
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


lowercase  : Tuple						   =						logging.get_logger(__name__)

# General docstring
lowercase  : Optional[int]						   =						"""ResNetConfig"""

# Base docstring
lowercase  : Any						   =						"""microsoft/resnet-50"""
lowercase  : Optional[Any]						   =						[1, 2048, 7, 7]

# Image classification docstring
lowercase  : str						   =						"""microsoft/resnet-50"""
lowercase  : List[str]						   =						"""tiger cat"""

lowercase  : List[Any]						   =						[
    """microsoft/resnet-50""",
    # See all resnet models at https://huggingface.co/models?filter=resnet
]

class __snake_case		(				nn.Module					):
    def __init__(  self		,snake_case		,snake_case		,snake_case = 3		,snake_case = 1		,snake_case = "relu"				):



           '''simple docstring'''







           super().__init__()
           lowercase							:					int	     =				nn.Convad(
               snake_case		,snake_case		,kernel_size=snake_case		,stride=snake_case		,padding=kernel_size // 2		,bias=snake_case				)
           lowercase							:					int	     =				nn.BatchNormad(snake_case				)
           lowercase							:					List[Any]	     =				ACTaFN[activation] if activation is not None else nn.Identity()
    def 				_SCREAMING_SNAKE_CASE	(  self		,snake_case				):



           '''simple docstring'''







           lowercase							:					Any	     =				self.convolution(snake_case				)
           lowercase							:					List[str]	     =				self.normalization(snake_case				)
           lowercase							:					List[Any]	     =				self.activation(snake_case				)
           return hidden_state






class __snake_case		(				nn.Module					):
    def __init__(  self		,snake_case				):



           '''simple docstring'''







           super().__init__()
           lowercase							:					Union[str, Any]	     =				ResNetConvLayer(
               config.num_channels		,config.embedding_size		,kernel_size=7		,stride=2		,activation=config.hidden_act				)
           lowercase							:					Union[str, Any]	     =				nn.MaxPoolad(kernel_size=3		,stride=2		,padding=1				)
           lowercase							:					Any	     =				config.num_channels
    def 				_SCREAMING_SNAKE_CASE	(  self		,snake_case				):



           '''simple docstring'''







           lowercase							:					Dict	     =				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							:					str	     =				self.embedder(snake_case				)
           lowercase							:					int	     =				self.pooler(snake_case				)
           return embedding






class __snake_case		(				nn.Module					):
    def __init__(  self		,snake_case		,snake_case		,snake_case = 2				):



           '''simple docstring'''







           super().__init__()
           lowercase							:					Tuple	     =				nn.Convad(snake_case		,snake_case		,kernel_size=1		,stride=snake_case		,bias=snake_case				)
           lowercase							:					Tuple	     =				nn.BatchNormad(snake_case				)
    def 				_SCREAMING_SNAKE_CASE	(  self		,snake_case				):



           '''simple docstring'''







           lowercase							:					Tuple	     =				self.convolution(snake_case				)
           lowercase							:					List[Any]	     =				self.normalization(snake_case				)
           return hidden_state






class __snake_case		(				nn.Module					):
    def __init__(  self		,snake_case		,snake_case		,snake_case = 1		,snake_case = "relu"				):



           '''simple docstring'''







           super().__init__()
           lowercase							:					int	     =				in_channels != out_channels or stride != 1
           lowercase							:					Optional[Any]	     =				(
               ResNetShortCut(snake_case		,snake_case		,stride=snake_case				) if should_apply_shortcut else nn.Identity()
           )
           lowercase							:					str	     =				nn.Sequential(
               ResNetConvLayer(snake_case		,snake_case		,stride=snake_case				)		,ResNetConvLayer(snake_case		,snake_case		,activation=snake_case				)		,)
           lowercase							:					Union[str, Any]	     =				ACTaFN[activation]
    def 				_SCREAMING_SNAKE_CASE	(  self		,snake_case				):



