

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()
_SCREAMING_SNAKE_CASE  :						Any         =    logging.get_logger(__name__)
def        UpperCAmelCase_     (		_A       ):





     '''simple docstring'''




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

     SCREAMING_SNAKE_CASE__					=					'''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__					=					BitConfig(
         conv_layer=_A   ,	num_labels=10_00   ,	idalabel=_A   ,	labelaid=_A   ,	)

     return config
def        UpperCAmelCase_     (		_A       ):





     '''simple docstring'''




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

     return name
def        UpperCAmelCase_     (		):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__					=					'''http://images.cocodataset.org/val2017/000000039769.jpg'''
     SCREAMING_SNAKE_CASE__					=					Image.open(requests.get(_A   ,	stream=_A       ).raw       )
     return im
@torch.no_grad()
def        UpperCAmelCase_     (		_A   ,	_A   ,	_A=False       ):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__					=					get_config(_A       )

     # load original model from timm
     SCREAMING_SNAKE_CASE__					=					create_model(_A   ,	pretrained=_A       )
     timm_model.eval()

     # load state_dict of original model
     SCREAMING_SNAKE_CASE__					=					timm_model.state_dict()
     for key in state_dict.copy().keys():
          SCREAMING_SNAKE_CASE__					=					state_dict.pop(_A       )
          SCREAMING_SNAKE_CASE__					=					val.squeeze() if '''head''' in key else val

     # load HuggingFace model
     SCREAMING_SNAKE_CASE__					=					BitForImageClassification(_A       )
     model.eval()
     model.load_state_dict(_A       )

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

     SCREAMING_SNAKE_CASE__					=					{
         '''bilinear''': PILImageResampling.BILINEAR,
         '''bicubic''': PILImageResampling.BICUBIC,
         '''nearest''': PILImageResampling.NEAREST,
     }

     SCREAMING_SNAKE_CASE__					=					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()   ,	)

     SCREAMING_SNAKE_CASE__					=					prepare_img()
     SCREAMING_SNAKE_CASE__					=					transform(_A       ).unsqueeze(0       )
     SCREAMING_SNAKE_CASE__					=					processor(_A   ,	return_tensors='''pt'''       ).pixel_values

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

     # verify logits
     with torch.no_grad():
          SCREAMING_SNAKE_CASE__					=					model(_A       )
          SCREAMING_SNAKE_CASE__					=					outputs.logits

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

       _SCREAMING_SNAKE_CASE  :						List[str]         =    parser.parse_args()
       convert_bit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)





import math
import time

from transformers import Trainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput, speed_metrics


if is_torch_tpu_available(check_device=False):
       import torch_xla.core.xla_model as xm
       import torch_xla.debug.metrics as met






class  UpperCAmelCase__	( A__ ):






  """simple docstring"""


  def __init__(							self		:	Tuple			,     *__lowerCamelCase		:	List[str]			,     __lowerCamelCase		:	Any=None			,     __lowerCamelCase		:	Optional[int]=None			,     **__lowerCamelCase		:	Optional[int]      )	->							List[str]:
       super().__init__(*__lowerCamelCase			,     **__lowerCamelCase      )
       SCREAMING_SNAKE_CASE__					=					eval_examples
       SCREAMING_SNAKE_CASE__					=					post_process_function






  def    lowercase_	(							self		:	Optional[Any]			,     __lowerCamelCase		:	Dict=None			,     __lowerCamelCase		:	int=None			,     __lowerCamelCase		:	str=None			,     __lowerCamelCase		:	str = "eval"      )	->							Optional[Any]:
       SCREAMING_SNAKE_CASE__					=					self.eval_dataset if eval_dataset is None else eval_dataset
       SCREAMING_SNAKE_CASE__					=					self.get_eval_dataloader(__lowerCamelCase      )
       SCREAMING_SNAKE_CASE__					=					self.eval_examples if eval_examples is None else eval_examples

