'''simple docstring'''

import unittest

from transformers import MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING, is_vision_available, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_vision_available():
  from PIL import Image
else:



  class       UpperCAmelCase  :





         '''simple docstring'''



         @staticmethod
         def _lowerCAmelCase(						*__lowerCAmelCase     ,       **__lowerCAmelCase			)  ->  Dict:
          pass



@is_pipeline_test
@require_vision
@require_torch
class       UpperCAmelCase  (						unittest.TestCase ):





       '''simple docstring'''



       SCREAMING_SNAKE_CASE 		=   MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING

       def _lowerCAmelCase(						self     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase			)  ->  Optional[int]:
        lowercase__    :   int						= pipeline(
            '''zero-shot-object-detection'''     ,       model='''hf-internal-testing/tiny-random-owlvit-object-detection'''			)

        lowercase__    :   Optional[int]						= [
            {
                '''image''': '''./tests/fixtures/tests_samples/COCO/000000039769.png''',
                '''candidate_labels''': ['''cat''', '''remote''', '''couch'''],
            }
        ]
        return object_detector, examples

       def _lowerCAmelCase(						self     ,       __lowerCAmelCase     ,       __lowerCAmelCase			)  ->  Tuple:
        lowercase__    :   Union[str, Any]						= object_detector(examples[0]     ,       threshold=0.0			)

        lowercase__    :   Any						= len(__lowerCAmelCase			)
        self.assertGreater(__lowerCAmelCase     ,       0			)
        self.assertEqual(
            __lowerCAmelCase     ,       [
                {
                    '''score''': ANY(__lowerCAmelCase			),
                    '''label''': ANY(__lowerCAmelCase			),
                    '''box''': {'''xmin''': ANY(__lowerCAmelCase			), '''ymin''': ANY(__lowerCAmelCase			), '''xmax''': ANY(__lowerCAmelCase			), '''ymax''': ANY(__lowerCAmelCase			)},
                }
                for i in range(__lowerCAmelCase			)
            ]     ,       )

       @require_tf
       @unittest.skip('''Zero Shot Object Detection not implemented in TF'''			)
       def _lowerCAmelCase(						self			)  ->  Tuple:
        pass

       @require_torch
       def _lowerCAmelCase(						self			)  ->  int:
        lowercase__    :   Any						= pipeline(
            '''zero-shot-object-detection'''     ,       model='''hf-internal-testing/tiny-random-owlvit-object-detection'''			)

        lowercase__    :   List[str]						= object_detector(
            '''./tests/fixtures/tests_samples/COCO/000000039769.png'''     ,       candidate_labels=['''cat''', '''remote''', '''couch''']     ,       threshold=0.6_4     ,       )

        self.assertEqual(
            nested_simplify(__lowerCAmelCase     ,       decimals=4			)     ,       [
                {'''score''': 0.7_2_3_5, '''label''': '''cat''', '''box''': {'''xmin''': 204, '''ymin''': 167, '''xmax''': 232, '''ymax''': 190}},
                {'''score''': 0.7_2_1_8, '''label''': '''remote''', '''box''': {'''xmin''': 204, '''ymin''': 167, '''xmax''': 232, '''ymax''': 190}},
                {'''score''': 0.7_1_8_4, '''label''': '''couch''', '''box''': {'''xmin''': 204, '''ymin''': 167, '''xmax''': 232, '''ymax''': 190}},
                {'''score''': 0.6_7_4_8, '''label''': '''remote''', '''box''': {'''xmin''': 571, '''ymin''': 83, '''xmax''': 598, '''ymax''': 103}},
                {'''score''': 0.6_6_5_6, '''label''': '''cat''', '''box''': {'''xmin''': 571, '''ymin''': 83, '''xmax''': 598, '''ymax''': 103}},
                {'''score''': 0.6_6_1_4, '''label''': '''couch''', '''box''': {'''xmin''': 571, '''ymin''': 83, '''xmax''': 598, '''ymax''': 103}},
                {'''score''': 0.6_4_5_6, '''label''': '''remote''', '''box''': {'''xmin''': 494, '''ymin''': 105, '''xmax''': 521, '''ymax''': 127}},
                {'''score''': 0.6_4_2, '''label''': '''remote''', '''box''': {'''xmin''': 67, '''ymin''': 274, '''xmax''': 93, '''ymax''': 297}},
                {'''score''': 0.6_4_1_9, '''label''': '''cat''', '''box''': {'''xmin''': 494, '''ymin''': 105, '''xmax''': 521, '''ymax''': 127}},
            ]     ,       )

