




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

import timm
import torch
import torch.nn as nn
from huggingface_hub import hf_hub_download
from torch import Tensor

from transformers import AutoImageProcessor, ResNetConfig, ResNetForImageClassification
from transformers.utils import logging


logging.set_verbosity_info()
UpperCAmelCase_		:				List[Any]									=     logging.get_logger()




@dataclass
class 		__A      :
						UpperCamelCase     						=					42
						UpperCamelCase     						=					field(default_factory=UpperCamelCase__			)
						UpperCamelCase     						=					field(default_factory=UpperCamelCase__			)

						def     A__						(		self   :Tuple    , __snake_case   :Optional[Any]    , __snake_case   :Tensor    , __snake_case   :Tensor ):
										'''simple docstring'''

										__magic_name__	:	Dict								=len(list(m.modules() ) ) == 1 or isinstance(__snake_case    , nn.Convad ) or isinstance(__snake_case    , nn.BatchNormad )
										if has_not_submodules:
														self.traced.append(__snake_case )

						def __call__(		self   :List[str]    , __snake_case   :Tensor ):
										'''simple docstring'''

										for m in self.module.modules():
														self.handles.append(m.register_forward_hook(self._forward_hook ) )
										self.module(__snake_case )
										[x.remove() for x in self.handles]
										return self


						@property
						def     A__						(		self   :Union[str, Any] ):
										'''simple docstring'''

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




@dataclass
class 		__A      :
						UpperCamelCase     						=					42
						UpperCamelCase     						=					42
						UpperCamelCase     						=					0
						UpperCamelCase     						=					field(default_factory=UpperCamelCase__			)
						UpperCamelCase     						=					field(default_factory=UpperCamelCase__			)

						def __call__(		self   :Dict    , __snake_case   :Tensor ):
										'''simple docstring'''

										__magic_name__	:	Tuple								=Tracker(self.dest )(__snake_case ).parametrized
										__magic_name__	:	str								=Tracker(self.src )(__snake_case ).parametrized

										__magic_name__	:	List[Any]								=list(filter(lambda __snake_case : type(__snake_case ) not in self.src_skip    , __snake_case ) )
										__magic_name__	:	Union[str, Any]								=list(filter(lambda __snake_case : type(__snake_case ) not in self.dest_skip    , __snake_case ) )

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

										for dest_m, src_m in zip(__snake_case    , __snake_case ):
														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	,     lowerCamelCase	,     lowerCamelCase	,     lowerCamelCase = True   ):
				print(F"Converting {name}..."   )
				with torch.no_grad():
								__magic_name__	:	Union[str, Any]								=timm.create_model(lowerCamelCase	,     pretrained=lowerCamelCase   ).eval()
								__magic_name__	:	str								=ResNetForImageClassification(lowerCamelCase   ).eval()
								__magic_name__	:	Optional[Any]								=ModuleTransfer(src=lowerCamelCase	,     dest=lowerCamelCase   )
								__magic_name__	:	List[str]								=torch.randn((1, 3, 224, 224)   )
								module_transfer(lowerCamelCase   )

				assert torch.allclose(from_model(lowerCamelCase   )	,     our_model(lowerCamelCase   ).logits   ), "The model logits don't match the original one."

				__magic_name__	:	Optional[Any]								=F"resnet{'-'.join(name.split('resnet'   )   )}"
				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__	:	List[Any]								=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	,     lowerCamelCase = None	,     lowerCamelCase = True   ):
				__magic_name__	:	int								="""imagenet-1k-id2label.json"""
				__magic_name__	:	List[str]								=1000
				__magic_name__	:	Tuple								=(1, num_labels)

				__magic_name__	:	Union[str, Any]								="""huggingface/label-files"""
				__magic_name__	:	int								=num_labels
				__magic_name__	:	str								=json.load(open(hf_hub_download(lowerCamelCase	,     lowerCamelCase	,     repo_type="""dataset"""   )	,     """r"""   )   )
				__magic_name__	:	Tuple								={int(lowerCamelCase   ): v for k, v in idalabel.items()}

