'''simple docstring'''




import inspect
import unittest

from transformers import BitConfig
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_backbone_common import BackboneTesterMixin
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 BitBackbone, BitForImageClassification, BitImageProcessor, BitModel
						from transformers.models.bit.modeling_bit import BIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
						from PIL import Image





class 					__magic_name__		:







			def __init__(			self       :						Tuple       ,    lowercase_       :						Tuple       ,    lowercase_       :						Any=3       ,    lowercase_       :						int=32       ,    lowercase_       :						str=3       ,    lowercase_       :						int=10       ,    lowercase_       :						Any=[8, 16, 32, 64]       ,    lowercase_       :						Tuple=[1, 1, 2, 1]       ,    lowercase_       :						Any=True       ,    lowercase_       :						int=True       ,    lowercase_       :						Any="relu"       ,    lowercase_       :						List[Any]=3       ,    lowercase_       :						Tuple=None       ,    lowercase_       :						Union[str, Any]=["stage2", "stage3", "stage4"]       ,    lowercase_       :						Optional[int]=[2, 3, 4]       ,    lowercase_       :						List[str]=1       ,    ):
										lowercase_      :    Any       		= parent
										lowercase_      :    str       		= batch_size
										lowercase_      :    Any       		= image_size
										lowercase_      :    Optional[Any]       		= num_channels
										lowercase_      :    Any       		= embeddings_size
										lowercase_      :    Union[str, Any]       		= hidden_sizes
										lowercase_      :    Any       		= depths
										lowercase_      :    Dict       		= is_training
										lowercase_      :    Tuple       		= use_labels
										lowercase_      :    str       		= hidden_act
										lowercase_      :    Optional[Any]       		= num_labels
										lowercase_      :    Tuple       		= scope
										lowercase_      :    Any       		= len(lowercase_					)
										lowercase_      :    Optional[Any]       		= out_features
										lowercase_      :    Tuple       		= out_indices
										lowercase_      :    str       		= num_groups







			def 			SCREAMING_SNAKE_CASE_  (			self       :						List[Any]					):
										lowercase_      :    int       		= floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]					)

										lowercase_      :    List[Any]       		= None
										if self.use_labels:
																	lowercase_      :    List[str]       		= ids_tensor([self.batch_size]       ,    self.num_labels					)

										lowercase_      :    int       		= self.get_config()

										return config, pixel_values, labels







			def 			SCREAMING_SNAKE_CASE_  (			self       :						Union[str, Any]					):
										return BitConfig(
										    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       ,    out_features=self.out_features       ,    out_indices=self.out_indices       ,    num_groups=self.num_groups       ,    )







			def 			SCREAMING_SNAKE_CASE_  (			self       :						Union[str, Any]       ,    lowercase_       :						int       ,    lowercase_       :						List[str]       ,    lowercase_       :						List[str]					):
										lowercase_      :    Optional[int]       		= BitModel(config=lowercase_					)
										model.to(lowercase_					)
										model.eval()
										lowercase_      :    List[Any]       		= model(lowercase_					)
										self.parent.assertEqual(
										    result.last_hidden_state.shape       ,    (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)       ,    )







			def 			SCREAMING_SNAKE_CASE_  (			self       :						Optional[Any]       ,    lowercase_       :						Tuple       ,    lowercase_       :						Optional[int]       ,    lowercase_       :						List[Any]					):
										lowercase_      :    Union[str, Any]       		= self.num_labels
										lowercase_      :    Tuple       		= BitForImageClassification(lowercase_					)
										model.to(lowercase_					)
										model.eval()
										lowercase_      :    Any       		= model(lowercase_       ,    labels=lowercase_					)
										self.parent.assertEqual(result.logits.shape       ,    (self.batch_size, self.num_labels)					)







			def 			SCREAMING_SNAKE_CASE_  (			self       :						Optional[Any]       ,    lowercase_       :						Union[str, Any]       ,    lowercase_       :						List[Any]       ,    lowercase_       :						Optional[int]					):
										lowercase_      :    Any       		= BitBackbone(config=lowercase_					)
										model.to(lowercase_					)
										model.eval()
										lowercase_      :    Dict       		= model(lowercase_					)

