
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 	A_ (							snake_case							:   Tuple ,      snake_case							:   List[str] ,      snake_case							:   int ,      snake_case							:   Optional[Any] ,      snake_case							:   List[Any] )      ->    str:



						'''simple docstring'''

						with open(snake_case ) as metadata_file:
												__UpperCamelCase       =			json.load(snake_case )
						__UpperCamelCase       =			LukeConfig(use_entity_aware_attention=snake_case ,      **metadata['''model_config'''] )

						# Load in the weights from the checkpoint_path
						__UpperCamelCase       =			torch.load(snake_case ,      map_location='''cpu''' )

						# Load the entity vocab file
						__UpperCamelCase       =			load_entity_vocab(snake_case )

						__UpperCamelCase       =			RobertaTokenizer.from_pretrained(metadata['''model_config''']['''bert_model_name'''] )

						# Add special tokens to the token vocabulary for downstream tasks
						__UpperCamelCase       =			AddedToken('''<ent>''' ,      lstrip=snake_case ,      rstrip=snake_case )
						__UpperCamelCase       =			AddedToken('''<ent2>''' ,      lstrip=snake_case ,      rstrip=snake_case )
						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(snake_case )
						with open(os.path.join(snake_case ,      LukeTokenizer.vocab_files_names['''entity_vocab_file'''] ) ,      '''w''' ) as f:
												json.dump(snake_case ,      snake_case )

						__UpperCamelCase       =			LukeTokenizer.from_pretrained(snake_case )

						# Initialize the embeddings of the special tokens
						__UpperCamelCase       =			state_dict['''embeddings.word_embeddings.weight''']
						__UpperCamelCase       =			word_emb[tokenizer.convert_tokens_to_ids(['''@'''] )[0]].unsqueeze(0 )
						__UpperCamelCase       =			word_emb[tokenizer.convert_tokens_to_ids(['''#'''] )[0]].unsqueeze(0 )
						__UpperCamelCase       =			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"]:
																		__UpperCamelCase       =			f"encoder.layer.{layer_index}.attention.self."
																		__UpperCamelCase       =			state_dict[prefix + matrix_name]
																		__UpperCamelCase       =			state_dict[prefix + matrix_name]
																		__UpperCamelCase       =			state_dict[prefix + matrix_name]

    # Initialize the embedding of the [MASK2] entity using that of the [MASK] entity for downstream tasks
						__UpperCamelCase       =			state_dict['''entity_embeddings.entity_embeddings.weight''']
						__UpperCamelCase       =			entity_emb[entity_vocab['''[MASK]''']]

						__UpperCamelCase       =			LukeModel(config=snake_case ).eval()

						__UpperCamelCase  ,							__UpperCamelCase       =			model.load_state_dict(snake_case ,      strict=snake_case )
						if not (len(snake_case ) == 1 and missing_keys[0] == "embeddings.position_ids"):
												raise ValueError(f"Missing keys {', '.join(snake_case )}. 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
						__UpperCamelCase       =			LukeTokenizer.from_pretrained(snake_case ,      task='''entity_classification''' )

						__UpperCamelCase       =			(
						    '''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 .'''
						)
						__UpperCamelCase       =			(39, 42)
						__UpperCamelCase       =			tokenizer(snake_case ,      entity_spans=[span] ,      add_prefix_space=snake_case ,      return_tensors='''pt''' )

						__UpperCamelCase       =			model(**snake_case )

						# Verify word hidden states
						if model_size == "large":
												__UpperCamelCase       =			torch.Size((1, 42, 1024) )
												__UpperCamelCase       =			torch.tensor(
												    [[0.0133, 0.0865, 0.0095], [0.3093, -0.2576, -0.7418], [-0.1720, -0.2117, -0.2869]] )
						else:  # base
												__UpperCamelCase       =			torch.Size((1, 42, 768) )
												__UpperCamelCase       =			torch.tensor([[0.0037, 0.1368, -0.0091], [0.1099, 0.3329, -0.1095], [0.0765, 0.5335, 0.1179]] )

						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] ,      snake_case ,      atol=1e-4 ):
												raise ValueError