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

				__magic_name__	:	int								=partial(lowerCamelCase	,     num_labels=lowerCamelCase	,     idalabel=lowerCamelCase	,     labelaid=lowerCamelCase   )

				__magic_name__	:	Dict								={
				    """resnet18""": ImageNetPreTrainedConfig(
				        depths=[2, 2, 2, 2]	,     hidden_sizes=[64, 128, 256, 512]	,     layer_type="""basic"""   ),
				    """resnet26""": ImageNetPreTrainedConfig(
				        depths=[2, 2, 2, 2]	,     hidden_sizes=[256, 512, 1024, 2048]	,     layer_type="""bottleneck"""   ),
				    """resnet34""": ImageNetPreTrainedConfig(
				        depths=[3, 4, 6, 3]	,     hidden_sizes=[64, 128, 256, 512]	,     layer_type="""basic"""   ),
				    """resnet50""": ImageNetPreTrainedConfig(
				        depths=[3, 4, 6, 3]	,     hidden_sizes=[256, 512, 1024, 2048]	,     layer_type="""bottleneck"""   ),
				    """resnet101""": ImageNetPreTrainedConfig(
				        depths=[3, 4, 23, 3]	,     hidden_sizes=[256, 512, 1024, 2048]	,     layer_type="""bottleneck"""   ),
				    """resnet152""": ImageNetPreTrainedConfig(
				        depths=[3, 8, 36, 3]	,     hidden_sizes=[256, 512, 1024, 2048]	,     layer_type="""bottleneck"""   ),
				}

				if model_name:
								convert_weight_and_push(lowerCamelCase	,     names_to_config[model_name]	,     lowerCamelCase	,     lowerCamelCase   )
				else:
								for model_name, config in names_to_config.items():
												convert_weight_and_push(lowerCamelCase	,     lowerCamelCase	,     lowerCamelCase	,     lowerCamelCase   )
				return config, expected_shape


if __name__ == "__main__":
							UpperCAmelCase_		:				Tuple									=     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: resnet18,26,34,50,101,152. 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(
							    "--push_to_hub",
							    default=True,
							    type=bool,
							    required=False,
							    help="If True, push model and image processor to the hub.",
							)

							UpperCAmelCase_		:				Optional[Any]									=     parser.parse_args()
							UpperCAmelCase_		:				Path									=     args.pytorch_dump_folder_path
							pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
							convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)










import argparse
import json
import os

import torch

from transformers import LukeConfig, LukeModel, LukeTokenizer, RobertaTokenizer
from transformers.tokenization_utils_base import AddedToken




@torch.no_grad()
def 		lowerCAmelCase_   (	lowerCamelCase	,     lowerCamelCase	,     lowerCamelCase	,     lowerCamelCase	,     lowerCamelCase   ):
				# Load configuration defined in the metadata file
				with open(lowerCamelCase   ) as metadata_file:
								__magic_name__	:	List[Any]								=json.load(lowerCamelCase   )
				__magic_name__	:	Optional[int]								=LukeConfig(use_entity_aware_attention=lowerCamelCase	,     **metadata["""model_config"""]   )

				# Load in the weights from the checkpoint_path
				__magic_name__	:	List[str]								=torch.load(lowerCamelCase	,     map_location="""cpu"""   )

				# Load the entity vocab file
				__magic_name__	:	Dict								=load_entity_vocab(lowerCamelCase   )

				__magic_name__	:	Union[str, Any]								=RobertaTokenizer.from_pretrained(metadata["""model_config"""]["""bert_model_name"""]   )

