
"""simple docstring"""






import copy
import os
import tempfile
from unittest import TestCase
from unittest.mock import patch

import numpy as np
import pyarrow as pa
import pyarrow.parquet as pq
import pytest

from datasets.arrow_writer import ArrowWriter, OptimizedTypedSequence, ParquetWriter, TypedSequence
from datasets.features import ArrayaD, ClassLabel, Features, Image, Value
from datasets.features.features import ArrayaDExtensionType, cast_to_python_objects
from datasets.keyhash import DuplicatedKeysError, InvalidKeyError

from .utils import require_pil





class 				a__					(						A__       ):



     def  lowerCamelCase_						(							self :str   ):







      '''simple docstring'''



      UpperCamelCase_     :					List[Any]   	=pa.array(TypedSequence([1, 2, 3]   )   )
      self.assertEqual(arr.type      ,  pa.intaa()   )



     def  lowerCamelCase_						(							self :str   ):







      '''simple docstring'''



      with self.assertRaises(_lowerCamelCase   ):
       UpperCamelCase_     :					Dict   	=pa.array(TypedSequence([1, 2, 3]   )      ,  type=pa.intaa()   )



     def  lowerCamelCase_						(							self :Optional[Any]   ):







      '''simple docstring'''



      with self.assertRaises(_lowerCamelCase   ):
       UpperCamelCase_     :					Optional[int]   	=pa.array(TypedSequence([1, 2, 3]      ,  try_type=Value('bool'   )      ,  type=Value('int64'   )   )   )



     def  lowerCamelCase_						(							self :Optional[Any]   ):







      '''simple docstring'''



      UpperCamelCase_     :					List[Any]   	=pa.array(TypedSequence([1, 2, 3]      ,  type=Value('int32'   )   )   )
      self.assertEqual(arr.type      ,  pa.intaa()   )



     def  lowerCamelCase_						(							self :str   ):







      '''simple docstring'''



      with self.assertRaises((TypeError, pa.lib.ArrowInvalid)   ):
       UpperCamelCase_     :					Optional[int]   	=pa.array(TypedSequence(['foo', 'bar']      ,  type=Value('int64'   )   )   )



     def  lowerCamelCase_						(							self :List[str]   ):







      '''simple docstring'''



      UpperCamelCase_     :					Optional[int]   	=pa.array(TypedSequence([1, 2, 3]      ,  try_type=Value('int32'   )   )   )
      self.assertEqual(arr.type      ,  pa.intaa()   )



     def  lowerCamelCase_						(							self :Optional[Any]   ):







      '''simple docstring'''



      UpperCamelCase_     :					int   	=pa.array(TypedSequence(['foo', 'bar']      ,  try_type=Value('int64'   )   )   )
      self.assertEqual(arr.type      ,  pa.string()   )



     def  lowerCamelCase_						(							self :List[Any]   ):







      '''simple docstring'''



      UpperCamelCase_     :					List[str]   	=pa.array(TypedSequence([[[1, 2, 3]]]      ,  type=ArrayaD((1, 3)      ,  'int64'   )   )   )
      self.assertEqual(arr.type      ,  ArrayaDExtensionType((1, 3)      ,  'int64'   )   )



     def  lowerCamelCase_						(							self :Optional[int]   ):







      '''simple docstring'''



      with self.assertRaises((TypeError, pa.lib.ArrowInvalid)   ):
       UpperCamelCase_     :					Any   	=pa.array(TypedSequence(['foo', 'bar']      ,  type=ArrayaD((1, 3)      ,  'int64'   )   )   )



     def  lowerCamelCase_						(							self :Dict   ):







      '''simple docstring'''



      UpperCamelCase_     :					List[Any]   	=pa.array(TypedSequence([[[1, 2, 3]]]      ,  try_type=ArrayaD((1, 3)      ,  'int64'   )   )   )
      self.assertEqual(arr.type      ,  ArrayaDExtensionType((1, 3)      ,  'int64'   )   )



     def  lowerCamelCase_						(							self :List[str]   ):







      '''simple docstring'''



      UpperCamelCase_     :					List[str]   	=pa.array(TypedSequence(['foo', 'bar']      ,  try_type=ArrayaD((1, 3)      ,  'int64'   )   )   )
      self.assertEqual(arr.type      ,  pa.string()   )




