



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()
__UpperCAmelCase          =       logging.get_logger(__name__)
def 		UpperCamelCase	(   snake_case__       :    List[Any]	)	->     int:
   UpperCamelCase    :  str							=	'huggingface/label-files'
   UpperCamelCase    :  Tuple							=	'imagenet-1k-id2label.json'
   UpperCamelCase    :  Dict							=	json.load(open(hf_hub_download(snake_case__       ,     snake_case__       ,     repo_type='dataset'	)       ,     'r'	)	)
   UpperCamelCase    :  Tuple							=	{int(snake_case__	): v for k, v in idalabel.items()}
   UpperCamelCase    :  Any							=	{v: k for k, v in idalabel.items()}

   UpperCamelCase    :  List[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"
   UpperCamelCase    :  List[Any]							=	BitConfig(
       conv_layer=snake_case__       ,     num_labels=1000       ,     idalabel=snake_case__       ,     labelaid=snake_case__       ,     )

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

   return name
def 		UpperCamelCase	(   )	->     Optional[Any]:
   UpperCamelCase    :  str							=	'http://images.cocodataset.org/val2017/000000039769.jpg'
   UpperCamelCase    :  Dict							=	Image.open(requests.get(snake_case__       ,     stream=snake_case__	).raw	)
   return im
@torch.no_grad()
def 		UpperCamelCase	(   snake_case__       :    Union[str, Any]       ,     snake_case__       :    List[Any]       ,     snake_case__       :    Optional[Any]=False	)	->     List[Any]:
   UpperCamelCase    :  str							=	get_config(snake_case__	)

   # load original model from timm
   UpperCamelCase    :  List[Any]							=	create_model(snake_case__       ,     pretrained=snake_case__	)
   timm_model.eval()

   # load state_dict of original model
   UpperCamelCase    :  Optional[Any]							=	timm_model.state_dict()
   for key in state_dict.copy().keys():
      UpperCamelCase    :  Dict							=	state_dict.pop(snake_case__	)
      UpperCamelCase    :  List[Any]							=	val.squeeze() if 'head' in key else val

   # load HuggingFace model
   UpperCamelCase    :  Dict							=	BitForImageClassification(snake_case__	)
   model.eval()
   model.load_state_dict(snake_case__	)

   # create image processor
   UpperCamelCase    :  str							=	create_transform(**resolve_data_config({}       ,     model=snake_case__	)	)
   UpperCamelCase    :  Optional[Any]							=	transform.transforms

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

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

   UpperCamelCase    :  str							=	prepare_img()
   UpperCamelCase    :  Tuple							=	transform(snake_case__	).unsqueeze(0	)
   UpperCamelCase    :  List[str]							=	processor(snake_case__       ,     return_tensors='pt'	).pixel_values

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

   # verify logits
   with torch.no_grad():
      UpperCamelCase    :  int							=	model(snake_case__	)
      UpperCamelCase    :  Optional[Any]							=	outputs.logits

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

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

   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__":
    __UpperCAmelCase          =       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.''',
    )

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








import json
import os
import tempfile
import unittest

import numpy as np
from datasets import load_dataset

from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin


if is_torch_available():
    import torch

if is_vision_available():
    from PIL import Image

    from transformers import ImageGPTImageProcessor



class 				lowerCAmelCase_		(  unittest.TestCase							):
       def __init__(			self,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_=7,			SCREAMING_SNAKE_CASE_=3,			SCREAMING_SNAKE_CASE_=18,			SCREAMING_SNAKE_CASE_=30,			SCREAMING_SNAKE_CASE_=400,			SCREAMING_SNAKE_CASE_=True,			SCREAMING_SNAKE_CASE_=None,			SCREAMING_SNAKE_CASE_=True,			)     ->		Union[str, Any]:
          UpperCamelCase    :  str							=	size if size is not None else {'height': 18, 'width': 18}
          UpperCamelCase    :  int							=	parent
          UpperCamelCase    :  List[Any]							=	batch_size
          UpperCamelCase    :  int							=	num_channels
          UpperCamelCase    :  Any							=	image_size
          UpperCamelCase    :  Optional[int]							=	min_resolution
          UpperCamelCase    :  Optional[Any]							=	max_resolution
          UpperCamelCase    :  Union[str, Any]							=	do_resize
          UpperCamelCase    :  List[Any]							=	size
          UpperCamelCase    :  int							=	do_normalize






       def 					snake_case_       (			self       )     ->		Tuple:
          return {
              # here we create 2 clusters for the sake of simplicity
              "clusters": np.asarray(
                  [
                      [0.88_66_44_36_34_03_32_03, 0.66_18_82_93_69_54_49_83, 0.38_91_74_64_01_78_68_04],
                      [-0.60_42_55_91_46_88_11_04, -0.0_22_95_00_88_60_52_84_69, 0.54_23_79_73_69_00_32_96],
                  ]       ),
              "do_resize": self.do_resize,
              "size": self.size,
              "do_normalize": self.do_normalize,
          }






