

'''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()
UpperCamelCase_    						=       logging.get_logger(__name__)






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

						lowercase :							Union[str, 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"
						lowercase :							Any    		=BitConfig(
						    conv_layer=__magic_name__   , num_labels=1000   , idalabel=__magic_name__   , labelaid=__magic_name__   , )

						return config






def 				_lowerCAmelCase			(   __magic_name__						:     Optional[Any]						)				->  List[str]:
						if "stem.conv" in name:
												lowercase :							Tuple    		=name.replace('''stem.conv'''   , '''bit.embedder.convolution'''						)
						if "blocks" in name:
												lowercase :							Union[str, Any]    		=name.replace('''blocks'''   , '''layers'''						)
						if "head.fc" in name:
												lowercase :							Tuple    		=name.replace('''head.fc'''   , '''classifier.1'''						)
						if name.startswith('''norm'''						):
												lowercase :							Tuple    		='''bit.''' + name
						if "bit" not in name and "classifier" not in name:
												lowercase :							Optional[int]    		='''bit.encoder.''' + name

						return name






def 				_lowerCAmelCase			(   )				->  Tuple:
						lowercase :							int    		='''http://images.cocodataset.org/val2017/000000039769.jpg'''
						lowercase :							Any    		=Image.open(requests.get(__magic_name__   , stream=__magic_name__						).raw						)
						return im






@torch.no_grad()
def 				_lowerCAmelCase			(   __magic_name__						:     Any   , __magic_name__						:     List[str]   , __magic_name__						:     List[Any]=False						)				->  Tuple:
						lowercase :							Optional[Any]    		=get_config(__magic_name__						)

						# load original model from timm
						lowercase :							Dict    		=create_model(__magic_name__   , pretrained=__magic_name__						)
						timm_model.eval()

						# load state_dict of original model
						lowercase :							List[str]    		=timm_model.state_dict()
						for key in state_dict.copy().keys():
												lowercase :							Union[str, Any]    		=state_dict.pop(__magic_name__						)
												lowercase :							List[Any]    		=val.squeeze() if '''head''' in key else val

						# load HuggingFace model
						lowercase :							str    		=BitForImageClassification(__magic_name__						)
						model.eval()
						model.load_state_dict(__magic_name__						)

						# create image processor
						lowercase :							str    		=create_transform(**resolve_data_config({}   , model=__magic_name__						)						)
						lowercase :							int    		=transform.transforms

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

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

						lowercase :							Optional[Any]    		=prepare_img()
						lowercase :							Optional[Any]    		=transform(__magic_name__						).unsqueeze(0						)
						lowercase :							Optional[Any]    		=processor(__magic_name__   , return_tensors='''pt'''						).pixel_values

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

						# verify logits
						with torch.no_grad():
												lowercase :							List[str]    		=model(__magic_name__						)
												lowercase :							Any    		=outputs.logits

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

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

						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_    						=       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_    						=       parser.parse_args()
							convert_bit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)








'''simple docstring'''





import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional

import numpy as np
from utils_multiple_choice import MultipleChoiceDataset, Split, processors

import transformers
from transformers import (
    AutoConfig,
    AutoModelForMultipleChoice,
    AutoTokenizer,
    DataCollatorWithPadding,
    EvalPrediction,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import is_main_process


UpperCamelCase_    						=       logging.getLogger(__name__)






def 				_lowerCAmelCase			(   __magic_name__						:     List[Any]   , __magic_name__						:     str						)				->  Union[str, Any]:
						return (preds == labels).mean()





@dataclass
class 		__SCREAMING_SNAKE_CASE							:
				lowerCamelCase_		       =    field(
				    metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'}      )
				lowerCamelCase_		       =    field(
				    default=lowercase__						,							metadata={'help': 'Pretrained config name or path if not the same as model_name'}      )
				lowerCamelCase_		       =    field(
				    default=lowercase__						,							metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'}      )
				lowerCamelCase_		       =    field(
				    default=lowercase__						,							metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'}						,							)





@dataclass
class 		__SCREAMING_SNAKE_CASE							:
				lowerCamelCase_		       =    field(metadata={'help': 'The name of the task to train on: ' + ', '.join(processors.keys()      )}      )
				lowerCamelCase_		       =    field(metadata={'help': 'Should contain the data files for the task.'}      )
				lowerCamelCase_		       =    field(
				    default=1_28						,							metadata={
				        'help': (
				            'The maximum total input sequence length after tokenization. Sequences longer '
				            'than this will be truncated, sequences shorter will be padded.'
				        )
				    }						,							)
				lowerCamelCase_		       =    field(
				    default=lowercase__						,							metadata={'help': 'Overwrite the cached training and evaluation sets'}      )






def 				_lowerCAmelCase			(   )				->  int:
						# See all possible arguments in src/transformers/training_args.py
						# or by passing the --help flag to this script.
						# We now keep distinct sets of args, for a cleaner separation of concerns.

