

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							:  Any   	=						logging.get_logger(__name__)







def 			A_      (    _UpperCAmelCase    ):
						SCREAMING_SNAKE_CASE_: Optional[int]  		=       "huggingface/label-files"
						SCREAMING_SNAKE_CASE_: Dict  		=       "imagenet-1k-id2label.json"
						SCREAMING_SNAKE_CASE_: Tuple  		=       json.load(open(hf_hub_download(_UpperCAmelCase				,	_UpperCAmelCase				,	repo_type="dataset"    )				,	"r"    )    )
						SCREAMING_SNAKE_CASE_: Tuple  		=       {int(_UpperCAmelCase    ): v for k, v in idalabel.items()}
						SCREAMING_SNAKE_CASE_: Optional[Any]  		=       {v: k for k, v in idalabel.items()}

						SCREAMING_SNAKE_CASE_: Optional[int]  		=       "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_: Union[str, Any]  		=       BitConfig(
						    conv_layer=_UpperCAmelCase				,	num_labels=10_00				,	idalabel=_UpperCAmelCase				,	labelaid=_UpperCAmelCase				,	)

						return config







def 			A_      (    _UpperCAmelCase    ):
						if "stem.conv" in name:
												SCREAMING_SNAKE_CASE_: Optional[Any]  		=       name.replace("stem.conv"				,	"bit.embedder.convolution"    )
						if "blocks" in name:
												SCREAMING_SNAKE_CASE_: Any  		=       name.replace("blocks"				,	"layers"    )
						if "head.fc" in name:
												SCREAMING_SNAKE_CASE_: int  		=       name.replace("head.fc"				,	"classifier.1"    )
						if name.startswith("norm"    ):
												SCREAMING_SNAKE_CASE_: Any  		=       "bit." + name
						if "bit" not in name and "classifier" not in name:
												SCREAMING_SNAKE_CASE_: int  		=       "bit.encoder." + name

						return name







def 			A_      (    ):
						SCREAMING_SNAKE_CASE_: Tuple  		=       "http://images.cocodataset.org/val2017/000000039769.jpg"
						SCREAMING_SNAKE_CASE_: Dict  		=       Image.open(requests.get(_UpperCAmelCase				,	stream=_UpperCAmelCase    ).raw    )
						return im







@torch.no_grad()
def 			A_      (    _UpperCAmelCase				,	_UpperCAmelCase				,	_UpperCAmelCase=False    ):
						SCREAMING_SNAKE_CASE_: str  		=       get_config(_UpperCAmelCase    )

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

						# load state_dict of original model
						SCREAMING_SNAKE_CASE_: List[Any]  		=       timm_model.state_dict()
						for key in state_dict.copy().keys():
												SCREAMING_SNAKE_CASE_: Optional[Any]  		=       state_dict.pop(_UpperCAmelCase    )
												SCREAMING_SNAKE_CASE_: Any  		=       val.squeeze() if "head" in key else val

						# load HuggingFace model
						SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       BitForImageClassification(_UpperCAmelCase    )
						model.eval()
						model.load_state_dict(_UpperCAmelCase    )

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

						SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       {
						    "bilinear": PILImageResampling.BILINEAR,
						    "bicubic": PILImageResampling.BICUBIC,
						    "nearest": PILImageResampling.NEAREST,
						}

						SCREAMING_SNAKE_CASE_: Tuple  		=       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()				,	)

						SCREAMING_SNAKE_CASE_: Dict  		=       prepare_img()
						SCREAMING_SNAKE_CASE_: int  		=       transform(_UpperCAmelCase    ).unsqueeze(0    )
						SCREAMING_SNAKE_CASE_: str  		=       processor(_UpperCAmelCase				,	return_tensors="pt"    ).pixel_values

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

						# verify logits
						with torch.no_grad():
												SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       model(_UpperCAmelCase    )
												SCREAMING_SNAKE_CASE_: Tuple  		=       outputs.logits

						print("Logits:"				,	logits[0, :3]    )
						print("Predicted class:"				,	model.config.idalabel[logits.argmax(-1    ).item()]    )
						SCREAMING_SNAKE_CASE_: int  		=       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__":
						lowerCAmelCase							:  Optional[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)









import os
import tempfile
import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch


if is_torch_available():
						import torch
						from torch import nn

						from transformers import (
						    Adafactor,
						    AdamW,
						    get_constant_schedule,
						    get_constant_schedule_with_warmup,
						    get_cosine_schedule_with_warmup,
						    get_cosine_with_hard_restarts_schedule_with_warmup,
						    get_inverse_sqrt_schedule,
						    get_linear_schedule_with_warmup,
						    get_polynomial_decay_schedule_with_warmup,
						)







