

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

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


def 					_UpperCAmelCase       (  ):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_									=						'http://images.cocodataset.org/val2017/000000039769.jpg'
    SCREAMING_SNAKE_CASE_									=						Image.open(requests.get(_SCREAMING_SNAKE_CASE ,   stream=_SCREAMING_SNAKE_CASE       ).raw       )
    return im


def 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		int       ):





    """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 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		Optional[int] ,   _SCREAMING_SNAKE_CASE					:		Union[str, Any] ,   _SCREAMING_SNAKE_CASE					:		Optional[Any]       ):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_									=						dct.pop(_SCREAMING_SNAKE_CASE       )
    SCREAMING_SNAKE_CASE_									=						val


def 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		Dict       ):





    """simple docstring"""


    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 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		Optional[Any] ,   _SCREAMING_SNAKE_CASE					:		str ,   _SCREAMING_SNAKE_CASE					:		Optional[Any]       ):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_									=						SwiftFormerConfig()

    # dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
    SCREAMING_SNAKE_CASE_									=						1_000
    SCREAMING_SNAKE_CASE_									=						'huggingface/label-files'
    SCREAMING_SNAKE_CASE_									=						'imagenet-1k-id2label.json'
    SCREAMING_SNAKE_CASE_									=						json.load(open(hf_hub_download(_SCREAMING_SNAKE_CASE ,   _SCREAMING_SNAKE_CASE ,   repo_type='dataset'       ) ,   'r'       )       )
    SCREAMING_SNAKE_CASE_									=						{int(_SCREAMING_SNAKE_CASE       ): 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_									=						[48, 56, 112, 220]

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

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

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

    # 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(_SCREAMING_SNAKE_CASE ,   map_location='cpu' ,   check_hash=_SCREAMING_SNAKE_CASE       )
        else:
            SCREAMING_SNAKE_CASE_									=						torch.load(_SCREAMING_SNAKE_CASE ,   map_location='cpu'       )
    SCREAMING_SNAKE_CASE_									=						checkpoint

    SCREAMING_SNAKE_CASE_									=						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
    SCREAMING_SNAKE_CASE_									=						SwiftFormerForImageClassification(_SCREAMING_SNAKE_CASE       ).eval()
    hf_model.load_state_dict(_SCREAMING_SNAKE_CASE       )

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

    # compare outputs from both models
    SCREAMING_SNAKE_CASE_									=						get_expected_output(_SCREAMING_SNAKE_CASE       )
    SCREAMING_SNAKE_CASE_									=						hf_model(inputs['pixel_values']       ).logits

    assert hf_logits.shape == torch.Size([1, 1_000]       )
    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__":
       UpperCamelCase__  :      str			=    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.")

       UpperCamelCase__  :      Union[str, Any]			=    parser.parse_args()
       convert_swiftformer_checkpoint(args.swiftformer_name, args.pytorch_dump_folder_path, args.original_ckpt)





import unittest

import numpy as np
from datasets import load_dataset

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
       import torch

if is_vision_available():
       from PIL import Image

       from transformers import BeitImageProcessor



class       __snake_case  (    unittest.TestCase       ):

  def __init__(					self						,		_A						,		_A=7						,		_A=3						,		_A=18						,		_A=30						,		_A=400						,		_A=True						,		_A=None						,		_A=True						,		_A=None						,		_A=True						,		_A=[0.5, 0.5, 0.5]						,		_A=[0.5, 0.5, 0.5]						,		_A=False						,		):
      SCREAMING_SNAKE_CASE_									=						size if size is not None else {'height': 20, 'width': 20}
      SCREAMING_SNAKE_CASE_									=						crop_size if crop_size is not None else {'height': 18, 'width': 18}
      SCREAMING_SNAKE_CASE_									=						parent
      SCREAMING_SNAKE_CASE_									=						batch_size
      SCREAMING_SNAKE_CASE_									=						num_channels
      SCREAMING_SNAKE_CASE_									=						image_size
      SCREAMING_SNAKE_CASE_									=						min_resolution
      SCREAMING_SNAKE_CASE_									=						max_resolution
      SCREAMING_SNAKE_CASE_									=						do_resize
      SCREAMING_SNAKE_CASE_									=						size
      SCREAMING_SNAKE_CASE_									=						do_center_crop
      SCREAMING_SNAKE_CASE_									=						crop_size
      SCREAMING_SNAKE_CASE_									=						do_normalize
      SCREAMING_SNAKE_CASE_									=						image_mean
      SCREAMING_SNAKE_CASE_									=						image_std
      SCREAMING_SNAKE_CASE_									=						do_reduce_labels


