import argparse

import requests
import torch
from PIL import Image

from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel






def 				__lowerCamelCase		(						lowerCAmelCase__     ):
       # vision encoder
       if "img_encoder.pos_embed" in name:
              lowerCAmelCase__					     = name.replace('img_encoder.pos_embed'     ,  'vision_model.embeddings.position_embeddings'     )
       if "img_encoder.patch_embed.proj" in name:
              lowerCAmelCase__					     = name.replace('img_encoder.patch_embed.proj'     ,  'vision_model.embeddings.patch_embeddings.projection'     )
       if "img_encoder.patch_embed.norm" in name:
              lowerCAmelCase__					     = name.replace('img_encoder.patch_embed.norm'     ,  'vision_model.embeddings.layernorm'     )
       if "img_encoder.layers" in name:
              lowerCAmelCase__					     = name.replace('img_encoder.layers'     ,  'vision_model.encoder.stages'     )
       if "blocks" in name and "res" not in name:
              lowerCAmelCase__					     = name.replace('blocks'     ,  'layers'     )
       if "attn" in name and "pre_assign" not in name:
              lowerCAmelCase__					     = name.replace('attn'     ,  'self_attn'     )
       if "proj" in name and "self_attn" in name and "text" not in name:
              lowerCAmelCase__					     = name.replace('proj'     ,  'out_proj'     )
       if "pre_assign_attn.attn.proj" in name:
              lowerCAmelCase__					     = name.replace('pre_assign_attn.attn.proj'     ,  'pre_assign_attn.attn.out_proj'     )
       if "norm1" in name:
              lowerCAmelCase__					     = name.replace('norm1'     ,  'layer_norm1'     )
       if "norm2" in name and "pre_assign" not in name:
              lowerCAmelCase__					     = name.replace('norm2'     ,  'layer_norm2'     )
       if "img_encoder.norm" in name:
              lowerCAmelCase__					     = name.replace('img_encoder.norm'     ,  'vision_model.layernorm'     )
       # text encoder
       if "text_encoder.token_embedding" in name:
              lowerCAmelCase__					     = name.replace('text_encoder.token_embedding'     ,  'text_model.embeddings.token_embedding'     )
       if "text_encoder.positional_embedding" in name:
              lowerCAmelCase__					     = name.replace('text_encoder.positional_embedding'     ,  'text_model.embeddings.position_embedding.weight'     )
       if "text_encoder.transformer.resblocks." in name:
              lowerCAmelCase__					     = name.replace('text_encoder.transformer.resblocks.'     ,  'text_model.encoder.layers.'     )
       if "ln_1" in name:
              lowerCAmelCase__					     = name.replace('ln_1'     ,  'layer_norm1'     )
       if "ln_2" in name:
              lowerCAmelCase__					     = name.replace('ln_2'     ,  'layer_norm2'     )
       if "c_fc" in name:
              lowerCAmelCase__					     = name.replace('c_fc'     ,  'fc1'     )
       if "c_proj" in name:
              lowerCAmelCase__					     = name.replace('c_proj'     ,  'fc2'     )
       if "text_encoder" in name:
              lowerCAmelCase__					     = name.replace('text_encoder'     ,  'text_model'     )
       if "ln_final" in name:
              lowerCAmelCase__					     = name.replace('ln_final'     ,  'final_layer_norm'     )
       # projection layers
       if "img_projector.linear_hidden." in name:
              lowerCAmelCase__					     = name.replace('img_projector.linear_hidden.'     ,  'visual_projection.'     )
       if "img_projector.linear_out." in name:
              lowerCAmelCase__					     = name.replace('img_projector.linear_out.'     ,  'visual_projection.3.'     )
       if "text_projector.linear_hidden" in name:
              lowerCAmelCase__					     = name.replace('text_projector.linear_hidden'     ,  'text_projection'     )
       if "text_projector.linear_out" in name:
              lowerCAmelCase__					     = name.replace('text_projector.linear_out'     ,  'text_projection.3'     )

