




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







def   _snake_case    (       lowercase__	):
				_lowerCamelCase  :    str					=					'huggingface/label-files'
				_lowerCamelCase  :    Dict					=					'imagenet-1k-id2label.json'
				_lowerCamelCase  :    Optional[Any]					=					json.load(open(hf_hub_download(lowercase__							,					lowercase__							,					repo_type='dataset'	)							,					'r'	)	)
				_lowerCamelCase  :    Dict					=					{int(lowercase__	): v for k, v in idalabel.items()}
				_lowerCamelCase  :    List[str]					=					{v: k for k, v in idalabel.items()}

				_lowerCamelCase  :    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"
				_lowerCamelCase  :    Dict					=					BitConfig(
				    conv_layer=lowercase__							,					num_labels=1000							,					idalabel=lowercase__							,					labelaid=lowercase__							,					)

				return config







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

				return name







def   _snake_case    (       ):
				_lowerCamelCase  :    List[str]					=					'http://images.cocodataset.org/val2017/000000039769.jpg'
				_lowerCamelCase  :    Dict					=					Image.open(requests.get(lowercase__							,					stream=lowercase__	).raw	)
				return im







@torch.no_grad()
def   _snake_case    (       lowercase__							,					lowercase__							,					lowercase__=False	):
				_lowerCamelCase  :    Optional[int]					=					get_config(lowercase__	)

				# load original model from timm
				_lowerCamelCase  :    List[str]					=					create_model(lowercase__							,					pretrained=lowercase__	)
				timm_model.eval()

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

				# load HuggingFace model
				_lowerCamelCase  :    str					=					BitForImageClassification(lowercase__	)
				model.eval()
				model.load_state_dict(lowercase__	)

				# create image processor
				_lowerCamelCase  :    Union[str, Any]					=					create_transform(**resolve_data_config({}							,					model=lowercase__	)	)
				_lowerCamelCase  :    Tuple					=					transform.transforms

				_lowerCamelCase  :    List[str]					=					{
				    'bilinear': PILImageResampling.BILINEAR,
				    'bicubic': PILImageResampling.BICUBIC,
				    'nearest': PILImageResampling.NEAREST,
				}

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

				_lowerCamelCase  :    List[str]					=					prepare_img()
				_lowerCamelCase  :    str					=					transform(lowercase__	).unsqueeze(0	)
				_lowerCamelCase  :    str					=					processor(lowercase__							,					return_tensors='pt'	).pixel_values

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

				# verify logits
				with torch.no_grad():
								_lowerCamelCase  :    Any					=					model(lowercase__	)
								_lowerCamelCase  :    Union[str, Any]					=					outputs.logits

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

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

				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)





"""simple docstring"""






from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowercase__  		=		logging.get_logger(__name__)

lowercase__  		=		{
    """google/realm-cc-news-pretrained-embedder""": (
        """https://huggingface.co/google/realm-cc-news-pretrained-embedder/resolve/main/config.json"""
    ),
    """google/realm-cc-news-pretrained-encoder""": (
        """https://huggingface.co/google/realm-cc-news-pretrained-encoder/resolve/main/config.json"""
    ),
    """google/realm-cc-news-pretrained-scorer""": (
        """https://huggingface.co/google/realm-cc-news-pretrained-scorer/resolve/main/config.json"""
    ),
    """google/realm-cc-news-pretrained-openqa""": (
        """https://huggingface.co/google/realm-cc-news-pretrained-openqa/aresolve/main/config.json"""
    ),
    """google/realm-orqa-nq-openqa""": """https://huggingface.co/google/realm-orqa-nq-openqa/resolve/main/config.json""",
    """google/realm-orqa-nq-reader""": """https://huggingface.co/google/realm-orqa-nq-reader/resolve/main/config.json""",
    """google/realm-orqa-wq-openqa""": """https://huggingface.co/google/realm-orqa-wq-openqa/resolve/main/config.json""",
    """google/realm-orqa-wq-reader""": """https://huggingface.co/google/realm-orqa-wq-reader/resolve/main/config.json""",
    # See all REALM models at https://huggingface.co/models?filter=realm
}






class lowerCAmelCase__			(  lowercase		):






			'''simple docstring'''



			lowerCamelCase__   =    """realm"""
			def __init__(						self						,   lowercase=30522						,   lowercase=768						,   lowercase=128						,   lowercase=12						,   lowercase=12						,   lowercase=8						,   lowercase=3072						,   lowercase="gelu_new"						,   lowercase=0.1						,   lowercase=0.1						,   lowercase=512						,   lowercase=2						,   lowercase=0.02						,   lowercase=1E-12						,   lowercase=256						,   lowercase=10						,   lowercase=1E-3						,   lowercase=5						,   lowercase=320						,   lowercase=13353718						,   lowercase=5000						,   lowercase=1						,   lowercase=0						,   lowercase=2						,   **lowercase						,   ):
							super().__init__(pad_token_id=lowercase						,   bos_token_id=lowercase						,   eos_token_id=lowercase						,   **lowercase						)

							# Common config
							_lowerCamelCase  :    str					=					vocab_size
							_lowerCamelCase  :    Dict					=					max_position_embeddings
							_lowerCamelCase  :    int					=					hidden_size
							_lowerCamelCase  :    Optional[Any]					=					retriever_proj_size
							_lowerCamelCase  :    Dict					=					num_hidden_layers
							_lowerCamelCase  :    Any					=					num_attention_heads
							_lowerCamelCase  :    int					=					num_candidates
							_lowerCamelCase  :    List[Any]					=					intermediate_size
							_lowerCamelCase  :    int					=					hidden_act
							_lowerCamelCase  :    Union[str, Any]					=					hidden_dropout_prob
							_lowerCamelCase  :    Dict					=					attention_probs_dropout_prob
							_lowerCamelCase  :    Union[str, Any]					=					initializer_range
							_lowerCamelCase  :    List[Any]					=					type_vocab_size
							_lowerCamelCase  :    int					=					layer_norm_eps

							# Reader config
							_lowerCamelCase  :    Tuple					=					span_hidden_size
							_lowerCamelCase  :    int					=					max_span_width
							_lowerCamelCase  :    Tuple					=					reader_layer_norm_eps
							_lowerCamelCase  :    Union[str, Any]					=					reader_beam_size
							_lowerCamelCase  :    Union[str, Any]					=					reader_seq_len

							# Retrieval config
							_lowerCamelCase  :    Optional[Any]					=					num_block_records
							_lowerCamelCase  :    str					=					searcher_beam_size