
'''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 A				(__lowerCamelCase				:Dict		):
						_lowerCAmelCase  						=			"""huggingface/label-files"""
						_lowerCAmelCase  						=			"""imagenet-1k-id2label.json"""
						_lowerCAmelCase  						=			json.load(open(hf_hub_download(__lowerCamelCase			,     __lowerCamelCase			,     repo_type="""dataset"""		)			,     """r"""		)		)
						_lowerCAmelCase  						=			{int(__lowerCamelCase		): v for k, v in idalabel.items()}
						_lowerCAmelCase  						=			{v: k for k, v in idalabel.items()}

						_lowerCAmelCase  						=			"""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  						=			BitConfig(
						    conv_layer=__lowerCamelCase			,     num_labels=1000			,     idalabel=__lowerCamelCase			,     labelaid=__lowerCamelCase			,     )

						return config


def A				(__lowerCamelCase				:Tuple		):
						if "stem.conv" in name:
												_lowerCAmelCase  						=			name.replace("""stem.conv"""			,     """bit.embedder.convolution"""		)
						if "blocks" in name:
												_lowerCAmelCase  						=			name.replace("""blocks"""			,     """layers"""		)
						if "head.fc" in name:
												_lowerCAmelCase  						=			name.replace("""head.fc"""			,     """classifier.1"""		)
						if name.startswith("""norm"""		):
												_lowerCAmelCase  						=			"""bit.""" + name
						if "bit" not in name and "classifier" not in name:
												_lowerCAmelCase  						=			"""bit.encoder.""" + name

						return name


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


@torch.no_grad()
def A				(__lowerCamelCase				:Dict			,     __lowerCamelCase				:List[Any]			,     __lowerCamelCase				:Any=False		):
						_lowerCAmelCase  						=			get_config(__lowerCamelCase		)

						# load original model from timm
						_lowerCAmelCase  						=			create_model(__lowerCamelCase			,     pretrained=__lowerCamelCase		)
						timm_model.eval()

						# load state_dict of original model
						_lowerCAmelCase  						=			timm_model.state_dict()
						for key in state_dict.copy().keys():
												_lowerCAmelCase  						=			state_dict.pop(__lowerCamelCase		)
												_lowerCAmelCase  						=			val.squeeze() if """head""" in key else val

						# load HuggingFace model
						_lowerCAmelCase  						=			BitForImageClassification(__lowerCamelCase		)
						model.eval()
						model.load_state_dict(__lowerCamelCase		)

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

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

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

						_lowerCAmelCase  						=			prepare_img()
						_lowerCAmelCase  						=			transform(__lowerCamelCase		).unsqueeze(0		)
						_lowerCAmelCase  						=			processor(__lowerCamelCase			,     return_tensors="""pt"""		).pixel_values

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

						# verify logits
						with torch.no_grad():
												_lowerCAmelCase  						=			model(__lowerCamelCase		)
												_lowerCAmelCase  						=			outputs.logits

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

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

						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'''



# Lint as: python3
# pylint: enable=line-too-long
# pylint: disable=g-import-not-at-top,g-bad-import-order,wrong-import-position

_lowercase    =		"""2.13.1"""

import platform

import pyarrow
from packaging import version


if version.parse(platform.python_version()) < version.parse("""3.7"""):
						raise ImportWarning(
						    """To use `datasets`, Python>=3.7 is required, and the current version of Python doesn't match this condition."""
						)

if version.parse(pyarrow.__version__).major < 8:
						raise ImportWarning(
						    """To use `datasets`, the module `pyarrow>=8.0.0` is required, and the current version of `pyarrow` doesn't match this condition.\n"""
						    """If you are running this in a Google Colab, you should probably just restart the runtime to use the right version of `pyarrow`."""
						)

del platform
del pyarrow
del version

from .arrow_dataset import Dataset
from .arrow_reader import ReadInstruction
from .builder import ArrowBasedBuilder, BeamBasedBuilder, BuilderConfig, DatasetBuilder, GeneratorBasedBuilder
from .combine import concatenate_datasets, interleave_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .download import *
from .features import *
from .fingerprint import disable_caching, enable_caching, is_caching_enabled, set_caching_enabled
from .info import DatasetInfo, MetricInfo
from .inspect import (
    get_dataset_config_info,
    get_dataset_config_names,
    get_dataset_infos,
    get_dataset_split_names,
    inspect_dataset,
    inspect_metric,
    list_datasets,
    list_metrics,
)
from .iterable_dataset import IterableDataset
from .load import load_dataset, load_dataset_builder, load_from_disk, load_metric
from .metric import Metric
from .splits import (
    NamedSplit,
    NamedSplitAll,
    Split,
    SplitBase,
    SplitDict,
    SplitGenerator,
    SplitInfo,
    SubSplitInfo,
    percent,
)
from .tasks import *
from .utils import *
from .utils import logging


# deprecated modules
from datasets import arrow_dataset as _arrow_dataset  # isort:skip
from datasets import utils as _utils  # isort:skip
from datasets.utils import download_manager as _deprecated_download_manager  # isort:skip

_lowercase    =		concatenate_datasets
_lowercase    =		DownloadConfig
_lowercase    =		DownloadManager
_lowercase    =		DownloadMode
_lowercase    =		DownloadConfig
_lowercase    =		DownloadMode
_lowercase    =		DownloadManager

del _arrow_dataset, _utils, _deprecated_download_manager
