

import argparse
import json
import os
from collections import OrderedDict

import torch

from transformers import LukeConfig, LukeForMaskedLM, MLukeTokenizer, XLMRobertaTokenizer
from transformers.tokenization_utils_base import AddedToken


@torch.no_grad()
def 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		str ,   _SCREAMING_SNAKE_CASE					:		Optional[Any] ,   _SCREAMING_SNAKE_CASE					:		Union[str, Any] ,   _SCREAMING_SNAKE_CASE					:		int ,   _SCREAMING_SNAKE_CASE					:		str       ):





				"""simple docstring"""


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

				# Load in the weights from the checkpoint_path
				SCREAMING_SNAKE_CASE_									=						torch.load(__snake_case ,   map_location='cpu'       )["module"]

				# Load the entity vocab file
				SCREAMING_SNAKE_CASE_									=						load_original_entity_vocab(__snake_case       )
				# add an entry for [MASK2]
				SCREAMING_SNAKE_CASE_									=						max(entity_vocab.values()       ) + 1
				config.entity_vocab_size += 1

				SCREAMING_SNAKE_CASE_									=						XLMRobertaTokenizer.from_pretrained(metadata['model_config']['bert_model_name']       )

				# Add special tokens to the token vocabulary for downstream tasks
				SCREAMING_SNAKE_CASE_									=						AddedToken('<ent>' ,   lstrip=__snake_case ,   rstrip=__snake_case       )
				SCREAMING_SNAKE_CASE_									=						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 ,   'tokenizer_config.json'       ) ,   'r'       ) as f:
								SCREAMING_SNAKE_CASE_									=						json.load(__snake_case       )
				SCREAMING_SNAKE_CASE_									=						"MLukeTokenizer"
				with open(os.path.join(__snake_case ,   'tokenizer_config.json'       ) ,   'w'       ) as f:
								json.dump(__snake_case ,   __snake_case       )

				with open(os.path.join(__snake_case ,   MLukeTokenizer.vocab_files_names['entity_vocab_file']       ) ,   'w'       ) as f:
								json.dump(__snake_case ,   __snake_case       )

				SCREAMING_SNAKE_CASE_									=						MLukeTokenizer.from_pretrained(__snake_case       )

				# Initialize the embeddings of the special tokens
				SCREAMING_SNAKE_CASE_									=						tokenizer.convert_tokens_to_ids(['@']       )[0]
				SCREAMING_SNAKE_CASE_									=						tokenizer.convert_tokens_to_ids(['#']       )[0]

				SCREAMING_SNAKE_CASE_									=						state_dict["embeddings.word_embeddings.weight"]
				SCREAMING_SNAKE_CASE_									=						word_emb[ent_init_index].unsqueeze(0       )
				SCREAMING_SNAKE_CASE_									=						word_emb[enta_init_index].unsqueeze(0       )
				SCREAMING_SNAKE_CASE_									=						torch.cat([word_emb, ent_emb, enta_emb]       )
				# add special tokens for 'entity_predictions.bias'
				for bias_name in ["lm_head.decoder.bias", "lm_head.bias"]:
								SCREAMING_SNAKE_CASE_									=						state_dict[bias_name]
								SCREAMING_SNAKE_CASE_									=						decoder_bias[ent_init_index].unsqueeze(0       )
								SCREAMING_SNAKE_CASE_									=						decoder_bias[enta_init_index].unsqueeze(0       )
								SCREAMING_SNAKE_CASE_									=						torch.cat([decoder_bias, ent_decoder_bias, enta_decoder_bias]       )

				# 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"]:
												SCREAMING_SNAKE_CASE_									=						f"""encoder.layer.{layer_index}.attention.self."""
												SCREAMING_SNAKE_CASE_									=						state_dict[prefix + matrix_name]
												SCREAMING_SNAKE_CASE_									=						state_dict[prefix + matrix_name]
												SCREAMING_SNAKE_CASE_									=						state_dict[prefix + matrix_name]

    # Initialize the embedding of the [MASK2] entity using that of the [MASK] entity for downstream tasks
				SCREAMING_SNAKE_CASE_									=						state_dict["entity_embeddings.entity_embeddings.weight"]
				SCREAMING_SNAKE_CASE_									=						entity_emb[entity_vocab["[MASK]"]].unsqueeze(0       )
				SCREAMING_SNAKE_CASE_									=						torch.cat([entity_emb, entity_mask_emb]       )
				# add [MASK2] for 'entity_predictions.bias'
				SCREAMING_SNAKE_CASE_									=						state_dict["entity_predictions.bias"]
				SCREAMING_SNAKE_CASE_									=						entity_prediction_bias[entity_vocab["[MASK]"]].unsqueeze(0       )
				SCREAMING_SNAKE_CASE_									=						torch.cat([entity_prediction_bias, entity_mask_bias]       )

