





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, 1_088, 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				):






		'''simple docstring'''






		def __init__(							self,   snake_case__,   snake_case__,   snake_case__ = 3,   snake_case__ = 1,   snake_case__ = 1,   snake_case__ = "relu",   )       -> str:






									"""simple docstring"""
									super().__init__()
									lowercase_	:   Union[str, Any]  				=     nn.Convad(
									    lowerCAmelCase_,   lowerCAmelCase_,   kernel_size=lowerCAmelCase_,   stride=lowerCAmelCase_,   padding=kernel_size // 2,   groups=lowerCAmelCase_,   bias=lowerCAmelCase_,   )
									lowercase_	:   str  				=     nn.BatchNormad(lowerCAmelCase_  )
									lowercase_	:   Optional[Any]  				=     ACTaFN[activation] if activation is not None else nn.Identity()






		def 						snake_case__				(							self,   snake_case__  )       -> Optional[Any]:






									"""simple docstring"""
									lowercase_	:   List[str]  				=     self.convolution(lowerCAmelCase_  )
									lowercase_	:   List[Any]  				=     self.normalization(lowerCAmelCase_  )
									lowercase_	:   Tuple  				=     self.activation(lowerCAmelCase_  )
									return hidden_state




class     UpperCamelCase__					(     nn.Module				):






		'''simple docstring'''






		def __init__(							self,   snake_case__  )       -> str:






									"""simple docstring"""
									super().__init__()
									lowercase_	:   List[str]  				=     RegNetConvLayer(
									    config.num_channels,   config.embedding_size,   kernel_size=3,   stride=2,   activation=config.hidden_act  )
									lowercase_	:   str  				=     config.num_channels






		def 						snake_case__				(							self,   snake_case__  )       -> Tuple:






									"""simple docstring"""
									lowercase_	:   List[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."""  )
									lowercase_	:   Optional[Any]  				=     self.embedder(lowerCAmelCase_  )
									return hidden_state




class     UpperCamelCase__					(     nn.Module				):






		'''simple docstring'''






		def __init__(							self,   snake_case__,   snake_case__,   snake_case__ = 2  )       -> Any:






									"""simple docstring"""
									super().__init__()
									lowercase_	:   int  				=     nn.Convad(lowerCAmelCase_,   lowerCAmelCase_,   kernel_size=1,   stride=lowerCAmelCase_,   bias=lowerCAmelCase_  )
									lowercase_	:   List[str]  				=     nn.BatchNormad(lowerCAmelCase_  )






		def 						snake_case__				(							self,   snake_case__  )       -> Dict:






									"""simple docstring"""
									lowercase_	:   List[str]  				=     self.convolution(lowerCAmelCase_  )
									lowercase_	:   Any  				=     self.normalization(lowerCAmelCase_  )
									return hidden_state




class     UpperCamelCase__					(     nn.Module				):






		'''simple docstring'''






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






									"""simple docstring"""
									super().__init__()

									lowercase_	:   List[Any]  				=     nn.AdaptiveAvgPoolad((1, 1)  )
									lowercase_	:   List[str]  				=     nn.Sequential(
									    nn.Convad(lowerCAmelCase_,   lowerCAmelCase_,   kernel_size=1  ),   nn.ReLU(),   nn.Convad(lowerCAmelCase_,   lowerCAmelCase_,   kernel_size=1  ),   nn.Sigmoid(),   )






		def 						snake_case__				(							self,   snake_case__  )       -> str:






									"""simple docstring"""
									# b c h w -> b c 1 1
									lowercase_	:   Any  				=     self.pooler(lowerCAmelCase_  )
									lowercase_	:   Dict  				=     self.attention(lowerCAmelCase_  )
									lowercase_	:   Optional[int]  				=     hidden_state * attention
									return hidden_state




class     UpperCamelCase__					(     nn.Module				):






		'''simple docstring'''






		def __init__(							self,   snake_case__,   snake_case__,   snake_case__,   snake_case__ = 1  )       -> List[str]:






									"""simple docstring"""
									super().__init__()
									lowercase_	:   Optional[int]  				=     in_channels != out_channels or stride != 1
									lowercase_	:   int  				=     max(1,   out_channels // config.groups_width  )
									lowercase_	:   Any  				=     (
									    RegNetShortCut(lowerCAmelCase_,   lowerCAmelCase_,   stride=lowerCAmelCase_  ) if should_apply_shortcut else nn.Identity()
									)
									lowercase_	:   Any  				=     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_  ),   )
									lowercase_	:   int  				=     ACTaFN[config.hidden_act]






		def 						snake_case__				(							self,   snake_case__  )       -> str:






									"""simple docstring"""
									lowercase_	:   Any  				=     hidden_state
									lowercase_	:   Any  				=     self.layer(lowerCAmelCase_  )
									lowercase_	:   int  				=     self.shortcut(lowerCAmelCase_  )
									hidden_state += residual
									lowercase_	:   List[str]  				=     self.activation(lowerCAmelCase_  )
									return hidden_state




class     UpperCamelCase__					(     nn.Module				):






