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

def 		UpperCamelCase						(   lowercase_ )		->						Union[str, Any]:


  '''simple docstring'''






  lowercase__	: List[str]     		=   """huggingface/label-files"""
  lowercase__	: Optional[int]     		=   """imagenet-1k-id2label.json"""
  lowercase__	: int     		=   json.load(open(hf_hub_download(lowercase_			,			lowercase_			,			repo_type="""dataset""" )			,			"""r""" ) )
  lowercase__	: int     		=   {int(lowercase_ ): v for k, v in idalabel.items()}
  lowercase__	: List[str]     		=   {v: k for k, v in idalabel.items()}

  lowercase__	: Tuple     		=   """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__	: int     		=   BitConfig(
      conv_layer=lowercase_			,			num_labels=10_00			,			idalabel=lowercase_			,			labelaid=lowercase_			,			)

  return config

def 		UpperCamelCase						(   lowercase_ )		->						Tuple:


  '''simple docstring'''






  if "stem.conv" in name:
    lowercase__	: Optional[int]     		=   name.replace("""stem.conv"""			,			"""bit.embedder.convolution""" )
  if "blocks" in name:
    lowercase__	: Tuple     		=   name.replace("""blocks"""			,			"""layers""" )
  if "head.fc" in name:
    lowercase__	: List[str]     		=   name.replace("""head.fc"""			,			"""classifier.1""" )
  if name.startswith("""norm""" ):
    lowercase__	: List[str]     		=   """bit.""" + name
  if "bit" not in name and "classifier" not in name:
    lowercase__	: Optional[int]     		=   """bit.encoder.""" + name

  return name

def 		UpperCamelCase						(   )		->						Dict:


  '''simple docstring'''






  lowercase__	: Tuple     		=   """http://images.cocodataset.org/val2017/000000039769.jpg"""
  lowercase__	: Union[str, Any]     		=   Image.open(requests.get(lowercase_			,			stream=lowercase_ ).raw )
  return im

@torch.no_grad()
def 		UpperCamelCase						(   lowercase_			,			lowercase_			,			lowercase_=False )		->						Union[str, Any]:


  '''simple docstring'''






  lowercase__	: Tuple     		=   get_config(lowercase_ )

  # load original model from timm
  lowercase__	: str     		=   create_model(lowercase_			,			pretrained=lowercase_ )
  timm_model.eval()

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

  # load HuggingFace model
  lowercase__	: List[str]     		=   BitForImageClassification(lowercase_ )
  model.eval()
  model.load_state_dict(lowercase_ )

  # create image processor
  lowercase__	: Tuple     		=   create_transform(**resolve_data_config({}			,			model=lowercase_ ) )
  lowercase__	: Any     		=   transform.transforms

  lowercase__	: Dict     		=   {
      """bilinear""": PILImageResampling.BILINEAR,
      """bicubic""": PILImageResampling.BICUBIC,
      """nearest""": PILImageResampling.NEAREST,
  }

  lowercase__	: Any     		=   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()			,			)

  lowercase__	: List[Any]     		=   prepare_img()
  lowercase__	: Any     		=   transform(lowercase_ ).unsqueeze(0 )
  lowercase__	: str     		=   processor(lowercase_			,			return_tensors="""pt""" ).pixel_values

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

  # verify logits
  with torch.no_grad():
    lowercase__	: Any     		=   model(lowercase_ )
    lowercase__	: Dict     		=   outputs.logits

  print("""Logits:"""			,			logits[0, :3] )
  print("""Predicted class:"""			,			model.config.idalabel[logits.argmax(-1 ).item()] )
  lowercase__	: List[str]     		=   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__":
     lowerCamelCase__     :		Optional[int]        = 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__     :		Dict        = parser.parse_args()
     convert_bit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)








from typing import Any, Dict, List, Union

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, ChunkPipeline


if is_vision_available():
     from PIL import Image

     from ..image_utils import load_image

if is_torch_available():
     import torch

     from transformers.modeling_outputs import BaseModelOutput

     from ..models.auto.modeling_auto import MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING

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



@add_end_docstrings(UpperCAmelCase_				)
class    _snake_case (						UpperCAmelCase_				):


 def __init__(       self       ,	**SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   super().__init__(**SCREAMING_SNAKE_CASE_)

   if self.framework == "tf":
     raise ValueError(f'The {self.__class__} is only available in PyTorch.')

   requires_backends(self       ,	"""vision""")
   self.check_model_type(SCREAMING_SNAKE_CASE_)


 def __call__(       self       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_ = None       ,	**SCREAMING_SNAKE_CASE_       ,	):


