

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   __lowercase				( UpperCAmelCase_	):




			"""simple docstring"""







			def        _SCREAMING_SNAKE_CASE			( self  :     int):
									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       pa.array(TypedSequence([1, 2, 3]))
									self.assertEqual(arr.type   ,						pa.intaa())



			def        _SCREAMING_SNAKE_CASE			( self  :     str):
									with self.assertRaises(lowerCAmelCase__):
															SCREAMING_SNAKE_CASE_: Optional[int]  		=       pa.array(TypedSequence([1, 2, 3])   ,						type=pa.intaa())



			def        _SCREAMING_SNAKE_CASE			( self  :     Dict):
									with self.assertRaises(lowerCAmelCase__):
															SCREAMING_SNAKE_CASE_: Dict  		=       pa.array(TypedSequence([1, 2, 3]   ,						try_type=Value("bool")   ,						type=Value("int64")))



			def        _SCREAMING_SNAKE_CASE			( self  :     str):
									SCREAMING_SNAKE_CASE_: Dict  		=       pa.array(TypedSequence([1, 2, 3]   ,						type=Value("int32")))
									self.assertEqual(arr.type   ,						pa.intaa())



			def        _SCREAMING_SNAKE_CASE			( self  :     str):
									with self.assertRaises((TypeError, pa.lib.ArrowInvalid)):
															SCREAMING_SNAKE_CASE_: List[str]  		=       pa.array(TypedSequence(["foo", "bar"]   ,						type=Value("int64")))



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



			def        _SCREAMING_SNAKE_CASE			( self  :     Optional[int]):
									SCREAMING_SNAKE_CASE_: Dict  		=       pa.array(TypedSequence(["foo", "bar"]   ,						try_type=Value("int64")))
									self.assertEqual(arr.type   ,						pa.string())



			def        _SCREAMING_SNAKE_CASE			( self  :     str):
									SCREAMING_SNAKE_CASE_: int  		=       pa.array(TypedSequence([[[1, 2, 3]]]   ,						type=ArrayaD((1, 3)   ,						"int64")))
									self.assertEqual(arr.type   ,						ArrayaDExtensionType((1, 3)   ,						"int64"))



			def        _SCREAMING_SNAKE_CASE			( self  :     str):
									with self.assertRaises((TypeError, pa.lib.ArrowInvalid)):
															SCREAMING_SNAKE_CASE_: List[str]  		=       pa.array(TypedSequence(["foo", "bar"]   ,						type=ArrayaD((1, 3)   ,						"int64")))



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



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




			@require_pil
			def        _SCREAMING_SNAKE_CASE			( self  :     str):
									import PIL.Image

									SCREAMING_SNAKE_CASE_: Tuple  		=       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:
															SCREAMING_SNAKE_CASE_: Tuple  		=       pa.array(TypedSequence([{"path": None, "bytes": b"image_bytes"}, pil_image]   ,						type=Image()))
															SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: Optional[int]  		=       mock_cast_to_python_objects.call_args_list[-1]
															self.assertIn("optimize_list_casting"   ,						lowerCAmelCase__)
															self.assertFalse(kwargs["optimize_list_casting"])







def 			A_      (    _UpperCAmelCase				,	_UpperCAmelCase    ):
						SCREAMING_SNAKE_CASE_: Dict  		=       pa.BufferReader(_UpperCAmelCase    ) if isinstance(_UpperCAmelCase				,	pa.Buffer    ) else pa.memory_map(_UpperCAmelCase    )
						SCREAMING_SNAKE_CASE_: Optional[int]  		=       pa.ipc.open_stream(_UpperCAmelCase    )
						SCREAMING_SNAKE_CASE_: 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_      (    _UpperCAmelCase				,	_UpperCAmelCase    ):
						SCREAMING_SNAKE_CASE_: Optional[int]  		=       pa.BufferOutputStream()
						SCREAMING_SNAKE_CASE_: Optional[Any]  		=       pa.schema(_UpperCAmelCase    ) if fields else None
						with ArrowWriter(stream=_UpperCAmelCase				,	schema=_UpperCAmelCase				,	writer_batch_size=_UpperCAmelCase    ) as writer:
												writer.write({"col_1": "foo", "col_2": 1}    )
												writer.write({"col_1": "bar", "col_2": 2}    )
												SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       writer.finalize()
						assert num_examples == 2
						assert num_bytes > 0
						if not fields:
												SCREAMING_SNAKE_CASE_: str  		=       {"col_1": pa.string(), "col_2": pa.intaa()}
						assert writer._schema == pa.schema(_UpperCAmelCase				,	metadata=writer._schema.metadata    )
						_check_output(output.getvalue()				,	expected_num_chunks=num_examples if writer_batch_size == 1 else 1    )