       return name






def 				__lowerCamelCase		(						lowerCAmelCase__     ,  lowerCAmelCase__     ):
       for key in orig_state_dict.copy().keys():
              lowerCAmelCase__					     = orig_state_dict.pop(lowerCAmelCase__     )

              if "qkv" in key:
                     # weights and biases of the key, value and query projections of vision encoder's attention layers require special treatment:
                     # we need to split them up into separate matrices/vectors
                     lowerCAmelCase__					     = key.split('.'     )
                     lowerCAmelCase__     , lowerCAmelCase__					     = int(key_split[2]     ), int(key_split[4]     )
                     lowerCAmelCase__					     = config.vision_config.hidden_size
                     if "weight" in key:
                            lowerCAmelCase__					     = val[:dim, :]
                            lowerCAmelCase__					     = val[dim : dim * 2, :]
                            lowerCAmelCase__					     = val[-dim:, :]
                     else:
                            lowerCAmelCase__					     = val[:dim]
                            lowerCAmelCase__					     = val[dim : dim * 2]
                            lowerCAmelCase__					     = val[-dim:]
              elif "in_proj" in key:
                     # weights and biases of the key, value and query projections of text encoder's attention layers require special treatment:
                     # we need to split them up into separate matrices/vectors
                     lowerCAmelCase__					     = key.split('.'     )
                     lowerCAmelCase__					     = int(key_split[3]     )
                     lowerCAmelCase__					     = config.text_config.hidden_size
                     if "weight" in key:
                            lowerCAmelCase__					     = val[:dim, :]
                            lowerCAmelCase__					     = val[
                                dim : dim * 2, :
                            ]
                            lowerCAmelCase__					     = val[-dim:, :]
                     else:
                            lowerCAmelCase__					     = val[:dim]
                            lowerCAmelCase__					     = val[dim : dim * 2]
                            lowerCAmelCase__					     = val[-dim:]
              else:
                     lowerCAmelCase__					     = rename_key(lowerCAmelCase__     )
                     # squeeze if necessary
                     if (
                         "text_projection.0" in new_name
                         or "text_projection.3" in new_name
                         or "visual_projection.0" in new_name
                         or "visual_projection.3" in new_name
                     ):
                            lowerCAmelCase__					     = val.squeeze_()
                     else:
                            lowerCAmelCase__					     = val

       return orig_state_dict






def 				__lowerCamelCase		(						):
       lowerCAmelCase__					     = 'http://images.cocodataset.org/val2017/000000039769.jpg'
       lowerCAmelCase__					     = Image.open(requests.get(lowerCAmelCase__     ,  stream=lowerCAmelCase__     ).raw     )
       return im






@torch.no_grad()
def 				__lowerCamelCase		(						lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__="groupvit-gcc-yfcc"     ,  lowerCAmelCase__=False     ):
       lowerCAmelCase__					     = GroupViTConfig()
       lowerCAmelCase__					     = GroupViTModel(lowerCAmelCase__     ).eval()

       lowerCAmelCase__					     = torch.load(lowerCAmelCase__     ,  map_location='cpu'     )['model']
       lowerCAmelCase__					     = convert_state_dict(lowerCAmelCase__     ,  lowerCAmelCase__     )
       lowerCAmelCase__     , lowerCAmelCase__					     = model.load_state_dict(lowerCAmelCase__     ,  strict=lowerCAmelCase__     )
       assert missing_keys == ["text_model.embeddings.position_ids"]
       assert (unexpected_keys == ["multi_label_logit_scale"]) or (len(lowerCAmelCase__     ) == 0)