										# verify feature maps
										self.parent.assertEqual(len(result.feature_maps					)       ,    len(config.out_features					)					)
										self.parent.assertListEqual(list(result.feature_maps[0].shape					)       ,    [self.batch_size, self.hidden_sizes[1], 4, 4]					)

										# verify channels
										self.parent.assertEqual(len(model.channels					)       ,    len(config.out_features					)					)
										self.parent.assertListEqual(model.channels       ,    config.hidden_sizes[1:]					)

										# verify backbone works with out_features=None
										lowercase_      :    List[str]       		= None
										lowercase_      :    Dict       		= BitBackbone(config=lowercase_					)
										model.to(lowercase_					)
										model.eval()
										lowercase_      :    Tuple       		= model(lowercase_					)

										# verify feature maps
										self.parent.assertEqual(len(result.feature_maps					)       ,    1					)
										self.parent.assertListEqual(list(result.feature_maps[0].shape					)       ,    [self.batch_size, self.hidden_sizes[-1], 1, 1]					)

										# verify channels
										self.parent.assertEqual(len(model.channels					)       ,    1					)
										self.parent.assertListEqual(model.channels       ,    [config.hidden_sizes[-1]]					)







			def 			SCREAMING_SNAKE_CASE_  (			self       :						Any					):
										lowercase_      :    Optional[int]       		= self.prepare_config_and_inputs()
										lowercase_		,		lowercase_		,		lowercase_      :    Optional[Any]       		= config_and_inputs
										lowercase_      :    Any       		= {"""pixel_values""": pixel_values}
										return config, inputs_dict





@require_torch
class 					__magic_name__		(					_UpperCAmelCase,  _UpperCAmelCase,  unittest.TestCase):
			UpperCamelCase__       = (BitModel, BitForImageClassification, BitBackbone) if is_torch_available() else ()
			UpperCamelCase__       = (
			    {'''feature-extraction''': BitModel, '''image-classification''': BitForImageClassification}
			    if is_torch_available()
			    else {}
			)

			UpperCamelCase__       = False
			UpperCamelCase__       = False
			UpperCamelCase__       = False
			UpperCamelCase__       = False
			UpperCamelCase__       = False







			def 			SCREAMING_SNAKE_CASE_  (			self       :						str					):
										lowercase_      :    int       		= BitModelTester(self					)
										lowercase_      :    Optional[int]       		= ConfigTester(self       ,    config_class=lowercase_       ,    has_text_modality=lowercase_					)







			def 			SCREAMING_SNAKE_CASE_  (			self       :						Any					):
										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 			SCREAMING_SNAKE_CASE_  (			self       :						List[str]					):
										return







			@unittest.skip(reason="""Bit does not output attentions"""					)
			def 			SCREAMING_SNAKE_CASE_  (			self       :						Any					):
										pass







			@unittest.skip(reason="""Bit does not use inputs_embeds"""					)
			def 			SCREAMING_SNAKE_CASE_  (			self       :						Any					):
										pass







			@unittest.skip(reason="""Bit does not support input and output embeddings"""					)
			def 			SCREAMING_SNAKE_CASE_  (			self       :						Optional[int]					):
										pass







			def 			SCREAMING_SNAKE_CASE_  (			self       :						Dict					):
										lowercase_		,		lowercase_      :    Optional[int]       		= self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
																	lowercase_      :    Optional[Any]       		= model_class(lowercase_					)
																	lowercase_      :    Union[str, Any]       		= inspect.signature(model.forward					)
																	# signature.parameters is an OrderedDict => so arg_names order is deterministic
																	lowercase_      :    Union[str, Any]       		= [*signature.parameters.keys()]

