




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 ...modeling_outputs import (
    BackboneOutput,
    BaseModelOutputWithNoAttention,
    BaseModelOutputWithPoolingAndNoAttention,
    ImageClassifierOutputWithNoAttention,
)
from ...modeling_utils import PreTrainedModel
from ...utils import (
    add_code_sample_docstrings,
    add_start_docstrings,
    add_start_docstrings_to_model_forward,
    logging,
    replace_return_docstrings,
)
from ...utils.backbone_utils import BackboneMixin
from .configuration_resnet import ResNetConfig


UpperCamelCase								=					logging.get_logger(__name__)

# General docstring
UpperCamelCase								=					'ResNetConfig'

# Base docstring
UpperCamelCase								=					'microsoft/resnet-50'
UpperCamelCase								=					[1, 2048, 7, 7]

# Image classification docstring
UpperCamelCase								=					'microsoft/resnet-50'
UpperCamelCase								=					'tiger cat'

UpperCamelCase								=					[
    'microsoft/resnet-50',
    # See all resnet models at https://huggingface.co/models?filter=resnet
]





class 			__lowerCamelCase	(   nn.Module   ):




    """simple docstring"""

    def __init__(	self					:					str       ,     SCREAMING_SNAKE_CASE__					:					int       ,     SCREAMING_SNAKE_CASE__					:					int       ,     SCREAMING_SNAKE_CASE__					:					int = 3       ,     SCREAMING_SNAKE_CASE__					:					int = 1       ,     SCREAMING_SNAKE_CASE__					:					str = "relu"       )				->	str:
         super().__init__()
         lowerCAmelCase__          =			nn.Convad(
             SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       ,     kernel_size=SCREAMING_SNAKE_CASE__       ,     stride=SCREAMING_SNAKE_CASE__       ,     padding=kernel_size // 2       ,     bias=SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			nn.BatchNormad(SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			ACTaFN[activation] if activation is not None else nn.Identity()


    def        a				(	self					:					int       ,     SCREAMING_SNAKE_CASE__					:					Tensor       )				->	Tensor:
         lowerCAmelCase__          =			self.convolution(SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			self.normalization(SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			self.activation(SCREAMING_SNAKE_CASE__       )
         return hidden_state






class 			__lowerCamelCase	(   nn.Module   ):




    """simple docstring"""

    def __init__(	self					:					Dict       ,     SCREAMING_SNAKE_CASE__					:					ResNetConfig       )				->	Optional[Any]:
         super().__init__()
         lowerCAmelCase__          =			ResNetConvLayer(
             config.num_channels       ,     config.embedding_size       ,     kernel_size=7       ,     stride=2       ,     activation=config.hidden_act       )
         lowerCAmelCase__          =			nn.MaxPoolad(kernel_size=3       ,     stride=2       ,     padding=1       )
         lowerCAmelCase__          =			config.num_channels


    def        a				(	self					:					Any       ,     SCREAMING_SNAKE_CASE__					:					Tensor       )				->	Tensor:
         lowerCAmelCase__          =			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."       )
         lowerCAmelCase__          =			self.embedder(SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			self.pooler(SCREAMING_SNAKE_CASE__       )
         return embedding






class 			__lowerCamelCase	(   nn.Module   ):




    """simple docstring"""

    def __init__(	self					:					Optional[int]       ,     SCREAMING_SNAKE_CASE__					:					int       ,     SCREAMING_SNAKE_CASE__					:					int       ,     SCREAMING_SNAKE_CASE__					:					int = 2       )				->	Union[str, Any]:
         super().__init__()
         lowerCAmelCase__          =			nn.Convad(SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       ,     kernel_size=1       ,     stride=SCREAMING_SNAKE_CASE__       ,     bias=SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			nn.BatchNormad(SCREAMING_SNAKE_CASE__       )


    def        a				(	self					:					Optional[Any]       ,     SCREAMING_SNAKE_CASE__					:					Tensor       )				->	Tensor:
         lowerCAmelCase__          =			self.convolution(SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			self.normalization(SCREAMING_SNAKE_CASE__       )
         return hidden_state






class 			__lowerCamelCase	(   nn.Module   ):




