from typing import Dict, List, Optional, Union

import numpy as np

from .feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from .utils import PaddingStrategy, TensorType, is_tf_tensor, is_torch_tensor, logging, to_numpy


A     :  str					     =       logging.get_logger(__name__)


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







  def __init__(							self :       Optional[Any]  ,     __magic_name__ :       int  ,     __magic_name__ :       int  ,     __magic_name__ :       float  ,     **__magic_name__ :       List[str]   )  ->	Dict:
        SCREAMING_SNAKE_CASE_          =  feature_size
        SCREAMING_SNAKE_CASE_          =  sampling_rate
        SCREAMING_SNAKE_CASE_          =  padding_value

        SCREAMING_SNAKE_CASE_          =  kwargs.pop("padding_side"  ,     "right"   )
        SCREAMING_SNAKE_CASE_          =  kwargs.pop("return_attention_mask"  ,     __magic_name__   )

        super().__init__(**__magic_name__   )







  def    __A     (							self :       List[str]  ,     __magic_name__ :       Union[
          BatchFeature,
          List[BatchFeature],
          Dict[str, BatchFeature],
          Dict[str, List[BatchFeature]],
          List[Dict[str, BatchFeature]],
      ]  ,     __magic_name__ :       Union[bool, str, PaddingStrategy] = True  ,     __magic_name__ :       Optional[int] = None  ,     __magic_name__ :       bool = False  ,     __magic_name__ :       Optional[int] = None  ,     __magic_name__ :       Optional[bool] = None  ,     __magic_name__ :       Optional[Union[str, TensorType]] = None  ,     )  ->	BatchFeature:
        # If we have a list of dicts, let's convert it in a dict of lists
        # We do this to allow using this method as a collate_fn function in PyTorch Dataloader
        if isinstance(__magic_name__  ,     (list, tuple)   ) and isinstance(processed_features[0]  ,     (dict, BatchFeature)   ):
              SCREAMING_SNAKE_CASE_          =  {
                  key: [example[key] for example in processed_features] for key in processed_features[0].keys()
              }

        # The model's main input name, usually `input_values`, has be passed for padding
        if self.model_input_names[0] not in processed_features:
              raise ValueError(
                  "You should supply an instance of `transformers.BatchFeature` or list of `transformers.BatchFeature`"
                  F''' to this method that includes {self.model_input_names[0]}, but you provided'''
                  F''' {list(processed_features.keys()   )}'''   )

        SCREAMING_SNAKE_CASE_          =  processed_features[self.model_input_names[0]]
        SCREAMING_SNAKE_CASE_          =  (
            return_attention_mask if return_attention_mask is not None else self.return_attention_mask
        )

        if len(__magic_name__   ) == 0:
              if return_attention_mask:
                    SCREAMING_SNAKE_CASE_          =  []
              return processed_features

        # If we have PyTorch/TF tensors or lists as inputs, we cast them as Numpy arrays
        # and rebuild them afterwards if no return_tensors is specified
        # Note that we lose the specific device the tensor may be on for PyTorch

        SCREAMING_SNAKE_CASE_          =  required_input[0]
        if isinstance(__magic_name__  ,     (list, tuple)   ):
              # first_element might be an empty list/tuple in some edge cases so we grab the first non empty element.
              SCREAMING_SNAKE_CASE_          =  0
              while len(required_input[index]   ) == 0:
                    index += 1
              if index < len(__magic_name__   ):
                    SCREAMING_SNAKE_CASE_          =  required_input[index][0]

        if return_tensors is None:
              if is_tf_tensor(__magic_name__   ):
                    SCREAMING_SNAKE_CASE_          =  "tf"
              elif is_torch_tensor(__magic_name__   ):
                    SCREAMING_SNAKE_CASE_          =  "pt"
              elif isinstance(__magic_name__  ,     (int, float, list, tuple, np.ndarray)   ):
                    SCREAMING_SNAKE_CASE_          =  "np"
              else:
                    raise ValueError(
                        F'''type of {first_element} unknown: {type(__magic_name__   )}. '''
                        "Should be one of a python, numpy, pytorch or tensorflow object."   )

        for key, value in processed_features.items():
              if isinstance(value[0]  ,     (int, float)   ):
                    SCREAMING_SNAKE_CASE_          =  to_numpy(__magic_name__   )
              else:
                    SCREAMING_SNAKE_CASE_          =  [to_numpy(__magic_name__   ) for v in value]

        # Convert padding_strategy in PaddingStrategy
        SCREAMING_SNAKE_CASE_          =  self._get_padding_strategies(padding=__magic_name__  ,     max_length=__magic_name__   )

        SCREAMING_SNAKE_CASE_          =  processed_features[self.model_input_names[0]]

        SCREAMING_SNAKE_CASE_          =  len(__magic_name__   )
        if not all(len(__magic_name__   ) == batch_size for v in processed_features.values()   ):
              raise ValueError("Some items in the output dictionary have a different batch size than others."   )

