
"""simple docstring"""




import collections
import inspect
import unittest

from transformers import FocalNetConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
  import torch
  from torch import nn

  from transformers import (
      FocalNetBackbone,
      FocalNetForImageClassification,
      FocalNetForMaskedImageModeling,
      FocalNetModel,
  )
  from transformers.models.focalnet.modeling_focalnet import FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
  from PIL import Image

  from transformers import AutoImageProcessor


class 			__magic_name__       :
       def __init__(     self ,				A_ ,				A_=13 ,				A_=32 ,				A_=2 ,				A_=3 ,				A_=16 ,				A_=[32, 64, 128] ,				A_=[1, 2, 1] ,				A_=[2, 2, 4] ,				A_=2 ,				A_=2.0 ,				A_=True ,				A_=0.0 ,				A_=0.0 ,				A_=0.1 ,				A_="gelu" ,				A_=False ,				A_=True ,				A_=0.02 ,				A_=1E-5 ,				A_=True ,				A_=None ,				A_=True ,				A_=10 ,				A_=8 ,				A_=["stage1", "stage2"] ,				A_=[1, 2] ,				)     ->	Any:





         """simple docstring"""




         _lowercase:			List[str]       =     parent
         _lowercase:			Dict       =     batch_size
         _lowercase:			Optional[Any]       =     image_size
         _lowercase:			int       =     patch_size
         _lowercase:			Union[str, Any]       =     num_channels
         _lowercase:			int       =     embed_dim
         _lowercase:			Optional[Any]       =     hidden_sizes
         _lowercase:			List[Any]       =     depths
         _lowercase:			List[Any]       =     num_heads
         _lowercase:			Dict       =     window_size
         _lowercase:			Dict       =     mlp_ratio
         _lowercase:			Union[str, Any]       =     qkv_bias
         _lowercase:			str       =     hidden_dropout_prob
         _lowercase:			Union[str, Any]       =     attention_probs_dropout_prob
         _lowercase:			List[Any]       =     drop_path_rate
         _lowercase:			Optional[Any]       =     hidden_act
         _lowercase:			Optional[Any]       =     use_absolute_embeddings
         _lowercase:			str       =     patch_norm
         _lowercase:			Union[str, Any]       =     layer_norm_eps
         _lowercase:			List[str]       =     initializer_range
         _lowercase:			str       =     is_training
         _lowercase:			List[Any]       =     scope
         _lowercase:			int       =     use_labels
         _lowercase:			Union[str, Any]       =     type_sequence_label_size
         _lowercase:			Optional[Any]       =     encoder_stride
         _lowercase:			Union[str, Any]       =     out_features
         _lowercase:			Tuple       =     out_indices
       def 							lowercase_							(     self       )     ->	List[str]:





         """simple docstring"""




         _lowercase:			Any       =     floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]       )

         _lowercase:			List[Any]       =     None
         if self.use_labels:
           _lowercase:			Dict       =     ids_tensor([self.batch_size] ,				self.type_sequence_label_size       )

         _lowercase:			Any       =     self.get_config()

         return config, pixel_values, labels
       def 							lowercase_							(     self       )     ->	int:





         """simple docstring"""




         return FocalNetConfig(
             image_size=self.image_size ,				patch_size=self.patch_size ,				num_channels=self.num_channels ,				embed_dim=self.embed_dim ,				hidden_sizes=self.hidden_sizes ,				depths=self.depths ,				num_heads=self.num_heads ,				window_size=self.window_size ,				mlp_ratio=self.mlp_ratio ,				qkv_bias=self.qkv_bias ,				hidden_dropout_prob=self.hidden_dropout_prob ,				attention_probs_dropout_prob=self.attention_probs_dropout_prob ,				drop_path_rate=self.drop_path_rate ,				hidden_act=self.hidden_act ,				use_absolute_embeddings=self.use_absolute_embeddings ,				path_norm=self.patch_norm ,				layer_norm_eps=self.layer_norm_eps ,				initializer_range=self.initializer_range ,				encoder_stride=self.encoder_stride ,				out_features=self.out_features ,				out_indices=self.out_indices ,				)
       def 							lowercase_							(     self ,				A_ ,				A_ ,				A_       )     ->	int:





         """simple docstring"""




         _lowercase:			Optional[int]       =     FocalNetModel(config=A_       )
         model.to(A_       )
         model.eval()
         _lowercase:			Tuple       =     model(A_       )

         _lowercase:			Optional[int]       =     ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths       ) - 1))
         _lowercase:			List[Any]       =     int(config.embed_dim * 2 ** (len(config.depths       ) - 1)       )

         self.parent.assertEqual(result.last_hidden_state.shape ,				(self.batch_size, expected_seq_len, expected_dim)       )
       def 							lowercase_							(     self ,				A_ ,				A_ ,				A_       )     ->	Dict:





         """simple docstring"""




         _lowercase:			Tuple       =     FocalNetBackbone(config=A_       )
         model.to(A_       )
         model.eval()
         _lowercase:			Union[str, Any]       =     model(A_       )

         # verify feature maps
         self.parent.assertEqual(len(result.feature_maps       ) ,				len(config.out_features       )       )
         self.parent.assertListEqual(list(result.feature_maps[0].shape       ) ,				[self.batch_size, self.image_size, 8, 8]       )

         # verify channels
         self.parent.assertEqual(len(model.channels       ) ,				len(config.out_features       )       )
         self.parent.assertListEqual(model.channels ,				config.hidden_sizes[:-1]       )

         # verify backbone works with out_features=None
         _lowercase:			Dict       =     None
         _lowercase:			Union[str, Any]       =     FocalNetBackbone(config=A_       )
         model.to(A_       )
         model.eval()
         _lowercase:			Tuple       =     model(A_       )

         # verify feature maps
         self.parent.assertEqual(len(result.feature_maps       ) ,				1       )
         self.parent.assertListEqual(list(result.feature_maps[0].shape       ) ,				[self.batch_size, self.image_size * 2, 4, 4]       )

         # verify channels
         self.parent.assertEqual(len(model.channels       ) ,				1       )
         self.parent.assertListEqual(model.channels ,				[config.hidden_sizes[-1]]       )
       def 							lowercase_							(     self ,				A_ ,				A_ ,				A_       )     ->	Optional[int]:





         """simple docstring"""




         _lowercase:			Dict       =     FocalNetForMaskedImageModeling(config=A_       )
         model.to(A_       )
         model.eval()
         _lowercase:			str       =     model(A_       )
         self.parent.assertEqual(
             result.reconstruction.shape ,				(self.batch_size, self.num_channels, self.image_size, self.image_size)       )

         # test greyscale images
         _lowercase:			List[Any]       =     1
         _lowercase:			Any       =     FocalNetForMaskedImageModeling(A_       )
         model.to(A_       )
         model.eval()

         _lowercase:			List[str]       =     floats_tensor([self.batch_size, 1, self.image_size, self.image_size]       )
         _lowercase:			Dict       =     model(A_       )
         self.parent.assertEqual(result.reconstruction.shape ,				(self.batch_size, 1, self.image_size, self.image_size)       )
       def 							lowercase_							(     self ,				A_ ,				A_ ,				A_       )     ->	str:





         """simple docstring"""




         _lowercase:			Dict       =     self.type_sequence_label_size
         _lowercase:			Tuple       =     FocalNetForImageClassification(A_       )
         model.to(A_       )
         model.eval()
         _lowercase:			Dict       =     model(A_ ,				labels=A_       )
         self.parent.assertEqual(result.logits.shape ,				(self.batch_size, self.type_sequence_label_size)       )

         # test greyscale images
         _lowercase:			List[str]       =     1
         _lowercase:			Tuple       =     FocalNetForImageClassification(A_       )
         model.to(A_       )
         model.eval()

