





from ...utils import is_torch_available, is_transformers_available


if is_transformers_available() and is_torch_available():
    from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline









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_						       =	logging.get_logger(__name__)

# General docstring
SCREAMING_SNAKE_CASE_						       =	"""RegNetConfig"""

# Base docstring
SCREAMING_SNAKE_CASE_						       =	"""facebook/regnet-y-040"""
SCREAMING_SNAKE_CASE_						       =	[1, 1_0_8_8, 7, 7]

# Image classification docstring
SCREAMING_SNAKE_CASE_						       =	"""facebook/regnet-y-040"""
SCREAMING_SNAKE_CASE_						       =	"""tabby, tabby cat"""

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

class 		UpperCamelCase__			(				nn.Module       ):



     '''simple docstring'''

     def __init__(							self						:   str						,lowerCamelCase__						:   int						,lowerCamelCase__						:   int						,lowerCamelCase__						:   int = 3						,lowerCamelCase__						:   int = 1						,lowerCamelCase__						:   int = 1						,lowerCamelCase__						:   Optional[str] = "relu"						,)					->		Union[str, Any]:




           '''simple docstring'''


           super().__init__()
           SCREAMING_SNAKE_CASE		=						nn.Convad(
               lowerCamelCase__						,lowerCamelCase__						,kernel_size=lowerCamelCase__						,stride=lowerCamelCase__						,padding=kernel_size // 2						,groups=lowerCamelCase__						,bias=lowerCamelCase__						,)
           SCREAMING_SNAKE_CASE		=						nn.BatchNormad(lowerCamelCase__	)
           SCREAMING_SNAKE_CASE		=						ACTaFN[activation] if activation is not None else nn.Identity()






     def     SCREAMING_SNAKE_CASE__  (							self						:   Optional[Any]						,lowerCamelCase__						:   Tuple	)					->		str:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						self.convolution(lowerCamelCase__	)
           SCREAMING_SNAKE_CASE		=						self.normalization(lowerCamelCase__	)
           SCREAMING_SNAKE_CASE		=						self.activation(lowerCamelCase__	)
           return hidden_state

class 		UpperCamelCase__			(				nn.Module       ):



     '''simple docstring'''

     def __init__(							self						:   Optional[Any]						,lowerCamelCase__						:   RegNetConfig	)					->		List[str]:




           '''simple docstring'''


           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						:   List[str]						,lowerCamelCase__						:   Optional[int]	)					->		Union[str, Any]:




           '''simple docstring'''


           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(lowerCamelCase__	)
           return hidden_state

class 		UpperCamelCase__			(				nn.Module       ):



     '''simple docstring'''

     def __init__(							self						:   List[Any]						,lowerCamelCase__						:   int						,lowerCamelCase__						:   int						,lowerCamelCase__						:   int = 2	)					->		List[str]:




           '''simple docstring'''


           super().__init__()
           SCREAMING_SNAKE_CASE		=						nn.Convad(lowerCamelCase__						,lowerCamelCase__						,kernel_size=1						,stride=lowerCamelCase__						,bias=lowerCamelCase__	)
           SCREAMING_SNAKE_CASE		=						nn.BatchNormad(lowerCamelCase__	)






     def     SCREAMING_SNAKE_CASE__  (							self						:   str						,lowerCamelCase__						:   Tensor	)					->		Tensor:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						self.convolution(lowerCamelCase__	)
           SCREAMING_SNAKE_CASE		=						self.normalization(lowerCamelCase__	)
           return hidden_state

class 		UpperCamelCase__			(				nn.Module       ):



     '''simple docstring'''

     def __init__(							self						:   List[Any]						,lowerCamelCase__						:   int						,lowerCamelCase__						:   int	)					->		int:




           '''simple docstring'''


           super().__init__()

           SCREAMING_SNAKE_CASE		=						nn.AdaptiveAvgPoolad((1, 1)	)
           SCREAMING_SNAKE_CASE		=						nn.Sequential(
               nn.Convad(lowerCamelCase__						,lowerCamelCase__						,kernel_size=1	)						,nn.ReLU()						,nn.Convad(lowerCamelCase__						,lowerCamelCase__						,kernel_size=1	)						,nn.Sigmoid()						,)






     def     SCREAMING_SNAKE_CASE__  (							self						:   List[str]						,lowerCamelCase__						:   Any	)					->		int:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						self.pooler(lowerCamelCase__	)
           SCREAMING_SNAKE_CASE		=						self.attention(lowerCamelCase__	)
           SCREAMING_SNAKE_CASE		=						hidden_state * attention
           return hidden_state

class 		UpperCamelCase__			(				nn.Module       ):