				# Add special tokens to the token vocabulary for downstream tasks
				__magic_name__	:	List[str]								=AddedToken("""<ent>"""	,     lstrip=lowerCamelCase	,     rstrip=lowerCamelCase   )
				__magic_name__	:	Union[str, Any]								=AddedToken("""<ent2>"""	,     lstrip=lowerCamelCase	,     rstrip=lowerCamelCase   )
				tokenizer.add_special_tokens({"""additional_special_tokens""": [entity_token_a, entity_token_a]}   )
				config.vocab_size += 2

				print(F"Saving tokenizer to {pytorch_dump_folder_path}"   )
				tokenizer.save_pretrained(lowerCamelCase   )
				with open(os.path.join(lowerCamelCase	,     LukeTokenizer.vocab_files_names["""entity_vocab_file"""]   )	,     """w"""   ) as f:
								json.dump(lowerCamelCase	,     lowerCamelCase   )

				__magic_name__	:	Optional[Any]								=LukeTokenizer.from_pretrained(lowerCamelCase   )

				# Initialize the embeddings of the special tokens
				__magic_name__	:	List[str]								=state_dict["""embeddings.word_embeddings.weight"""]
				__magic_name__	:	Optional[int]								=word_emb[tokenizer.convert_tokens_to_ids(["""@"""]   )[0]].unsqueeze(0   )
				__magic_name__	:	Optional[int]								=word_emb[tokenizer.convert_tokens_to_ids(["""#"""]   )[0]].unsqueeze(0   )
				__magic_name__	:	Tuple								=torch.cat([word_emb, ent_emb, enta_emb]   )

				# Initialize the query layers of the entity-aware self-attention mechanism
				for layer_index in range(config.num_hidden_layers   ):
								for matrix_name in ["query.weight", "query.bias"]:
												__magic_name__	:	Optional[Any]								=F"encoder.layer.{layer_index}.attention.self."
												__magic_name__	:	Optional[Any]								=state_dict[prefix + matrix_name]
												__magic_name__	:	Any								=state_dict[prefix + matrix_name]
												__magic_name__	:	Dict								=state_dict[prefix + matrix_name]

    # Initialize the embedding of the [MASK2] entity using that of the [MASK] entity for downstream tasks
				__magic_name__	:	str								=state_dict["""entity_embeddings.entity_embeddings.weight"""]
				__magic_name__	:	Union[str, Any]								=entity_emb[entity_vocab["""[MASK]"""]]

				__magic_name__	:	Optional[int]								=LukeModel(config=lowerCamelCase   ).eval()

				__magic_name__				, __magic_name__	:	Any								=model.load_state_dict(lowerCamelCase	,     strict=lowerCamelCase   )
				if not (len(lowerCamelCase   ) == 1 and missing_keys[0] == "embeddings.position_ids"):
								raise ValueError(F"Missing keys {', '.join(lowerCamelCase   )}. Expected only missing embeddings.position_ids"   )
				if not (all(key.startswith("""entity_predictions"""   ) or key.startswith("""lm_head"""   ) for key in unexpected_keys   )):
								raise ValueError(
								    """Unexpected keys"""
								    F" {', '.join([key for key in unexpected_keys if not (key.startswith('entity_predictions'   ) or key.startswith('lm_head'   ))]   )}"   )

				# Check outputs
				__magic_name__	:	Dict								=LukeTokenizer.from_pretrained(lowerCamelCase	,     task="""entity_classification"""   )

				__magic_name__	:	int								=(
				    """Top seed Ana Ivanovic said on Thursday she could hardly believe her luck as a fortuitous netcord helped the"""
				    """ new world number one avoid a humiliating second- round exit at Wimbledon ."""
				)
				__magic_name__	:	Optional[Any]								=(39, 42)
				__magic_name__	:	Dict								=tokenizer(lowerCamelCase	,     entity_spans=[span]	,     add_prefix_space=lowerCamelCase	,     return_tensors="""pt"""   )