       # verify result
       lowerCAmelCase__					     = CLIPProcessor.from_pretrained('openai/clip-vit-base-patch32'     )
       lowerCAmelCase__					     = prepare_img()
       lowerCAmelCase__					     = processor(text=['a photo of a cat', 'a photo of a dog']     ,  images=lowerCAmelCase__     ,  padding=lowerCAmelCase__     ,  return_tensors='pt'     )

       with torch.no_grad():
              lowerCAmelCase__					     = model(**lowerCAmelCase__     )

       if model_name == "groupvit-gcc-yfcc":
              lowerCAmelCase__					     = torch.tensor([[1_3.3_5_2_3, 6.3_6_2_9]]     )
       elif model_name == "groupvit-gcc-redcaps":
              lowerCAmelCase__					     = torch.tensor([[1_6.1_8_7_3, 8.6_2_3_0]]     )
       else:
              raise ValueError(F"""Model name {model_name} not supported."""     )
       assert torch.allclose(outputs.logits_per_image     ,  lowerCAmelCase__     ,  atol=1e-3     )

       processor.save_pretrained(lowerCAmelCase__     )
       model.save_pretrained(lowerCAmelCase__     )
       print('Successfully saved processor and model to'     ,  lowerCAmelCase__     )

       if push_to_hub:
              print('Pushing to the hub...'     )
              processor.push_to_hub(lowerCAmelCase__     ,  organization='nielsr'     )
              model.push_to_hub(lowerCAmelCase__     ,  organization='nielsr'     )


if __name__ == "__main__":
    lowerCAmelCase__     						=			argparse.ArgumentParser()
    parser.add_argument(
        '--pytorch_dump_folder_path', default=None, type=str, help='Path to dump the processor and PyTorch model.'
    )
    parser.add_argument('--checkpoint_path', default=None, type=str, help='Path to GroupViT checkpoint')
    parser.add_argument(
        '--model_name',
        default='groupvit-gccy-fcc',
        type=str,
        help='Name of the model. Expecting either \'groupvit-gcc-yfcc\' or \'groupvit-gcc-redcaps\'',
    )
    parser.add_argument(
        '--push_to_hub',
        action='store_true',
        help='Whether or not to push the converted model and processor to the 🤗 hub using the provided `model_name`.',
    )
    lowerCAmelCase__     						=			parser.parse_args()

    convert_groupvit_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.model_name, args.push_to_hub)

from copy import deepcopy
from typing import Optional, Union

import numpy as np

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, is_tf_available, is_torch_available


if is_torch_available():
    import torch

if is_tf_available():
    import tensorflow as tf
class     a_			(						SCREAMING_SNAKE_CASE						):






 '''simple docstring'''






 UpperCAmelCase_     			= ['image_processor']
 UpperCAmelCase_     			= 'SamImageProcessor'




 def __init__(      self :  Tuple      ,   lowercase__ :  Dict):







        '''simple docstring'''



        super().__init__(lowercase__)
        lowerCAmelCase__					     = self.image_processor
        lowerCAmelCase__					     = -10
        lowerCAmelCase__					     = self.image_processor.size['longest_edge']




 def __call__(      self :  List[Any]      ,   lowercase__ :  Optional[int]=None      ,   lowercase__ :  Any=None      ,   lowercase__ :  Tuple=None      ,   lowercase__ :  List[str]=None      ,   lowercase__ :  Optional[Union[str, TensorType]] = None      ,   **lowercase__ :  Dict      ,   ):







        '''simple docstring'''



        lowerCAmelCase__					     = self.image_processor(
            lowercase__      ,   return_tensors=lowercase__      ,   **lowercase__      ,   )

        # pop arguments that are not used in the foward but used nevertheless
        lowerCAmelCase__					     = encoding_image_processor['original_sizes']

        if hasattr(lowercase__      ,   'numpy'):  # Checks if Torch or TF tensor
               lowerCAmelCase__					     = original_sizes.numpy()

        lowerCAmelCase__     , lowerCAmelCase__     , lowerCAmelCase__					     = self._check_and_preprocess_points(
            input_points=lowercase__      ,   input_labels=lowercase__      ,   input_boxes=lowercase__      ,   )

        lowerCAmelCase__					     = self._normalize_and_convert(
            lowercase__      ,   lowercase__      ,   input_points=lowercase__      ,   input_labels=lowercase__      ,   input_boxes=lowercase__      ,   return_tensors=lowercase__      ,   )

        return encoding_image_processor




 def 		__snake_case						(      self :  Optional[Any]      ,   lowercase__ :  List[str]      ,   lowercase__ :  List[str]      ,   lowercase__ :  str=None      ,   lowercase__ :  Optional[int]=None      ,   lowercase__ :  str=None      ,   lowercase__ :  Optional[Any]="pt"      ,   ):







