


'''simple docstring'''







import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import FEATURE_EXTRACTOR_NAME, is_vision_available


if is_vision_available():
   from PIL import Image

   from transformers import ChineseCLIPImageProcessor, ChineseCLIPProcessor


@require_vision
class 			__lowerCamelCase			(	unittest.TestCase	):
    """simple docstring"""


    def        A   (  self      :							List[Any]):
           _A					:     int     = tempfile.mkdtemp()

           _A					:     int     = [
               '[UNK]',
               '[CLS]',
               '[SEP]',
               '[PAD]',
               '[MASK]',
               '的',
               '价',
               '格',
               '是',
               '15',
               '便',
               'alex',
               '##andra',
               '，',
               '。',
               '-',
               't',
               'shirt',
           ]
           _A					:     Any     = os.path.join(self.tmpdirname				,							VOCAB_FILES_NAMES['vocab_file'])
           with open(self.vocab_file				,							'w'				,							encoding='utf-8') as vocab_writer:
                  vocab_writer.write(''.join([x + '\n' for x in vocab_tokens]))

           _A					:     List[str]     = {
               'do_resize': True,
               'size': {'height': 224, 'width': 224},
               'do_center_crop': True,
               'crop_size': {'height': 18, 'width': 18},
               'do_normalize': True,
               'image_mean': [0.4814_5466, 0.457_8275, 0.4082_1073],
               'image_std': [0.2686_2954, 0.2613_0258, 0.2757_7711],
               'do_convert_rgb': True,
           }
           _A					:     List[str]     = os.path.join(self.tmpdirname				,							SCREAMING_SNAKE_CASE)
           with open(self.image_processor_file				,							'w'				,							encoding='utf-8') as fp:
                  json.dump(SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE)


    def        A   (  self      :							Optional[int]				,							**SCREAMING_SNAKE_CASE      :							Optional[Any]):
           return BertTokenizer.from_pretrained(self.tmpdirname				,							**SCREAMING_SNAKE_CASE)


    def        A   (  self      :							Union[str, Any]				,							**SCREAMING_SNAKE_CASE      :							Optional[int]):
           return BertTokenizerFast.from_pretrained(self.tmpdirname				,							**SCREAMING_SNAKE_CASE)


    def        A   (  self      :							List[Any]				,							**SCREAMING_SNAKE_CASE      :							Union[str, Any]):
           return ChineseCLIPImageProcessor.from_pretrained(self.tmpdirname				,							**SCREAMING_SNAKE_CASE)


    def        A   (  self      :							Optional[int]):
           shutil.rmtree(self.tmpdirname)


    def        A   (  self      :							Optional[Any]):
           _A					:     Optional[int]     = [np.random.randint(255				,							size=(3, 30, 400)				,							dtype=np.uinta)]

           _A					:     Dict     = [Image.fromarray(np.moveaxis(SCREAMING_SNAKE_CASE				,							0				,							-1)) for x in image_inputs]

           return image_inputs


    def        A   (  self      :							Optional[int]):
           _A					:     int     = self.get_tokenizer()
           _A					:     Optional[int]     = self.get_rust_tokenizer()
           _A					:     Union[str, Any]     = self.get_image_processor()

           _A					:     Dict     = ChineseCLIPProcessor(tokenizer=SCREAMING_SNAKE_CASE				,							image_processor=SCREAMING_SNAKE_CASE)
           processor_slow.save_pretrained(self.tmpdirname)
           _A					:     str     = ChineseCLIPProcessor.from_pretrained(self.tmpdirname				,							use_fast=SCREAMING_SNAKE_CASE)

