


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 	UpperCAmelCase_					(					UpperCamelCase    ):

   '''simple docstring'''




   def _snake_case    (		self ):






     """simple docstring"""





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






   def _snake_case    (		self ):






     """simple docstring"""





     with self.assertRaises(__A ):
       lowerCamelCase :					Any    =				pa.array(TypedSequence([1, 2, 3] )						,				type=pa.intaa() )






   def _snake_case    (		self ):






     """simple docstring"""





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






   def _snake_case    (		self ):






     """simple docstring"""





     lowerCamelCase :					Optional[Any]    =				pa.array(TypedSequence([1, 2, 3]						,				type=Value("int32" ) ) )
     self.assertEqual(arr.type						,				pa.intaa() )






   def _snake_case    (		self ):






     """simple docstring"""





     with self.assertRaises((TypeError, pa.lib.ArrowInvalid) ):
       lowerCamelCase :					Tuple    =				pa.array(TypedSequence(["foo", "bar"]						,				type=Value("int64" ) ) )






   def _snake_case    (		self ):






     """simple docstring"""





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






   def _snake_case    (		self ):






     """simple docstring"""





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






   def _snake_case    (		self ):






     """simple docstring"""





     lowerCamelCase :					Optional[int]    =				pa.array(TypedSequence([[[1, 2, 3]]]						,				type=ArrayaD((1, 3)						,				"int64" ) ) )
     self.assertEqual(arr.type						,				ArrayaDExtensionType((1, 3)						,				"int64" ) )






   def _snake_case    (		self ):






     """simple docstring"""





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






   def _snake_case    (		self ):






     """simple docstring"""





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






   def _snake_case    (		self ):






     """simple docstring"""





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






   @require_pil
   def _snake_case    (		self ):






     """simple docstring"""





     import PIL.Image

     lowerCamelCase :					Union[str, Any]    =				PIL.Image.fromarray(np.arange(10						,				dtype=np.uinta ).reshape(2						,				5 ) )
     with patch(
         "datasets.arrow_writer.cast_to_python_objects"						,				side_effect=__A ) as mock_cast_to_python_objects:
       lowerCamelCase :					List[str]    =				pa.array(TypedSequence([{"path": None, "bytes": B"image_bytes"}, pil_image]						,				type=Image() ) )
       lowerCamelCase    ,	lowerCamelCase :					Tuple    =				mock_cast_to_python_objects.call_args_list[-1]
       self.assertIn("optimize_list_casting"						,				__A )
       self.assertFalse(kwargs["optimize_list_casting"] )






def 		lowercase_(  SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_	):


  '''simple docstring'''

  lowerCamelCase :					Any    =				pa.BufferReader(SCREAMING_SNAKE_CASE_	) if isinstance(SCREAMING_SNAKE_CASE_      ,						pa.Buffer	) else pa.memory_map(SCREAMING_SNAKE_CASE_	)
  lowerCamelCase :					List[str]    =				pa.ipc.open_stream(SCREAMING_SNAKE_CASE_	)
  lowerCamelCase :					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 		lowercase_(  SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_	):


  '''simple docstring'''

  lowerCamelCase :					List[Any]    =				pa.BufferOutputStream()
  lowerCamelCase :					Optional[Any]    =				pa.schema(SCREAMING_SNAKE_CASE_	) if fields else None
  with ArrowWriter(stream=SCREAMING_SNAKE_CASE_      ,						schema=SCREAMING_SNAKE_CASE_      ,						writer_batch_size=SCREAMING_SNAKE_CASE_	) as writer:
    writer.write({"col_1": "foo", "col_2": 1}	)
    writer.write({"col_1": "bar", "col_2": 2}	)
    lowerCamelCase    ,	lowerCamelCase :					str    =				writer.finalize()
  assert num_examples == 2
  assert num_bytes > 0
  if not fields:
    lowerCamelCase :					int    =				{"col_1": pa.string(), "col_2": pa.intaa()}
  assert writer._schema == pa.schema(SCREAMING_SNAKE_CASE_      ,						metadata=writer._schema.metadata	)
  _check_output(output.getvalue()      ,						expected_num_chunks=num_examples if writer_batch_size == 1 else 1	)






def 		lowercase_(  ):


