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




@dataclass
class 					UpperCamelCase     :

				"""simple docstring"""



				UpperCAmelCase_    :				nn.Module
				UpperCAmelCase_    :				List[nn.Module]						  =    field(default_factory=__magic_name__				)
				UpperCAmelCase_    :				list						  =    field(default_factory=__magic_name__				)


				def 							A (    self		,     lowercase__		,     lowercase__		,     lowercase__      )   ->   Optional[Any]:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =     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__      )   ->   Union[str, Any]:
									"""simple docstring"""







									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 							A (    self      )   ->   Tuple:
									"""simple docstring"""







									return list(filter(lambda lowercase__      : len(list(x.state_dict().keys()      )      ) > 0		,     self.traced      )      )






@dataclass
class 					UpperCamelCase     :

				"""simple docstring"""



				UpperCAmelCase_    :				nn.Module
				UpperCAmelCase_    :				nn.Module
				UpperCAmelCase_    :				int						  =    0
				UpperCAmelCase_    :				List						  =    field(default_factory=__magic_name__				)
				UpperCAmelCase_    :				List						  =    field(default_factory=__magic_name__				)


				def __call__(    self		,     lowercase__      )   ->   List[Any]:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =     Tracker(self.dest      )(lowercase__      ).parametrized
									SCREAMING_SNAKE_CASE           =     Tracker(self.src      )(lowercase__      ).parametrized

									SCREAMING_SNAKE_CASE           =     list(filter(lambda lowercase__      : type(lowercase__      ) not in self.src_skip		,     lowercase__      )      )
									SCREAMING_SNAKE_CASE           =     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   UpperCamelCase_  (		SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_      ):
					# nn.Parameter cannot be tracked by the Tracker, thus we need to manually convert them
					SCREAMING_SNAKE_CASE           =     from_model.state_dict()
					SCREAMING_SNAKE_CASE           =     our_model.state_dict()
					SCREAMING_SNAKE_CASE           =     our_model.config
					SCREAMING_SNAKE_CASE           =     []
					for stage_idx in range(len(config.hidden_sizes      )      ):
										for block_id in range(config.depths[stage_idx]      ):
															SCREAMING_SNAKE_CASE           =     f'''block{stage_idx + 1}.{block_id}.layer_scale_1'''
															SCREAMING_SNAKE_CASE           =     f'''van.encoder.stages.{stage_idx}.layers.{block_id}.attention_scaling.weight'''

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

					our_model.load_state_dict(SCREAMING_SNAKE_CASE_      )
					return our_model

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

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

					SCREAMING_SNAKE_CASE           =     name
					print(SCREAMING_SNAKE_CASE_      )

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

										# we can use the convnext one
										SCREAMING_SNAKE_CASE           =     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=SCREAMING_SNAKE_CASE_,					)

										print(f'''Pushed {checkpoint_name}'''      )

def   UpperCamelCase_  (		SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_ = None,					SCREAMING_SNAKE_CASE_ = True      ):
					SCREAMING_SNAKE_CASE           =     'imagenet-1k-id2label.json'
					SCREAMING_SNAKE_CASE           =     1_0_0_0

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

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

					SCREAMING_SNAKE_CASE           =     partial(SCREAMING_SNAKE_CASE_,					num_labels=SCREAMING_SNAKE_CASE_,					idalabel=SCREAMING_SNAKE_CASE_,					labelaid=SCREAMING_SNAKE_CASE_      )

					SCREAMING_SNAKE_CASE           =     {
					    '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],					),
					}

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

					SCREAMING_SNAKE_CASE           =     {
					    '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(
										    SCREAMING_SNAKE_CASE_,					names_to_config[model_name],					checkpoint=names_to_original_checkpoints[model_name],					from_model=names_to_original_models[model_name](),					save_directory=SCREAMING_SNAKE_CASE_,					push_to_hub=SCREAMING_SNAKE_CASE_,					)
					else:
										for model_name, config in names_to_config.items():
															convert_weight_and_push(
															    SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_,					checkpoint=names_to_original_checkpoints[model_name],					from_model=names_to_original_models[model_name](),					save_directory=SCREAMING_SNAKE_CASE_,					push_to_hub=SCREAMING_SNAKE_CASE_,					)


if __name__ == "__main__":
	snake_case			       =		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.',
	)

	snake_case			       =		parser.parse_args()
	snake_case			       =		args.pytorch_dump_folder_path
	pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
	snake_case			       =		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 os
from typing import Optional, Union

import numpy as np
from huggingface_hub import hf_hub_download

from ... import AutoTokenizer
from ...utils import logging


snake_case			       =		'block_records.npy'


snake_case			       =		logging.get_logger(__name__)

def   UpperCamelCase_  (		SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_      ):
					import tensorflow.compat.va as tf

					SCREAMING_SNAKE_CASE           =     tf.data.TFRecordDataset(SCREAMING_SNAKE_CASE_,					buffer_size=5_1_2 * 1_0_2_4 * 1_0_2_4      )
					SCREAMING_SNAKE_CASE           =     blocks_dataset.batch(SCREAMING_SNAKE_CASE_,					drop_remainder=SCREAMING_SNAKE_CASE_      )
					SCREAMING_SNAKE_CASE           =     next(blocks_dataset.take(1      ).as_numpy_iterator()      )

					return np_record




class 					UpperCamelCase     :

