






"""simple docstring"""

import collections

from .utils import ExplicitEnum, is_torch_available, logging


if is_torch_available():
			import torch


lowercase           =							logging.get_logger(__name__)

class     A_				:







				def __init__(				self				:    Dict    ,     __lowerCamelCase				:    Optional[int]    ,     __lowerCamelCase				:    Tuple=2_1    ,     __lowerCamelCase				:    Any=[]    ,     __lowerCamelCase				:    Optional[int]=None						)					->       Dict:
										__magic_name__      =  model
										__magic_name__      =  trace_batch_nums
										__magic_name__      =  abort_after_batch_num

										# keep a LIFO buffer of frames to dump as soon as inf/nan is encountered to give context to the problem emergence
										__magic_name__      =  collections.deque([]    ,     __lowerCamelCase						)
										__magic_name__      =  []
										__magic_name__      =  0
										__magic_name__      =  0
										__magic_name__      =  False
										__magic_name__      =  "                 "

										self.analyse_model()

										self.register_forward_hook()







				def      _snake_case       (				self				:    List[str]    ,     __lowerCamelCase				:    List[str]=None						)					->       Dict:
										if frame is not None:
																self.expand_frame(__lowerCamelCase						)
										self.frames.append("\n".join(self.frame						)						)
										__magic_name__      =  []  # start a new frame







				def      _snake_case       (				self				:    Any    ,     __lowerCamelCase				:    Tuple						)					->       Dict:
										self.frame.append(__lowerCamelCase						)







				def      _snake_case       (				self				:    Tuple						)					->       int:
										print("\n".join(self.frames						)						)
										__magic_name__      =  []







				def      _snake_case       (				self				:    List[str]						)					->       Optional[int]:
										__magic_name__      =  []







				def      _snake_case       (				self				:    str						)					->       List[str]:
										print(f'''\nDetected inf/nan during batch_number={self.batch_number}'''						)
										print(f'''Last {len(self.frames						)} forward frames:'''						)
										print(f'''{"abs min":8} {"abs max":8} metadata'''						)
										print("\n".join(self.frames						)						)
										print("\n\n"						)
										__magic_name__      =  []







				def      _snake_case       (				self				:    Tuple						)					->       int:
										# extract the fully qualified module names, to be able to report at run time. e.g.:
										# encoder.block.2.layer.0.SelfAttention.o
										#
										# for shared weights only the first shared module name will be registered
										__magic_name__      =  {m: name for name, m in self.model.named_modules()}
										# self.longest_module_name = max(len(v) for v in self.module_names.values())







				def      _snake_case       (				self				:    Any    ,     __lowerCamelCase				:    str    ,     __lowerCamelCase				:    str						)					->       int:
										if torch.is_tensor(__lowerCamelCase						):
																self.expand_frame(get_abs_min_max(__lowerCamelCase    ,     __lowerCamelCase						)						)
																if detect_overflow(__lowerCamelCase    ,     __lowerCamelCase						):
																						__magic_name__      =  True
										elif var is None:
																self.expand_frame(f'''{"None":>17} {ctx}'''						)
										else:
																self.expand_frame(f'''{"not a tensor":>17} {ctx}'''						)







				def      _snake_case       (				self				:    List[Any]						)					->       Tuple:
										self.expand_frame(f'''\n\n{self.prefix} *** Starting batch number={self.batch_number} ***'''						)
										self.expand_frame(f'''{"abs min":8} {"abs max":8} metadata'''						)







				def      _snake_case       (				self				:    Optional[int]						)					->       Tuple:
										self.expand_frame(f'''{self.prefix} *** Finished batch number={self.batch_number-1} ***\n\n'''						)







				def      _snake_case       (				self				:    str    ,     __lowerCamelCase				:    str    ,     __lowerCamelCase				:    List[Any]    ,     __lowerCamelCase				:    Dict						)					->       List[Any]:
										self.expand_frame(f'''{self.prefix} {self.module_names[module]} {module.__class__.__name__}'''						)

										# params
										for name, p in module.named_parameters(recurse=__lowerCamelCase						):
																self.analyse_variable(__lowerCamelCase    ,     __lowerCamelCase						)

										# inputs
										if isinstance(__lowerCamelCase    ,     __lowerCamelCase						):
																for i, x in enumerate(__lowerCamelCase						):
																						self.analyse_variable(__lowerCamelCase    ,     f'''input[{i}]'''						)
										else:
																self.analyse_variable(__lowerCamelCase    ,     "input"						)

