


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()
a_ :Optional[Any]  							=			logging.get_logger(__name__)

a_ :str  							=			torch.device('cpu')




def    a   (							)	-> int:




      '''simple docstring'''

      SCREAMING_SNAKE_CASE__       :  Union[str, Any]						     =		'''http://images.cocodataset.org/val2017/000000039769.jpg'''
      SCREAMING_SNAKE_CASE__       :  Dict						     =		Image.open(requests.get(A__      ,    stream=A__							).raw							)
      return im




def    a   (							A__							)	-> List[str]:




      '''simple docstring'''

      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    a   (							A__      ,    A__      ,    A__							)	-> Any:




      '''simple docstring'''

      SCREAMING_SNAKE_CASE__       :  Dict						     =		dct.pop(A__							)
      SCREAMING_SNAKE_CASE__       :  Optional[Any]						     =		val




def    a   (							A__							)	-> int:




      '''simple docstring'''

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




@torch.no_grad()
def    a   (							A__      ,    A__      ,    A__							)	-> str:




      '''simple docstring'''

      SCREAMING_SNAKE_CASE__       :  int						     =		SwiftFormerConfig()

      # dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
      SCREAMING_SNAKE_CASE__       :  Tuple						     =		1_0_0_0
      SCREAMING_SNAKE_CASE__       :  Optional[Any]						     =		'''huggingface/label-files'''
      SCREAMING_SNAKE_CASE__       :  Optional[int]						     =		'''imagenet-1k-id2label.json'''
      SCREAMING_SNAKE_CASE__       :  Any						     =		json.load(open(hf_hub_download(A__      ,    A__      ,    repo_type='''dataset'''							)      ,    '''r'''							)							)
      SCREAMING_SNAKE_CASE__       :  List[Any]						     =		{int(A__							): v for k, v in idalabel.items()}
      SCREAMING_SNAKE_CASE__       :  List[str]						     =		idalabel
      SCREAMING_SNAKE_CASE__       :  str						     =		{v: k for k, v in idalabel.items()}

      # size of the architecture
      if swiftformer_name == "swiftformer_xs":
            SCREAMING_SNAKE_CASE__       :  Union[str, Any]						     =		[3, 3, 6, 4]
            SCREAMING_SNAKE_CASE__       :  Union[str, Any]						     =		[4_8, 5_6, 1_1_2, 2_2_0]

      elif swiftformer_name == "swiftformer_s":
            SCREAMING_SNAKE_CASE__       :  Tuple						     =		[3, 3, 9, 6]
            SCREAMING_SNAKE_CASE__       :  List[str]						     =		[4_8, 6_4, 1_6_8, 2_2_4]

      elif swiftformer_name == "swiftformer_l1":
            SCREAMING_SNAKE_CASE__       :  Any						     =		[4, 3, 1_0, 5]
            SCREAMING_SNAKE_CASE__       :  int						     =		[4_8, 9_6, 1_9_2, 3_8_4]

      elif swiftformer_name == "swiftformer_l3":
            SCREAMING_SNAKE_CASE__       :  Optional[Any]						     =		[4, 4, 1_2, 6]
            SCREAMING_SNAKE_CASE__       :  Dict						     =		[6_4, 1_2_8, 3_2_0, 5_1_2]

      # load state_dict of original model, remove and rename some keys
      if original_ckpt:
            if original_ckpt.startswith('''https'''							):
                  SCREAMING_SNAKE_CASE__       :  List[Any]						     =		torch.hub.load_state_dict_from_url(A__      ,    map_location='''cpu'''      ,    check_hash=A__							)
            else:
                  SCREAMING_SNAKE_CASE__       :  Tuple						     =		torch.load(A__      ,    map_location='''cpu'''							)
      SCREAMING_SNAKE_CASE__       :  Optional[int]						     =		checkpoint

      SCREAMING_SNAKE_CASE__       :  Dict						     =		create_rename_keys(A__							)
      for rename_key_src, rename_key_dest in rename_keys:
            rename_key(A__      ,    A__      ,    A__							)

      # load HuggingFace model
      SCREAMING_SNAKE_CASE__       :  str						     =		SwiftFormerForImageClassification(A__							).eval()
      hf_model.load_state_dict(A__							)