  def 						lowerCAmelCase__     (					self):
      return {
          "do_resize": self.do_resize,
          "size": self.size,
          "do_center_crop": self.do_center_crop,
          "crop_size": self.crop_size,
          "do_normalize": self.do_normalize,
          "image_mean": self.image_mean,
          "image_std": self.image_std,
          "do_reduce_labels": self.do_reduce_labels,
      }






def 					_UpperCAmelCase       (  ):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_									=						load_dataset('hf-internal-testing/fixtures_ade20k' ,   split='test'       )

    SCREAMING_SNAKE_CASE_									=						Image.open(dataset[0]['file']       )
    SCREAMING_SNAKE_CASE_									=						Image.open(dataset[1]['file']       )

    return image, map


def 					_UpperCAmelCase       (  ):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_									=						load_dataset('hf-internal-testing/fixtures_ade20k' ,   split='test'       )

    SCREAMING_SNAKE_CASE_									=						Image.open(ds[0]['file']       )
    SCREAMING_SNAKE_CASE_									=						Image.open(ds[1]['file']       )
    SCREAMING_SNAKE_CASE_									=						Image.open(ds[2]['file']       )
    SCREAMING_SNAKE_CASE_									=						Image.open(ds[3]['file']       )

    return [imagea, imagea], [mapa, mapa]





@require_torch
@require_vision
class       __snake_case  (    lowerCAmelCase__     , unittest.TestCase       ):
  __lowerCAmelCase     :	Union[str, Any]						 =  BeitImageProcessor if is_vision_available() else None

  def 						lowerCAmelCase__     (					self):
      SCREAMING_SNAKE_CASE_									=						BeitImageProcessingTester(self)

  @property
  def 						lowerCAmelCase__     (					self):
      return self.image_processor_tester.prepare_image_processor_dict()

  def 						lowerCAmelCase__     (					self):
      SCREAMING_SNAKE_CASE_									=						self.image_processing_class(**self.image_processor_dict)
      self.assertTrue(hasattr(_A						,		'do_resize'))
      self.assertTrue(hasattr(_A						,		'size'))
      self.assertTrue(hasattr(_A						,		'do_center_crop'))
      self.assertTrue(hasattr(_A						,		'center_crop'))
      self.assertTrue(hasattr(_A						,		'do_normalize'))
      self.assertTrue(hasattr(_A						,		'image_mean'))
      self.assertTrue(hasattr(_A						,		'image_std'))

  def 						lowerCAmelCase__     (					self):
      SCREAMING_SNAKE_CASE_									=						self.image_processing_class.from_dict(self.image_processor_dict)
      self.assertEqual(image_processor.size						,		{'height': 20, 'width': 20})
      self.assertEqual(image_processor.crop_size						,		{'height': 18, 'width': 18})
      self.assertEqual(image_processor.do_reduce_labels						,		_A)