     '''simple docstring'''

     def __init__(							self						:   Optional[int]						,lowerCamelCase__						:   RegNetConfig						,lowerCamelCase__						:   int						,lowerCamelCase__						:   int						,lowerCamelCase__						:   int = 1	)					->		str:




           '''simple docstring'''


           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(lowerCamelCase__						,lowerCamelCase__						,stride=lowerCamelCase__	) if should_apply_shortcut else nn.Identity()
           )
           SCREAMING_SNAKE_CASE		=						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__	)						,)
           SCREAMING_SNAKE_CASE		=						ACTaFN[config.hidden_act]






     def     SCREAMING_SNAKE_CASE__  (							self						:   Dict						,lowerCamelCase__						:   Optional[Any]	)					->		str:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						hidden_state
           SCREAMING_SNAKE_CASE		=						self.layer(lowerCamelCase__	)
           SCREAMING_SNAKE_CASE		=						self.shortcut(lowerCamelCase__	)
           hidden_state += residual
           SCREAMING_SNAKE_CASE		=						self.activation(lowerCamelCase__	)
           return hidden_state

class 		UpperCamelCase__			(				nn.Module       ):



     '''simple docstring'''

     def __init__(							self						:   Dict						,lowerCamelCase__						:   RegNetConfig						,lowerCamelCase__						:   int						,lowerCamelCase__						:   int						,lowerCamelCase__						:   int = 1	)					->		Optional[int]:




           '''simple docstring'''


           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(lowerCamelCase__						,lowerCamelCase__						,stride=lowerCamelCase__	) if should_apply_shortcut else nn.Identity()
           )
           SCREAMING_SNAKE_CASE		=						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__	)						,)
           SCREAMING_SNAKE_CASE		=						ACTaFN[config.hidden_act]






     def     SCREAMING_SNAKE_CASE__  (							self						:   Dict						,lowerCamelCase__						:   Tuple	)					->		Tuple:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						hidden_state
           SCREAMING_SNAKE_CASE		=						self.layer(lowerCamelCase__	)
           SCREAMING_SNAKE_CASE		=						self.shortcut(lowerCamelCase__	)
           hidden_state += residual
           SCREAMING_SNAKE_CASE		=						self.activation(lowerCamelCase__	)
           return hidden_state

class 		UpperCamelCase__			(				nn.Module       ):



     '''simple docstring'''

     def __init__(							self						:   int						,lowerCamelCase__						:   RegNetConfig						,lowerCamelCase__						:   int						,lowerCamelCase__						:   int						,lowerCamelCase__						:   int = 2						,lowerCamelCase__						:   int = 2						,)					->		Tuple:




           '''simple docstring'''


           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(
                   lowerCamelCase__						,lowerCamelCase__						,lowerCamelCase__						,stride=lowerCamelCase__						,)						,*[layer(lowerCamelCase__						,lowerCamelCase__						,lowerCamelCase__	) for _ in range(depth - 1	)]						,)






     def     SCREAMING_SNAKE_CASE__  (							self						:   Dict						,lowerCamelCase__						:   Union[str, Any]	)					->		Optional[Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						self.layers(lowerCamelCase__	)
           return hidden_state

class 		UpperCamelCase__			(				nn.Module       ):



     '''simple docstring'''

     def __init__(							self						:   List[Any]						,lowerCamelCase__						:   RegNetConfig	)					->		str:




           '''simple docstring'''


           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(
                   lowerCamelCase__						,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(lowerCamelCase__						,config.depths[1:]	):
                 self.stages.append(RegNetStage(lowerCamelCase__						,lowerCamelCase__						,lowerCamelCase__						,depth=lowerCamelCase__	)	)






     def     SCREAMING_SNAKE_CASE__  (							self						:   int						,lowerCamelCase__						:   Tensor						,lowerCamelCase__						:   bool = False						,lowerCamelCase__						:   bool = True	)					->		BaseModelOutputWithNoAttention:




           '''simple docstring'''


           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(lowerCamelCase__	)

           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=lowerCamelCase__						,hidden_states=lowerCamelCase__	)

class 		UpperCamelCase__			(				lowerCAmelCase_       ):



     '''simple docstring'''

     __snake_case   :     List[Any]		     =      RegNetConfig
     __snake_case   :     Union[str, Any]		     =      "regnet"
     __snake_case   :     Optional[Any]		     =      "pixel_values"
     __snake_case   :     List[Any]		     =      True






     def     SCREAMING_SNAKE_CASE__  (							self						:   List[str]						,lowerCamelCase__						:   int	)					->		Any:




           '''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     SCREAMING_SNAKE_CASE__  (							self						:   Union[str, Any]						,lowerCamelCase__						:   Tuple						,lowerCamelCase__						:   str=False	)					->		str:




           '''simple docstring'''


           if isinstance(lowerCamelCase__						,lowerCamelCase__	):
                 SCREAMING_SNAKE_CASE		=						value
SCREAMING_SNAKE_CASE_						       =	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_						       =	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 		UpperCamelCase__			(				lowerCAmelCase_       ):



     '''simple docstring'''

     def __init__(							self						:   str						,lowerCamelCase__						:   str	)					->		Any:




           '''simple docstring'''


           super().__init__(lowerCamelCase__	)
           SCREAMING_SNAKE_CASE		=						config
           SCREAMING_SNAKE_CASE		=						RegNetEmbeddings(lowerCamelCase__	)
           SCREAMING_SNAKE_CASE		=						RegNetEncoder(lowerCamelCase__	)
           SCREAMING_SNAKE_CASE		=						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     SCREAMING_SNAKE_CASE__  (							self						:   Union[str, Any]						,lowerCamelCase__						:   Tensor						,lowerCamelCase__						:   Optional[bool] = None						,lowerCamelCase__						:   Optional[bool] = None	)					->		BaseModelOutputWithPoolingAndNoAttention:




           '''simple docstring'''


           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(lowerCamelCase__	)

           SCREAMING_SNAKE_CASE		=						self.encoder(
               lowerCamelCase__						,output_hidden_states=lowerCamelCase__						,return_dict=lowerCamelCase__	)

           SCREAMING_SNAKE_CASE		=						encoder_outputs[0]

           SCREAMING_SNAKE_CASE		=						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(
    "\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    "							,				lowerCAmelCase_							,				)
# Copied from transformers.models.resnet.modeling_resnet.ResNetForImageClassification with RESNET->REGNET,ResNet->RegNet,resnet->regnet
class 		UpperCamelCase__			(				lowerCAmelCase_       ):



     '''simple docstring'''

     def __init__(							self						:   Any						,lowerCamelCase__						:   Optional[int]	)					->		List[Any]:




           '''simple docstring'''


           super().__init__(lowerCamelCase__	)
           SCREAMING_SNAKE_CASE		=						config.num_labels
           SCREAMING_SNAKE_CASE		=						RegNetModel(lowerCamelCase__	)
           # 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(lowerCamelCase__	)
     @add_code_sample_docstrings(
         checkpoint=_IMAGE_CLASS_CHECKPOINT						,output_type=lowerCamelCase__						,config_class=_CONFIG_FOR_DOC						,expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT						,)
     def     SCREAMING_SNAKE_CASE__  (							self						:   str						,lowerCamelCase__						:   Optional[torch.FloatTensor] = None						,lowerCamelCase__						:   Optional[torch.LongTensor] = None						,lowerCamelCase__						:   Optional[bool] = None						,lowerCamelCase__						:   Optional[bool] = None						,)					->		ImageClassifierOutputWithNoAttention:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						return_dict if return_dict is not None else self.config.use_return_dict

           SCREAMING_SNAKE_CASE		=						self.regnet(lowerCamelCase__						,output_hidden_states=lowerCamelCase__						,return_dict=lowerCamelCase__	)

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

           SCREAMING_SNAKE_CASE		=						self.classifier(lowerCamelCase__	)

           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(lowerCamelCase__						,lowerCamelCase__	)
                 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(lowerCamelCase__						,lowerCamelCase__	)

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

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


