


"""simple docstring"""







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__      				=   logging.get_logger(__name__)

lowerCAmelCase__      				=   torch.device('''cpu''')
def 					a__			(				):


  '''simple docstring'''







  lowerCAmelCase     :     List[Any]								=							"http://images.cocodataset.org/val2017/000000039769.jpg"
  lowerCAmelCase     :     Union[str, Any]								=							Image.open(requests.get(SCREAMING_SNAKE_CASE				,      stream=SCREAMING_SNAKE_CASE				).raw				)
  return im
def 					a__			(				SCREAMING_SNAKE_CASE    :    Dict				):


  '''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 					a__			(				SCREAMING_SNAKE_CASE    :    Tuple				,      SCREAMING_SNAKE_CASE    :    List[str]				,      SCREAMING_SNAKE_CASE    :    int				):


  '''simple docstring'''







  lowerCAmelCase     :     int								=							dct.pop(SCREAMING_SNAKE_CASE				)
  lowerCAmelCase     :     List[str]								=							val
def 					a__			(				SCREAMING_SNAKE_CASE    :    Tuple				):


  '''simple docstring'''







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


  '''simple docstring'''







  lowerCAmelCase     :     Union[str, Any]								=							SwiftFormerConfig()

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

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

  elif swiftformer_name == "swiftformer_s":
    lowerCAmelCase     :     Optional[int]								=							[3, 3, 9, 6]
    lowerCAmelCase     :     Union[str, Any]								=							[4_8, 6_4, 1_6_8, 2_2_4]

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

  elif swiftformer_name == "swiftformer_l3":
    lowerCAmelCase     :     List[Any]								=							[4, 4, 1_2, 6]
    lowerCAmelCase     :     str								=							[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"				):
      lowerCAmelCase     :     Tuple								=							torch.hub.load_state_dict_from_url(SCREAMING_SNAKE_CASE				,      map_location="cpu"				,      check_hash=SCREAMING_SNAKE_CASE				)
    else:
      lowerCAmelCase     :     Any								=							torch.load(SCREAMING_SNAKE_CASE				,      map_location="cpu"				)
  lowerCAmelCase     :     Tuple								=							checkpoint

  lowerCAmelCase     :     Optional[int]								=							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
  lowerCAmelCase     :     Union[str, Any]								=							SwiftFormerForImageClassification(SCREAMING_SNAKE_CASE				).eval()
  hf_model.load_state_dict(SCREAMING_SNAKE_CASE				)

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

  # compare outputs from both models
  lowerCAmelCase     :     int								=							get_expected_output(SCREAMING_SNAKE_CASE				)
  lowerCAmelCase     :     Tuple								=							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]				,      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__":
     lowerCAmelCase__      				=   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__      				=   parser.parse_args()
     convert_swiftformer_checkpoint(args.swiftformer_name, args.pytorch_dump_folder_path, args.original_ckpt)









"""simple docstring"""







from typing import Any, Dict, List, Optional, Tuple, Union

import torch
from torch import nn
from torch.utils.data import DistributedSampler, RandomSampler

from transformers import PreTrainedModel, Trainer, logging
from transformers.integrations import is_fairscale_available
from transformers.models.fsmt.configuration_fsmt import FSMTConfig
from transformers.optimization import (
    Adafactor,
    AdamW,
    get_constant_schedule,
    get_constant_schedule_with_warmup,
    get_cosine_schedule_with_warmup,
    get_cosine_with_hard_restarts_schedule_with_warmup,
    get_linear_schedule_with_warmup,
    get_polynomial_decay_schedule_with_warmup,
)
from transformers.trainer_pt_utils import get_tpu_sampler
from transformers.training_args import ParallelMode
from transformers.utils import is_torch_tpu_available


if is_fairscale_available():
     from fairscale.optim import OSS


lowerCAmelCase__      				=   logging.get_logger(__name__)

lowerCAmelCase__      				=   {
    '''linear''': get_linear_schedule_with_warmup,
    '''cosine''': get_cosine_schedule_with_warmup,
    '''cosine_w_restarts''': get_cosine_with_hard_restarts_schedule_with_warmup,
    '''polynomial''': get_polynomial_decay_schedule_with_warmup,
    '''constant''': get_constant_schedule,
    '''constant_w_warmup''': get_constant_schedule_with_warmup,
}


class       SCREAMING_SNAKE_CASE__				(		lowercase				):
       """simple docstring"""


       def __init__(						self		,     snake_case__=None		,     snake_case__=None		,     *snake_case__		,     **snake_case__						):