  '''simple docstring'''

  lowerCamelCase :					Any    =				pa.BufferOutputStream()
  lowerCamelCase :					int    =				Features({"labels": ClassLabel(names=["neg", "pos"]	)}	)
  with ArrowWriter(stream=SCREAMING_SNAKE_CASE_      ,						features=SCREAMING_SNAKE_CASE_	) as writer:
    writer.write({"labels": 0}	)
    writer.write({"labels": 1}	)
    lowerCamelCase    ,	lowerCamelCase :					str    =				writer.finalize()
  assert num_examples == 2
  assert num_bytes > 0
  assert writer._schema == features.arrow_schema
  assert writer._schema.metadata == features.arrow_schema.metadata
  lowerCamelCase :					Union[str, Any]    =				pa.BufferReader(output.getvalue()	)
  lowerCamelCase :					List[str]    =				pa.ipc.open_stream(SCREAMING_SNAKE_CASE_	)
  lowerCamelCase :					pa.Table    =				f.read_all()
  lowerCamelCase :					str    =				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(SCREAMING_SNAKE_CASE_	)






@pytest.mark.parametrize("writer_batch_size"      ,						[None, 1, 10]	)
def 		lowercase_(  SCREAMING_SNAKE_CASE_	):


  '''simple docstring'''

  lowerCamelCase :					Dict    =				pa.BufferOutputStream()
  with ArrowWriter(
      stream=SCREAMING_SNAKE_CASE_      ,						writer_batch_size=SCREAMING_SNAKE_CASE_      ,						hash_salt="split_name"      ,						check_duplicates=SCREAMING_SNAKE_CASE_      ,						) as writer:
    with pytest.raises(SCREAMING_SNAKE_CASE_	):
      writer.write({"col_1": "foo", "col_2": 1}      ,						key=[1, 2]	)
      lowerCamelCase    ,	lowerCamelCase :					Dict    =				writer.finalize()






@pytest.mark.parametrize("writer_batch_size"      ,						[None, 2, 10]	)
def 		lowercase_(  SCREAMING_SNAKE_CASE_	):


  '''simple docstring'''

  lowerCamelCase :					Optional[int]    =				pa.BufferOutputStream()
  with ArrowWriter(
      stream=SCREAMING_SNAKE_CASE_      ,						writer_batch_size=SCREAMING_SNAKE_CASE_      ,						hash_salt="split_name"      ,						check_duplicates=SCREAMING_SNAKE_CASE_      ,						) as writer:
    with pytest.raises(SCREAMING_SNAKE_CASE_	):
      writer.write({"col_1": "foo", "col_2": 1}      ,						key=10	)
      writer.write({"col_1": "bar", "col_2": 2}      ,						key=10	)
      lowerCamelCase    ,	lowerCamelCase :					List[Any]    =				writer.finalize()






@pytest.mark.parametrize("writer_batch_size"      ,						[None, 2, 10]	)
def 		lowercase_(  SCREAMING_SNAKE_CASE_	):


  '''simple docstring'''

  lowerCamelCase :					Optional[Any]    =				pa.BufferOutputStream()
  with ArrowWriter(
      stream=SCREAMING_SNAKE_CASE_      ,						writer_batch_size=SCREAMING_SNAKE_CASE_      ,						hash_salt="split_name"      ,						check_duplicates=SCREAMING_SNAKE_CASE_      ,						) as writer:
    writer.write({"col_1": "foo", "col_2": 1}      ,						key=1	)
    writer.write({"col_1": "bar", "col_2": 2}      ,						key=2	)
    lowerCamelCase    ,	lowerCamelCase :					Optional[Any]    =				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 		lowercase_(  SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_	):