     @require_pil
     def  lowerCamelCase_						(							self :Dict   ):







      '''simple docstring'''



      import PIL.Image

      UpperCamelCase_     :					List[Any]   	=PIL.Image.fromarray(np.arange(10      ,  dtype=np.uinta   ).reshape(2      ,  5   )   )
      with patch(
          'datasets.arrow_writer.cast_to_python_objects'      ,  side_effect=_lowerCamelCase   ) as mock_cast_to_python_objects:
       UpperCamelCase_     :					Dict   	=pa.array(TypedSequence([{'path': None, 'bytes': b'image_bytes'}, pil_image]      ,  type=Image()   )   )
       UpperCamelCase_	,     UpperCamelCase_     :					Optional[Any]   	=mock_cast_to_python_objects.call_args_list[-1]
       self.assertIn('optimize_list_casting'      ,  _lowerCamelCase   )
       self.assertFalse(kwargs['optimize_list_casting']   )




def       A_  (				__lowercase						,	__lowercase	):
 UpperCamelCase_     :					int   	=pa.BufferReader(__lowercase	) if isinstance(__lowercase						,	pa.Buffer	) else pa.memory_map(__lowercase	)
 UpperCamelCase_     :					int   	=pa.ipc.open_stream(__lowercase	)
 UpperCamelCase_     :					pa.Table   	=f.read_all()
 assert len(pa_table.to_batches()	) == expected_num_chunks
 assert pa_table.to_pydict() == {"col_1": ["foo", "bar"], "col_2": [1, 2]}
 del pa_table




@pytest.mark.parametrize('writer_batch_size'						,	[None, 1, 10]	)
@pytest.mark.parametrize(
    'fields'						,	[None, {'col_1': pa.string(), 'col_2': pa.intaa()}, {'col_1': pa.string(), 'col_2': pa.intaa()}]	)
def       A_  (				__lowercase						,	__lowercase	):
 UpperCamelCase_     :					Any   	=pa.BufferOutputStream()
 UpperCamelCase_     :					Optional[int]   	=pa.schema(__lowercase	) if fields else None
 with ArrowWriter(stream=__lowercase						,	schema=__lowercase						,	writer_batch_size=__lowercase	) as writer:
  writer.write({'col_1': 'foo', 'col_2': 1}	)
  writer.write({'col_1': 'bar', 'col_2': 2}	)
  UpperCamelCase_	,     UpperCamelCase_     :					int   	=writer.finalize()
 assert num_examples == 2
 assert num_bytes > 0
 if not fields:
  UpperCamelCase_     :					Any   	={'col_1': pa.string(), 'col_2': pa.intaa()}
 assert writer._schema == pa.schema(__lowercase						,	metadata=writer._schema.metadata	)
 _check_output(output.getvalue()						,	expected_num_chunks=num_examples if writer_batch_size == 1 else 1	)




def       A_  (				):
 UpperCamelCase_     :					List[str]   	=pa.BufferOutputStream()
 UpperCamelCase_     :					Any   	=Features({'labels': ClassLabel(names=['neg', 'pos']	)}	)
 with ArrowWriter(stream=__lowercase						,	features=__lowercase	) as writer:
  writer.write({'labels': 0}	)
  writer.write({'labels': 1}	)
  UpperCamelCase_	,     UpperCamelCase_     :					Union[str, Any]   	=writer.finalize()
 assert num_examples == 2
 assert num_bytes > 0
 assert writer._schema == features.arrow_schema
 assert writer._schema.metadata == features.arrow_schema.metadata
 UpperCamelCase_     :					Optional[Any]   	=pa.BufferReader(output.getvalue()	)
 UpperCamelCase_     :					Dict   	=pa.ipc.open_stream(__lowercase	)
 UpperCamelCase_     :					pa.Table   	=f.read_all()
 UpperCamelCase_     :					Tuple   	=pa_table.schema
 assert pa_table.num_rows == 2
 assert schema == features.arrow_schema
 assert schema.metadata == features.arrow_schema.metadata
 assert features == Features.from_arrow_schema(__lowercase	)