         """simple docstring"""





         super().__init__(*snake_case__		,     **snake_case__						)

         if config is None:
           assert isinstance(self.model		,     snake_case__						), (
               "If no `config` is passed the model to be trained has to be of type `PreTrainedModel`, but is"
               f""" {self.model.__class__}"""
           )
           lowerCAmelCase     :     Optional[int]								=							self.model.config
         else:
           lowerCAmelCase     :     List[str]								=							config

         lowerCAmelCase     :     Any								=							data_args
         lowerCAmelCase     :     Tuple								=							self.config.tgt_vocab_size if isinstance(self.config		,     snake_case__						) else self.config.vocab_size

         if self.args.label_smoothing != 0 or (self.data_args is not None and self.data_args.ignore_pad_token_for_loss):
           assert self.config.pad_token_id is not None, (
               "Make sure that `config.pad_token_id` is correcly defined when ignoring `pad_token` for loss"
               " calculation or doing label smoothing."
           )

         if self.config.pad_token_id is None and self.config.eos_token_id is not None:
           logger.warning(
               f"""The `config.pad_token_id` is `None`. Using `config.eos_token_id` = {self.config.eos_token_id} for"""
               " padding.."						)

         if self.args.label_smoothing == 0:
           lowerCAmelCase     :     int								=							torch.nn.CrossEntropyLoss(ignore_index=self.config.pad_token_id						)
         else:
           # dynamically import label_smoothed_nll_loss
           from utils import label_smoothed_nll_loss

           lowerCAmelCase     :     Tuple								=							label_smoothed_nll_loss

       def        lowercase__							(						self		,     snake_case__						):






         """simple docstring"""





         if self.optimizer is None:
           lowerCAmelCase     :     Optional[int]								=							["bias", "LayerNorm.weight"]
           lowerCAmelCase     :     str								=							[
               {
                   "params": [p for n, p in self.model.named_parameters() if not any(nd in n for nd in no_decay						)],
                   "weight_decay": self.args.weight_decay,
               },
               {
                   "params": [p for n, p in self.model.named_parameters() if any(nd in n for nd in no_decay						)],
                   "weight_decay": 0.0,
               },
           ]
           lowerCAmelCase     :     Union[str, Any]								=							Adafactor if self.args.adafactor else AdamW
           if self.args.adafactor:
             lowerCAmelCase     :     Dict								=							Adafactor
             lowerCAmelCase     :     Optional[int]								=							{"scale_parameter": False, "relative_step": False}
           else:
             lowerCAmelCase     :     int								=							AdamW
             lowerCAmelCase     :     int								=							{
                 "betas": (self.args.adam_betaa, self.args.adam_betaa),
                 "eps": self.args.adam_epsilon,
             }
           lowerCAmelCase     :     Any								=							self.args.learning_rate
           if self.sharded_ddp:
             lowerCAmelCase     :     int								=							OSS(
                 params=snake_case__		,     optim=snake_case__		,     **snake_case__		,     )
           else:
             lowerCAmelCase     :     Any								=							optimizer_cls(snake_case__		,     **snake_case__						)

         if self.lr_scheduler is None:
           lowerCAmelCase     :     Tuple								=							self._get_lr_scheduler(snake_case__						)
         else:  # ignoring --lr_scheduler
           logger.warning("scheduler is passed to `Seq2SeqTrainer`, `--lr_scheduler` arg is ignored."						)

       def        lowercase__							(						self		,     snake_case__						):






         """simple docstring"""





         lowerCAmelCase     :     Optional[int]								=							arg_to_scheduler[self.args.lr_scheduler]
         if self.args.lr_scheduler == "constant":
           lowerCAmelCase     :     Tuple								=							schedule_func(self.optimizer						)
         elif self.args.lr_scheduler == "constant_w_warmup":
           lowerCAmelCase     :     Any								=							schedule_func(self.optimizer		,     num_warmup_steps=self.args.warmup_steps						)
         else:
           lowerCAmelCase     :     str								=							schedule_func(
               self.optimizer		,     num_warmup_steps=self.args.warmup_steps		,     num_training_steps=snake_case__						)
         return scheduler

       def        lowercase__							(						self						):