    """simple docstring"""

    def __init__(	self					:					List[str]       ,     SCREAMING_SNAKE_CASE__					:					int       ,     SCREAMING_SNAKE_CASE__					:					int       ,     SCREAMING_SNAKE_CASE__					:					int = 1       ,     SCREAMING_SNAKE_CASE__					:					str = "relu"       )				->	Dict:
         super().__init__()
         lowerCAmelCase__          =			in_channels != out_channels or stride != 1
         lowerCAmelCase__          =			(
             ResNetShortCut(SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       ,     stride=SCREAMING_SNAKE_CASE__       ) if should_apply_shortcut else nn.Identity()
         )
         lowerCAmelCase__          =			nn.Sequential(
             ResNetConvLayer(SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       ,     stride=SCREAMING_SNAKE_CASE__       )       ,     ResNetConvLayer(SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       ,     activation=SCREAMING_SNAKE_CASE__       )       ,     )
         lowerCAmelCase__          =			ACTaFN[activation]


    def        a				(	self					:					List[Any]       ,     SCREAMING_SNAKE_CASE__					:					int       )				->	Dict:
         lowerCAmelCase__          =			hidden_state
         lowerCAmelCase__          =			self.layer(SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			self.shortcut(SCREAMING_SNAKE_CASE__       )
         hidden_state += residual
         lowerCAmelCase__          =			self.activation(SCREAMING_SNAKE_CASE__       )
         return hidden_state






class 			__lowerCamelCase	(   nn.Module   ):




    """simple docstring"""

    def __init__(	self					:					Optional[Any]       ,     SCREAMING_SNAKE_CASE__					:					int       ,     SCREAMING_SNAKE_CASE__					:					int       ,     SCREAMING_SNAKE_CASE__					:					int = 1       ,     SCREAMING_SNAKE_CASE__					:					str = "relu"       ,     SCREAMING_SNAKE_CASE__					:					int = 4       )				->	Dict:
         super().__init__()
         lowerCAmelCase__          =			in_channels != out_channels or stride != 1
         lowerCAmelCase__          =			out_channels // reduction
         lowerCAmelCase__          =			(
             ResNetShortCut(SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       ,     stride=SCREAMING_SNAKE_CASE__       ) if should_apply_shortcut else nn.Identity()
         )
         lowerCAmelCase__          =			nn.Sequential(
             ResNetConvLayer(SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       ,     kernel_size=1       )       ,     ResNetConvLayer(SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       ,     stride=SCREAMING_SNAKE_CASE__       )       ,     ResNetConvLayer(SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       ,     kernel_size=1       ,     activation=SCREAMING_SNAKE_CASE__       )       ,     )
         lowerCAmelCase__          =			ACTaFN[activation]


    def        a				(	self					:					Any       ,     SCREAMING_SNAKE_CASE__					:					List[Any]       )				->	Optional[int]:
         lowerCAmelCase__          =			hidden_state
         lowerCAmelCase__          =			self.layer(SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			self.shortcut(SCREAMING_SNAKE_CASE__       )
         hidden_state += residual
         lowerCAmelCase__          =			self.activation(SCREAMING_SNAKE_CASE__       )
         return hidden_state






class 			__lowerCamelCase	(   nn.Module   ):




    """simple docstring"""

    def __init__(	self					:					int       ,     SCREAMING_SNAKE_CASE__					:					ResNetConfig       ,     SCREAMING_SNAKE_CASE__					:					int       ,     SCREAMING_SNAKE_CASE__					:					int       ,     SCREAMING_SNAKE_CASE__					:					int = 2       ,     SCREAMING_SNAKE_CASE__					:					int = 2       ,     )				->	List[Any]:
         super().__init__()

         lowerCAmelCase__          =			ResNetBottleNeckLayer if config.layer_type == "bottleneck" else ResNetBasicLayer

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


    def        a				(	self					:					Dict       ,     SCREAMING_SNAKE_CASE__					:					Tensor       )				->	Tensor:
         lowerCAmelCase__          =			input
         for layer in self.layers:
              lowerCAmelCase__          =			layer(SCREAMING_SNAKE_CASE__       )
         return hidden_state






class 			__lowerCamelCase	(   nn.Module   ):




    """simple docstring"""

    def __init__(	self					:					Union[str, Any]       ,     SCREAMING_SNAKE_CASE__					:					ResNetConfig       )				->	Optional[Any]:
         super().__init__()
         lowerCAmelCase__          =			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(
             ResNetStage(
                 SCREAMING_SNAKE_CASE__       ,     config.embedding_size       ,     config.hidden_sizes[0]       ,     stride=2 if config.downsample_in_first_stage else 1       ,     depth=config.depths[0]       ,     )       )
         lowerCAmelCase__          =			zip(config.hidden_sizes       ,     config.hidden_sizes[1:]       )
         for (in_channels, out_channels), depth in zip(SCREAMING_SNAKE_CASE__       ,     config.depths[1:]       ):
              self.stages.append(ResNetStage(SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       ,     depth=SCREAMING_SNAKE_CASE__       )       )


    def        a				(	self					:					Optional[int]       ,     SCREAMING_SNAKE_CASE__					:					Tensor       ,     SCREAMING_SNAKE_CASE__					:					bool = False       ,     SCREAMING_SNAKE_CASE__					:					bool = True       )				->	BaseModelOutputWithNoAttention:
         lowerCAmelCase__          =			() if output_hidden_states else None