@pytest.mark.parametrize('writer_batch_size'						,	[None, 1, 10]	)
def       A_  (				__lowercase	):
 UpperCamelCase_     :					Dict   	=pa.BufferOutputStream()
 with ArrowWriter(
     stream=__lowercase						,	writer_batch_size=__lowercase						,	hash_salt='split_name'						,	check_duplicates=__lowercase						,	) as writer:
  with pytest.raises(__lowercase	):
   writer.write({'col_1': 'foo', 'col_2': 1}						,	key=[1, 2]	)
   UpperCamelCase_	,     UpperCamelCase_     :					List[str]   	=writer.finalize()




@pytest.mark.parametrize('writer_batch_size'						,	[None, 2, 10]	)
def       A_  (				__lowercase	):
 UpperCamelCase_     :					Optional[int]   	=pa.BufferOutputStream()
 with ArrowWriter(
     stream=__lowercase						,	writer_batch_size=__lowercase						,	hash_salt='split_name'						,	check_duplicates=__lowercase						,	) as writer:
  with pytest.raises(__lowercase	):
   writer.write({'col_1': 'foo', 'col_2': 1}						,	key=10	)
   writer.write({'col_1': 'bar', 'col_2': 2}						,	key=10	)
   UpperCamelCase_	,     UpperCamelCase_     :					Tuple   	=writer.finalize()




@pytest.mark.parametrize('writer_batch_size'						,	[None, 2, 10]	)
def       A_  (				__lowercase	):
 UpperCamelCase_     :					Optional[int]   	=pa.BufferOutputStream()
 with ArrowWriter(
     stream=__lowercase						,	writer_batch_size=__lowercase						,	hash_salt='split_name'						,	check_duplicates=__lowercase						,	) as writer:
  writer.write({'col_1': 'foo', 'col_2': 1}						,	key=1	)
  writer.write({'col_1': 'bar', 'col_2': 2}						,	key=2	)
  UpperCamelCase_	,     UpperCamelCase_     :					int   	=writer.finalize()
 assert num_examples == 2
 assert num_bytes > 0
 _check_output(output.getvalue()						,	expected_num_chunks=num_examples if writer_batch_size == 1 else 1	)




@pytest.mark.parametrize('writer_batch_size'						,	[None, 1, 10]	)
@pytest.mark.parametrize(
    'fields'						,	[None, {'col_1': pa.string(), 'col_2': pa.intaa()}, {'col_1': pa.string(), 'col_2': pa.intaa()}]	)
def       A_  (				__lowercase						,	__lowercase	):
 UpperCamelCase_     :					Optional[int]   	=pa.BufferOutputStream()
 UpperCamelCase_     :					List[str]   	=pa.schema(__lowercase	) if fields else None
 with ArrowWriter(stream=__lowercase						,	schema=__lowercase						,	writer_batch_size=__lowercase	) as writer:
  writer.write_batch({'col_1': ['foo', 'bar'], 'col_2': [1, 2]}	)
  writer.write_batch({'col_1': [], 'col_2': []}	)
  UpperCamelCase_	,     UpperCamelCase_     :					List[str]   	=writer.finalize()
 assert num_examples == 2
 assert num_bytes > 0
 if not fields:
  UpperCamelCase_     :					Union[str, Any]   	={'col_1': pa.string(), 'col_2': pa.intaa()}
 assert writer._schema == pa.schema(__lowercase						,	metadata=writer._schema.metadata	)
 _check_output(output.getvalue()						,	expected_num_chunks=num_examples if writer_batch_size == 1 else 1	)




@pytest.mark.parametrize('writer_batch_size'						,	[None, 1, 10]	)
@pytest.mark.parametrize(
    'fields'						,	[None, {'col_1': pa.string(), 'col_2': pa.intaa()}, {'col_1': pa.string(), 'col_2': pa.intaa()}]	)
def       A_  (				__lowercase						,	__lowercase	):
 UpperCamelCase_     :					List[str]   	=pa.BufferOutputStream()
 UpperCamelCase_     :					List[Any]   	=pa.schema(__lowercase	) if fields else None
 with ArrowWriter(stream=__lowercase						,	schema=__lowercase						,	writer_batch_size=__lowercase	) as writer:
  writer.write_table(pa.Table.from_pydict({'col_1': ['foo', 'bar'], 'col_2': [1, 2]}	)	)
  UpperCamelCase_	,     UpperCamelCase_     :					str   	=writer.finalize()
 assert num_examples == 2
 assert num_bytes > 0
 if not fields:
  UpperCamelCase_     :					List[str]   	={'col_1': pa.string(), 'col_2': pa.intaa()}
 assert writer._schema == pa.schema(__lowercase						,	metadata=writer._schema.metadata	)
 _check_output(output.getvalue()						,	expected_num_chunks=num_examples if writer_batch_size == 1 else 1	)




