


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					:       List[str]			    =     logging.get_logger(__name__)



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

      a__	:  Dict     =   "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__	:  Tuple     =   BitConfig(
          conv_layer=__a			,				num_labels=1_000			,				idalabel=__a			,				labelaid=__a			,				)

      return config



def UpperCamelCase_				(				__a      )	->   Any:
      if "stem.conv" in name:
            a__	:  Dict     =   name.replace("stem.conv"			,				"bit.embedder.convolution"      )
      if "blocks" in name:
            a__	:  str     =   name.replace("blocks"			,				"layers"      )
      if "head.fc" in name:
            a__	:  Union[str, Any]     =   name.replace("head.fc"			,				"classifier.1"      )
      if name.startswith("norm"      ):
            a__	:  List[Any]     =   "bit." + name
      if "bit" not in name and "classifier" not in name:
            a__	:  List[Any]     =   "bit.encoder." + name

      return name



def UpperCamelCase_				(				)	->   Optional[Any]:
      a__	:  Dict     =   "http://images.cocodataset.org/val2017/000000039769.jpg"
      a__	:  Optional[Any]     =   Image.open(requests.get(__a			,				stream=__a      ).raw      )
      return im



@torch.no_grad()
def UpperCamelCase_				(				__a			,				__a			,				__a=False      )	->   Any:
      a__	:  Optional[int]     =   get_config(__a      )

      # load original model from timm
      a__	:  List[str]     =   create_model(__a			,				pretrained=__a      )
      timm_model.eval()

      # load state_dict of original model
      a__	:  Optional[int]     =   timm_model.state_dict()
      for key in state_dict.copy().keys():
            a__	:  Tuple     =   state_dict.pop(__a      )
            a__	:  str     =   val.squeeze() if "head" in key else val

      # load HuggingFace model
      a__	:  str     =   BitForImageClassification(__a      )
      model.eval()
      model.load_state_dict(__a      )

      # create image processor
      a__	:  List[Any]     =   create_transform(**resolve_data_config({}			,				model=__a      )      )
      a__	:  Any     =   transform.transforms

      a__	:  Optional[int]     =   {
          "bilinear": PILImageResampling.BILINEAR,
          "bicubic": PILImageResampling.BICUBIC,
          "nearest": PILImageResampling.NEAREST,
      }

      a__	:  str     =   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__	:  Tuple     =   prepare_img()
      a__	:  Dict     =   transform(__a      ).unsqueeze(0      )
      a__	:  List[Any]     =   processor(__a			,				return_tensors="pt"      ).pixel_values

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

      # verify logits
      with torch.no_grad():
            a__	:  int     =   model(__a      )
            a__	:  str     =   outputs.logits

      print("Logits:"			,				logits[0, :3]      )
      print("Predicted class:"			,				model.config.idalabel[logits.argmax(-1      ).item()]      )
      a__	:  Tuple     =   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__":
    UpperCamelCase					:       Dict			    =     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					:       List[Any]			    =     parser.parse_args()
    convert_bit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)






import os
import time
from dataclasses import dataclass, field
from enum import Enum
from typing import Dict, List, Optional, Union

import torch
from filelock import FileLock
from torch.utils.data import Dataset

from ...models.auto.modeling_auto import MODEL_FOR_QUESTION_ANSWERING_MAPPING
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
from ..processors.squad import SquadFeatures, SquadVaProcessor, SquadVaProcessor, squad_convert_examples_to_features


__snake_case     :Optional[Any]			       =logging.get_logger(__name__)

__snake_case     :int			       =list(MODEL_FOR_QUESTION_ANSWERING_MAPPING.keys())
__snake_case     :Optional[int]			       =tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)



@dataclass
class        lowerCAmelCase__					:
				A_   :     str								=     field(
				    default=_lowerCamelCase ,				metadata={'help': 'Model type selected in the list: ' + ', '.join(_lowerCamelCase       )}       )
				A_   :     str								=     field(
				    default=_lowerCamelCase ,				metadata={'help': 'The input data dir. Should contain the .json files for the SQuAD task.'}       )
				A_   :     int								=     field(
				    default=1_2_8 ,				metadata={
				        'help': (
				            'The maximum total input sequence length after tokenization. Sequences longer '
				            'than this will be truncated, sequences shorter will be padded.'
				        )
				    } ,				)
				A_   :     int								=     field(
				    default=1_2_8 ,				metadata={'help': 'When splitting up a long document into chunks, how much stride to take between chunks.'} ,				)
				A_   :     int								=     field(
				    default=6_4 ,				metadata={
				        'help': (
				            'The maximum number of tokens for the question. Questions longer than this will '
				            'be truncated to this length.'
				        )
				    } ,				)
				A_   :     int								=     field(
				    default=3_0 ,				metadata={
				        'help': (
				            'The maximum length of an answer that can be generated. This is needed because the start '
				            'and end predictions are not conditioned on one another.'
				        )
				    } ,				)
				A_   :     bool								=     field(
				    default=_lowerCamelCase ,				metadata={'help': 'Overwrite the cached training and evaluation sets'}       )
				A_   :     bool								=     field(
				    default=_lowerCamelCase ,				metadata={'help': 'If true, the SQuAD examples contain some that do not have an answer.'}       )
				A_   :     float								=     field(
				    default=0.0 ,				metadata={'help': 'If null_score - best_non_null is greater than the threshold predict null.'}       )
				A_   :     int								=     field(
				    default=2_0 ,				metadata={'help': 'If null_score - best_non_null is greater than the threshold predict null.'}       )
				A_   :     int								=     field(
				    default=0 ,				metadata={
				        'help': (
				            'language id of input for language-specific xlm models (see'
				            ' tokenization_xlm.PRETRAINED_INIT_CONFIGURATION)'
				        )
				    } ,				)
				A_   :     int								=     field(default=1 ,				metadata={'help': 'multiple threads for converting example to features'}       )