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

              lowerCAmelCase__          =			stage_module(SCREAMING_SNAKE_CASE__       )

         if output_hidden_states:
              lowerCAmelCase__          =			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=SCREAMING_SNAKE_CASE__       ,     hidden_states=SCREAMING_SNAKE_CASE__       ,     )






class 			__lowerCamelCase	(   UpperCamelCase__   ):




    """simple docstring"""

    snake_case__ =				ResNetConfig
    snake_case__ =				"resnet"
    snake_case__ =				"pixel_values"
    snake_case__ =				True


    def        a				(	self					:					List[str]       ,     SCREAMING_SNAKE_CASE__					:					Tuple       )				->	str:
         if isinstance(SCREAMING_SNAKE_CASE__       ,     nn.Convad       ):
              nn.init.kaiming_normal_(module.weight       ,     mode="fan_out"       ,     nonlinearity="relu"       )
         elif isinstance(SCREAMING_SNAKE_CASE__       ,     (nn.BatchNormad, nn.GroupNorm)       ):
              nn.init.constant_(module.weight       ,     1       )
              nn.init.constant_(module.bias       ,     0       )


    def        a				(	self					:					List[str]       ,     SCREAMING_SNAKE_CASE__					:					int       ,     SCREAMING_SNAKE_CASE__					:					Tuple=False       )				->	List[Any]:
         if isinstance(SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       ):
              lowerCAmelCase__          =			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 ([`ResNetConfig`]): 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 [`~utils.ModelOutput`] instead of a plain tuple.\n'





@add_start_docstrings(
    "The bare ResNet model outputting raw features without any specific head on top."     ,    UpperCamelCase__     ,    )
class 			__lowerCamelCase	(   UpperCamelCase__   ):




    """simple docstring"""

    def __init__(	self					:					Optional[int]       ,     SCREAMING_SNAKE_CASE__					:					int       )				->	Dict:
         super().__init__(SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			config
         lowerCAmelCase__          =			ResNetEmbeddings(SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			ResNetEncoder(SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			nn.AdaptiveAvgPoolad((1, 1)       )
         # Initialize weights and apply final processing
         self.post_init()


    @add_start_docstrings_to_model_forward(SCREAMING_SNAKE_CASE__       )
    @add_code_sample_docstrings(
        checkpoint=_CHECKPOINT_FOR_DOC       ,     output_type=SCREAMING_SNAKE_CASE__       ,     config_class=_CONFIG_FOR_DOC       ,     modality="vision"       ,     expected_output=_EXPECTED_OUTPUT_SHAPE       ,     )
    def        a				(	self					:					Dict       ,     SCREAMING_SNAKE_CASE__					:					Tensor       ,     SCREAMING_SNAKE_CASE__					:					Optional[bool] = None       ,     SCREAMING_SNAKE_CASE__					:					Optional[bool] = None       )				->	BaseModelOutputWithPoolingAndNoAttention:
         lowerCAmelCase__          =			(
             output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
         )
         lowerCAmelCase__          =			return_dict if return_dict is not None else self.config.use_return_dict

         lowerCAmelCase__          =			self.embedder(SCREAMING_SNAKE_CASE__       )

         lowerCAmelCase__          =			self.encoder(
             SCREAMING_SNAKE_CASE__       ,     output_hidden_states=SCREAMING_SNAKE_CASE__       ,     return_dict=SCREAMING_SNAKE_CASE__       )

         lowerCAmelCase__          =			encoder_outputs[0]

         lowerCAmelCase__          =			self.pooler(SCREAMING_SNAKE_CASE__       )

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

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






@add_start_docstrings(
    "\n    ResNet Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for\n    ImageNet.\n    "     ,    UpperCamelCase__     ,    )
class 			__lowerCamelCase	(   UpperCamelCase__   ):