  '''simple docstring'''

  lowerCamelCase :					Any    =				pa.BufferOutputStream()
  lowerCamelCase :					Optional[int]    =				pa.schema(SCREAMING_SNAKE_CASE_	) if fields else None
  with ArrowWriter(stream=SCREAMING_SNAKE_CASE_      ,						schema=SCREAMING_SNAKE_CASE_      ,						writer_batch_size=SCREAMING_SNAKE_CASE_	) as writer:
    writer.write_batch({"col_1": ["foo", "bar"], "col_2": [1, 2]}	)
    writer.write_batch({"col_1": [], "col_2": []}	)
    lowerCamelCase    ,	lowerCamelCase :					List[Any]    =				writer.finalize()
  assert num_examples == 2
  assert num_bytes > 0
  if not fields:
    lowerCamelCase :					List[Any]    =				{"col_1": pa.string(), "col_2": pa.intaa()}
  assert writer._schema == pa.schema(SCREAMING_SNAKE_CASE_      ,						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 		lowercase_(  SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_	):


  '''simple docstring'''

  lowerCamelCase :					Optional[int]    =				pa.BufferOutputStream()
  lowerCamelCase :					List[Any]    =				pa.schema(SCREAMING_SNAKE_CASE_	) if fields else None
  with ArrowWriter(stream=SCREAMING_SNAKE_CASE_      ,						schema=SCREAMING_SNAKE_CASE_      ,						writer_batch_size=SCREAMING_SNAKE_CASE_	) as writer:
    writer.write_table(pa.Table.from_pydict({"col_1": ["foo", "bar"], "col_2": [1, 2]}	)	)
    lowerCamelCase    ,	lowerCamelCase :					Union[str, Any]    =				writer.finalize()
  assert num_examples == 2
  assert num_bytes > 0
  if not fields:
    lowerCamelCase :					Optional[int]    =				{"col_1": pa.string(), "col_2": pa.intaa()}
  assert writer._schema == pa.schema(SCREAMING_SNAKE_CASE_      ,						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 		lowercase_(  SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_	):


  '''simple docstring'''

  lowerCamelCase :					List[str]    =				pa.BufferOutputStream()
  lowerCamelCase :					List[str]    =				pa.schema(SCREAMING_SNAKE_CASE_	) if fields else None
  with ArrowWriter(stream=SCREAMING_SNAKE_CASE_      ,						schema=SCREAMING_SNAKE_CASE_      ,						writer_batch_size=SCREAMING_SNAKE_CASE_	) 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]}	)	)
    lowerCamelCase    ,	lowerCamelCase :					Dict    =				writer.finalize()
  assert num_examples == 2
  assert num_bytes > 0
  if not fields:
    lowerCamelCase :					Optional[Any]    =				{"col_1": pa.string(), "col_2": pa.intaa()}
  assert writer._schema == pa.schema(SCREAMING_SNAKE_CASE_      ,						metadata=writer._schema.metadata	)
  _check_output(output.getvalue()      ,						expected_num_chunks=num_examples if writer_batch_size == 1 else 1	)






def 		lowercase_(  ):


  '''simple docstring'''

  with tempfile.TemporaryDirectory() as tmp_dir:
    lowerCamelCase :					Any    =				{"col_1": pa.string(), "col_2": pa.intaa()}
    lowerCamelCase :					Any    =				os.path.join(SCREAMING_SNAKE_CASE_      ,						"test.arrow"	)
    with ArrowWriter(path=SCREAMING_SNAKE_CASE_      ,						schema=pa.schema(SCREAMING_SNAKE_CASE_	)	) as writer:
      writer.write_batch({"col_1": ["foo", "bar"], "col_2": [1, 2]}	)
      lowerCamelCase    ,	lowerCamelCase :					int    =				writer.finalize()
    assert num_examples == 2
    assert num_bytes > 0
    assert writer._schema == pa.schema(SCREAMING_SNAKE_CASE_      ,						metadata=writer._schema.metadata	)
    _check_output(SCREAMING_SNAKE_CASE_      ,						1	)






def 		lowercase_(  SCREAMING_SNAKE_CASE_	):


