
import argparse

import requests
import torch
from PIL import Image

from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel


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

      return name


def 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__ ,		UpperCamelCase__		):
      for key in orig_state_dict.copy().keys():
            UpperCamelCase__    :   Any					=				orig_state_dict.pop(UpperCamelCase__		)

            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
                  UpperCamelCase__    :   Optional[int]					=				key.split('''.'''		)
                  UpperCamelCase__    :   str					=				int(key_split[2]		), int(key_split[4]		)
                  UpperCamelCase__    :   List[Any]					=				config.vision_config.hidden_size
                  if "weight" in key:
                        UpperCamelCase__    :   List[str]					=				val[:dim, :]
                        UpperCamelCase__    :   Any					=				val[dim : dim * 2, :]
                        UpperCamelCase__    :   Any					=				val[-dim:, :]
                  else:
                        UpperCamelCase__    :   List[str]					=				val[:dim]
                        UpperCamelCase__    :   List[Any]					=				val[dim : dim * 2]
                        UpperCamelCase__    :   Dict					=				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
                  UpperCamelCase__    :   str					=				key.split('''.'''		)
                  UpperCamelCase__    :   str					=				int(key_split[3]		)
                  UpperCamelCase__    :   Dict					=				config.text_config.hidden_size
                  if "weight" in key:
                        UpperCamelCase__    :   Any					=				val[:dim, :]
                        UpperCamelCase__    :   Optional[int]					=				val[
                            dim : dim * 2, :
                        ]
                        UpperCamelCase__    :   List[Any]					=				val[-dim:, :]
                  else:
                        UpperCamelCase__    :   Tuple					=				val[:dim]
                        UpperCamelCase__    :   Dict					=				val[dim : dim * 2]
                        UpperCamelCase__    :   Union[str, Any]					=				val[-dim:]
            else:
                  UpperCamelCase__    :   List[str]					=				rename_key(UpperCamelCase__		)
                  # 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
                  ):
                        UpperCamelCase__    :   str					=				val.squeeze_()
                  else:
                        UpperCamelCase__    :   Union[str, Any]					=				val

      return orig_state_dict


def 							SCREAMING_SNAKE_CASE_      (			):
      UpperCamelCase__    :   Union[str, Any]					=				'''http://images.cocodataset.org/val2017/000000039769.jpg'''
      UpperCamelCase__    :   Union[str, Any]					=				Image.open(requests.get(UpperCamelCase__ ,		stream=UpperCamelCase__		).raw		)
      return im


@torch.no_grad()
def 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__ ,		UpperCamelCase__ ,		UpperCamelCase__="groupvit-gcc-yfcc" ,		UpperCamelCase__=False		):
      UpperCamelCase__    :   List[Any]					=				GroupViTConfig()
      UpperCamelCase__    :   Dict					=				GroupViTModel(UpperCamelCase__		).eval()

      UpperCamelCase__    :   Tuple					=				torch.load(UpperCamelCase__ ,		map_location='''cpu'''		)['''model''']
      UpperCamelCase__    :   Optional[int]					=				convert_state_dict(UpperCamelCase__ ,		UpperCamelCase__		)
      UpperCamelCase__    :   List[Any]					=				model.load_state_dict(UpperCamelCase__ ,		strict=UpperCamelCase__		)
      assert missing_keys == ["text_model.embeddings.position_ids"]
      assert (unexpected_keys == ["multi_label_logit_scale"]) or (len(UpperCamelCase__		) == 0)

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

      with torch.no_grad():
            UpperCamelCase__    :   Optional[int]					=				model(**UpperCamelCase__		)

      if model_name == "groupvit-gcc-yfcc":
            UpperCamelCase__    :   str					=				torch.tensor([[13.3523, 6.3629]]		)
      elif model_name == "groupvit-gcc-redcaps":
            UpperCamelCase__    :   Optional[Any]					=				torch.tensor([[16.1873, 8.6230]]		)
      else:
            raise ValueError(f'''Model name {model_name} not supported.'''		)
      assert torch.allclose(outputs.logits_per_image ,		UpperCamelCase__ ,		atol=1e-3		)

