

"""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


A_							:			int											= logging.get_logger(__name__)

# General docstring
A_							:			Optional[Any]											= "RegNetConfig"

# Base docstring
A_							:			int											= "facebook/regnet-y-040"
A_							:			List[str]											= [1, 1_088, 7, 7]

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

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





class    lowerCamelCase						(nn.Module			):

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





			def 					SCREAMING_SNAKE_CASE				(					self				:						Optional[Any]    ,				__UpperCAmelCase				:						List[Any]       )   ->    int:
						SCREAMING_SNAKE_CASE__								=      self.convolution(__UpperCAmelCase       )
						SCREAMING_SNAKE_CASE__								=      self.normalization(__UpperCAmelCase       )
						SCREAMING_SNAKE_CASE__								=      self.activation(__UpperCAmelCase       )
						return hidden_state





class    lowerCamelCase						(nn.Module			):

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





			def 					SCREAMING_SNAKE_CASE				(					self				:						Dict    ,				__UpperCAmelCase				:						List[Any]       )   ->    Optional[Any]:
						SCREAMING_SNAKE_CASE__								=      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."""       )
						SCREAMING_SNAKE_CASE__								=      self.embedder(__UpperCAmelCase       )
						return hidden_state





class    lowerCamelCase						(nn.Module			):

			def __init__(					self				:						Tuple    ,				__UpperCAmelCase				:						int    ,				__UpperCAmelCase				:						int    ,				__UpperCAmelCase				:						int = 2       )   ->    Union[str, Any]:
						super().__init__()
						SCREAMING_SNAKE_CASE__								=      nn.Convad(__UpperCAmelCase    ,				__UpperCAmelCase    ,				kernel_size=1    ,				stride=__UpperCAmelCase    ,				bias=__UpperCAmelCase       )
						SCREAMING_SNAKE_CASE__								=      nn.BatchNormad(__UpperCAmelCase       )





			def 					SCREAMING_SNAKE_CASE				(					self				:						List[Any]    ,				__UpperCAmelCase				:						Tensor       )   ->    Tensor:
						SCREAMING_SNAKE_CASE__								=      self.convolution(__UpperCAmelCase       )
						SCREAMING_SNAKE_CASE__								=      self.normalization(__UpperCAmelCase       )
						return hidden_state





class    lowerCamelCase						(nn.Module			):

			def __init__(					self				:						Any    ,				__UpperCAmelCase				:						int    ,				__UpperCAmelCase				:						int       )   ->    List[str]:
						super().__init__()

						SCREAMING_SNAKE_CASE__								=      nn.AdaptiveAvgPoolad((1, 1)       )
						SCREAMING_SNAKE_CASE__								=      nn.Sequential(
						    nn.Convad(__UpperCAmelCase    ,				__UpperCAmelCase    ,				kernel_size=1       )    ,				nn.ReLU()    ,				nn.Convad(__UpperCAmelCase    ,				__UpperCAmelCase    ,				kernel_size=1       )    ,				nn.Sigmoid()    ,				)





			def 					SCREAMING_SNAKE_CASE				(					self				:						int    ,				__UpperCAmelCase				:						int       )   ->    Union[str, Any]:
						# b c h w -> b c 1 1
						SCREAMING_SNAKE_CASE__								=      self.pooler(__UpperCAmelCase       )
						SCREAMING_SNAKE_CASE__								=      self.attention(__UpperCAmelCase       )
						SCREAMING_SNAKE_CASE__								=      hidden_state * attention
						return hidden_state





class    lowerCamelCase						(nn.Module			):

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





			def 					SCREAMING_SNAKE_CASE				(					self				:						int    ,				__UpperCAmelCase				:						List[str]       )   ->    Union[str, Any]:
						SCREAMING_SNAKE_CASE__								=      hidden_state
						SCREAMING_SNAKE_CASE__								=      self.layer(__UpperCAmelCase       )
						SCREAMING_SNAKE_CASE__								=      self.shortcut(__UpperCAmelCase       )
						hidden_state += residual
						SCREAMING_SNAKE_CASE__								=      self.activation(__UpperCAmelCase       )
						return hidden_state





class    lowerCamelCase						(nn.Module			):

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





			def 					SCREAMING_SNAKE_CASE				(					self				:						List[str]    ,				__UpperCAmelCase				:						List[Any]       )   ->    Union[str, Any]:
						SCREAMING_SNAKE_CASE__								=      hidden_state
						SCREAMING_SNAKE_CASE__								=      self.layer(__UpperCAmelCase       )
						SCREAMING_SNAKE_CASE__								=      self.shortcut(__UpperCAmelCase       )
						hidden_state += residual
						SCREAMING_SNAKE_CASE__								=      self.activation(__UpperCAmelCase       )
						return hidden_state





class    lowerCamelCase						(nn.Module			):

			def __init__(					self				:						Optional[Any]    ,				__UpperCAmelCase				:						RegNetConfig    ,				__UpperCAmelCase				:						int    ,				__UpperCAmelCase				:						int    ,				__UpperCAmelCase				:						int = 2    ,				__UpperCAmelCase				:						int = 2    ,				)   ->    Optional[int]:
						super().__init__()

