


'''simple docstring'''

import argparse
import json
import sys
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import List

import torch
import torch.nn as nn
from huggingface_hub import cached_download, hf_hub_download
from torch import Tensor

from transformers import AutoImageProcessor, VanConfig, VanForImageClassification
from transformers.models.deprecated.van.modeling_van import VanLayerScaling
from transformers.utils import logging


logging.set_verbosity_info()
_UpperCAmelCase					:			Dict				     =					logging.get_logger(__name__)


@dataclass
class    a__					:







				"""simple docstring"""



				__UpperCamelCase     :		nn.Module
				__UpperCamelCase     :		List[nn.Module]						=						field(default_factory=__A   )
				__UpperCamelCase     :		list						=						field(default_factory=__A   )




				def        _snake_case	(self							,      __lowercase							,      __lowercase							,      __lowercase		):
										__lowerCAmelCase											=					len(list(m.modules()		)		) == 1 or isinstance(__lowercase							,      nn.Convad		) or isinstance(__lowercase							,      nn.BatchNormad		)
										if has_not_submodules:
																if not isinstance(__lowercase							,      __lowercase		):
																						self.traced.append(__lowercase		)




				def __call__(self							,      __lowercase		):
										for m in self.module.modules():
																self.handles.append(m.register_forward_hook(self._forward_hook		)		)
										self.module(__lowercase		)
										[x.remove() for x in self.handles]
										return self




				@property
				def        _snake_case	(self		):
										# check the len of the state_dict keys to see if we have learnable params
										return list(filter(lambda __lowercase		: len(list(x.state_dict().keys()		)		) > 0							,      self.traced		)		)


@dataclass
class    a__					:







				"""simple docstring"""



				__UpperCamelCase     :		nn.Module
				__UpperCamelCase     :		nn.Module
				__UpperCamelCase     :		int						=						0
				__UpperCamelCase     :		List						=						field(default_factory=__A   )
				__UpperCamelCase     :		List						=						field(default_factory=__A   )




				def __call__(self							,      __lowercase		):
										__lowerCAmelCase											=					Tracker(self.dest		)(__lowercase		).parametrized
										__lowerCAmelCase											=					Tracker(self.src		)(__lowercase		).parametrized

										__lowerCAmelCase											=					list(filter(lambda __lowercase		: type(__lowercase		) not in self.src_skip							,      __lowercase		)		)
										__lowerCAmelCase											=					list(filter(lambda __lowercase		: type(__lowercase		) not in self.dest_skip							,      __lowercase		)		)

										if len(__lowercase		) != len(__lowercase		):
																raise Exception(
																    F"""Numbers of operations are different. Source module has {len(__lowercase		)} operations while"""
																    F""" destination module has {len(__lowercase		)}."""		)

										for dest_m, src_m in zip(__lowercase							,      __lowercase		):
																dest_m.load_state_dict(src_m.state_dict()		)
																if self.verbose == 1:
																						print(F"""Transfered from={src_m} to={dest_m}"""		)

def 				__magic_name__(    lowerCamelCase,					lowerCamelCase):
						# nn.Parameter cannot be tracked by the Tracker, thus we need to manually convert them
						__lowerCAmelCase											=					from_model.state_dict()
						__lowerCAmelCase											=					our_model.state_dict()
						__lowerCAmelCase											=					our_model.config
						__lowerCAmelCase											=					[]
						for stage_idx in range(len(config.hidden_sizes)):
												for block_id in range(config.depths[stage_idx]):
																		__lowerCAmelCase											=					F"""block{stage_idx + 1}.{block_id}.layer_scale_1"""
																		__lowerCAmelCase											=					F"""van.encoder.stages.{stage_idx}.layers.{block_id}.attention_scaling.weight"""

																		all_keys.append((from_key, to_key))
																		__lowerCAmelCase											=					F"""block{stage_idx + 1}.{block_id}.layer_scale_2"""
																		__lowerCAmelCase											=					F"""van.encoder.stages.{stage_idx}.layers.{block_id}.mlp_scaling.weight"""

																		all_keys.append((from_key, to_key))