																	lowercase_      :    Optional[int]       		= ["""pixel_values"""]
																	self.assertListEqual(arg_names[:1]       ,    lowercase_					)







			def 			SCREAMING_SNAKE_CASE_  (			self       :						Any					):
										lowercase_      :    int       		= self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*lowercase_					)







			def 			SCREAMING_SNAKE_CASE_  (			self       :						Tuple					):
										lowercase_      :    Any       		= self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_backbone(*lowercase_					)







			def 			SCREAMING_SNAKE_CASE_  (			self       :						Any					):
										lowercase_		,		lowercase_      :    int       		= self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
																	lowercase_      :    List[Any]       		= model_class(config=lowercase_					)
																	for name, module in model.named_modules():
																								if isinstance(lowercase_       ,    (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 			SCREAMING_SNAKE_CASE_  (			self       :						Optional[Any]					):
										def check_hidden_states_output(lowercase_       :						Optional[int]       ,    lowercase_       :						int       ,    lowercase_       :						int					):
																	lowercase_      :    Optional[Any]       		= model_class(lowercase_					)
																	model.to(lowercase_					)
																	model.eval()

																	with torch.no_grad():
																								lowercase_      :    List[Any]       		= model(**self._prepare_for_class(lowercase_       ,    lowercase_					)					)

																	lowercase_      :    str       		= outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

																	lowercase_      :    Optional[int]       		= self.model_tester.num_stages
																	self.assertEqual(len(lowercase_					)       ,    expected_num_stages + 1					)

																	# Bit'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 // 4, self.model_tester.image_size // 4]       ,    )

										lowercase_		,		lowercase_      :    List[Any]       		= self.model_tester.prepare_config_and_inputs_for_common()
										lowercase_      :    Dict       		= ["""preactivation""", """bottleneck"""]
										for model_class in self.all_model_classes:
																	for layer_type in layers_type:
																								lowercase_      :    Union[str, Any]       		= layer_type
																								lowercase_      :    Optional[Any]       		= True
																								check_hidden_states_output(lowercase_       ,    lowercase_       ,    lowercase_					)

																								# check that output_hidden_states also work using config
																								del inputs_dict["output_hidden_states"]
																								lowercase_      :    Union[str, Any]       		= True

																								check_hidden_states_output(lowercase_       ,    lowercase_       ,    lowercase_					)







			@unittest.skip(reason="""Bit does not use feedforward chunking"""					)
			def 			SCREAMING_SNAKE_CASE_  (			self       :						Tuple					):
										pass







			def 			SCREAMING_SNAKE_CASE_  (			self       :						Optional[Any]					):
										lowercase_      :    Tuple       		= self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_image_classification(*lowercase_					)







			@slow
			def 			SCREAMING_SNAKE_CASE_  (			self       :						Tuple					):
										for model_name in BIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																	lowercase_      :    List[str]       		= BitModel.from_pretrained(lowercase_					)
																	self.assertIsNotNone(lowercase_					)




def 							lowerCamelCase			(   )    ->   Optional[Any]:
							lowercase_      :    List[str]       		= Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""				)
							return image





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







			@cached_property
			def 			SCREAMING_SNAKE_CASE_  (			self       :						List[str]					):
										return (
										    BitImageProcessor.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]					) if is_vision_available() else None
										)







			@slow
			def 			SCREAMING_SNAKE_CASE_  (			self       :						int					):
										lowercase_      :    List[str]       		= BitForImageClassification.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]					).to(lowercase_					)

										lowercase_      :    int       		= self.default_image_processor
										lowercase_      :    List[Any]       		= prepare_img()
										lowercase_      :    Dict       		= image_processor(images=lowercase_       ,    return_tensors="""pt"""					).to(lowercase_					)

										# forward pass
										with torch.no_grad():
																	lowercase_      :    str       		= model(**lowercase_					)