def 			A_      (    ):
						SCREAMING_SNAKE_CASE_: Optional[Any]  		=       pa.BufferOutputStream()
						SCREAMING_SNAKE_CASE_: int  		=       Features({"labels": ClassLabel(names=["neg", "pos"]    )}    )
						with ArrowWriter(stream=_UpperCAmelCase				,	features=_UpperCAmelCase    ) as writer:
												writer.write({"labels": 0}    )
												writer.write({"labels": 1}    )
												SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: Optional[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
						SCREAMING_SNAKE_CASE_: Any  		=       pa.BufferReader(output.getvalue()    )
						SCREAMING_SNAKE_CASE_: Optional[int]  		=       pa.ipc.open_stream(_UpperCAmelCase    )
						SCREAMING_SNAKE_CASE_: pa.Table  		=       f.read_all()
						SCREAMING_SNAKE_CASE_: 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(_UpperCAmelCase    )







@pytest.mark.parametrize("writer_batch_size"				,	[None, 1, 10]    )
def 			A_      (    _UpperCAmelCase    ):
						SCREAMING_SNAKE_CASE_: str  		=       pa.BufferOutputStream()
						with ArrowWriter(
						    stream=_UpperCAmelCase				,	writer_batch_size=_UpperCAmelCase				,	hash_salt="split_name"				,	check_duplicates=_UpperCAmelCase				,	) as writer:
												with pytest.raises(_UpperCAmelCase    ):
																		writer.write({"col_1": "foo", "col_2": 1}				,	key=[1, 2]    )
																		SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       writer.finalize()