						for from_key, to_key in all_keys:
												__lowerCAmelCase											=					from_state_dict.pop(lowerCamelCase)

						our_model.load_state_dict(lowerCamelCase)
						return our_model

def 				__magic_name__(    lowerCamelCase,					lowerCamelCase,					lowerCamelCase,					lowerCamelCase,					lowerCamelCase,					lowerCamelCase = True,					):
						print(F"""Downloading weights for {name}...""")
						__lowerCAmelCase											=					cached_download(lowerCamelCase)
						print(F"""Converting {name}...""")
						__lowerCAmelCase											=					torch.load(lowerCamelCase)['''state_dict''']
						from_model.load_state_dict(lowerCamelCase)
						from_model.eval()
						with torch.no_grad():
												__lowerCAmelCase											=					VanForImageClassification(lowerCamelCase).eval()
												__lowerCAmelCase											=					ModuleTransfer(src=lowerCamelCase,					dest=lowerCamelCase)
												__lowerCAmelCase											=					torch.randn((1, 3, 2_2_4, 2_2_4))
												module_transfer(lowerCamelCase)
												__lowerCAmelCase											=					copy_parameters(lowerCamelCase,					lowerCamelCase)

						if not torch.allclose(from_model(lowerCamelCase),					our_model(lowerCamelCase).logits):
												raise ValueError('''The model logits don\'t match the original one.''')

						__lowerCAmelCase											=					name
						print(lowerCamelCase)

						if push_to_hub:
												our_model.push_to_hub(
												    repo_path_or_name=save_directory / checkpoint_name,					commit_message='''Add model''',					use_temp_dir=lowerCamelCase,					)

												# we can use the convnext one
												__lowerCAmelCase											=					AutoImageProcessor.from_pretrained('''facebook/convnext-base-224-22k-1k''')
												image_processor.push_to_hub(
												    repo_path_or_name=save_directory / checkpoint_name,					commit_message='''Add image processor''',					use_temp_dir=lowerCamelCase,					)

												print(F"""Pushed {checkpoint_name}""")

def 				__magic_name__(    lowerCamelCase,					lowerCamelCase = None,					lowerCamelCase = True):
						__lowerCAmelCase											=					'''imagenet-1k-id2label.json'''
						__lowerCAmelCase											=					1_0_0_0

						__lowerCAmelCase											=					'''huggingface/label-files'''
						__lowerCAmelCase											=					num_labels
						__lowerCAmelCase											=					json.load(open(hf_hub_download(lowerCamelCase,					lowerCamelCase,					repo_type='''dataset'''),					'''r'''))
						__lowerCAmelCase											=					{int(lowerCamelCase): v for k, v in idalabel.items()}

						__lowerCAmelCase											=					idalabel
						__lowerCAmelCase											=					{v: k for k, v in idalabel.items()}

						__lowerCAmelCase											=					partial(lowerCamelCase,					num_labels=lowerCamelCase,					idalabel=lowerCamelCase,					labelaid=lowerCamelCase)

						__lowerCAmelCase											=					{
						    '''van-tiny''': ImageNetPreTrainedConfig(
						        hidden_sizes=[3_2, 6_4, 1_6_0, 2_5_6],					depths=[3, 3, 5, 2],					mlp_ratios=[8, 8, 4, 4],					),
						    '''van-small''': ImageNetPreTrainedConfig(
						        hidden_sizes=[6_4, 1_2_8, 3_2_0, 5_1_2],					depths=[2, 2, 4, 2],					mlp_ratios=[8, 8, 4, 4],					),
						    '''van-base''': ImageNetPreTrainedConfig(
						        hidden_sizes=[6_4, 1_2_8, 3_2_0, 5_1_2],					depths=[3, 3, 1_2, 3],					mlp_ratios=[8, 8, 4, 4],					),
						    '''van-large''': ImageNetPreTrainedConfig(
						        hidden_sizes=[6_4, 1_2_8, 3_2_0, 5_1_2],					depths=[3, 5, 2_7, 3],					mlp_ratios=[8, 8, 4, 4],					),
						}