      # prepare test inputs
      SCREAMING_SNAKE_CASE__       :  Optional[Any]						     =		prepare_img()
      SCREAMING_SNAKE_CASE__       :  Optional[Any]						     =		ViTImageProcessor.from_pretrained('''preprocessor_config'''							)
      SCREAMING_SNAKE_CASE__       :  str						     =		processor(images=A__      ,    return_tensors='''pt'''							)

      # compare outputs from both models
      SCREAMING_SNAKE_CASE__       :  Union[str, Any]						     =		get_expected_output(A__							)
      SCREAMING_SNAKE_CASE__       :  Optional[Any]						     =		hf_model(inputs['''pixel_values''']							).logits

      assert hf_logits.shape == torch.Size([1, 1_0_0_0]							)
      assert torch.allclose(hf_logits[0, 0:5]      ,    A__      ,    atol=1e-3							)

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


if __name__ == "__main__":
    a_ :List[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.')

    a_ :int  							=			parser.parse_args()
    convert_swiftformer_checkpoint(args.swiftformer_name, args.pytorch_dump_folder_path, args.original_ckpt)










'''simple docstring'''
import logging
import os

import quant_trainer
import torch
from torch.utils.data import DataLoader

from transformers import Trainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput


lowerCAmelCase   : Dict             =				logging.getLogger(__name__)

if is_torch_tpu_available(check_device=False):
					import torch_xla.core.xla_model as xm
					import torch_xla.debug.metrics as met




class        _UpperCamelCase	(    SCREAMING_SNAKE_CASE):


				'''simple docstring'''


				def __init__(     self							,      *a_							,      a_=None							,      a_=None							,      a_=None							,      **a_							)    ->  Any:
					super().__init__(*a_							,      **a_							)
					lowercase						:		Optional[Any]   					=		eval_examples
					lowercase						:		Any   					=		post_process_function
					lowercase						:		Dict   					=		quant_trainer_args
					lowercase						:		List[Any]   					=		1_2_8  # default number of calibration samples

				def 	a__		(     self							,      a_=None							)    ->  List[str]:
					if calib_dataset is None and self.calib_dataset is None:
						raise ValueError("Trainer: calibration requires an calib_dataset."							)
					lowercase						:		Dict   					=		calib_dataset if calib_dataset is not None else self.calib_dataset

					lowercase						:		Optional[int]   					=		self._remove_unused_columns(a_							,      description="Calibration"							)

					return DataLoader(
					    a_							,      batch_size=self.args.eval_batch_size							,      collate_fn=self.data_collator							,      drop_last=self.args.dataloader_drop_last							,      num_workers=self.args.dataloader_num_workers							,      pin_memory=self.args.dataloader_pin_memory							,      shuffle=a_							,      )

				def 	a__		(     self							,      a_=None							)    ->  str:
					lowercase						:		Optional[Any]   					=		self.train_dataset if calib_dataset is None else calib_dataset
					lowercase						:		Optional[Any]   					=		self.get_calib_dataloader(a_							)

					lowercase						:		Union[str, Any]   					=		self.model
					quant_trainer.configure_model(a_							,      self.quant_trainer_args							,      calib=a_							)
					model.eval()
					quant_trainer.enable_calibration(a_							)

					logger.info("***** Running calibration *****"							)
					logger.info(F'''  Num examples = {self.calib_num}'''							)
					logger.info(F'''  Batch size = {calib_dataloader.batch_size}'''							)

					for step, inputs in enumerate(a_							):
						# Prediction step
						lowercase					,		lowercase					,		lowercase						:		Union[str, Any]   					=		self.prediction_step(a_							,      a_							,      prediction_loss_only=a_							)
						if (step + 1) * calib_dataloader.batch_size >= self.calib_num:
							break

					quant_trainer.finish_calibration(a_							,      self.quant_trainer_args							)
					lowercase						:		List[Any]   					=		model

				def 	a__		(     self							,      a_=None							,      a_=None							,      a_=None							,      a_ = "eval"							)    ->  int:
					lowercase						:		List[str]   					=		self.eval_dataset if eval_dataset is None else eval_dataset
					lowercase						:		Union[str, Any]   					=		self.get_eval_dataloader(a_							)
					lowercase						:		Tuple   					=		self.eval_examples if eval_examples is None else eval_examples