        '''simple docstring'''



        if input_points is not None:
               if len(lowercase__) != len(lowercase__):
                      lowerCAmelCase__					     = [
                          self._normalize_coordinates(self.target_size      ,   lowercase__      ,   original_sizes[0]) for point in input_points
                      ]
               else:
                      lowerCAmelCase__					     = [
                          self._normalize_coordinates(self.target_size      ,   lowercase__      ,   lowercase__)
                          for point, original_size in zip(lowercase__      ,   lowercase__)
                      ]
               # check that all arrays have the same shape
               if not all(point.shape == input_points[0].shape for point in input_points):
                      if input_labels is not None:
                             lowerCAmelCase__     , lowerCAmelCase__					     = self._pad_points_and_labels(lowercase__      ,   lowercase__)

               lowerCAmelCase__					     = np.array(lowercase__)

        if input_labels is not None:
               lowerCAmelCase__					     = np.array(lowercase__)

        if input_boxes is not None:
               if len(lowercase__) != len(lowercase__):
                      lowerCAmelCase__					     = [
                          self._normalize_coordinates(self.target_size      ,   lowercase__      ,   original_sizes[0]      ,   is_bounding_box=lowercase__)
                          for box in input_boxes
                      ]
               else:
                      lowerCAmelCase__					     = [
                          self._normalize_coordinates(self.target_size      ,   lowercase__      ,   lowercase__      ,   is_bounding_box=lowercase__)
                          for box, original_size in zip(lowercase__      ,   lowercase__)
                      ]
               lowerCAmelCase__					     = np.array(lowercase__)

        if input_boxes is not None:
               if return_tensors == "pt":
                      lowerCAmelCase__					     = torch.from_numpy(lowercase__)
                      # boxes batch size of 1 by default
                      lowerCAmelCase__					     = input_boxes.unsqueeze(1) if len(input_boxes.shape) != 3 else input_boxes
               elif return_tensors == "tf":
                      lowerCAmelCase__					     = tf.convert_to_tensor(lowercase__)
                      # boxes batch size of 1 by default
                      lowerCAmelCase__					     = tf.expand_dims(lowercase__      ,   1) if len(input_boxes.shape) != 3 else input_boxes
               encoding_image_processor.update({'input_boxes': input_boxes})
        if input_points is not None:
               if return_tensors == "pt":
                      lowerCAmelCase__					     = torch.from_numpy(lowercase__)
                      # point batch size of 1 by default
                      lowerCAmelCase__					     = input_points.unsqueeze(1) if len(input_points.shape) != 4 else input_points
               elif return_tensors == "tf":
                      lowerCAmelCase__					     = tf.convert_to_tensor(lowercase__)
                      # point batch size of 1 by default
                      lowerCAmelCase__					     = tf.expand_dims(lowercase__      ,   1) if len(input_points.shape) != 4 else input_points
               encoding_image_processor.update({'input_points': input_points})
        if input_labels is not None:
               if return_tensors == "pt":
                      lowerCAmelCase__					     = torch.from_numpy(lowercase__)
                      # point batch size of 1 by default
                      lowerCAmelCase__					     = input_labels.unsqueeze(1) if len(input_labels.shape) != 3 else input_labels
               elif return_tensors == "tf":
                      lowerCAmelCase__					     = tf.convert_to_tensor(lowercase__)
                      # point batch size of 1 by default
                      lowerCAmelCase__					     = tf.expand_dims(lowercase__      ,   1) if len(input_labels.shape) != 3 else input_labels
               encoding_image_processor.update({'input_labels': input_labels})

        return encoding_image_processor




 def 		__snake_case						(      self :  str      ,   lowercase__ :  Optional[int]      ,   lowercase__ :  Optional[Any]):







