





'''simple docstring'''

import pytest

from datasets.splits import SplitDict, SplitInfo
from datasets.utils.py_utils import asdict







@pytest.mark.parametrize(
    "split_dict"		,							[
        SplitDict(),
        SplitDict({"train": SplitInfo(name="train"		,							num_bytes=1337		,							num_examples=42		,							dataset_name="my_dataset"							)}							),
        SplitDict({"train": SplitInfo(name="train"		,							num_bytes=1337		,							num_examples=42							)}							),
        SplitDict({"train": SplitInfo()}							),
    ]		,							)
def 							_lowercase    (	lowerCAmelCase_							):

				"""simple docstring"""






				lowercase         = split_dict._to_yaml_list()
				assert len(UpperCamelCase__							) == len(UpperCamelCase__							)
				lowercase         = SplitDict._from_yaml_list(UpperCamelCase__							)
				for split_name, split_info in split_dict.items():
								# dataset_name field is deprecated, and is therefore not part of the YAML dump
								lowercase         = None
								# the split name of split_dict takes over the name of the split info object
								lowercase         = split_name
				assert split_dict == reloaded







@pytest.mark.parametrize(
    "split_info"		,							[SplitInfo(), SplitInfo(dataset_name=UpperCamelCase__							), SplitInfo(dataset_name="my_dataset"							)]							)
def 							_lowercase    (	lowerCAmelCase_							):

				"""simple docstring"""






				lowercase         = asdict(SplitDict({"train": split_info}							)							)
				assert "dataset_name" in split_dict_asdict["train"]
				assert split_dict_asdict["train"]["dataset_name"] == split_info.dataset_name











'''simple docstring'''

import inspect
import unittest

from transformers import RegNetConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device

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 torch import nn

			from transformers import RegNetForImageClassification, RegNetModel
			from transformers.models.regnet.modeling_regnet import REGNET_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
			from PIL import Image

			from transformers import AutoImageProcessor

class       UpperCAmelCase :




		def __init__(self      :	Dict							,			A__      :	str							,			A__      :	int=3							,			A__      :	Dict=3_2							,			A__      :	str=3							,			A__      :	str=1_0							,			A__      :	Optional[int]=[1_0, 2_0, 3_0, 4_0]							,			A__      :	Tuple=[1, 1, 2, 1]							,			A__      :	int=True							,			A__      :	List[Any]=True							,			A__      :	List[Any]="relu"							,			A__      :	Any=3							,			A__      :	Any=None							,			)					-> Tuple:
						lowercase         = parent
						lowercase         = batch_size
						lowercase         = image_size
						lowercase         = num_channels
						lowercase         = embeddings_size
						lowercase         = hidden_sizes
						lowercase         = depths
						lowercase         = is_training
						lowercase         = use_labels
						lowercase         = hidden_act
						lowercase         = num_labels
						lowercase         = scope
						lowercase         = len(A__				)




		def 				UpperCAmelCase__   (self      :	str				)					-> List[str]:
						lowercase         = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]				)

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

						lowercase         = self.get_config()

						return config, pixel_values, labels




		def 				UpperCAmelCase__   (self      :	Optional[Any]				)					-> List[str]:
						return RegNetConfig(
						    num_channels=self.num_channels							,			embeddings_size=self.embeddings_size							,			hidden_sizes=self.hidden_sizes							,			depths=self.depths							,			hidden_act=self.hidden_act							,			num_labels=self.num_labels							,			)




		def 				UpperCAmelCase__   (self      :	Dict							,			A__      :	Union[str, Any]							,			A__      :	Optional[int]							,			A__      :	Dict				)					-> str:
						lowercase         = RegNetModel(config=A__				)
						model.to(A__				)
						model.eval()
						lowercase         = model(A__				)
						# expected last hidden states: B, C, H // 32, W // 32
						self.parent.assertEqual(
						    result.last_hidden_state.shape							,			(self.batch_size, self.hidden_sizes[-1], self.image_size // 3_2, self.image_size // 3_2)							,			)




		def 				UpperCAmelCase__   (self      :	List[str]							,			A__      :	List[str]							,			A__      :	Union[str, Any]							,			A__      :	str				)					-> Dict:
						lowercase         = self.num_labels
						lowercase         = RegNetForImageClassification(A__				)
						model.to(A__				)
						model.eval()
						lowercase         = model(A__							,			labels=A__				)
						self.parent.assertEqual(result.logits.shape							,			(self.batch_size, self.num_labels)				)





		def 				UpperCAmelCase__   (self      :	Any				)					-> Union[str, Any]:
						lowercase         = self.prepare_config_and_inputs()
						lowercase   ,							lowercase   ,							lowercase         = config_and_inputs
						lowercase         = {"pixel_values": pixel_values}
						return config, inputs_dict