				SCREAMING_SNAKE_CASE_									=						LukeForMaskedLM(config=__snake_case       ).eval()

				state_dict.pop('entity_predictions.decoder.weight'       )
				state_dict.pop('lm_head.decoder.weight'       )
				state_dict.pop('lm_head.decoder.bias'       )
				SCREAMING_SNAKE_CASE_									=						OrderedDict()
				for key, value in state_dict.items():
								if not (key.startswith('lm_head'       ) or key.startswith('entity_predictions'       )):
												SCREAMING_SNAKE_CASE_									=						state_dict[key]
								else:
												SCREAMING_SNAKE_CASE_									=						state_dict[key]

				SCREAMING_SNAKE_CASE_									=						model.load_state_dict(__snake_case ,   strict=__snake_case       )

				if set(__snake_case       ) != {"luke.embeddings.position_ids"}:
								raise ValueError(f"""Unexpected unexpected_keys: {unexpected_keys}"""       )
				if set(__snake_case       ) != {
				    "lm_head.decoder.weight",
				    "lm_head.decoder.bias",
				    "entity_predictions.decoder.weight",
				}:
								raise ValueError(f"""Unexpected missing_keys: {missing_keys}"""       )

				model.tie_weights()
				assert (model.luke.embeddings.word_embeddings.weight == model.lm_head.decoder.weight).all()
				assert (model.luke.entity_embeddings.entity_embeddings.weight == model.entity_predictions.decoder.weight).all()

				# Check outputs
				SCREAMING_SNAKE_CASE_									=						MLukeTokenizer.from_pretrained(__snake_case ,   task='entity_classification'       )

				SCREAMING_SNAKE_CASE_									=						"ISO 639-3 uses the code fas for the dialects spoken across Iran and アフガニスタン (Afghanistan)."
				SCREAMING_SNAKE_CASE_									=						(0, 9)
				SCREAMING_SNAKE_CASE_									=						tokenizer(__snake_case ,   entity_spans=[span] ,   return_tensors='pt'       )

				SCREAMING_SNAKE_CASE_									=						model(**__snake_case       )

				# Verify word hidden states
				if model_size == "large":
								raise NotImplementedError
				else:  # base
								SCREAMING_SNAKE_CASE_									=						torch.Size((1, 33, 768)       )
								SCREAMING_SNAKE_CASE_									=						torch.tensor([[0.0892, 0.0596, -0.2819], [0.0134, 0.1199, 0.0573], [-0.0169, 0.0927, 0.0644]]       )

				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":
								raise NotImplementedError
				else:  # base
								SCREAMING_SNAKE_CASE_									=						torch.Size((1, 1, 768)       )
								SCREAMING_SNAKE_CASE_									=						torch.tensor([[-0.1482, 0.0609, 0.0322]]       )

				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

				# Verify masked word/entity prediction
				SCREAMING_SNAKE_CASE_									=						MLukeTokenizer.from_pretrained(__snake_case       )
				SCREAMING_SNAKE_CASE_									=						"Tokyo is the capital of <mask>."
				SCREAMING_SNAKE_CASE_									=						(24, 30)
				SCREAMING_SNAKE_CASE_									=						tokenizer(__snake_case ,   entity_spans=[span] ,   return_tensors='pt'       )

				SCREAMING_SNAKE_CASE_									=						model(**__snake_case       )

				SCREAMING_SNAKE_CASE_									=						encoding["input_ids"][0].tolist()
				SCREAMING_SNAKE_CASE_									=						input_ids.index(tokenizer.convert_tokens_to_ids('<mask>'       )       )
				SCREAMING_SNAKE_CASE_									=						outputs.logits[0][mask_position_id].argmax(dim=-1       )
				assert "Japan" == tokenizer.decode(__snake_case       )

				SCREAMING_SNAKE_CASE_									=						outputs.entity_logits[0][0].argmax().item()
				SCREAMING_SNAKE_CASE_									=						[
				    entity for entity, entity_id in tokenizer.entity_vocab.items() if entity_id == predicted_entity_id
				]
				assert [e for e in multilingual_predicted_entities if e.startswith('en:'       )][0] == "en:Japan"