        lowercase__    :   Union[str, Any]						= object_detector(
            [
                {
                    '''image''': '''./tests/fixtures/tests_samples/COCO/000000039769.png''',
                    '''candidate_labels''': ['''cat''', '''remote''', '''couch'''],
                }
            ]     ,       threshold=0.6_4     ,       )

        self.assertEqual(
            nested_simplify(__lowerCAmelCase     ,       decimals=4			)     ,       [
                [
                    {'''score''': 0.7_2_3_5, '''label''': '''cat''', '''box''': {'''xmin''': 204, '''ymin''': 167, '''xmax''': 232, '''ymax''': 190}},
                    {'''score''': 0.7_2_1_8, '''label''': '''remote''', '''box''': {'''xmin''': 204, '''ymin''': 167, '''xmax''': 232, '''ymax''': 190}},
                    {'''score''': 0.7_1_8_4, '''label''': '''couch''', '''box''': {'''xmin''': 204, '''ymin''': 167, '''xmax''': 232, '''ymax''': 190}},
                    {'''score''': 0.6_7_4_8, '''label''': '''remote''', '''box''': {'''xmin''': 571, '''ymin''': 83, '''xmax''': 598, '''ymax''': 103}},
                    {'''score''': 0.6_6_5_6, '''label''': '''cat''', '''box''': {'''xmin''': 571, '''ymin''': 83, '''xmax''': 598, '''ymax''': 103}},
                    {'''score''': 0.6_6_1_4, '''label''': '''couch''', '''box''': {'''xmin''': 571, '''ymin''': 83, '''xmax''': 598, '''ymax''': 103}},
                    {'''score''': 0.6_4_5_6, '''label''': '''remote''', '''box''': {'''xmin''': 494, '''ymin''': 105, '''xmax''': 521, '''ymax''': 127}},
                    {'''score''': 0.6_4_2, '''label''': '''remote''', '''box''': {'''xmin''': 67, '''ymin''': 274, '''xmax''': 93, '''ymax''': 297}},
                    {'''score''': 0.6_4_1_9, '''label''': '''cat''', '''box''': {'''xmin''': 494, '''ymin''': 105, '''xmax''': 521, '''ymax''': 127}},
                ]
            ]     ,       )

       @require_torch
       @slow
       def _lowerCAmelCase(						self			)  ->  List[Any]:
        lowercase__    :   Dict						= pipeline('''zero-shot-object-detection'''			)

        lowercase__    :   List[str]						= object_detector(
            '''http://images.cocodataset.org/val2017/000000039769.jpg'''     ,       candidate_labels=['''cat''', '''remote''', '''couch''']     ,       )
        self.assertEqual(
            nested_simplify(__lowerCAmelCase     ,       decimals=4			)     ,       [
                {'''score''': 0.2_8_6_8, '''label''': '''cat''', '''box''': {'''xmin''': 324, '''ymin''': 20, '''xmax''': 640, '''ymax''': 373}},
                {'''score''': 0.2_7_7, '''label''': '''remote''', '''box''': {'''xmin''': 40, '''ymin''': 72, '''xmax''': 177, '''ymax''': 115}},
                {'''score''': 0.2_5_3_7, '''label''': '''cat''', '''box''': {'''xmin''': 1, '''ymin''': 55, '''xmax''': 315, '''ymax''': 472}},
                {'''score''': 0.1_4_7_4, '''label''': '''remote''', '''box''': {'''xmin''': 335, '''ymin''': 74, '''xmax''': 371, '''ymax''': 187}},
                {'''score''': 0.1_2_0_8, '''label''': '''couch''', '''box''': {'''xmin''': 4, '''ymin''': 0, '''xmax''': 642, '''ymax''': 476}},
            ]     ,       )