						__lowerCAmelCase											=					{
						    '''van-tiny''': van_tiny,
						    '''van-small''': van_small,
						    '''van-base''': van_base,
						    '''van-large''': van_large,
						}

						__lowerCAmelCase											=					{
						    '''van-tiny''': (
						        '''https://huggingface.co/Visual-Attention-Network/VAN-Tiny-original/resolve/main/van_tiny_754.pth.tar'''
						    ),
						    '''van-small''': (
						        '''https://huggingface.co/Visual-Attention-Network/VAN-Small-original/resolve/main/van_small_811.pth.tar'''
						    ),
						    '''van-base''': (
						        '''https://huggingface.co/Visual-Attention-Network/VAN-Base-original/resolve/main/van_base_828.pth.tar'''
						    ),
						    '''van-large''': (
						        '''https://huggingface.co/Visual-Attention-Network/VAN-Large-original/resolve/main/van_large_839.pth.tar'''
						    ),
						}

						if model_name:
												convert_weight_and_push(
												    lowerCamelCase,					names_to_config[model_name],					checkpoint=names_to_original_checkpoints[model_name],					from_model=names_to_original_models[model_name](),					save_directory=lowerCamelCase,					push_to_hub=lowerCamelCase,					)
						else:
												for model_name, config in names_to_config.items():
																		convert_weight_and_push(
																		    lowerCamelCase,					lowerCamelCase,					checkpoint=names_to_original_checkpoints[model_name],					from_model=names_to_original_models[model_name](),					save_directory=lowerCamelCase,					push_to_hub=lowerCamelCase,					)


if __name__ == "__main__":
	_UpperCAmelCase					:			str				     =					argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    """--model-name""",
	    default=None,
	    type=str,
	    help=(
	        """The name of the model you wish to convert, it must be one of the supported resnet* architecture,"""
	        """ currently: van-tiny/small/base/large. If `None`, all of them will the converted."""
	    ),
	)
	parser.add_argument(
	    """--pytorch_dump_folder_path""",
	    default=None,
	    type=Path,
	    required=True,
	    help="""Path to the output PyTorch model directory.""",
	)
	parser.add_argument(
	    """--van_dir""",
	    required=True,
	    type=Path,
	    help=(
	        """A path to VAN's original implementation directory. You can download from here:"""
	        """ https://github.com/Visual-Attention-Network/VAN-Classification"""
	    ),
	)
	parser.add_argument(
	    """--push_to_hub""",
	    default=True,
	    type=bool,
	    required=False,
	    help="""If True, push model and image processor to the hub.""",
	)

	_UpperCAmelCase					:			List[str]				     =					parser.parse_args()
	_UpperCAmelCase					:			Path				     =					args.pytorch_dump_folder_path
	pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
	_UpperCAmelCase					:			List[Any]				     =					args.van_dir
	# append the path to the parents to maskformer dir
	sys.path.append(str(van_dir.parent))
	from van.models.van import van_base, van_large, van_small, van_tiny

	convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)





'''simple docstring'''

import logging

import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss

from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.albert.modeling_albert import (
    ALBERT_INPUTS_DOCSTRING,
    ALBERT_START_DOCSTRING,
    AlbertModel,
    AlbertPreTrainedModel,
    AlbertTransformer,
)


_UpperCAmelCase					:			List[str]				     =					logging.getLogger(__name__)


class    a__					(			__A   ):







				"""simple docstring"""



				def        _snake_case	(self							,      __lowercase							,      __lowercase							,      __lowercase=None							,      __lowercase=None		):
										if current_layer == 0:
																__lowerCAmelCase											=					self.embedding_hidden_mapping_in(__lowercase		)
										else:
																__lowerCAmelCase											=					hidden_states[0]

										__lowerCAmelCase											=					int(self.config.num_hidden_layers / self.config.num_hidden_groups		)

										# Index of the hidden group
										__lowerCAmelCase											=					int(current_layer / (self.config.num_hidden_layers / self.config.num_hidden_groups)		)

										__lowerCAmelCase											=					self.albert_layer_groups[group_idx](
										    __lowercase							,      __lowercase							,      head_mask[group_idx * layers_per_group : (group_idx + 1) * layers_per_group]							,      )
										__lowerCAmelCase											=					layer_group_output[0]

										return (hidden_states,)