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







@pytest.mark.parametrize("writer_batch_size"				,	[None, 2, 10]    )
def 			A_      (    _UpperCAmelCase    ):
						SCREAMING_SNAKE_CASE_: int  		=       pa.BufferOutputStream()
						with ArrowWriter(
						    stream=_UpperCAmelCase				,	writer_batch_size=_UpperCAmelCase				,	hash_salt="split_name"				,	check_duplicates=_UpperCAmelCase				,	) as writer:
												writer.write({"col_1": "foo", "col_2": 1}				,	key=1    )
												writer.write({"col_1": "bar", "col_2": 2}				,	key=2    )
												SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: 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 			A_      (    _UpperCAmelCase				,	_UpperCAmelCase    ):
						SCREAMING_SNAKE_CASE_: Tuple  		=       pa.BufferOutputStream()
						SCREAMING_SNAKE_CASE_: List[Any]  		=       pa.schema(_UpperCAmelCase    ) if fields else None
						with ArrowWriter(stream=_UpperCAmelCase				,	schema=_UpperCAmelCase				,	writer_batch_size=_UpperCAmelCase    ) as writer:
												writer.write_batch({"col_1": ["foo", "bar"], "col_2": [1, 2]}    )
												writer.write_batch({"col_1": [], "col_2": []}    )
												SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       writer.finalize()
						assert num_examples == 2
						assert num_bytes > 0
						if not fields:
												SCREAMING_SNAKE_CASE_: List[str]  		=       {"col_1": pa.string(), "col_2": pa.intaa()}
						assert writer._schema == pa.schema(_UpperCAmelCase				,	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_      (    _UpperCAmelCase				,	_UpperCAmelCase    ):
						SCREAMING_SNAKE_CASE_: Any  		=       pa.BufferOutputStream()
						SCREAMING_SNAKE_CASE_: str  		=       pa.schema(_UpperCAmelCase    ) if fields else None
						with ArrowWriter(stream=_UpperCAmelCase				,	schema=_UpperCAmelCase				,	writer_batch_size=_UpperCAmelCase    ) as writer:
												writer.write_table(pa.Table.from_pydict({"col_1": ["foo", "bar"], "col_2": [1, 2]}    )    )
												SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: Optional[Any]  		=       writer.finalize()
						assert num_examples == 2
						assert num_bytes > 0
						if not fields:
												SCREAMING_SNAKE_CASE_: Dict  		=       {"col_1": pa.string(), "col_2": pa.intaa()}
						assert writer._schema == pa.schema(_UpperCAmelCase				,	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_      (    _UpperCAmelCase				,	_UpperCAmelCase    ):
						SCREAMING_SNAKE_CASE_: Tuple  		=       pa.BufferOutputStream()
						SCREAMING_SNAKE_CASE_: Any  		=       pa.schema(_UpperCAmelCase    ) if fields else None
						with ArrowWriter(stream=_UpperCAmelCase				,	schema=_UpperCAmelCase				,	writer_batch_size=_UpperCAmelCase    ) 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]}    )    )
												SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       writer.finalize()
						assert num_examples == 2
						assert num_bytes > 0
						if not fields:
												SCREAMING_SNAKE_CASE_: List[str]  		=       {"col_1": pa.string(), "col_2": pa.intaa()}
						assert writer._schema == pa.schema(_UpperCAmelCase				,	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:
												SCREAMING_SNAKE_CASE_: Optional[int]  		=       {"col_1": pa.string(), "col_2": pa.intaa()}
												SCREAMING_SNAKE_CASE_: Any  		=       os.path.join(_UpperCAmelCase				,	"test.arrow"    )
												with ArrowWriter(path=_UpperCAmelCase				,	schema=pa.schema(_UpperCAmelCase    )    ) as writer:
																		writer.write_batch({"col_1": ["foo", "bar"], "col_2": [1, 2]}    )
																		SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: List[Any]  		=       writer.finalize()
												assert num_examples == 2
												assert num_bytes > 0
												assert writer._schema == pa.schema(_UpperCAmelCase				,	metadata=writer._schema.metadata    )
												_check_output(_UpperCAmelCase				,	1    )







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







def 			A_      (    _UpperCAmelCase				,	_UpperCAmelCase    ):
						if isinstance(lst[0]				,	_UpperCAmelCase    ):
												change_first_primitive_element_in_list(lst[0]				,	_UpperCAmelCase    )
						else:
												SCREAMING_SNAKE_CASE_: 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_      (    _UpperCAmelCase				,	_UpperCAmelCase				,	_UpperCAmelCase    ):
						SCREAMING_SNAKE_CASE_: Tuple  		=       pa.array(TypedSequence(_UpperCAmelCase				,	optimized_int_type=_UpperCAmelCase    )    )
						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_      (    _UpperCAmelCase				,	_UpperCAmelCase				,	_UpperCAmelCase    ):
						# in range
						SCREAMING_SNAKE_CASE_: List[str]  		=       pa.array(OptimizedTypedSequence(_UpperCAmelCase				,	col=_UpperCAmelCase    )    )
						assert get_base_dtype(arr.type    ) == expected_dtype