						# Verify entity hidden states
						if model_size == "large":
												__UpperCamelCase       =			torch.Size((1, 1, 1024) )
												__UpperCamelCase       =			torch.tensor([[0.0466, -0.0106, -0.0179]] )
						else:  # base
												__UpperCamelCase       =			torch.Size((1, 1, 768) )
												__UpperCamelCase       =			torch.tensor([[0.1457, 0.1044, 0.0174]] )

						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] ,      snake_case ,      atol=1e-4 ):
												raise ValueError

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




def 	A_ (							snake_case							:   Optional[Any] )      ->    Union[str, Any]:



						'''simple docstring'''

						__UpperCamelCase       =			{}
						with open(snake_case ,      '''r''' ,      encoding='''utf-8''' ) as f:
												for index, line in enumerate(snake_case ):
																		__UpperCamelCase  ,							__UpperCamelCase       =			line.rstrip().split('''\t''' )
																		__UpperCamelCase       =			index

						return entity_vocab


if __name__ == "__main__":
				lowercase__							:							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."
				)
				lowercase__							:							Any									=			parser.parse_args()
				convert_luke_checkpoint(
				    args.checkpoint_path,
				    args.metadata_path,
				    args.entity_vocab_path,
				    args.pytorch_dump_folder_path,
				    args.model_size,
				)






import argparse
import collections
import json
from pathlib import Path

import requests
import torch
import yaml
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    MobileViTImageProcessor,
    MobileViTVaConfig,
    MobileViTVaForImageClassification,
    MobileViTVaForSemanticSegmentation,
)
from transformers.utils import logging


logging.set_verbosity_info()
lowercase__							:							Union[str, Any]									=			logging.get_logger(__name__)




def 	A_ (							snake_case							:   List[str] )      ->    List[str]:



						'''simple docstring'''

						print('''Loading config file...''' )

						def flatten_yaml_as_dict(snake_case							:   Optional[int] ,      snake_case							:   List[Any]="" ,      snake_case							:   str="." ):
												__UpperCamelCase       =			[]
												for k, v in d.items():
																		__UpperCamelCase       =			parent_key + sep + k if parent_key else k
																		if isinstance(snake_case ,      collections.abc.MutableMapping ):
																								items.extend(flatten_yaml_as_dict(snake_case ,      snake_case ,      sep=snake_case ).items() )
																		else:
																								items.append((new_key, v) )
												return dict(snake_case )

						__UpperCamelCase       =			argparse.Namespace()
						with open(snake_case ,      '''r''' ) as yaml_file:
												try:
																		__UpperCamelCase       =			yaml.load(snake_case ,      Loader=yaml.FullLoader )

																		__UpperCamelCase       =			flatten_yaml_as_dict(snake_case )
																		for k, v in flat_cfg.items():
																								setattr(snake_case ,      snake_case ,      snake_case )
												except yaml.YAMLError as exc:
																		logger.error('''Error while loading config file: {}. Error message: {}'''.format(snake_case ,      str(snake_case ) ) )
						return config




def 	A_ (							snake_case							:   List[Any] ,      snake_case							:   Optional[Any] )      ->    Union[str, Any]:



						'''simple docstring'''

						__UpperCamelCase       =			MobileViTVaConfig()

						__UpperCamelCase       =			False

						# dataset
						if task_name.startswith('''imagenet1k_''' ):
												__UpperCamelCase       =			1000
												if int(task_name.strip().split('''_''' )[-1] ) == 384:
																		__UpperCamelCase       =			384
												else:
																		__UpperCamelCase       =			256
												__UpperCamelCase       =			'''imagenet-1k-id2label.json'''
						elif task_name.startswith('''imagenet21k_to_1k_''' ):
												__UpperCamelCase       =			21000
												if int(task_name.strip().split('''_''' )[-1] ) == 384:
																		__UpperCamelCase       =			384
												else:
																		__UpperCamelCase       =			256
												__UpperCamelCase       =			'''imagenet-22k-id2label.json'''
						elif task_name.startswith('''ade20k_''' ):
												__UpperCamelCase       =			151
												__UpperCamelCase       =			512
												__UpperCamelCase       =			'''ade20k-id2label.json'''
												__UpperCamelCase       =			True
						elif task_name.startswith('''voc_''' ):
												__UpperCamelCase       =			21
												__UpperCamelCase       =			512
												__UpperCamelCase       =			'''pascal-voc-id2label.json'''
												__UpperCamelCase       =			True

