





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

_lowerCAmelCase							:					Union[str, Any]		    =		torch.device('''cpu''')

def    __snake_case  (    )       ->  Any:
   A_    : Optional[Any]								=	"http://images.cocodataset.org/val2017/000000039769.jpg"
   A_    : Any								=	Image.open(requests.get(lowerCAmelCase__ , stream=lowerCAmelCase__		).raw		)
   return im

def    __snake_case  (    _lowerCAmelCase       :       Tuple		)       ->  List[Any]:
   if swiftformer_name == "swiftformer_xs":
      return torch.tensor([-2.1_703e00, 2.1_107e00, -2.0_811e00, 8.8_685e-01, 2.4_360e-01]		)

   elif swiftformer_name == "swiftformer_s":
      return torch.tensor([3.9_636e-01, 2.3_478e-01, -1.6_963e00, -1.7_381e00, -8.6_337e-01]		)

   elif swiftformer_name == "swiftformer_l1":
      return torch.tensor([-4.2_768e-01, -4.7_429e-01, -1.0_897e00, -1.0_248e00, 3.5_523e-02]		)

   elif swiftformer_name == "swiftformer_l3":
      return torch.tensor([-2.5_330e-01, 2.4_211e-01, -6.0_185e-01, -8.2_789e-01, -6.0_446e-02]		)

def    __snake_case  (    _lowerCAmelCase       :       str , _lowerCAmelCase       :       Union[str, Any] , _lowerCAmelCase       :       Optional[int]		)       ->  Dict:
   A_    : str								=	dct.pop(lowerCAmelCase__		)
   A_    : List[str]								=	val

def    __snake_case  (    _lowerCAmelCase       :       Optional[Any]		)       ->  int:
   A_    : Optional[Any]								=	[]
   for k in state_dict.keys():
      A_    : int								=	k
      if ".pwconv" in k:
         A_    : str								=	k_new.replace(".pwconv" , ".point_wise_conv"		)
      if ".dwconv" in k:
         A_    : int								=	k_new.replace(".dwconv" , ".depth_wise_conv"		)
      if ".Proj." in k:
         A_    : List[str]								=	k_new.replace(".Proj." , ".proj."		)
      if "patch_embed" in k_new:
         A_    : Dict								=	k_new.replace("patch_embed" , "swiftformer.patch_embed.patch_embedding"		)
      if "network" in k_new:
         A_    : List[Any]								=	k_new.split("."		)
         if ls[2].isdigit():
            A_    : List[str]								=	"swiftformer.encoder.network." + ls[1] + ".blocks." + ls[2] + "." + ".".join(ls[3:]		)
         else:
            A_    : Union[str, Any]								=	k_new.replace("network" , "swiftformer.encoder.network"		)
      rename_keys.append((k, k_new)		)
   return rename_keys

@torch.no_grad()
def    __snake_case  (    _lowerCAmelCase       :       Tuple , _lowerCAmelCase       :       List[Any] , _lowerCAmelCase       :       Optional[int]		)       ->  Any:
   A_    : Optional[Any]								=	SwiftFormerConfig()

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

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

   elif swiftformer_name == "swiftformer_s":
      A_    : List[Any]								=	[3, 3, 9, 6]
      A_    : Optional[Any]								=	[48, 64, 168, 224]

   elif swiftformer_name == "swiftformer_l1":
      A_    : List[Any]								=	[4, 3, 10, 5]
      A_    : List[str]								=	[48, 96, 192, 384]

   elif swiftformer_name == "swiftformer_l3":
      A_    : Union[str, Any]								=	[4, 4, 12, 6]
      A_    : Dict								=	[64, 128, 320, 512]

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

   A_    : Optional[int]								=	create_rename_keys(lowerCAmelCase__		)
   for rename_key_src, rename_key_dest in rename_keys:
      rename_key(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__		)

   # load HuggingFace model
   A_    : int								=	SwiftFormerForImageClassification(lowerCAmelCase__		).eval()
   hf_model.load_state_dict(lowerCAmelCase__		)

   # prepare test inputs
   A_    : List[Any]								=	prepare_img()
   A_    : Any								=	ViTImageProcessor.from_pretrained("preprocessor_config"		)
   A_    : int								=	processor(images=lowerCAmelCase__ , return_tensors="pt"		)

   # compare outputs from both models
   A_    : List[str]								=	get_expected_output(lowerCAmelCase__		)
   A_    : str								=	hf_model(inputs["pixel_values"]		).logits

   assert hf_logits.shape == torch.Size([1, 1000]		)
   assert torch.allclose(hf_logits[0, 0:5] , lowerCAmelCase__ , atol=1e-3		)

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


if __name__ == "__main__":
 _lowerCAmelCase							:					Union[str, Any]		    =		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.''')