         """simple docstring"""





         if isinstance(self.train_dataset		,     torch.utils.data.IterableDataset						):
           return None
         elif is_torch_tpu_available():
           return get_tpu_sampler(self.train_dataset						)
         else:
           if self.args.sortish_sampler:
             self.train_dataset.make_sortish_sampler(
                 self.args.per_device_train_batch_size		,     distributed=(self.args.parallel_mode == ParallelMode.DISTRIBUTED)		,     )

           return (
               RandomSampler(self.train_dataset						)
               if self.args.local_rank == -1
               else DistributedSampler(self.train_dataset						)
           )

       def        lowercase__							(						self		,     snake_case__		,     snake_case__		,     snake_case__						):






         """simple docstring"""





         if self.args.label_smoothing == 0:
           if self.data_args is not None and self.data_args.ignore_pad_token_for_loss:
             # force training to ignore pad token
             lowerCAmelCase     :     Dict								=							model(**snake_case__		,     use_cache=snake_case__						)[0]
             lowerCAmelCase     :     List[Any]								=							self.loss_fn(logits.view(-1		,     logits.shape[-1]						)		,     labels.view(-1						)						)
           else:
             # compute usual loss via models
             lowerCAmelCase						,  lowerCAmelCase     :     str								=							model(**snake_case__		,     labels=snake_case__		,     use_cache=snake_case__						)[:2]
         else:
           # compute label smoothed loss
           lowerCAmelCase     :     int								=							model(**snake_case__		,     use_cache=snake_case__						)[0]
           lowerCAmelCase     :     List[Any]								=							torch.nn.functional.log_softmax(snake_case__		,     dim=-1						)
           lowerCAmelCase						,  lowerCAmelCase     :     str								=							self.loss_fn(snake_case__		,     snake_case__		,     self.args.label_smoothing		,     ignore_index=self.config.pad_token_id						)
         return loss, logits

       def        lowercase__							(						self		,     snake_case__		,     snake_case__						):






         """simple docstring"""





         lowerCAmelCase     :     Tuple								=							inputs.pop("labels"						)
         lowerCAmelCase						,  lowerCAmelCase     :     str								=							self._compute_loss(snake_case__		,     snake_case__		,     snake_case__						)
         return loss

       def        lowercase__							(						self		,     snake_case__		,     snake_case__		,     snake_case__		,     snake_case__ = None		,     ):






         """simple docstring"""





         lowerCAmelCase     :     List[str]								=							self._prepare_inputs(snake_case__						)

         lowerCAmelCase     :     Union[str, Any]								=							{
             "max_length": self.data_args.val_max_target_length
             if self.data_args is not None
             else self.config.max_length,
             "num_beams": self.data_args.eval_beams if self.data_args is not None else self.config.num_beams,
         }

         if self.args.predict_with_generate and not self.args.prediction_loss_only:
           lowerCAmelCase     :     Dict								=							self.model.generate(
               inputs["input_ids"]		,     attention_mask=inputs["attention_mask"]		,     **snake_case__		,     )
           # in case the batch is shorter than max length, the output should be padded
           if generated_tokens.shape[-1] < gen_kwargs["max_length"]:
             lowerCAmelCase     :     Dict								=							self._pad_tensors_to_max_len(snake_case__		,     gen_kwargs["max_length"]						)

         lowerCAmelCase     :     Optional[Any]								=							inputs.pop("labels"						)
         with torch.no_grad():
           # compute loss on predict data
           lowerCAmelCase						,  lowerCAmelCase     :     Dict								=							self._compute_loss(snake_case__		,     snake_case__		,     snake_case__						)

         lowerCAmelCase     :     List[str]								=							loss.mean().detach()
         if self.args.prediction_loss_only:
           return (loss, None, None)

         lowerCAmelCase     :     int								=							generated_tokens if self.args.predict_with_generate else logits

         if labels.shape[-1] < gen_kwargs["max_length"]:
           lowerCAmelCase     :     Optional[int]								=							self._pad_tensors_to_max_len(snake_case__		,     gen_kwargs["max_length"]						)

         return (loss, logits, labels)







       def        lowercase__							(						self		,     snake_case__		,     snake_case__						):






         """simple docstring"""





         lowerCAmelCase     :     List[Any]								=							self.config.pad_token_id if self.config.pad_token_id is not None else self.config.eos_token_id

         if pad_token_id is None:
           raise ValueError(
               "Make sure that either `config.pad_token_id` or `config.eos_token_id` is defined if tensor has to be"
               f""" padded to `max_length`={max_length}"""						)

         lowerCAmelCase     :     Optional[Any]								=							pad_token_id * torch.ones(
             (tensor.shape[0], max_length)		,     dtype=tensor.dtype		,     device=tensor.device						)
         lowerCAmelCase     :     int								=							tensor
         return padded_tensor