@pytest.mark.parametrize('writer_batch_size'						,	[None, 1, 10]	)
@pytest.mark.parametrize(
    'fields'						,	[None, {'col_1': pa.string(), 'col_2': pa.intaa()}, {'col_1': pa.string(), 'col_2': pa.intaa()}]	)
def       A_  (				__lowercase						,	__lowercase	):
 UpperCamelCase_     :					Optional[int]   	=pa.BufferOutputStream()
 UpperCamelCase_     :					Optional[Any]   	=pa.schema(__lowercase	) if fields else None
 with ArrowWriter(stream=__lowercase						,	schema=__lowercase						,	writer_batch_size=__lowercase	) as writer:
  writer.write_row(pa.Table.from_pydict({'col_1': ['foo'], 'col_2': [1]}	)	)
  writer.write_row(pa.Table.from_pydict({'col_1': ['bar'], 'col_2': [2]}	)	)
  UpperCamelCase_	,     UpperCamelCase_     :					int   	=writer.finalize()
 assert num_examples == 2
 assert num_bytes > 0
 if not fields:
  UpperCamelCase_     :					int   	={'col_1': pa.string(), 'col_2': pa.intaa()}
 assert writer._schema == pa.schema(__lowercase						,	metadata=writer._schema.metadata	)
 _check_output(output.getvalue()						,	expected_num_chunks=num_examples if writer_batch_size == 1 else 1	)




def       A_  (				):
 with tempfile.TemporaryDirectory() as tmp_dir:
  UpperCamelCase_     :					Dict   	={'col_1': pa.string(), 'col_2': pa.intaa()}
  UpperCamelCase_     :					Optional[int]   	=os.path.join(__lowercase						,	'test.arrow'	)
  with ArrowWriter(path=__lowercase						,	schema=pa.schema(__lowercase	)	) as writer:
   writer.write_batch({'col_1': ['foo', 'bar'], 'col_2': [1, 2]}	)
   UpperCamelCase_	,     UpperCamelCase_     :					int   	=writer.finalize()
  assert num_examples == 2
  assert num_bytes > 0
  assert writer._schema == pa.schema(__lowercase						,	metadata=writer._schema.metadata	)
  _check_output(__lowercase						,	1	)




def       A_  (				__lowercase	):
 if pa.types.is_list(__lowercase	):
  return get_base_dtype(arr_type.value_type	)
 else:
  return arr_type




def       A_  (				__lowercase						,	__lowercase	):
 if isinstance(lst[0]						,	__lowercase	):
  change_first_primitive_element_in_list(lst[0]						,	__lowercase	)
 else:
  UpperCamelCase_     :					Optional[int]   	=value




@pytest.mark.parametrize('optimized_int_type, expected_dtype'						,	[(None, pa.intaa()), (Value('int32'	), pa.intaa())]	)
@pytest.mark.parametrize('sequence'						,	[[1, 2, 3], [[1, 2, 3]], [[[1, 2, 3]]]]	)
def       A_  (				__lowercase						,	__lowercase						,	__lowercase	):
 UpperCamelCase_     :					Dict   	=pa.array(TypedSequence(__lowercase						,	optimized_int_type=__lowercase	)	)
 assert get_base_dtype(arr.type	) == expected_dtype