 _lowerCAmelCase							:					Dict		    =		parser.parse_args()
 convert_swiftformer_checkpoint(args.swiftformer_name, args.pytorch_dump_folder_path, args.original_ckpt)


import copy
import os
from typing import Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowercase__		:Dict   =    logging.get_logger(__name__)

lowercase__		:Optional[int]   =    {
    "BAAI/AltCLIP": "https://huggingface.co/BAAI/AltCLIP/resolve/main/config.json",
    # See all AltCLIP models at https://huggingface.co/models?filter=altclip
}







class 				lowercase		(					SCREAMING_SNAKE_CASE__		):
							lowercase_       :       Any       					='''altclip_text_model'''




							def __init__(				self   ,A__=2_5_0_0_0_2   ,A__=1_0_2_4   ,A__=2_4   ,A__=1_6   ,A__=4_0_9_6   ,A__="gelu"   ,A__=0.1   ,A__=0.1   ,A__=5_1_4   ,A__=1   ,A__=0.02   ,A__=0.02   ,A__=1E-05   ,A__=1   ,A__=0   ,A__=2   ,A__="absolute"   ,A__=True   ,A__=7_6_8   ,**A__   ,):
												super().__init__(pad_token_id=A__   ,bos_token_id=A__   ,eos_token_id=A__   ,**A__)

												lowercase							=						vocab_size
												lowercase							=						hidden_size
												lowercase							=						num_hidden_layers
												lowercase							=						num_attention_heads
												lowercase							=						hidden_act
												lowercase							=						intermediate_size
												lowercase							=						hidden_dropout_prob
												lowercase							=						attention_probs_dropout_prob
												lowercase							=						max_position_embeddings
												lowercase							=						type_vocab_size
												lowercase							=						initializer_range
												lowercase							=						initializer_factor
												lowercase							=						layer_norm_eps
												lowercase							=						position_embedding_type
												lowercase							=						use_cache
												lowercase							=						project_dim







class 				lowercase		(					SCREAMING_SNAKE_CASE__		):
							lowercase_       :       Optional[int]       					='''altclip_vision_model'''




							def __init__(				self   ,A__=7_6_8   ,A__=3_0_7_2   ,A__=5_1_2   ,A__=1_2   ,A__=1_2   ,A__=3   ,A__=2_2_4   ,A__=3_2   ,A__="quick_gelu"   ,A__=1E-5   ,A__=0.0   ,A__=0.02   ,A__=1.0   ,**A__   ,):
												super().__init__(**A__)

												lowercase							=						hidden_size
												lowercase							=						intermediate_size
												lowercase							=						projection_dim
												lowercase							=						num_hidden_layers
												lowercase							=						num_attention_heads
												lowercase							=						num_channels
												lowercase							=						patch_size
												lowercase							=						image_size
												lowercase							=						initializer_range
												lowercase							=						initializer_factor
												lowercase							=						attention_dropout
												lowercase							=						layer_norm_eps
												lowercase							=						hidden_act





							@classmethod
							def  A__      (				cls   ,A__   ,**A__):
												cls._set_token_in_kwargs(A__)

												lowercase	,						lowercase							=						cls.get_config_dict(A__   ,**A__)

												# get the vision config dict if we are loading from AltCLIPConfig
												if config_dict.get('''model_type''') == "altclip":
																	lowercase							=						config_dict['''vision_config''']

												if "model_type" in config_dict and hasattr(cls   ,'''model_type''') and config_dict["model_type"] != cls.model_type:
																	logger.warning(
																	    f'You are using a model of type {config_dict["model_type"]} to instantiate a model of type '
																	    f'{cls.model_type}. This is not supported for all configurations of models and can yield errors.')