        lowercase__    :   str						= object_detector(
            [
                {
                    '''image''': '''http://images.cocodataset.org/val2017/000000039769.jpg''',
                    '''candidate_labels''': ['''cat''', '''remote''', '''couch'''],
                },
                {
                    '''image''': '''http://images.cocodataset.org/val2017/000000039769.jpg''',
                    '''candidate_labels''': ['''cat''', '''remote''', '''couch'''],
                },
            ]     ,       )
        self.assertEqual(
            nested_simplify(__lowerCAmelCase     ,       decimals=4			)     ,       [
                [
                    {'''score''': 0.2_8_6_8, '''label''': '''cat''', '''box''': {'''xmin''': 324, '''ymin''': 20, '''xmax''': 640, '''ymax''': 373}},
                    {'''score''': 0.2_7_7, '''label''': '''remote''', '''box''': {'''xmin''': 40, '''ymin''': 72, '''xmax''': 177, '''ymax''': 115}},
                    {'''score''': 0.2_5_3_7, '''label''': '''cat''', '''box''': {'''xmin''': 1, '''ymin''': 55, '''xmax''': 315, '''ymax''': 472}},
                    {'''score''': 0.1_4_7_4, '''label''': '''remote''', '''box''': {'''xmin''': 335, '''ymin''': 74, '''xmax''': 371, '''ymax''': 187}},
                    {'''score''': 0.1_2_0_8, '''label''': '''couch''', '''box''': {'''xmin''': 4, '''ymin''': 0, '''xmax''': 642, '''ymax''': 476}},
                ],
                [
                    {'''score''': 0.2_8_6_8, '''label''': '''cat''', '''box''': {'''xmin''': 324, '''ymin''': 20, '''xmax''': 640, '''ymax''': 373}},
                    {'''score''': 0.2_7_7, '''label''': '''remote''', '''box''': {'''xmin''': 40, '''ymin''': 72, '''xmax''': 177, '''ymax''': 115}},
                    {'''score''': 0.2_5_3_7, '''label''': '''cat''', '''box''': {'''xmin''': 1, '''ymin''': 55, '''xmax''': 315, '''ymax''': 472}},
                    {'''score''': 0.1_4_7_4, '''label''': '''remote''', '''box''': {'''xmin''': 335, '''ymin''': 74, '''xmax''': 371, '''ymax''': 187}},
                    {'''score''': 0.1_2_0_8, '''label''': '''couch''', '''box''': {'''xmin''': 4, '''ymin''': 0, '''xmax''': 642, '''ymax''': 476}},
                ],
            ]     ,       )

       @require_tf
       @unittest.skip('''Zero Shot Object Detection not implemented in TF'''			)
       def _lowerCAmelCase(						self			)  ->  Optional[Any]:
        pass

       @require_torch
       @slow
       def _lowerCAmelCase(						self			)  ->  str:
        lowercase__    :   str						= 0.2
        lowercase__    :   Any						= pipeline('''zero-shot-object-detection'''			)

        lowercase__    :   Tuple						= object_detector(
            '''http://images.cocodataset.org/val2017/000000039769.jpg'''     ,       candidate_labels=['''cat''', '''remote''', '''couch''']     ,       threshold=__lowerCAmelCase     ,       )
        self.assertEqual(
            nested_simplify(__lowerCAmelCase     ,       decimals=4			)     ,       [
                {'''score''': 0.2_8_6_8, '''label''': '''cat''', '''box''': {'''xmin''': 324, '''ymin''': 20, '''xmax''': 640, '''ymax''': 373}},
                {'''score''': 0.2_7_7, '''label''': '''remote''', '''box''': {'''xmin''': 40, '''ymin''': 72, '''xmax''': 177, '''ymax''': 115}},
                {'''score''': 0.2_5_3_7, '''label''': '''cat''', '''box''': {'''xmin''': 1, '''ymin''': 55, '''xmax''': 315, '''ymax''': 472}},
            ]     ,       )


       @require_torch
       @slow
       def _lowerCAmelCase(						self			)  ->  Dict:
        lowercase__    :   int						= 2
        lowercase__    :   Union[str, Any]						= pipeline('''zero-shot-object-detection'''			)

        lowercase__    :   List[Any]						= object_detector(
            '''http://images.cocodataset.org/val2017/000000039769.jpg'''     ,       candidate_labels=['''cat''', '''remote''', '''couch''']     ,       top_k=__lowerCAmelCase     ,       )
        self.assertEqual(
            nested_simplify(__lowerCAmelCase     ,       decimals=4			)     ,       [
                {'''score''': 0.2_8_6_8, '''label''': '''cat''', '''box''': {'''xmin''': 324, '''ymin''': 20, '''xmax''': 640, '''ymax''': 373}},
                {'''score''': 0.2_7_7, '''label''': '''remote''', '''box''': {'''xmin''': 40, '''ymin''': 72, '''xmax''': 177, '''ymax''': 115}},
            ]     ,       )


'''simple docstring'''

import io
import itertools
import json
from dataclasses import dataclass
from typing import Optional

import pyarrow as pa
import pyarrow.json as paj

import datasets
from datasets.table import table_cast
from datasets.utils.file_utils import readline


