




"""simple docstring"""







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


a      =							logging.get_logger(__name__)

# General docstring
a      =							'RegNetConfig'

# Base docstring
a      =							'facebook/regnet-y-040'
a      =							[1, 1_0_8_8, 7, 7]

# Image classification docstring
a      =							'facebook/regnet-y-040'
a      =							'tabby, tabby cat'

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






class       SCREAMING_SNAKE_CASE__					(					nn.Module					):


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




						def   __lowercase (							self				:			int						,						lowerCAmelCase				:			int					):
										lowerCAmelCase  =				self.convolution(lowerCAmelCase					)
										lowerCAmelCase  =				self.normalization(lowerCAmelCase					)
										lowerCAmelCase  =				self.activation(lowerCAmelCase					)
										return hidden_state






class       SCREAMING_SNAKE_CASE__					(					nn.Module					):


						def __init__(							self				:			Union[str, Any]						,						lowerCAmelCase				:			RegNetConfig					):
										super().__init__()
										lowerCAmelCase  =				RegNetConvLayer(
										    config.num_channels						,						config.embedding_size						,						kernel_size=3						,						stride=2						,						activation=config.hidden_act					)
										lowerCAmelCase  =				config.num_channels




						def   __lowercase (							self				:			List[str]						,						lowerCAmelCase				:			List[str]					):
										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(lowerCAmelCase					)
										return hidden_state






class       SCREAMING_SNAKE_CASE__					(					nn.Module					):


						def __init__(							self				:			Tuple						,						lowerCAmelCase				:			int						,						lowerCAmelCase				:			int						,						lowerCAmelCase				:			int = 2					):
										super().__init__()
										lowerCAmelCase  =				nn.Convad(lowerCAmelCase						,						lowerCAmelCase						,						kernel_size=1						,						stride=lowerCAmelCase						,						bias=lowerCAmelCase					)
										lowerCAmelCase  =				nn.BatchNormad(lowerCAmelCase					)




						def   __lowercase (							self				:			Tuple						,						lowerCAmelCase				:			Tensor					):
										lowerCAmelCase  =				self.convolution(lowerCAmelCase					)
										lowerCAmelCase  =				self.normalization(lowerCAmelCase					)
										return hidden_state






class       SCREAMING_SNAKE_CASE__					(					nn.Module					):


						def __init__(							self				:			Union[str, Any]						,						lowerCAmelCase				:			int						,						lowerCAmelCase				:			int					):
										super().__init__()

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




						def   __lowercase (							self				:			int						,						lowerCAmelCase				:			Dict					):
										# b c h w -> b c 1 1
										lowerCAmelCase  =				self.pooler(lowerCAmelCase					)
										lowerCAmelCase  =				self.attention(lowerCAmelCase					)
										lowerCAmelCase  =				hidden_state * attention
										return hidden_state






class       SCREAMING_SNAKE_CASE__					(					nn.Module					):


						def __init__(							self				:			Dict						,						lowerCAmelCase				:			RegNetConfig						,						lowerCAmelCase				:			int						,						lowerCAmelCase				:			int						,						lowerCAmelCase				:			int = 1					):
										super().__init__()
										lowerCAmelCase  =				in_channels != out_channels or stride != 1
										lowerCAmelCase  =				max(1						,						out_channels // config.groups_width					)
										lowerCAmelCase  =				(
										    RegNetShortCut(lowerCAmelCase						,						lowerCAmelCase						,						stride=lowerCAmelCase					) if should_apply_shortcut else nn.Identity()
										)
										lowerCAmelCase  =				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					)						,						)
										lowerCAmelCase  =				ACTaFN[config.hidden_act]




						def   __lowercase (							self				:			Tuple						,						lowerCAmelCase				:			List[str]					):
										lowerCAmelCase  =				hidden_state
										lowerCAmelCase  =				self.layer(lowerCAmelCase					)
										lowerCAmelCase  =				self.shortcut(lowerCAmelCase					)
										hidden_state += residual
										lowerCAmelCase  =				self.activation(lowerCAmelCase					)
										return hidden_state






class       SCREAMING_SNAKE_CASE__					(					nn.Module					):


						def __init__(							self				:			int						,						lowerCAmelCase				:			RegNetConfig						,						lowerCAmelCase				:			int						,						lowerCAmelCase				:			int						,						lowerCAmelCase				:			int = 1					):
										super().__init__()
										lowerCAmelCase  =				in_channels != out_channels or stride != 1
										lowerCAmelCase  =				max(1						,						out_channels // config.groups_width					)
										lowerCAmelCase  =				(
										    RegNetShortCut(lowerCAmelCase						,						lowerCAmelCase						,						stride=lowerCAmelCase					) if should_apply_shortcut else nn.Identity()
										)
										lowerCAmelCase  =				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					)						,						)
										lowerCAmelCase  =				ACTaFN[config.hidden_act]