										# verify the logits
										lowercase_      :    Optional[int]       		= torch.Size((1, 1000)					)
										self.assertEqual(outputs.logits.shape       ,    lowercase_					)

										lowercase_      :    Union[str, Any]       		= torch.tensor([[-0.65_26, -0.52_63, -1.43_98]]					).to(lowercase_					)

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





@require_torch
class 					__magic_name__		(					_UpperCAmelCase,  unittest.TestCase):
			UpperCamelCase__       = (BitBackbone,) if is_torch_available() else ()
			UpperCamelCase__       = BitConfig

			UpperCamelCase__       = False







			def 			SCREAMING_SNAKE_CASE_  (			self       :						List[Any]					):
										lowercase_      :    Union[str, Any]       		= BitModelTester(self					)






'''simple docstring'''




from typing import Optional

import torch
import torch.utils.checkpoint
from torch import Tensor, nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss

from ...activations import ACTaFN
from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
from ...modeling_outputs import (
    BaseModelOutputWithNoAttention,
    BaseModelOutputWithPoolingAndNoAttention,
    ImageClassifierOutputWithNoAttention,
)
from ...modeling_utils import PreTrainedModel
from ...utils import logging
from .configuration_regnet import RegNetConfig


_lowercase		:				Tuple	       =		logging.get_logger(__name__)

# General docstring
_lowercase		:				List[str]	       =		"RegNetConfig"

# Base docstring
_lowercase		:				Dict	       =		"facebook/regnet-y-040"
_lowercase		:				Union[str, Any]	       =		[1, 1088, 7, 7]

# Image classification docstring
_lowercase		:				Optional[Any]	       =		"facebook/regnet-y-040"
_lowercase		:				Union[str, Any]	       =		"tabby, tabby cat"

_lowercase		:				str	       =		[
    "facebook/regnet-y-040",
    # See all regnet models at https://huggingface.co/models?filter=regnet
]





class 					__magic_name__		(					nn.Module):







			def __init__(			self       :						Union[str, Any]       ,    lowercase_       :						int       ,    lowercase_       :						int       ,    lowercase_       :						int = 3       ,    lowercase_       :						int = 1       ,    lowercase_       :						int = 1       ,    lowercase_       :						Optional[str] = "relu"       ,    ):
										super().__init__()
										lowercase_      :    List[Any]       		= nn.Convad(
										    lowercase_       ,    lowercase_       ,    kernel_size=lowercase_       ,    stride=lowercase_       ,    padding=kernel_size // 2       ,    groups=lowercase_       ,    bias=lowercase_       ,    )
										lowercase_      :    str       		= nn.BatchNormad(lowercase_					)
										lowercase_      :    Optional[int]       		= ACTaFN[activation] if activation is not None else nn.Identity()







			def 			SCREAMING_SNAKE_CASE_  (			self       :						List[Any]       ,    lowercase_       :						List[str]					):
										lowercase_      :    Dict       		= self.convolution(lowercase_					)
										lowercase_      :    str       		= self.normalization(lowercase_					)
										lowercase_      :    Optional[Any]       		= self.activation(lowercase_					)
										return hidden_state





class 					__magic_name__		(					nn.Module):







			def __init__(			self       :						List[Any]       ,    lowercase_       :						RegNetConfig					):
										super().__init__()
										lowercase_      :    str       		= RegNetConvLayer(
										    config.num_channels       ,    config.embedding_size       ,    kernel_size=3       ,    stride=2       ,    activation=config.hidden_act					)
										lowercase_      :    Any       		= config.num_channels







			def 			SCREAMING_SNAKE_CASE_  (			self       :						Dict       ,    lowercase_       :						Optional[Any]					):
										lowercase_      :    List[str]       		= pixel_values.shape[1]
										if num_channels != self.num_channels:
																	raise ValueError(
																	    """Make sure that the channel dimension of the pixel values match with the one set in the configuration."""					)
										lowercase_      :    Any       		= self.embedder(lowercase_					)
										return hidden_state