__a:			Tuple	       =       datasets.utils.logging.get_logger(__name__)



@dataclass
class       UpperCAmelCase  (						datasets.BuilderConfig ):





       '''simple docstring'''



       SCREAMING_SNAKE_CASE 		=   None
       SCREAMING_SNAKE_CASE 		=   "utf-8"
       SCREAMING_SNAKE_CASE 		=   None
       SCREAMING_SNAKE_CASE 		=   None
       SCREAMING_SNAKE_CASE 		=   True  # deprecated
       SCREAMING_SNAKE_CASE 		=   None  # deprecated
       SCREAMING_SNAKE_CASE 		=   1_0 << 2_0  # 10MB
       SCREAMING_SNAKE_CASE 		=   None



class       UpperCAmelCase  (						datasets.ArrowBasedBuilder ):





       '''simple docstring'''



       SCREAMING_SNAKE_CASE 		=   JsonConfig

       def _lowerCAmelCase(						self			)  ->  Any:
        if self.config.block_size is not None:
         logger.warning('''The JSON loader parameter `block_size` is deprecated. Please use `chunksize` instead'''			)
         lowercase__    :   Tuple						= self.config.block_size
        if self.config.use_threads is not True:
         logger.warning(
             '''The JSON loader parameter `use_threads` is deprecated and doesn\'t have any effect anymore.'''			)
        if self.config.newlines_in_values is not None:
         raise ValueError('''The JSON loader parameter `newlines_in_values` is no longer supported'''			)
        return datasets.DatasetInfo(features=self.config.features			)

       def _lowerCAmelCase(						self     ,       __lowerCAmelCase			)  ->  Optional[Any]:
        if not self.config.data_files:
         raise ValueError(F"""At least one data file must be specified, but got data_files={self.config.data_files}"""			)
        lowercase__    :   List[str]						= dl_manager.download_and_extract(self.config.data_files			)
        if isinstance(__lowerCAmelCase     ,       (str, list, tuple)			):
         lowercase__    :   List[str]						= data_files
         if isinstance(__lowerCAmelCase     ,       __lowerCAmelCase			):
          lowercase__    :   Union[str, Any]						= [files]
         lowercase__    :   Dict						= [dl_manager.iter_files(__lowerCAmelCase			) for file in files]
         return [datasets.SplitGenerator(name=datasets.Split.TRAIN     ,       gen_kwargs={'''files''': files}			)]
        lowercase__    :   str						= []
        for split_name, files in data_files.items():
         if isinstance(__lowerCAmelCase     ,       __lowerCAmelCase			):
          lowercase__    :   List[str]						= [files]
         lowercase__    :   Optional[Any]						= [dl_manager.iter_files(__lowerCAmelCase			) for file in files]
         splits.append(datasets.SplitGenerator(name=__lowerCAmelCase     ,       gen_kwargs={'''files''': files}			)			)
        return splits

       def _lowerCAmelCase(						self     ,       __lowerCAmelCase			)  ->  pa.Table:
        if self.config.features is not None:
         # adding missing columns
         for column_name in set(self.config.features			) - set(pa_table.column_names			):
          lowercase__    :   Optional[int]						= self.config.features.arrow_schema.field(__lowerCAmelCase			).type
          lowercase__    :   Union[str, Any]						= pa_table.append_column(__lowerCAmelCase     ,       pa.array([None] * len(__lowerCAmelCase			)     ,       type=__lowerCAmelCase			)			)
         # more expensive cast to support nested structures with keys in a different order
         # allows str <-> int/float or str to Audio for example
         lowercase__    :   Dict						= table_cast(__lowerCAmelCase     ,       self.config.features.arrow_schema			)
        return pa_table


       def _lowerCAmelCase(						self     ,       __lowerCAmelCase			)  ->  Optional[int]:
        for file_idx, file in enumerate(itertools.chain.from_iterable(__lowerCAmelCase			)			):
         # If the file is one json object and if we need to look at the list of items in one specific field
         if self.config.field is not None:
          with open(__lowerCAmelCase     ,       encoding=self.config.encoding     ,       errors=self.config.encoding_errors			) as f:
           lowercase__    :   Dict						= json.load(__lowerCAmelCase			)

