





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


_SCREAMING_SNAKE_CASE 		=						logging.get_logger(__name__)

# General docstring
_SCREAMING_SNAKE_CASE 		=						"""RegNetConfig"""

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

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

_SCREAMING_SNAKE_CASE 		=						[
    """facebook/regnet-y-040""",
    # See all regnet models at https://huggingface.co/models?filter=regnet
]
class SCREAMING_SNAKE_CASE_  (   nn.Module   ):






						def __init__( self : Any						,				_A : int						,				_A : int						,				_A : int = 3						,				_A : int = 1						,				_A : int = 1						,				_A : Optional[str] = "relu"						,				)							->      Tuple:







							"""simple docstring"""


							super().__init__()
							snake_case_						:     Optional[Any]     =		nn.Convad(
							    _A						,				_A						,				kernel_size=_A						,				stride=_A						,				padding=kernel_size // 2						,				groups=_A						,				bias=_A						,				)
							snake_case_						:     str     =		nn.BatchNormad(_A		)
							snake_case_						:     Any     =		ACTaFN[activation] if activation is not None else nn.Identity()






						def UpperCAmelCase_			( self : List[str]						,				_A : str		)							->      Optional[int]:







							"""simple docstring"""


							snake_case_						:     Tuple     =		self.convolution(_A		)
							snake_case_						:     Dict     =		self.normalization(_A		)
							snake_case_						:     Any     =		self.activation(_A		)
							return hidden_state
class SCREAMING_SNAKE_CASE_  (   nn.Module   ):






						def __init__( self : Union[str, Any]						,				_A : RegNetConfig		)							->      str:







							"""simple docstring"""


							super().__init__()
							snake_case_						:     Optional[Any]     =		RegNetConvLayer(
							    config.num_channels						,				config.embedding_size						,				kernel_size=3						,				stride=2						,				activation=config.hidden_act		)
							snake_case_						:     Optional[int]     =		config.num_channels






						def UpperCAmelCase_			( self : List[str]						,				_A : str		)							->      List[str]:







							"""simple docstring"""


							snake_case_						:     Optional[Any]     =		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.'		)
							snake_case_						:     int     =		self.embedder(_A		)
							return hidden_state
class SCREAMING_SNAKE_CASE_  (   nn.Module   ):






						def __init__( self : Optional[Any]						,				_A : int						,				_A : int						,				_A : int = 2		)							->      Tuple:







							"""simple docstring"""


							super().__init__()
							snake_case_						:     str     =		nn.Convad(_A						,				_A						,				kernel_size=1						,				stride=_A						,				bias=_A		)
							snake_case_						:     Tuple     =		nn.BatchNormad(_A		)






						def UpperCAmelCase_			( self : str						,				_A : Tensor		)							->      Tensor:







							"""simple docstring"""


							snake_case_						:     Optional[Any]     =		self.convolution(_A		)
							snake_case_						:     List[str]     =		self.normalization(_A		)
							return hidden_state
class SCREAMING_SNAKE_CASE_  (   nn.Module   ):






						def __init__( self : Optional[int]						,				_A : int						,				_A : int		)							->      str:







							"""simple docstring"""


							super().__init__()

							snake_case_						:     Dict     =		nn.AdaptiveAvgPoolad((1, 1)		)
							snake_case_						:     List[str]     =		nn.Sequential(
							    nn.Convad(_A						,				_A						,				kernel_size=1		)						,				nn.ReLU()						,				nn.Convad(_A						,				_A						,				kernel_size=1		)						,				nn.Sigmoid()						,				)






						def UpperCAmelCase_			( self : Union[str, Any]						,				_A : Optional[Any]		)							->      str:







							"""simple docstring"""


							snake_case_						:     Union[str, Any]     =		self.pooler(_A		)
							snake_case_						:     Dict     =		self.attention(_A		)
							snake_case_						:     Optional[Any]     =		hidden_state * attention
							return hidden_state
class SCREAMING_SNAKE_CASE_  (   nn.Module   ):






						def __init__( self : Any						,				_A : RegNetConfig						,				_A : int						,				_A : int						,				_A : int = 1		)							->      Tuple:







							"""simple docstring"""


							super().__init__()
							snake_case_						:     List[str]     =		in_channels != out_channels or stride != 1
							snake_case_						:     Tuple     =		max(1						,				out_channels // config.groups_width		)
							snake_case_						:     Union[str, Any]     =		(
							    RegNetShortCut(_A						,				_A						,				stride=_A		) if should_apply_shortcut else nn.Identity()
							)
							snake_case_						:     Any     =		nn.Sequential(
							    RegNetConvLayer(_A						,				_A						,				kernel_size=1						,				activation=config.hidden_act		)						,				RegNetConvLayer(_A						,				_A						,				stride=_A						,				groups=_A						,				activation=config.hidden_act		)						,				RegNetConvLayer(_A						,				_A						,				kernel_size=1						,				activation=_A		)						,				)
							snake_case_						:     int     =		ACTaFN[config.hidden_act]






						def UpperCAmelCase_			( self : Dict						,				_A : Union[str, Any]		)							->      List[Any]:







							"""simple docstring"""


							snake_case_						:     Optional[Any]     =		hidden_state
							snake_case_						:     str     =		self.layer(_A		)
							snake_case_						:     str     =		self.shortcut(_A		)
							hidden_state += residual
							snake_case_						:     Tuple     =		self.activation(_A		)
							return hidden_state
class SCREAMING_SNAKE_CASE_  (   nn.Module   ):






						def __init__( self : Dict						,				_A : RegNetConfig						,				_A : int						,				_A : int						,				_A : int = 1		)							->      Union[str, Any]:







							"""simple docstring"""


							super().__init__()
							snake_case_						:     Optional[int]     =		in_channels != out_channels or stride != 1
							snake_case_						:     int     =		max(1						,				out_channels // config.groups_width		)
							snake_case_						:     Dict     =		(
							    RegNetShortCut(_A						,				_A						,				stride=_A		) if should_apply_shortcut else nn.Identity()
							)
							snake_case_						:     List[str]     =		nn.Sequential(
							    RegNetConvLayer(_A						,				_A						,				kernel_size=1						,				activation=config.hidden_act		)						,				RegNetConvLayer(_A						,				_A						,				stride=_A						,				groups=_A						,				activation=config.hidden_act		)						,				RegNetSELayer(_A						,				reduced_channels=int(round(in_channels / 4		)		)		)						,				RegNetConvLayer(_A						,				_A						,				kernel_size=1						,				activation=_A		)						,				)
							snake_case_						:     Dict     =		ACTaFN[config.hidden_act]






						def UpperCAmelCase_			( self : Union[str, Any]						,				_A : str		)							->      Tuple:







							"""simple docstring"""


							snake_case_						:     str     =		hidden_state
							snake_case_						:     Any     =		self.layer(_A		)
							snake_case_						:     Any     =		self.shortcut(_A		)
							hidden_state += residual
							snake_case_						:     Optional[Any]     =		self.activation(_A		)
							return hidden_state
class SCREAMING_SNAKE_CASE_  (   nn.Module   ):






						def __init__( self : str						,				_A : RegNetConfig						,				_A : int						,				_A : int						,				_A : int = 2						,				_A : int = 2						,				)							->      Optional[Any]:







							"""simple docstring"""


							super().__init__()

							snake_case_						:     List[Any]     =		RegNetXLayer if config.layer_type == 'x' else RegNetYLayer

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






						def UpperCAmelCase_			( self : Optional[int]						,				_A : List[str]		)							->      int:







							"""simple docstring"""


							snake_case_						:     Dict     =		self.layers(_A		)
							return hidden_state
class SCREAMING_SNAKE_CASE_  (   nn.Module   ):






						def __init__( self : Dict						,				_A : RegNetConfig		)							->      int:







							"""simple docstring"""


							super().__init__()
							snake_case_						:     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(
							    RegNetStage(
							        _A						,				config.embedding_size						,				config.hidden_sizes[0]						,				stride=2 if config.downsample_in_first_stage else 1						,				depth=config.depths[0]						,				)		)
							snake_case_						:     Optional[int]     =		zip(config.hidden_sizes						,				config.hidden_sizes[1:]		)
							for (in_channels, out_channels), depth in zip(_A						,				config.depths[1:]		):
								self.stages.append(RegNetStage(_A						,				_A						,				_A						,				depth=_A		)		)






						def UpperCAmelCase_			( self : Union[str, Any]						,				_A : Tensor						,				_A : bool = False						,				_A : bool = True		)							->      BaseModelOutputWithNoAttention:







							"""simple docstring"""


							snake_case_						:     Any     =		() if output_hidden_states else None

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

								snake_case_						:     Tuple     =		stage_module(_A		)

							if output_hidden_states:
								snake_case_						:     Dict     =		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=_A						,				hidden_states=_A		)
class SCREAMING_SNAKE_CASE_  (   snake_case_   ):
						__magic_name__:     int	 =    RegNetConfig
						__magic_name__:     Optional[int]	 =    "regnet"
						__magic_name__:     List[str]	 =    "pixel_values"
						__magic_name__:     Dict	 =    True






						def UpperCAmelCase_			( self : List[Any]						,				_A : Dict		)							->      Dict:







							"""simple docstring"""


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






						def UpperCAmelCase_			( self : Optional[Any]						,				_A : Optional[Any]						,				_A : int=False		)							->      Dict:







							"""simple docstring"""


							if isinstance(_A						,				_A		):
								snake_case_						:     List[str]     =		value
_SCREAMING_SNAKE_CASE 		=						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.
"""