												return cls.from_dict(A__   ,**A__)







class 				lowercase		(					SCREAMING_SNAKE_CASE__		):
							lowercase_       :       Any       					='''altclip'''
							lowercase_       :       List[Any]       					=True




							def __init__(				self   ,A__=None   ,A__=None   ,A__=7_6_8   ,A__=2.6592   ,**A__):
												# If `_config_dict` exist, we use them for the backward compatibility.
												# We pop out these 2 attributes before calling `super().__init__` to avoid them being saved (which causes a lot
												# of confusion!).
												lowercase							=						kwargs.pop('''text_config_dict'''   ,A__)
												lowercase							=						kwargs.pop('''vision_config_dict'''   ,A__)

												super().__init__(**A__)

												# Instead of simply assigning `[text|vision]_config_dict` to `[text|vision]_config`, we use the values in
												# `[text|vision]_config_dict` to update the values in `[text|vision]_config`. The values should be same in most
												# cases, but we don't want to break anything regarding `_config_dict` that existed before commit `8827e1b2`.
												if text_config_dict is not None:
																	if text_config is None:
																						lowercase							=						{}

																	# This is the complete result when using `text_config_dict`.
																	lowercase							=						AltCLIPTextConfig(**A__).to_dict()

																	# Give a warning if the values exist in both `_text_config_dict` and `text_config` but being different.
																	for key, value in _text_config_dict.items():
																						if key in text_config and value != text_config[key] and key not in ["transformers_version"]:
																											# If specified in `text_config_dict`
																											if key in text_config_dict:
																																lowercase							=						(
																																    f'`{key}` is found in both `text_config_dict` and `text_config` but with different values. '
																																    f'The value `text_config_dict["{key}"]` will be used instead.'
																																)
																											# If inferred from default argument values (just to be super careful)
																											else:
																																lowercase							=						(
																																    f'`text_config_dict` is provided which will be used to initialize `AltCLIPTextConfig`. The '
																																    f'value `text_config["{key}"]` will be overriden.'
																																)
																											logger.warning(A__)

            # Update all values in `text_config` with the ones in `_text_config_dict`.
																	text_config.update(_text_config_dict)

												if vision_config_dict is not None:
																	if vision_config is None:
																						lowercase							=						{}

																	# This is the complete result when using `vision_config_dict`.
																	lowercase							=						AltCLIPVisionConfig(**A__).to_dict()
																	# convert keys to string instead of integer
																	if "id2label" in _vision_config_dict:
																						lowercase							=						{
																						    str(A__): value for key, value in _vision_config_dict['''id2label'''].items()
																						}

																	# Give a warning if the values exist in both `_vision_config_dict` and `vision_config` but being different.
																	for key, value in _vision_config_dict.items():
																						if key in vision_config and value != vision_config[key] and key not in ["transformers_version"]:
																											# If specified in `vision_config_dict`
																											if key in vision_config_dict:
																																lowercase							=						(
																																    f'`{key}` is found in both `vision_config_dict` and `vision_config` but with different '
																																    f'values. The value `vision_config_dict["{key}"]` will be used instead.'
																																)
																											# If inferred from default argument values (just to be super careful)
																											else:
																																lowercase							=						(
																																    f'`vision_config_dict` is provided which will be used to initialize `AltCLIPVisionConfig`. '
																																    f'The value `vision_config["{key}"]` will be overriden.'
																																)
																											logger.warning(A__)

            # Update all values in `vision_config` with the ones in `_vision_config_dict`.
																	vision_config.update(_vision_config_dict)

												if text_config is None:
																	lowercase							=						{}
																	logger.info('''`text_config` is `None`. Initializing the `AltCLIPTextConfig` with default values.''')

												if vision_config is None:
																	lowercase							=						{}
																	logger.info('''`vision_config` is `None`. initializing the `AltCLIPVisionConfig` with default values.''')

												lowercase							=						AltCLIPTextConfig(**A__)
												lowercase							=						AltCLIPVisionConfig(**A__)

												lowercase							=						projection_dim
												lowercase							=						logit_scale_init_value
												lowercase							=						1.0




							@classmethod
							def  A__      (				cls   ,A__   ,A__   ,**A__):
												return cls(text_config=text_config.to_dict()   ,vision_config=vision_config.to_dict()   ,**A__)





							def  A__      (				self):
												lowercase							=						copy.deepcopy(self.__dict__)
												lowercase							=						self.text_config.to_dict()
												lowercase							=						self.vision_config.to_dict()
												lowercase							=						self.__class__.model_type
												return output