           '''simple docstring'''







           lowercase							:					List[Any]	     =				hidden_state
           lowercase							:					str	     =				self.layer(snake_case				)
           lowercase							:					List[Any]	     =				self.shortcut(snake_case				)
           hidden_state += residual
           lowercase							:					str	     =				self.activation(snake_case				)
           return hidden_state






class __snake_case		(				nn.Module					):
    def __init__(  self		,snake_case		,snake_case		,snake_case = 1		,snake_case = "relu"		,snake_case = 4				):



           '''simple docstring'''







           super().__init__()
           lowercase							:					Optional[Any]	     =				in_channels != out_channels or stride != 1
           lowercase							:					List[Any]	     =				out_channels // reduction
           lowercase							:					Optional[Any]	     =				(
               ResNetShortCut(snake_case		,snake_case		,stride=snake_case				) if should_apply_shortcut else nn.Identity()
           )
           lowercase							:					Dict	     =				nn.Sequential(
               ResNetConvLayer(snake_case		,snake_case		,kernel_size=1				)		,ResNetConvLayer(snake_case		,snake_case		,stride=snake_case				)		,ResNetConvLayer(snake_case		,snake_case		,kernel_size=1		,activation=snake_case				)		,)
           lowercase							:					Union[str, Any]	     =				ACTaFN[activation]
    def 				_SCREAMING_SNAKE_CASE	(  self		,snake_case				):



           '''simple docstring'''







           lowercase							:					int	     =				hidden_state
           lowercase							:					int	     =				self.layer(snake_case				)
           lowercase							:					Dict	     =				self.shortcut(snake_case				)
           hidden_state += residual
           lowercase							:					str	     =				self.activation(snake_case				)
           return hidden_state






class __snake_case		(				nn.Module					):
    def __init__(  self		,snake_case		,snake_case		,snake_case		,snake_case = 2		,snake_case = 2		,):



           '''simple docstring'''







           super().__init__()

           lowercase							:					Tuple	     =				ResNetBottleNeckLayer if config.layer_type == """bottleneck""" else ResNetBasicLayer

           lowercase							:					List[Any]	     =				nn.Sequential(
               # downsampling is done in the first layer with stride of 2
               layer(snake_case		,snake_case		,stride=snake_case		,activation=config.hidden_act				)		,*[layer(snake_case		,snake_case		,activation=config.hidden_act				) for _ in range(depth - 1				)]		,)
    def 				_SCREAMING_SNAKE_CASE	(  self		,snake_case				):



           '''simple docstring'''







           lowercase							:					Optional[Any]	     =				input
           for layer in self.layers:
                  lowercase							:					int	     =				layer(snake_case				)
           return hidden_state






class __snake_case		(				nn.Module					):
    def __init__(  self		,snake_case				):



           '''simple docstring'''







           super().__init__()
           lowercase							:					List[str]	     =				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(
                   snake_case		,config.embedding_size		,config.hidden_sizes[0]		,stride=2 if config.downsample_in_first_stage else 1		,depth=config.depths[0]		,)				)
           lowercase							:					str	     =				zip(config.hidden_sizes		,config.hidden_sizes[1:]				)
           for (in_channels, out_channels), depth in zip(snake_case		,config.depths[1:]				):
                  self.stages.append(ResNetStage(snake_case		,snake_case		,snake_case		,depth=snake_case				)				)
    def 				_SCREAMING_SNAKE_CASE	(  self		,snake_case		,snake_case = False		,snake_case = True				):



           '''simple docstring'''







           lowercase							:					str	     =				() if output_hidden_states else None

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

                  lowercase							:					Optional[int]	     =				stage_module(snake_case				)