@require_torch
@require_vision
class 				lowerCAmelCase_		(  a__       ,    unittest.TestCase							):
       UpperCAmelCase__				:					Any        =  ImageGPTImageProcessor if is_vision_available() else None
       def 					snake_case_       (			self       )     ->		int:
          UpperCamelCase    :  str							=	ImageGPTImageProcessingTester(self       )
       @property
       def 					snake_case_       (			self       )     ->		Optional[Any]:
          return self.image_processor_tester.prepare_image_processor_dict()
       def 					snake_case_       (			self       )     ->		str:
          UpperCamelCase    :  int							=	self.image_processing_class(**self.image_processor_dict       )
          self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_,			'clusters'       )       )
          self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_,			'do_resize'       )       )
          self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_,			'size'       )       )
          self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_,			'do_normalize'       )       )
       def 					snake_case_       (			self       )     ->		str:
          UpperCamelCase    :  Tuple							=	self.image_processing_class.from_dict(self.image_processor_dict       )
          self.assertEqual(image_processor.size,			{'height': 18, 'width': 18}       )

          UpperCamelCase    :  Dict							=	self.image_processing_class.from_dict(self.image_processor_dict,			size=42       )
          self.assertEqual(image_processor.size,			{'height': 42, 'width': 42}       )
       def 					snake_case_       (			self       )     ->		List[str]:
          UpperCamelCase    :  str							=	self.image_processing_class(**self.image_processor_dict       )
          UpperCamelCase    :  int							=	json.loads(image_processor.to_json_string()       )
          for key, value in self.image_processor_dict.items():
             if key == "clusters":
                self.assertTrue(np.array_equal(SCREAMING_SNAKE_CASE_,			obj[key]       )       )
             else:
                self.assertEqual(obj[key],			SCREAMING_SNAKE_CASE_       )
       def 					snake_case_       (			self       )     ->		Dict:
          UpperCamelCase    :  Tuple							=	self.image_processing_class(**self.image_processor_dict       )

          with tempfile.TemporaryDirectory() as tmpdirname:
             UpperCamelCase    :  List[str]							=	os.path.join(SCREAMING_SNAKE_CASE_,			'image_processor.json'       )
             image_processor_first.to_json_file(SCREAMING_SNAKE_CASE_       )
             UpperCamelCase    :  Tuple							=	self.image_processing_class.from_json_file(SCREAMING_SNAKE_CASE_       ).to_dict()

          UpperCamelCase    :  List[Any]							=	image_processor_first.to_dict()
          for key, value in image_processor_first.items():
             if key == "clusters":
                self.assertTrue(np.array_equal(SCREAMING_SNAKE_CASE_,			image_processor_second[key]       )       )
             else:
                self.assertEqual(image_processor_first[key],			SCREAMING_SNAKE_CASE_       )
       def 					snake_case_       (			self       )     ->		Optional[int]:
          UpperCamelCase    :  Optional[Any]							=	self.image_processing_class(**self.image_processor_dict       )

          with tempfile.TemporaryDirectory() as tmpdirname:
             image_processor_first.save_pretrained(SCREAMING_SNAKE_CASE_       )
             UpperCamelCase    :  str							=	self.image_processing_class.from_pretrained(SCREAMING_SNAKE_CASE_       ).to_dict()

          UpperCamelCase    :  Union[str, Any]							=	image_processor_first.to_dict()
          for key, value in image_processor_first.items():
             if key == "clusters":
                self.assertTrue(np.array_equal(SCREAMING_SNAKE_CASE_,			image_processor_second[key]       )       )
             else:
                self.assertEqual(image_processor_first[key],			SCREAMING_SNAKE_CASE_       )






       @unittest.skip('ImageGPT requires clusters at initialization'       )
       def 					snake_case_       (			self       )     ->		str:
          pass






def 		UpperCamelCase	(   )	->     int:
   UpperCamelCase    :  Optional[int]							=	load_dataset('hf-internal-testing/fixtures_image_utils'       ,     split='test'	)

   UpperCamelCase    :  int							=	Image.open(dataset[4]['file']	)
   UpperCamelCase    :  Optional[Any]							=	Image.open(dataset[5]['file']	)

   UpperCamelCase    :  str							=	[imagea, imagea]

   return images



@require_vision
@require_torch
class 				lowerCAmelCase_		(  unittest.TestCase							):
       @slow
       def 					snake_case_       (			self       )     ->		str:
          UpperCamelCase    :  List[str]							=	ImageGPTImageProcessor.from_pretrained('openai/imagegpt-small'       )

          UpperCamelCase    :  List[str]							=	prepare_images()

          # test non-batched
          UpperCamelCase    :  int							=	image_processing(images[0],			return_tensors='pt'       )

          self.assertIsInstance(encoding.input_ids,			torch.LongTensor       )
          self.assertEqual(encoding.input_ids.shape,			(1, 1024)       )

          UpperCamelCase    :  Union[str, Any]							=	[306, 191, 191]
          self.assertEqual(encoding.input_ids[0, :3].tolist(),			SCREAMING_SNAKE_CASE_       )

          # test batched
          UpperCamelCase    :  Tuple							=	image_processing(SCREAMING_SNAKE_CASE_,			return_tensors='pt'       )

          self.assertIsInstance(encoding.input_ids,			torch.LongTensor       )
          self.assertEqual(encoding.input_ids.shape,			(2, 1024)       )

          UpperCamelCase    :  Optional[Any]							=	[303, 13, 13]
          self.assertEqual(encoding.input_ids[1, -3:].tolist(),			SCREAMING_SNAKE_CASE_       )