      SCREAMING_SNAKE_CASE_									=						self.image_processing_class.from_dict(
          self.image_processor_dict						,		size=42						,		crop_size=84						,		reduce_labels=_A)
      self.assertEqual(image_processor.size						,		{'height': 42, 'width': 42})
      self.assertEqual(image_processor.crop_size						,		{'height': 84, 'width': 84})
      self.assertEqual(image_processor.do_reduce_labels						,		_A)

  def 						lowerCAmelCase__     (					self):
      pass

  def 						lowerCAmelCase__     (					self):
      # Initialize image_processing
      SCREAMING_SNAKE_CASE_									=						self.image_processing_class(**self.image_processor_dict)
      # create random PIL images
      SCREAMING_SNAKE_CASE_									=						prepare_image_inputs(self.image_processor_tester						,		equal_resolution=_A)
      for image in image_inputs:
          self.assertIsInstance(_A						,		Image.Image)

      # Test not batched input
      SCREAMING_SNAKE_CASE_									=						image_processing(image_inputs[0]						,		return_tensors='pt').pixel_values
      self.assertEqual(
          encoded_images.shape						,		(
              1,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['height'],
              self.image_processor_tester.crop_size['width'],
          )						,		)

      # Test batched
      SCREAMING_SNAKE_CASE_									=						image_processing(_A						,		return_tensors='pt').pixel_values
      self.assertEqual(
          encoded_images.shape						,		(
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['height'],
              self.image_processor_tester.crop_size['width'],
          )						,		)

  def 						lowerCAmelCase__     (					self):
      # Initialize image_processing
      SCREAMING_SNAKE_CASE_									=						self.image_processing_class(**self.image_processor_dict)
      # create random numpy tensors
      SCREAMING_SNAKE_CASE_									=						prepare_image_inputs(self.image_processor_tester						,		equal_resolution=_A						,		numpify=_A)
      for image in image_inputs:
          self.assertIsInstance(_A						,		np.ndarray)

      # Test not batched input
      SCREAMING_SNAKE_CASE_									=						image_processing(image_inputs[0]						,		return_tensors='pt').pixel_values
      self.assertEqual(
          encoded_images.shape						,		(
              1,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['height'],
              self.image_processor_tester.crop_size['width'],
          )						,		)

      # Test batched
      SCREAMING_SNAKE_CASE_									=						image_processing(_A						,		return_tensors='pt').pixel_values
      self.assertEqual(
          encoded_images.shape						,		(
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['height'],
              self.image_processor_tester.crop_size['width'],
          )						,		)

  def 						lowerCAmelCase__     (					self):
      # Initialize image_processing
      SCREAMING_SNAKE_CASE_									=						self.image_processing_class(**self.image_processor_dict)
      # create random PyTorch tensors
      SCREAMING_SNAKE_CASE_									=						prepare_image_inputs(self.image_processor_tester						,		equal_resolution=_A						,		torchify=_A)
      for image in image_inputs:
          self.assertIsInstance(_A						,		torch.Tensor)

      # Test not batched input
      SCREAMING_SNAKE_CASE_									=						image_processing(image_inputs[0]						,		return_tensors='pt').pixel_values
      self.assertEqual(
          encoded_images.shape						,		(
              1,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['height'],
              self.image_processor_tester.crop_size['width'],
          )						,		)

      # Test batched
      SCREAMING_SNAKE_CASE_									=						image_processing(_A						,		return_tensors='pt').pixel_values
      self.assertEqual(
          encoded_images.shape						,		(
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['height'],
              self.image_processor_tester.crop_size['width'],
          )						,		)

  def 						lowerCAmelCase__     (					self):
      # Initialize image_processing
      SCREAMING_SNAKE_CASE_									=						self.image_processing_class(**self.image_processor_dict)
      # create random PyTorch tensors
      SCREAMING_SNAKE_CASE_									=						prepare_image_inputs(self.image_processor_tester						,		equal_resolution=_A						,		torchify=_A)
      SCREAMING_SNAKE_CASE_									=						[]
      for image in image_inputs:
          self.assertIsInstance(_A						,		torch.Tensor)
          maps.append(torch.zeros(image.shape[-2:]).long())