				__magic_name__	:	str								=model(**lowerCamelCase   )

				# Verify word hidden states
				if model_size == "large":
								__magic_name__	:	Tuple								=torch.Size((1, 42, 1024)   )
								__magic_name__	:	int								=torch.tensor(
								    [[0.0_1_3_3, 0.0_8_6_5, 0.0_0_9_5], [0.3_0_9_3, -0.2_5_7_6, -0.7_4_1_8], [-0.1_7_2_0, -0.2_1_1_7, -0.2_8_6_9]]   )
				else:  # base
								__magic_name__	:	Dict								=torch.Size((1, 42, 768)   )
								__magic_name__	:	List[Any]								=torch.tensor([[0.0_0_3_7, 0.1_3_6_8, -0.0_0_9_1], [0.1_0_9_9, 0.3_3_2_9, -0.1_0_9_5], [0.0_7_6_5, 0.5_3_3_5, 0.1_1_7_9]]   )

				if not (outputs.last_hidden_state.shape == expected_shape):
								raise ValueError(
								    F"Outputs.last_hidden_state.shape is {outputs.last_hidden_state.shape}, Expected shape is {expected_shape}"   )
				if not torch.allclose(outputs.last_hidden_state[0, :3, :3]	,     lowerCamelCase	,     atol=1E-4   ):
								raise ValueError

				# Verify entity hidden states
				if model_size == "large":
								__magic_name__	:	Optional[Any]								=torch.Size((1, 1, 1024)   )
								__magic_name__	:	Tuple								=torch.tensor([[0.0_4_6_6, -0.0_1_0_6, -0.0_1_7_9]]   )
				else:  # base
								__magic_name__	:	List[Any]								=torch.Size((1, 1, 768)   )
								__magic_name__	:	Dict								=torch.tensor([[0.1_4_5_7, 0.1_0_4_4, 0.0_1_7_4]]   )

				if not (outputs.entity_last_hidden_state.shape != expected_shape):
								raise ValueError(
								    F"Outputs.entity_last_hidden_state.shape is {outputs.entity_last_hidden_state.shape}, Expected shape is"
								    F" {expected_shape}"   )
				if not torch.allclose(outputs.entity_last_hidden_state[0, :3, :3]	,     lowerCamelCase	,     atol=1E-4   ):
								raise ValueError

				# Finally, save our PyTorch model and tokenizer
				print("""Saving PyTorch model to {}""".format(lowerCamelCase   )   )
				model.save_pretrained(lowerCamelCase   )




def 		lowerCAmelCase_   (	lowerCamelCase   ):
				__magic_name__	:	Tuple								={}
				with open(lowerCamelCase	,     """r"""	,     encoding="""utf-8"""   ) as f:
								for index, line in enumerate(lowerCamelCase   ):
												__magic_name__				, __magic_name__	:	Optional[Any]								=line.rstrip().split("""\t"""   )
												__magic_name__	:	Union[str, Any]								=index

				return entity_vocab


if __name__ == "__main__":
							UpperCAmelCase_		:				Optional[Any]									=     argparse.ArgumentParser()
							# Required parameters
							parser.add_argument("--checkpoint_path", type=str, help="Path to a pytorch_model.bin file.")
							parser.add_argument(
							    "--metadata_path", default=None, type=str, help="Path to a metadata.json file, defining the configuration."
							)
							parser.add_argument(
							    "--entity_vocab_path",
							    default=None,
							    type=str,
							    help="Path to an entity_vocab.tsv file, containing the entity vocabulary.",
							)
							parser.add_argument(
							    "--pytorch_dump_folder_path", default=None, type=str, help="Path to where to dump the output PyTorch model."
							)
							parser.add_argument(
							    "--model_size", default="base", type=str, choices=["base", "large"], help="Size of the model to be converted."
							)
							UpperCAmelCase_		:				int									=     parser.parse_args()
							convert_luke_checkpoint(
							    args.checkpoint_path,
							    args.metadata_path,
							    args.entity_vocab_path,
							    args.pytorch_dump_folder_path,
							    args.model_size,
							)