           _A					:     Tuple     = ChineseCLIPProcessor(tokenizer=SCREAMING_SNAKE_CASE				,							image_processor=SCREAMING_SNAKE_CASE)
           processor_fast.save_pretrained(self.tmpdirname)
           _A					:     int     = ChineseCLIPProcessor.from_pretrained(self.tmpdirname)

           self.assertEqual(processor_slow.tokenizer.get_vocab()				,							tokenizer_slow.get_vocab())
           self.assertEqual(processor_fast.tokenizer.get_vocab()				,							tokenizer_fast.get_vocab())
           self.assertEqual(tokenizer_slow.get_vocab()				,							tokenizer_fast.get_vocab())
           self.assertIsInstance(processor_slow.tokenizer				,							SCREAMING_SNAKE_CASE)
           self.assertIsInstance(processor_fast.tokenizer				,							SCREAMING_SNAKE_CASE)

           self.assertEqual(processor_slow.image_processor.to_json_string()				,							image_processor.to_json_string())
           self.assertEqual(processor_fast.image_processor.to_json_string()				,							image_processor.to_json_string())
           self.assertIsInstance(processor_slow.image_processor				,							SCREAMING_SNAKE_CASE)
           self.assertIsInstance(processor_fast.image_processor				,							SCREAMING_SNAKE_CASE)


    def        A   (  self      :							List[str]):
           _A					:     List[Any]     = ChineseCLIPProcessor(tokenizer=self.get_tokenizer()				,							image_processor=self.get_image_processor())
           processor.save_pretrained(self.tmpdirname)

           _A					:     List[str]     = self.get_tokenizer(cls_token='(CLS)'				,							sep_token='(SEP)')
           _A					:     Tuple     = self.get_image_processor(do_normalize=SCREAMING_SNAKE_CASE)

           _A					:     Union[str, Any]     = ChineseCLIPProcessor.from_pretrained(
               self.tmpdirname				,							cls_token='(CLS)'				,							sep_token='(SEP)'				,							do_normalize=SCREAMING_SNAKE_CASE)

           self.assertEqual(processor.tokenizer.get_vocab()				,							tokenizer_add_kwargs.get_vocab())
           self.assertIsInstance(processor.tokenizer				,							SCREAMING_SNAKE_CASE)

           self.assertEqual(processor.image_processor.to_json_string()				,							image_processor_add_kwargs.to_json_string())
           self.assertIsInstance(processor.image_processor				,							SCREAMING_SNAKE_CASE)


    def        A   (  self      :							Optional[int]):
           _A					:     Union[str, Any]     = self.get_image_processor()
           _A					:     Optional[Any]     = self.get_tokenizer()

           _A					:     Dict     = ChineseCLIPProcessor(tokenizer=SCREAMING_SNAKE_CASE				,							image_processor=SCREAMING_SNAKE_CASE)

           _A					:     Any     = self.prepare_image_inputs()

           _A					:     Any     = image_processor(SCREAMING_SNAKE_CASE				,							return_tensors='np')
           _A					:     Any     = processor(images=SCREAMING_SNAKE_CASE				,							return_tensors='np')

           for key in input_feat_extract.keys():
                  self.assertAlmostEqual(input_feat_extract[key].sum()				,							input_processor[key].sum()				,							delta=1e-2)


    def        A   (  self      :							Optional[int]):
           _A					:     List[str]     = self.get_image_processor()
           _A					:     str     = self.get_tokenizer()

           _A					:     Any     = ChineseCLIPProcessor(tokenizer=SCREAMING_SNAKE_CASE				,							image_processor=SCREAMING_SNAKE_CASE)

           _A					:     Optional[Any]     = 'Alexandra，T-shirt的价格是15便士。'

           _A					:     Any     = processor(text=SCREAMING_SNAKE_CASE)

           _A					:     str     = tokenizer(SCREAMING_SNAKE_CASE)

           for key in encoded_tok.keys():
                  self.assertListEqual(encoded_tok[key]				,							encoded_processor[key])


    def        A   (  self      :							List[str]):
           _A					:     int     = self.get_image_processor()
           _A					:     List[Any]     = self.get_tokenizer()

           _A					:     List[Any]     = ChineseCLIPProcessor(tokenizer=SCREAMING_SNAKE_CASE				,							image_processor=SCREAMING_SNAKE_CASE)

           _A					:     Dict     = 'Alexandra，T-shirt的价格是15便士。'
           _A					:     Optional[Any]     = self.prepare_image_inputs()

           _A					:     Optional[Any]     = processor(text=SCREAMING_SNAKE_CASE				,							images=SCREAMING_SNAKE_CASE)

           self.assertListEqual(list(inputs.keys())				,							['input_ids', 'token_type_ids', 'attention_mask', 'pixel_values'])