   '''simple docstring'''




   if "text_queries" in kwargs:
     lowercase__	: Any     		=   kwargs.pop("""text_queries""")

   if isinstance(SCREAMING_SNAKE_CASE_       ,	(str, Image.Image)):
     lowercase__	: Optional[Any]     		=   {"""image""": image, """candidate_labels""": candidate_labels}
   else:
     lowercase__	: int     		=   image
   lowercase__	: List[str]     		=   super().__call__(SCREAMING_SNAKE_CASE_       ,	**SCREAMING_SNAKE_CASE_)
   return results


 def   lowercase__  (       self       ,	**SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   lowercase__	: Tuple     		=   {}
   if "threshold" in kwargs:
     lowercase__	: List[Any]     		=   kwargs["""threshold"""]
   if "top_k" in kwargs:
     lowercase__	: int     		=   kwargs["""top_k"""]
   return {}, {}, postprocess_params


 def   lowercase__  (       self       ,	SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   lowercase__	: str     		=   load_image(inputs["""image"""])
   lowercase__	: Any     		=   inputs["""candidate_labels"""]
   if isinstance(SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_):
     lowercase__	: List[str]     		=   candidate_labels.split(""",""")

   lowercase__	: Tuple     		=   torch.tensor([[image.height, image.width]]       ,	dtype=torch.intaa)
   for i, candidate_label in enumerate(SCREAMING_SNAKE_CASE_):
     lowercase__	: Optional[Any]     		=   self.tokenizer(SCREAMING_SNAKE_CASE_       ,	return_tensors=self.framework)
     lowercase__	: Union[str, Any]     		=   self.image_processor(SCREAMING_SNAKE_CASE_       ,	return_tensors=self.framework)
     yield {
         "is_last": i == len(SCREAMING_SNAKE_CASE_) - 1,
         "target_size": target_size,
         "candidate_label": candidate_label,
         **text_inputs,
         **image_features,
     }


 def   lowercase__  (       self       ,	SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   lowercase__	: str     		=   model_inputs.pop("""target_size""")
   lowercase__	: Optional[int]     		=   model_inputs.pop("""candidate_label""")
   lowercase__	: Dict     		=   model_inputs.pop("""is_last""")

   lowercase__	: Union[str, Any]     		=   self.model(**SCREAMING_SNAKE_CASE_)

   lowercase__	: Union[str, Any]     		=   {"""target_size""": target_size, """candidate_label""": candidate_label, """is_last""": is_last, **outputs}
   return model_outputs


 def   lowercase__  (       self       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_=0.1       ,	SCREAMING_SNAKE_CASE_=None):


   '''simple docstring'''




   lowercase__	: Union[str, Any]     		=   []
   for model_output in model_outputs:
     lowercase__	: Optional[int]     		=   model_output["""candidate_label"""]
     lowercase__	: Tuple     		=   BaseModelOutput(SCREAMING_SNAKE_CASE_)
     lowercase__	: List[str]     		=   self.image_processor.post_process_object_detection(
         outputs=SCREAMING_SNAKE_CASE_       ,	threshold=SCREAMING_SNAKE_CASE_       ,	target_sizes=model_output["""target_size"""])[0]

     for index in outputs["scores"].nonzero():
       lowercase__	: Optional[Any]     		=   outputs["""scores"""][index].item()
       lowercase__	: Optional[Any]     		=   self._get_bounding_box(outputs["""boxes"""][index][0])

       lowercase__	: Tuple     		=   {"""score""": score, """label""": label, """box""": box}
       results.append(SCREAMING_SNAKE_CASE_)

   lowercase__	: int     		=   sorted(SCREAMING_SNAKE_CASE_       ,	key=lambda SCREAMING_SNAKE_CASE_: x["score"]       ,	reverse=SCREAMING_SNAKE_CASE_)
   if top_k:
     lowercase__	: Any     		=   results[:top_k]

   return results



 def   lowercase__  (       self       ,	SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   if self.framework != "pt":
     raise ValueError("""The ZeroShotObjectDetectionPipeline is only available in PyTorch.""")
   lowercase__		,     lowercase__		,     lowercase__		,     lowercase__	: List[Any]     		=   box.int().tolist()
   lowercase__	: Optional[int]     		=   {
       """xmin""": xmin,
       """ymin""": ymin,
       """xmax""": xmax,
       """ymax""": ymax,
   }
   return bbox






