# Lint as: python3
# pylint: enable=line-too-long
# pylint: disable=g-import-not-at-top,g-bad-import-order,wrong-import-position

_lowerCamelCase   :				List[Any]     			=						'''2.13.1'''

import platform

import pyarrow
from packaging import version


if version.parse(platform.python_version()) < version.parse('''3.7'''):
							raise ImportWarning(
							    '''To use `datasets`, Python>=3.7 is required, and the current version of Python doesn\'t match this condition.'''
							)

if version.parse(pyarrow.__version__).major < 8:
							raise ImportWarning(
							    '''To use `datasets`, the module `pyarrow>=8.0.0` is required, and the current version of `pyarrow` doesn\'t match this condition.\n'''
							    '''If you are running this in a Google Colab, you should probably just restart the runtime to use the right version of `pyarrow`.'''
							)

del platform
del pyarrow
del version

from .arrow_dataset import Dataset
from .arrow_reader import ReadInstruction
from .builder import ArrowBasedBuilder, BeamBasedBuilder, BuilderConfig, DatasetBuilder, GeneratorBasedBuilder
from .combine import concatenate_datasets, interleave_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .download import *
from .features import *
from .fingerprint import disable_caching, enable_caching, is_caching_enabled, set_caching_enabled
from .info import DatasetInfo, MetricInfo
from .inspect import (
    get_dataset_config_info,
    get_dataset_config_names,
    get_dataset_infos,
    get_dataset_split_names,
    inspect_dataset,
    inspect_metric,
    list_datasets,
    list_metrics,
)
from .iterable_dataset import IterableDataset
from .load import load_dataset, load_dataset_builder, load_from_disk, load_metric
from .metric import Metric
from .splits import (
    NamedSplit,
    NamedSplitAll,
    Split,
    SplitBase,
    SplitDict,
    SplitGenerator,
    SplitInfo,
    SubSplitInfo,
    percent,
)
from .tasks import *
from .utils import *
from .utils import logging


# deprecated modules
from datasets import arrow_dataset as _arrow_dataset  # isort:skip
from datasets import utils as _utils  # isort:skip
from datasets.utils import download_manager as _deprecated_download_manager  # isort:skip

_lowerCamelCase   :				Tuple     			=						concatenate_datasets
_lowerCamelCase   :				List[Any]     			=						DownloadConfig
_lowerCamelCase   :				Optional[int]     			=						DownloadManager
_lowerCamelCase   :				Tuple     			=						DownloadMode
_lowerCamelCase   :				List[str]     			=						DownloadConfig
_lowerCamelCase   :				Optional[int]     			=						DownloadMode
_lowerCamelCase   :				List[str]     			=						DownloadManager

del _arrow_dataset, _utils, _deprecated_download_manager





import inspect
import unittest

from transformers import MobileNetVaConfig
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_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
							import torch

							from transformers import MobileNetVaForImageClassification, MobileNetVaModel
							from transformers.models.mobilenet_va.modeling_mobilenet_va import MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
							from PIL import Image

							from transformers import MobileNetVaImageProcessor


class   lowercase      (       a       ):




							def 		__snake_case(  self				:   Optional[int] )   ->			str:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    self.config_class(**self.inputs_dict )
												self.parent.assertTrue(hasattr(_UpperCamelCase		,							"tf_padding" ) )
												self.parent.assertTrue(hasattr(_UpperCamelCase		,							"depth_multiplier" ) )



class   lowercase      :




							def __init__(  self				:   Tuple		,							_UpperCamelCase				:   Optional[Any]		,							_UpperCamelCase				:   Any=13		,							_UpperCamelCase				:   Any=3		,							_UpperCamelCase				:   Union[str, Any]=32		,							_UpperCamelCase				:   Optional[Any]=0.2_5		,							_UpperCamelCase				:   int=8		,							_UpperCamelCase				:   str=True		,							_UpperCamelCase				:   Any=1_024		,							_UpperCamelCase				:   Tuple=32		,							_UpperCamelCase				:   List[str]="relu6"		,							_UpperCamelCase				:   Tuple=0.1		,							_UpperCamelCase				:   List[str]=0.0_2		,							_UpperCamelCase				:   int=True		,							_UpperCamelCase				:   int=True		,							_UpperCamelCase				:   Optional[Any]=10		,							_UpperCamelCase				:   List[str]=None		,							)   ->			Any:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    parent
												SCREAMING_SNAKE_CASE           =    batch_size
												SCREAMING_SNAKE_CASE           =    num_channels
												SCREAMING_SNAKE_CASE           =    image_size
												SCREAMING_SNAKE_CASE           =    depth_multiplier
												SCREAMING_SNAKE_CASE           =    min_depth
												SCREAMING_SNAKE_CASE           =    tf_padding
												SCREAMING_SNAKE_CASE           =    int(last_hidden_size * depth_multiplier )
												SCREAMING_SNAKE_CASE           =    output_stride
												SCREAMING_SNAKE_CASE           =    hidden_act
												SCREAMING_SNAKE_CASE           =    classifier_dropout_prob
												SCREAMING_SNAKE_CASE           =    use_labels
												SCREAMING_SNAKE_CASE           =    is_training
												SCREAMING_SNAKE_CASE           =    num_labels
												SCREAMING_SNAKE_CASE           =    initializer_range
												SCREAMING_SNAKE_CASE           =    scope




							def 		__snake_case(  self				:   Dict )   ->			List[str]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )

												SCREAMING_SNAKE_CASE           =    None
												SCREAMING_SNAKE_CASE           =    None
												if self.use_labels:
																	SCREAMING_SNAKE_CASE           =    ids_tensor([self.batch_size]		,							self.num_labels )
																	SCREAMING_SNAKE_CASE           =    ids_tensor([self.batch_size, self.image_size, self.image_size]		,							self.num_labels )

												SCREAMING_SNAKE_CASE           =    self.get_config()

												return config, pixel_values, labels, pixel_labels




							def 		__snake_case(  self				:   Optional[Any] )   ->			str:



												'''simple docstring'''

												return MobileNetVaConfig(
												    num_channels=self.num_channels		,							image_size=self.image_size		,							depth_multiplier=self.depth_multiplier		,							min_depth=self.min_depth		,							tf_padding=self.tf_padding		,							hidden_act=self.hidden_act		,							classifier_dropout_prob=self.classifier_dropout_prob		,							initializer_range=self.initializer_range		,							)




							def 		__snake_case(  self				:   int		,							_UpperCamelCase				:   Optional[int]		,							_UpperCamelCase				:   Any		,							_UpperCamelCase				:   Union[str, Any]		,							_UpperCamelCase				:   int )   ->			List[str]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    MobileNetVaModel(config=_UpperCamelCase )
												model.to(_UpperCamelCase )
												model.eval()
												SCREAMING_SNAKE_CASE           =    model(_UpperCamelCase )
												self.parent.assertEqual(
												    result.last_hidden_state.shape		,							(
												        self.batch_size,
												        self.last_hidden_size,
												        self.image_size // self.output_stride,
												        self.image_size // self.output_stride,
												    )		,							)




							def 		__snake_case(  self				:   Dict		,							_UpperCamelCase				:   Tuple		,							_UpperCamelCase				:   Any		,							_UpperCamelCase				:   str		,							_UpperCamelCase				:   List[Any] )   ->			Optional[int]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    self.num_labels
												SCREAMING_SNAKE_CASE           =    MobileNetVaForImageClassification(_UpperCamelCase )
												model.to(_UpperCamelCase )
												model.eval()
												SCREAMING_SNAKE_CASE           =    model(_UpperCamelCase		,							labels=_UpperCamelCase )
												self.parent.assertEqual(result.logits.shape		,							(self.batch_size, self.num_labels) )





							def 		__snake_case(  self				:   Optional[Any] )   ->			str:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    self.prepare_config_and_inputs()
												SCREAMING_SNAKE_CASE     ,				SCREAMING_SNAKE_CASE     ,				SCREAMING_SNAKE_CASE     ,				SCREAMING_SNAKE_CASE           =    config_and_inputs
												SCREAMING_SNAKE_CASE           =    {"pixel_values": pixel_values}
												return config, inputs_dict



@require_torch
class   lowercase      (       a			,    a			,    unittest.TestCase       ):
							lowercase__  :    Dict									=							(MobileNetVaModel, MobileNetVaForImageClassification) if is_torch_available() else ()
							lowercase__  :    Tuple									=							(
							    {"""feature-extraction""": MobileNetVaModel, """image-classification""": MobileNetVaForImageClassification}
							    if is_torch_available()
							    else {}
							)

							lowercase__  :    Union[str, Any]									=							False
							lowercase__  :    Union[str, Any]									=							False
							lowercase__  :    Tuple									=							False
							lowercase__  :    List[str]									=							False




							def 		__snake_case(  self				:   List[str] )   ->			List[str]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    MobileNetVaModelTester(self )
												SCREAMING_SNAKE_CASE           =    MobileNetVaConfigTester(self		,							config_class=_UpperCamelCase		,							has_text_modality=_UpperCamelCase )




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



												'''simple docstring'''

												self.config_tester.run_common_tests()




							@unittest.skip(reason="MobileNetV1 does not use inputs_embeds" )
							def 		__snake_case(  self				:   Tuple )   ->			Tuple:



												'''simple docstring'''

												pass




							@unittest.skip(reason="MobileNetV1 does not support input and output embeddings" )
							def 		__snake_case(  self				:   Optional[int] )   ->			Dict:



												'''simple docstring'''

												pass




							@unittest.skip(reason="MobileNetV1 does not output attentions" )
							def 		__snake_case(  self				:   Any )   ->			List[str]:



												'''simple docstring'''

												pass




							def 		__snake_case(  self				:   List[Any] )   ->			str:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE     ,				SCREAMING_SNAKE_CASE           =    self.model_tester.prepare_config_and_inputs_for_common()

												for model_class in self.all_model_classes:
																	SCREAMING_SNAKE_CASE           =    model_class(_UpperCamelCase )
																	SCREAMING_SNAKE_CASE           =    inspect.signature(model.forward )
																	# signature.parameters is an OrderedDict => so arg_names order is deterministic
																	SCREAMING_SNAKE_CASE           =    [*signature.parameters.keys()]

																	SCREAMING_SNAKE_CASE           =    ["pixel_values"]
																	self.assertListEqual(arg_names[:1]		,							_UpperCamelCase )




							def 		__snake_case(  self				:   List[Any] )   ->			Dict:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_model(*_UpperCamelCase )




							def 		__snake_case(  self				:   List[str] )   ->			Optional[int]:



												'''simple docstring'''

												def check_hidden_states_output(_UpperCamelCase				:   Any		,							_UpperCamelCase				:   Dict		,							_UpperCamelCase				:   Tuple ):
																	SCREAMING_SNAKE_CASE           =    model_class(_UpperCamelCase )
																	model.to(_UpperCamelCase )
																	model.eval()

																	with torch.no_grad():
																						SCREAMING_SNAKE_CASE           =    model(**self._prepare_for_class(_UpperCamelCase		,							_UpperCamelCase ) )

																	SCREAMING_SNAKE_CASE           =    outputs.hidden_states

																	SCREAMING_SNAKE_CASE           =    26
																	self.assertEqual(len(_UpperCamelCase )		,							_UpperCamelCase )

												SCREAMING_SNAKE_CASE     ,				SCREAMING_SNAKE_CASE           =    self.model_tester.prepare_config_and_inputs_for_common()

												for model_class in self.all_model_classes:
																	SCREAMING_SNAKE_CASE           =    True
																	check_hidden_states_output(_UpperCamelCase		,							_UpperCamelCase		,							_UpperCamelCase )

																	# check that output_hidden_states also work using config
																	del inputs_dict["output_hidden_states"]
																	SCREAMING_SNAKE_CASE           =    True

																	check_hidden_states_output(_UpperCamelCase		,							_UpperCamelCase		,							_UpperCamelCase )




							def 		__snake_case(  self				:   Any )   ->			Optional[int]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_image_classification(*_UpperCamelCase )





							@slow
							def 		__snake_case(  self				:   int )   ->			str:



												'''simple docstring'''

												for model_name in MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																	SCREAMING_SNAKE_CASE           =    MobileNetVaModel.from_pretrained(_UpperCamelCase )
																	self.assertIsNotNone(_UpperCamelCase )





def 		__lowerCamelCase							():
					SCREAMING_SNAKE_CASE           =    Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"					)
					return image


@require_torch
@require_vision
class   lowercase      (       unittest.TestCase       ):




							@cached_property
							def 		__snake_case(  self				:   List[Any] )   ->			Optional[int]:



												'''simple docstring'''

												return (
												    MobileNetVaImageProcessor.from_pretrained("google/mobilenet_v1_1.0_224" ) if is_vision_available() else None
												)





							@slow
							def 		__snake_case(  self				:   str )   ->			Union[str, Any]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    MobileNetVaForImageClassification.from_pretrained("google/mobilenet_v1_1.0_224" ).to(_UpperCamelCase )

												SCREAMING_SNAKE_CASE           =    self.default_image_processor
												SCREAMING_SNAKE_CASE           =    prepare_img()
												SCREAMING_SNAKE_CASE           =    image_processor(images=_UpperCamelCase		,							return_tensors="pt" ).to(_UpperCamelCase )

												# forward pass
												with torch.no_grad():
																	SCREAMING_SNAKE_CASE           =    model(**_UpperCamelCase )

												# verify the logits
												SCREAMING_SNAKE_CASE           =    torch.Size((1, 1_001) )
												self.assertEqual(outputs.logits.shape		,							_UpperCamelCase )

												SCREAMING_SNAKE_CASE           =    torch.tensor([-4.1_7_3_9, -1.1_2_3_3, 3.1_2_0_5] ).to(_UpperCamelCase )

												self.assertTrue(torch.allclose(outputs.logits[0, :3]		,							_UpperCamelCase		,							atol=1e-4 ) )