           # test if it raises when no input is passed
           with pytest.raises(SCREAMING_SNAKE_CASE):
                  processor()


    def        A   (  self      :							Any):
           _A					:     Optional[int]     = self.get_image_processor()
           _A					:     List[Any]     = self.get_tokenizer()

           _A					:     Tuple     = ChineseCLIPProcessor(tokenizer=SCREAMING_SNAKE_CASE				,							image_processor=SCREAMING_SNAKE_CASE)

           _A					:     str     = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

           _A					:     Dict     = processor.batch_decode(SCREAMING_SNAKE_CASE)
           _A					:     Optional[Any]     = tokenizer.batch_decode(SCREAMING_SNAKE_CASE)

           self.assertListEqual(SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE)


    def        A   (  self      :							Union[str, Any]):
           _A					:     int     = self.get_image_processor()
           _A					:     Optional[int]     = self.get_tokenizer()

           _A					:     Tuple     = ChineseCLIPProcessor(tokenizer=SCREAMING_SNAKE_CASE				,							image_processor=SCREAMING_SNAKE_CASE)

           _A					:     int     = 'Alexandra，T-shirt的价格是15便士。'
           _A					:     int     = self.prepare_image_inputs()

           _A					:     Tuple     = processor(text=SCREAMING_SNAKE_CASE				,							images=SCREAMING_SNAKE_CASE)

           self.assertListEqual(list(inputs.keys())				,							processor.model_input_names)










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

A				:					Union[str, Any]    						=			torch.device('''cpu''')






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






def 				lowerCAmelCase__	(    lowerCamelCase					:      List[Any]       ):
       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 				lowerCAmelCase__	(    lowerCamelCase					:      Union[str, Any]  ,lowerCamelCase					:      List[Any]  ,lowerCamelCase					:      Dict       ):
       _A					:     Union[str, Any]     = dct.pop(lowerCamelCase       )
       _A					:     List[str]     = val






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






@torch.no_grad()
def 				lowerCAmelCase__	(    lowerCamelCase					:      int  ,lowerCamelCase					:      str  ,lowerCamelCase					:      List[str]       ):
       _A					:     Dict     = SwiftFormerConfig()

       # dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
       _A					:     Any     = 1000
       _A					:     int     = 'huggingface/label-files'
       _A					:     List[Any]     = 'imagenet-1k-id2label.json'
       _A					:     Optional[int]     = json.load(open(hf_hub_download(lowerCamelCase  ,lowerCamelCase  ,repo_type='dataset'       )  ,'r'       )       )
       _A					:     Dict     = {int(lowerCamelCase       ): v for k, v in idalabel.items()}
       _A					:     Optional[int]     = idalabel
       _A					:     Any     = {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					:     Optional[int]     = [48, 56, 112, 220]

       elif swiftformer_name == "swiftformer_s":
              _A					:     List[Any]     = [3, 3, 9, 6]
              _A					:     Tuple     = [48, 64, 168, 224]

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

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

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

       _A					:     List[str]     = create_rename_keys(lowerCamelCase       )
       for rename_key_src, rename_key_dest in rename_keys:
              rename_key(lowerCamelCase  ,lowerCamelCase  ,lowerCamelCase       )

       # load HuggingFace model
       _A					:     str     = SwiftFormerForImageClassification(lowerCamelCase       ).eval()
       hf_model.load_state_dict(lowerCamelCase       )

       # prepare test inputs
       _A					:     Any     = prepare_img()
       _A					:     Optional[int]     = ViTImageProcessor.from_pretrained('preprocessor_config'       )
       _A					:     Any     = processor(images=lowerCamelCase  ,return_tensors='pt'       )

       # compare outputs from both models
       _A					:     int     = get_expected_output(lowerCamelCase       )
       _A					:     Optional[int]     = hf_model(inputs['pixel_values']       ).logits

       assert hf_logits.shape == torch.Size([1, 1000]       )
       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				:					Dict    						=			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[str]    						=			parser.parse_args()
   convert_swiftformer_checkpoint(args.swiftformer_name, args.pytorch_dump_folder_path, args.original_ckpt)