						# orig_config
						__UpperCamelCase       =			load_orig_config_file(snake_case )
						assert getattr(snake_case ,      '''model.classification.name''' ,      -1 ) == "mobilevit_v2", "Invalid model"
						__UpperCamelCase       =			getattr(snake_case ,      '''model.classification.mitv2.width_multiplier''' ,      1.0 )
						assert (
						    getattr(snake_case ,      '''model.classification.mitv2.attn_norm_layer''' ,      -1 ) == "layer_norm_2d"
						), "Norm layers other than layer_norm_2d is not supported"
						__UpperCamelCase       =			getattr(snake_case ,      '''model.classification.activation.name''' ,      '''swish''' )
						# config.image_size == getattr(orig_config,  'sampler.bs.crop_size_width', 256)

						if is_segmentation_model:
												__UpperCamelCase       =			getattr(snake_case ,      '''model.segmentation.output_stride''' ,      16 )
												if "_deeplabv3" in task_name:
																		__UpperCamelCase       =			getattr(snake_case ,      '''model.segmentation.deeplabv3.aspp_rates''' ,      [12, 24, 36] )
																		__UpperCamelCase       =			getattr(snake_case ,      '''model.segmentation.deeplabv3.aspp_out_channels''' ,      512 )
																		__UpperCamelCase       =			getattr(snake_case ,      '''model.segmentation.deeplabv3.aspp_dropout''' ,      0.1 )

    # id2label
						__UpperCamelCase       =			'''huggingface/label-files'''
						__UpperCamelCase       =			json.load(open(hf_hub_download(snake_case ,      snake_case ,      repo_type='''dataset''' ) ,      '''r''' ) )
						__UpperCamelCase       =			{int(snake_case ): v for k, v in idalabel.items()}
						__UpperCamelCase       =			idalabel
						__UpperCamelCase       =			{v: k for k, v in idalabel.items()}

						return config




def 	A_ (							snake_case							:   List[Any] ,      snake_case							:   int ,      snake_case							:   Any )      ->    str:



						'''simple docstring'''

						__UpperCamelCase       =			dct.pop(snake_case )
						__UpperCamelCase       =			val




def 	A_ (							snake_case							:   int ,      snake_case							:   List[Any]=False )      ->    Optional[Any]:



						'''simple docstring'''

						if base_model:
												__UpperCamelCase       =			''''''
						else:
												__UpperCamelCase       =			'''mobilevitv2.'''

						__UpperCamelCase       =			[]
						for k in state_dict.keys():
												if k[:8] == "encoder.":
																		__UpperCamelCase       =			k[8:]
												else:
																		__UpperCamelCase       =			k

												if ".block." in k:
																		__UpperCamelCase       =			k_new.replace('''.block.''' ,      '''.''' )
												if ".conv." in k:
																		__UpperCamelCase       =			k_new.replace('''.conv.''' ,      '''.convolution.''' )
												if ".norm." in k:
																		__UpperCamelCase       =			k_new.replace('''.norm.''' ,      '''.normalization.''' )

												if "conv_1." in k:
																		__UpperCamelCase       =			k_new.replace('''conv_1.''' ,      f"{model_prefix}conv_stem." )
												for i in [1, 2]:
																		if f"layer_{i}." in k:
																								__UpperCamelCase       =			k_new.replace(f"layer_{i}." ,      f"{model_prefix}encoder.layer.{i-1}.layer." )
												if ".exp_1x1." in k:
																		__UpperCamelCase       =			k_new.replace('''.exp_1x1.''' ,      '''.expand_1x1.''' )
												if ".red_1x1." in k:
																		__UpperCamelCase       =			k_new.replace('''.red_1x1.''' ,      '''.reduce_1x1.''' )