						SCREAMING_SNAKE_CASE__								=      RegNetXLayer if config.layer_type == """x""" else RegNetYLayer

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





			def 					SCREAMING_SNAKE_CASE				(					self				:						Dict    ,				__UpperCAmelCase				:						int       )   ->    Optional[int]:
						SCREAMING_SNAKE_CASE__								=      self.layers(__UpperCAmelCase       )
						return hidden_state





class    lowerCamelCase						(nn.Module			):

			def __init__(					self				:						Dict    ,				__UpperCAmelCase				:						RegNetConfig       )   ->    Tuple:
						super().__init__()
						SCREAMING_SNAKE_CASE__								=      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(
						        __UpperCAmelCase    ,				config.embedding_size    ,				config.hidden_sizes[0]    ,				stride=2 if config.downsample_in_first_stage else 1    ,				depth=config.depths[0]    ,				)       )
						SCREAMING_SNAKE_CASE__								=      zip(config.hidden_sizes    ,				config.hidden_sizes[1:]       )
						for (in_channels, out_channels), depth in zip(__UpperCAmelCase    ,				config.depths[1:]       ):
									self.stages.append(RegNetStage(__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				depth=__UpperCAmelCase       )       )





			def 					SCREAMING_SNAKE_CASE				(					self				:						Optional[Any]    ,				__UpperCAmelCase				:						Tensor    ,				__UpperCAmelCase				:						bool = False    ,				__UpperCAmelCase				:						bool = True       )   ->    BaseModelOutputWithNoAttention:
						SCREAMING_SNAKE_CASE__								=      () if output_hidden_states else None

						for stage_module in self.stages:
									if output_hidden_states:
												SCREAMING_SNAKE_CASE__								=      hidden_states + (hidden_state,)

									SCREAMING_SNAKE_CASE__								=      stage_module(__UpperCAmelCase       )

						if output_hidden_states:
									SCREAMING_SNAKE_CASE__								=      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=__UpperCAmelCase    ,				hidden_states=__UpperCAmelCase       )





class    lowerCamelCase						(A__			):
			lowerCamelCase__   :		List[str]							=							RegNetConfig
			lowerCamelCase__   :		Union[str, Any]							=							'regnet'
			lowerCamelCase__   :		Optional[Any]							=							'pixel_values'
			lowerCamelCase__   :		Union[str, Any]							=							True

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





			def 					SCREAMING_SNAKE_CASE				(					self				:						Dict    ,				__UpperCAmelCase				:						List[Any]    ,				__UpperCAmelCase				:						Union[str, Any]=False       )   ->    Union[str, Any]:
						if isinstance(__UpperCAmelCase    ,				__UpperCAmelCase       ):
									SCREAMING_SNAKE_CASE__								=      value
A_							:			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"

A_							:			Tuple											= 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.'			,A__			,)
# Copied from transformers.models.resnet.modeling_resnet.ResNetModel with RESNET->REGNET,ResNet->RegNet
class    lowerCamelCase						(A__			):

			def __init__(					self				:						List[str]    ,				__UpperCAmelCase				:						List[str]       )   ->    int:
						super().__init__(__UpperCAmelCase       )
						SCREAMING_SNAKE_CASE__								=      config
						SCREAMING_SNAKE_CASE__								=      RegNetEmbeddings(__UpperCAmelCase       )
						SCREAMING_SNAKE_CASE__								=      RegNetEncoder(__UpperCAmelCase       )
						SCREAMING_SNAKE_CASE__								=      nn.AdaptiveAvgPoolad((1, 1)       )
						# Initialize weights and apply final processing
						self.post_init()





			@add_start_docstrings_to_model_forward(__UpperCAmelCase       )
			@add_code_sample_docstrings(
			    checkpoint=_CHECKPOINT_FOR_DOC    ,				output_type=__UpperCAmelCase    ,				config_class=_CONFIG_FOR_DOC    ,				modality="""vision"""    ,				expected_output=_EXPECTED_OUTPUT_SHAPE    ,				)
			def 					SCREAMING_SNAKE_CASE				(					self				:						str    ,				__UpperCAmelCase				:						Tensor    ,				__UpperCAmelCase				:						Optional[bool] = None    ,				__UpperCAmelCase				:						Optional[bool] = None       )   ->    BaseModelOutputWithPoolingAndNoAttention:
						SCREAMING_SNAKE_CASE__								=      (
						    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
						)
						SCREAMING_SNAKE_CASE__								=      return_dict if return_dict is not None else self.config.use_return_dict

