



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_							:      str      =  logging.get_logger(__name__)

# General docstring
UpperCAmelCase_							:      Any      =  "RegNetConfig"

# Base docstring
UpperCAmelCase_							:      List[str]      =  "facebook/regnet-y-040"
UpperCAmelCase_							:      Union[str, Any]      =  [1, 1_088, 7, 7]

# Image classification docstring
UpperCAmelCase_							:      Any      =  "facebook/regnet-y-040"
UpperCAmelCase_							:      Dict      =  "tabby, tabby cat"

UpperCAmelCase_							:      Union[str, Any]      =  [
    "facebook/regnet-y-040",
    # See all regnet models at https://huggingface.co/models?filter=regnet
]






class      UpperCamelCase    (						nn.Module     ):



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



	def 			__A							(       self			,	UpperCAmelCase__		):
							A__							=       self.convolution(UpperCAmelCase__		)
							A__							=       self.normalization(UpperCAmelCase__		)
							A__							=       self.activation(UpperCAmelCase__		)
							return hidden_state






class      UpperCamelCase    (						nn.Module     ):



	def __init__(       self			,	UpperCAmelCase__		):
							super().__init__()
							A__							=       RegNetConvLayer(
							    config.num_channels			,	config.embedding_size			,	kernel_size=3			,	stride=2			,	activation=config.hidden_act		)
							A__							=       config.num_channels



	def 			__A							(       self			,	UpperCAmelCase__		):
							A__							=       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."		)
							A__							=       self.embedder(UpperCAmelCase__		)
							return hidden_state






class      UpperCamelCase    (						nn.Module     ):



	def __init__(       self			,	UpperCAmelCase__			,	UpperCAmelCase__			,	UpperCAmelCase__ = 2		):
							super().__init__()
							A__							=       nn.Convad(UpperCAmelCase__			,	UpperCAmelCase__			,	kernel_size=1			,	stride=UpperCAmelCase__			,	bias=UpperCAmelCase__		)
							A__							=       nn.BatchNormad(UpperCAmelCase__		)



	def 			__A							(       self			,	UpperCAmelCase__		):
							A__							=       self.convolution(UpperCAmelCase__		)
							A__							=       self.normalization(UpperCAmelCase__		)
							return hidden_state






class      UpperCamelCase    (						nn.Module     ):



	def __init__(       self			,	UpperCAmelCase__			,	UpperCAmelCase__		):
							super().__init__()

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



	def 			__A							(       self			,	UpperCAmelCase__		):
							# b c h w -> b c 1 1
							A__							=       self.pooler(UpperCAmelCase__		)
							A__							=       self.attention(UpperCAmelCase__		)
							A__							=       hidden_state * attention
							return hidden_state






class      UpperCamelCase    (						nn.Module     ):



	def __init__(       self			,	UpperCAmelCase__			,	UpperCAmelCase__			,	UpperCAmelCase__			,	UpperCAmelCase__ = 1		):
							super().__init__()
							A__							=       in_channels != out_channels or stride != 1
							A__							=       max(1			,	out_channels // config.groups_width		)
							A__							=       (
							    RegNetShortCut(UpperCAmelCase__			,	UpperCAmelCase__			,	stride=UpperCAmelCase__		) if should_apply_shortcut else nn.Identity()
							)
							A__							=       nn.Sequential(
							    RegNetConvLayer(UpperCAmelCase__			,	UpperCAmelCase__			,	kernel_size=1			,	activation=config.hidden_act		)			,	RegNetConvLayer(UpperCAmelCase__			,	UpperCAmelCase__			,	stride=UpperCAmelCase__			,	groups=UpperCAmelCase__			,	activation=config.hidden_act		)			,	RegNetConvLayer(UpperCAmelCase__			,	UpperCAmelCase__			,	kernel_size=1			,	activation=UpperCAmelCase__		)			,	)
							A__							=       ACTaFN[config.hidden_act]



	def 			__A							(       self			,	UpperCAmelCase__		):
							A__							=       hidden_state
							A__							=       self.layer(UpperCAmelCase__		)
							A__							=       self.shortcut(UpperCAmelCase__		)
							hidden_state += residual
							A__							=       self.activation(UpperCAmelCase__		)
							return hidden_state