class        lowerCAmelCase__					(  _lowerCamelCase       ):
				A_   :     str								=     'train'
				A_   :     str								=     'dev'





class        lowerCAmelCase__					(  _lowerCamelCase       ):
				A_   :     SquadDataTrainingArguments
				A_   :     List[SquadFeatures]
				A_   :     Split
				A_   :     bool


				def __init__(							self      :  Optional[int]						, __UpperCamelCase      :  SquadDataTrainingArguments						, __UpperCamelCase      :  PreTrainedTokenizer						, __UpperCamelCase      :  Optional[int] = None						, __UpperCamelCase      :  Union[str, Split] = Split.train						, __UpperCamelCase      :  Optional[bool] = False						, __UpperCamelCase      :  Optional[str] = None						, __UpperCamelCase      :  Optional[str] = "pt"						, )		->     Any:
											A      						=    args
											A      						=    is_language_sensitive
											A      						=    SquadVaProcessor() if args.version_2_with_negative else SquadVaProcessor()
											if isinstance(__UpperCamelCase						, __UpperCamelCase       ):
																		try:
																									A      						=    Split[mode]
																		except KeyError:
																									raise KeyError('mode is not a valid split name'       )
											A      						=    mode
											# Load data features from cache or dataset file
											A      						=    'v2' if args.version_2_with_negative else 'v1'
											A      						=    os.path.join(
											    cache_dir if cache_dir is not None else args.data_dir						, f'''cached_{mode.value}_{tokenizer.__class__.__name__}_{args.max_seq_length}_{version_tag}'''						, )

											# Make sure only the first process in distributed training processes the dataset,
											# and the others will use the cache.
											A      						=    cached_features_file + '.lock'
											with FileLock(__UpperCamelCase       ):
																		if os.path.exists(__UpperCamelCase       ) and not args.overwrite_cache:
																									A      						=    time.time()
																									A      						=    torch.load(__UpperCamelCase       )

																									# Legacy cache files have only features, while new cache files
																									# will have dataset and examples also.
																									A      						=    self.old_features['features']
																									A      						=    self.old_features.get('dataset'						, __UpperCamelCase       )
																									A      						=    self.old_features.get('examples'						, __UpperCamelCase       )
																									logger.info(
																									    f'''Loading features from cached file {cached_features_file} [took %.3f s]'''						, time.time() - start       )

																									if self.dataset is None or self.examples is None:
																																logger.warning(
																																    f'''Deleting cached file {cached_features_file} will allow dataset and examples to be cached in'''
																																    ' future run'       )
																		else:
																									if mode == Split.dev:
																																A      						=    self.processor.get_dev_examples(args.data_dir       )
																									else:
																																A      						=    self.processor.get_train_examples(args.data_dir       )

																									A						,   A      						=    squad_convert_examples_to_features(
																									    examples=self.examples						, tokenizer=__UpperCamelCase						, max_seq_length=args.max_seq_length						, doc_stride=args.doc_stride						, max_query_length=args.max_query_length						, is_training=mode == Split.train						, threads=args.threads						, return_dataset=__UpperCamelCase						, )

																									A      						=    time.time()
																									torch.save(
																									    {'features': self.features, 'dataset': self.dataset, 'examples': self.examples}						, __UpperCamelCase						, )
																									# ^ This seems to take a lot of time so I want to investigate why and how we can improve.
																									logger.info(
																									    f'''Saving features into cached file {cached_features_file} [took {time.time() - start:.3f} s]'''       )


				def __len__(							self      :  Optional[Any]       )		->     Tuple:
											return len(self.features       )


				def __getitem__(							self      :  Tuple						, __UpperCamelCase      :  List[Any]       )		->     Dict[str, torch.Tensor]:
											# Convert to Tensors and build dataset
											A      						=    self.features[i]

											A      						=    torch.tensor(feature.input_ids						, dtype=torch.long       )
											A      						=    torch.tensor(feature.attention_mask						, dtype=torch.long       )
											A      						=    torch.tensor(feature.token_type_ids						, dtype=torch.long       )
											A      						=    torch.tensor(feature.cls_index						, dtype=torch.long       )
											A      						=    torch.tensor(feature.p_mask						, dtype=torch.float       )
											A      						=    torch.tensor(feature.is_impossible						, dtype=torch.float       )

											A      						=    {
											    'input_ids': input_ids,
											    'attention_mask': attention_mask,
											    'token_type_ids': token_type_ids,
											}

											if self.args.model_type in ["xlm", "roberta", "distilbert", "camembert"]:
																		del inputs["token_type_ids"]

											if self.args.model_type in ["xlnet", "xlm"]:
																		inputs.update({'cls_index': cls_index, 'p_mask': p_mask}       )
																		if self.args.version_2_with_negative:
																									inputs.update({'is_impossible': is_impossible}       )
																		if self.is_language_sensitive:
																									inputs.update({'langs': (torch.ones(input_ids.shape						, dtype=torch.intaa       ) * self.args.lang_id)}       )

											if self.mode == Split.train:
																		A      						=    torch.tensor(feature.start_position						, dtype=torch.long       )
																		A      						=    torch.tensor(feature.end_position						, dtype=torch.long       )
																		inputs.update({'start_positions': start_positions, 'end_positions': end_positions}       )

											return inputs