@pytest.mark.parametrize(
    'col, expected_dtype'						,	[
        ('attention_mask', pa.inta()),
        ('special_tokens_mask', pa.inta()),
        ('token_type_ids', pa.inta()),
        ('input_ids', pa.intaa()),
        ('other', pa.intaa()),
    ]						,	)
@pytest.mark.parametrize('sequence'						,	[[1, 2, 3], [[1, 2, 3]], [[[1, 2, 3]]]]	)
def       A_  (				__lowercase						,	__lowercase						,	__lowercase	):
 # in range
 UpperCamelCase_     :					Optional[int]   	=pa.array(OptimizedTypedSequence(__lowercase						,	col=__lowercase	)	)
 assert get_base_dtype(arr.type	) == expected_dtype

 # not in range
 if col != "other":
  # avoids errors due to in-place modifications
  UpperCamelCase_     :					Optional[Any]   	=copy.deepcopy(__lowercase	)
  UpperCamelCase_     :					Optional[int]   	=np.iinfo(expected_dtype.to_pandas_dtype()	).max + 1
  change_first_primitive_element_in_list(__lowercase						,	__lowercase	)
  UpperCamelCase_     :					Optional[int]   	=pa.array(OptimizedTypedSequence(__lowercase						,	col=__lowercase	)	)
  assert get_base_dtype(arr.type	) == pa.intaa()




@pytest.mark.parametrize('raise_exception'						,	[False, True]	)
def       A_  (				__lowercase						,	__lowercase	):
 UpperCamelCase_     :					Union[str, Any]   	=str(tmp_path / 'dataset-train.arrow'	)
 try:
  with ArrowWriter(path=__lowercase	) as writer:
   if raise_exception:
    raise pa.lib.ArrowInvalid()
   else:
    writer.stream.close()
 except pa.lib.ArrowInvalid:
  pass
 finally:
  assert writer.stream.closed




def       A_  (				__lowercase	):
 UpperCamelCase_     :					Dict   	='mock://dataset-train.arrow'
 with ArrowWriter(path=__lowercase						,	storage_options=mockfs.storage_options	) as writer:
  assert isinstance(writer._fs						,	type(__lowercase	)	)
  assert writer._fs.storage_options == mockfs.storage_options
  writer.write({'col_1': 'foo', 'col_2': 1}	)
  writer.write({'col_1': 'bar', 'col_2': 2}	)
  UpperCamelCase_	,     UpperCamelCase_     :					Tuple   	=writer.finalize()
 assert num_examples == 2
 assert num_bytes > 0
 assert mockfs.exists(__lowercase	)




def       A_  (				):
 UpperCamelCase_     :					Dict   	=pa.BufferOutputStream()
 with ParquetWriter(stream=__lowercase	) as writer:
  writer.write({'col_1': 'foo', 'col_2': 1}	)
  writer.write({'col_1': 'bar', 'col_2': 2}	)
  UpperCamelCase_	,     UpperCamelCase_     :					List[str]   	=writer.finalize()
 assert num_examples == 2
 assert num_bytes > 0
 UpperCamelCase_     :					List[Any]   	=pa.BufferReader(output.getvalue()	)
 UpperCamelCase_     :					pa.Table   	=pq.read_table(__lowercase	)
 assert pa_table.to_pydict() == {"col_1": ["foo", "bar"], "col_2": [1, 2]}




@require_pil
@pytest.mark.parametrize('embed_local_files'						,	[False, True]	)
def       A_  (				__lowercase						,	__lowercase	):
 import PIL.Image

 UpperCamelCase_     :					Any   	=str(tmp_path / 'test_image_rgb.jpg'	)
 PIL.Image.fromarray(np.zeros((5, 5)						,	dtype=np.uinta	)	).save(__lowercase						,	format='png'	)
 UpperCamelCase_     :					Union[str, Any]   	=pa.BufferOutputStream()
 with ParquetWriter(
     stream=__lowercase						,	features=Features({'image': Image()}	)						,	embed_local_files=__lowercase	) as writer:
  writer.write({'image': image_path}	)
  writer.finalize()
 UpperCamelCase_     :					List[Any]   	=pa.BufferReader(output.getvalue()	)
 UpperCamelCase_     :					pa.Table   	=pq.read_table(__lowercase	)
 UpperCamelCase_     :					Dict   	=pa_table.to_pydict()
 if embed_local_files:
  assert isinstance(out['image'][0]['path']						,	__lowercase	)
  with open(__lowercase						,	'rb'	) as f:
   assert out["image"][0]["bytes"] == f.read()
 else:
  assert out["image"][0]["path"] == image_path
  assert out["image"][0]["bytes"] is None





def       A_  (				):
 UpperCamelCase_     :					Union[str, Any]   	=pa.schema([pa.field('col_1'						,	pa.string()						,	nullable=__lowercase	)]	)
 UpperCamelCase_     :					str   	=pa.BufferOutputStream()
 with ArrowWriter(stream=__lowercase	) as writer:
  writer._build_writer(inferred_schema=__lowercase	)
  assert writer._schema == pa.schema([pa.field('col_1'						,	pa.string()	)]	)