  '''simple docstring'''

  if pa.types.is_list(SCREAMING_SNAKE_CASE_	):
    return get_base_dtype(arr_type.value_type	)
  else:
    return arr_type






def 		lowercase_(  SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_	):


  '''simple docstring'''

  if isinstance(lst[0]      ,						SCREAMING_SNAKE_CASE_	):
    change_first_primitive_element_in_list(lst[0]      ,						SCREAMING_SNAKE_CASE_	)
  else:
    lowerCamelCase :					List[str]    =				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 		lowercase_(  SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_	):


  '''simple docstring'''

  lowerCamelCase :					Optional[Any]    =				pa.array(TypedSequence(SCREAMING_SNAKE_CASE_      ,						optimized_int_type=SCREAMING_SNAKE_CASE_	)	)
  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 		lowercase_(  SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_	):


  '''simple docstring'''

  lowerCamelCase :					Optional[Any]    =				pa.array(OptimizedTypedSequence(SCREAMING_SNAKE_CASE_      ,						col=SCREAMING_SNAKE_CASE_	)	)
  assert get_base_dtype(arr.type	) == expected_dtype

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






@pytest.mark.parametrize("raise_exception"      ,						[False, True]	)
def 		lowercase_(  SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_	):


  '''simple docstring'''

  lowerCamelCase :					Optional[Any]    =				str(tmp_path / "dataset-train.arrow"	)
  try:
    with ArrowWriter(path=SCREAMING_SNAKE_CASE_	) as writer:
      if raise_exception:
        raise pa.lib.ArrowInvalid()
      else:
        writer.stream.close()
  except pa.lib.ArrowInvalid:
    pass
  finally:
    assert writer.stream.closed






def 		lowercase_(  SCREAMING_SNAKE_CASE_	):


  '''simple docstring'''

  lowerCamelCase :					Dict    =				"mock://dataset-train.arrow"
  with ArrowWriter(path=SCREAMING_SNAKE_CASE_      ,						storage_options=mockfs.storage_options	) as writer:
    assert isinstance(writer._fs      ,						type(SCREAMING_SNAKE_CASE_	)	)
    assert writer._fs.storage_options == mockfs.storage_options
    writer.write({"col_1": "foo", "col_2": 1}	)
    writer.write({"col_1": "bar", "col_2": 2}	)
    lowerCamelCase    ,	lowerCamelCase :					Optional[int]    =				writer.finalize()
  assert num_examples == 2
  assert num_bytes > 0
  assert mockfs.exists(SCREAMING_SNAKE_CASE_	)






def 		lowercase_(  ):


  '''simple docstring'''

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






@require_pil
@pytest.mark.parametrize("embed_local_files"      ,						[False, True]	)
def 		lowercase_(  SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_	):


  '''simple docstring'''

  import PIL.Image

  lowerCamelCase :					Dict    =				str(tmp_path / "test_image_rgb.jpg"	)
  PIL.Image.fromarray(np.zeros((5, 5)      ,						dtype=np.uinta	)	).save(SCREAMING_SNAKE_CASE_      ,						format="png"	)
  lowerCamelCase :					Any    =				pa.BufferOutputStream()
  with ParquetWriter(
      stream=SCREAMING_SNAKE_CASE_      ,						features=Features({"image": Image()}	)      ,						embed_local_files=SCREAMING_SNAKE_CASE_	) as writer:
    writer.write({"image": image_path}	)
    writer.finalize()
  lowerCamelCase :					Dict    =				pa.BufferReader(output.getvalue()	)
  lowerCamelCase :					pa.Table    =				pq.read_table(SCREAMING_SNAKE_CASE_	)
  lowerCamelCase :					Tuple    =				pa_table.to_pydict()
  if embed_local_files:
    assert isinstance(out["image"][0]["path"]      ,						SCREAMING_SNAKE_CASE_	)
    with open(SCREAMING_SNAKE_CASE_      ,						"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 		lowercase_(  ):