_SCREAMING_SNAKE_CASE 		=						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."    ,			snake_case_    ,			)
# Copied from transformers.models.resnet.modeling_resnet.ResNetModel with RESNET->REGNET,ResNet->RegNet
class SCREAMING_SNAKE_CASE_  (   snake_case_   ):






						def __init__( self : Optional[Any]						,				_A : str		)							->      int:







							"""simple docstring"""


							super().__init__(_A		)
							snake_case_						:     Any     =		config
							snake_case_						:     List[str]     =		RegNetEmbeddings(_A		)
							snake_case_						:     str     =		RegNetEncoder(_A		)
							snake_case_						:     Union[str, Any]     =		nn.AdaptiveAvgPoolad((1, 1)		)
							# Initialize weights and apply final processing
							self.post_init()






						@add_start_docstrings_to_model_forward(_A		)
						@add_code_sample_docstrings(
						    checkpoint=_CHECKPOINT_FOR_DOC						,				output_type=_A						,				config_class=_CONFIG_FOR_DOC						,				modality='vision'						,				expected_output=_EXPECTED_OUTPUT_SHAPE						,				)
						def UpperCAmelCase_			( self : Optional[Any]						,				_A : Tensor						,				_A : Optional[bool] = None						,				_A : Optional[bool] = None		)							->      BaseModelOutputWithPoolingAndNoAttention:







							"""simple docstring"""


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

							snake_case_						:     Any     =		self.embedder(_A		)

							snake_case_						:     int     =		self.encoder(
							    _A						,				output_hidden_states=_A						,				return_dict=_A		)

							snake_case_						:     Optional[Any]     =		encoder_outputs[0]

							snake_case_						:     Dict     =		self.pooler(_A		)

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

							return BaseModelOutputWithPoolingAndNoAttention(
							    last_hidden_state=_A						,				pooler_output=_A						,				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    "    ,			snake_case_    ,			)
# Copied from transformers.models.resnet.modeling_resnet.ResNetForImageClassification with RESNET->REGNET,ResNet->RegNet,resnet->regnet
class SCREAMING_SNAKE_CASE_  (   snake_case_   ):






						def __init__( self : Optional[int]						,				_A : int		)							->      Dict:







							"""simple docstring"""


							super().__init__(_A		)
							snake_case_						:     List[Any]     =		config.num_labels
							snake_case_						:     Optional[Any]     =		RegNetModel(_A		)
							# classification head
							snake_case_						:     Any     =		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(_A		)
						@add_code_sample_docstrings(
						    checkpoint=_IMAGE_CLASS_CHECKPOINT						,				output_type=_A						,				config_class=_CONFIG_FOR_DOC						,				expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT						,				)
						def UpperCAmelCase_			( self : Union[str, Any]						,				_A : Optional[torch.FloatTensor] = None						,				_A : Optional[torch.LongTensor] = None						,				_A : Optional[bool] = None						,				_A : Optional[bool] = None						,				)							->      ImageClassifierOutputWithNoAttention:







							"""simple docstring"""


							snake_case_						:     Optional[int]     =		return_dict if return_dict is not None else self.config.use_return_dict

							snake_case_						:     Union[str, Any]     =		self.regnet(_A						,				output_hidden_states=_A						,				return_dict=_A		)

							snake_case_						:     str     =		outputs.pooler_output if return_dict else outputs[1]

							snake_case_						:     str     =		self.classifier(_A		)

							snake_case_						:     List[str]     =		None

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

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

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








from __future__ import annotations

import math


def 							SCREAMING_SNAKE_CASE__  (      __a				,	__a				):
	snake_case_						:     Optional[int]     =		u
	for i in range(1				,	__a				):
		snake_case_						:     Optional[Any]     =		temp * (u - i)
	return temp


def 							SCREAMING_SNAKE_CASE__  (      ):
	snake_case_						:     Dict     =		int(input('enter the numbers of values: '				)				)
	snake_case_						:     list[list[float]]     =		[]
	for _ in range(__a				):
		y.append([]				)
	for i in range(__a				):
		for j in range(__a				):
			y[i].append(__a				)
			snake_case_						:     str     =		0

	print('enter the values of parameters in a list: '				)
	snake_case_						:     int     =		list(map(__a				,	input().split()				)				)

	print('enter the values of corresponding parameters: '				)
	for i in range(__a				):
		snake_case_						:     Union[str, Any]     =		float(input()				)

	snake_case_						:     int     =		int(input('enter the value to interpolate: '				)				)
	snake_case_						:     List[Any]     =		(value - x[0]) / (x[1] - x[0])

	# for calculating forward difference table

	for i in range(1				,	__a				):
		for j in range(n - i				):
			snake_case_						:     int     =		y[j + 1][i - 1] - y[j][i - 1]

	snake_case_						:     str     =		y[0][0]
	for i in range(1				,	__a				):
		summ += (ucal(__a				,	__a				) * y[0][i]) / math.factorial(__a				)

	print(f"""the value at {value} is {summ}"""				)


if __name__ == "__main__":
						main()