         _lowercase:			List[str]       =     floats_tensor([self.batch_size, 1, self.image_size, self.image_size]       )
         _lowercase:			List[str]       =     model(A_       )
         self.parent.assertEqual(result.logits.shape ,				(self.batch_size, self.type_sequence_label_size)       )



       def 							lowercase_							(     self       )     ->	int:





         """simple docstring"""




         _lowercase:			Tuple       =     self.prepare_config_and_inputs()

         _lowercase ,				_lowercase ,				_lowercase:			Tuple       =     config_and_inputs
         _lowercase:			Optional[int]       =     {'''pixel_values''': pixel_values}
         return config, inputs_dict


@require_torch
class 			__magic_name__       (       SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__   ,					unittest.TestCase ):
       UpperCamelCase_   =		(
           (
               FocalNetModel,
               FocalNetForImageClassification,
               FocalNetForMaskedImageModeling,
               FocalNetBackbone,
           )
           if is_torch_available()
           else ()
       )
       UpperCamelCase_   =		(
           {'''feature-extraction''': FocalNetModel, '''image-classification''': FocalNetForImageClassification}
           if is_torch_available()
           else {}
       )
       UpperCamelCase_   =		False

       UpperCamelCase_   =		False
       UpperCamelCase_   =		False
       UpperCamelCase_   =		False
       UpperCamelCase_   =		False
       def 							lowercase_							(     self       )     ->	str:





         """simple docstring"""




         _lowercase:			List[Any]       =     FocalNetModelTester(self       )
         _lowercase:			Tuple       =     ConfigTester(self ,				config_class=A_ ,				embed_dim=37 ,				has_text_modality=A_       )
       def 							lowercase_							(     self       )     ->	Union[str, Any]:





         """simple docstring"""




         self.create_and_test_config_common_properties()
         self.config_tester.create_and_test_config_to_json_string()
         self.config_tester.create_and_test_config_to_json_file()
         self.config_tester.create_and_test_config_from_and_save_pretrained()
         self.config_tester.create_and_test_config_with_num_labels()
         self.config_tester.check_config_can_be_init_without_params()
         self.config_tester.check_config_arguments_init()
       def 							lowercase_							(     self       )     ->	Dict:





         """simple docstring"""




         return
       def 							lowercase_							(     self       )     ->	str:





         """simple docstring"""




         _lowercase:			str       =     self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*A_       )
       def 							lowercase_							(     self       )     ->	List[str]:





         """simple docstring"""




         _lowercase:			Optional[Any]       =     self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_backbone(*A_       )
       def 							lowercase_							(     self       )     ->	List[Any]:





         """simple docstring"""




         _lowercase:			Any       =     self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_masked_image_modeling(*A_       )
       def 							lowercase_							(     self       )     ->	List[Any]:





         """simple docstring"""




         _lowercase:			List[Any]       =     self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_image_classification(*A_       )
       @unittest.skip(reason='''FocalNet does not use inputs_embeds'''       )
       def 							lowercase_							(     self       )     ->	Tuple:





         """simple docstring"""




         pass
       @unittest.skip(reason='''FocalNet does not use feedforward chunking'''       )
       def 							lowercase_							(     self       )     ->	Any:





         """simple docstring"""




         pass
       def 							lowercase_							(     self       )     ->	List[str]:





         """simple docstring"""




         _lowercase ,				_lowercase:			List[Any]       =     self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes[:-1]:
           _lowercase:			List[str]       =     model_class(A_       )
           self.assertIsInstance(model.get_input_embeddings() ,				(nn.Module)       )
           _lowercase:			Any       =     model.get_output_embeddings()
           self.assertTrue(x is None or isinstance(A_ ,				nn.Linear       )       )
       def 							lowercase_							(     self       )     ->	Dict:





         """simple docstring"""




