






'''simple docstring'''






from collections import defaultdict
from typing import Optional

from ..image_utils import load_image
from ..utils import (
    add_end_docstrings,
    is_torch_available,
    logging,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, ChunkPipeline


if is_torch_available():
      import torch

      from ..models.auto.modeling_auto import MODEL_FOR_MASK_GENERATION_MAPPING

__A    	=   logging.get_logger(__name__)




@add_end_docstrings(UpperCamelCase_							)
class a_    (    UpperCamelCase_							):







   def __init__(self					,   **__a)			->      Optional[Any]:
      """simple docstring"""






      super().__init__(**__a)
      requires_backends(self					,   'vision')
      requires_backends(self					,   'torch')

      if self.framework != "pt":
         raise ValueError(F"""The {self.__class__} is only available in PyTorch.""")

      self.check_model_type(__a)







   def     SCREAMING_SNAKE_CASE__					(self					,   **__a)			->      int:
      """simple docstring"""






      __snake_case				:      List[Any]     							= {}
      __snake_case				:      List[str]     							= {}
      __snake_case				:      int     							= {}
      # preprocess args
      if "points_per_batch" in kwargs:
         __snake_case				:      Optional[int]     							= kwargs['points_per_batch']
      if "points_per_crop" in kwargs:
         __snake_case				:      Dict     							= kwargs['points_per_crop']
      if "crops_n_layers" in kwargs:
         __snake_case				:      Dict     							= kwargs['crops_n_layers']
      if "crop_overlap_ratio" in kwargs:
         __snake_case				:      int     							= kwargs['crop_overlap_ratio']
      if "crop_n_points_downscale_factor" in kwargs:
         __snake_case				:      int     							= kwargs['crop_n_points_downscale_factor']
      # postprocess args
      if "pred_iou_thresh" in kwargs:
         __snake_case				:      Union[str, Any]     							= kwargs['pred_iou_thresh']
      if "stability_score_offset" in kwargs:
         __snake_case				:      str     							= kwargs['stability_score_offset']
      if "mask_threshold" in kwargs:
         __snake_case				:      Optional[int]     							= kwargs['mask_threshold']
      if "stability_score_thresh" in kwargs:
         __snake_case				:      Dict     							= kwargs['stability_score_thresh']
      if "crops_nms_thresh" in kwargs:
         __snake_case				:      List[str]     							= kwargs['crops_nms_thresh']
      if "output_rle_mask" in kwargs:
         __snake_case				:      Union[str, Any]     							= kwargs['output_rle_mask']
      if "output_bboxes_mask" in kwargs:
         __snake_case				:      List[Any]     							= kwargs['output_bboxes_mask']
      return preprocess_kwargs, forward_params, postprocess_kwargs







   def __call__(self					,   __a					,   *__a					,   __a=None					,   __a=None					,   **__a)			->      Dict:
      """simple docstring"""






      return super().__call__(__a					,   *__a					,   num_workers=__a					,   batch_size=__a					,   **__a)







   def     SCREAMING_SNAKE_CASE__					(self					,   __a					,   __a=6_4					,   __a = 0					,   __a = 5_1_2 / 1_5_0_0					,   __a = 3_2					,   __a = 1					,   )			->      List[Any]:
      """simple docstring"""






      __snake_case				:      Tuple     							= load_image(__a)
      __snake_case				:      List[Any]     							= self.image_processor.size['longest_edge']
      __snake_case							,__snake_case							,__snake_case							,__snake_case				:      List[Any]     							= self.image_processor.generate_crop_boxes(
          __a					,   __a					,   __a					,   __a					,   __a					,   __a)
      __snake_case				:      Union[str, Any]     							= self.image_processor(images=__a					,   return_tensors='pt')

      with self.device_placement():
         if self.framework == "pt":
            __snake_case				:      List[str]     							= self.get_inference_context()
            with inference_context():
               __snake_case				:      List[Any]     							= self._ensure_tensor_on_device(__a					,   device=self.device)
               __snake_case				:      Optional[int]     							= self.model.get_image_embeddings(model_inputs.pop('pixel_values'))
               __snake_case				:      str     							= image_embeddings

      __snake_case				:      Optional[Any]     							= grid_points.shape[1]
      __snake_case				:      Dict     							= points_per_batch if points_per_batch is not None else n_points

      if points_per_batch <= 0:
         raise ValueError(
             'Cannot have points_per_batch<=0. Must be >=1 to returned batched outputs. '
             'To return all points at once, set points_per_batch to None')

      for i in range(0					,   __a					,   __a):
         __snake_case				:      Union[str, Any]     							= grid_points[:, i : i + points_per_batch, :, :]
         __snake_case				:      Any     							= input_labels[:, i : i + points_per_batch]
         __snake_case				:      Any     							= i == n_points - points_per_batch
         yield {
             "input_points": batched_points,
             "input_labels": labels,
             "input_boxes": crop_boxes,
             "is_last": is_last,
             **model_inputs,
         }







   def     SCREAMING_SNAKE_CASE__					(self					,   __a					,   __a=0.88					,   __a=0.95					,   __a=0					,   __a=1					,   )			->      Optional[int]:
      """simple docstring"""






