


from math import acos, sin
from typing import List, Tuple, Union

import numpy as np
import torch
from PIL import Image

from ...models import AutoencoderKL, UNetaDConditionModel
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import AudioPipelineOutput, BaseOutput, DiffusionPipeline, ImagePipelineOutput
from .mel import Mel





class   __a			(    __UpperCamelCase      ):
 __lowercase :							Any        = ['vqvae']
 def __init__(   self				, lowerCAmelCase__				, lowerCAmelCase__				, lowerCAmelCase__				, lowerCAmelCase__				, )					->				Union[str, Any]:




        '''simple docstring'''
        super().__init__()
        self.register_modules(unet=lowerCAmelCase__				, scheduler=lowerCAmelCase__				, mel=lowerCAmelCase__				, vqvae=lowerCAmelCase__ )
 def 		SCREAMING_SNAKE_CASE__   (   self )					->				int:




        '''simple docstring'''
        return 50 if isinstance(self.scheduler				, lowerCAmelCase__ ) else 1_000
 @torch.no_grad()
 def __call__(   self				, lowerCAmelCase__ = 1				, lowerCAmelCase__ = None				, lowerCAmelCase__ = None				, lowerCAmelCase__ = 0				, lowerCAmelCase__ = 0				, lowerCAmelCase__ = None				, lowerCAmelCase__ = None				, lowerCAmelCase__ = 0				, lowerCAmelCase__ = 0				, lowerCAmelCase__ = None				, lowerCAmelCase__ = 0				, lowerCAmelCase__ = None				, lowerCAmelCase__ = None				, lowerCAmelCase__=True				, )					->				Union[
     Union[AudioPipelineOutput, ImagePipelineOutput],
     Tuple[List[Image.Image], Tuple[int, List[np.ndarray]]],
 ]:




        '''simple docstring'''
        lowercase__:   Union[str, Any]  			=   steps or self.get_default_steps()
        self.scheduler.set_timesteps(lowerCAmelCase__ )
        lowercase__:   Optional[int]  			=   step_generator or generator
        # For backwards compatibility
        if type(self.unet.config.sample_size ) == int:
               lowercase__:   Optional[int]  			=   (self.unet.config.sample_size, self.unet.config.sample_size)
        if noise is None:
               lowercase__:   List[str]  			=   randn_tensor(
                   (
                       batch_size,
                       self.unet.config.in_channels,
                       self.unet.config.sample_size[0],
                       self.unet.config.sample_size[1],
                   )				, generator=lowerCAmelCase__				, device=self.device				, )
        lowercase__:   List[Any]  			=   noise
        lowercase__:   int  			=   None

        if audio_file is not None or raw_audio is not None:
               self.mel.load_audio(lowerCAmelCase__				, lowerCAmelCase__ )
               lowercase__:   int  			=   self.mel.audio_slice_to_image(lowerCAmelCase__ )
               lowercase__:   int  			=   np.frombuffer(input_image.tobytes()				, dtype='uint8' ).reshape(
                   (input_image.height, input_image.width) )
               lowercase__:   str  			=   (input_image / 255) * 2 - 1
               lowercase__:   Union[str, Any]  			=   torch.tensor(input_image[np.newaxis, :, :]				, dtype=torch.float ).to(self.device )

               if self.vqvae is not None:
                      lowercase__:   Optional[int]  			=   self.vqvae.encode(torch.unsqueeze(lowerCAmelCase__				, 0 ) ).latent_dist.sample(
                          generator=lowerCAmelCase__ )[0]
                      lowercase__:   Dict  			=   self.vqvae.config.scaling_factor * input_images

               if start_step > 0:
                      lowercase__:   List[Any]  			=   self.scheduler.add_noise(lowerCAmelCase__				, lowerCAmelCase__				, self.scheduler.timesteps[start_step - 1] )

               lowercase__:   str  			=   (
                   self.unet.config.sample_size[1] * self.mel.get_sample_rate() / self.mel.x_res / self.mel.hop_length
               )
               lowercase__:   Dict  			=   int(mask_start_secs * pixels_per_second )
               lowercase__:   Tuple  			=   int(mask_end_secs * pixels_per_second )
               lowercase__:   List[Any]  			=   self.scheduler.add_noise(lowerCAmelCase__				, lowerCAmelCase__				, torch.tensor(self.scheduler.timesteps[start_step:] ) )