         _lowercase ,				_lowercase:			str       =     self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes[:-1]:
           _lowercase:			str       =     model_class(A_       )
           _lowercase:			List[Any]       =     inspect.signature(model.forward       )
           # signature.parameters is an OrderedDict => so arg_names order is deterministic
           _lowercase:			Optional[Any]       =     [*signature.parameters.keys()]

           _lowercase:			Optional[int]       =     ['''pixel_values''']
           self.assertListEqual(arg_names[:1] ,				A_       )
       def 							lowercase_							(     self ,				A_ ,				A_ ,				A_ ,				A_       )     ->	List[Any]:





         """simple docstring"""




         _lowercase:			Tuple       =     model_class(A_       )
         model.to(A_       )
         model.eval()

         with torch.no_grad():
           _lowercase:			Optional[Any]       =     model(**self._prepare_for_class(A_ ,				A_       )       )

         _lowercase:			Optional[Any]       =     outputs.hidden_states

         _lowercase:			List[str]       =     getattr(
             self.model_tester ,				'''expected_num_hidden_layers''' ,				len(self.model_tester.depths       ) + 1       )
         self.assertEqual(len(A_       ) ,				A_       )

         # FocalNet has a different seq_length
         _lowercase:			int       =     (
             config.patch_size
             if isinstance(config.patch_size ,				collections.abc.Iterable       )
             else (config.patch_size, config.patch_size)
         )

         _lowercase:			Union[str, Any]       =     (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])

         self.assertListEqual(
             list(hidden_states[0].shape[-2:]       ) ,				[num_patches, self.model_tester.embed_dim] ,				)

         _lowercase:			Optional[Any]       =     outputs.reshaped_hidden_states
         self.assertEqual(len(A_       ) ,				A_       )

         _lowercase ,				_lowercase ,				_lowercase ,				_lowercase:			Optional[int]       =     reshaped_hidden_states[0].shape
         _lowercase:			Tuple       =     (
             reshaped_hidden_states[0].view(A_ ,				A_ ,				height * width       ).permute(0 ,				2 ,				1       )
         )
         self.assertListEqual(
             list(reshaped_hidden_states.shape[-2:]       ) ,				[num_patches, self.model_tester.embed_dim] ,				)
       def 							lowercase_							(     self       )     ->	Optional[Any]:





         """simple docstring"""




         _lowercase ,				_lowercase:			Dict       =     self.model_tester.prepare_config_and_inputs_for_common()

         _lowercase:			List[Any]       =     (
             self.model_tester.image_size
             if isinstance(self.model_tester.image_size ,				collections.abc.Iterable       )
             else (self.model_tester.image_size, self.model_tester.image_size)
         )

         for model_class in self.all_model_classes[:-1]:
           _lowercase:			List[str]       =     True
           self.check_hidden_states_output(A_ ,				A_ ,				A_ ,				A_       )

           # check that output_hidden_states also work using config
           del inputs_dict["output_hidden_states"]
           _lowercase:			str       =     True

           self.check_hidden_states_output(A_ ,				A_ ,				A_ ,				A_       )
       def 							lowercase_							(     self       )     ->	Any:





         """simple docstring"""




         _lowercase ,				_lowercase:			Any       =     self.model_tester.prepare_config_and_inputs_for_common()
         _lowercase:			Optional[Any]       =     3

         _lowercase:			Optional[int]       =     (
             self.model_tester.image_size
             if isinstance(self.model_tester.image_size ,				collections.abc.Iterable       )
             else (self.model_tester.image_size, self.model_tester.image_size)
         )
         _lowercase:			Union[str, Any]       =     (
             config.patch_size
             if isinstance(config.patch_size ,				collections.abc.Iterable       )
             else (config.patch_size, config.patch_size)
         )

         _lowercase:			Union[str, Any]       =     image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
         _lowercase:			Tuple       =     image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

         for model_class in self.all_model_classes[:-1]:
           _lowercase:			str       =     True
           self.check_hidden_states_output(A_ ,				A_ ,				A_ ,				(padded_height, padded_width)       )