      __snake_case				:      List[Any]     							= model_inputs.pop('input_boxes')
      __snake_case				:      str     							= model_inputs.pop('is_last')
      __snake_case				:      int     							= model_inputs.pop('original_sizes').tolist()
      __snake_case				:      str     							= model_inputs.pop('reshaped_input_sizes').tolist()

      __snake_case				:      Tuple     							= self.model(**__a)

      # post processing happens here in order to avoid CPU GPU copies of ALL the masks
      __snake_case				:      str     							= model_outputs['pred_masks']
      __snake_case				:      Any     							= self.image_processor.post_process_masks(
          __a					,   __a					,   __a					,   __a					,   binarize=__a)
      __snake_case				:      Optional[Any]     							= model_outputs['iou_scores']
      __snake_case							,__snake_case							,__snake_case				:      Optional[int]     							= self.image_processor.filter_masks(
          masks[0]					,   iou_scores[0]					,   original_sizes[0]					,   input_boxes[0]					,   __a					,   __a					,   __a					,   __a					,   )
      return {
          "masks": masks,
          "is_last": is_last,
          "boxes": boxes,
          "iou_scores": iou_scores,
      }







   def     SCREAMING_SNAKE_CASE__					(self					,   __a					,   __a=False					,   __a=False					,   __a=0.7					,   )			->      List[str]:
      """simple docstring"""






      __snake_case				:      int     							= []
      __snake_case				:      Optional[Any]     							= []
      __snake_case				:      int     							= []
      for model_output in model_outputs:
         all_scores.append(model_output.pop('iou_scores'))
         all_masks.extend(model_output.pop('masks'))
         all_boxes.append(model_output.pop('boxes'))

      __snake_case				:      Union[str, Any]     							= torch.cat(__a)
      __snake_case				:      Tuple     							= torch.cat(__a)
      __snake_case							,__snake_case							,__snake_case							,__snake_case				:      List[str]     							= self.image_processor.post_process_for_mask_generation(
          __a					,   __a					,   __a					,   __a)

      __snake_case				:      Any     							= defaultdict(__a)
      for output in model_outputs:
         for k, v in output.items():
            extra[k].append(__a)

      __snake_case				:      List[str]     							= {}
      if output_rle_mask:
         __snake_case				:      Optional[int]     							= rle_mask

      if output_bboxes_mask:
         __snake_case				:      int     							= bounding_boxes

      return {"masks": output_masks, "scores": iou_scores, **optional, **extra}







'''simple docstring'''






import argparse
import json
from collections import OrderedDict
from functools import partial
from pathlib import Path

import timm
import torch
from huggingface_hub import hf_hub_download

from transformers import LevitConfig, LevitForImageClassificationWithTeacher, LevitImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
__A    	=   logging.get_logger()




def        _SCREAMING_SNAKE_CASE  (				A  :       int  ,		A  :       str  ,		A  :       LevitConfig  ,		A  :       Path  ,		A  :       bool = True  )    -> Dict:







   """simple docstring"""
   print(F"""Converting {name}..."""  )

   with torch.no_grad():
      if hidden_sizes == 1_28:
         if name[-1] == "S":
            __snake_case				:      Optional[int]     							= timm.create_model('levit_128s'  ,		pretrained=A  )
         else:
            __snake_case				:      Tuple     							= timm.create_model('levit_128'  ,		pretrained=A  )
      if hidden_sizes == 1_92:
         __snake_case				:      int     							= timm.create_model('levit_192'  ,		pretrained=A  )
      if hidden_sizes == 2_56:
         __snake_case				:      List[Any]     							= timm.create_model('levit_256'  ,		pretrained=A  )
      if hidden_sizes == 3_84:
         __snake_case				:      int     							= timm.create_model('levit_384'  ,		pretrained=A  )

      from_model.eval()
      __snake_case				:      str     							= LevitForImageClassificationWithTeacher(A  ).eval()
      __snake_case				:      int     							= OrderedDict()

      __snake_case				:      Optional[Any]     							= from_model.state_dict()
      __snake_case				:      Tuple     							= list(from_model.state_dict().keys()  )
      __snake_case				:      List[str]     							= list(our_model.state_dict().keys()  )
      print(len(A  )  ,		len(A  )  )
      for i in range(len(A  )  ):
         __snake_case				:      Optional[int]     							= weights[og_keys[i]]
      our_model.load_state_dict(A  )

      __snake_case				:      Tuple     							= torch.randn((2, 3, 2_24, 2_24)  )
      __snake_case				:      Union[str, Any]     							= from_model(A  )
      __snake_case				:      List[str]     							= our_model(A  ).logits

   assert torch.allclose(A  ,		A  ), "The model logits don't match the original one."