				"""simple docstring"""



				def __init__(    self		,     lowercase__		,     lowercase__		,     lowercase__=2		,     lowercase__=1000		,     lowercase__=100		,     lowercase__=100000		,     )   ->   Dict:
									"""simple docstring"""







									from scann.scann_ops.py.scann_ops_pybind import builder as Builder

									SCREAMING_SNAKE_CASE           =     Builder(db=lowercase__		,     num_neighbors=lowercase__		,     distance_measure='dot_product'      )
									SCREAMING_SNAKE_CASE           =     builder.tree(
									    num_leaves=lowercase__		,     num_leaves_to_search=lowercase__		,     training_sample_size=lowercase__      )
									SCREAMING_SNAKE_CASE           =     builder.score_ah(dimensions_per_block=lowercase__      )

									SCREAMING_SNAKE_CASE           =     builder.build()




				def 							A (    self		,     lowercase__      )   ->   str:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE     ,   SCREAMING_SNAKE_CASE           =     self.searcher.search_batched(question_projection.detach().cpu()      )
									return retrieved_block_ids.astype('int64'      )






class 					UpperCamelCase     :

				"""simple docstring"""



				def __init__(    self		,     lowercase__		,     lowercase__      )   ->   Dict:
									"""simple docstring"""







									super().__init__()
									SCREAMING_SNAKE_CASE           =     block_records
									SCREAMING_SNAKE_CASE           =     tokenizer


				def __call__(    self		,     lowercase__		,     lowercase__		,     lowercase__		,     lowercase__=None		,     lowercase__="pt"      )   ->   Optional[int]:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =     np.take(self.block_records		,     indices=lowercase__		,     axis=0      )

									SCREAMING_SNAKE_CASE           =     self.tokenizer.decode(question_input_ids[0]		,     skip_special_tokens=lowercase__      )

									SCREAMING_SNAKE_CASE           =     []
									SCREAMING_SNAKE_CASE           =     []
									for retrieved_block in retrieved_blocks:
														text.append(lowercase__      )
														text_pair.append(retrieved_block.decode()      )

									SCREAMING_SNAKE_CASE           =     self.tokenizer(
									    lowercase__		,     lowercase__		,     padding=lowercase__		,     truncation=lowercase__		,     return_special_tokens_mask=lowercase__		,     max_length=lowercase__      )
									SCREAMING_SNAKE_CASE           =     concat_inputs.convert_to_tensors(lowercase__      )

									if answer_ids is not None:
														return self.block_has_answer(lowercase__		,     lowercase__      ) + (concat_inputs_tensors,)
									else:
														return (None, None, None, concat_inputs_tensors)


				@classmethod
				def 							A (    cls		,     lowercase__		,     *lowercase__		,     **lowercase__      )   ->   Optional[Any]:
									"""simple docstring"""







									if os.path.isdir(lowercase__      ):
														SCREAMING_SNAKE_CASE           =     os.path.join(lowercase__		,     _REALM_BLOCK_RECORDS_FILENAME      )
									else:
														SCREAMING_SNAKE_CASE           =     hf_hub_download(
														    repo_id=lowercase__		,     filename=_REALM_BLOCK_RECORDS_FILENAME		,     **lowercase__      )
									SCREAMING_SNAKE_CASE           =     np.load(lowercase__		,     allow_pickle=lowercase__      )

									SCREAMING_SNAKE_CASE           =     AutoTokenizer.from_pretrained(lowercase__		,     *lowercase__		,     **lowercase__      )

									return cls(lowercase__		,     lowercase__      )


				def 							A (    self		,     lowercase__      )   ->   List[str]:
									"""simple docstring"""







									np.save(os.path.join(lowercase__		,     _REALM_BLOCK_RECORDS_FILENAME      )		,     self.block_records      )
									# save tokenizer
									self.tokenizer.save_pretrained(lowercase__      )




				def 							A (    self		,     lowercase__		,     lowercase__      )   ->   Optional[Any]:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =     []
									SCREAMING_SNAKE_CASE           =     []
									SCREAMING_SNAKE_CASE           =     []
									SCREAMING_SNAKE_CASE           =     0

									for input_id in concat_inputs.input_ids:
														SCREAMING_SNAKE_CASE           =     input_id.tolist()
														# Check answers between two [SEP] tokens
														SCREAMING_SNAKE_CASE           =     input_id_list.index(self.tokenizer.sep_token_id      )
														SCREAMING_SNAKE_CASE           =     first_sep_idx + 1 + input_id_list[first_sep_idx + 1 :].index(self.tokenizer.sep_token_id      )

														start_pos.append([]      )
														end_pos.append([]      )
														for answer in answer_ids:
																			for idx in range(first_sep_idx + 1		,     lowercase__      ):
																								if answer[0] == input_id_list[idx]:
																													if input_id_list[idx : idx + len(lowercase__      )] == answer:
																																		start_pos[-1].append(lowercase__      )
																																		end_pos[-1].append(idx + len(lowercase__      ) - 1      )

														if len(start_pos[-1]      ) == 0:
																			has_answers.append(lowercase__      )
														else:
																			has_answers.append(lowercase__      )
																			if len(start_pos[-1]      ) > max_answers:
																								SCREAMING_SNAKE_CASE           =     len(start_pos[-1]      )

        # Pad -1 to max_answers
									for start_pos_, end_pos_ in zip(lowercase__		,     lowercase__      ):
														if len(start_pos_      ) < max_answers:
																			SCREAMING_SNAKE_CASE           =     [-1] * (max_answers - len(start_pos_      ))
																			start_pos_ += padded
																			end_pos_ += padded
									return has_answers, start_pos, end_pos
