






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


SCREAMING_SNAKE_CASE__      :			Optional[Any]							=		logging.get_logger(__name__)

# General docstring
SCREAMING_SNAKE_CASE__      :			Union[str, Any]							=		"""RegNetConfig"""

# Base docstring
SCREAMING_SNAKE_CASE__      :			Any							=		"""facebook/regnet-y-040"""
SCREAMING_SNAKE_CASE__      :			Optional[int]							=		[1, 10_88, 7, 7]

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

SCREAMING_SNAKE_CASE__      :			Optional[Any]							=		[
    """facebook/regnet-y-040""",
    # See all regnet models at https://huggingface.co/models?filter=regnet
]
class 						lowerCamelCase_				(      nn.Module ):







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







				def 			A	(						self       ,					__lowerCAmelCase     ):
								"""simple docstring"""
								__magic_name__		:Optional[int]        =      self.convolution(__lowerCAmelCase     )
								__magic_name__		:Optional[Any]        =      self.normalization(__lowerCAmelCase     )
								__magic_name__		:Any        =      self.activation(__lowerCAmelCase     )
								return hidden_state







class 						lowerCamelCase_				(      nn.Module ):







				def __init__(						self       ,					__lowerCAmelCase     ):
								"""simple docstring"""
								super().__init__()
								__magic_name__		:Optional[Any]        =      RegNetConvLayer(
								    config.num_channels       ,					config.embedding_size       ,					kernel_size=3       ,					stride=2       ,					activation=config.hidden_act     )
								__magic_name__		:Dict        =      config.num_channels







				def 			A	(						self       ,					__lowerCAmelCase     ):
								"""simple docstring"""
								__magic_name__		: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.'''     )
								__magic_name__		:List[Any]        =      self.embedder(__lowerCAmelCase     )
								return hidden_state







class 						lowerCamelCase_				(      nn.Module ):







				def __init__(						self       ,					__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase = 2     ):
								"""simple docstring"""
								super().__init__()
								__magic_name__		:Union[str, Any]        =      nn.Convad(__lowerCAmelCase       ,					__lowerCAmelCase       ,					kernel_size=1       ,					stride=__lowerCAmelCase       ,					bias=__lowerCAmelCase     )
								__magic_name__		:Optional[Any]        =      nn.BatchNormad(__lowerCAmelCase     )







				def 			A	(						self       ,					__lowerCAmelCase     ):
								"""simple docstring"""
								__magic_name__		:List[Any]        =      self.convolution(__lowerCAmelCase     )
								__magic_name__		:int        =      self.normalization(__lowerCAmelCase     )
								return hidden_state







class 						lowerCamelCase_				(      nn.Module ):







				def __init__(						self       ,					__lowerCAmelCase       ,					__lowerCAmelCase     ):
								"""simple docstring"""
								super().__init__()

								__magic_name__		:Union[str, Any]        =      nn.AdaptiveAvgPoolad((1, 1)     )
								__magic_name__		:int        =      nn.Sequential(
								    nn.Convad(__lowerCAmelCase       ,					__lowerCAmelCase       ,					kernel_size=1     )       ,					nn.ReLU()       ,					nn.Convad(__lowerCAmelCase       ,					__lowerCAmelCase       ,					kernel_size=1     )       ,					nn.Sigmoid()       ,					)







				def 			A	(						self       ,					__lowerCAmelCase     ):
								"""simple docstring"""
								# b c h w -> b c 1 1
								__magic_name__		:Optional[int]        =      self.pooler(__lowerCAmelCase     )
								__magic_name__		:Union[str, Any]        =      self.attention(__lowerCAmelCase     )
								__magic_name__		:List[Any]        =      hidden_state * attention
								return hidden_state







class 						lowerCamelCase_				(      nn.Module ):







				def __init__(						self       ,					__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase = 1     ):
								"""simple docstring"""
								super().__init__()
								__magic_name__		:Dict        =      in_channels != out_channels or stride != 1
								__magic_name__		:Optional[Any]        =      max(1       ,					out_channels // config.groups_width     )
								__magic_name__		:Union[str, Any]        =      (
								    RegNetShortCut(__lowerCAmelCase       ,					__lowerCAmelCase       ,					stride=__lowerCAmelCase     ) if should_apply_shortcut else nn.Identity()
								)
								__magic_name__		:Any        =      nn.Sequential(
								    RegNetConvLayer(__lowerCAmelCase       ,					__lowerCAmelCase       ,					kernel_size=1       ,					activation=config.hidden_act     )       ,					RegNetConvLayer(__lowerCAmelCase       ,					__lowerCAmelCase       ,					stride=__lowerCAmelCase       ,					groups=__lowerCAmelCase       ,					activation=config.hidden_act     )       ,					RegNetConvLayer(__lowerCAmelCase       ,					__lowerCAmelCase       ,					kernel_size=1       ,					activation=__lowerCAmelCase     )       ,					)
								__magic_name__		:List[str]        =      ACTaFN[config.hidden_act]