@add_start_docstrings(
    'The bare ALBERT Model transformer with PABEE outputting raw hidden-states without any specific head on top.'				,    __A				,    )
class    a__					(			__A   ):







				"""simple docstring"""



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

										__lowerCAmelCase											=					AlbertTransformerWithPabee(__lowercase		)

										self.init_weights()
										__lowerCAmelCase											=					0
										__lowerCAmelCase											=					0
										__lowerCAmelCase											=					0

										__lowerCAmelCase											=					0




				def        _snake_case	(self							,      __lowercase		):
										__lowerCAmelCase											=					threshold




				def        _snake_case	(self							,      __lowercase		):
										__lowerCAmelCase											=					patience




				def        _snake_case	(self		):
										__lowerCAmelCase											=					0
										__lowerCAmelCase											=					0




				def        _snake_case	(self		):
										__lowerCAmelCase											=					self.inference_layers_num / self.inference_instances_num
										__lowerCAmelCase											=					(
										    F"""*** Patience = {self.patience} Avg. Inference Layers = {avg_inf_layers:.2f} Speed Up ="""
										    F""" {1 - avg_inf_layers / self.config.num_hidden_layers:.2f} ***"""
										)
										print(__lowercase		)




				@add_start_docstrings_to_model_forward(__lowercase		)
				def        _snake_case	(self							,      __lowercase=None							,      __lowercase=None							,      __lowercase=None							,      __lowercase=None							,      __lowercase=None							,      __lowercase=None							,      __lowercase=None							,      __lowercase=None							,      __lowercase=False							,      ):

										if input_ids is not None and inputs_embeds is not None:
																raise ValueError('''You cannot specify both input_ids and inputs_embeds at the same time'''		)
										elif input_ids is not None:
																__lowerCAmelCase											=					input_ids.size()
										elif inputs_embeds is not None:
																__lowerCAmelCase											=					inputs_embeds.size()[:-1]
										else:
																raise ValueError('''You have to specify either input_ids or inputs_embeds'''		)

										__lowerCAmelCase											=					input_ids.device if input_ids is not None else inputs_embeds.device

										if attention_mask is None:
																__lowerCAmelCase											=					torch.ones(__lowercase							,      device=__lowercase		)
										if token_type_ids is None:
																__lowerCAmelCase											=					torch.zeros(__lowercase							,      dtype=torch.long							,      device=__lowercase		)

										__lowerCAmelCase											=					attention_mask.unsqueeze(1		).unsqueeze(2		)
										__lowerCAmelCase											=					extended_attention_mask.to(dtype=self.dtype		)  # fp16 compatibility
										__lowerCAmelCase											=					(1.0 - extended_attention_mask) * -1_0_0_0_0.0
										__lowerCAmelCase											=					self.get_head_mask(__lowercase							,      self.config.num_hidden_layers		)

										__lowerCAmelCase											=					self.embeddings(
										    __lowercase							,      position_ids=__lowercase							,      token_type_ids=__lowercase							,      inputs_embeds=__lowercase		)
										__lowerCAmelCase											=					embedding_output

										if self.training:
																__lowerCAmelCase											=					[]
																for i in range(self.config.num_hidden_layers		):
																						__lowerCAmelCase											=					self.encoder.adaptive_forward(
																						    __lowercase							,      current_layer=__lowercase							,      attention_mask=__lowercase							,      head_mask=__lowercase							,      )

																						__lowerCAmelCase											=					self.pooler_activation(self.pooler(encoder_outputs[0][:, 0]		)		)
																						__lowerCAmelCase											=					output_layers[i](output_dropout(__lowercase		)		)
																						res.append(__lowercase		)
										elif self.patience == 0:  # Use all layers for inference
																__lowerCAmelCase											=					self.encoder(__lowercase							,      __lowercase							,      head_mask=__lowercase		)
																__lowerCAmelCase											=					self.pooler_activation(self.pooler(encoder_outputs[0][:, 0]		)		)
																__lowerCAmelCase											=					[output_layers[self.config.num_hidden_layers - 1](__lowercase		)]
										else:
																__lowerCAmelCase											=					0
																__lowerCAmelCase											=					None
																__lowerCAmelCase											=					0
																for i in range(self.config.num_hidden_layers		):
																						calculated_layer_num += 1
																						__lowerCAmelCase											=					self.encoder.adaptive_forward(
																						    __lowercase							,      current_layer=__lowercase							,      attention_mask=__lowercase							,      head_mask=__lowercase							,      )