           # check that output_hidden_states also work using config
           del inputs_dict["output_hidden_states"]
           _lowercase:			List[str]       =     True
           self.check_hidden_states_output(A_ ,				A_ ,				A_ ,				(padded_height, padded_width)       )
       @slow
       def 							lowercase_							(     self       )     ->	List[str]:





         """simple docstring"""




         for model_name in FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           _lowercase:			Optional[Any]       =     FocalNetModel.from_pretrained(A_       )
           self.assertIsNotNone(A_       )



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





         """simple docstring"""




         _lowercase ,				_lowercase:			Tuple       =     self.model_tester.prepare_config_and_inputs_for_common()

         _lowercase:			List[str]       =     _config_zero_init(A_       )
         for model_class in self.all_model_classes:
           _lowercase:			Any       =     model_class(config=A_       )
           for name, param in model.named_parameters():
             if "embeddings" not in name and param.requires_grad:
               self.assertIn(
                   ((param.data.mean() * 1E9).round() / 1E9).item() ,				[0.0, 1.0] ,				msg=f'''Parameter {name} of model {model_class} seems not properly initialized''' ,				)


@require_vision
@require_torch
class 			__magic_name__       (       unittest.TestCase ):
       @cached_property
       def 							lowercase_							(     self       )     ->	Dict:





         """simple docstring"""




         return AutoImageProcessor.from_pretrained('''microsoft/focalnet-tiny'''       ) if is_vision_available() else None



       @slow
       def 							lowercase_							(     self       )     ->	Optional[int]:





         """simple docstring"""




         _lowercase:			Optional[Any]       =     FocalNetForImageClassification.from_pretrained('''microsoft/focalnet-tiny'''       ).to(A_       )
         _lowercase:			Optional[int]       =     self.default_image_processor

         _lowercase:			Optional[Any]       =     Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''       )
         _lowercase:			List[str]       =     image_processor(images=A_ ,				return_tensors='''pt'''       ).to(A_       )

         # forward pass
         with torch.no_grad():
           _lowercase:			str       =     model(**A_       )

         # verify the logits
         _lowercase:			Any       =     torch.Size((1, 1000)       )
         self.assertEqual(outputs.logits.shape ,				A_       )
         _lowercase:			str       =     torch.tensor([0.21_66, -0.43_68, 0.21_91]       ).to(A_       )
         self.assertTrue(torch.allclose(outputs.logits[0, :3] ,				A_ ,				atol=1E-4       )       )
         self.assertTrue(outputs.logits.argmax(dim=-1       ).item() ,				281       )


@require_torch
class 			__magic_name__       (       SCREAMING_SNAKE_CASE__   ,					unittest.TestCase ):
       UpperCamelCase_   =		(FocalNetBackbone,) if is_torch_available() else ()
       UpperCamelCase_   =		FocalNetConfig

       UpperCamelCase_   =		False
       def 							lowercase_							(     self       )     ->	int:





         """simple docstring"""




         _lowercase:			int       =     FocalNetModelTester(self       )


"""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__   :					List[Any]							=   datasets.utils.logging.get_logger(__name__)


@dataclass
class 			__magic_name__       (       datasets.BuilderConfig ):
       UpperCamelCase_   =		None
       UpperCamelCase_   =		"utf-8"
       UpperCamelCase_   =		None
       UpperCamelCase_   =		None
       UpperCamelCase_   =		True  # deprecated
       UpperCamelCase_   =		None  # deprecated
       UpperCamelCase_   =		10 << 20  # 10MB
       UpperCamelCase_   =		None


class 			__magic_name__       (       datasets.ArrowBasedBuilder ):
       UpperCamelCase_   =		JsonConfig
       def 							lowercase_							(     self       )     ->	str:





         """simple docstring"""




         if self.config.block_size is not None:
           logger.warning('''The JSON loader parameter `block_size` is deprecated. Please use `chunksize` instead'''       )
           _lowercase:			List[str]       =     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 							lowercase_							(     self ,				A_       )     ->	Any:





         """simple docstring"""




         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:			int       =     dl_manager.download_and_extract(self.config.data_files       )
         if isinstance(A_ ,				(str, list, tuple)       ):
           _lowercase:			Tuple       =     data_files
           if isinstance(A_ ,				A_       ):
             _lowercase:			Optional[Any]       =     [files]
           _lowercase:			Dict       =     [dl_manager.iter_files(A_       ) 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(A_ ,				A_       ):
             _lowercase:			Optional[Any]       =     [files]
           _lowercase:			str       =     [dl_manager.iter_files(A_       ) for file in files]
           splits.append(datasets.SplitGenerator(name=A_ ,				gen_kwargs={'''files''': files}       )       )
         return splits
       def 							lowercase_							(     self ,				A_       )     ->	pa.Table:





         """simple docstring"""




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



       def 							lowercase_							(     self ,				A_       )     ->	str:





         """simple docstring"""




         for file_idx, file in enumerate(itertools.chain.from_iterable(A_       )       ):
           # 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(A_ ,				encoding=self.config.encoding ,				errors=self.config.encoding_errors       ) as f:
               _lowercase:			Optional[int]       =     json.load(A_       )

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

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

           # If the file has one json object per line
           else:
             with open(A_ ,				'''rb'''       ) as f:
               _lowercase:			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:			Optional[int]       =     max(self.config.chunksize // 32 ,				16 << 10       )
               _lowercase:			List[Any]       =     (
                   self.config.encoding_errors if self.config.encoding_errors is not None else '''strict'''
               )
               while True:
                 _lowercase:			Union[str, Any]       =     f.read(self.config.chunksize       )
                 if not batch:
                   break
                 # Finish current line
                 try:
                   batch += f.readline()
                 except (AttributeError, io.UnsupportedOperation):
                   batch += readline(A_       )
                 # PyArrow only accepts utf-8 encoded bytes
                 if self.config.encoding != "utf-8":
                   _lowercase:			Any       =     batch.decode(self.config.encoding ,				errors=A_       ).encode('''utf-8'''       )
                 try:
                   while True:
                     try:
                       _lowercase:			Optional[int]       =     paj.read_json(
                           io.BytesIO(A_       ) ,				read_options=paj.ReadOptions(block_size=A_       )       )
                       break
                     except (pa.ArrowInvalid, pa.ArrowNotImplementedError) as e:
                       if (
                           isinstance(A_ ,				pa.ArrowInvalid       )
                           and "straddling" not in str(A_       )
                           or block_size > len(A_       )
                       ):
                         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(A_       )} 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(
                         A_ ,				encoding=self.config.encoding ,				errors=self.config.encoding_errors       ) as f:
                       _lowercase:			Optional[Any]       =     json.load(A_       )
                   except json.JSONDecodeError:
                     logger.error(f'''Failed to read file \'{file}\' with error {type(A_       )}: {e}'''       )
                     raise e
                   # If possible, parse the file as a list of json objects and exit the loop
                   if isinstance(A_ ,				A_       ):  # list is the only sequence type supported in JSON
                     try:
                       _lowercase:			Optional[int]       =     set().union(*[row.keys() for row in dataset]       )
                       _lowercase:			Tuple       =     {col: [row.get(A_       ) for row in dataset] for col in keys}
                       _lowercase:			str       =     pa.Table.from_pydict(A_       )
                     except (pa.ArrowInvalid, AttributeError) as e:
                       logger.error(f'''Failed to read file \'{file}\' with error {type(A_       )}: {e}'''       )
                       raise ValueError(f'''Not able to read records in the JSON file at {file}.'''       ) from None
                     yield file_idx, self._cast_table(A_       )
                     break
                   else:
                     logger.error(f'''Failed to read file \'{file}\' with error {type(A_       )}: {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(A_       )
                 batch_idx += 1