												for i in [3, 4, 5]:
																		if f"layer_{i}.0." in k:
																								__UpperCamelCase       =			k_new.replace(f"layer_{i}.0." ,      f"{model_prefix}encoder.layer.{i-1}.downsampling_layer." )
																		if f"layer_{i}.1.local_rep.0." in k:
																								__UpperCamelCase       =			k_new.replace(f"layer_{i}.1.local_rep.0." ,      f"{model_prefix}encoder.layer.{i-1}.conv_kxk." )
																		if f"layer_{i}.1.local_rep.1." in k:
																								__UpperCamelCase       =			k_new.replace(f"layer_{i}.1.local_rep.1." ,      f"{model_prefix}encoder.layer.{i-1}.conv_1x1." )

												for i in [3, 4, 5]:
																		if i == 3:
																								__UpperCamelCase       =			[0, 1]
																		elif i == 4:
																								__UpperCamelCase       =			[0, 1, 2, 3]
																		elif i == 5:
																								__UpperCamelCase       =			[0, 1, 2]

																		for j in j_in:
																								if f"layer_{i}.1.global_rep.{j}." in k:
																														__UpperCamelCase       =			k_new.replace(
																														    f"layer_{i}.1.global_rep.{j}." ,      f"{model_prefix}encoder.layer.{i-1}.transformer.layer.{j}." )
																		if f"layer_{i}.1.global_rep.{j+1}." in k:
																								__UpperCamelCase       =			k_new.replace(
																								    f"layer_{i}.1.global_rep.{j+1}." ,      f"{model_prefix}encoder.layer.{i-1}.layernorm." )

																		if f"layer_{i}.1.conv_proj." in k:
																								__UpperCamelCase       =			k_new.replace(f"layer_{i}.1.conv_proj." ,      f"{model_prefix}encoder.layer.{i-1}.conv_projection." )

												if "pre_norm_attn.0." in k:
																		__UpperCamelCase       =			k_new.replace('''pre_norm_attn.0.''' ,      '''layernorm_before.''' )
												if "pre_norm_attn.1." in k:
																		__UpperCamelCase       =			k_new.replace('''pre_norm_attn.1.''' ,      '''attention.''' )
												if "pre_norm_ffn.0." in k:
																		__UpperCamelCase       =			k_new.replace('''pre_norm_ffn.0.''' ,      '''layernorm_after.''' )
												if "pre_norm_ffn.1." in k:
																		__UpperCamelCase       =			k_new.replace('''pre_norm_ffn.1.''' ,      '''ffn.conv1.''' )
												if "pre_norm_ffn.3." in k:
																		__UpperCamelCase       =			k_new.replace('''pre_norm_ffn.3.''' ,      '''ffn.conv2.''' )

												if "classifier.1." in k:
																		__UpperCamelCase       =			k_new.replace('''classifier.1.''' ,      '''classifier.''' )

												if "seg_head." in k:
																		__UpperCamelCase       =			k_new.replace('''seg_head.''' ,      '''segmentation_head.''' )
												if ".aspp_layer." in k:
																		__UpperCamelCase       =			k_new.replace('''.aspp_layer.''' ,      '''.''' )
												if ".aspp_pool." in k:
																		__UpperCamelCase       =			k_new.replace('''.aspp_pool.''' ,      '''.''' )

												rename_keys.append((k, k_new) )
						return rename_keys




def 	A_ (							snake_case							:   List[str] )      ->    str:



						'''simple docstring'''

						__UpperCamelCase       =			[]
						for k in state_dict.keys():
												if k.startswith('''seg_head.aux_head.''' ):
																		keys_to_ignore.append(snake_case )
						for k in keys_to_ignore:
												state_dict.pop(snake_case ,      snake_case )




def 	A_ (							)      ->    str:



						'''simple docstring'''

						__UpperCamelCase       =			'''http://images.cocodataset.org/val2017/000000039769.jpg'''
						# url = "https://cdn.britannica.com/86/141086-050-9D7C75EE/Gulfstream-G450-business-jet-passengers.jpg"
						__UpperCamelCase       =			Image.open(requests.get(snake_case ,      stream=snake_case ).raw )
						return im




@torch.no_grad()
def 	A_ (							snake_case							:   Dict ,      snake_case							:   List[str] ,      snake_case							:   Optional[Any] ,      snake_case							:   Optional[int] )      ->    int:



						'''simple docstring'''