  '''simple docstring'''

  lowerCamelCase :					Tuple    =				pa.schema([pa.field("col_1"      ,						pa.string()      ,						nullable=SCREAMING_SNAKE_CASE_	)]	)
  lowerCamelCase :					int    =				pa.BufferOutputStream()
  with ArrowWriter(stream=SCREAMING_SNAKE_CASE_	) as writer:
    writer._build_writer(inferred_schema=SCREAMING_SNAKE_CASE_	)
    assert writer._schema == pa.schema([pa.field("col_1"      ,						pa.string()	)]	)







from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_snake_case             =     logging.get_logger(__name__)

_snake_case             =     {
    '''google/mobilenet_v2_1.4_224''': '''https://huggingface.co/google/mobilenet_v2_1.4_224/resolve/main/config.json''',
    '''google/mobilenet_v2_1.0_224''': '''https://huggingface.co/google/mobilenet_v2_1.0_224/resolve/main/config.json''',
    '''google/mobilenet_v2_0.75_160''': '''https://huggingface.co/google/mobilenet_v2_0.75_160/resolve/main/config.json''',
    '''google/mobilenet_v2_0.35_96''': '''https://huggingface.co/google/mobilenet_v2_0.35_96/resolve/main/config.json''',
    # See all MobileNetV2 models at https://huggingface.co/models?filter=mobilenet_v2
}
class 	UpperCAmelCase_					(					UpperCamelCase    ):

   '''simple docstring'''




   __A :      Dict     		=		"mobilenet_v2"






   def __init__(		self						,				__A=3						,				__A=224						,				__A=1.0						,				__A=8						,				__A=8						,				__A=6						,				__A=32						,				__A=True						,				__A=True						,				__A="relu6"						,				__A=True						,				__A=0.8						,				__A=0.02						,				__A=0.001						,				__A=255						,				**__A						,				):






     """simple docstring"""





     super().__init__(**__A )

     if depth_multiplier <= 0:
       raise ValueError("depth_multiplier must be greater than zero." )

     lowerCamelCase :					str    =				num_channels
     lowerCamelCase :					Any    =				image_size
     lowerCamelCase :					Union[str, Any]    =				depth_multiplier
     lowerCamelCase :					Tuple    =				depth_divisible_by
     lowerCamelCase :					Dict    =				min_depth
     lowerCamelCase :					Dict    =				expand_ratio
     lowerCamelCase :					Optional[Any]    =				output_stride
     lowerCamelCase :					int    =				first_layer_is_expansion
     lowerCamelCase :					Union[str, Any]    =				finegrained_output
     lowerCamelCase :					Optional[Any]    =				hidden_act
     lowerCamelCase :					Optional[Any]    =				tf_padding
     lowerCamelCase :					Optional[Any]    =				classifier_dropout_prob
     lowerCamelCase :					Dict    =				initializer_range
     lowerCamelCase :					str    =				layer_norm_eps
     lowerCamelCase :					Optional[Any]    =				semantic_loss_ignore_index



class 	UpperCAmelCase_					(					UpperCamelCase    ):

   '''simple docstring'''




   __A :      Union[str, Any]     		=		version.parse("1.11"    )






   @property
   def _snake_case    (		self ):






     """simple docstring"""





     return OrderedDict([("pixel_values", {0: "batch"})] )






   @property
   def _snake_case    (		self ):






     """simple docstring"""





     if self.task == "image-classification":
       return OrderedDict([("logits", {0: "batch"})] )
     else:
       return OrderedDict([("last_hidden_state", {0: "batch"}), ("pooler_output", {0: "batch"})] )






   @property
   def _snake_case    (		self ):






     """simple docstring"""





     return 1e-4