class 					__magic_name__		(					nn.Module):







			def __init__(			self       :						Optional[int]       ,    lowercase_       :						int       ,    lowercase_       :						int       ,    lowercase_       :						int = 2					):
										super().__init__()
										lowercase_      :    Optional[Any]       		= nn.Convad(lowercase_       ,    lowercase_       ,    kernel_size=1       ,    stride=lowercase_       ,    bias=lowercase_					)
										lowercase_      :    Union[str, Any]       		= nn.BatchNormad(lowercase_					)







			def 			SCREAMING_SNAKE_CASE_  (			self       :						Tuple       ,    lowercase_       :						Tensor					):
										lowercase_      :    Tuple       		= self.convolution(lowercase_					)
										lowercase_      :    str       		= self.normalization(lowercase_					)
										return hidden_state





class 					__magic_name__		(					nn.Module):







			def __init__(			self       :						str       ,    lowercase_       :						int       ,    lowercase_       :						int					):
										super().__init__()

										lowercase_      :    int       		= nn.AdaptiveAvgPoolad((1, 1)					)
										lowercase_      :    int       		= nn.Sequential(
										    nn.Convad(lowercase_       ,    lowercase_       ,    kernel_size=1					)       ,    nn.ReLU()       ,    nn.Convad(lowercase_       ,    lowercase_       ,    kernel_size=1					)       ,    nn.Sigmoid()       ,    )







			def 			SCREAMING_SNAKE_CASE_  (			self       :						Optional[int]       ,    lowercase_       :						Any					):
										# b c h w -> b c 1 1
										lowercase_      :    List[str]       		= self.pooler(lowercase_					)
										lowercase_      :    Optional[int]       		= self.attention(lowercase_					)
										lowercase_      :    Any       		= hidden_state * attention
										return hidden_state





class 					__magic_name__		(					nn.Module):







			def __init__(			self       :						Optional[int]       ,    lowercase_       :						RegNetConfig       ,    lowercase_       :						int       ,    lowercase_       :						int       ,    lowercase_       :						int = 1					):
										super().__init__()
										lowercase_      :    List[Any]       		= in_channels != out_channels or stride != 1
										lowercase_      :    Optional[int]       		= max(1       ,    out_channels // config.groups_width					)
										lowercase_      :    Dict       		= (
										    RegNetShortCut(lowercase_       ,    lowercase_       ,    stride=lowercase_					) if should_apply_shortcut else nn.Identity()
										)
										lowercase_      :    List[Any]       		= nn.Sequential(
										    RegNetConvLayer(lowercase_       ,    lowercase_       ,    kernel_size=1       ,    activation=config.hidden_act					)       ,    RegNetConvLayer(lowercase_       ,    lowercase_       ,    stride=lowercase_       ,    groups=lowercase_       ,    activation=config.hidden_act					)       ,    RegNetConvLayer(lowercase_       ,    lowercase_       ,    kernel_size=1       ,    activation=lowercase_					)       ,    )
										lowercase_      :    int       		= ACTaFN[config.hidden_act]







			def 			SCREAMING_SNAKE_CASE_  (			self       :						Any       ,    lowercase_       :						Any					):
										lowercase_      :    Any       		= hidden_state
										lowercase_      :    Union[str, Any]       		= self.layer(lowercase_					)
										lowercase_      :    Union[str, Any]       		= self.shortcut(lowercase_					)
										hidden_state += residual
										lowercase_      :    str       		= self.activation(lowercase_					)
										return hidden_state





class 					__magic_name__		(					nn.Module):