					# Temporarily disable metric computation, we will do it in the loop here.
					lowercase						:		List[str]   					=		self.compute_metrics
					lowercase						:		Dict   					=		None
					lowercase						:		int   					=		self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
					try:
						lowercase						:		Tuple   					=		eval_loop(
						    a_							,      description="Evaluation"							,      prediction_loss_only=True if compute_metrics is None else None							,      ignore_keys=a_							,      )
					finally:
						lowercase						:		List[Any]   					=		compute_metrics

					if self.post_process_function is not None and self.compute_metrics is not None:
						lowercase						:		Union[str, Any]   					=		self.post_process_function(a_							,      a_							,      output.predictions							)
						lowercase						:		Union[str, Any]   					=		self.compute_metrics(a_							)

						# Prefix all keys with metric_key_prefix + '_'
						for key in list(metrics.keys()							):
							if not key.startswith(F'''{metric_key_prefix}_'''							):
								lowercase						:		Dict   					=		metrics.pop(a_							)

						self.log(a_							)
					else:
						lowercase						:		Tuple   					=		{}

					if self.args.tpu_metrics_debug or self.args.debug:
						# tpu-comment: Logging debug metrics for PyTorch/XLA (compile, execute times, ops, etc.)
						xm.master_print(met.metrics_report()							)

					lowercase						:		int   					=		self.callback_handler.on_evaluate(self.args							,      self.state							,      self.control							,      a_							)
					return metrics

				def 	a__		(     self							,      a_							,      a_							,      a_=None							,      a_ = "test"							)    ->  Optional[int]:
					lowercase						:		Dict   					=		self.get_test_dataloader(a_							)

					# Temporarily disable metric computation, we will do it in the loop here.
					lowercase						:		List[str]   					=		self.compute_metrics
					lowercase						:		List[str]   					=		None
					lowercase						:		int   					=		self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
					try:
						lowercase						:		Optional[Any]   					=		eval_loop(
						    a_							,      description="Prediction"							,      prediction_loss_only=True if compute_metrics is None else None							,      ignore_keys=a_							,      )
					finally:
						lowercase						:		int   					=		compute_metrics

					if self.post_process_function is None or self.compute_metrics is None:
						return output

					lowercase						:		List[str]   					=		self.post_process_function(a_							,      a_							,      output.predictions							,      "predict"							)
					lowercase						:		Dict   					=		self.compute_metrics(a_							)

					# Prefix all keys with metric_key_prefix + '_'
					for key in list(metrics.keys()							):
						if not key.startswith(F'''{metric_key_prefix}_'''							):
							lowercase						:		Union[str, Any]   					=		metrics.pop(a_							)

					return PredictionOutput(predictions=predictions.predictions							,      label_ids=predictions.label_ids							,      metrics=a_							)


				def 	a__		(     self							,      a_="./"							)    ->  Optional[Any]:
					lowercase						:		str   					=		self.eval_dataset
					lowercase						:		Dict   					=		self.get_eval_dataloader(a_							)

					lowercase						:		Any   					=		next(iter(a_							)							)

					# saving device - to make it consistent
					lowercase						:		List[Any]   					=		torch.device("cuda" if torch.cuda.is_available() else "cpu"							)

					# convert to tuple
					lowercase						:		Tuple   					=		tuple(v.to(a_							) for k, v in batch.items()							)

					logger.info("Converting model to be onnx compatible"							)
					from pytorch_quantization.nn import TensorQuantizer

					lowercase						:		Optional[Any]   					=		True

					lowercase						:		int   					=		self.model.to(a_							)

					model.eval()
					model.float()

					lowercase						:		Union[str, Any]   					=		model.module if hasattr(a_							,      "module"							) else model
					quant_trainer.configure_model(a_							,      self.quant_trainer_args							)

					lowercase						:		List[str]   					=		os.path.join(a_							,      "model.onnx"							)
					logger.info(F'''exporting model to {output_model_file}'''							)

					lowercase						:		Any   					=		{0: "batch_size", 1: "seq_len"}

					torch.onnx.export(
					    a_							,      a_							,      a_							,      export_params=a_							,      opset_version=1_3							,      do_constant_folding=a_							,      input_names=["input_ids", "attention_mask", "token_type_ids"]							,      output_names=["output_start_logits", "output_end_logits"]							,      dynamic_axes={
					        "input_ids": axes,
					        "attention_mask": axes,
					        "token_type_ids": axes,
					        "output_start_logits": axes,
					        "output_end_logits": axes,
					    }							,      verbose=a_							,      )
					logger.info("onnx export finished"							)