										# outputs
										if isinstance(__lowerCamelCase    ,     __lowerCamelCase						):
																for i, x in enumerate(__lowerCamelCase						):
																						# possibly a tuple of tuples
																						if isinstance(__lowerCamelCase    ,     __lowerCamelCase						):
																												for j, y in enumerate(__lowerCamelCase						):
																																		self.analyse_variable(__lowerCamelCase    ,     f'''output[{i}][{j}]'''						)
																						else:
																												self.analyse_variable(__lowerCamelCase    ,     f'''output[{i}]'''						)
										else:
																self.analyse_variable(__lowerCamelCase    ,     "output"						)

										self.save_frame()







				def      _snake_case       (				self				:    Dict						)					->       int:
										self.model.apply(self._register_forward_hook						)







				def      _snake_case       (				self				:    Any    ,     __lowerCamelCase				:    List[str]						)					->       Optional[int]:
										module.register_forward_hook(self.forward_hook						)







				def      _snake_case       (				self				:    Any    ,     __lowerCamelCase				:    Tuple    ,     __lowerCamelCase				:    Optional[int]    ,     __lowerCamelCase				:    Union[str, Any]						)					->       Dict:
										# - input is a tuple of packed inputs (could be non-Tensors)
										# - output could be a Tensor or a tuple of Tensors and non-Tensors

										__magic_name__      =  False

										__magic_name__      =  True if self.batch_number in self.trace_batch_nums else False
										if trace_mode:
																self.reset_saved_frames()

										if self.total_calls == 0:
																self.batch_start_frame()
										self.total_calls += 1

										# count batch numbers - the very first forward hook of the batch will be called when the
										# batch completes - i.e. it gets called very last - we know this batch has finished
										if module == self.model:
																self.batch_number += 1
																__magic_name__      =  True

										self.create_frame(__lowerCamelCase    ,     __lowerCamelCase    ,     __lowerCamelCase						)

										# if last_frame_of_batch:
										#     self.batch_end_frame()

										if trace_mode:
																self.trace_frames()

										if last_frame_of_batch:
																self.batch_start_frame()

										if self.detected_overflow and not trace_mode:
																self.dump_saved_frames()

																# now we can abort, as it's pointless to continue running
																raise ValueError(
																    "DebugUnderflowOverflow: inf/nan detected, aborting as there is no point running further. "
																    "Please scroll up above this traceback to see the activation values prior to this event."						)

										# abort after certain batch if requested to do so
										if self.abort_after_batch_num is not None and self.batch_number > self.abort_after_batch_num:
																raise ValueError(
																    f'''DebugUnderflowOverflow: aborting after {self.batch_number} batches due to'''
																    f''' `abort_after_batch_num={self.abort_after_batch_num}` arg'''						)






def _lowerCAmelCase						(	__lowerCamelCase:Union[str, Any]			,    __lowerCamelCase:Optional[int] ):

						'''simple docstring'''






						__magic_name__      =  var.abs()
						return f'''{abs_var.min():8.2e} {abs_var.max():8.2e} {ctx}'''






def _lowerCAmelCase						(	__lowerCamelCase:str			,    __lowerCamelCase:Tuple ):

						'''simple docstring'''






						__magic_name__      =  False
						if torch.isnan(__lowerCamelCase ).any().item():
												__magic_name__      =  True
												print(f'''{ctx} has nans''' )
						if torch.isinf(__lowerCamelCase ).any().item():
												__magic_name__      =  True
												print(f'''{ctx} has infs''' )

						# if needed to monitor large elements can enable the following
						if 0:  # and detected:
												__magic_name__      =  var[torch.ge(var.abs()			,    1_0_0 )]
												if naaa.numel() > 0:
																		print(f'''{ctx}:  n100={naaa.numel()}''' )
												__magic_name__      =  var[torch.ge(var.abs()			,    1_0_0_0 )]
												if naaaa.numel() > 0:
																		print(f'''{ctx}: n1000={naaaa.numel()}''' )
												__magic_name__      =  var[torch.ge(var.abs()			,    1_0_0_0_0 )]
												if naaaaa.numel() > 0:
																		print(f'''{ctx}: n10000={naaaaa.numel()}''' )

						if 0:
												print(f'''min={var.min():9.2e} max={var.max():9.2e}''' )

						if 0:
												print(f'''min={var.min():9.2e} max={var.max():9.2e} var={var.var():9.2e} mean={var.mean():9.2e} ({ctx})''' )

						return detected


class     A_				(   snake_case_	):
				UpperCAmelCase__										=					'''underflow_overflow'''
				UpperCAmelCase__										=					'''tpu_metrics_debug'''











"""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()
lowercase           =							logging.get_logger(__name__)