        '''simple docstring'''



        lowerCAmelCase__					     = max([point.shape[0] for point in input_points])
        lowerCAmelCase__					     = []
        for i, point in enumerate(lowercase__):
               if point.shape[0] != expected_nb_points:
                      lowerCAmelCase__					     = np.concatenate(
                          [point, np.zeros((expected_nb_points - point.shape[0], 2)) + self.point_pad_value]      ,   axis=0)
                      lowerCAmelCase__					     = np.append(input_labels[i]      ,   [self.point_pad_value])
               processed_input_points.append(lowercase__)
        lowerCAmelCase__					     = processed_input_points
        return input_points, input_labels




 def 		__snake_case						(      self :  Optional[Any]      ,   lowercase__ :  int      ,   lowercase__ :  np.ndarray      ,   lowercase__ :  int      ,   lowercase__ :  Optional[Any]=False):







        '''simple docstring'''



        lowerCAmelCase__     , lowerCAmelCase__					     = original_size
        lowerCAmelCase__     , lowerCAmelCase__					     = self.image_processor._get_preprocess_shape(lowercase__      ,   longest_edge=lowercase__)
        lowerCAmelCase__					     = deepcopy(lowercase__).astype(lowercase__)

        if is_bounding_box:
               lowerCAmelCase__					     = coords.reshape(-1      ,   2      ,   2)

        lowerCAmelCase__					     = coords[..., 0] * (new_w / old_w)
        lowerCAmelCase__					     = coords[..., 1] * (new_h / old_h)

        if is_bounding_box:
               lowerCAmelCase__					     = coords.reshape(-1      ,   4)

        return coords




 def 		__snake_case						(      self :  Dict      ,   lowercase__ :  Optional[Any]=None      ,   lowercase__ :  Tuple=None      ,   lowercase__ :  int=None      ,   ):







        '''simple docstring'''



        if input_points is not None:
               if hasattr(lowercase__      ,   'numpy'):  # Checks for TF or Torch tensor
                      lowerCAmelCase__					     = input_points.numpy().tolist()

               if not isinstance(lowercase__      ,   lowercase__) or not isinstance(input_points[0]      ,   lowercase__):
                      raise ValueError('Input points must be a list of list of floating points.')
               lowerCAmelCase__					     = [np.array(lowercase__) for input_point in input_points]
        else:
               lowerCAmelCase__					     = None

        if input_labels is not None:
               if hasattr(lowercase__      ,   'numpy'):
                      lowerCAmelCase__					     = input_labels.numpy().tolist()

               if not isinstance(lowercase__      ,   lowercase__) or not isinstance(input_labels[0]      ,   lowercase__):
                      raise ValueError('Input labels must be a list of list integers.')
               lowerCAmelCase__					     = [np.array(lowercase__) for label in input_labels]
        else:
               lowerCAmelCase__					     = None

        if input_boxes is not None:
               if hasattr(lowercase__      ,   'numpy'):
                      lowerCAmelCase__					     = input_boxes.numpy().tolist()

               if (
                   not isinstance(lowercase__      ,   lowercase__)
                   or not isinstance(input_boxes[0]      ,   lowercase__)
                   or not isinstance(input_boxes[0][0]      ,   lowercase__)
               ):
                      raise ValueError('Input boxes must be a list of list of list of floating points.')
               lowerCAmelCase__					     = [np.array(lowercase__).astype(np.floataa) for box in input_boxes]
        else:
               lowerCAmelCase__					     = None

        return input_points, input_labels, input_boxes




 @property
 def 		__snake_case						(      self :  List[Any]):







        '''simple docstring'''



        lowerCAmelCase__					     = self.image_processor.model_input_names
        return list(dict.fromkeys(lowercase__))




 def 		__snake_case						(      self :  int      ,   *lowercase__ :  int      ,   **lowercase__ :  int):







        '''simple docstring'''



        return self.image_processor.post_process_masks(*lowercase__      ,   **lowercase__)