			def __init__(			self       :						Optional[Any]       ,    lowercase_       :						RegNetConfig       ,    lowercase_       :						int       ,    lowercase_       :						int       ,    lowercase_       :						int = 1					):
										super().__init__()
										lowercase_      :    str       		= in_channels != out_channels or stride != 1
										lowercase_      :    int       		= max(1       ,    out_channels // config.groups_width					)
										lowercase_      :    int       		= (
										    RegNetShortCut(lowercase_       ,    lowercase_       ,    stride=lowercase_					) if should_apply_shortcut else nn.Identity()
										)
										lowercase_      :    Union[str, Any]       		= nn.Sequential(
										    RegNetConvLayer(lowercase_       ,    lowercase_       ,    kernel_size=1       ,    activation=config.hidden_act					)       ,    RegNetConvLayer(lowercase_       ,    lowercase_       ,    stride=lowercase_       ,    groups=lowercase_       ,    activation=config.hidden_act					)       ,    RegNetSELayer(lowercase_       ,    reduced_channels=int(round(in_channels / 4					)					)					)       ,    RegNetConvLayer(lowercase_       ,    lowercase_       ,    kernel_size=1       ,    activation=lowercase_					)       ,    )
										lowercase_      :    Optional[int]       		= ACTaFN[config.hidden_act]







			def 			SCREAMING_SNAKE_CASE_  (			self       :						List[str]       ,    lowercase_       :						Union[str, Any]					):
										lowercase_      :    Optional[int]       		= hidden_state
										lowercase_      :    str       		= self.layer(lowercase_					)
										lowercase_      :    int       		= self.shortcut(lowercase_					)
										hidden_state += residual
										lowercase_      :    Optional[Any]       		= self.activation(lowercase_					)
										return hidden_state





class 					__magic_name__		(					nn.Module):







			def __init__(			self       :						str       ,    lowercase_       :						RegNetConfig       ,    lowercase_       :						int       ,    lowercase_       :						int       ,    lowercase_       :						int = 2       ,    lowercase_       :						int = 2       ,    ):
										super().__init__()

										lowercase_      :    str       		= RegNetXLayer if config.layer_type == """x""" else RegNetYLayer

										lowercase_      :    str       		= nn.Sequential(
										    # downsampling is done in the first layer with stride of 2
										    layer(
										        lowercase_       ,    lowercase_       ,    lowercase_       ,    stride=lowercase_       ,    )       ,    *[layer(lowercase_       ,    lowercase_       ,    lowercase_					) for _ in range(depth - 1					)]       ,    )







			def 			SCREAMING_SNAKE_CASE_  (			self       :						Union[str, Any]       ,    lowercase_       :						List[str]					):
										lowercase_      :    Tuple       		= self.layers(lowercase_					)
										return hidden_state





class 					__magic_name__		(					nn.Module):







			def __init__(			self       :						Dict       ,    lowercase_       :						RegNetConfig					):
										super().__init__()
										lowercase_      :    Optional[Any]       		= nn.ModuleList([]					)
										# based on `downsample_in_first_stage`, the first layer of the first stage may or may not downsample the input
										self.stages.append(
										    RegNetStage(
										        lowercase_       ,    config.embedding_size       ,    config.hidden_sizes[0]       ,    stride=2 if config.downsample_in_first_stage else 1       ,    depth=config.depths[0]       ,    )					)
										lowercase_      :    Optional[Any]       		= zip(config.hidden_sizes       ,    config.hidden_sizes[1:]					)
										for (in_channels, out_channels), depth in zip(lowercase_       ,    config.depths[1:]					):
																	self.stages.append(RegNetStage(lowercase_       ,    lowercase_       ,    lowercase_       ,    depth=lowercase_					)					)







			def 			SCREAMING_SNAKE_CASE_  (			self       :						str       ,    lowercase_       :						Tensor       ,    lowercase_       :						bool = False       ,    lowercase_       :						bool = True					):
										lowercase_      :    Tuple       		= () if output_hidden_states else None

										for stage_module in self.stages:
																	if output_hidden_states:
																								lowercase_      :    Union[str, Any]       		= hidden_states + (hidden_state,)

																	lowercase_      :    Dict       		= stage_module(lowercase_					)

