




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	:       Optional[int]     =	logging.get_logger(__name__)




def     __magic_name__       (				A	:      str     ):






			'''simple docstring'''







			a					=    "huggingface/label-files"
			a					=    "imagenet-1k-id2label.json"
			a					=    json.load(open(hf_hub_download(A,						A,						repo_type="dataset"     ),						"r"     )     )
			a					=    {int(A     ): 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=A,						num_labels=1000,						idalabel=A,						labelaid=A,						)

			return config




def     __magic_name__       (				A	:      Tuple     ):






			'''simple docstring'''







			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     __magic_name__       (				):






			'''simple docstring'''







			a					=    "http://images.cocodataset.org/val2017/000000039769.jpg"
			a					=    Image.open(requests.get(A,						stream=A     ).raw     )
			return im




@torch.no_grad()
def     __magic_name__       (				A	:      Optional[int],						A	:      int,						A	:      int=False     ):






			'''simple docstring'''







			a					=    get_config(A     )

			# load original model from timm
			a					=    create_model(A,						pretrained=A     )
			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(A     )
						a					=    val.squeeze() if "head" in key else val

			# load HuggingFace model
			a					=    BitForImageClassification(A     )
			model.eval()
			model.load_state_dict(A     )

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

			a					=    {
			    "bilinear": PILImageResampling.BILINEAR,
			    "bicubic": PILImageResampling.BICUBIC,
			    "nearest": PILImageResampling.NEAREST,
			}

			a					=    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(),						)

			a					=    prepare_img()
			a					=    transform(A     ).unsqueeze(0     )
			a					=    processor(A,						return_tensors="pt"     ).pixel_values

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

			# verify logits
			with torch.no_grad():
						a					=    model(A     )
						a					=    outputs.logits

			print("Logits:",						logits[0, :3]     )
			print("Predicted class:",						model.config.idalabel[logits.argmax(-1     ).item()]     )
			a					=    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	:       Any     =	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	:       int     =	parser.parse_args()
		convert_bit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)












from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


__lowerCAmelCase	:       Dict     =	{
    'configuration_blip': [
        'BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'BlipConfig',
        'BlipTextConfig',
        'BlipVisionConfig',
    ],
    'processing_blip': ['BlipProcessor'],
}

try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__lowerCAmelCase	:       Optional[Any]     =	['BlipImageProcessor']


try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__lowerCAmelCase	:       List[Any]     =	[
		    'BLIP_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'BlipModel',
		    'BlipPreTrainedModel',
		    'BlipForConditionalGeneration',
		    'BlipForQuestionAnswering',
		    'BlipVisionModel',
		    'BlipTextModel',
		    'BlipForImageTextRetrieval',
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__lowerCAmelCase	:       Any     =	[
		    'TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'TFBlipModel',
		    'TFBlipPreTrainedModel',
		    'TFBlipForConditionalGeneration',
		    'TFBlipForQuestionAnswering',
		    'TFBlipVisionModel',
		    'TFBlipTextModel',
		    'TFBlipForImageTextRetrieval',
		]

if TYPE_CHECKING:
		from .configuration_blip import BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP, BlipConfig, BlipTextConfig, BlipVisionConfig
		from .processing_blip import BlipProcessor

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .image_processing_blip import BlipImageProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_blip import (
				    BLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
				    BlipForConditionalGeneration,
				    BlipForImageTextRetrieval,
				    BlipForQuestionAnswering,
				    BlipModel,
				    BlipPreTrainedModel,
				    BlipTextModel,
				    BlipVisionModel,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_blip import (
				    TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFBlipForConditionalGeneration,
				    TFBlipForImageTextRetrieval,
				    TFBlipForQuestionAnswering,
				    TFBlipModel,
				    TFBlipPreTrainedModel,
				    TFBlipTextModel,
				    TFBlipVisionModel,
				)

else:
		import sys

		__lowerCAmelCase	:       List[str]     =	_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)