				def 			A	(						self       ,					__lowerCAmelCase     ):
								"""simple docstring"""
								__magic_name__		:Dict        =      hidden_state
								__magic_name__		:List[str]        =      self.layer(__lowerCAmelCase     )
								__magic_name__		:List[Any]        =      self.shortcut(__lowerCAmelCase     )
								hidden_state += residual
								__magic_name__		:Optional[Any]        =      self.activation(__lowerCAmelCase     )
								return hidden_state







class 						lowerCamelCase_				(      nn.Module ):







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







				def 			A	(						self       ,					__lowerCAmelCase     ):
								"""simple docstring"""
								__magic_name__		:int        =      hidden_state
								__magic_name__		:Dict        =      self.layer(__lowerCAmelCase     )
								__magic_name__		:Optional[Any]        =      self.shortcut(__lowerCAmelCase     )
								hidden_state += residual
								__magic_name__		:int        =      self.activation(__lowerCAmelCase     )
								return hidden_state







class 						lowerCamelCase_				(      nn.Module ):







				def __init__(						self       ,					__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase = 2       ,					__lowerCAmelCase = 2       ,					):
								"""simple docstring"""
								super().__init__()

								__magic_name__		:Optional[Any]        =      RegNetXLayer if config.layer_type == '''x''' else RegNetYLayer

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







				def 			A	(						self       ,					__lowerCAmelCase     ):
								"""simple docstring"""
								__magic_name__		:Optional[int]        =      self.layers(__lowerCAmelCase     )
								return hidden_state







class 						lowerCamelCase_				(      nn.Module ):







				def __init__(						self       ,					__lowerCAmelCase     ):
								"""simple docstring"""
								super().__init__()
								__magic_name__		:Tuple        =      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(
								        __lowerCAmelCase       ,					config.embedding_size       ,					config.hidden_sizes[0]       ,					stride=2 if config.downsample_in_first_stage else 1       ,					depth=config.depths[0]       ,					)     )
								__magic_name__		:str        =      zip(config.hidden_sizes       ,					config.hidden_sizes[1:]     )
								for (in_channels, out_channels), depth in zip(__lowerCAmelCase       ,					config.depths[1:]     ):
												self.stages.append(RegNetStage(__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase       ,					depth=__lowerCAmelCase     )     )







				def 			A	(						self       ,					__lowerCAmelCase       ,					__lowerCAmelCase = False       ,					__lowerCAmelCase = True     ):
								"""simple docstring"""
								__magic_name__		:str        =      () if output_hidden_states else None

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

												__magic_name__		:Union[str, Any]        =      stage_module(__lowerCAmelCase     )

								if output_hidden_states:
												__magic_name__		:List[str]        =      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=__lowerCAmelCase       ,					hidden_states=__lowerCAmelCase     )







class 						lowerCamelCase_				(      lowerCamelCase ):
				a__					       =  RegNetConfig
				a__					       =  '''regnet'''
				a__					       =  '''pixel_values'''
				a__					       =  True







				def 			A	(						self       ,					__lowerCAmelCase     ):
								"""simple docstring"""
								if isinstance(__lowerCAmelCase       ,					nn.Convad     ):
												nn.init.kaiming_normal_(module.weight       ,					mode='''fan_out'''       ,					nonlinearity='''relu'''     )
								elif isinstance(__lowerCAmelCase       ,					(nn.BatchNormad, nn.GroupNorm)     ):
												nn.init.constant_(module.weight       ,					1     )
												nn.init.constant_(module.bias       ,					0     )







				def 			A	(						self       ,					__lowerCAmelCase       ,					__lowerCAmelCase=False     ):
								"""simple docstring"""
								if isinstance(__lowerCAmelCase       ,					__lowerCAmelCase     ):
												__magic_name__		:Optional[Any]        =      value







SCREAMING_SNAKE_CASE__      :			Any							=		r"""
    This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it
    as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
    behavior.

