import dataclasses
import json
import warnings
from dataclasses import dataclass, field
from time import time
from typing import List

from ..utils import logging


__UpperCamelCase	: Optional[int]    		=  logging.get_logger(__name__)
def    A			(					_lowercase=None	,						_lowercase=None		):
						return field(default_factory=lambda: default	,						metadata=_lowercase		)





@dataclass
class       lowercase__		:
						UpperCamelCase_  =	list_field(
						    default=[]			,							metadata={
						        """help""": (
						            """Model checkpoints to be provided to the AutoModel classes. Leave blank to benchmark the base version"""
						            """ of all available models"""
						        )
						    }			,							)

						UpperCamelCase_  =	list_field(
						    default=[8]			,							metadata={"""help""": """List of batch sizes for which memory and time performance will be evaluated"""})

						UpperCamelCase_  =	list_field(
						    default=[8, 32, 128, 512]			,							metadata={"""help""": """List of sequence lengths for which memory and time performance will be evaluated"""}			,							)

						UpperCamelCase_  =	field(
						    default=UpperCamelCase_			,							metadata={"""help""": """Whether to benchmark inference of model. Inference can be disabled via --no-inference."""}			,							)
						UpperCamelCase_  =	field(
						    default=UpperCamelCase_			,							metadata={"""help""": """Whether to run on available cuda devices. Cuda can be disabled via --no-cuda."""}			,							)
						UpperCamelCase_  =	field(
						    default=UpperCamelCase_			,							metadata={"""help""": """Whether to run on available tpu devices. TPU can be disabled via --no-tpu."""})
						UpperCamelCase_  =	field(default=UpperCamelCase_			,							metadata={"""help""": """Use FP16 to accelerate inference."""})
						UpperCamelCase_  =	field(default=UpperCamelCase_			,							metadata={"""help""": """Benchmark training of model"""})
						UpperCamelCase_  =	field(default=UpperCamelCase_			,							metadata={"""help""": """Verbose memory tracing"""})
						UpperCamelCase_  =	field(
						    default=UpperCamelCase_			,							metadata={"""help""": """Whether to perform speed measurements. Speed measurements can be disabled via --no-speed."""}			,							)
						UpperCamelCase_  =	field(
						    default=UpperCamelCase_			,							metadata={
						        """help""": """Whether to perform memory measurements. Memory measurements can be disabled via --no-memory"""
						    }			,							)
						UpperCamelCase_  =	field(default=UpperCamelCase_			,							metadata={"""help""": """Trace memory line by line"""})
						UpperCamelCase_  =	field(default=UpperCamelCase_			,							metadata={"""help""": """Save result to a CSV file"""})
						UpperCamelCase_  =	field(default=UpperCamelCase_			,							metadata={"""help""": """Save all print statements in a log file"""})
						UpperCamelCase_  =	field(default=UpperCamelCase_			,							metadata={"""help""": """Whether to print environment information"""})
						UpperCamelCase_  =	field(
						    default=UpperCamelCase_			,							metadata={
						        """help""": (
						            """Whether to use multiprocessing for memory and speed measurement. It is highly recommended to use"""
						            """ multiprocessing for accurate CPU and GPU memory measurements. This option should only be disabled"""
						            """ for debugging / testing and on TPU."""
						        )
						    }			,							)
						UpperCamelCase_  =	field(
						    default=f"inference_time_{round(time())}.csv"			,							metadata={"""help""": """CSV filename used if saving time results to csv."""}			,							)
						UpperCamelCase_  =	field(
						    default=f"inference_memory_{round(time())}.csv"			,							metadata={"""help""": """CSV filename used if saving memory results to csv."""}			,							)
						UpperCamelCase_  =	field(
						    default=f"train_time_{round(time())}.csv"			,							metadata={"""help""": """CSV filename used if saving time results to csv for training."""}			,							)
						UpperCamelCase_  =	field(
						    default=f"train_memory_{round(time())}.csv"			,							metadata={"""help""": """CSV filename used if saving memory results to csv for training."""}			,							)
						UpperCamelCase_  =	field(
						    default=f"env_info_{round(time())}.csv"			,							metadata={"""help""": """CSV filename used if saving environment information."""}			,							)
						UpperCamelCase_  =	field(
						    default=f"log_{round(time())}.csv"			,							metadata={"""help""": """Log filename used if print statements are saved in log."""}			,							)
						UpperCamelCase_  =	field(default=3			,							metadata={"""help""": """Times an experiment will be run."""})
						UpperCamelCase_  =	field(
						    default=UpperCamelCase_			,							metadata={
						        """help""": (
						            """Instead of loading the model as defined in `config.architectures` if exists, just load the pretrain"""
						            """ model weights."""
						        )
						    }			,							)


						def  __A ( self			:       Dict		):





												'''simple docstring'''





												warnings.warn(
												    f"""The class {self.__class__} is deprecated. Hugging Face Benchmarking utils"""
												    ''' are deprecated in general and it is advised to use external Benchmarking libraries '''
												    ''' to benchmark Transformer models.'''					,	UpperCamelCase__					,	)


						def  __A ( self			:       Any		):