class      UpperCamelCase    (						nn.Module     ):



	def __init__(       self			,	UpperCAmelCase__			,	UpperCAmelCase__			,	UpperCAmelCase__			,	UpperCAmelCase__ = 1		):
							super().__init__()
							A__							=       in_channels != out_channels or stride != 1
							A__							=       max(1			,	out_channels // config.groups_width		)
							A__							=       (
							    RegNetShortCut(UpperCAmelCase__			,	UpperCAmelCase__			,	stride=UpperCAmelCase__		) if should_apply_shortcut else nn.Identity()
							)
							A__							=       nn.Sequential(
							    RegNetConvLayer(UpperCAmelCase__			,	UpperCAmelCase__			,	kernel_size=1			,	activation=config.hidden_act		)			,	RegNetConvLayer(UpperCAmelCase__			,	UpperCAmelCase__			,	stride=UpperCAmelCase__			,	groups=UpperCAmelCase__			,	activation=config.hidden_act		)			,	RegNetSELayer(UpperCAmelCase__			,	reduced_channels=int(round(in_channels / 4		)		)		)			,	RegNetConvLayer(UpperCAmelCase__			,	UpperCAmelCase__			,	kernel_size=1			,	activation=UpperCAmelCase__		)			,	)
							A__							=       ACTaFN[config.hidden_act]



	def 			__A							(       self			,	UpperCAmelCase__		):
							A__							=       hidden_state
							A__							=       self.layer(UpperCAmelCase__		)
							A__							=       self.shortcut(UpperCAmelCase__		)
							hidden_state += residual
							A__							=       self.activation(UpperCAmelCase__		)
							return hidden_state






class      UpperCamelCase    (						nn.Module     ):



	def __init__(       self			,	UpperCAmelCase__			,	UpperCAmelCase__			,	UpperCAmelCase__			,	UpperCAmelCase__ = 2			,	UpperCAmelCase__ = 2			,	):
							super().__init__()

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

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



	def 			__A							(       self			,	UpperCAmelCase__		):
							A__							=       self.layers(UpperCAmelCase__		)
							return hidden_state






class      UpperCamelCase    (						nn.Module     ):



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



	def 			__A							(       self			,	UpperCAmelCase__			,	UpperCAmelCase__ = False			,	UpperCAmelCase__ = True		):
							A__							=       () if output_hidden_states else None

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

													A__							=       stage_module(UpperCAmelCase__		)

							if output_hidden_states:
													A__							=       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=UpperCAmelCase__			,	hidden_states=UpperCAmelCase__		)






class      UpperCamelCase    (						_UpperCAmelCase     ):
	lowerCAmelCase	:   Any				=	RegNetConfig
	lowerCAmelCase	:   Tuple				=	"""regnet"""
	lowerCAmelCase	:   Optional[Any]				=	"""pixel_values"""
	lowerCAmelCase	:   Optional[int]				=	True



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



	def 			__A							(       self			,	UpperCAmelCase__			,	UpperCAmelCase__=False		):
							if isinstance(UpperCAmelCase__			,	UpperCAmelCase__		):
													A__							=       value





UpperCAmelCase_							:      Tuple      =  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_							:      Any      =  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."""       ,    _UpperCAmelCase       ,    )
# Copied from transformers.models.resnet.modeling_resnet.ResNetModel with RESNET->REGNET,ResNet->RegNet
class      UpperCamelCase    (						_UpperCAmelCase     ):



	def __init__(       self			,	UpperCAmelCase__		):
							super().__init__(UpperCAmelCase__		)
							A__							=       config
							A__							=       RegNetEmbeddings(UpperCAmelCase__		)
							A__							=       RegNetEncoder(UpperCAmelCase__		)
							A__							=       nn.AdaptiveAvgPoolad((1, 1)		)
							# Initialize weights and apply final processing
							self.post_init()