       # Temporarily disable metric computation, we will do it in the loop here.
       SCREAMING_SNAKE_CASE__					=					self.compute_metrics
       SCREAMING_SNAKE_CASE__					=					None
       SCREAMING_SNAKE_CASE__					=					self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
       SCREAMING_SNAKE_CASE__					=					time.time()
       try:
            SCREAMING_SNAKE_CASE__					=					eval_loop(
                __lowerCamelCase			,     description='''Evaluation'''			,     prediction_loss_only=True if compute_metrics is None else None			,     ignore_keys=__lowerCamelCase			,     metric_key_prefix=__lowerCamelCase			,     )
       finally:
            SCREAMING_SNAKE_CASE__					=					compute_metrics
       SCREAMING_SNAKE_CASE__					=					self.args.eval_batch_size * self.args.world_size
       if f'''{metric_key_prefix}_jit_compilation_time''' in output.metrics:
            start_time += output.metrics[f'''{metric_key_prefix}_jit_compilation_time''']
       output.metrics.update(
           speed_metrics(
               __lowerCamelCase			,     __lowerCamelCase			,     num_samples=output.num_samples			,     num_steps=math.ceil(output.num_samples / total_batch_size      )			,     )      )
       if self.post_process_function is not None and self.compute_metrics is not None and self.args.should_save:
            # Only the main node write the results by default
            SCREAMING_SNAKE_CASE__					=					self.post_process_function(__lowerCamelCase			,     __lowerCamelCase			,     output.predictions      )
            SCREAMING_SNAKE_CASE__					=					self.compute_metrics(__lowerCamelCase      )

            # Prefix all keys with metric_key_prefix + '_'
            for key in list(metrics.keys()      ):
                 if not key.startswith(f'''{metric_key_prefix}_'''      ):
                      SCREAMING_SNAKE_CASE__					=					metrics.pop(__lowerCamelCase      )
            metrics.update(output.metrics      )
       else:
            SCREAMING_SNAKE_CASE__					=					output.metrics

       if self.args.should_log:
            # Only the main node log the results by default
            self.log(__lowerCamelCase      )

       if self.args.tpu_metrics_debug or self.args.debug:
            # tpu-comment: Logging debug metrics for PyTorch/XLA (compile, execute times, ops, etc.)
            xm.master_print(met.metrics_report()      )

       SCREAMING_SNAKE_CASE__					=					self.callback_handler.on_evaluate(self.args			,     self.state			,     self.control			,     __lowerCamelCase      )
       return metrics







  def    lowercase_	(							self		:	Dict			,     __lowerCamelCase		:	Dict			,     __lowerCamelCase		:	str			,     __lowerCamelCase		:	str=None			,     __lowerCamelCase		:	str = "test"      )	->							Any:
       SCREAMING_SNAKE_CASE__					=					self.get_test_dataloader(__lowerCamelCase      )

       # Temporarily disable metric computation, we will do it in the loop here.
       SCREAMING_SNAKE_CASE__					=					self.compute_metrics
       SCREAMING_SNAKE_CASE__					=					None
       SCREAMING_SNAKE_CASE__					=					self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
       SCREAMING_SNAKE_CASE__					=					time.time()
       try:
            SCREAMING_SNAKE_CASE__					=					eval_loop(
                __lowerCamelCase			,     description='''Prediction'''			,     prediction_loss_only=True if compute_metrics is None else None			,     ignore_keys=__lowerCamelCase			,     metric_key_prefix=__lowerCamelCase			,     )
       finally:
            SCREAMING_SNAKE_CASE__					=					compute_metrics
       SCREAMING_SNAKE_CASE__					=					self.args.eval_batch_size * self.args.world_size
       if f'''{metric_key_prefix}_jit_compilation_time''' in output.metrics:
            start_time += output.metrics[f'''{metric_key_prefix}_jit_compilation_time''']
       output.metrics.update(
           speed_metrics(
               __lowerCamelCase			,     __lowerCamelCase			,     num_samples=output.num_samples			,     num_steps=math.ceil(output.num_samples / total_batch_size      )			,     )      )

       if self.post_process_function is None or self.compute_metrics is None:
            return output

       SCREAMING_SNAKE_CASE__					=					self.post_process_function(__lowerCamelCase			,     __lowerCamelCase			,     output.predictions			,     '''predict'''      )
       SCREAMING_SNAKE_CASE__					=					self.compute_metrics(__lowerCamelCase      )

       # Prefix all keys with metric_key_prefix + '_'
       for key in list(metrics.keys()      ):
            if not key.startswith(f'''{metric_key_prefix}_'''      ):
                 SCREAMING_SNAKE_CASE__					=					metrics.pop(__lowerCamelCase      )
       metrics.update(output.metrics      )
       return PredictionOutput(predictions=predictions.predictions			,     label_ids=predictions.label_ids			,     metrics=__lowerCamelCase      )