						def   __lowercase (							self				:			Dict						,						lowerCAmelCase				:			Dict					):
										lowerCAmelCase  =				hidden_state
										lowerCAmelCase  =				self.layer(lowerCAmelCase					)
										lowerCAmelCase  =				self.shortcut(lowerCAmelCase					)
										hidden_state += residual
										lowerCAmelCase  =				self.activation(lowerCAmelCase					)
										return hidden_state






class       SCREAMING_SNAKE_CASE__					(					nn.Module					):


						def __init__(							self				:			Union[str, Any]						,						lowerCAmelCase				:			RegNetConfig						,						lowerCAmelCase				:			int						,						lowerCAmelCase				:			int						,						lowerCAmelCase				:			int = 2						,						lowerCAmelCase				:			int = 2						,						):
										super().__init__()

										lowerCAmelCase  =				RegNetXLayer if config.layer_type == """x""" else RegNetYLayer

										lowerCAmelCase  =				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   __lowercase (							self				:			Tuple						,						lowerCAmelCase				:			Tuple					):
										lowerCAmelCase  =				self.layers(lowerCAmelCase					)
										return hidden_state






class       SCREAMING_SNAKE_CASE__					(					nn.Module					):


						def __init__(							self				:			int						,						lowerCAmelCase				:			RegNetConfig					):
										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(
										    RegNetStage(
										        lowerCAmelCase						,						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(lowerCAmelCase						,						config.depths[1:]					):
														self.stages.append(RegNetStage(lowerCAmelCase						,						lowerCAmelCase						,						lowerCAmelCase						,						depth=lowerCAmelCase					)					)




						def   __lowercase (							self				:			Dict						,						lowerCAmelCase				:			Tensor						,						lowerCAmelCase				:			bool = False						,						lowerCAmelCase				:			bool = True					):
										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(lowerCAmelCase					)

										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=lowerCAmelCase						,						hidden_states=lowerCAmelCase					)






class       SCREAMING_SNAKE_CASE__					(					_a					):
						_a    						=						RegNetConfig
						_a    						=						'regnet'
						_a    						=						'pixel_values'
						_a    						=						True


						def   __lowercase (							self				:			int						,						lowerCAmelCase				:			str					):
										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   __lowercase (							self				:			List[str]						,						lowerCAmelCase				:			str						,						lowerCAmelCase				:			Union[str, Any]=False					):
										if isinstance(lowerCAmelCase						,						lowerCAmelCase					):
														lowerCAmelCase  =				value





a      =							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'

a      =							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       SCREAMING_SNAKE_CASE__					(					_a					):


						def __init__(							self				:			Optional[Any]						,						lowerCAmelCase				:			List[Any]					):
										super().__init__(lowerCAmelCase					)
										lowerCAmelCase  =				config
										lowerCAmelCase  =				RegNetEmbeddings(lowerCAmelCase					)
										lowerCAmelCase  =				RegNetEncoder(lowerCAmelCase					)
										lowerCAmelCase  =				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   __lowercase (							self				:			Any						,						lowerCAmelCase				:			Tensor						,						lowerCAmelCase				:			Optional[bool] = None						,						lowerCAmelCase				:			Optional[bool] = None					):
										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(lowerCAmelCase					)

										lowerCAmelCase  =				self.encoder(
										    lowerCAmelCase						,						output_hidden_states=lowerCAmelCase						,						return_dict=lowerCAmelCase					)

										lowerCAmelCase  =				encoder_outputs[0]

										lowerCAmelCase  =				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    '						,				_a						,				)
# Copied from transformers.models.resnet.modeling_resnet.ResNetForImageClassification with RESNET->REGNET,ResNet->RegNet,resnet->regnet
class       SCREAMING_SNAKE_CASE__					(					_a					):


						def __init__(							self				:			Optional[Any]						,						lowerCAmelCase				:			Any					):
										super().__init__(lowerCAmelCase					)
										lowerCAmelCase  =				config.num_labels
										lowerCAmelCase  =				RegNetModel(lowerCAmelCase					)
										# 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(lowerCAmelCase					)
						@add_code_sample_docstrings(
						    checkpoint=_IMAGE_CLASS_CHECKPOINT						,						output_type=lowerCAmelCase						,						config_class=_CONFIG_FOR_DOC						,						expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT						,						)
						def   __lowercase (							self				:			Optional[int]						,						lowerCAmelCase				:			Optional[torch.FloatTensor] = None						,						lowerCAmelCase				:			Optional[torch.LongTensor] = None						,						lowerCAmelCase				:			Optional[bool] = None						,						lowerCAmelCase				:			Optional[bool] = None						,						):
										lowerCAmelCase  =				return_dict if return_dict is not None else self.config.use_return_dict

										lowerCAmelCase  =				self.regnet(lowerCAmelCase						,						output_hidden_states=lowerCAmelCase						,						return_dict=lowerCAmelCase					)