												'''simple docstring'''





												return json.dumps(dataclasses.asdict(self		)					,	indent=2		)


						@property
						def  __A ( self			:       List[str]		):





												'''simple docstring'''





												if len(self.models		) <= 0:
																		raise ValueError(
																		    '''Please make sure you provide at least one model name / model identifier, *e.g.* `--models'''
																		    ''' bert-base-cased` or `args.models = [\'bert-base-cased\'].'''		)
												return self.models



						@property
						def  __A ( self			:       List[Any]		):





												'''simple docstring'''





												if not self.multi_process:
																		return False
												elif self.is_tpu:
																		logger.info('''Multiprocessing is currently not possible on TPU.'''		)
																		return False
												else:
																		return True






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()
__UpperCamelCase	: str    		=  logging.get_logger(__name__)
def    A			(					_lowercase		):
						SCREAMING_SNAKE_CASE							:					Any           =							'''huggingface/label-files'''
						SCREAMING_SNAKE_CASE							:					Any           =							'''imagenet-1k-id2label.json'''
						SCREAMING_SNAKE_CASE							:					Any           =							json.load(open(hf_hub_download(_lowercase	,						_lowercase	,						repo_type='''dataset'''		)	,						'''r'''		)		)
						SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							{int(_lowercase		): v for k, v in idalabel.items()}
						SCREAMING_SNAKE_CASE							:					Dict           =							{v: k for k, v in idalabel.items()}

						SCREAMING_SNAKE_CASE							:					str           =							'''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							:					Optional[int]           =							BitConfig(
						    conv_layer=_lowercase	,						num_labels=1_000	,						idalabel=_lowercase	,						labelaid=_lowercase	,						)

						return config
def    A			(					_lowercase		):
						if "stem.conv" in name:
												SCREAMING_SNAKE_CASE							:					Optional[int]           =							name.replace('''stem.conv'''	,						'''bit.embedder.convolution'''		)
						if "blocks" in name:
												SCREAMING_SNAKE_CASE							:					Tuple           =							name.replace('''blocks'''	,						'''layers'''		)
						if "head.fc" in name:
												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							name.replace('''head.fc'''	,						'''classifier.1'''		)
						if name.startswith('''norm'''		):
												SCREAMING_SNAKE_CASE							:					str           =							'''bit.''' + name
						if "bit" not in name and "classifier" not in name:
												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							'''bit.encoder.''' + name

						return name
def    A			(					):
						SCREAMING_SNAKE_CASE							:					Any           =							'''http://images.cocodataset.org/val2017/000000039769.jpg'''
						SCREAMING_SNAKE_CASE							:					List[str]           =							Image.open(requests.get(_lowercase	,						stream=_lowercase		).raw		)
						return im
@torch.no_grad()
def    A			(					_lowercase	,						_lowercase	,						_lowercase=False		):
						SCREAMING_SNAKE_CASE							:					List[Any]           =							get_config(_lowercase		)

						# load original model from timm
						SCREAMING_SNAKE_CASE							:					Optional[Any]           =							create_model(_lowercase	,						pretrained=_lowercase		)
						timm_model.eval()

						# load state_dict of original model
						SCREAMING_SNAKE_CASE							:					Optional[int]           =							timm_model.state_dict()
						for key in state_dict.copy().keys():
												SCREAMING_SNAKE_CASE							:					Dict           =							state_dict.pop(_lowercase		)
												SCREAMING_SNAKE_CASE							:					Optional[int]           =							val.squeeze() if '''head''' in key else val

						# load HuggingFace model
						SCREAMING_SNAKE_CASE							:					str           =							BitForImageClassification(_lowercase		)
						model.eval()
						model.load_state_dict(_lowercase		)

						# create image processor
						SCREAMING_SNAKE_CASE							:					Optional[Any]           =							create_transform(**resolve_data_config({}	,						model=_lowercase		)		)
						SCREAMING_SNAKE_CASE							:					List[str]           =							transform.transforms

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

						SCREAMING_SNAKE_CASE							:					Tuple           =							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()	,						)

						SCREAMING_SNAKE_CASE							:					Any           =							prepare_img()
						SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							transform(_lowercase		).unsqueeze(0		)
						SCREAMING_SNAKE_CASE							:					Optional[int]           =							processor(_lowercase	,						return_tensors='''pt'''		).pixel_values

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

						# verify logits
						with torch.no_grad():
												SCREAMING_SNAKE_CASE							:					Dict           =							model(_lowercase		)
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							outputs.logits

						print('''Logits:'''	,						logits[0, :3]		)
						print('''Predicted class:'''	,						model.config.idalabel[logits.argmax(-1		).item()]		)
						SCREAMING_SNAKE_CASE							:					List[Any]           =							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__":
					__UpperCamelCase	: 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.',
					)

					__UpperCamelCase	: Optional[int]    		=  parser.parse_args()
					convert_bit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)





