



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()
SCREAMING_SNAKE_CASE     				=						logging.get_logger(__name__)

SCREAMING_SNAKE_CASE     				=						torch.device("cpu")







def snake_case__  (			)		->							Optional[int]:
		UpperCAmelCase_						=				"http://images.cocodataset.org/val2017/000000039769.jpg"
		UpperCAmelCase_						=				Image.open(requests.get(__SCREAMING_SNAKE_CASE			,     stream=__SCREAMING_SNAKE_CASE ).raw )
		return im







def snake_case__  (			__SCREAMING_SNAKE_CASE )		->							Tuple:
		if swiftformer_name == "swiftformer_xs":
				return torch.tensor([-2.17_03E00, 2.11_07E00, -2.08_11E00, 8.86_85E-01, 2.43_60E-01] )

		elif swiftformer_name == "swiftformer_s":
				return torch.tensor([3.96_36E-01, 2.34_78E-01, -1.69_63E00, -1.73_81E00, -8.63_37E-01] )

		elif swiftformer_name == "swiftformer_l1":
				return torch.tensor([-4.27_68E-01, -4.74_29E-01, -1.08_97E00, -1.02_48E00, 3.55_23E-02] )

		elif swiftformer_name == "swiftformer_l3":
				return torch.tensor([-2.53_30E-01, 2.42_11E-01, -6.01_85E-01, -8.27_89E-01, -6.04_46E-02] )







def snake_case__  (			__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE )		->							Dict:
		UpperCAmelCase_						=				dct.pop(__SCREAMING_SNAKE_CASE )
		UpperCAmelCase_						=				val







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







@torch.no_grad()
def snake_case__  (			__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE )		->							List[str]:
		UpperCAmelCase_						=				SwiftFormerConfig()

		# dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
		UpperCAmelCase_						=				1000
		UpperCAmelCase_						=				"huggingface/label-files"
		UpperCAmelCase_						=				"imagenet-1k-id2label.json"
		UpperCAmelCase_						=				json.load(open(hf_hub_download(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     repo_type="dataset" )			,     "r" ) )
		UpperCAmelCase_						=				{int(__SCREAMING_SNAKE_CASE ): v for k, v in idalabel.items()}
		UpperCAmelCase_						=				idalabel
		UpperCAmelCase_						=				{v: k for k, v in idalabel.items()}

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

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

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

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

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

		UpperCAmelCase_						=				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
		UpperCAmelCase_						=				SwiftFormerForImageClassification(__SCREAMING_SNAKE_CASE ).eval()
		hf_model.load_state_dict(__SCREAMING_SNAKE_CASE )

		# prepare test inputs
		UpperCAmelCase_						=				prepare_img()
		UpperCAmelCase_						=				ViTImageProcessor.from_pretrained("preprocessor_config" )
		UpperCAmelCase_						=				processor(images=__SCREAMING_SNAKE_CASE			,     return_tensors="pt" )

		# compare outputs from both models
		UpperCAmelCase_						=				get_expected_output(__SCREAMING_SNAKE_CASE )
		UpperCAmelCase_						=				hf_model(inputs["pixel_values"] ).logits

		assert hf_logits.shape == torch.Size([1, 1000] )
		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__":
			SCREAMING_SNAKE_CASE     				=						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.")

			SCREAMING_SNAKE_CASE     				=						parser.parse_args()
			convert_swiftformer_checkpoint(args.swiftformer_name, args.pytorch_dump_folder_path, args.original_ckpt)






import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ....tokenization_utils_fast import PreTrainedTokenizerFast
from ....utils import logging
from .tokenization_retribert import RetriBertTokenizer


SCREAMING_SNAKE_CASE     				=						logging.get_logger(__name__)

SCREAMING_SNAKE_CASE     				=						{"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}

SCREAMING_SNAKE_CASE     				=						{
    "vocab_file": {
        "yjernite/retribert-base-uncased": (
            "https://huggingface.co/yjernite/retribert-base-uncased/resolve/main/vocab.txt"
        ),
    },
    "tokenizer_file": {
        "yjernite/retribert-base-uncased": (
            "https://huggingface.co/yjernite/retribert-base-uncased/resolve/main/tokenizer.json"
        ),
    },
}