						SCREAMING_SNAKE_CASE__								=      self.embedder(__UpperCAmelCase       )

						SCREAMING_SNAKE_CASE__								=      self.encoder(
						    __UpperCAmelCase    ,				output_hidden_states=__UpperCAmelCase    ,				return_dict=__UpperCAmelCase       )

						SCREAMING_SNAKE_CASE__								=      encoder_outputs[0]

						SCREAMING_SNAKE_CASE__								=      self.pooler(__UpperCAmelCase       )

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

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





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

			def __init__(					self				:						Optional[int]    ,				__UpperCAmelCase				:						str       )   ->    Dict:
						super().__init__(__UpperCAmelCase       )
						SCREAMING_SNAKE_CASE__								=      config.num_labels
						SCREAMING_SNAKE_CASE__								=      RegNetModel(__UpperCAmelCase       )
						# classification head
						SCREAMING_SNAKE_CASE__								=      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(__UpperCAmelCase       )
			@add_code_sample_docstrings(
			    checkpoint=_IMAGE_CLASS_CHECKPOINT    ,				output_type=__UpperCAmelCase    ,				config_class=_CONFIG_FOR_DOC    ,				expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT    ,				)
			def 					SCREAMING_SNAKE_CASE				(					self				:						Union[str, Any]    ,				__UpperCAmelCase				:						Optional[torch.FloatTensor] = None    ,				__UpperCAmelCase				:						Optional[torch.LongTensor] = None    ,				__UpperCAmelCase				:						Optional[bool] = None    ,				__UpperCAmelCase				:						Optional[bool] = None    ,				)   ->    ImageClassifierOutputWithNoAttention:
						SCREAMING_SNAKE_CASE__								=      return_dict if return_dict is not None else self.config.use_return_dict

						SCREAMING_SNAKE_CASE__								=      self.regnet(__UpperCAmelCase    ,				output_hidden_states=__UpperCAmelCase    ,				return_dict=__UpperCAmelCase       )

						SCREAMING_SNAKE_CASE__								=      outputs.pooler_output if return_dict else outputs[1]

						SCREAMING_SNAKE_CASE__								=      self.classifier(__UpperCAmelCase       )

						SCREAMING_SNAKE_CASE__								=      None

						if labels is not None:
									if self.config.problem_type is None:
												if self.num_labels == 1:
															SCREAMING_SNAKE_CASE__								=      """regression"""
												elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
															SCREAMING_SNAKE_CASE__								=      """single_label_classification"""
												else:
															SCREAMING_SNAKE_CASE__								=      """multi_label_classification"""
									if self.config.problem_type == "regression":
												SCREAMING_SNAKE_CASE__								=      MSELoss()
												if self.num_labels == 1:
															SCREAMING_SNAKE_CASE__								=      loss_fct(logits.squeeze()    ,				labels.squeeze()       )
												else:
															SCREAMING_SNAKE_CASE__								=      loss_fct(__UpperCAmelCase    ,				__UpperCAmelCase       )
									elif self.config.problem_type == "single_label_classification":
												SCREAMING_SNAKE_CASE__								=      CrossEntropyLoss()
												SCREAMING_SNAKE_CASE__								=      loss_fct(logits.view(-1    ,				self.num_labels       )    ,				labels.view(-1       )       )
									elif self.config.problem_type == "multi_label_classification":
												SCREAMING_SNAKE_CASE__								=      BCEWithLogitsLoss()
												SCREAMING_SNAKE_CASE__								=      loss_fct(__UpperCAmelCase    ,				__UpperCAmelCase       )

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

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







"""simple docstring"""




import pytest
import requests

from datasets.utils.file_utils import http_head

from .utils import OfflineSimulationMode, RequestWouldHangIndefinitelyError, offline

@pytest.mark.integration
def 	A       (     ):




			'''simple docstring'''


			with offline(OfflineSimulationMode.CONNECTION_TIMES_OUT    ):
						with pytest.raises(snake_case__    ):
									requests.request("""GET"""		,				"""https://huggingface.co"""    )
						with pytest.raises(requests.exceptions.ConnectTimeout    ):
									requests.request("""GET"""		,				"""https://huggingface.co"""		,				timeout=1.0    )

@pytest.mark.integration
def 	A       (     ):




			'''simple docstring'''


			with offline(OfflineSimulationMode.CONNECTION_FAILS    ):
						with pytest.raises(requests.exceptions.ConnectionError    ):
									requests.request("""GET"""		,				"""https://huggingface.co"""    )




def 	A       (     ):




			'''simple docstring'''


			with offline(OfflineSimulationMode.HF_DATASETS_OFFLINE_SET_TO_1    ):
						with pytest.raises(snake_case__    ):
									http_head("""https://huggingface.co"""    )