@dataclass
class     A_				:
				UpperCAmelCase__										=					42
				UpperCAmelCase__										=					field(default_factory=snake_case_	)
				UpperCAmelCase__										=					field(default_factory=snake_case_	)







				def      _snake_case       (				self				:    Any    ,     __lowerCamelCase				:    Any    ,     __lowerCamelCase				:    Tensor    ,     __lowerCamelCase				:    Tensor						)					->       Dict:
										__magic_name__      =  len(list(m.modules()						)						) == 1 or isinstance(__lowerCamelCase    ,     nn.Convad						) or isinstance(__lowerCamelCase    ,     nn.BatchNormad						)
										if has_not_submodules:
																if not isinstance(__lowerCamelCase    ,     __lowerCamelCase						):
																						self.traced.append(__lowerCamelCase						)







				def __call__(				self				:    int    ,     __lowerCamelCase				:    Tensor						)					->       List[str]:
										for m in self.module.modules():
																self.handles.append(m.register_forward_hook(self._forward_hook						)						)
										self.module(__lowerCamelCase						)
										[x.remove() for x in self.handles]
										return self







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





@dataclass
class     A_				:
				UpperCAmelCase__										=					42
				UpperCAmelCase__										=					42
				UpperCAmelCase__										=					0
				UpperCAmelCase__										=					field(default_factory=snake_case_	)
				UpperCAmelCase__										=					field(default_factory=snake_case_	)







				def __call__(				self				:    Union[str, Any]    ,     __lowerCamelCase				:    Tensor						)					->       str:
										__magic_name__      =  Tracker(self.dest						)(__lowerCamelCase						).parametrized
										__magic_name__      =  Tracker(self.src						)(__lowerCamelCase						).parametrized

										__magic_name__      =  list(filter(lambda __lowerCamelCase						: type(__lowerCamelCase						) not in self.src_skip    ,     __lowerCamelCase						)						)
										__magic_name__      =  list(filter(lambda __lowerCamelCase						: type(__lowerCamelCase						) not in self.dest_skip    ,     __lowerCamelCase						)						)

										if len(__lowerCamelCase						) != len(__lowerCamelCase						):
																raise Exception(
																    f'''Numbers of operations are different. Source module has {len(__lowerCamelCase						)} operations while'''
																    f''' destination module has {len(__lowerCamelCase						)}.'''						)

										for dest_m, src_m in zip(__lowerCamelCase    ,     __lowerCamelCase						):
																dest_m.load_state_dict(src_m.state_dict()						)
																if self.verbose == 1:
																						print(f'''Transfered from={src_m} to={dest_m}'''						)






def _lowerCAmelCase						(	__lowerCamelCase:nn.Module			,    __lowerCamelCase:nn.Module ):

						'''simple docstring'''






						__magic_name__      =  from_model.state_dict()
						__magic_name__      =  our_model.state_dict()
						__magic_name__      =  our_model.config
						__magic_name__      =  []
						for stage_idx in range(len(config.hidden_sizes ) ):
												for block_id in range(config.depths[stage_idx] ):
																		__magic_name__      =  f'''block{stage_idx + 1}.{block_id}.layer_scale_1'''
																		__magic_name__      =  f'''van.encoder.stages.{stage_idx}.layers.{block_id}.attention_scaling.weight'''

																		all_keys.append((from_key, to_key) )
																		__magic_name__      =  f'''block{stage_idx + 1}.{block_id}.layer_scale_2'''
																		__magic_name__      =  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:
												__magic_name__      =  from_state_dict.pop(__lowerCamelCase )

						our_model.load_state_dict(__lowerCamelCase )
						return our_model






def _lowerCAmelCase						(	__lowerCamelCase:str			,    __lowerCamelCase:VanConfig			,    __lowerCamelCase:str			,    __lowerCamelCase:nn.Module			,    __lowerCamelCase:Path			,    __lowerCamelCase:bool = True			,    ):

						'''simple docstring'''






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

						__magic_name__      =  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
												__magic_name__      =  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 _lowerCAmelCase						(	__lowerCamelCase:Path			,    __lowerCamelCase:str = None			,    __lowerCamelCase:bool = True ):

						'''simple docstring'''






						__magic_name__      =  "imagenet-1k-id2label.json"
						__magic_name__      =  1_0_0_0

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

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

						__magic_name__      =  partial(__lowerCamelCase			,    num_labels=__lowerCamelCase			,    idalabel=__lowerCamelCase			,    labelaid=__lowerCamelCase )

						__magic_name__      =  {
						    "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]			,    ),
						}

						__magic_name__      =  {
						    "van-tiny": van_tiny,
						    "van-small": van_small,
						    "van-base": van_base,
						    "van-large": van_large,
						}

						__magic_name__      =  {
						    "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__":
			lowercase           =							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.''',
			)

			lowercase           =							parser.parse_args()
			lowercase           =							args.pytorch_dump_folder_path
			pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
			lowercase           =							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)



