


import json
import os
from typing import Optional, Tuple

import regex as re

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


__lowerCAmelCase		       =							logging.get_logger(__name__)

__lowerCAmelCase		       =							{
    '''vocab_file''': '''vocab.json''',
    '''merges_file''': '''merges.txt''',
}

__lowerCAmelCase		       =							{
    '''vocab_file''': {'''ctrl''': '''https://raw.githubusercontent.com/salesforce/ctrl/master/ctrl-vocab.json'''},
    '''merges_file''': {'''ctrl''': '''https://raw.githubusercontent.com/salesforce/ctrl/master/ctrl-merges.txt'''},
}

__lowerCAmelCase		       =							{
    '''ctrl''': 2_56,
}

__lowerCAmelCase		       =							{
    '''Pregnancy''': 16_86_29,
    '''Christianity''': 76_75,
    '''Explain''': 10_64_23,
    '''Fitness''': 6_34_40,
    '''Saving''': 6_31_63,
    '''Ask''': 2_71_71,
    '''Ass''': 9_59_85,
    '''Joke''': 16_35_09,
    '''Questions''': 4_56_22,
    '''Thoughts''': 4_96_05,
    '''Retail''': 5_23_42,
    '''Feminism''': 16_43_38,
    '''Writing''': 1_19_92,
    '''Atheism''': 19_22_63,
    '''Netflix''': 4_86_16,
    '''Computing''': 3_96_39,
    '''Opinion''': 4_32_13,
    '''Alone''': 4_49_67,
    '''Funny''': 5_89_17,
    '''Gaming''': 4_03_58,
    '''Human''': 40_88,
    '''India''': 13_31,
    '''Joker''': 7_71_38,
    '''Diet''': 3_62_06,
    '''Legal''': 1_18_59,
    '''Norman''': 49_39,
    '''Tip''': 7_26_89,
    '''Weight''': 5_23_43,
    '''Movies''': 4_62_73,
    '''Running''': 2_34_25,
    '''Science''': 20_90,
    '''Horror''': 3_77_93,
    '''Confession''': 6_05_72,
    '''Finance''': 1_22_50,
    '''Politics''': 1_63_60,
    '''Scary''': 19_19_85,
    '''Support''': 1_26_54,
    '''Technologies''': 3_25_16,
    '''Teenage''': 6_61_60,
    '''Event''': 3_27_69,
    '''Learned''': 6_74_60,
    '''Notion''': 18_27_70,
    '''Wikipedia''': 3_75_83,
    '''Books''': 66_65,
    '''Extract''': 7_60_50,
    '''Confessions''': 10_27_01,
    '''Conspiracy''': 7_59_32,
    '''Links''': 6_36_74,
    '''Narcissus''': 15_04_25,
    '''Relationship''': 5_47_66,
    '''Relationships''': 13_47_96,
    '''Reviews''': 4_16_71,
    '''News''': 42_56,
    '''Translation''': 2_68_20,
    '''multilingual''': 12_84_06,
}




def    snake_case_     (  snake_case     )  ->				List[str]:
       lowercase__:   Any  			=   set()
       lowercase__:   List[str]  			=   word[0]
       for char in word[1:]:
              pairs.add((prev_char, char)     )
              lowercase__:   Any  			=   char

       lowercase__:   Dict  			=   set(snake_case     )
       return pairs





class   __a			(    __UpperCamelCase      ):
 __lowercase :							Optional[Any]        = VOCAB_FILES_NAMES
 __lowercase :							str        = PRETRAINED_VOCAB_FILES_MAP
 __lowercase :							Tuple        = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 __lowercase :							Union[str, Any]        = CONTROL_CODES
 def __init__(   self				, lowerCAmelCase__				, lowerCAmelCase__				, lowerCAmelCase__="<unk>"				, **lowerCAmelCase__ )					->				List[str]:




        '''simple docstring'''
        super().__init__(unk_token=lowerCAmelCase__				, **lowerCAmelCase__ )

        with open(lowerCAmelCase__				, encoding='utf-8' ) as vocab_handle:
               lowercase__:   Optional[Any]  			=   json.load(lowerCAmelCase__ )
        lowercase__:   List[str]  			=   {v: k for k, v in self.encoder.items()}
        with open(lowerCAmelCase__				, encoding='utf-8' ) as merges_handle:
               lowercase__:   Tuple  			=   merges_handle.read().split('\n' )[1:-1]
        lowercase__:   Union[str, Any]  			=   [tuple(merge.split() ) for merge in merges]
        lowercase__:   Optional[Any]  			=   dict(zip(lowerCAmelCase__				, range(len(lowerCAmelCase__ ) ) ) )
        lowercase__:   str  			=   {}
 @property
 def 		SCREAMING_SNAKE_CASE__   (   self )					->				Any:




        '''simple docstring'''
        return len(self.encoder )
 def 		SCREAMING_SNAKE_CASE__   (   self )					->				Union[str, Any]:




        '''simple docstring'''
        return dict(self.encoder				, **self.added_tokens_encoder )
 def 		SCREAMING_SNAKE_CASE__   (   self				, lowerCAmelCase__ )					->				Any:




        '''simple docstring'''
        if token in self.cache:
               return self.cache[token]
        lowercase__:   Union[str, Any]  			=   tuple(lowerCAmelCase__ )
        lowercase__:   Dict  			=   tuple(list(word[:-1] ) + [word[-1] + '</w>'] )
        lowercase__:   Union[str, Any]  			=   get_pairs(lowerCAmelCase__ )

        if not pairs:
               return token

        while True:
               lowercase__:   str  			=   min(lowerCAmelCase__				, key=lambda lowerCAmelCase__ : self.bpe_ranks.get(lowerCAmelCase__				, float('inf' ) ) )
               if bigram not in self.bpe_ranks:
                      break
               lowercase__			,						lowercase__:   Optional[Any]  			=   bigram
               lowercase__:   Union[str, Any]  			=   []
               lowercase__:   Optional[int]  			=   0
               while i < len(lowerCAmelCase__ ):
                      try:
                             lowercase__:   Tuple  			=   word.index(lowerCAmelCase__				, lowerCAmelCase__ )
                      except ValueError:
                             new_word.extend(word[i:] )
                             break
                      else:
                             new_word.extend(word[i:j] )
                             lowercase__:   str  			=   j

                      if word[i] == first and i < len(lowerCAmelCase__ ) - 1 and word[i + 1] == second:
                             new_word.append(first + second )
                             i += 2
                      else:
                             new_word.append(word[i] )
                             i += 1
               lowercase__:   Union[str, Any]  			=   tuple(lowerCAmelCase__ )
               lowercase__:   Optional[int]  			=   new_word
               if len(lowerCAmelCase__ ) == 1:
                      break
               else:
                      lowercase__:   Optional[Any]  			=   get_pairs(lowerCAmelCase__ )
        lowercase__:   Any  			=   '@@ '.join(lowerCAmelCase__ )
        lowercase__:   int  			=   word[:-4]
        lowercase__:   str  			=   word
        return word
 def 		SCREAMING_SNAKE_CASE__   (   self				, lowerCAmelCase__ )					->				Optional[int]:




        '''simple docstring'''
        lowercase__:   Tuple  			=   []

        lowercase__:   List[str]  			=   re.findall(R'\S+\n?'				, lowerCAmelCase__ )

        for token in words:
               split_tokens.extend(list(self.bpe(lowerCAmelCase__ ).split(' ' ) ) )
        return split_tokens
 def 		SCREAMING_SNAKE_CASE__   (   self				, lowerCAmelCase__ )					->				Optional[Any]:




        '''simple docstring'''
        return self.encoder.get(lowerCAmelCase__				, self.encoder.get(self.unk_token ) )
 def 		SCREAMING_SNAKE_CASE__   (   self				, lowerCAmelCase__ )					->				Optional[Any]:




        '''simple docstring'''
        return self.decoder.get(lowerCAmelCase__				, self.unk_token )
 def 		SCREAMING_SNAKE_CASE__   (   self				, lowerCAmelCase__ )					->				Dict:




        '''simple docstring'''
        lowercase__:   List[str]  			=   ' '.join(lowerCAmelCase__ ).replace('@@ '				, '' ).strip()
        return out_string
 def 		SCREAMING_SNAKE_CASE__   (   self				, lowerCAmelCase__				, lowerCAmelCase__ = None )					->				Tuple[str]:




        '''simple docstring'''
        if not os.path.isdir(lowerCAmelCase__ ):
               logger.error(F'Vocabulary path ({save_directory}) should be a directory' )
               return
        lowercase__:   Tuple  			=   os.path.join(
            lowerCAmelCase__				, (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'] )
        lowercase__:   Union[str, Any]  			=   os.path.join(
            lowerCAmelCase__				, (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['merges_file'] )

        with open(lowerCAmelCase__				, 'w'				, encoding='utf-8' ) as f:
               f.write(json.dumps(self.encoder				, indent=2				, sort_keys=lowerCAmelCase__				, ensure_ascii=lowerCAmelCase__ ) + '\n' )

        lowercase__:   List[Any]  			=   0
        with open(lowerCAmelCase__				, 'w'				, encoding='utf-8' ) as writer:
               writer.write('#version: 0.2\n' )
               for bpe_tokens, token_index in sorted(self.bpe_ranks.items()				, key=lambda lowerCAmelCase__ : kv[1] ):
                      if index != token_index:
                             logger.warning(
                                 F'Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'
                                 ' Please check that the tokenizer is not corrupted!' )
                             lowercase__:   List[Any]  			=   token_index
                      writer.write(' '.join(lowerCAmelCase__ ) + '\n' )
                      index += 1

        return vocab_file, merge_file

 # def decode(self, token_ids, skip_special_tokens=False, clean_up_tokenization_spaces=True):
 #     filtered_tokens = ' '.join(self.convert_ids_to_tokens(token_ids, skip_special_tokens=skip_special_tokens))
 #     tokens_generated_so_far = re.sub('(@@ )', '', string=filtered_tokens)
 #     tokens_generated_so_far = re.sub('(@@ ?$)', '', string=tokens_generated_so_far)
 #     return ''.join(tokens_generated_so_far)






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 )