      # Test not batched input
      SCREAMING_SNAKE_CASE_									=						image_processing(image_inputs[0]						,		maps[0]						,		return_tensors='pt')
      self.assertEqual(
          encoding['pixel_values'].shape						,		(
              1,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['height'],
              self.image_processor_tester.crop_size['width'],
          )						,		)
      self.assertEqual(
          encoding['labels'].shape						,		(
              1,
              self.image_processor_tester.crop_size['height'],
              self.image_processor_tester.crop_size['width'],
          )						,		)
      self.assertEqual(encoding['labels'].dtype						,		torch.long)
      self.assertTrue(encoding['labels'].min().item() >= 0)
      self.assertTrue(encoding['labels'].max().item() <= 255)

      # Test batched
      SCREAMING_SNAKE_CASE_									=						image_processing(_A						,		_A						,		return_tensors='pt')
      self.assertEqual(
          encoding['pixel_values'].shape						,		(
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['height'],
              self.image_processor_tester.crop_size['width'],
          )						,		)
      self.assertEqual(
          encoding['labels'].shape						,		(
              self.image_processor_tester.batch_size,
              self.image_processor_tester.crop_size['height'],
              self.image_processor_tester.crop_size['width'],
          )						,		)
      self.assertEqual(encoding['labels'].dtype						,		torch.long)
      self.assertTrue(encoding['labels'].min().item() >= 0)
      self.assertTrue(encoding['labels'].max().item() <= 255)

      # Test not batched input (PIL images)
      SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_									=						prepare_semantic_single_inputs()

      SCREAMING_SNAKE_CASE_									=						image_processing(_A						,		_A						,		return_tensors='pt')
      self.assertEqual(
          encoding['pixel_values'].shape						,		(
              1,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['height'],
              self.image_processor_tester.crop_size['width'],
          )						,		)
      self.assertEqual(
          encoding['labels'].shape						,		(
              1,
              self.image_processor_tester.crop_size['height'],
              self.image_processor_tester.crop_size['width'],
          )						,		)
      self.assertEqual(encoding['labels'].dtype						,		torch.long)
      self.assertTrue(encoding['labels'].min().item() >= 0)
      self.assertTrue(encoding['labels'].max().item() <= 255)

      # Test batched input (PIL images)
      SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_									=						prepare_semantic_batch_inputs()

      SCREAMING_SNAKE_CASE_									=						image_processing(_A						,		_A						,		return_tensors='pt')
      self.assertEqual(
          encoding['pixel_values'].shape						,		(
              2,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['height'],
              self.image_processor_tester.crop_size['width'],
          )						,		)
      self.assertEqual(
          encoding['labels'].shape						,		(
              2,
              self.image_processor_tester.crop_size['height'],
              self.image_processor_tester.crop_size['width'],
          )						,		)
      self.assertEqual(encoding['labels'].dtype						,		torch.long)
      self.assertTrue(encoding['labels'].min().item() >= 0)
      self.assertTrue(encoding['labels'].max().item() <= 255)


  def 						lowerCAmelCase__     (					self):
      # Initialize image_processing
      SCREAMING_SNAKE_CASE_									=						self.image_processing_class(**self.image_processor_dict)

      # ADE20k has 150 classes, and the background is included, so labels should be between 0 and 150
      SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_									=						prepare_semantic_single_inputs()
      SCREAMING_SNAKE_CASE_									=						image_processing(_A						,		_A						,		return_tensors='pt')
      self.assertTrue(encoding['labels'].min().item() >= 0)
      self.assertTrue(encoding['labels'].max().item() <= 150)

      SCREAMING_SNAKE_CASE_									=						True
      SCREAMING_SNAKE_CASE_									=						image_processing(_A						,		_A						,		return_tensors='pt')
      self.assertTrue(encoding['labels'].min().item() >= 0)
      self.assertTrue(encoding['labels'].max().item() <= 255)