        SCREAMING_SNAKE_CASE_          =  []
        for i in range(__magic_name__   ):
              SCREAMING_SNAKE_CASE_          =  {k: v[i] for k, v in processed_features.items()}
              # truncation
              SCREAMING_SNAKE_CASE_          =  self._truncate(
                  __magic_name__  ,     max_length=__magic_name__  ,     pad_to_multiple_of=__magic_name__  ,     truncation=__magic_name__  ,     )
              truncated_inputs.append(__magic_name__   )

        if padding_strategy == PaddingStrategy.LONGEST:
              # make sure that `max_length` cannot be longer than the longest truncated length
              SCREAMING_SNAKE_CASE_          =  max(len(input_slice[self.model_input_names[0]]   ) for input_slice in truncated_inputs   )
              SCREAMING_SNAKE_CASE_          =  PaddingStrategy.MAX_LENGTH

        SCREAMING_SNAKE_CASE_          =  {}
        for i in range(__magic_name__   ):
              # padding
              SCREAMING_SNAKE_CASE_          =  self._pad(
                  truncated_inputs[i]  ,     max_length=__magic_name__  ,     padding_strategy=__magic_name__  ,     pad_to_multiple_of=__magic_name__  ,     return_attention_mask=__magic_name__  ,     )

              for key, value in outputs.items():
                    if key not in batch_outputs:
                          SCREAMING_SNAKE_CASE_          =  []
                    if value.dtype is np.dtype(np.floataa   ):
                          SCREAMING_SNAKE_CASE_          =  value.astype(np.floataa   )
                    batch_outputs[key].append(__magic_name__   )

        return BatchFeature(__magic_name__  ,     tensor_type=__magic_name__   )







  def    __A     (							self :       Any  ,     __magic_name__ :       Union[Dict[str, np.ndarray], BatchFeature]  ,     __magic_name__ :       Optional[int] = None  ,     __magic_name__ :       PaddingStrategy = PaddingStrategy.DO_NOT_PAD  ,     __magic_name__ :       Optional[int] = None  ,     __magic_name__ :       Optional[bool] = None  ,     )  ->	dict:
        SCREAMING_SNAKE_CASE_          =  processed_features[self.model_input_names[0]]

        if padding_strategy == PaddingStrategy.LONGEST:
              SCREAMING_SNAKE_CASE_          =  len(__magic_name__   )

        if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
              SCREAMING_SNAKE_CASE_          =  ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

        SCREAMING_SNAKE_CASE_          =  padding_strategy != PaddingStrategy.DO_NOT_PAD and len(__magic_name__   ) < max_length

        if return_attention_mask and "attention_mask" not in processed_features:
              SCREAMING_SNAKE_CASE_          =  np.ones(len(__magic_name__   )  ,     dtype=np.intaa   )

        if needs_to_be_padded:
              SCREAMING_SNAKE_CASE_          =  max_length - len(__magic_name__   )
              if self.padding_side == "right":
                    if return_attention_mask:
                          SCREAMING_SNAKE_CASE_          =  np.pad(
                              processed_features["attention_mask"]  ,     (0, difference)   )
                    SCREAMING_SNAKE_CASE_          =  ((0, difference), (0, 0)) if self.feature_size > 1 else (0, difference)
                    SCREAMING_SNAKE_CASE_          =  np.pad(
                        __magic_name__  ,     __magic_name__  ,     "constant"  ,     constant_values=self.padding_value   )
              elif self.padding_side == "left":
                    if return_attention_mask:
                          SCREAMING_SNAKE_CASE_          =  np.pad(
                              processed_features["attention_mask"]  ,     (difference, 0)   )
                    SCREAMING_SNAKE_CASE_          =  ((difference, 0), (0, 0)) if self.feature_size > 1 else (difference, 0)
                    SCREAMING_SNAKE_CASE_          =  np.pad(
                        __magic_name__  ,     __magic_name__  ,     "constant"  ,     constant_values=self.padding_value   )
              else:
                    raise ValueError("Invalid padding strategy:" + str(self.padding_side   )   )

        return processed_features







  def    __A     (							self :       Optional[Any]  ,     __magic_name__ :       Union[Dict[str, np.ndarray], BatchFeature]  ,     __magic_name__ :       Optional[int] = None  ,     __magic_name__ :       Optional[int] = None  ,     __magic_name__ :       Optional[bool] = None  ,     )  ->	str:
        if not truncation:
              return processed_features
        elif truncation and max_length is None:
              raise ValueError("When setting ``truncation=True``, make sure that ``max_length`` is defined."   )

        SCREAMING_SNAKE_CASE_          =  processed_features[self.model_input_names[0]]

        # find `max_length` that fits `pad_to_multiple_of`
        if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
              SCREAMING_SNAKE_CASE_          =  ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

