



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()
SCREAMING_SNAKE_CASE__       =      logging.get_logger(__name__)


def A						(       __UpperCamelCase					)						->      Dict:
			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_000 ,	idalabel=__UpperCamelCase ,	labelaid=__UpperCamelCase ,	)

			return config


def A						(       __UpperCamelCase					)						->      List[str]:
			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 A						(       )						->      List[Any]:
			A__       =    'http://images.cocodataset.org/val2017/000000039769.jpg'
			A__       =    Image.open(requests.get(__UpperCamelCase ,	stream=__UpperCamelCase					).raw					)
			return im


@torch.no_grad()
def A						(       __UpperCamelCase ,	__UpperCamelCase ,	__UpperCamelCase=False					)						->      Tuple:
			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__":
							SCREAMING_SNAKE_CASE__       =      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.''',
							)

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









import tempfile
import unittest

from make_student import create_student_by_copying_alternating_layers

from transformers import AutoConfig
from transformers.file_utils import cached_property
from transformers.testing_utils import require_torch


SCREAMING_SNAKE_CASE__       =      '''sshleifer/bart-tiny-random'''
SCREAMING_SNAKE_CASE__       =      '''patrickvonplaten/t5-tiny-random'''


@require_torch
class      __lowerCAmelCase     (		unittest.TestCase      ):
					"""simple docstring"""






					@cached_property
					def     _a							(     self					:       Optional[int]					):






								"""simple docstring"""


								return AutoConfig.from_pretrained(_snake_case					)



					def     _a							(     self					:       Optional[Any]					):






								"""simple docstring"""


								A__     , *A__       =    create_student_by_copying_alternating_layers(_snake_case				,					tempfile.mkdtemp()				,					e=1				,					d=1					)
								self.assertEqual(student.config.num_hidden_layers				,					1					)



					def     _a							(     self					:       Optional[int]					):






								"""simple docstring"""


								A__     , *A__       =    create_student_by_copying_alternating_layers(_snake_case				,					tempfile.mkdtemp()				,					e=1				,					d=_snake_case					)



					def     _a							(     self					:       int					):






								"""simple docstring"""


								A__     , *A__       =    create_student_by_copying_alternating_layers(_snake_case				,					tempfile.mkdtemp()				,					e=1				,					d=_snake_case					)
								self.assertEqual(student.config.encoder_layers				,					1					)
								self.assertEqual(student.config.decoder_layers				,					self.teacher_config.encoder_layers					)



					def     _a							(     self					:       str					):






								"""simple docstring"""


								A__     , *A__       =    create_student_by_copying_alternating_layers(_snake_case				,					tempfile.mkdtemp()				,					e=1				,					d=1					)
								self.assertEqual(student.config.encoder_layers				,					1					)
								self.assertEqual(student.config.decoder_layers				,					1					)



					def     _a							(     self					:       str					):






								"""simple docstring"""


								with self.assertRaises(_snake_case					):
											create_student_by_copying_alternating_layers(_snake_case				,					tempfile.mkdtemp()				,					e=_snake_case				,					d=_snake_case					)