						lowercase :							Dict    		=HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)						)
						lowercase		,	lowercase		,	lowercase :							List[Any]    		=parser.parse_args_into_dataclasses()

						if (
						    os.path.exists(training_args.output_dir						)
						    and os.listdir(training_args.output_dir						)
						    and training_args.do_train
						    and not training_args.overwrite_output_dir
						):
												raise ValueError(
												    f'''Output directory ({training_args.output_dir}) already exists and is not empty. Use'''
												    ''' --overwrite_output_dir to overcome.'''						)

						# Setup logging
						logging.basicConfig(
						    format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s'''   , datefmt='''%m/%d/%Y %H:%M:%S'''   , level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN   , )
						logger.warning(
						    '''Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s'''   , training_args.local_rank   , training_args.device   , training_args.n_gpu   , bool(training_args.local_rank != -1						)   , training_args.fpaa   , )
						# Set the verbosity to info of the Transformers logger (on main process only):
						if is_main_process(training_args.local_rank						):
												transformers.utils.logging.set_verbosity_info()
												transformers.utils.logging.enable_default_handler()
												transformers.utils.logging.enable_explicit_format()
						logger.info('''Training/evaluation parameters %s'''   , __magic_name__						)

						# Set seed
						set_seed(training_args.seed						)

						try:
												lowercase :							Any    		=processors[data_args.task_name]()
												lowercase :							Optional[int]    		=processor.get_labels()
												lowercase :							str    		=len(__magic_name__						)
						except KeyError:
												raise ValueError('''Task not found: %s''' % (data_args.task_name)						)

						# Load pretrained model and tokenizer
						#
						# Distributed training:
						# The .from_pretrained methods guarantee that only one local process can concurrently
						# download model & vocab.

						lowercase :							List[str]    		=AutoConfig.from_pretrained(
						    model_args.config_name if model_args.config_name else model_args.model_name_or_path   , num_labels=__magic_name__   , finetuning_task=data_args.task_name   , cache_dir=model_args.cache_dir   , )
						lowercase :							int    		=AutoTokenizer.from_pretrained(
						    model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path   , cache_dir=model_args.cache_dir   , )
						lowercase :							Any    		=AutoModelForMultipleChoice.from_pretrained(
						    model_args.model_name_or_path   , from_tf=bool('''.ckpt''' in model_args.model_name_or_path						)   , config=__magic_name__   , cache_dir=model_args.cache_dir   , )

						# Get datasets
						lowercase :							int    		=(
						    MultipleChoiceDataset(
						        data_dir=data_args.data_dir   , tokenizer=__magic_name__   , task=data_args.task_name   , max_seq_length=data_args.max_seq_length   , overwrite_cache=data_args.overwrite_cache   , mode=Split.train   , )
						    if training_args.do_train
						    else None
						)
						lowercase :							Union[str, Any]    		=(
						    MultipleChoiceDataset(
						        data_dir=data_args.data_dir   , tokenizer=__magic_name__   , task=data_args.task_name   , max_seq_length=data_args.max_seq_length   , overwrite_cache=data_args.overwrite_cache   , mode=Split.dev   , )
						    if training_args.do_eval
						    else None
						)

						def compute_metrics(__magic_name__						:     EvalPrediction						) -> Dict:
												lowercase :							Dict    		=np.argmax(p.predictions   , axis=1						)
												return {"acc": simple_accuracy(__magic_name__   , p.label_ids						)}

						# Data collator
						lowercase :							List[str]    		=DataCollatorWithPadding(__magic_name__   , pad_to_multiple_of=8						) if training_args.fpaa else None

						# Initialize our Trainer
						lowercase :							Dict    		=Trainer(
						    model=__magic_name__   , args=__magic_name__   , train_dataset=__magic_name__   , eval_dataset=__magic_name__   , compute_metrics=__magic_name__   , data_collator=__magic_name__   , )

						# Training
						if training_args.do_train:
												trainer.train(
												    model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path						) else None						)
												trainer.save_model()
												# For convenience, we also re-save the tokenizer to the same directory,
												# so that you can share your model easily on huggingface.co/models =)
												if trainer.is_world_master():
																		tokenizer.save_pretrained(training_args.output_dir						)

    # Evaluation
						lowercase :							Optional[Any]    		={}
						if training_args.do_eval:
												logger.info('''*** Evaluate ***'''						)

												lowercase :							List[Any]    		=trainer.evaluate()

												lowercase :							Any    		=os.path.join(training_args.output_dir   , '''eval_results.txt'''						)
												if trainer.is_world_master():
																		with open(__magic_name__   , '''w'''						) as writer:
																								logger.info('''***** Eval results *****'''						)
																								for key, value in result.items():
																														logger.info('''  %s = %s'''   , __magic_name__   , __magic_name__						)
																														writer.write('''%s = %s\n''' % (key, value)						)

																								results.update(__magic_name__						)

						return results






def 				_lowerCAmelCase			(   __magic_name__						:     Any						)				->  Optional[Any]:
						# For xla_spawn (TPUs)
						main()


if __name__ == "__main__":
							main()