@require_torch
class       UpperCAmelCase (						_lowercase			,       _lowercase			,       unittest.TestCase  ):
		UpperCAmelCase					:	str 			=		(RegNetModel, RegNetForImageClassification) if is_torch_available() else ()
		UpperCAmelCase					:	Dict 			=		(
		    {'''feature-extraction''': RegNetModel, '''image-classification''': RegNetForImageClassification}
		    if is_torch_available()
		    else {}
		)

		UpperCAmelCase					:	Dict 			=		False
		UpperCAmelCase					:	int 			=		False
		UpperCAmelCase					:	Tuple 			=		False
		UpperCAmelCase					:	Tuple 			=		False




		def 				UpperCAmelCase__   (self      :	Optional[int]				)					-> Tuple:
						lowercase         = RegNetModelTester(self				)
						lowercase         = ConfigTester(self							,			config_class=A__							,			has_text_modality=A__				)




		def 				UpperCAmelCase__   (self      :	List[Any]				)					-> str:
						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 				UpperCAmelCase__   (self      :	Optional[Any]				)					-> int:
						return




		@unittest.skip(reason="RegNet does not use inputs_embeds"				)
		def 				UpperCAmelCase__   (self      :	int				)					-> Optional[int]:
						pass




		@unittest.skip(reason="RegNet does not support input and output embeddings"				)
		def 				UpperCAmelCase__   (self      :	List[Any]				)					-> Optional[Any]:
						pass




		def 				UpperCAmelCase__   (self      :	Any				)					-> str:
						lowercase   ,							lowercase         = self.model_tester.prepare_config_and_inputs_for_common()

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

										lowercase         = ["pixel_values"]
										self.assertListEqual(arg_names[:1]							,			A__				)




		def 				UpperCAmelCase__   (self      :	List[Any]				)					-> Union[str, Any]:
						lowercase         = self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*A__				)




		def 				UpperCAmelCase__   (self      :	List[str]				)					-> Tuple:
						lowercase   ,							lowercase         = self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
										lowercase         = model_class(config=A__				)
										for name, module in model.named_modules():
														if isinstance(A__							,			(nn.BatchNormad, nn.GroupNorm)				):
																		self.assertTrue(
																		    torch.all(module.weight == 1				)							,			msg=f'Parameter {name} of model {model_class} seems not properly initialized'							,			)
																		self.assertTrue(
																		    torch.all(module.bias == 0				)							,			msg=f'Parameter {name} of model {model_class} seems not properly initialized'							,			)




		def 				UpperCAmelCase__   (self      :	Optional[Any]				)					-> List[str]:
						def check_hidden_states_output(A__      :	Optional[Any]							,			A__      :	List[str]							,			A__      :	Tuple				):
										lowercase         = model_class(A__				)
										model.to(A__				)
										model.eval()

										with torch.no_grad():
														lowercase         = model(**self._prepare_for_class(A__							,			A__				)				)

										lowercase         = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

										lowercase         = self.model_tester.num_stages
										self.assertEqual(len(A__				)							,			expected_num_stages + 1				)

										# RegNet's feature maps are of shape (batch_size, num_channels, height, width)
										self.assertListEqual(
										    list(hidden_states[0].shape[-2:]				)							,			[self.model_tester.image_size // 2, self.model_tester.image_size // 2]							,			)

						lowercase   ,							lowercase         = self.model_tester.prepare_config_and_inputs_for_common()
						lowercase         = ["basic", "bottleneck"]
						for model_class in self.all_model_classes:
										for layer_type in layers_type:
														lowercase         = layer_type
														lowercase         = True
														check_hidden_states_output(A__							,			A__							,			A__				)

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

														check_hidden_states_output(A__							,			A__							,			A__				)




		def 				UpperCAmelCase__   (self      :	List[Any]				)					-> Tuple:
						lowercase         = self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_image_classification(*A__				)





		@slow
		def 				UpperCAmelCase__   (self      :	Tuple				)					-> Any:
						for model_name in REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
										lowercase         = RegNetModel.from_pretrained(A__				)
										self.assertIsNotNone(A__				)







def 							UpperCAmelCase_    (	):

				"""simple docstring"""






				lowercase         = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"							)
				return image


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




		@cached_property
		def 				UpperCAmelCase__   (self      :	Optional[int]				)					-> Union[str, Any]:
						return (
						    AutoImageProcessor.from_pretrained(REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]				)
						    if is_vision_available()
						    else None
						)





		@slow
		def 				UpperCAmelCase__   (self      :	List[str]				)					-> int:
						lowercase         = RegNetForImageClassification.from_pretrained(REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]				).to(A__				)

						lowercase         = self.default_image_processor
						lowercase         = prepare_img()
						lowercase         = image_processor(images=A__							,			return_tensors="pt"				).to(A__				)

						# forward pass
						with torch.no_grad():
										lowercase         = model(**A__				)

						# verify the logits
						lowercase         = torch.Size((1, 1_0_0_0)				)
						self.assertEqual(outputs.logits.shape							,			A__				)

						lowercase         = torch.tensor([-0.4_1_8_0, -1.5_0_5_1, -3.4_8_3_6]				).to(A__				)

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