def 			A_      (    _UpperCAmelCase				,	_UpperCAmelCase=10    ):
						SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       []
						for _ in range(_UpperCAmelCase    ):
												lrs.append(scheduler.get_lr()[0]    )
												scheduler.step()
						return lrs







def 			A_      (    _UpperCAmelCase				,	_UpperCAmelCase=10    ):
						SCREAMING_SNAKE_CASE_: List[str]  		=       []
						for step in range(_UpperCAmelCase    ):
												lrs.append(scheduler.get_lr()[0]    )
												scheduler.step()
												if step == num_steps // 2:
																		with tempfile.TemporaryDirectory() as tmpdirname:
																								SCREAMING_SNAKE_CASE_: Optional[int]  		=       os.path.join(_UpperCAmelCase				,	"schedule.bin"    )
																								torch.save(scheduler.state_dict()				,	_UpperCAmelCase    )

																								SCREAMING_SNAKE_CASE_: Optional[Any]  		=       torch.load(_UpperCAmelCase    )
																								scheduler.load_state_dict(_UpperCAmelCase    )
						return lrs



@require_torch
class   __lowercase				( unittest.TestCase	):




			"""simple docstring"""







			def        _SCREAMING_SNAKE_CASE			( self  :     Dict   ,						lowerCAmelCase__  :     int   ,						lowerCAmelCase__  :     Dict   ,						lowerCAmelCase__  :     Tuple):
									self.assertEqual(len(lowerCAmelCase__)   ,						len(lowerCAmelCase__))
									for a, b in zip(lowerCAmelCase__   ,						lowerCAmelCase__):
															self.assertAlmostEqual(lowerCAmelCase__   ,						lowerCAmelCase__   ,						delta=lowerCAmelCase__)



			def        _SCREAMING_SNAKE_CASE			( self  :     Dict):
									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       torch.tensor([0.1, -0.2, -0.1]   ,						requires_grad=lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_: str  		=       torch.tensor([0.4, 0.2, -0.5])
									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       nn.MSELoss()
									# No warmup, constant schedule, no gradient clipping
									SCREAMING_SNAKE_CASE_: int  		=       AdamW(params=[w]   ,						lr=2E-1   ,						weight_decay=0.0)
									for _ in range(100):
															SCREAMING_SNAKE_CASE_: Dict  		=       criterion(lowerCAmelCase__   ,						lowerCAmelCase__)
															loss.backward()
															optimizer.step()
															w.grad.detach_()  # No zero_grad() function on simple tensors. we do it ourselves.
															w.grad.zero_()
									self.assertListAlmostEqual(w.tolist()   ,						[0.4, 0.2, -0.5]   ,						tol=1E-2)




			def        _SCREAMING_SNAKE_CASE			( self  :     Dict):
									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       torch.tensor([0.1, -0.2, -0.1]   ,						requires_grad=lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_: int  		=       torch.tensor([0.4, 0.2, -0.5])
									SCREAMING_SNAKE_CASE_: Any  		=       nn.MSELoss()
									# No warmup, constant schedule, no gradient clipping
									SCREAMING_SNAKE_CASE_: int  		=       Adafactor(
									    params=[w]   ,						lr=1E-2   ,						eps=(1E-30, 1E-3)   ,						clip_threshold=1.0   ,						decay_rate=-0.8   ,						betaa=lowerCAmelCase__   ,						weight_decay=0.0   ,						relative_step=lowerCAmelCase__   ,						scale_parameter=lowerCAmelCase__   ,						warmup_init=lowerCAmelCase__   ,						)
									for _ in range(1000):
															SCREAMING_SNAKE_CASE_: List[Any]  		=       criterion(lowerCAmelCase__   ,						lowerCAmelCase__)
															loss.backward()
															optimizer.step()
															w.grad.detach_()  # No zero_grad() function on simple tensors. we do it ourselves.
															w.grad.zero_()
									self.assertListAlmostEqual(w.tolist()   ,						[0.4, 0.2, -0.5]   ,						tol=1E-2)







@require_torch
class   __lowercase				( unittest.TestCase	):




			"""simple docstring"""







