




import os
import unittest

from transformers import BatchEncoding
from transformers.models.bert.tokenization_bert import (
    BasicTokenizer,
    WordpieceTokenizer,
    _is_control,
    _is_punctuation,
    _is_whitespace,
)
from transformers.models.prophetnet.tokenization_prophetnet import VOCAB_FILES_NAMES, ProphetNetTokenizer
from transformers.testing_utils import require_torch, slow

from ...test_tokenization_common import TokenizerTesterMixin







class     A(		UpperCamelCase				, unittest.TestCase  ):



 '''simple docstring'''


 UpperCamelCase		  =		ProphetNetTokenizer
 UpperCamelCase		  =		False

 def 							a__					(							self   :    List[Any]    )  ->      List[str]:

       """simple docstring"""






       super().setUp()

       lowerCamelCase_							=       [
           '[UNK]',
           '[CLS]',
           '[SEP]',
           '[PAD]',
           '[MASK]',
           'want',
           '##want',
           '##ed',
           'wa',
           'un',
           'runn',
           '##ing',
           ',',
           'low',
           'lowest',
       ]
       lowerCamelCase_							=       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]    )    )

 def 							a__					(							self   :    Union[str, Any]		,				A_   :    str    )  ->      List[Any]:

       """simple docstring"""






       lowerCamelCase_							=       'UNwant\u00E9d,running'
       lowerCamelCase_							=       'unwanted, running'
       return input_text, output_text

 def 							a__					(							self   :    Optional[int]    )  ->      int:

       """simple docstring"""






       lowerCamelCase_							=       self.tokenizer_class(self.vocab_file    )

       lowerCamelCase_							=       tokenizer.tokenize('UNwant\u00E9d,running'    )
       self.assertListEqual(A_		,				['un', '##want', '##ed', ',', 'runn', '##ing']    )
       self.assertListEqual(tokenizer.convert_tokens_to_ids(A_    )		,				[9, 6, 7, 12, 10, 11]    )

 def 							a__					(							self   :    List[str]    )  ->      Optional[Any]:

       """simple docstring"""






       lowerCamelCase_							=       BasicTokenizer()

       self.assertListEqual(tokenizer.tokenize('ah\u535A\u63A8zz'    )		,				['ah', '\u535A', '\u63A8', 'zz']    )

 def 							a__					(							self   :    List[Any]    )  ->      Dict:

       """simple docstring"""






       lowerCamelCase_							=       BasicTokenizer(do_lower_case=A_    )

       self.assertListEqual(
           tokenizer.tokenize(' \tHeLLo!how  \n Are yoU?  '    )		,				['hello', '!', 'how', 'are', 'you', '?']    )
       self.assertListEqual(tokenizer.tokenize('H\u00E9llo'    )		,				['hello']    )

 def 							a__					(							self   :    Optional[Any]    )  ->      Optional[int]:

       """simple docstring"""






       lowerCamelCase_							=       BasicTokenizer(do_lower_case=A_		,				strip_accents=A_    )

       self.assertListEqual(
           tokenizer.tokenize(' \tHäLLo!how  \n Are yoU?  '    )		,				['hällo', '!', 'how', 'are', 'you', '?']    )
       self.assertListEqual(tokenizer.tokenize('H\u00E9llo'    )		,				['h\u00E9llo']    )

 def 							a__					(							self   :    List[str]    )  ->      Optional[int]:

       """simple docstring"""






       lowerCamelCase_							=       BasicTokenizer(do_lower_case=A_		,				strip_accents=A_    )

       self.assertListEqual(
           tokenizer.tokenize(' \tHäLLo!how  \n Are yoU?  '    )		,				['hallo', '!', 'how', 'are', 'you', '?']    )
       self.assertListEqual(tokenizer.tokenize('H\u00E9llo'    )		,				['hello']    )

 def 							a__					(							self   :    Union[str, Any]    )  ->      Tuple:

       """simple docstring"""






       lowerCamelCase_							=       BasicTokenizer(do_lower_case=A_    )

       self.assertListEqual(
           tokenizer.tokenize(' \tHäLLo!how  \n Are yoU?  '    )		,				['hallo', '!', 'how', 'are', 'you', '?']    )
       self.assertListEqual(tokenizer.tokenize('H\u00E9llo'    )		,				['hello']    )

 def 							a__					(							self   :    Tuple    )  ->      Any:

       """simple docstring"""






       lowerCamelCase_							=       BasicTokenizer(do_lower_case=A_    )

       self.assertListEqual(
           tokenizer.tokenize(' \tHeLLo!how  \n Are yoU?  '    )		,				['HeLLo', '!', 'how', 'Are', 'yoU', '?']    )

 def 							a__					(							self   :    Union[str, Any]    )  ->      Dict:

       """simple docstring"""






       lowerCamelCase_							=       BasicTokenizer(do_lower_case=A_		,				strip_accents=A_    )

       self.assertListEqual(
           tokenizer.tokenize(' \tHäLLo!how  \n Are yoU?  '    )		,				['HäLLo', '!', 'how', 'Are', 'yoU', '?']    )

 def 							a__					(							self   :    Union[str, Any]    )  ->      Optional[int]:

       """simple docstring"""






       lowerCamelCase_							=       BasicTokenizer(do_lower_case=A_		,				strip_accents=A_    )

       self.assertListEqual(
           tokenizer.tokenize(' \tHäLLo!how  \n Are yoU?  '    )		,				['HaLLo', '!', 'how', 'Are', 'yoU', '?']    )

 def 							a__					(							self   :    Union[str, Any]    )  ->      int:

       """simple docstring"""






       lowerCamelCase_							=       BasicTokenizer(do_lower_case=A_		,				never_split=['[UNK]']    )

       self.assertListEqual(
           tokenizer.tokenize(' \tHeLLo!how  \n Are yoU? [UNK]'    )		,				['HeLLo', '!', 'how', 'Are', 'yoU', '?', '[UNK]']    )

 def 							a__					(							self   :    List[str]    )  ->      Dict:

       """simple docstring"""






       lowerCamelCase_							=       ['[UNK]', '[CLS]', '[SEP]', 'want', '##want', '##ed', 'wa', 'un', 'runn', '##ing']

       lowerCamelCase_							=       {}
       for i, token in enumerate(A_    ):
             lowerCamelCase_							=       i
       lowerCamelCase_							=       WordpieceTokenizer(vocab=A_		,				unk_token='[UNK]'    )

       self.assertListEqual(tokenizer.tokenize(''    )		,				[]    )

       self.assertListEqual(tokenizer.tokenize('unwanted running'    )		,				['un', '##want', '##ed', 'runn', '##ing']    )

       self.assertListEqual(tokenizer.tokenize('unwantedX running'    )		,				['[UNK]', 'runn', '##ing']    )

 @require_torch
 def 							a__					(							self   :    Union[str, Any]    )  ->      Dict:

       """simple docstring"""






       lowerCamelCase_							=       self.tokenizer_class.from_pretrained('microsoft/prophetnet-large-uncased'    )

       lowerCamelCase_							=       ['A long paragraph for summarization.', 'Another paragraph for summarization.']
       lowerCamelCase_							=       [1037, 2146, 20423, 2005, 7680, 7849, 3989, 1012, 102]
       lowerCamelCase_							=       tokenizer(A_		,				padding=A_		,				return_tensors='pt'    )
       self.assertIsInstance(A_		,				A_    )
       lowerCamelCase_							=       list(batch.input_ids.numpy()[0]    )
       self.assertListEqual(A_		,				A_    )

       self.assertEqual((2, 9)		,				batch.input_ids.shape    )
       self.assertEqual((2, 9)		,				batch.attention_mask.shape    )

 def 							a__					(							self   :    int    )  ->      Union[str, Any]:

       """simple docstring"""






       self.assertTrue(_is_whitespace(' '    )    )
       self.assertTrue(_is_whitespace('\t'    )    )
       self.assertTrue(_is_whitespace('\r'    )    )
       self.assertTrue(_is_whitespace('\n'    )    )
       self.assertTrue(_is_whitespace('\u00A0'    )    )

       self.assertFalse(_is_whitespace('A'    )    )
       self.assertFalse(_is_whitespace('-'    )    )

 def 							a__					(							self   :    List[str]    )  ->      Union[str, Any]:

       """simple docstring"""






       self.assertTrue(_is_control('\u0005'    )    )

       self.assertFalse(_is_control('A'    )    )
       self.assertFalse(_is_control(' '    )    )
       self.assertFalse(_is_control('\t'    )    )
       self.assertFalse(_is_control('\r'    )    )

 def 							a__					(							self   :    int    )  ->      str:

       """simple docstring"""






       self.assertTrue(_is_punctuation('-'    )    )
       self.assertTrue(_is_punctuation('$'    )    )
       self.assertTrue(_is_punctuation('`'    )    )
       self.assertTrue(_is_punctuation('.'    )    )

       self.assertFalse(_is_punctuation('A'    )    )
       self.assertFalse(_is_punctuation(' '    )    )





 @slow
 def 							a__					(							self   :    int    )  ->      int:

       """simple docstring"""






       lowerCamelCase_							=       self.tokenizer_class.from_pretrained('microsoft/prophetnet-large-uncased'    )

       lowerCamelCase_							=       tokenizer.encode('sequence builders'		,				add_special_tokens=A_    )
       lowerCamelCase_							=       tokenizer.encode('multi-sequence build'		,				add_special_tokens=A_    )

       lowerCamelCase_							=       tokenizer.build_inputs_with_special_tokens(A_    )
       lowerCamelCase_							=       tokenizer.build_inputs_with_special_tokens(A_		,				A_    )

       assert encoded_sentence == text + [102]
       assert encoded_pair == text + [102] + text_a + [102]








import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm import create_model
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform

from transformers import BitConfig, BitForImageClassification, BitImageProcessor
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


logging.set_verbosity_info()
_lowercase    	=		logging.get_logger(__name__)



def 		lowerCAmelCase__     (       UpperCamelCase_					:							str						)->	int:
    A__					=       '''huggingface/label-files'''
    A__					=       '''imagenet-1k-id2label.json'''
    A__					=       json.load(open(hf_hub_download(UpperCamelCase_			,							UpperCamelCase_			,							repo_type='''dataset'''						)			,							'''r'''						)						)
    A__					=       {int(UpperCamelCase_						): v for k, v in idalabel.items()}
    A__					=       {v: k for k, v in idalabel.items()}

    A__					=       '''std_conv''' if '''bit''' in model_name else False

    # note that when using BiT as backbone for ViT-hybrid checkpoints,
    # one needs to additionally set config.layer_type = "bottleneck", config.stem_type = "same",
    # config.conv_layer = "std_conv_same"
    A__					=       BitConfig(
        conv_layer=UpperCamelCase_			,							num_labels=1_0_0_0			,							idalabel=UpperCamelCase_			,							labelaid=UpperCamelCase_			,							)

    return config



def 		lowerCAmelCase__     (       UpperCamelCase_					:							List[Any]						)->	Optional[int]:
    if "stem.conv" in name:
        A__					=       name.replace('''stem.conv'''			,							'''bit.embedder.convolution'''						)
    if "blocks" in name:
        A__					=       name.replace('''blocks'''			,							'''layers'''						)
    if "head.fc" in name:
        A__					=       name.replace('''head.fc'''			,							'''classifier.1'''						)
    if name.startswith('''norm'''						):
        A__					=       '''bit.''' + name
    if "bit" not in name and "classifier" not in name:
        A__					=       '''bit.encoder.''' + name

    return name



def 		lowerCAmelCase__     (       )->	str:
    A__					=       '''http://images.cocodataset.org/val2017/000000039769.jpg'''
    A__					=       Image.open(requests.get(UpperCamelCase_			,							stream=UpperCamelCase_						).raw						)
    return im



