
"""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()
A_    							=					logging.get_logger(__name__)

A_    							=					torch.device("""cpu""")






def  lowercase						(	):
			lowerCamelCase_					=  '''http://images.cocodataset.org/val2017/000000039769.jpg'''
			lowerCamelCase_					=  Image.open(requests.get(lowerCAmelCase__	,stream=lowerCAmelCase__						).raw						)
			return im






def  lowercase						(	lowerCAmelCase__						):
			if swiftformer_name == "swiftformer_xs":
						return torch.tensor([-2.17_03E00, 2.11_07E00, -2.08_11E00, 8.86_85E-01, 2.43_60E-01]						)

			elif swiftformer_name == "swiftformer_s":
						return torch.tensor([3.96_36E-01, 2.34_78E-01, -1.69_63E00, -1.73_81E00, -8.63_37E-01]						)

			elif swiftformer_name == "swiftformer_l1":
						return torch.tensor([-4.27_68E-01, -4.74_29E-01, -1.08_97E00, -1.02_48E00, 3.55_23E-02]						)

			elif swiftformer_name == "swiftformer_l3":
						return torch.tensor([-2.53_30E-01, 2.42_11E-01, -6.01_85E-01, -8.27_89E-01, -6.04_46E-02]						)






def  lowercase						(	lowerCAmelCase__	,lowerCAmelCase__	,lowerCAmelCase__						):
			lowerCamelCase_					=  dct.pop(lowerCAmelCase__						)
			lowerCamelCase_					=  val






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






@torch.no_grad()
def  lowercase						(	lowerCAmelCase__	,lowerCAmelCase__	,lowerCAmelCase__						):
			lowerCamelCase_					=  SwiftFormerConfig()

			# dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
			lowerCamelCase_					=  1_000
			lowerCamelCase_					=  '''huggingface/label-files'''
			lowerCamelCase_					=  '''imagenet-1k-id2label.json'''
			lowerCamelCase_					=  json.load(open(hf_hub_download(lowerCAmelCase__	,lowerCAmelCase__	,repo_type='''dataset'''						)	,'''r'''						)						)
			lowerCamelCase_					=  {int(lowerCAmelCase__						): v for k, v in idalabel.items()}
			lowerCamelCase_					=  idalabel
			lowerCamelCase_					=  {v: k for k, v in idalabel.items()}

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

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

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

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

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

			lowerCamelCase_					=  create_rename_keys(lowerCAmelCase__						)
			for rename_key_src, rename_key_dest in rename_keys:
						rename_key(lowerCAmelCase__	,lowerCAmelCase__	,lowerCAmelCase__						)

			# load HuggingFace model
			lowerCamelCase_					=  SwiftFormerForImageClassification(lowerCAmelCase__						).eval()
			hf_model.load_state_dict(lowerCAmelCase__						)

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

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

			assert hf_logits.shape == torch.Size([1, 1_000]						)
			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__":
						A_    							=					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_    							=					parser.parse_args()
						convert_swiftformer_checkpoint(args.swiftformer_name, args.pytorch_dump_folder_path, args.original_ckpt)








import unittest

from transformers import is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_vision_available():
   from PIL import Image
else:


   class 	lowerCamelCase__	:



    """simple docstring"""

    @staticmethod
    def     snake_case_      (    *__lowerCAmelCase					:					Optional[Any]			,						**__lowerCAmelCase					:					Any      )   ->	Any:
       pass


@is_pipeline_test
@require_vision
class 	lowerCamelCase__	(       unittest.TestCase):



 """simple docstring"""

 @require_torch
 def     snake_case_      (    self					:					Tuple      )   ->	Tuple:
    _A      =       pipeline(
        model='''hf-internal-testing/tiny-random-clip-zero-shot-image-classification'''			,						)
    _A      =       Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''      )
    _A      =       image_classifier(__lowerCAmelCase			,						candidate_labels=['''a''', '''b''', '''c''']      )

    # The floating scores are so close, we enter floating error approximation and the order is not guaranteed across
    # python and torch versions.
    self.assertIn(
        nested_simplify(__lowerCAmelCase      )			,						[
            [{'''score''': 0.333, '''label''': '''a'''}, {'''score''': 0.333, '''label''': '''b'''}, {'''score''': 0.333, '''label''': '''c'''}],
            [{'''score''': 0.333, '''label''': '''a'''}, {'''score''': 0.333, '''label''': '''c'''}, {'''score''': 0.333, '''label''': '''b'''}],
        ]			,						)

    _A      =       image_classifier([image] * 5			,						candidate_labels=['''A''', '''B''', '''C''']			,						batch_size=2      )
    self.assertEqual(
        nested_simplify(__lowerCAmelCase      )			,						[
            [
                {'''score''': 0.333, '''label''': ANY(__lowerCAmelCase      )},
                {'''score''': 0.333, '''label''': ANY(__lowerCAmelCase      )},
                {'''score''': 0.333, '''label''': ANY(__lowerCAmelCase      )},
            ],
            [
                {'''score''': 0.333, '''label''': ANY(__lowerCAmelCase      )},
                {'''score''': 0.333, '''label''': ANY(__lowerCAmelCase      )},
                {'''score''': 0.333, '''label''': ANY(__lowerCAmelCase      )},
            ],
            [
                {'''score''': 0.333, '''label''': ANY(__lowerCAmelCase      )},
                {'''score''': 0.333, '''label''': ANY(__lowerCAmelCase      )},
                {'''score''': 0.333, '''label''': ANY(__lowerCAmelCase      )},
            ],
            [
                {'''score''': 0.333, '''label''': ANY(__lowerCAmelCase      )},
                {'''score''': 0.333, '''label''': ANY(__lowerCAmelCase      )},
                {'''score''': 0.333, '''label''': ANY(__lowerCAmelCase      )},
            ],
            [
                {'''score''': 0.333, '''label''': ANY(__lowerCAmelCase      )},
                {'''score''': 0.333, '''label''': ANY(__lowerCAmelCase      )},
                {'''score''': 0.333, '''label''': ANY(__lowerCAmelCase      )},
            ],
        ]			,						)