        SCREAMING_SNAKE_CASE_          =  len(__magic_name__   ) > max_length

        if needs_to_be_truncated:
              SCREAMING_SNAKE_CASE_          =  processed_features[self.model_input_names[0]][:max_length]
              if "attention_mask" in processed_features:
                    SCREAMING_SNAKE_CASE_          =  processed_features["attention_mask"][:max_length]

        return processed_features







  def    __A     (							self :       Optional[int]  ,     __magic_name__ :       List[Any]=False  ,     __magic_name__ :       int=None   )  ->	Dict:

        # Get padding strategy
        if padding is not False:
              if padding is True:
                    SCREAMING_SNAKE_CASE_          =  PaddingStrategy.LONGEST  # Default to pad to the longest sequence in the batch
              elif not isinstance(__magic_name__  ,     __magic_name__   ):
                    SCREAMING_SNAKE_CASE_          =  PaddingStrategy(__magic_name__   )
              elif isinstance(__magic_name__  ,     __magic_name__   ):
                    SCREAMING_SNAKE_CASE_          =  padding
        else:
              SCREAMING_SNAKE_CASE_          =  PaddingStrategy.DO_NOT_PAD

        # Set max length if needed
        if max_length is None:
              if padding_strategy == PaddingStrategy.MAX_LENGTH:
                    raise ValueError(
                        F'''When setting ``padding={PaddingStrategy.MAX_LENGTH}``, make sure that max_length is defined'''   )

        # Test if we have a padding value
        if padding_strategy != PaddingStrategy.DO_NOT_PAD and (self.padding_value is None):
              raise ValueError(
                  "Asking to pad but the feature_extractor does not have a padding value. Please select a value to use"
                  " as `padding_value`. For example: `feature_extractor.padding_value = 0.0`."   )

        return padding_strategy



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


def      a__ (		):
      SCREAMING_SNAKE_CASE_          =  "http://images.cocodataset.org/val2017/000000039769.jpg"
      SCREAMING_SNAKE_CASE_          =  Image.open(requests.get(__UpperCamelCase     ,      stream=__UpperCamelCase				).raw				)
      return im


def      a__ (		__UpperCamelCase				):
      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__ (		__UpperCamelCase     ,      __UpperCamelCase     ,      __UpperCamelCase				):
      SCREAMING_SNAKE_CASE_          =  dct.pop(__UpperCamelCase				)
      SCREAMING_SNAKE_CASE_          =  val


def      a__ (		__UpperCamelCase				):
      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      a__ (		__UpperCamelCase     ,      __UpperCamelCase     ,      __UpperCamelCase				):
      SCREAMING_SNAKE_CASE_          =  SwiftFormerConfig()

      # dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
      SCREAMING_SNAKE_CASE_          =  1_0_0_0
      SCREAMING_SNAKE_CASE_          =  "huggingface/label-files"
      SCREAMING_SNAKE_CASE_          =  "imagenet-1k-id2label.json"
      SCREAMING_SNAKE_CASE_          =  json.load(open(hf_hub_download(__UpperCamelCase     ,      __UpperCamelCase     ,      repo_type="dataset"				)     ,      "r"				)				)
      SCREAMING_SNAKE_CASE_          =  {int(__UpperCamelCase				): 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_          =  [4_8, 5_6, 1_1_2, 2_2_0]

      elif swiftformer_name == "swiftformer_s":
            SCREAMING_SNAKE_CASE_          =  [3, 3, 9, 6]
            SCREAMING_SNAKE_CASE_          =  [4_8, 6_4, 1_6_8, 2_2_4]

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

      elif swiftformer_name == "swiftformer_l3":
            SCREAMING_SNAKE_CASE_          =  [4, 4, 1_2, 6]
            SCREAMING_SNAKE_CASE_          =  [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_          =  torch.hub.load_state_dict_from_url(__UpperCamelCase     ,      map_location="cpu"     ,      check_hash=__UpperCamelCase				)
            else:
                  SCREAMING_SNAKE_CASE_          =  torch.load(__UpperCamelCase     ,      map_location="cpu"				)
      SCREAMING_SNAKE_CASE_          =  checkpoint

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

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

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

      # compare outputs from both models
      SCREAMING_SNAKE_CASE_          =  get_expected_output(__UpperCamelCase				)
      SCREAMING_SNAKE_CASE_          =  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]     ,      __UpperCamelCase     ,      atol=1E-3				)

      Path(__UpperCamelCase				).mkdir(exist_ok=__UpperCamelCase				)
      print(F'''Saving model {swiftformer_name} to {pytorch_dump_folder_path}'''				)
      hf_model.save_pretrained(__UpperCamelCase				)


if __name__ == "__main__":
      A     :  Optional[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     :  List[Any]					     =       parser.parse_args()
      convert_swiftformer_checkpoint(args.swiftformer_name, args.pytorch_dump_folder_path, args.original_ckpt)


