





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


UpperCAmelCase_           =	logging.get_logger(__name__)

# General docstring
UpperCAmelCase_           =	"RegNetConfig"

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

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

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




class 			__UpperCamelCase    (							nn.Module    ):





     def __init__(     self				,							_UpperCamelCase				,							_UpperCamelCase				,							_UpperCamelCase = 3				,							_UpperCamelCase = 1				,							_UpperCamelCase = 1				,							_UpperCamelCase = "relu"				,							):
        super().__init__()
        _UpperCAmelCase           =   nn.Convad(
            _UpperCamelCase				,							_UpperCamelCase				,							kernel_size=_UpperCamelCase				,							stride=_UpperCamelCase				,							padding=kernel_size // 2				,							groups=_UpperCamelCase				,							bias=_UpperCamelCase				,							)
        _UpperCAmelCase           =   nn.BatchNormad(_UpperCamelCase					)
        _UpperCAmelCase           =   ACTaFN[activation] if activation is not None else nn.Identity()





     def 		UpperCamelCase(     self				,							_UpperCamelCase					):
        _UpperCAmelCase           =   self.convolution(_UpperCamelCase					)
        _UpperCAmelCase           =   self.normalization(_UpperCamelCase					)
        _UpperCAmelCase           =   self.activation(_UpperCamelCase					)
        return hidden_state




class 			__UpperCamelCase    (							nn.Module    ):





     def __init__(     self				,							_UpperCamelCase					):
        super().__init__()
        _UpperCAmelCase           =   RegNetConvLayer(
            config.num_channels				,							config.embedding_size				,							kernel_size=3				,							stride=2				,							activation=config.hidden_act					)
        _UpperCAmelCase           =   config.num_channels





     def 		UpperCamelCase(     self				,							_UpperCamelCase					):
        _UpperCAmelCase           =   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.'''					)
        _UpperCAmelCase           =   self.embedder(_UpperCamelCase					)
        return hidden_state




class 			__UpperCamelCase    (							nn.Module    ):





     def __init__(     self				,							_UpperCamelCase				,							_UpperCamelCase				,							_UpperCamelCase = 2					):
        super().__init__()
        _UpperCAmelCase           =   nn.Convad(_UpperCamelCase				,							_UpperCamelCase				,							kernel_size=1				,							stride=_UpperCamelCase				,							bias=_UpperCamelCase					)
        _UpperCAmelCase           =   nn.BatchNormad(_UpperCamelCase					)





     def 		UpperCamelCase(     self				,							_UpperCamelCase					):
        _UpperCAmelCase           =   self.convolution(_UpperCamelCase					)
        _UpperCAmelCase           =   self.normalization(_UpperCamelCase					)
        return hidden_state




class 			__UpperCamelCase    (							nn.Module    ):





     def __init__(     self				,							_UpperCamelCase				,							_UpperCamelCase					):
        super().__init__()

        _UpperCAmelCase           =   nn.AdaptiveAvgPoolad((1, 1)					)
        _UpperCAmelCase           =   nn.Sequential(
            nn.Convad(_UpperCamelCase				,							_UpperCamelCase				,							kernel_size=1					)				,							nn.ReLU()				,							nn.Convad(_UpperCamelCase				,							_UpperCamelCase				,							kernel_size=1					)				,							nn.Sigmoid()				,							)





     def 		UpperCamelCase(     self				,							_UpperCamelCase					):
        # b c h w -> b c 1 1
        _UpperCAmelCase           =   self.pooler(_UpperCamelCase					)
        _UpperCAmelCase           =   self.attention(_UpperCamelCase					)
        _UpperCAmelCase           =   hidden_state * attention
        return hidden_state




class 			__UpperCamelCase    (							nn.Module    ):





     def __init__(     self				,							_UpperCamelCase				,							_UpperCamelCase				,							_UpperCamelCase				,							_UpperCamelCase = 1					):
        super().__init__()
        _UpperCAmelCase           =   in_channels != out_channels or stride != 1
        _UpperCAmelCase           =   max(1				,							out_channels // config.groups_width					)
        _UpperCAmelCase           =   (
            RegNetShortCut(_UpperCamelCase				,							_UpperCamelCase				,							stride=_UpperCamelCase					) if should_apply_shortcut else nn.Identity()
        )
        _UpperCAmelCase           =   nn.Sequential(
            RegNetConvLayer(_UpperCamelCase				,							_UpperCamelCase				,							kernel_size=1				,							activation=config.hidden_act					)				,							RegNetConvLayer(_UpperCamelCase				,							_UpperCamelCase				,							stride=_UpperCamelCase				,							groups=_UpperCamelCase				,							activation=config.hidden_act					)				,							RegNetConvLayer(_UpperCamelCase				,							_UpperCamelCase				,							kernel_size=1				,							activation=_UpperCamelCase					)				,							)
        _UpperCAmelCase           =   ACTaFN[config.hidden_act]





     def 		UpperCamelCase(     self				,							_UpperCamelCase					):
        _UpperCAmelCase           =   hidden_state
        _UpperCAmelCase           =   self.layer(_UpperCamelCase					)
        _UpperCAmelCase           =   self.shortcut(_UpperCamelCase					)
        hidden_state += residual
        _UpperCAmelCase           =   self.activation(_UpperCamelCase					)
        return hidden_state




class 			__UpperCamelCase    (							nn.Module    ):