           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=snake_case		,hidden_states=snake_case		,)






class __snake_case		(				lowerCAmelCase					):
    _a		:						Optional[int]=    ResNetConfig
    _a		:						int=    "resnet"
    _a		:						Union[str, Any]=    "pixel_values"
    _a		:						int=    True
    def 				_SCREAMING_SNAKE_CASE	(  self		,snake_case				):



           '''simple docstring'''







           if isinstance(snake_case		,nn.Convad				):
                  nn.init.kaiming_normal_(module.weight		,mode="""fan_out"""		,nonlinearity="""relu"""				)
           elif isinstance(snake_case		,(nn.BatchNormad, nn.GroupNorm)				):
                  nn.init.constant_(module.weight		,1				)
                  nn.init.constant_(module.bias		,0				)
    def 				_SCREAMING_SNAKE_CASE	(  self		,snake_case		,snake_case=False				):



           '''simple docstring'''







           if isinstance(snake_case		,snake_case				):
                  lowercase							:					Union[str, Any]	     =				value






lowercase  : Union[str, Any]						   =						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 ([`ResNetConfig`]): 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.
"""

lowercase  : Dict						   =						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 [`~utils.ModelOutput`] instead of a plain tuple.
"""

@add_start_docstrings(
    "The bare ResNet model outputting raw features without any specific head on top." ,     lowerCAmelCase ,     )
class __snake_case		(				lowerCAmelCase					):
    def __init__(  self		,snake_case				):



           '''simple docstring'''







           super().__init__(snake_case				)
           lowercase							:					Union[str, Any]	     =				config
           lowercase							:					Optional[int]	     =				ResNetEmbeddings(snake_case				)
           lowercase							:					Any	     =				ResNetEncoder(snake_case				)
           lowercase							:					str	     =				nn.AdaptiveAvgPoolad((1, 1)				)
           # Initialize weights and apply final processing
           self.post_init()
    @add_start_docstrings_to_model_forward(snake_case				)
    @add_code_sample_docstrings(
        checkpoint=_CHECKPOINT_FOR_DOC		,output_type=snake_case		,config_class=_CONFIG_FOR_DOC		,modality="""vision"""		,expected_output=_EXPECTED_OUTPUT_SHAPE		,)
    def 				_SCREAMING_SNAKE_CASE	(  self		,snake_case		,snake_case = None		,snake_case = None				):



           '''simple docstring'''







           lowercase							:					List[str]	     =				(
               output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
           )
           lowercase							:					List[str]	     =				return_dict if return_dict is not None else self.config.use_return_dict

           lowercase							:					str	     =				self.embedder(snake_case				)

           lowercase							:					Optional[int]	     =				self.encoder(
               snake_case		,output_hidden_states=snake_case		,return_dict=snake_case				)

           lowercase							:					Dict	     =				encoder_outputs[0]

           lowercase							:					List[Any]	     =				self.pooler(snake_case				)

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

           return BaseModelOutputWithPoolingAndNoAttention(
               last_hidden_state=snake_case		,pooler_output=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    " ,     lowerCAmelCase ,     )
class __snake_case		(				lowerCAmelCase					):
    def __init__(  self		,snake_case				):



           '''simple docstring'''







           super().__init__(snake_case				)
           lowercase							:					Union[str, Any]	     =				config.num_labels
           lowercase							:					Optional[int]	     =				ResNetModel(snake_case				)
           # classification head
           lowercase							:					int	     =				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(snake_case				)
    @add_code_sample_docstrings(
        checkpoint=_IMAGE_CLASS_CHECKPOINT		,output_type=snake_case		,config_class=_CONFIG_FOR_DOC		,expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT		,)
    def 				_SCREAMING_SNAKE_CASE	(  self		,snake_case = None		,snake_case = None		,snake_case = None		,snake_case = None		,):