			_UpperCAmelCase   :							Union[str, Any]										=     nn.Linear(50		,       50	) if is_torch_available() else None
			_UpperCAmelCase   :							List[Any]										=     AdamW(m.parameters()		,       lr=10.0	) if is_torch_available() else None
			_UpperCAmelCase   :							Optional[Any]										=     10



			def        _SCREAMING_SNAKE_CASE			( self  :     Optional[int]   ,						lowerCAmelCase__  :     List[Any]   ,						lowerCAmelCase__  :     int   ,						lowerCAmelCase__  :     int   ,						lowerCAmelCase__  :     Optional[Any]=None):
									self.assertEqual(len(lowerCAmelCase__)   ,						len(lowerCAmelCase__))
									for a, b in zip(lowerCAmelCase__   ,						lowerCAmelCase__):
															self.assertAlmostEqual(lowerCAmelCase__   ,						lowerCAmelCase__   ,						delta=lowerCAmelCase__   ,						msg=lowerCAmelCase__)




			def        _SCREAMING_SNAKE_CASE			( self  :     int):
									SCREAMING_SNAKE_CASE_: Dict  		=       {"num_warmup_steps": 2, "num_training_steps": 10}
									# schedulers doct format
									# function: (sched_args_dict, expected_learning_rates)
									SCREAMING_SNAKE_CASE_: Dict  		=       {
									    get_constant_schedule: ({}, [10.0] * self.num_steps),
									    get_constant_schedule_with_warmup: (
									        {"num_warmup_steps": 4},
									        [0.0, 2.5, 5.0, 7.5, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0],
									    ),
									    get_linear_schedule_with_warmup: (
									        {**common_kwargs},
									        [0.0, 5.0, 10.0, 8.75, 7.5, 6.25, 5.0, 3.75, 2.5, 1.25],
									    ),
									    get_cosine_schedule_with_warmup: (
									        {**common_kwargs},
									        [0.0, 5.0, 10.0, 9.61, 8.53, 6.91, 5.0, 3.08, 1.46, 0.38],
									    ),
									    get_cosine_with_hard_restarts_schedule_with_warmup: (
									        {**common_kwargs, "num_cycles": 2},
									        [0.0, 5.0, 10.0, 8.53, 5.0, 1.46, 10.0, 8.53, 5.0, 1.46],
									    ),
									    get_polynomial_decay_schedule_with_warmup: (
									        {**common_kwargs, "power": 2.0, "lr_end": 1E-7},
									        [0.0, 5.0, 10.0, 7.656, 5.625, 3.906, 2.5, 1.406, 0.625, 0.156],
									    ),
									    get_inverse_sqrt_schedule: (
									        {"num_warmup_steps": 2},
									        [0.0, 5.0, 10.0, 8.165, 7.071, 6.325, 5.774, 5.345, 5.0, 4.714],
									    ),
									}

									for scheduler_func, data in scheds.items():
															SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: Tuple  		=       data

															SCREAMING_SNAKE_CASE_: List[Any]  		=       scheduler_func(self.optimizer   ,						**lowerCAmelCase__)
															self.assertEqual(len([scheduler.get_lr()[0]])   ,						1)
															SCREAMING_SNAKE_CASE_: int  		=       unwrap_schedule(lowerCAmelCase__   ,						self.num_steps)
															self.assertListAlmostEqual(
															    lowerCAmelCase__   ,						lowerCAmelCase__   ,						tol=1E-2   ,						msg=F"failed for {scheduler_func} in normal scheduler"   ,						)

															SCREAMING_SNAKE_CASE_: List[str]  		=       scheduler_func(self.optimizer   ,						**lowerCAmelCase__)
															if scheduler_func.__name__ != "get_constant_schedule":
																					LambdaScheduleWrapper.wrap_scheduler(lowerCAmelCase__)  # wrap to test picklability of the schedule
															SCREAMING_SNAKE_CASE_: Tuple  		=       unwrap_and_save_reload_schedule(lowerCAmelCase__   ,						self.num_steps)
															self.assertListEqual(lowerCAmelCase__   ,						lowerCAmelCase__   ,						msg=F"failed for {scheduler_func} in save and reload")







class   __lowercase				:




			"""simple docstring"""







			def __init__( self  :     str   ,						lowerCAmelCase__  :     List[str]):
									SCREAMING_SNAKE_CASE_: List[Any]  		=       fn



			def __call__( self  :     Optional[int]   ,						*lowerCAmelCase__  :     List[Any]   ,						**lowerCAmelCase__  :     Tuple):
									return self.fn(*lowerCAmelCase__   ,						**lowerCAmelCase__)




			@classmethod
			def        _SCREAMING_SNAKE_CASE			( self  :     Optional[int]   ,						lowerCAmelCase__  :     str):
									SCREAMING_SNAKE_CASE_: str  		=       list(map(self   ,						scheduler.lr_lambdas))