		'''simple docstring'''






		def __init__(							self,   snake_case__,   snake_case__,   snake_case__,   snake_case__ = 1  )       -> List[Any]:






									"""simple docstring"""
									super().__init__()
									lowercase_	:   Optional[Any]  				=     in_channels != out_channels or stride != 1
									lowercase_	:   List[str]  				=     max(1,   out_channels // config.groups_width  )
									lowercase_	:   List[Any]  				=     (
									    RegNetShortCut(lowerCAmelCase_,   lowerCAmelCase_,   stride=lowerCAmelCase_  ) if should_apply_shortcut else nn.Identity()
									)
									lowercase_	:   Union[str, Any]  				=     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_  ),   )
									lowercase_	:   Dict  				=     ACTaFN[config.hidden_act]






		def 						snake_case__				(							self,   snake_case__  )       -> Union[str, Any]:






									"""simple docstring"""
									lowercase_	:   Union[str, Any]  				=     hidden_state
									lowercase_	:   Any  				=     self.layer(lowerCAmelCase_  )
									lowercase_	:   List[str]  				=     self.shortcut(lowerCAmelCase_  )
									hidden_state += residual
									lowercase_	:   Any  				=     self.activation(lowerCAmelCase_  )
									return hidden_state




class     UpperCamelCase__					(     nn.Module				):






		'''simple docstring'''






		def __init__(							self,   snake_case__,   snake_case__,   snake_case__,   snake_case__ = 2,   snake_case__ = 2,   )       -> Optional[int]:






									"""simple docstring"""
									super().__init__()

									lowercase_	:   Tuple  				=     RegNetXLayer if config.layer_type == """x""" else RegNetYLayer

									lowercase_	:   int  				=     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 						snake_case__				(							self,   snake_case__  )       -> str:






									"""simple docstring"""
									lowercase_	:   int  				=     self.layers(lowerCAmelCase_  )
									return hidden_state




class     UpperCamelCase__					(     nn.Module				):






		'''simple docstring'''






		def __init__(							self,   snake_case__  )       -> str:






									"""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(
									    RegNetStage(
									        lowerCAmelCase_,   config.embedding_size,   config.hidden_sizes[0],   stride=2 if config.downsample_in_first_stage else 1,   depth=config.depths[0],   )  )
									lowercase_	:   Optional[Any]  				=     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 						snake_case__				(							self,   snake_case__,   snake_case__ = False,   snake_case__ = True  )       -> Tuple:






									"""simple docstring"""
									lowercase_	:   Optional[Any]  				=     () if output_hidden_states else None

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

																lowercase_	:   List[Any]  				=     stage_module(lowerCAmelCase_  )

									if output_hidden_states:
																lowercase_	:   str  				=     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     UpperCamelCase__					(     _SCREAMING_SNAKE_CASE				):






		'''simple docstring'''






		__a       :       Dict       =	RegNetConfig
		__a       :       int       =	"""regnet"""
		__a       :       Dict       =	"""pixel_values"""
		__a       :       Union[str, Any]       =	True






		def 						snake_case__				(							self,   snake_case__  )       -> int:






									"""simple docstring"""
									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 						snake_case__				(							self,   snake_case__,   snake_case__=False  )       -> Dict:






									"""simple docstring"""
									if isinstance(lowerCAmelCase_,   lowerCAmelCase_  ):
																lowercase_	:   Dict  				=     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."""				, _SCREAMING_SNAKE_CASE				, )
# Copied from transformers.models.resnet.modeling_resnet.ResNetModel with RESNET->REGNET,ResNet->RegNet
class     UpperCamelCase__					(     _SCREAMING_SNAKE_CASE				):






		'''simple docstring'''






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






									"""simple docstring"""
									super().__init__(lowerCAmelCase_  )
									lowercase_	:   Any  				=     config
									lowercase_	:   Union[str, Any]  				=     RegNetEmbeddings(lowerCAmelCase_  )
									lowercase_	:   Optional[int]  				=     RegNetEncoder(lowerCAmelCase_  )
									lowercase_	:   List[str]  				=     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 						snake_case__				(							self,   snake_case__,   snake_case__ = None,   snake_case__ = None  )       -> List[Any]:






									"""simple docstring"""
									lowercase_	:   Any  				=     (
									    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
									)
									lowercase_	:   Any  				=     return_dict if return_dict is not None else self.config.use_return_dict

									lowercase_	:   Union[str, Any]  				=     self.embedder(lowerCAmelCase_  )

									lowercase_	:   Any  				=     self.encoder(
									    lowerCAmelCase_,   output_hidden_states=lowerCAmelCase_,   return_dict=lowerCAmelCase_  )

									lowercase_	:   Dict  				=     encoder_outputs[0]

									lowercase_	:   List[str]  				=     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(
    """
    RegNet Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for
    ImageNet.
    """				, _SCREAMING_SNAKE_CASE				, )
# Copied from transformers.models.resnet.modeling_resnet.ResNetForImageClassification with RESNET->REGNET,ResNet->RegNet,resnet->regnet
class     UpperCamelCase__					(     _SCREAMING_SNAKE_CASE				):