    Parameters:
        config ([`RegNetConfig`]): Model configuration class with all the parameters of the model.
            Initializing with a config file does not load the weights associated with the model, only the
            configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""

SCREAMING_SNAKE_CASE__      :			List[str]							=		r"""
    Args:
        pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
            Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See
            [`ConvNextImageProcessor.__call__`] for details.

        output_hidden_states (`bool`, *optional*):
            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
            more detail.
        return_dict (`bool`, *optional*):
            Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple.
"""
@add_start_docstrings(
    '''The bare RegNet model outputting raw features without any specific head on top.'''			,				lowerCamelCase			,				)
# Copied from transformers.models.resnet.modeling_resnet.ResNetModel with RESNET->REGNET,ResNet->RegNet
class 						lowerCamelCase_				(      lowerCamelCase ):







				def __init__(						self       ,					__lowerCAmelCase     ):
								"""simple docstring"""
								super().__init__(__lowerCAmelCase     )
								__magic_name__		:str        =      config
								__magic_name__		:Any        =      RegNetEmbeddings(__lowerCAmelCase     )
								__magic_name__		:str        =      RegNetEncoder(__lowerCAmelCase     )
								__magic_name__		:Tuple        =      nn.AdaptiveAvgPoolad((1, 1)     )
								# Initialize weights and apply final processing
								self.post_init()







				@add_start_docstrings_to_model_forward(__lowerCAmelCase     )
				@add_code_sample_docstrings(
				    checkpoint=_CHECKPOINT_FOR_DOC       ,					output_type=__lowerCAmelCase       ,					config_class=_CONFIG_FOR_DOC       ,					modality='''vision'''       ,					expected_output=_EXPECTED_OUTPUT_SHAPE       ,					)
				def 			A	(						self       ,					__lowerCAmelCase       ,					__lowerCAmelCase = None       ,					__lowerCAmelCase = None     ):
								"""simple docstring"""
								__magic_name__		:List[str]        =      (
								    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
								)
								__magic_name__		:Optional[int]        =      return_dict if return_dict is not None else self.config.use_return_dict

								__magic_name__		:Tuple        =      self.embedder(__lowerCAmelCase     )

								__magic_name__		:Dict        =      self.encoder(
								    __lowerCAmelCase       ,					output_hidden_states=__lowerCAmelCase       ,					return_dict=__lowerCAmelCase     )

								__magic_name__		:Any        =      encoder_outputs[0]

								__magic_name__		:Union[str, Any]        =      self.pooler(__lowerCAmelCase     )

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

								return BaseModelOutputWithPoolingAndNoAttention(
								    last_hidden_state=__lowerCAmelCase       ,					pooler_output=__lowerCAmelCase       ,					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.
    '''			,				lowerCamelCase			,				)
# Copied from transformers.models.resnet.modeling_resnet.ResNetForImageClassification with RESNET->REGNET,ResNet->RegNet,resnet->regnet
class 						lowerCamelCase_				(      lowerCamelCase ):







				def __init__(						self       ,					__lowerCAmelCase     ):
								"""simple docstring"""
								super().__init__(__lowerCAmelCase     )
								__magic_name__		:Union[str, Any]        =      config.num_labels
								__magic_name__		:Dict        =      RegNetModel(__lowerCAmelCase     )
								# classification head
								__magic_name__		:Optional[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(__lowerCAmelCase     )
				@add_code_sample_docstrings(
				    checkpoint=_IMAGE_CLASS_CHECKPOINT       ,					output_type=__lowerCAmelCase       ,					config_class=_CONFIG_FOR_DOC       ,					expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT       ,					)
				def 			A	(						self       ,					__lowerCAmelCase = None       ,					__lowerCAmelCase = None       ,					__lowerCAmelCase = None       ,					__lowerCAmelCase = None       ,					):
								"""simple docstring"""
								__magic_name__		:str        =      return_dict if return_dict is not None else self.config.use_return_dict

								__magic_name__		:str        =      self.regnet(__lowerCAmelCase       ,					output_hidden_states=__lowerCAmelCase       ,					return_dict=__lowerCAmelCase     )

								__magic_name__		:Tuple        =      outputs.pooler_output if return_dict else outputs[1]

								__magic_name__		:Union[str, Any]        =      self.classifier(__lowerCAmelCase     )

								__magic_name__		:Optional[int]        =      None

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

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

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














from urllib.parse import quote

import pytest

from datasets.utils.hub import hf_hub_url

@pytest.mark.parametrize('''repo_id''',  ['''canonical_dataset_name''', '''org-name/dataset-name'''] )
@pytest.mark.parametrize('''path''',  ['''filename.csv''', '''filename with blanks.csv'''] )
@pytest.mark.parametrize('''revision''',  [None, '''v2'''] )
def      __lowercase					( snake_case,  snake_case,  snake_case ):







				"""simple docstring"""






				__magic_name__		:Tuple        =      hf_hub_url(repo_id=snake_case,  path=snake_case,  revision=snake_case )
				assert url == f'''https://huggingface.co/datasets/{repo_id}/resolve/{revision or "main"}/{quote(snake_case )}'''