	@add_start_docstrings_to_model_forward(UpperCAmelCase__		)
	@add_code_sample_docstrings(
	    checkpoint=_CHECKPOINT_FOR_DOC			,	output_type=UpperCAmelCase__			,	config_class=_CONFIG_FOR_DOC			,	modality="vision"			,	expected_output=_EXPECTED_OUTPUT_SHAPE			,	)
	def 			__A							(       self			,	UpperCAmelCase__			,	UpperCAmelCase__ = None			,	UpperCAmelCase__ = None		):
							A__							=       (
							    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
							)
							A__							=       return_dict if return_dict is not None else self.config.use_return_dict

							A__							=       self.embedder(UpperCAmelCase__		)

							A__							=       self.encoder(
							    UpperCAmelCase__			,	output_hidden_states=UpperCAmelCase__			,	return_dict=UpperCAmelCase__		)

							A__							=       encoder_outputs[0]

							A__							=       self.pooler(UpperCAmelCase__		)

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

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



	def __init__(       self			,	UpperCAmelCase__		):
							super().__init__(UpperCAmelCase__		)
							A__							=       config.num_labels
							A__							=       RegNetModel(UpperCAmelCase__		)
							# classification head
							A__							=       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(UpperCAmelCase__		)
	@add_code_sample_docstrings(
	    checkpoint=_IMAGE_CLASS_CHECKPOINT			,	output_type=UpperCAmelCase__			,	config_class=_CONFIG_FOR_DOC			,	expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT			,	)
	def 			__A							(       self			,	UpperCAmelCase__ = None			,	UpperCAmelCase__ = None			,	UpperCAmelCase__ = None			,	UpperCAmelCase__ = None			,	):
							A__							=       return_dict if return_dict is not None else self.config.use_return_dict

							A__							=       self.regnet(UpperCAmelCase__			,	output_hidden_states=UpperCAmelCase__			,	return_dict=UpperCAmelCase__		)

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

							A__							=       self.classifier(UpperCAmelCase__		)

							A__							=       None

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

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

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










import argparse

import torch
from huggingface_hub import hf_hub_download

from transformers import AutoTokenizer, RobertaPreLayerNormConfig, RobertaPreLayerNormForMaskedLM
from transformers.utils import logging


logging.set_verbosity_info()
UpperCAmelCase_							:      Union[str, Any]      =  logging.get_logger(__name__)
def  UpperCamelCase						(       _A			:				str			,							_A			:				str    )->       Any:





						"""simple docstring"""





						A__							=       RobertaPreLayerNormConfig.from_pretrained(
						    _A			,							architectures=["RobertaPreLayerNormForMaskedLM"]    )

						# convert state_dict
						A__							=       torch.load(hf_hub_download(repo_id=_A			,							filename="pytorch_model.bin"    )    )
						A__							=       {}
						for tensor_key, tensor_value in original_state_dict.items():
												# The transformer implementation gives the model a unique name, rather than overwiriting 'roberta'
												if tensor_key.startswith("roberta."    ):
																		A__							=       "roberta_prelayernorm." + tensor_key[len("roberta."    ) :]

												# The original implementation contains weights which are not used, remove them from the state_dict
												if tensor_key.endswith(".self.LayerNorm.weight"    ) or tensor_key.endswith(".self.LayerNorm.bias"    ):
																		continue

												A__							=       tensor_value

						A__							=       RobertaPreLayerNormForMaskedLM.from_pretrained(
						    pretrained_model_name_or_path=_A			,							config=_A			,							state_dict=_A    )
						model.save_pretrained(_A    )

						# convert tokenizer
						A__							=       AutoTokenizer.from_pretrained(_A    )
						tokenizer.save_pretrained(_A    )


if __name__ == "__main__":
						UpperCAmelCase_							:      str      =  argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    "--checkpoint-repo",
						    default=None,
						    type=str,
						    required=True,
						    help="Path the official PyTorch dump, e.g. 'andreasmadsen/efficient_mlm_m0.40'.",
						)
						parser.add_argument(
						    "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
						)
						UpperCAmelCase_							:      List[Any]      =  parser.parse_args()
						convert_roberta_prelayernorm_checkpoint_to_pytorch(args.checkpoint_repo, args.pytorch_dump_folder_path)