						__UpperCamelCase       =			get_mobilevitva_config(snake_case ,      snake_case )

						# load original state_dict
						__UpperCamelCase       =			torch.load(snake_case ,      map_location='''cpu''' )

						# load huggingface model
						if task_name.startswith('''ade20k_''' ) or task_name.startswith('''voc_''' ):
												__UpperCamelCase       =			MobileViTVaForSemanticSegmentation(snake_case ).eval()
												__UpperCamelCase       =			False
						else:
												__UpperCamelCase       =			MobileViTVaForImageClassification(snake_case ).eval()
												__UpperCamelCase       =			False

						# remove and rename some keys of load the original model
						__UpperCamelCase       =			checkpoint
						remove_unused_keys(snake_case )
						__UpperCamelCase       =			create_rename_keys(snake_case ,      base_model=snake_case )
						for rename_key_src, rename_key_dest in rename_keys:
												rename_key(snake_case ,      snake_case ,      snake_case )

						# load modified state_dict
						model.load_state_dict(snake_case )

						# Check outputs on an image, prepared by MobileViTImageProcessor
						__UpperCamelCase       =			MobileViTImageProcessor(crop_size=config.image_size ,      size=config.image_size + 32 )
						__UpperCamelCase       =			image_processor(images=prepare_img() ,      return_tensors='''pt''' )
						__UpperCamelCase       =			model(**snake_case )

						# verify classification model
						if task_name.startswith('''imagenet''' ):
												__UpperCamelCase       =			outputs.logits
												__UpperCamelCase       =			logits.argmax(-1 ).item()
												print('''Predicted class:''' ,      model.config.idalabel[predicted_class_idx] )
												if task_name.startswith('''imagenet1k_256''' ) and config.width_multiplier == 1.0:
																		# expected_logits for base variant
																		__UpperCamelCase       =			torch.tensor([-1.63_36e00, -7.32_04e-02, -5.18_83e-01] )
																		assert torch.allclose(logits[0, :3] ,      snake_case ,      atol=1e-4 )

						Path(snake_case ).mkdir(exist_ok=snake_case )
						print(f"Saving model {task_name} to {pytorch_dump_folder_path}" )
						model.save_pretrained(snake_case )
						print(f"Saving image processor to {pytorch_dump_folder_path}" )
						image_processor.save_pretrained(snake_case )


if __name__ == "__main__":
				lowercase__							:							Any									=			argparse.ArgumentParser()
				# Required parameters
				parser.add_argument(
				    "--task",
				    default="imagenet1k_256",
				    type=str,
				    help=(
				        "Name of the task for which the MobileViTV2 model you'd like to convert is trained on . "
				        "\n                Classification (ImageNet-1k)\n                    - MobileViTV2 (256x256) : imagenet1k_256\n                    - MobileViTV2 (Trained on 256x256 and Finetuned on 384x384) : imagenet1k_384\n                    - MobileViTV2 (Trained on ImageNet-21k and Finetuned on ImageNet-1k 256x256) :\n                      imagenet21k_to_1k_256\n                    - MobileViTV2 (Trained on ImageNet-21k, Finetuned on ImageNet-1k 256x256, and Finetuned on\n                      ImageNet-1k 384x384) : imagenet21k_to_1k_384\n                Segmentation\n                    - ADE20K Dataset : ade20k_deeplabv3\n                    - Pascal VOC 2012 Dataset: voc_deeplabv3\n            "
				    ),
				    choices=[
				        "imagenet1k_256",
				        "imagenet1k_384",
				        "imagenet21k_to_1k_256",
				        "imagenet21k_to_1k_384",
				        "ade20k_deeplabv3",
				        "voc_deeplabv3",
				    ],
				)

				parser.add_argument(
				    "--orig_checkpoint_path", required=True, type=str, help="Path to the original state dict (.pt file)."
				)
				parser.add_argument("--orig_config_path", required=True, type=str, help="Path to the original config file.")
				parser.add_argument(
				    "--pytorch_dump_folder_path", required=True, type=str, help="Path to the output PyTorch model directory."
				)

				lowercase__							:							Tuple									=			parser.parse_args()
				convert_mobilevitva_checkpoint(
				    args.task, args.orig_checkpoint_path, args.orig_config_path, args.pytorch_dump_folder_path
				)




