





"""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					(       )       ->int:



    '''simple docstring'''







    __A   :						str									=					'''http://images.cocodataset.org/val2017/000000039769.jpg'''
    __A   :						Optional[int]									=					Image.open(requests.get(snake_case_    ,stream=snake_case_						).raw						)
    return im







def 		__lowercase					(       snake_case_  :  Optional[int]						)       ->List[Any]:



    '''simple docstring'''







    if swiftformer_name == "swiftformer_xs":
        return torch.tensor([-2.1703e00, 2.1107e00, -2.0811e00, 8.8685e-01, 2.4360e-01]						)

    elif swiftformer_name == "swiftformer_s":
        return torch.tensor([3.9636e-01, 2.3478e-01, -1.6963e00, -1.7381e00, -8.6337e-01]						)

    elif swiftformer_name == "swiftformer_l1":
        return torch.tensor([-4.2768e-01, -4.7429e-01, -1.0897e00, -1.0248e00, 3.5523e-02]						)

    elif swiftformer_name == "swiftformer_l3":
        return torch.tensor([-2.5330e-01, 2.4211e-01, -6.0185e-01, -8.2789e-01, -6.0446e-02]						)







def 		__lowercase					(       snake_case_  :  Dict    ,snake_case_  :  int    ,snake_case_  :  Tuple						)       ->Union[str, Any]:



    '''simple docstring'''







    __A   :						Any									=					dct.pop(snake_case_						)
    __A   :						Optional[int]									=					val







def 		__lowercase					(       snake_case_  :  Dict						)       ->Dict:



    '''simple docstring'''







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







@torch.no_grad()
def 		__lowercase					(       snake_case_  :  Union[str, Any]    ,snake_case_  :  str    ,snake_case_  :  Union[str, Any]						)       ->Optional[int]:



    '''simple docstring'''







    __A   :						str									=					SwiftFormerConfig()

    # dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
    __A   :						Union[str, Any]									=					1000
    __A   :						Optional[Any]									=					'''huggingface/label-files'''
    __A   :						List[Any]									=					'''imagenet-1k-id2label.json'''
    __A   :						List[str]									=					json.load(open(hf_hub_download(snake_case_    ,snake_case_    ,repo_type='''dataset'''						)    ,'''r'''						)						)
    __A   :						Tuple									=					{int(snake_case_						): v for k, v in idalabel.items()}
    __A   :						List[str]									=					idalabel
    __A   :						Dict									=					{v: k for k, v in idalabel.items()}

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

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

    elif swiftformer_name == "swiftformer_l1":
        __A   :						List[Any]									=					[4, 3, 10, 5]
        __A   :						str									=					[48, 96, 192, 384]

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

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

    __A   :						Union[str, Any]									=					create_rename_keys(snake_case_						)
    for rename_key_src, rename_key_dest in rename_keys:
        rename_key(snake_case_    ,snake_case_    ,snake_case_						)

    # load HuggingFace model
    __A   :						Dict									=					SwiftFormerForImageClassification(snake_case_						).eval()
    hf_model.load_state_dict(snake_case_						)

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

    # compare outputs from both models
    __A   :						Dict									=					get_expected_output(snake_case_						)
    __A   :						List[str]									=					hf_model(inputs['''pixel_values''']						).logits

    assert hf_logits.shape == torch.Size([1, 1000]						)
    assert torch.allclose(hf_logits[0, 0:5]    ,snake_case_    ,atol=1e-3						)

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


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)











"""simple docstring"""



import json
import os
import unittest

from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin






@require_tokenizers
class 		__snake_case (		SCREAMING_SNAKE_CASE__							,							unittest.TestCase					):





 """simple docstring"""


 _lowerCamelCase				=					MgpstrTokenizer
 _lowerCamelCase				=					False
 _lowerCamelCase				=					{}
 _lowerCamelCase				=					False

 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     super().setUp()

     # fmt: off
     __A   :						int									=					['''[GO]''', '''[s]''', '''0''', '''1''', '''2''', '''3''', '''4''', '''5''', '''6''', '''7''', '''8''', '''9''', '''a''', '''b''', '''c''', '''d''', '''e''', '''f''', '''g''', '''h''', '''i''', '''j''', '''k''', '''l''', '''m''', '''n''', '''o''', '''p''', '''q''', '''r''', '''s''', '''t''', '''u''', '''v''', '''w''', '''x''', '''y''', '''z''']
     # fmt: on
     __A   :						Dict									=					dict(zip(__lowerCamelCase ,						range(len(__lowerCamelCase  )  )  )  )

     __A   :						Optional[Any]									=					os.path.join(self.tmpdirname ,						VOCAB_FILES_NAMES['''vocab_file''']  )
     with open(self.vocab_file ,						'''w''' ,						encoding='''utf-8'''  ) as fp:
         fp.write(json.dumps(__lowerCamelCase  ) + '''\n'''  )

 def  UpperCamelCase__(   self ,						**__lowerCamelCase  ):



     '''simple docstring'''




     return MgpstrTokenizer.from_pretrained(self.tmpdirname ,						**__lowerCamelCase  )

 def  UpperCamelCase__(   self ,						__lowerCamelCase  ):



     '''simple docstring'''




     __A   :						List[str]									=					'''tester'''
     __A   :						Dict									=					'''tester'''
     return input_text, output_text

 @unittest.skip('''MGP-STR always lower cases letters.'''  )
 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     pass

 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     __A   :						List[Any]									=					self.get_tokenizers(do_lower_case=__lowerCamelCase  )
     for tokenizer in tokenizers:
         with self.subTest(F"""{tokenizer.__class__.__name__}"""  ):
             __A   :						Union[str, Any]									=					'''[SPECIAL_TOKEN]'''

             tokenizer.add_special_tokens({'''cls_token''': special_token}  )
             __A   :						Optional[Any]									=					tokenizer.encode([special_token] ,						add_special_tokens=__lowerCamelCase  )
             self.assertEqual(len(__lowerCamelCase  ) ,						1  )

             __A   :						List[Any]									=					tokenizer.decode(__lowerCamelCase ,						skip_special_tokens=__lowerCamelCase  )
             self.assertTrue(special_token not in decoded  )

 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     __A   :						Tuple									=					self.get_tokenizers()
     for tokenizer in tokenizers:
         with self.subTest(F"""{tokenizer.__class__.__name__}"""  ):
             __A			,	__A   :						str									=					self.get_input_output_texts(__lowerCamelCase  )

             __A   :						Union[str, Any]									=					tokenizer.tokenize(__lowerCamelCase  )
             __A   :						Union[str, Any]									=					tokenizer.convert_tokens_to_ids(__lowerCamelCase  )
             __A   :						Union[str, Any]									=					tokenizer.encode(__lowerCamelCase ,						add_special_tokens=__lowerCamelCase  )
             self.assertListEqual(__lowerCamelCase ,						__lowerCamelCase  )

             __A   :						Optional[Any]									=					tokenizer.convert_ids_to_tokens(__lowerCamelCase  )
             self.assertNotEqual(len(__lowerCamelCase  ) ,						0  )
             __A   :						Union[str, Any]									=					tokenizer.decode(__lowerCamelCase  )
             self.assertIsInstance(__lowerCamelCase ,						__lowerCamelCase  )

             self.assertEqual(text_a.replace(''' ''' ,						''''''  ) ,						__lowerCamelCase  )

 @unittest.skip('''MGP-STR tokenizer only handles one sequence.'''  )
 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     pass







 @unittest.skip('''inputs cannot be pretokenized in MgpstrTokenizer'''  )
 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     pass