"""simple docstring"""






from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


__SCREAMING_SNAKE_CASE							=     logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE							=     {
    'microsoft/focalnet-tiny': 'https://huggingface.co/microsoft/focalnet-tiny/resolve/main/config.json',
}





class 				a__					(						A__   ,							A__       ):
     UpperCAmelCase__         =      '''focalnet'''



     def __init__(							self :Any      ,  _lowerCamelCase :List[Any]=224      ,  _lowerCamelCase :str=4      ,  _lowerCamelCase :List[str]=3      ,  _lowerCamelCase :Optional[Any]=96      ,  _lowerCamelCase :Dict=False      ,  _lowerCamelCase :Optional[Any]=[192, 384, 768, 768]      ,  _lowerCamelCase :Tuple=[2, 2, 6, 2]      ,  _lowerCamelCase :List[Any]=[2, 2, 2, 2]      ,  _lowerCamelCase :Optional[int]=[3, 3, 3, 3]      ,  _lowerCamelCase :Optional[Any]="gelu"      ,  _lowerCamelCase :Tuple=4.0      ,  _lowerCamelCase :Optional[Any]=0.0      ,  _lowerCamelCase :int=0.1      ,  _lowerCamelCase :List[str]=False      ,  _lowerCamelCase :str=1E-4      ,  _lowerCamelCase :Optional[int]=False      ,  _lowerCamelCase :List[str]=False      ,  _lowerCamelCase :str=False      ,  _lowerCamelCase :Optional[int]=0.02      ,  _lowerCamelCase :int=1E-5      ,  _lowerCamelCase :Tuple=32      ,  _lowerCamelCase :List[str]=None      ,  _lowerCamelCase :Dict=None      ,  **_lowerCamelCase :List[Any]      ,  ):







      '''simple docstring'''



      super().__init__(**_lowerCamelCase   )

      UpperCamelCase_     :					Any   	=image_size
      UpperCamelCase_     :					int   	=patch_size
      UpperCamelCase_     :					int   	=num_channels
      UpperCamelCase_     :					Union[str, Any]   	=embed_dim
      UpperCamelCase_     :					int   	=use_conv_embed
      UpperCamelCase_     :					Optional[int]   	=hidden_sizes
      UpperCamelCase_     :					str   	=depths
      UpperCamelCase_     :					Any   	=focal_levels
      UpperCamelCase_     :					List[str]   	=focal_windows
      UpperCamelCase_     :					str   	=hidden_act
      UpperCamelCase_     :					Dict   	=mlp_ratio
      UpperCamelCase_     :					List[str]   	=hidden_dropout_prob
      UpperCamelCase_     :					Optional[int]   	=drop_path_rate
      UpperCamelCase_     :					str   	=use_layerscale
      UpperCamelCase_     :					List[Any]   	=layerscale_value
      UpperCamelCase_     :					Optional[int]   	=use_post_layernorm
      UpperCamelCase_     :					Dict   	=use_post_layernorm_in_modulation
      UpperCamelCase_     :					Optional[Any]   	=normalize_modulator
      UpperCamelCase_     :					List[Any]   	=initializer_range
      UpperCamelCase_     :					List[str]   	=layer_norm_eps
      UpperCamelCase_     :					List[Any]   	=encoder_stride
      UpperCamelCase_     :					str   	=['stem'] + [f'''stage{idx}''' for idx in range(1      ,  len(self.depths   ) + 1   )]
      UpperCamelCase_	,     UpperCamelCase_     :					int   	=get_aligned_output_features_output_indices(
          out_features=_lowerCamelCase      ,  out_indices=_lowerCamelCase      ,  stage_names=self.stage_names   )
