

"""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 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()
a					:Dict    = logging.get_logger(__name__)




def  _lowercase					(    __lowerCAmelCase							)       ->				List[str]:
      SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	"""huggingface/label-files"""
      SCREAMING_SNAKE_CASE__   :      Tuple      		=	"""imagenet-1k-id2label.json"""
      SCREAMING_SNAKE_CASE__   :      Tuple      		=	json.load(open(hf_hub_download(__lowerCAmelCase  ,					__lowerCAmelCase  ,					repo_type="""dataset"""							)  ,					"""r"""							)							)
      SCREAMING_SNAKE_CASE__   :      str      		=	{int(__lowerCAmelCase							): v for k, v in idalabel.items()}
      SCREAMING_SNAKE_CASE__   :      str      		=	{v: k for k, v in idalabel.items()}

      SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	"""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"
      SCREAMING_SNAKE_CASE__   :      Dict      		=	BitConfig(
          conv_layer=__lowerCAmelCase  ,					num_labels=1000  ,					idalabel=__lowerCAmelCase  ,					labelaid=__lowerCAmelCase  ,					)

      return config




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

      return name




def  _lowercase					(    )       ->				List[str]:
      SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	"""http://images.cocodataset.org/val2017/000000039769.jpg"""
      SCREAMING_SNAKE_CASE__   :      Tuple      		=	Image.open(requests.get(__lowerCAmelCase  ,					stream=__lowerCAmelCase							).raw							)
      return im




@torch.no_grad()
def  _lowercase					(    __lowerCAmelCase  ,					__lowerCAmelCase  ,					__lowerCAmelCase=False							)       ->				Optional[int]:
      SCREAMING_SNAKE_CASE__   :      List[Any]      		=	get_config(__lowerCAmelCase							)

      # load original model from timm
      SCREAMING_SNAKE_CASE__   :      str      		=	create_model(__lowerCAmelCase  ,					pretrained=__lowerCAmelCase							)
      timm_model.eval()

      # load state_dict of original model
      SCREAMING_SNAKE_CASE__   :      int      		=	timm_model.state_dict()
      for key in state_dict.copy().keys():
            SCREAMING_SNAKE_CASE__   :      int      		=	state_dict.pop(__lowerCAmelCase							)
            SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	val.squeeze() if """head""" in key else val

      # load HuggingFace model
      SCREAMING_SNAKE_CASE__   :      Any      		=	BitForImageClassification(__lowerCAmelCase							)
      model.eval()
      model.load_state_dict(__lowerCAmelCase							)

      # create image processor
      SCREAMING_SNAKE_CASE__   :      Dict      		=	create_transform(**resolve_data_config({}  ,					model=__lowerCAmelCase							)							)
      SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	transform.transforms

      SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	{
          """bilinear""": PILImageResampling.BILINEAR,
          """bicubic""": PILImageResampling.BICUBIC,
          """nearest""": PILImageResampling.NEAREST,
      }

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

      SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	prepare_img()
      SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	transform(__lowerCAmelCase							).unsqueeze(0							)
      SCREAMING_SNAKE_CASE__   :      Any      		=	processor(__lowerCAmelCase  ,					return_tensors="""pt"""							).pixel_values

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

      # verify logits
      with torch.no_grad():
            SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	model(__lowerCAmelCase							)
            SCREAMING_SNAKE_CASE__   :      List[Any]      		=	outputs.logits

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

      if pytorch_dump_folder_path is not None:
            Path(__lowerCAmelCase							).mkdir(exist_ok=__lowerCAmelCase							)
            print(F'''Saving model {model_name} and processor to {pytorch_dump_folder_path}'''							)
            model.save_pretrained(__lowerCAmelCase							)
            processor.save_pretrained(__lowerCAmelCase							)

      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__":
  a					:str    = 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.",
  )

  a					:List[Any]    = parser.parse_args()
  convert_bit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)







"""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 IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
  from PIL import Image

  from transformers import AlignProcessor, EfficientNetImageProcessor

@require_vision
class      __a							(unittest.TestCase):






     '''simple docstring'''

     def 					_a			(						self  )  ->      int:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      List[Any]      		=	tempfile.mkdtemp()

           SCREAMING_SNAKE_CASE__   :      Dict      		=	[
               """[UNK]""",
               """[CLS]""",
               """[SEP]""",
               """[PAD]""",
               """[MASK]""",
               """want""",
               """##want""",
               """##ed""",
               """wa""",
               """un""",
               """runn""",
               """##ing""",
               """,""",
               """low""",
               """lowest""",
           ]
           SCREAMING_SNAKE_CASE__   :      Dict      		=	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]  )  )

           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	{
               """do_resize""": True,
               """size""": 20,
               """do_center_crop""": True,
               """crop_size""": 18,
               """do_normalize""": True,
               """image_mean""": [0.48_145_466, 0.4_578_275, 0.40_821_073],
               """image_std""": [0.26_862_954, 0.26_130_258, 0.27_577_711],
           }
           SCREAMING_SNAKE_CASE__   :      Tuple      		=	os.path.join(self.tmpdirname	,    _a  )
           with open(self.image_processor_file	,    """w"""	,    encoding="""utf-8"""  ) as fp:
                 json.dump(_a	,    _a  )

     def 					_a			(						self	,    **_a  )  ->      List[Any]:


           """simple docstring"""


           return BertTokenizer.from_pretrained(self.tmpdirname	,    **_a  )

     def 					_a			(						self	,    **_a  )  ->      List[Any]:


           """simple docstring"""


           return BertTokenizerFast.from_pretrained(self.tmpdirname	,    **_a  )

     def 					_a			(						self	,    **_a  )  ->      Any:


           """simple docstring"""


           return EfficientNetImageProcessor.from_pretrained(self.tmpdirname	,    **_a  )

     def 					_a			(						self  )  ->      List[Any]:


           """simple docstring"""


           shutil.rmtree(self.tmpdirname  )

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


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Tuple      		=	[np.random.randint(255	,    size=(3, 30, 400)	,    dtype=np.uinta  )]
           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	[Image.fromarray(np.moveaxis(_a	,    0	,    -1  )  ) for x in image_inputs]
           return image_inputs

     def 					_a			(						self  )  ->      List[str]:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Tuple      		=	self.get_tokenizer()
           SCREAMING_SNAKE_CASE__   :      str      		=	self.get_rust_tokenizer()
           SCREAMING_SNAKE_CASE__   :      str      		=	self.get_image_processor()

           SCREAMING_SNAKE_CASE__   :      Tuple      		=	AlignProcessor(tokenizer=_a	,    image_processor=_a  )
           processor_slow.save_pretrained(self.tmpdirname  )
           SCREAMING_SNAKE_CASE__   :      str      		=	AlignProcessor.from_pretrained(self.tmpdirname	,    use_fast=_a  )

           SCREAMING_SNAKE_CASE__   :      int      		=	AlignProcessor(tokenizer=_a	,    image_processor=_a  )
           processor_fast.save_pretrained(self.tmpdirname  )
           SCREAMING_SNAKE_CASE__   :      Any      		=	AlignProcessor.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	,    _a  )
           self.assertIsInstance(processor_fast.tokenizer	,    _a  )

           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	,    _a  )
           self.assertIsInstance(processor_fast.image_processor	,    _a  )

     def 					_a			(						self  )  ->      str:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	AlignProcessor(tokenizer=self.get_tokenizer()	,    image_processor=self.get_image_processor()  )
           processor.save_pretrained(self.tmpdirname  )

           SCREAMING_SNAKE_CASE__   :      Any      		=	self.get_tokenizer(bos_token="""(BOS)"""	,    eos_token="""(EOS)"""  )
           SCREAMING_SNAKE_CASE__   :      Dict      		=	self.get_image_processor(do_normalize=_a	,    padding_value=1.0  )

           SCREAMING_SNAKE_CASE__   :      Dict      		=	AlignProcessor.from_pretrained(
               self.tmpdirname	,    bos_token="""(BOS)"""	,    eos_token="""(EOS)"""	,    do_normalize=_a	,    padding_value=1.0  )

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

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

     def 					_a			(						self  )  ->      List[Any]:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	self.get_image_processor()
           SCREAMING_SNAKE_CASE__   :      str      		=	self.get_tokenizer()

           SCREAMING_SNAKE_CASE__   :      List[str]      		=	AlignProcessor(tokenizer=_a	,    image_processor=_a  )

           SCREAMING_SNAKE_CASE__   :      Any      		=	self.prepare_image_inputs()

           SCREAMING_SNAKE_CASE__   :      List[Any]      		=	image_processor(_a	,    return_tensors="""np"""  )
           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	processor(images=_a	,    return_tensors="""np"""  )

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

     def 					_a			(						self  )  ->      Any:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      int      		=	self.get_image_processor()
           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	self.get_tokenizer()

           SCREAMING_SNAKE_CASE__   :      Any      		=	AlignProcessor(tokenizer=_a	,    image_processor=_a  )

           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	"""lower newer"""

           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	processor(text=_a  )

           SCREAMING_SNAKE_CASE__   :      Any      		=	tokenizer(_a	,    padding="""max_length"""	,    max_length=64  )

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

     def 					_a			(						self  )  ->      Optional[Any]:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      str      		=	self.get_image_processor()
           SCREAMING_SNAKE_CASE__   :      int      		=	self.get_tokenizer()

           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	AlignProcessor(tokenizer=_a	,    image_processor=_a  )

           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	"""lower newer"""
           SCREAMING_SNAKE_CASE__   :      List[Any]      		=	self.prepare_image_inputs()

           SCREAMING_SNAKE_CASE__   :      Any      		=	processor(text=_a	,    images=_a  )

           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(_a  ):
                 processor()

     def 					_a			(						self  )  ->      Any:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Any      		=	self.get_image_processor()
           SCREAMING_SNAKE_CASE__   :      Dict      		=	self.get_tokenizer()

           SCREAMING_SNAKE_CASE__   :      Tuple      		=	AlignProcessor(tokenizer=_a	,    image_processor=_a  )

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

           SCREAMING_SNAKE_CASE__   :      List[Any]      		=	processor.batch_decode(_a  )
           SCREAMING_SNAKE_CASE__   :      List[Any]      		=	tokenizer.batch_decode(_a  )

           self.assertListEqual(_a	,    _a  )





     def 					_a			(						self  )  ->      str:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      List[str]      		=	self.get_image_processor()
           SCREAMING_SNAKE_CASE__   :      Tuple      		=	self.get_tokenizer()

           SCREAMING_SNAKE_CASE__   :      Dict      		=	AlignProcessor(tokenizer=_a	,    image_processor=_a  )

           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	"""lower newer"""
           SCREAMING_SNAKE_CASE__   :      List[str]      		=	self.prepare_image_inputs()

           SCREAMING_SNAKE_CASE__   :      List[str]      		=	processor(text=_a	,    images=_a  )

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