@torch.no_grad()
def 		lowerCAmelCase__     (       UpperCamelCase_					:							List[str]			,							UpperCamelCase_					:							Tuple			,							UpperCamelCase_					:							List[Any]=False						)->	Optional[int]:
    A__					=       get_config(UpperCamelCase_						)

    # load original model from timm
    A__					=       create_model(UpperCamelCase_			,							pretrained=UpperCamelCase_						)
    timm_model.eval()

    # load state_dict of original model
    A__					=       timm_model.state_dict()
    for key in state_dict.copy().keys():
        A__					=       state_dict.pop(UpperCamelCase_						)
        A__					=       val.squeeze() if '''head''' in key else val

    # load HuggingFace model
    A__					=       BitForImageClassification(UpperCamelCase_						)
    model.eval()
    model.load_state_dict(UpperCamelCase_						)

    # create image processor
    A__					=       create_transform(**resolve_data_config({}			,							model=UpperCamelCase_						)						)
    A__					=       transform.transforms

    A__					=       {
        '''bilinear''': PILImageResampling.BILINEAR,
        '''bicubic''': PILImageResampling.BICUBIC,
        '''nearest''': PILImageResampling.NEAREST,
    }

    A__					=       BitImageProcessor(
        do_resize=UpperCamelCase_			,							size={'''shortest_edge''': timm_transforms[0].size}			,							resample=pillow_resamplings[timm_transforms[0].interpolation.value]			,							do_center_crop=UpperCamelCase_			,							crop_size={'''height''': timm_transforms[1].size[0], '''width''': timm_transforms[1].size[1]}			,							do_normalize=UpperCamelCase_			,							image_mean=timm_transforms[-1].mean.tolist()			,							image_std=timm_transforms[-1].std.tolist()			,							)

    A__					=       prepare_img()
    A__					=       transform(UpperCamelCase_						).unsqueeze(0						)
    A__					=       processor(UpperCamelCase_			,							return_tensors='''pt'''						).pixel_values

    # verify pixel values
    assert torch.allclose(UpperCamelCase_			,							UpperCamelCase_						)

    # verify logits
    with torch.no_grad():
        A__					=       model(UpperCamelCase_						)
        A__					=       outputs.logits

    print('''Logits:'''			,							logits[0, :3]						)
    print('''Predicted class:'''			,							model.config.idalabel[logits.argmax(-1						).item()]						)
    A__					=       timm_model(UpperCamelCase_						)
    assert timm_logits.shape == outputs.logits.shape
    assert torch.allclose(UpperCamelCase_			,							outputs.logits			,							atol=1E-3						)
    print('''Looks ok!'''						)

    if pytorch_dump_folder_path is not None:
        Path(UpperCamelCase_						).mkdir(exist_ok=UpperCamelCase_						)
        print(f"Saving model {model_name} and processor to {pytorch_dump_folder_path}"						)
        model.save_pretrained(UpperCamelCase_						)
        processor.save_pretrained(UpperCamelCase_						)

    if push_to_hub:
        print(f"Pushing model {model_name} and processor to the hub"						)
        model.push_to_hub(f"ybelkada/{model_name}"						)
        processor.push_to_hub(f"ybelkada/{model_name}"						)


if __name__ == "__main__":
   _lowercase    	=		argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       "--model_name",
       default="resnetv2_50x1_bitm",
       type=str,
       help="Name of the BiT timm model you'd like to convert.",
   )
   parser.add_argument(
       "--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
   )
   parser.add_argument(
       "--push_to_hub",
       action="store_true",
       help="Whether to push the model to the hub.",
   )

   _lowercase    	=		parser.parse_args()
   convert_bit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
