     SCREAMING_SNAKE_CASE		)







 @slow
 @require_torch
 def       __lowercase      (	self      :     int		):


      '''simple docstring'''






      UpperCamelCase__    :     Optional[Any]        =						pipeline("image-classification"							,     model="deepmind/vision-perceiver-conv"		)
      UpperCamelCase__    :     Any        =						image_classifier("http://images.cocodataset.org/val2017/000000039769.jpg"		)
      self.assertEqual(
          nested_simplify(SCREAMING_SNAKE_CASE							,     decimals=4		)							,     [
              {"score": 0.4_3_8_5, "label": "tabby, tabby cat"},
              {"score": 0.3_2_1, "label": "tiger cat"},
              {"score": 0.0_5_0_2, "label": "Egyptian cat"},
              {"score": 0.0_1_3_7, "label": "crib, cot"},
              {"score": 0.0_0_7, "label": "radiator"},
          ]							,     )

      UpperCamelCase__    :     Optional[Any]        =						pipeline("image-classification"							,     model="deepmind/vision-perceiver-fourier"		)
      UpperCamelCase__    :     List[str]        =						image_classifier("http://images.cocodataset.org/val2017/000000039769.jpg"		)
      self.assertEqual(
          nested_simplify(SCREAMING_SNAKE_CASE							,     decimals=4		)							,     [
              {"score": 0.5_6_5_8, "label": "tabby, tabby cat"},
              {"score": 0.1_3_0_9, "label": "tiger cat"},
              {"score": 0.0_7_2_2, "label": "Egyptian cat"},
              {"score": 0.0_7_0_7, "label": "remote control, remote"},
              {"score": 0.0_0_8_2, "label": "computer keyboard, keypad"},
          ]							,     )

      UpperCamelCase__    :     List[str]        =						pipeline("image-classification"							,     model="deepmind/vision-perceiver-learned"		)
      UpperCamelCase__    :     Optional[int]        =						image_classifier("http://images.cocodataset.org/val2017/000000039769.jpg"		)
      self.assertEqual(
          nested_simplify(SCREAMING_SNAKE_CASE							,     decimals=4		)							,     [
              {"score": 0.3_0_2_2, "label": "tabby, tabby cat"},
              {"score": 0.2_3_6_2, "label": "Egyptian cat"},
              {"score": 0.1_8_5_6, "label": "tiger cat"},
              {"score": 0.0_3_2_4, "label": "remote control, remote"},
              {"score": 0.0_0_9_6, "label": "quilt, comforter, comfort, puff"},
          ]							,     )