										if output_hidden_states:
																	lowercase_      :    Optional[Any]       		= hidden_states + (hidden_state,)

										if not return_dict:
																	return tuple(v for v in [hidden_state, hidden_states] if v is not None					)

										return BaseModelOutputWithNoAttention(last_hidden_state=lowercase_       ,    hidden_states=lowercase_					)





class 					__magic_name__		(					_UpperCAmelCase):
			UpperCamelCase__       = RegNetConfig
			UpperCamelCase__       = '''regnet'''
			UpperCamelCase__       = '''pixel_values'''
			UpperCamelCase__       = True







			def 			SCREAMING_SNAKE_CASE_  (			self       :						int       ,    lowercase_       :						Optional[Any]					):
										if isinstance(lowercase_       ,    nn.Convad					):
																	nn.init.kaiming_normal_(module.weight       ,    mode="""fan_out"""       ,    nonlinearity="""relu"""					)
										elif isinstance(lowercase_       ,    (nn.BatchNormad, nn.GroupNorm)					):
																	nn.init.constant_(module.weight       ,    1					)
																	nn.init.constant_(module.bias       ,    0					)







			def 			SCREAMING_SNAKE_CASE_  (			self       :						List[Any]       ,    lowercase_       :						Optional[int]       ,    lowercase_       :						Any=False					):
										if isinstance(lowercase_       ,    lowercase_					):
																	lowercase_      :    List[str]       		= value




_lowercase		:				Dict	       =		r"\n    This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it\n    as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and\n    behavior.\n\n    Parameters:\n        config ([`RegNetConfig`]): Model configuration class with all the parameters of the model.\n            Initializing with a config file does not load the weights associated with the model, only the\n            configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.\n"

_lowercase		:				Any	       =		r"\n    Args:\n        pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):\n            Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See\n            [`ConvNextImageProcessor.__call__`] for details.\n\n        output_hidden_states (`bool`, *optional*):\n            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for\n            more detail.\n        return_dict (`bool`, *optional*):\n            Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple.\n"





@add_start_docstrings(
    '''The bare RegNet model outputting raw features without any specific head on top.''',  _UpperCAmelCase,  )
# Copied from transformers.models.resnet.modeling_resnet.ResNetModel with RESNET->REGNET,ResNet->RegNet
class 					__magic_name__		(					_UpperCAmelCase):







			def __init__(			self       :						Any       ,    lowercase_       :						Any					):
										super().__init__(lowercase_					)
										lowercase_      :    List[str]       		= config
										lowercase_      :    Union[str, Any]       		= RegNetEmbeddings(lowercase_					)
										lowercase_      :    Union[str, Any]       		= RegNetEncoder(lowercase_					)
										lowercase_      :    str       		= nn.AdaptiveAvgPoolad((1, 1)					)
										# Initialize weights and apply final processing
										self.post_init()







			@add_start_docstrings_to_model_forward(lowercase_					)
			@add_code_sample_docstrings(
			    checkpoint=_CHECKPOINT_FOR_DOC       ,    output_type=lowercase_       ,    config_class=_CONFIG_FOR_DOC       ,    modality="""vision"""       ,    expected_output=_EXPECTED_OUTPUT_SHAPE       ,    )
			def 			SCREAMING_SNAKE_CASE_  (			self       :						str       ,    lowercase_       :						Tensor       ,    lowercase_       :						Optional[bool] = None       ,    lowercase_       :						Optional[bool] = None					):
										lowercase_      :    List[Any]       		= (
										    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
										)
										lowercase_      :    Tuple       		= return_dict if return_dict is not None else self.config.use_return_dict

										lowercase_      :    str       		= self.embedder(lowercase_					)

										lowercase_      :    Optional[Any]       		= self.encoder(
										    lowercase_       ,    output_hidden_states=lowercase_       ,    return_dict=lowercase_					)

										lowercase_      :    List[Any]       		= encoder_outputs[0]

										lowercase_      :    str       		= self.pooler(lowercase_					)

										if not return_dict:
																	return (last_hidden_state, pooled_output) + encoder_outputs[1:]

										return BaseModelOutputWithPoolingAndNoAttention(
										    last_hidden_state=lowercase_       ,    pooler_output=lowercase_       ,    hidden_states=encoder_outputs.hidden_states       ,    )