        for step, t in enumerate(self.progress_bar(self.scheduler.timesteps[start_step:] ) ):
               if isinstance(self.unet				, lowerCAmelCase__ ):
                      lowercase__:   Union[str, Any]  			=   self.unet(lowerCAmelCase__				, lowerCAmelCase__				, lowerCAmelCase__ )['sample']
               else:
                      lowercase__:   Optional[Any]  			=   self.unet(lowerCAmelCase__				, lowerCAmelCase__ )['sample']

               if isinstance(self.scheduler				, lowerCAmelCase__ ):
                      lowercase__:   List[str]  			=   self.scheduler.step(
                          model_output=lowerCAmelCase__				, timestep=lowerCAmelCase__				, sample=lowerCAmelCase__				, eta=lowerCAmelCase__				, generator=lowerCAmelCase__				, )['prev_sample']
               else:
                      lowercase__:   int  			=   self.scheduler.step(
                          model_output=lowerCAmelCase__				, timestep=lowerCAmelCase__				, sample=lowerCAmelCase__				, generator=lowerCAmelCase__				, )['prev_sample']

               if mask is not None:
                      if mask_start > 0:
                             lowercase__:   List[Any]  			=   mask[:, step, :, :mask_start]
                      if mask_end > 0:
                             lowercase__:   Optional[int]  			=   mask[:, step, :, -mask_end:]

        if self.vqvae is not None:
               # 0.18215 was scaling factor used in training to ensure unit variance
               lowercase__:   Union[str, Any]  			=   1 / self.vqvae.config.scaling_factor * images
               lowercase__:   Optional[Any]  			=   self.vqvae.decode(lowerCAmelCase__ )['sample']

        lowercase__:   Dict  			=   (images / 2 + 0.5).clamp(0				, 1 )
        lowercase__:   List[Any]  			=   images.cpu().permute(0				, 2				, 3				, 1 ).numpy()
        lowercase__:   int  			=   (images * 255).round().astype('uint8' )
        lowercase__:   Optional[Any]  			=   list(
            (Image.fromarray(_[:, :, 0] ) for _ in images)
            if images.shape[3] == 1
            else (Image.fromarray(lowerCAmelCase__				, mode='RGB' ).convert('L' ) for _ in images) )

        lowercase__:   Dict  			=   [self.mel.image_to_audio(lowerCAmelCase__ ) for _ in images]
        if not return_dict:
               return images, (self.mel.get_sample_rate(), audios)

        return BaseOutput(**AudioPipelineOutput(np.array(lowerCAmelCase__ )[:, np.newaxis, :] )				, **ImagePipelineOutput(lowerCAmelCase__ ) )
 @torch.no_grad()
 def 		SCREAMING_SNAKE_CASE__   (   self				, lowerCAmelCase__				, lowerCAmelCase__ = 50 )					->				np.ndarray:




        '''simple docstring'''
        assert isinstance(self.scheduler				, lowerCAmelCase__ )
        self.scheduler.set_timesteps(lowerCAmelCase__ )
        lowercase__:   List[str]  			=   np.array(
            [np.frombuffer(image.tobytes()				, dtype='uint8' ).reshape((1, image.height, image.width) ) for image in images] )
        lowercase__:   str  			=   (sample / 255) * 2 - 1
        lowercase__:   str  			=   torch.Tensor(lowerCAmelCase__ ).to(self.device )

        for t in self.progress_bar(torch.flip(self.scheduler.timesteps				, (0,) ) ):
               lowercase__:   Union[str, Any]  			=   t - self.scheduler.config.num_train_timesteps // self.scheduler.num_inference_steps
               lowercase__:   Optional[Any]  			=   self.scheduler.alphas_cumprod[t]
               lowercase__:   str  			=   (
                   self.scheduler.alphas_cumprod[prev_timestep]
                   if prev_timestep >= 0
                   else self.scheduler.final_alpha_cumprod
               )
               lowercase__:   str  			=   1 - alpha_prod_t
               lowercase__:   int  			=   self.unet(lowerCAmelCase__				, lowerCAmelCase__ )['sample']
               lowercase__:   int  			=   (1 - alpha_prod_t_prev) ** 0.5 * model_output
               lowercase__:   Optional[int]  			=   (sample - pred_sample_direction) * alpha_prod_t_prev ** (-0.5)
               lowercase__:   Any  			=   sample * alpha_prod_t ** 0.5 + beta_prod_t ** 0.5 * model_output

        return sample
 @staticmethod
 def 		SCREAMING_SNAKE_CASE__   (   lowerCAmelCase__				, lowerCAmelCase__				, lowerCAmelCase__ )					->				torch.Tensor:




        '''simple docstring'''
        lowercase__:   Any  			=   acos(torch.dot(torch.flatten(lowerCAmelCase__ )				, torch.flatten(lowerCAmelCase__ ) ) / torch.norm(lowerCAmelCase__ ) / torch.norm(lowerCAmelCase__ ) )
        return sin((1 - alpha) * theta ) * xa / sin(lowerCAmelCase__ ) + sin(alpha * theta ) * xa / sin(lowerCAmelCase__ )






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


__lowerCAmelCase		       =							logging.get_logger(__name__)

# General docstring
__lowerCAmelCase		       =							'''RegNetConfig'''

# Base docstring
__lowerCAmelCase		       =							'''facebook/regnet-y-040'''
__lowerCAmelCase		       =							[1, 10_88, 7, 7]

# Image classification docstring
__lowerCAmelCase		       =							'''facebook/regnet-y-040'''
__lowerCAmelCase		       =							'''tabby, tabby cat'''

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





class   __a			(    nn.Module      ):
 def __init__(   self				, lowerCAmelCase__				, lowerCAmelCase__				, lowerCAmelCase__ = 3				, lowerCAmelCase__ = 1				, lowerCAmelCase__ = 1				, lowerCAmelCase__ = "relu"				, )					->				Optional[Any]:




        '''simple docstring'''
        super().__init__()
        lowercase__:   Any  			=   nn.Convad(
            lowerCAmelCase__				, lowerCAmelCase__				, kernel_size=lowerCAmelCase__				, stride=lowerCAmelCase__				, padding=kernel_size // 2				, groups=lowerCAmelCase__				, bias=lowerCAmelCase__				, )
        lowercase__:   str  			=   nn.BatchNormad(lowerCAmelCase__ )
        lowercase__:   Union[str, Any]  			=   ACTaFN[activation] if activation is not None else nn.Identity()
 def 		SCREAMING_SNAKE_CASE__   (   self				, lowerCAmelCase__ )					->				Dict:




        '''simple docstring'''
        lowercase__:   List[str]  			=   self.convolution(lowerCAmelCase__ )
        lowercase__:   Optional[Any]  			=   self.normalization(lowerCAmelCase__ )
        lowercase__:   Union[str, Any]  			=   self.activation(lowerCAmelCase__ )
        return hidden_state





class   __a			(    nn.Module      ):
 def __init__(   self				, lowerCAmelCase__ )					->				Union[str, Any]:




        '''simple docstring'''
        super().__init__()
        lowercase__:   Dict  			=   RegNetConvLayer(
            config.num_channels				, config.embedding_size				, kernel_size=3				, stride=2				, activation=config.hidden_act )
        lowercase__:   Dict  			=   config.num_channels
 def 		SCREAMING_SNAKE_CASE__   (   self				, lowerCAmelCase__ )					->				int:




        '''simple docstring'''
        lowercase__:   Tuple  			=   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__:   Optional[int]  			=   self.embedder(lowerCAmelCase__ )
        return hidden_state





class   __a			(    nn.Module      ):
 def __init__(   self				, lowerCAmelCase__				, lowerCAmelCase__				, lowerCAmelCase__ = 2 )					->				Optional[Any]:




        '''simple docstring'''
        super().__init__()
        lowercase__:   Optional[Any]  			=   nn.Convad(lowerCAmelCase__				, lowerCAmelCase__				, kernel_size=1				, stride=lowerCAmelCase__				, bias=lowerCAmelCase__ )
        lowercase__:   Union[str, Any]  			=   nn.BatchNormad(lowerCAmelCase__ )
 def 		SCREAMING_SNAKE_CASE__   (   self				, lowerCAmelCase__ )					->				Tensor:




        '''simple docstring'''
        lowercase__:   Any  			=   self.convolution(lowerCAmelCase__ )
        lowercase__:   str  			=   self.normalization(lowerCAmelCase__ )
        return hidden_state





class   __a			(    nn.Module      ):
 def __init__(   self				, lowerCAmelCase__				, lowerCAmelCase__ )					->				List[str]:




        '''simple docstring'''
        super().__init__()

        lowercase__:   Any  			=   nn.AdaptiveAvgPoolad((1, 1) )
        lowercase__:   str  			=   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				, lowerCAmelCase__ )					->				Optional[Any]:




        '''simple docstring'''
        # b c h w -> b c 1 1
        lowercase__:   str  			=   self.pooler(lowerCAmelCase__ )
        lowercase__:   List[str]  			=   self.attention(lowerCAmelCase__ )
        lowercase__:   List[Any]  			=   hidden_state * attention
        return hidden_state





class   __a			(    nn.Module      ):
 def __init__(   self				, lowerCAmelCase__				, lowerCAmelCase__				, lowerCAmelCase__				, lowerCAmelCase__ = 1 )					->				Dict:




        '''simple docstring'''
        super().__init__()
        lowercase__:   str  			=   in_channels != out_channels or stride != 1
        lowercase__:   Optional[int]  			=   max(1				, out_channels // config.groups_width )
        lowercase__:   Union[str, Any]  			=   (
            RegNetShortCut(lowerCAmelCase__				, lowerCAmelCase__				, stride=lowerCAmelCase__ ) if should_apply_shortcut else nn.Identity()
        )
        lowercase__:   Dict  			=   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__ )				, )
        lowercase__:   Tuple  			=   ACTaFN[config.hidden_act]
 def 		SCREAMING_SNAKE_CASE__   (   self				, lowerCAmelCase__ )					->				int:




        '''simple docstring'''
        lowercase__:   Dict  			=   hidden_state
        lowercase__:   Union[str, Any]  			=   self.layer(lowerCAmelCase__ )
        lowercase__:   int  			=   self.shortcut(lowerCAmelCase__ )
        hidden_state += residual
        lowercase__:   Optional[int]  			=   self.activation(lowerCAmelCase__ )
        return hidden_state





class   __a			(    nn.Module      ):
 def __init__(   self				, lowerCAmelCase__				, lowerCAmelCase__				, lowerCAmelCase__				, lowerCAmelCase__ = 1 )					->				Dict:




        '''simple docstring'''
        super().__init__()
        lowercase__:   Optional[int]  			=   in_channels != out_channels or stride != 1
        lowercase__:   List[str]  			=   max(1				, out_channels // config.groups_width )
        lowercase__:   Any  			=   (
            RegNetShortCut(lowerCAmelCase__				, lowerCAmelCase__				, stride=lowerCAmelCase__ ) if should_apply_shortcut else nn.Identity()
        )
        lowercase__:   str  			=   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__ )				, )
        lowercase__:   Union[str, Any]  			=   ACTaFN[config.hidden_act]
 def 		SCREAMING_SNAKE_CASE__   (   self				, lowerCAmelCase__ )					->				List[str]:




        '''simple docstring'''
        lowercase__:   Optional[Any]  			=   hidden_state
        lowercase__:   Optional[int]  			=   self.layer(lowerCAmelCase__ )
        lowercase__:   str  			=   self.shortcut(lowerCAmelCase__ )
        hidden_state += residual
        lowercase__:   Optional[int]  			=   self.activation(lowerCAmelCase__ )
        return hidden_state





class   __a			(    nn.Module      ):
 def __init__(   self				, lowerCAmelCase__				, lowerCAmelCase__				, lowerCAmelCase__				, lowerCAmelCase__ = 2				, lowerCAmelCase__ = 2				, )					->				Tuple:




        '''simple docstring'''
        super().__init__()

        lowercase__:   Optional[int]  			=   RegNetXLayer if config.layer_type == 'x' else RegNetYLayer

        lowercase__:   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 		SCREAMING_SNAKE_CASE__   (   self				, lowerCAmelCase__ )					->				Optional[Any]:




        '''simple docstring'''
        lowercase__:   str  			=   self.layers(lowerCAmelCase__ )
        return hidden_state





class   __a			(    nn.Module      ):
 def __init__(   self				, lowerCAmelCase__ )					->				Dict:




        '''simple docstring'''
        super().__init__()
        lowercase__:   int  			=   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]				, ) )
        lowercase__:   int  			=   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				, lowerCAmelCase__				, lowerCAmelCase__ = False				, lowerCAmelCase__ = True )					->				BaseModelOutputWithNoAttention:




        '''simple docstring'''
        lowercase__:   List[str]  			=   () if output_hidden_states else None

        for stage_module in self.stages:
               if output_hidden_states:
                      lowercase__:   Optional[Any]  			=   hidden_states + (hidden_state,)

               lowercase__:   List[Any]  			=   stage_module(lowerCAmelCase__ )

        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=lowerCAmelCase__				, hidden_states=lowerCAmelCase__ )





class   __a			(    __UpperCamelCase      ):
 __lowercase :							Dict        = RegNetConfig
 __lowercase :							Dict        = 'regnet'
 __lowercase :							str        = 'pixel_values'
 __lowercase :							List[str]        = True
 def 		SCREAMING_SNAKE_CASE__   (   self				, lowerCAmelCase__ )					->				List[str]:




        '''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				, lowerCAmelCase__				, lowerCAmelCase__=False )					->				Optional[Any]:




        '''simple docstring'''
        if isinstance(lowerCAmelCase__				, lowerCAmelCase__ ):
               lowercase__:   Any  			=   value





__lowerCAmelCase		       =							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.
'''

__lowerCAmelCase		       =							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.'       ,       __UpperCamelCase       ,       )
# Copied from transformers.models.resnet.modeling_resnet.ResNetModel with RESNET->REGNET,ResNet->RegNet
class   __a			(    __UpperCamelCase      ):
 def __init__(   self				, lowerCAmelCase__ )					->				List[str]:




        '''simple docstring'''
        super().__init__(lowerCAmelCase__ )
        lowercase__:   Tuple  			=   config
        lowercase__:   List[str]  			=   RegNetEmbeddings(lowerCAmelCase__ )
        lowercase__:   Optional[int]  			=   RegNetEncoder(lowerCAmelCase__ )
        lowercase__:   Optional[Any]  			=   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				, lowerCAmelCase__				, lowerCAmelCase__ = None				, lowerCAmelCase__ = None )					->				BaseModelOutputWithPoolingAndNoAttention:




        '''simple docstring'''
        lowercase__:   List[Any]  			=   (
            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
        )
        lowercase__:   Union[str, Any]  			=   return_dict if return_dict is not None else self.config.use_return_dict

        lowercase__:   Any  			=   self.embedder(lowerCAmelCase__ )

        lowercase__:   List[Any]  			=   self.encoder(
            lowerCAmelCase__				, output_hidden_states=lowerCAmelCase__				, return_dict=lowerCAmelCase__ )

        lowercase__:   Optional[Any]  			=   encoder_outputs[0]

        lowercase__:   Optional[int]  			=   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    '       ,       __UpperCamelCase       ,       )
# Copied from transformers.models.resnet.modeling_resnet.ResNetForImageClassification with RESNET->REGNET,ResNet->RegNet,resnet->regnet
class   __a			(    __UpperCamelCase      ):
 def __init__(   self				, lowerCAmelCase__ )					->				str:




        '''simple docstring'''
        super().__init__(lowerCAmelCase__ )
        lowercase__:   Dict  			=   config.num_labels
        lowercase__:   Dict  			=   RegNetModel(lowerCAmelCase__ )
        # classification head
        lowercase__:   str  			=   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				, lowerCAmelCase__ = None				, lowerCAmelCase__ = None				, lowerCAmelCase__ = None				, lowerCAmelCase__ = None				, )					->				ImageClassifierOutputWithNoAttention:




        '''simple docstring'''
        lowercase__:   str  			=   return_dict if return_dict is not None else self.config.use_return_dict

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

        lowercase__:   Dict  			=   outputs.pooler_output if return_dict else outputs[1]

        lowercase__:   List[str]  			=   self.classifier(lowerCAmelCase__ )

        lowercase__:   Optional[Any]  			=   None

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

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

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