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


def 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		Any       ):





				"""simple docstring"""


				SCREAMING_SNAKE_CASE_									=						["[MASK]", "[PAD]", "[UNK]"]

				SCREAMING_SNAKE_CASE_									=						[json.loads(__snake_case       ) for line in open(__snake_case       )]

				SCREAMING_SNAKE_CASE_									=						{}
				for entry in data:
								SCREAMING_SNAKE_CASE_									=						entry["id"]
								for entity_name, language in entry["entities"]:
												if entity_name in SPECIAL_TOKENS:
																SCREAMING_SNAKE_CASE_									=						entity_id
																break
												SCREAMING_SNAKE_CASE_									=						f"""{language}:{entity_name}"""
												SCREAMING_SNAKE_CASE_									=						entity_id
				return new_mapping


if __name__ == "__main__":
							UpperCamelCase__  :      Union[str, 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__  :      Tuple			=    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 json
from pathlib import Path

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

from transformers import (
    SwiftFormerConfig,
    SwiftFormerForImageClassification,
    ViTImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
UpperCamelCase__  :      Optional[int]			=    logging.get_logger(__name__)

UpperCamelCase__  :      List[Any]			=    torch.device("cpu")


def 					_UpperCAmelCase       (  ):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_									=						'http://images.cocodataset.org/val2017/000000039769.jpg'
    SCREAMING_SNAKE_CASE_									=						Image.open(requests.get(_SCREAMING_SNAKE_CASE ,   stream=_SCREAMING_SNAKE_CASE       ).raw       )
    return im


def 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		int       ):





    """simple docstring"""


    if swiftformer_name == "swiftformer_xs":
        return torch.tensor([-2.1_7_0_3E0_0, 2.1_1_0_7E0_0, -2.0_8_1_1E0_0, 8.8_6_8_5E-0_1, 2.4_3_6_0E-0_1]       )

    elif swiftformer_name == "swiftformer_s":
        return torch.tensor([3.9_6_3_6E-0_1, 2.3_4_7_8E-0_1, -1.6_9_6_3E0_0, -1.7_3_8_1E0_0, -8.6_3_3_7E-0_1]       )

    elif swiftformer_name == "swiftformer_l1":
        return torch.tensor([-4.2_7_6_8E-0_1, -4.7_4_2_9E-0_1, -1.0_8_9_7E0_0, -1.0_2_4_8E0_0, 3.5_5_2_3E-0_2]       )

    elif swiftformer_name == "swiftformer_l3":
        return torch.tensor([-2.5_3_3_0E-0_1, 2.4_2_1_1E-0_1, -6.0_1_8_5E-0_1, -8.2_7_8_9E-0_1, -6.0_4_4_6E-0_2]       )


def 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		Optional[int] ,   _SCREAMING_SNAKE_CASE					:		Union[str, Any] ,   _SCREAMING_SNAKE_CASE					:		Optional[Any]       ):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_									=						dct.pop(_SCREAMING_SNAKE_CASE       )
    SCREAMING_SNAKE_CASE_									=						val


def 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		Dict       ):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_									=						[]
    for k in state_dict.keys():
        SCREAMING_SNAKE_CASE_									=						k
        if ".pwconv" in k:
            SCREAMING_SNAKE_CASE_									=						k_new.replace('.pwconv' ,   '.point_wise_conv'       )
        if ".dwconv" in k:
            SCREAMING_SNAKE_CASE_									=						k_new.replace('.dwconv' ,   '.depth_wise_conv'       )
        if ".Proj." in k:
            SCREAMING_SNAKE_CASE_									=						k_new.replace('.Proj.' ,   '.proj.'       )
        if "patch_embed" in k_new:
            SCREAMING_SNAKE_CASE_									=						k_new.replace('patch_embed' ,   'swiftformer.patch_embed.patch_embedding'       )
        if "network" in k_new:
            SCREAMING_SNAKE_CASE_									=						k_new.split('.'       )
            if ls[2].isdigit():
                SCREAMING_SNAKE_CASE_									=						'swiftformer.encoder.network.' + ls[1] + '.blocks.' + ls[2] + '.' + '.'.join(ls[3:]       )
            else:
                SCREAMING_SNAKE_CASE_									=						k_new.replace('network' ,   'swiftformer.encoder.network'       )
        rename_keys.append((k, k_new)       )
    return rename_keys