SCREAMING_SNAKE_CASE     				=						{
    "yjernite/retribert-base-uncased": 512,
}


SCREAMING_SNAKE_CASE     				=						{
    "yjernite/retribert-base-uncased": {"do_lower_case": True},
}



class 	lowerCamelCase      (						lowercase__  ):







					'''simple docstring'''







					lowerCAmelCase_				:    List[str]									=  VOCAB_FILES_NAMES
					lowerCAmelCase_				:    int									=  PRETRAINED_VOCAB_FILES_MAP
					lowerCAmelCase_				:    Any									=  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					lowerCAmelCase_				:    Dict									=  PRETRAINED_INIT_CONFIGURATION
					lowerCAmelCase_				:    List[str]									=  RetriBertTokenizer
					lowerCAmelCase_				:    Union[str, Any]									=  ['input_ids', 'attention_mask']



					def __init__(			self    ,	lowerCAmelCase=None    ,	lowerCAmelCase=None    ,	lowerCAmelCase=True    ,	lowerCAmelCase="[UNK]"    ,	lowerCAmelCase="[SEP]"    ,	lowerCAmelCase="[PAD]"    ,	lowerCAmelCase="[CLS]"    ,	lowerCAmelCase="[MASK]"    ,	lowerCAmelCase=True    ,	lowerCAmelCase=None    ,	**lowerCAmelCase    ,	):
							super().__init__(
							    lowerCAmelCase    ,	tokenizer_file=lowerCAmelCase    ,	do_lower_case=lowerCAmelCase    ,	unk_token=lowerCAmelCase    ,	sep_token=lowerCAmelCase    ,	pad_token=lowerCAmelCase    ,	cls_token=lowerCAmelCase    ,	mask_token=lowerCAmelCase    ,	tokenize_chinese_chars=lowerCAmelCase    ,	strip_accents=lowerCAmelCase    ,	**lowerCAmelCase    ,	)

							UpperCAmelCase_						=				json.loads(self.backend_tokenizer.normalizer.__getstate__()     )
							if (
							    normalizer_state.get("lowercase"    ,	lowerCAmelCase     ) != do_lower_case
							    or normalizer_state.get("strip_accents"    ,	lowerCAmelCase     ) != strip_accents
							    or normalizer_state.get("handle_chinese_chars"    ,	lowerCAmelCase     ) != tokenize_chinese_chars
							):
									UpperCAmelCase_						=				getattr(lowerCAmelCase    ,	normalizer_state.pop("type"     )     )
									UpperCAmelCase_						=				do_lower_case
									UpperCAmelCase_						=				strip_accents
									UpperCAmelCase_						=				tokenize_chinese_chars
									UpperCAmelCase_						=				normalizer_class(**lowerCAmelCase     )

							UpperCAmelCase_						=				do_lower_case



					def    A__		(			self    ,	lowerCAmelCase    ,	lowerCAmelCase=None     ):
							UpperCAmelCase_						=				[self.cls_token_id] + token_ids_a + [self.sep_token_id]

							if token_ids_a:
									output += token_ids_a + [self.sep_token_id]

							return output



					def    A__		(			self    ,	lowerCAmelCase    ,	lowerCAmelCase = None     ):
							UpperCAmelCase_						=				[self.sep_token_id]
							UpperCAmelCase_						=				[self.cls_token_id]
							if token_ids_a is None:
									return len(cls + token_ids_a + sep     ) * [0]
							return len(cls + token_ids_a + sep     ) * [0] + len(token_ids_a + sep     ) * [1]



					def    A__		(			self    ,	lowerCAmelCase    ,	lowerCAmelCase = None     ):
							UpperCAmelCase_						=				self._tokenizer.model.save(lowerCAmelCase    ,	name=lowerCAmelCase     )
							return tuple(lowerCAmelCase     )