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







@pytest.mark.parametrize("raise_exception"				,	[False, True]    )
def 			A_      (    _UpperCAmelCase				,	_UpperCAmelCase    ):
						SCREAMING_SNAKE_CASE_: Any  		=       str(tmp_path / "dataset-train.arrow"    )
						try:
												with ArrowWriter(path=_UpperCAmelCase    ) 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_      (    _UpperCAmelCase    ):
						SCREAMING_SNAKE_CASE_: str  		=       "mock://dataset-train.arrow"
						with ArrowWriter(path=_UpperCAmelCase				,	storage_options=mockfs.storage_options    ) as writer:
												assert isinstance(writer._fs				,	type(_UpperCAmelCase    )    )
												assert writer._fs.storage_options == mockfs.storage_options
												writer.write({"col_1": "foo", "col_2": 1}    )
												writer.write({"col_1": "bar", "col_2": 2}    )
												SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: Any  		=       writer.finalize()
						assert num_examples == 2
						assert num_bytes > 0
						assert mockfs.exists(_UpperCAmelCase    )







def 			A_      (    ):
						SCREAMING_SNAKE_CASE_: List[str]  		=       pa.BufferOutputStream()
						with ParquetWriter(stream=_UpperCAmelCase    ) as writer:
												writer.write({"col_1": "foo", "col_2": 1}    )
												writer.write({"col_1": "bar", "col_2": 2}    )
												SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: Dict  		=       writer.finalize()
						assert num_examples == 2
						assert num_bytes > 0
						SCREAMING_SNAKE_CASE_: int  		=       pa.BufferReader(output.getvalue()    )
						SCREAMING_SNAKE_CASE_: pa.Table  		=       pq.read_table(_UpperCAmelCase    )
						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_      (    _UpperCAmelCase				,	_UpperCAmelCase    ):
						import PIL.Image

						SCREAMING_SNAKE_CASE_: Any  		=       str(tmp_path / "test_image_rgb.jpg"    )
						PIL.Image.fromarray(np.zeros((5, 5)				,	dtype=np.uinta    )    ).save(_UpperCAmelCase				,	format="png"    )
						SCREAMING_SNAKE_CASE_: Any  		=       pa.BufferOutputStream()
						with ParquetWriter(
						    stream=_UpperCAmelCase				,	features=Features({"image": Image()}    )				,	embed_local_files=_UpperCAmelCase    ) as writer:
												writer.write({"image": image_path}    )
												writer.finalize()
						SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       pa.BufferReader(output.getvalue()    )
						SCREAMING_SNAKE_CASE_: pa.Table  		=       pq.read_table(_UpperCAmelCase    )
						SCREAMING_SNAKE_CASE_: int  		=       pa_table.to_pydict()
						if embed_local_files:
												assert isinstance(out["image"][0]["path"]				,	_UpperCAmelCase    )
												with open(_UpperCAmelCase				,	"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_      (    ):
						SCREAMING_SNAKE_CASE_: Dict  		=       pa.schema([pa.field("col_1"				,	pa.string()				,	nullable=_UpperCAmelCase    )]    )
						SCREAMING_SNAKE_CASE_: int  		=       pa.BufferOutputStream()
						with ArrowWriter(stream=_UpperCAmelCase    ) as writer:
												writer._build_writer(inferred_schema=_UpperCAmelCase    )
												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


UpperCAmelCase_     							= logging.get_logger(__name__)

UpperCAmelCase_     							= {
    '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     lowercase__       (	__lowerCamelCase				):

   '''simple docstring'''


   a						:							List[str]   		=		"mobilenet_v2"




   def __init__(    self,		__magic_name__=3,		__magic_name__=224,		__magic_name__=1.0,		__magic_name__=8,		__magic_name__=8,		__magic_name__=6,		__magic_name__=32,		__magic_name__=True,		__magic_name__=True,		__magic_name__="relu6",		__magic_name__=True,		__magic_name__=0.8,		__magic_name__=0.02,		__magic_name__=0.001,		__magic_name__=255,		**__magic_name__,		) ->      List[Any]:






       """simple docstring"""
       super().__init__(**__magic_name__							)

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

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






class     lowercase__       (	__lowerCamelCase				):

   '''simple docstring'''


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




   @property
   def       UpperCamelCase__  (    self							) ->      Mapping[str, Mapping[int, str]]:






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




   @property
   def       UpperCamelCase__  (    self							) ->      Mapping[str, Mapping[int, str]]:






       """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       UpperCamelCase__  (    self							) ->      float:






       """simple docstring"""
       return 1E-4