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





     def 		UpperCamelCase(     self				,							_UpperCamelCase					):
        _UpperCAmelCase           =   hidden_state
        _UpperCAmelCase           =   self.layer(_UpperCamelCase					)
        _UpperCAmelCase           =   self.shortcut(_UpperCamelCase					)
        hidden_state += residual
        _UpperCAmelCase           =   self.activation(_UpperCamelCase					)
        return hidden_state




class 			__UpperCamelCase    (							nn.Module    ):





     def __init__(     self				,							_UpperCamelCase				,							_UpperCamelCase				,							_UpperCamelCase				,							_UpperCamelCase = 2				,							_UpperCamelCase = 2				,							):
        super().__init__()

        _UpperCAmelCase           =   RegNetXLayer if config.layer_type == '''x''' else RegNetYLayer

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





     def 		UpperCamelCase(     self				,							_UpperCamelCase					):
        _UpperCAmelCase           =   self.layers(_UpperCamelCase					)
        return hidden_state




class 			__UpperCamelCase    (							nn.Module    ):





     def __init__(     self				,							_UpperCamelCase					):
        super().__init__()
        _UpperCAmelCase           =   nn.ModuleList([]					)
        # based on `downsample_in_first_stage`, the first layer of the first stage may or may not downsample the input
        self.stages.append(
            RegNetStage(
                _UpperCamelCase				,							config.embedding_size				,							config.hidden_sizes[0]				,							stride=2 if config.downsample_in_first_stage else 1				,							depth=config.depths[0]				,							)					)
        _UpperCAmelCase           =   zip(config.hidden_sizes				,							config.hidden_sizes[1:]					)
        for (in_channels, out_channels), depth in zip(_UpperCamelCase				,							config.depths[1:]					):
           self.stages.append(RegNetStage(_UpperCamelCase				,							_UpperCamelCase				,							_UpperCamelCase				,							depth=_UpperCamelCase					)					)





     def 		UpperCamelCase(     self				,							_UpperCamelCase				,							_UpperCamelCase = False				,							_UpperCamelCase = True					):
        _UpperCAmelCase           =   () if output_hidden_states else None

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

           _UpperCAmelCase           =   stage_module(_UpperCamelCase					)

        if output_hidden_states:
           _UpperCAmelCase           =   hidden_states + (hidden_state,)

        if not return_dict:
           return tuple(v for v in [hidden_state, hidden_states] if v is not None					)

        return BaseModelOutputWithNoAttention(last_hidden_state=_UpperCamelCase				,							hidden_states=_UpperCamelCase					)




class 			__UpperCamelCase    (							A__    ):
     __A	:  List[str]							  =   RegNetConfig
     __A	:  Any							  =   """regnet"""
     __A	:  Optional[int]							  =   """pixel_values"""
     __A	:  Any							  =   True





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





     def 		UpperCamelCase(     self				,							_UpperCamelCase				,							_UpperCamelCase=False					):
        if isinstance(_UpperCamelCase				,							_UpperCamelCase					):
           _UpperCAmelCase           =   value




UpperCAmelCase_           =	r"\n    This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it\n    as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and\n    behavior.\n\n    Parameters:\n        config ([`RegNetConfig`]): Model configuration class with all the parameters of the model.\n            Initializing with a config file does not load the weights associated with the model, only the\n            configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.\n"

UpperCAmelCase_           =	r"\n    Args:\n        pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):\n            Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See\n            [`ConvNextImageProcessor.__call__`] for details.\n\n        output_hidden_states (`bool`, *optional*):\n            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for\n            more detail.\n        return_dict (`bool`, *optional*):\n            Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple.\n"




@add_start_docstrings(
    """The bare RegNet model outputting raw features without any specific head on top."""	, A__	, )
# Copied from transformers.models.resnet.modeling_resnet.ResNetModel with RESNET->REGNET,ResNet->RegNet
class 			__UpperCamelCase    (							A__    ):





     def __init__(     self				,							_UpperCamelCase					):
        super().__init__(_UpperCamelCase					)
        _UpperCAmelCase           =   config
        _UpperCAmelCase           =   RegNetEmbeddings(_UpperCamelCase					)
        _UpperCAmelCase           =   RegNetEncoder(_UpperCamelCase					)
        _UpperCAmelCase           =   nn.AdaptiveAvgPoolad((1, 1)					)
        # Initialize weights and apply final processing
        self.post_init()





     @add_start_docstrings_to_model_forward(_UpperCamelCase					)
     @add_code_sample_docstrings(
         checkpoint=_CHECKPOINT_FOR_DOC				,							output_type=_UpperCamelCase				,							config_class=_CONFIG_FOR_DOC				,							modality='''vision'''				,							expected_output=_EXPECTED_OUTPUT_SHAPE				,							)
     def 		UpperCamelCase(     self				,							_UpperCamelCase				,							_UpperCamelCase = None				,							_UpperCamelCase = None					):
        _UpperCAmelCase           =   (
            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
        )
        _UpperCAmelCase           =   return_dict if return_dict is not None else self.config.use_return_dict