																						__lowerCAmelCase											=					self.pooler_activation(self.pooler(encoder_outputs[0][:, 0]		)		)
																						__lowerCAmelCase											=					output_layers[i](__lowercase		)
																						if regression:
																												__lowerCAmelCase											=					logits.detach()
																												if patient_result is not None:
																																		__lowerCAmelCase											=					patient_result.detach()
																												if (patient_result is not None) and torch.abs(patient_result - labels		) < self.regression_threshold:
																																		patient_counter += 1
																												else:
																																		__lowerCAmelCase											=					0
																						else:
																												__lowerCAmelCase											=					logits.detach().argmax(dim=1		)
																												if patient_result is not None:
																																		__lowerCAmelCase											=					patient_result.detach().argmax(dim=1		)
																												if (patient_result is not None) and torch.all(labels.eq(__lowercase		)		):
																																		patient_counter += 1
																												else:
																																		__lowerCAmelCase											=					0

																						__lowerCAmelCase											=					logits
																						if patient_counter == self.patience:
																												break
																__lowerCAmelCase											=					[patient_result]
																self.inference_layers_num += calculated_layer_num
																self.inference_instances_num += 1

										return res


@add_start_docstrings(
    'Albert Model transformer with PABEE and a sequence classification/regression head on top (a linear layer on top of\n    the pooled output) e.g. for GLUE tasks. '				,    __A				,    )
class    a__					(			__A   ):







				"""simple docstring"""



				def __init__(self							,      __lowercase		):
										super().__init__(__lowercase		)
										__lowerCAmelCase											=					config.num_labels

										__lowerCAmelCase											=					AlbertModelWithPabee(__lowercase		)
										__lowerCAmelCase											=					nn.Dropout(config.classifier_dropout_prob		)
										__lowerCAmelCase											=					nn.ModuleList(
										    [nn.Linear(config.hidden_size							,      self.config.num_labels		) for _ in range(config.num_hidden_layers		)]		)

										self.init_weights()




				@add_start_docstrings_to_model_forward(__lowercase		)
				def        _snake_case	(self							,      __lowercase=None							,      __lowercase=None							,      __lowercase=None							,      __lowercase=None							,      __lowercase=None							,      __lowercase=None							,      __lowercase=None							,      ):
										__lowerCAmelCase											=					self.albert(
										    input_ids=__lowercase							,      attention_mask=__lowercase							,      token_type_ids=__lowercase							,      position_ids=__lowercase							,      head_mask=__lowercase							,      inputs_embeds=__lowercase							,      output_dropout=self.dropout							,      output_layers=self.classifiers							,      regression=self.num_labels == 1							,      )

										__lowerCAmelCase											=					(logits[-1],)

										if labels is not None:
																__lowerCAmelCase											=					None
																__lowerCAmelCase											=					0
																for ix, logits_item in enumerate(__lowercase		):
																						if self.num_labels == 1:
																												#  We are doing regression
																												__lowerCAmelCase											=					MSELoss()
																												__lowerCAmelCase											=					loss_fct(logits_item.view(-1		)							,      labels.view(-1		)		)
																						else:
																												__lowerCAmelCase											=					CrossEntropyLoss()
																												__lowerCAmelCase											=					loss_fct(logits_item.view(-1							,      self.num_labels		)							,      labels.view(-1		)		)
																						if total_loss is None:
																												__lowerCAmelCase											=					loss
																						else:
																												total_loss += loss * (ix + 1)
																						total_weights += ix + 1
																__lowerCAmelCase											=					(total_loss / total_weights,) + outputs

										return outputs