@torch.no_grad()
def 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		Optional[Any] ,   _SCREAMING_SNAKE_CASE					:		str ,   _SCREAMING_SNAKE_CASE					:		Optional[Any]       ):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_									=						SwiftFormerConfig()

    # dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
    SCREAMING_SNAKE_CASE_									=						1_000
    SCREAMING_SNAKE_CASE_									=						'huggingface/label-files'
    SCREAMING_SNAKE_CASE_									=						'imagenet-1k-id2label.json'
    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()}

    # size of the architecture
    if swiftformer_name == "swiftformer_xs":
        SCREAMING_SNAKE_CASE_									=						[3, 3, 6, 4]
        SCREAMING_SNAKE_CASE_									=						[48, 56, 112, 220]

    elif swiftformer_name == "swiftformer_s":
        SCREAMING_SNAKE_CASE_									=						[3, 3, 9, 6]
        SCREAMING_SNAKE_CASE_									=						[48, 64, 168, 224]

    elif swiftformer_name == "swiftformer_l1":
        SCREAMING_SNAKE_CASE_									=						[4, 3, 10, 5]
        SCREAMING_SNAKE_CASE_									=						[48, 96, 192, 384]

    elif swiftformer_name == "swiftformer_l3":
        SCREAMING_SNAKE_CASE_									=						[4, 4, 12, 6]
        SCREAMING_SNAKE_CASE_									=						[64, 128, 320, 512]

    # load state_dict of original model, remove and rename some keys
    if original_ckpt:
        if original_ckpt.startswith('https'       ):
            SCREAMING_SNAKE_CASE_									=						torch.hub.load_state_dict_from_url(_SCREAMING_SNAKE_CASE ,   map_location='cpu' ,   check_hash=_SCREAMING_SNAKE_CASE       )
        else:
            SCREAMING_SNAKE_CASE_									=						torch.load(_SCREAMING_SNAKE_CASE ,   map_location='cpu'       )
    SCREAMING_SNAKE_CASE_									=						checkpoint

    SCREAMING_SNAKE_CASE_									=						create_rename_keys(_SCREAMING_SNAKE_CASE       )
    for rename_key_src, rename_key_dest in rename_keys:
        rename_key(_SCREAMING_SNAKE_CASE ,   _SCREAMING_SNAKE_CASE ,   _SCREAMING_SNAKE_CASE       )

    # load HuggingFace model
    SCREAMING_SNAKE_CASE_									=						SwiftFormerForImageClassification(_SCREAMING_SNAKE_CASE       ).eval()
    hf_model.load_state_dict(_SCREAMING_SNAKE_CASE       )

    # prepare test inputs
    SCREAMING_SNAKE_CASE_									=						prepare_img()
    SCREAMING_SNAKE_CASE_									=						ViTImageProcessor.from_pretrained('preprocessor_config'       )
    SCREAMING_SNAKE_CASE_									=						processor(images=_SCREAMING_SNAKE_CASE ,   return_tensors='pt'       )

    # compare outputs from both models
    SCREAMING_SNAKE_CASE_									=						get_expected_output(_SCREAMING_SNAKE_CASE       )
    SCREAMING_SNAKE_CASE_									=						hf_model(inputs['pixel_values']       ).logits

    assert hf_logits.shape == torch.Size([1, 1_000]       )
    assert torch.allclose(hf_logits[0, 0:5] ,   _SCREAMING_SNAKE_CASE ,   atol=1E-3       )

    Path(_SCREAMING_SNAKE_CASE       ).mkdir(exist_ok=_SCREAMING_SNAKE_CASE       )
    print(f"""Saving model {swiftformer_name} to {pytorch_dump_folder_path}"""       )
    hf_model.save_pretrained(_SCREAMING_SNAKE_CASE       )


if __name__ == "__main__":
       UpperCamelCase__  :      str			=    argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           "--swiftformer_name",
           default="swiftformer_xs",
           choices=["swiftformer_xs", "swiftformer_s", "swiftformer_l1", "swiftformer_l3"],
           type=str,
           help="Name of the SwiftFormer model you'd like to convert.",
       )
       parser.add_argument(
           "--pytorch_dump_folder_path",
           default="./converted_outputs/",
           type=str,
           help="Path to the output PyTorch model directory.",
       )
       parser.add_argument("--original_ckpt", default=None, type=str, help="Path to the original model checkpoint.")

       UpperCamelCase__  :      Union[str, Any]			=    parser.parse_args()
       convert_swiftformer_checkpoint(args.swiftformer_name, args.pytorch_dump_folder_path, args.original_ckpt)