           '''simple docstring'''







           lowercase							:					Any	     =				return_dict if return_dict is not None else self.config.use_return_dict

           lowercase							:					Tuple	     =				self.resnet(snake_case		,output_hidden_states=snake_case		,return_dict=snake_case				)

           lowercase							:					Any	     =				outputs.pooler_output if return_dict else outputs[1]

           lowercase							:					Tuple	     =				self.classifier(snake_case				)

           lowercase							:					int	     =				None

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

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

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






@add_start_docstrings(
    "\n    ResNet backbone, to be used with frameworks like DETR and MaskFormer.\n    " ,     lowerCAmelCase ,     )
class __snake_case		(				lowerCAmelCase ,     lowerCAmelCase					):
    def __init__(  self		,snake_case				):



           '''simple docstring'''







           super().__init__(snake_case				)
           super()._init_backbone(snake_case				)

           lowercase							:					List[str]	     =				[config.embedding_size] + config.hidden_sizes
           lowercase							:					List[Any]	     =				ResNetEmbeddings(snake_case				)
           lowercase							:					List[str]	     =				ResNetEncoder(snake_case				)

           # initialize weights and apply final processing
           self.post_init()
    @add_start_docstrings_to_model_forward(snake_case				)
    @replace_return_docstrings(output_type=snake_case		,config_class=_CONFIG_FOR_DOC				)
    def 				_SCREAMING_SNAKE_CASE	(  self		,snake_case		,snake_case = None		,snake_case = None				):



           '''simple docstring'''







           lowercase							:					str	     =				return_dict if return_dict is not None else self.config.use_return_dict
           lowercase							:					Union[str, Any]	     =				(
               output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
           )

           lowercase							:					List[str]	     =				self.embedder(snake_case				)

           lowercase							:					List[str]	     =				self.encoder(snake_case		,output_hidden_states=snake_case		,return_dict=snake_case				)

           lowercase							:					Optional[Any]	     =				outputs.hidden_states

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

           if not return_dict:
                  lowercase							:					Optional[Any]	     =				(feature_maps,)
                  if output_hidden_states:
                         output += (outputs.hidden_states,)
                  return output

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


import math
from datetime import datetime, timedelta







def 						_snake_case(  SCREAMING_SNAKE_CASE__    )			->   datetime:
       lowercase							:					Any	     =				year % 19
       lowercase							:					Optional[int]	     =				year % 4
       lowercase							:					Any	     =				year % 7
       lowercase							:					str	     =				math.floor(year / 100    )
       lowercase							:					List[str]	     =				math.floor((13 + 8 * leap_day_inhibits) / 25    )
       lowercase							:					Tuple	     =				leap_day_inhibits / 4
       lowercase							:					Any	     =				(
           15 - lunar_orbit_correction + leap_day_inhibits - leap_day_reinstall_number
       ) % 30
       lowercase							:					Union[str, Any]	     =				(4 + leap_day_inhibits - leap_day_reinstall_number) % 7

       # days to be added to March 21
       lowercase							:					str	     =				(19 * metonic_cycle + secular_moon_shift) % 30

       # PHM -> Paschal Full Moon
       lowercase							:					List[Any]	     =				(
           2 * julian_leap_year
           + 4 * non_leap_year
           + 6 * days_to_add
           + century_starting_point
       ) % 7

       if days_to_add == 29 and days_from_phm_to_sunday == 6:
              return datetime(SCREAMING_SNAKE_CASE__ ,     4 ,     19    )
       elif days_to_add == 28 and days_from_phm_to_sunday == 6:
              return datetime(SCREAMING_SNAKE_CASE__ ,     4 ,     18    )
       else:
              return datetime(SCREAMING_SNAKE_CASE__ ,     3 ,     22    ) + timedelta(
                  days=int(days_to_add + days_from_phm_to_sunday    )    )


if __name__ == "__main__":
  for year in (1994, 2000, 2010, 2021, 2023):
    lowercase  : List[str]						   =							"""will be""" if year > datetime.now().year else """was"""
    print(F'''Easter in {year} {tense} {gauss_easter(year)}''')