		'''simple docstring'''






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






									"""simple docstring"""
									super().__init__(lowerCAmelCase_  )
									lowercase_	:   str  				=     config.num_labels
									lowercase_	:   Tuple  				=     RegNetModel(lowerCAmelCase_  )
									# classification head
									lowercase_	:   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(lowerCAmelCase_  )
		@add_code_sample_docstrings(
		    checkpoint=_IMAGE_CLASS_CHECKPOINT,   output_type=lowerCAmelCase_,   config_class=_CONFIG_FOR_DOC,   expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT,   )
		def 						snake_case__				(							self,   snake_case__ = None,   snake_case__ = None,   snake_case__ = None,   snake_case__ = None,   )       -> List[str]:






									"""simple docstring"""
									lowercase_	:   Dict  				=     return_dict if return_dict is not None else self.config.use_return_dict

									lowercase_	:   Union[str, Any]  				=     self.regnet(lowerCAmelCase_,   output_hidden_states=lowerCAmelCase_,   return_dict=lowerCAmelCase_  )

									lowercase_	:   Optional[int]  				=     outputs.pooler_output if return_dict else outputs[1]

									lowercase_	:   List[str]  				=     self.classifier(lowerCAmelCase_  )

									lowercase_	:   Union[str, Any]  				=     None

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

									if not return_dict:
																lowercase_	:   List[str]  				=     (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 math
import os
import sys






def       __UpperCamelCase						(		snake_case__       ):
	A_		:    Optional[Any]						     = """"""
	try:
		with open(snake_case__ , """rb"""       ) as binary_file:
			A_		:    Union[str, Any]						     = binary_file.read()
		for dat in data:
			A_		:    Dict						     = F"""{dat:08b}"""
			result += curr_byte
		return result
	except OSError:
		print("""File not accessible"""       )
		sys.exit()






def       __UpperCamelCase						(		snake_case__ , snake_case__ , snake_case__ , snake_case__       ):
	lexicon.pop(snake_case__       )
	A_		:    List[str]						     = last_match_id

	if math.loga(snake_case__       ).is_integer():
		for curr_key in lexicon:
			A_		:    Dict						     = """0""" + lexicon[curr_key]

	A_		:    int						     = bin(snake_case__       )[2:]






def       __UpperCamelCase						(		snake_case__       ):
	A_		:    Dict						     = {"""0""": """0""", """1""": """1"""}
	A_     ,  A_		:    Optional[int]						     = """""", """"""
	A_		:    Tuple						     = len(snake_case__       )

	for i in range(len(snake_case__       )       ):
		curr_string += data_bits[i]
		if curr_string not in lexicon:
			continue

		A_		:    List[str]						     = lexicon[curr_string]
		result += last_match_id
		add_key_to_lexicon(snake_case__ , snake_case__ , snake_case__ , snake_case__       )
		index += 1
		A_		:    int						     = """"""

	while curr_string != "" and curr_string not in lexicon:
		curr_string += "0"

	if curr_string != "":
		A_		:    Any						     = lexicon[curr_string]
		result += last_match_id

	return result






def       __UpperCamelCase						(		snake_case__ , snake_case__       ):
	A_		:    Optional[int]						     = os.path.getsize(snake_case__       )
	A_		:    Dict						     = bin(snake_case__       )[2:]
	A_		:    Optional[Any]						     = len(snake_case__       )

	return "0" * (length_length - 1) + file_length_binary + compressed






def       __UpperCamelCase						(		snake_case__ , snake_case__       ):
	A_		:    Tuple						     = 8
	try:
		with open(snake_case__ , """wb"""       ) as opened_file:
			A_		:    Dict						     = [
			    to_write[i : i + byte_length]
			    for i in range(0 , len(snake_case__       ) , snake_case__       )
			]

			if len(result_byte_array[-1]       ) % byte_length == 0:
				result_byte_array.append("""10000000"""       )
			else:
				result_byte_array[-1] += "1" + "0" * (
				    byte_length - len(result_byte_array[-1]       ) - 1
				)

			for elem in result_byte_array:
				opened_file.write(int(snake_case__ , 2       ).to_bytes(1 , byteorder="""big"""       )       )
	except OSError:
		print("""File not accessible"""       )
		sys.exit()






def       __UpperCamelCase						(		snake_case__ , snake_case__       ):
	A_		:    List[str]						     = read_file_binary(snake_case__       )
	A_		:    str						     = compress_data(snake_case__       )
	A_		:    int						     = add_file_length(snake_case__ , snake_case__       )
	write_file_binary(snake_case__ , snake_case__       )


if __name__ == "__main__":
							compress(sys.argv[1], sys.argv[2])