    """simple docstring"""

    def __init__(	self					:					str       ,     SCREAMING_SNAKE_CASE__					:					Dict       )				->	List[str]:
         super().__init__(SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			config.num_labels
         lowerCAmelCase__          =			ResNetModel(SCREAMING_SNAKE_CASE__       )
         # classification head
         lowerCAmelCase__          =			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(SCREAMING_SNAKE_CASE__       )
    @add_code_sample_docstrings(
        checkpoint=_IMAGE_CLASS_CHECKPOINT       ,     output_type=SCREAMING_SNAKE_CASE__       ,     config_class=_CONFIG_FOR_DOC       ,     expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT       ,     )
    def        a				(	self					:					int       ,     SCREAMING_SNAKE_CASE__					:					Optional[torch.FloatTensor] = None       ,     SCREAMING_SNAKE_CASE__					:					Optional[torch.LongTensor] = None       ,     SCREAMING_SNAKE_CASE__					:					Optional[bool] = None       ,     SCREAMING_SNAKE_CASE__					:					Optional[bool] = None       ,     )				->	ImageClassifierOutputWithNoAttention:
         lowerCAmelCase__          =			return_dict if return_dict is not None else self.config.use_return_dict

         lowerCAmelCase__          =			self.resnet(SCREAMING_SNAKE_CASE__       ,     output_hidden_states=SCREAMING_SNAKE_CASE__       ,     return_dict=SCREAMING_SNAKE_CASE__       )

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

         lowerCAmelCase__          =			self.classifier(SCREAMING_SNAKE_CASE__       )

         lowerCAmelCase__          =			None

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

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

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






@add_start_docstrings(
    "\n    ResNet backbone, to be used with frameworks like DETR and MaskFormer.\n    "     ,    UpperCamelCase__     ,    )
class 			__lowerCamelCase	(   UpperCamelCase__     ,    UpperCamelCase__   ):




    """simple docstring"""

    def __init__(	self					:					str       ,     SCREAMING_SNAKE_CASE__					:					int       )				->	Tuple:
         super().__init__(SCREAMING_SNAKE_CASE__       )
         super()._init_backbone(SCREAMING_SNAKE_CASE__       )

         lowerCAmelCase__          =			[config.embedding_size] + config.hidden_sizes
         lowerCAmelCase__          =			ResNetEmbeddings(SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			ResNetEncoder(SCREAMING_SNAKE_CASE__       )

         # initialize weights and apply final processing
         self.post_init()


    @add_start_docstrings_to_model_forward(SCREAMING_SNAKE_CASE__       )
    @replace_return_docstrings(output_type=SCREAMING_SNAKE_CASE__       ,     config_class=_CONFIG_FOR_DOC       )
    def        a				(	self					:					List[str]       ,     SCREAMING_SNAKE_CASE__					:					Tensor       ,     SCREAMING_SNAKE_CASE__					:					Optional[bool] = None       ,     SCREAMING_SNAKE_CASE__					:					Optional[bool] = None       )				->	BackboneOutput:
         lowerCAmelCase__          =			return_dict if return_dict is not None else self.config.use_return_dict
         lowerCAmelCase__          =			(
             output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
         )

         lowerCAmelCase__          =			self.embedder(SCREAMING_SNAKE_CASE__       )

         lowerCAmelCase__          =			self.encoder(SCREAMING_SNAKE_CASE__       ,     output_hidden_states=SCREAMING_SNAKE_CASE__       ,     return_dict=SCREAMING_SNAKE_CASE__       )

         lowerCAmelCase__          =			outputs.hidden_states

         lowerCAmelCase__          =			()
         for idx, stage in enumerate(self.stage_names       ):
              if stage in self.out_features:
                   feature_maps += (hidden_states[idx],)

         if not return_dict:
              lowerCAmelCase__          =			(feature_maps,)
              if output_hidden_states:
                   output += (outputs.hidden_states,)
              return output

         return BackboneOutput(
             feature_maps=SCREAMING_SNAKE_CASE__       ,     hidden_states=outputs.hidden_states if output_hidden_states else None       ,     attentions=SCREAMING_SNAKE_CASE__       ,     )






import fire

from transformers import AutoConfig, AutoModelForSeqaSeqLM, AutoTokenizer



def 							_A       (						lowerCAmelCase_					: str       ,    lowerCAmelCase_					: str       ,    **lowerCAmelCase_					: str      ):
     """simple docstring"""

     lowerCAmelCase__          =			AutoConfig.from_pretrained(lowerCAmelCase_       ,    **lowerCAmelCase_      )
     lowerCAmelCase__          =			AutoModelForSeqaSeqLM.from_config(lowerCAmelCase_      )
     model.save_pretrained(lowerCAmelCase_      )
     AutoTokenizer.from_pretrained(lowerCAmelCase_      ).save_pretrained(lowerCAmelCase_      )
     return model


if __name__ == "__main__":
     fire.Fire(save_randomly_initialized_version)