@add_start_docstrings(
    '''
    RegNet Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for
    ImageNet.
    ''',  _UpperCAmelCase,  )
# Copied from transformers.models.resnet.modeling_resnet.ResNetForImageClassification with RESNET->REGNET,ResNet->RegNet,resnet->regnet
class 					__magic_name__		(					_UpperCAmelCase):







			def __init__(			self       :						Dict       ,    lowercase_       :						str					):
										super().__init__(lowercase_					)
										lowercase_      :    Any       		= config.num_labels
										lowercase_      :    List[str]       		= RegNetModel(lowercase_					)
										# classification head
										lowercase_      :    Any       		= nn.Sequential(
										    nn.Flatten()       ,    nn.Linear(config.hidden_sizes[-1]       ,    config.num_labels					) if config.num_labels > 0 else nn.Identity()       ,    )
										# initialize weights and apply final processing
										self.post_init()







			@add_start_docstrings_to_model_forward(lowercase_					)
			@add_code_sample_docstrings(
			    checkpoint=_IMAGE_CLASS_CHECKPOINT       ,    output_type=lowercase_       ,    config_class=_CONFIG_FOR_DOC       ,    expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT       ,    )
			def 			SCREAMING_SNAKE_CASE_  (			self       :						List[str]       ,    lowercase_       :						Optional[torch.FloatTensor] = None       ,    lowercase_       :						Optional[torch.LongTensor] = None       ,    lowercase_       :						Optional[bool] = None       ,    lowercase_       :						Optional[bool] = None       ,    ):
										lowercase_      :    Union[str, Any]       		= return_dict if return_dict is not None else self.config.use_return_dict

										lowercase_      :    Optional[int]       		= self.regnet(lowercase_       ,    output_hidden_states=lowercase_       ,    return_dict=lowercase_					)

										lowercase_      :    Optional[int]       		= outputs.pooler_output if return_dict else outputs[1]

										lowercase_      :    List[Any]       		= self.classifier(lowercase_					)

										lowercase_      :    Optional[int]       		= None

										if labels is not None:
																	if self.config.problem_type is None:
																								if self.num_labels == 1:
																															lowercase_      :    Optional[int]       		= """regression"""
																								elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
																															lowercase_      :    str       		= """single_label_classification"""
																								else:
																															lowercase_      :    str       		= """multi_label_classification"""
																	if self.config.problem_type == "regression":
																								lowercase_      :    str       		= MSELoss()
																								if self.num_labels == 1:
																															lowercase_      :    List[Any]       		= loss_fct(logits.squeeze()       ,    labels.squeeze()					)
																								else:
																															lowercase_      :    List[str]       		= loss_fct(lowercase_       ,    lowercase_					)
																	elif self.config.problem_type == "single_label_classification":
																								lowercase_      :    Optional[int]       		= CrossEntropyLoss()
																								lowercase_      :    Union[str, Any]       		= loss_fct(logits.view(-1       ,    self.num_labels					)       ,    labels.view(-1					)					)
																	elif self.config.problem_type == "multi_label_classification":
																								lowercase_      :    Dict       		= BCEWithLogitsLoss()
																								lowercase_      :    Tuple       		= loss_fct(lowercase_       ,    lowercase_					)

										if not return_dict:
																	lowercase_      :    Tuple       		= (logits,) + outputs[2:]
																	return (loss,) + output if loss is not None else output

										return ImageClassifierOutputWithNoAttention(loss=lowercase_       ,    logits=lowercase_       ,    hidden_states=outputs.hidden_states					)