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

										lowerCAmelCase  =				self.classifier(lowerCAmelCase					)

										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(lowerCAmelCase						,						lowerCAmelCase					)
														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(lowerCAmelCase						,						lowerCAmelCase					)

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

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











"""simple docstring"""







import os
import string
import sys


a      =							1 << 8

a      =							{
    'tab': ord('\t'),
    'newline': ord('\r'),
    'esc': 2_7,
    'up': 6_5 + ARROW_KEY_FLAG,
    'down': 6_6 + ARROW_KEY_FLAG,
    'right': 6_7 + ARROW_KEY_FLAG,
    'left': 6_8 + ARROW_KEY_FLAG,
    'mod_int': 9_1,
    'undefined': sys.maxsize,
    'interrupt': 3,
    'insert': 5_0,
    'delete': 5_1,
    'pg_up': 5_3,
    'pg_down': 5_4,
}

a      =							KEYMAP['up']
a      =							KEYMAP['left']

if sys.platform == "win32":
		a      =							[]
		a      =							{
		    b'\xe0H': KEYMAP['up'] - ARROW_KEY_FLAG,
		    b'\x00H': KEYMAP['up'] - ARROW_KEY_FLAG,
		    b'\xe0P': KEYMAP['down'] - ARROW_KEY_FLAG,
		    b'\x00P': KEYMAP['down'] - ARROW_KEY_FLAG,
		    b'\xe0M': KEYMAP['right'] - ARROW_KEY_FLAG,
		    b'\x00M': KEYMAP['right'] - ARROW_KEY_FLAG,
		    b'\xe0K': KEYMAP['left'] - ARROW_KEY_FLAG,
		    b'\x00K': KEYMAP['left'] - ARROW_KEY_FLAG,
		}

for i in range(1_0):
		a      =							ord(str(i))





def 							lowercase					()     ->  str:


				'''simple docstring'''






				if os.name == "nt":
								import msvcrt

								lowerCAmelCase  =				"""mbcs"""
								# Flush the keyboard buffer
								while msvcrt.kbhit():
												msvcrt.getch()
								if len(snake_case__ ) == 0:
												# Read the keystroke
												lowerCAmelCase  =				msvcrt.getch()

												# If it is a prefix char, get second part
												if ch in (b"\x00", b"\xe0"):
																lowerCAmelCase  =				ch + msvcrt.getch()
																# Translate actual Win chars to bullet char types
																try:
																				lowerCAmelCase  =				chr(WIN_KEYMAP[cha] )
																				WIN_CH_BUFFER.append(chr(KEYMAP["""mod_int"""] ) )
																				WIN_CH_BUFFER.append(snake_case__ )
																				if ord(snake_case__ ) in (
																				    KEYMAP["insert"] - 1 << 9,
																				    KEYMAP["delete"] - 1 << 9,
																				    KEYMAP["pg_up"] - 1 << 9,
																				    KEYMAP["pg_down"] - 1 << 9,
																				):
																								WIN_CH_BUFFER.append(chr(126 ) )
																				lowerCAmelCase  =				chr(KEYMAP["""esc"""] )
																except KeyError:
																				lowerCAmelCase  =				cha[1]
												else:
																lowerCAmelCase  =				ch.decode(snake_case__ )
								else:
												lowerCAmelCase  =				WIN_CH_BUFFER.pop(0 )
				elif os.name == "posix":
								import termios
								import tty

								lowerCAmelCase  =				sys.stdin.fileno()
								lowerCAmelCase  =				termios.tcgetattr(snake_case__ )
								try:
												tty.setraw(snake_case__ )
												lowerCAmelCase  =				sys.stdin.read(1 )
								finally:
												termios.tcsetattr(snake_case__      ,       termios.TCSADRAIN      ,       snake_case__ )
				return ch






def 							lowercase					()     ->  List[str]:


				'''simple docstring'''






				lowerCAmelCase  =				get_raw_chars()
				if ord(snake_case__ ) in [KEYMAP["interrupt"], KEYMAP["newline"]]:
								return char

				elif ord(snake_case__ ) == KEYMAP["esc"]:
								lowerCAmelCase  =				get_raw_chars()
								if ord(snake_case__ ) == KEYMAP["mod_int"]:
												lowerCAmelCase  =				get_raw_chars()
												if ord(snake_case__ ) >= KEYMAP["arrow_begin"] - ARROW_KEY_FLAG and ord(snake_case__ ) <= KEYMAP["arrow_end"] - ARROW_KEY_FLAG:
																return chr(ord(snake_case__ ) + ARROW_KEY_FLAG )
												else:
																return KEYMAP["undefined"]
								else:
												return get_raw_chars()

				else:
								if char in string.printable:
												return char
								else:
												return KEYMAP["undefined"]





