

"""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()
lowerCAmelCase__							=				logging.get_logger(__name__)



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

		SCREAMING_SNAKE_CASE_						 = '''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_						 = BitConfig(
		    conv_layer=__a,							num_labels=1_000,							idalabel=__a,							labelaid=__a,							)

		return config



def        _lowerCamelCase ( __a    ):
		if "stem.conv" in name:
				SCREAMING_SNAKE_CASE_						 = name.replace('''stem.conv''',							'''bit.embedder.convolution'''    )
		if "blocks" in name:
				SCREAMING_SNAKE_CASE_						 = name.replace('''blocks''',							'''layers'''    )
		if "head.fc" in name:
				SCREAMING_SNAKE_CASE_						 = name.replace('''head.fc''',							'''classifier.1'''    )
		if name.startswith('''norm'''    ):
				SCREAMING_SNAKE_CASE_						 = '''bit.''' + name
		if "bit" not in name and "classifier" not in name:
				SCREAMING_SNAKE_CASE_						 = '''bit.encoder.''' + name

		return name



def        _lowerCamelCase ( ):
		SCREAMING_SNAKE_CASE_						 = '''http://images.cocodataset.org/val2017/000000039769.jpg'''
		SCREAMING_SNAKE_CASE_						 = Image.open(requests.get(__a,							stream=__a    ).raw    )
		return im



@torch.no_grad()
def        _lowerCamelCase ( __a,							__a,							__a=False    ):
		SCREAMING_SNAKE_CASE_						 = get_config(__a    )

		# load original model from timm
		SCREAMING_SNAKE_CASE_						 = create_model(__a,							pretrained=__a    )
		timm_model.eval()

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

		# load HuggingFace model
		SCREAMING_SNAKE_CASE_						 = BitForImageClassification(__a    )
		model.eval()
		model.load_state_dict(__a    )

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

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

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

		SCREAMING_SNAKE_CASE_						 = prepare_img()
		SCREAMING_SNAKE_CASE_						 = transform(__a    ).unsqueeze(0    )
		SCREAMING_SNAKE_CASE_						 = processor(__a,							return_tensors='''pt'''    ).pixel_values

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

		# verify logits
		with torch.no_grad():
				SCREAMING_SNAKE_CASE_						 = model(__a    )
				SCREAMING_SNAKE_CASE_						 = outputs.logits

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

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

		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__":
						lowerCAmelCase__							=				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.',
						)

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

"""simple docstring"""




import argparse

from transformers import BigBirdConfig, BigBirdForPreTraining, BigBirdForQuestionAnswering, load_tf_weights_in_big_bird
from transformers.utils import logging


logging.set_verbosity_info()



def        _lowerCamelCase ( __a,							__a,							__a,							__a    ):
		# Initialise PyTorch model
		SCREAMING_SNAKE_CASE_						 = BigBirdConfig.from_json_file(__a    )
		print(F'Building PyTorch model from configuration: {config}'    )

		if is_trivia_qa:
				SCREAMING_SNAKE_CASE_						 = BigBirdForQuestionAnswering(__a    )
		else:
				SCREAMING_SNAKE_CASE_						 = BigBirdForPreTraining(__a    )

		# Load weights from tf checkpoint
		load_tf_weights_in_big_bird(__a,							__a,							is_trivia_qa=__a    )

		# Save pytorch-model
		print(F'Save PyTorch model to {pytorch_dump_path}'    )
		model.save_pretrained(__a    )


if __name__ == "__main__":
						lowerCAmelCase__							=				argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    '--tf_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
						)
						parser.add_argument(
						    '--big_bird_config_file',
						    default=None,
						    type=str,
						    required=True,
						    help=(
						        'The config json file corresponding to the pre-trained BERT model. \n'
						        'This specifies the model architecture.'
						    ),
						)
						parser.add_argument(
						    '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
						)
						parser.add_argument(
						    '--is_trivia_qa', action='store_true', help='Whether to convert a model with a trivia_qa head.'
						)
						lowerCAmelCase__							=				parser.parse_args()
						convert_tf_checkpoint_to_pytorch(
						    args.tf_checkpoint_path, args.big_bird_config_file, args.pytorch_dump_path, args.is_trivia_qa
						)