          # We keep only the field we are interested in
          lowercase__    :   Any						= dataset[self.config.field]

          # We accept two format: a list of dicts or a dict of lists
          if isinstance(__lowerCAmelCase     ,       (list, tuple)			):
           lowercase__    :   List[Any]						= set().union(*[row.keys() for row in dataset]			)
           lowercase__    :   List[Any]						= {col: [row.get(__lowerCAmelCase			) for row in dataset] for col in keys}
          else:
           lowercase__    :   Union[str, Any]						= dataset
          lowercase__    :   Optional[int]						= pa.Table.from_pydict(__lowerCAmelCase			)
          yield file_idx, self._cast_table(__lowerCAmelCase			)

         # If the file has one json object per line
         else:
          with open(__lowerCAmelCase     ,       '''rb'''			) as f:
           lowercase__    :   Optional[int]						= 0
           # Use block_size equal to the chunk size divided by 32 to leverage multithreading
           # Set a default minimum value of 16kB if the chunk size is really small
           lowercase__    :   List[str]						= max(self.config.chunksize // 32     ,       16 << 10			)
           lowercase__    :   Union[str, Any]						= (
               self.config.encoding_errors if self.config.encoding_errors is not None else '''strict'''
           )
           while True:
            lowercase__    :   Tuple						= f.read(self.config.chunksize			)
            if not batch:
             break
            # Finish current line
            try:
             batch += f.readline()
            except (AttributeError, io.UnsupportedOperation):
             batch += readline(__lowerCAmelCase			)
            # PyArrow only accepts utf-8 encoded bytes
            if self.config.encoding != "utf-8":
             lowercase__    :   Dict						= batch.decode(self.config.encoding     ,       errors=__lowerCAmelCase			).encode('''utf-8'''			)
            try:
             while True:
              try:
               lowercase__    :   str						= paj.read_json(
                   io.BytesIO(__lowerCAmelCase			)     ,       read_options=paj.ReadOptions(block_size=__lowerCAmelCase			)			)
               break
              except (pa.ArrowInvalid, pa.ArrowNotImplementedError) as e:
               if (
                   isinstance(__lowerCAmelCase     ,       pa.ArrowInvalid			)
                   and "straddling" not in str(__lowerCAmelCase			)
                   or block_size > len(__lowerCAmelCase			)
               ):
                raise
               else:
                # Increase the block size in case it was too small.
                # The block size will be reset for the next file.
                logger.debug(
                    F"""Batch of {len(__lowerCAmelCase			)} bytes couldn't be parsed with block_size={block_size}. Retrying with block_size={block_size * 2}."""			)
                block_size *= 2
            except pa.ArrowInvalid as e:
             try:
              with open(
                  __lowerCAmelCase     ,       encoding=self.config.encoding     ,       errors=self.config.encoding_errors			) as f:
               lowercase__    :   Optional[int]						= json.load(__lowerCAmelCase			)
             except json.JSONDecodeError:
              logger.error(F"""Failed to read file '{file}' with error {type(__lowerCAmelCase			)}: {e}"""			)
              raise e
             # If possible, parse the file as a list of json objects and exit the loop
             if isinstance(__lowerCAmelCase     ,       __lowerCAmelCase			):  # list is the only sequence type supported in JSON
              try:
               lowercase__    :   List[Any]						= set().union(*[row.keys() for row in dataset]			)
               lowercase__    :   str						= {col: [row.get(__lowerCAmelCase			) for row in dataset] for col in keys}
               lowercase__    :   Optional[int]						= pa.Table.from_pydict(__lowerCAmelCase			)
              except (pa.ArrowInvalid, AttributeError) as e:
               logger.error(F"""Failed to read file '{file}' with error {type(__lowerCAmelCase			)}: {e}"""			)
               raise ValueError(F"""Not able to read records in the JSON file at {file}."""			) from None
              yield file_idx, self._cast_table(__lowerCAmelCase			)
              break
             else:
              logger.error(F"""Failed to read file '{file}' with error {type(__lowerCAmelCase			)}: {e}"""			)
              raise ValueError(
                  F"""Not able to read records in the JSON file at {file}. """
                  F"""You should probably indicate the field of the JSON file containing your records. """
                  F"""This JSON file contain the following fields: {str(list(dataset.keys()			)			)}. """
                  F"""Select the correct one and provide it as `field='XXX'` to the dataset loading method. """			) from None
                        # Uncomment for debugging (will print the Arrow table size and elements)
                        # logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
                        # logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
            yield (file_idx, batch_idx), self._cast_table(__lowerCAmelCase			)
            batch_idx += 1