        _UpperCAmelCase           =   self.embedder(_UpperCamelCase					)

        _UpperCAmelCase           =   self.encoder(
            _UpperCamelCase				,							output_hidden_states=_UpperCamelCase				,							return_dict=_UpperCamelCase					)

        _UpperCAmelCase           =   encoder_outputs[0]

        _UpperCAmelCase           =   self.pooler(_UpperCamelCase					)

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

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




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





     def __init__(     self				,							_UpperCamelCase					):
        super().__init__(_UpperCamelCase					)
        _UpperCAmelCase           =   config.num_labels
        _UpperCAmelCase           =   RegNetModel(_UpperCamelCase					)
        # classification head
        _UpperCAmelCase           =   nn.Sequential(
            nn.Flatten()				,							nn.Linear(config.hidden_sizes[-1]				,							config.num_labels					) if config.num_labels > 0 else nn.Identity()				,							)
        # initialize weights and apply final processing
        self.post_init()





     @add_start_docstrings_to_model_forward(_UpperCamelCase					)
     @add_code_sample_docstrings(
         checkpoint=_IMAGE_CLASS_CHECKPOINT				,							output_type=_UpperCamelCase				,							config_class=_CONFIG_FOR_DOC				,							expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT				,							)
     def 		UpperCamelCase(     self				,							_UpperCamelCase = None				,							_UpperCamelCase = None				,							_UpperCamelCase = None				,							_UpperCamelCase = None				,							):
        _UpperCAmelCase           =   return_dict if return_dict is not None else self.config.use_return_dict

        _UpperCAmelCase           =   self.regnet(_UpperCamelCase				,							output_hidden_states=_UpperCamelCase				,							return_dict=_UpperCamelCase					)

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

        _UpperCAmelCase           =   self.classifier(_UpperCamelCase					)

        _UpperCAmelCase           =   None

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

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

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






from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType




class 			__UpperCamelCase    (							A__    ):
     __A	:  Dict							  =   ["""image_processor""", """tokenizer"""]
     __A	:  List[str]							  =   """BridgeTowerImageProcessor"""
     __A	:  str							  =   ("""RobertaTokenizer""", """RobertaTokenizerFast""")





     def __init__(     self				,							_UpperCamelCase				,							_UpperCamelCase					):
        super().__init__(_UpperCamelCase				,							_UpperCamelCase					)





     def __call__(     self				,							_UpperCamelCase				,							_UpperCamelCase = None				,							_UpperCamelCase = True				,							_UpperCamelCase = False				,							_UpperCamelCase = None				,							_UpperCamelCase = None				,							_UpperCamelCase = 0				,							_UpperCamelCase = None				,							_UpperCamelCase = None				,							_UpperCamelCase = None				,							_UpperCamelCase = False				,							_UpperCamelCase = False				,							_UpperCamelCase = False				,							_UpperCamelCase = False				,							_UpperCamelCase = True				,							_UpperCamelCase = None				,							**_UpperCamelCase				,							):
        _UpperCAmelCase           =   self.tokenizer(
            text=_UpperCamelCase				,							add_special_tokens=_UpperCamelCase				,							padding=_UpperCamelCase				,							truncation=_UpperCamelCase				,							max_length=_UpperCamelCase				,							stride=_UpperCamelCase				,							pad_to_multiple_of=_UpperCamelCase				,							return_token_type_ids=_UpperCamelCase				,							return_attention_mask=_UpperCamelCase				,							return_overflowing_tokens=_UpperCamelCase				,							return_special_tokens_mask=_UpperCamelCase				,							return_offsets_mapping=_UpperCamelCase				,							return_length=_UpperCamelCase				,							verbose=_UpperCamelCase				,							return_tensors=_UpperCamelCase				,							**_UpperCamelCase				,							)
        # add pixel_values + pixel_mask
        _UpperCAmelCase           =   self.image_processor(
            _UpperCamelCase				,							return_tensors=_UpperCamelCase				,							do_normalize=_UpperCamelCase				,							do_center_crop=_UpperCamelCase				,							**_UpperCamelCase					)
        encoding.update(_UpperCamelCase					)

        return encoding





     def 		UpperCamelCase(     self				,							*_UpperCamelCase				,							**_UpperCamelCase					):
        return self.tokenizer.batch_decode(*_UpperCamelCase				,							**_UpperCamelCase					)





     def 		UpperCamelCase(     self				,							*_UpperCamelCase				,							**_UpperCamelCase					):
        return self.tokenizer.decode(*_UpperCamelCase				,							**_UpperCamelCase					)





     @property
     def 		UpperCamelCase(     self					):
        _UpperCAmelCase           =   self.tokenizer.model_input_names
        _UpperCAmelCase           =   self.image_processor.model_input_names
        return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names					)					)