 @require_tf
 def     snake_case_      (    self					:					int      )   ->	Optional[int]:
    _A      =       pipeline(
        model='''hf-internal-testing/tiny-random-clip-zero-shot-image-classification'''			,						framework='''tf'''      )
    _A      =       Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''      )
    _A      =       image_classifier(__lowerCAmelCase			,						candidate_labels=['''a''', '''b''', '''c''']      )

    self.assertEqual(
        nested_simplify(__lowerCAmelCase      )			,						[{'''score''': 0.333, '''label''': '''a'''}, {'''score''': 0.333, '''label''': '''b'''}, {'''score''': 0.333, '''label''': '''c'''}]			,						)

    _A      =       image_classifier([image] * 5			,						candidate_labels=['''A''', '''B''', '''C''']			,						batch_size=2      )
    self.assertEqual(
        nested_simplify(__lowerCAmelCase      )			,						[
            [
                {'''score''': 0.333, '''label''': ANY(__lowerCAmelCase      )},
                {'''score''': 0.333, '''label''': ANY(__lowerCAmelCase      )},
                {'''score''': 0.333, '''label''': ANY(__lowerCAmelCase      )},
            ],
            [
                {'''score''': 0.333, '''label''': ANY(__lowerCAmelCase      )},
                {'''score''': 0.333, '''label''': ANY(__lowerCAmelCase      )},
                {'''score''': 0.333, '''label''': ANY(__lowerCAmelCase      )},
            ],
            [
                {'''score''': 0.333, '''label''': ANY(__lowerCAmelCase      )},
                {'''score''': 0.333, '''label''': ANY(__lowerCAmelCase      )},
                {'''score''': 0.333, '''label''': ANY(__lowerCAmelCase      )},
            ],
            [
                {'''score''': 0.333, '''label''': ANY(__lowerCAmelCase      )},
                {'''score''': 0.333, '''label''': ANY(__lowerCAmelCase      )},
                {'''score''': 0.333, '''label''': ANY(__lowerCAmelCase      )},
            ],
            [
                {'''score''': 0.333, '''label''': ANY(__lowerCAmelCase      )},
                {'''score''': 0.333, '''label''': ANY(__lowerCAmelCase      )},
                {'''score''': 0.333, '''label''': ANY(__lowerCAmelCase      )},
            ],
        ]			,						)

 @slow
 @require_torch
 def     snake_case_      (    self					:					Optional[int]      )   ->	int:
    _A      =       pipeline(
        task='''zero-shot-image-classification'''			,						model='''openai/clip-vit-base-patch32'''			,						)
    # This is an image of 2 cats with remotes and no planes
    _A      =       Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''      )
    _A      =       image_classifier(__lowerCAmelCase			,						candidate_labels=['''cat''', '''plane''', '''remote''']      )

    self.assertEqual(
        nested_simplify(__lowerCAmelCase      )			,						[
            {'''score''': 0.511, '''label''': '''remote'''},
            {'''score''': 0.485, '''label''': '''cat'''},
            {'''score''': 0.004, '''label''': '''plane'''},
        ]			,						)

    _A      =       image_classifier([image] * 5			,						candidate_labels=['''cat''', '''plane''', '''remote''']			,						batch_size=2      )
    self.assertEqual(
        nested_simplify(__lowerCAmelCase      )			,						[
            [
                {'''score''': 0.511, '''label''': '''remote'''},
                {'''score''': 0.485, '''label''': '''cat'''},
                {'''score''': 0.004, '''label''': '''plane'''},
            ],
        ]
        * 5			,						)

 @slow
 @require_tf
 def     snake_case_      (    self					:					Optional[int]      )   ->	Dict:
    _A      =       pipeline(
        task='''zero-shot-image-classification'''			,						model='''openai/clip-vit-base-patch32'''			,						framework='''tf'''      )
    # This is an image of 2 cats with remotes and no planes
    _A      =       Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''      )
    _A      =       image_classifier(__lowerCAmelCase			,						candidate_labels=['''cat''', '''plane''', '''remote''']      )
    self.assertEqual(
        nested_simplify(__lowerCAmelCase      )			,						[
            {'''score''': 0.511, '''label''': '''remote'''},
            {'''score''': 0.485, '''label''': '''cat'''},
            {'''score''': 0.004, '''label''': '''plane'''},
        ]			,						)

    _A      =       image_classifier([image] * 5			,						candidate_labels=['''cat''', '''plane''', '''remote''']			,						batch_size=2      )
    self.assertEqual(
        nested_simplify(__lowerCAmelCase      )			,						[
            [
                {'''score''': 0.511, '''label''': '''remote'''},
                {'''score''': 0.485, '''label''': '''cat'''},
                {'''score''': 0.004, '''label''': '''plane'''},
            ],
        ]
        * 5			,						)