   __snake_case				:      int     							= name
   print(A  )

   if push_to_hub:
      our_model.save_pretrained(save_directory / checkpoint_name  )
      __snake_case				:      int     							= LevitImageProcessor()
      image_processor.save_pretrained(save_directory / checkpoint_name  )

      print(F"""Pushed {checkpoint_name}"""  )




def        _SCREAMING_SNAKE_CASE  (				A  :       Path  ,		A  :       str = None  ,		A  :       bool = True  )    -> List[Any]:







   """simple docstring"""
   __snake_case				:      Optional[Any]     							= 'imagenet-1k-id2label.json'
   __snake_case				:      Tuple     							= 10_00
   __snake_case				:      Dict     							= (1, num_labels)

   __snake_case				:      List[str]     							= 'huggingface/label-files'
   __snake_case				:      Any     							= num_labels
   __snake_case				:      str     							= json.load(open(hf_hub_download(A  ,		A  ,		repo_type='dataset'  )  ,		'r'  )  )
   __snake_case				:      Any     							= {int(A  ): v for k, v in idalabel.items()}

   __snake_case				:      int     							= idalabel
   __snake_case				:      Union[str, Any]     							= {v: k for k, v in idalabel.items()}

   __snake_case				:      Optional[int]     							= partial(A  ,		num_labels=A  ,		idalabel=A  ,		labelaid=A  )

   __snake_case				:      Dict     							= {
       'levit-128S': 1_28,
       'levit-128': 1_28,
       'levit-192': 1_92,
       'levit-256': 2_56,
       'levit-384': 3_84,
   }

   __snake_case				:      Union[str, Any]     							= {
       'levit-128S': ImageNetPreTrainedConfig(
           hidden_sizes=[1_28, 2_56, 3_84]  ,		num_attention_heads=[4, 6, 8]  ,		depths=[2, 3, 4]  ,		key_dim=[16, 16, 16]  ,		drop_path_rate=0  ,		),
       'levit-128': ImageNetPreTrainedConfig(
           hidden_sizes=[1_28, 2_56, 3_84]  ,		num_attention_heads=[4, 8, 12]  ,		depths=[4, 4, 4]  ,		key_dim=[16, 16, 16]  ,		drop_path_rate=0  ,		),
       'levit-192': ImageNetPreTrainedConfig(
           hidden_sizes=[1_92, 2_88, 3_84]  ,		num_attention_heads=[3, 5, 6]  ,		depths=[4, 4, 4]  ,		key_dim=[32, 32, 32]  ,		drop_path_rate=0  ,		),
       'levit-256': ImageNetPreTrainedConfig(
           hidden_sizes=[2_56, 3_84, 5_12]  ,		num_attention_heads=[4, 6, 8]  ,		depths=[4, 4, 4]  ,		key_dim=[32, 32, 32]  ,		drop_path_rate=0  ,		),
       'levit-384': ImageNetPreTrainedConfig(
           hidden_sizes=[3_84, 5_12, 7_68]  ,		num_attention_heads=[6, 9, 12]  ,		depths=[4, 4, 4]  ,		key_dim=[32, 32, 32]  ,		drop_path_rate=0.1  ,		),
   }

   if model_name:
      convert_weight_and_push(
          names_to_hidden_sizes[model_name]  ,		A  ,		names_to_config[model_name]  ,		A  ,		A  )
   else:
      for model_name, config in names_to_config.items():
         convert_weight_and_push(names_to_hidden_sizes[model_name]  ,		A  ,		A  ,		A  ,		A  )
   return config, expected_shape


if __name__ == "__main__":
      __A    	=   argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          '''--model_name''',
          default=None,
          type=str,
          help='''The name of the model you wish to convert, it must be one of the supported Levit* architecture,''',
      )
      parser.add_argument(
          '''--pytorch_dump_folder_path''',
          default='''levit-dump-folder/''',
          type=Path,
          required=False,
          help='''Path to the output PyTorch model directory.''',
      )
      parser.add_argument('''--push_to_hub''', action='''store_true''', help='''Push model and image processor to the hub''')
      parser.add_argument(
          '''--no-push_to_hub''',
          dest='''push_to_hub''',
          action='''store_false''',
          help='''Do not push model and image processor to the hub''',
      )

      __A    	=   parser.parse_args()
      __A    	=   args.pytorch_dump_folder_path
      pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
      convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)