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

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


if __name__ == "__main__":
    lowerCamelCase     :   str      =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     :   Optional[int]      =parser.parse_args()

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




from typing import Dict, Iterable, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format, to_pil_image
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_pytesseract_available, is_vision_available, logging, requires_backends


if is_vision_available():
    import PIL

# soft dependency
if is_pytesseract_available():
    import pytesseract

lowerCamelCase      =logging.get_logger(__name__)


def 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__ ,		UpperCamelCase__ ,		UpperCamelCase__		):
      return [
          int(1_0_0_0 * (box[0] / width)		),
          int(1_0_0_0 * (box[1] / height)		),
          int(1_0_0_0 * (box[2] / width)		),
          int(1_0_0_0 * (box[3] / height)		),
      ]


def 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__ ,		UpperCamelCase__ ,		UpperCamelCase__		):
      UpperCamelCase__    :   List[Any]					=				to_pil_image(UpperCamelCase__		)
      UpperCamelCase__				,UpperCamelCase__    :   Optional[int]					=				pil_image.size
      UpperCamelCase__    :   str					=				pytesseract.image_to_data(UpperCamelCase__ ,		lang=UpperCamelCase__ ,		output_type='''dict''' ,		config=UpperCamelCase__		)
      UpperCamelCase__				,UpperCamelCase__				,UpperCamelCase__				,UpperCamelCase__				,UpperCamelCase__    :   Tuple					=				data['''text'''], data['''left'''], data['''top'''], data['''width'''], data['''height''']

      # filter empty words and corresponding coordinates
      UpperCamelCase__    :   Optional[Any]					=				[idx for idx, word in enumerate(UpperCamelCase__		) if not word.strip()]
      UpperCamelCase__    :   int					=				[word for idx, word in enumerate(UpperCamelCase__		) if idx not in irrelevant_indices]
      UpperCamelCase__    :   int					=				[coord for idx, coord in enumerate(UpperCamelCase__		) if idx not in irrelevant_indices]
      UpperCamelCase__    :   List[Any]					=				[coord for idx, coord in enumerate(UpperCamelCase__		) if idx not in irrelevant_indices]
      UpperCamelCase__    :   List[Any]					=				[coord for idx, coord in enumerate(UpperCamelCase__		) if idx not in irrelevant_indices]
      UpperCamelCase__    :   Dict					=				[coord for idx, coord in enumerate(UpperCamelCase__		) if idx not in irrelevant_indices]

      # turn coordinates into (left, top, left+width, top+height) format
      UpperCamelCase__    :   str					=				[]
      for x, y, w, h in zip(UpperCamelCase__ ,		UpperCamelCase__ ,		UpperCamelCase__ ,		UpperCamelCase__		):
            UpperCamelCase__    :   List[Any]					=				[x, y, x + w, y + h]
            actual_boxes.append(UpperCamelCase__		)

      # finally, normalize the bounding boxes
      UpperCamelCase__    :   str					=				[]
      for box in actual_boxes:
            normalized_boxes.append(normalize_box(UpperCamelCase__ ,		UpperCamelCase__ ,		UpperCamelCase__		)		)

      assert len(UpperCamelCase__		) == len(UpperCamelCase__		), "Not as many words as there are bounding boxes"

      return words, normalized_boxes




class 				_lowerCamelCase (      UpperCamelCase_ ):





     """simple docstring"""







     SCREAMING_SNAKE_CASE_		   =  ['''pixel_values''']






     def __init__(							self						,						__SCREAMING_SNAKE_CASE = True						,						__SCREAMING_SNAKE_CASE = None						,						__SCREAMING_SNAKE_CASE = PILImageResampling.BILINEAR						,						__SCREAMING_SNAKE_CASE = True						,						__SCREAMING_SNAKE_CASE = 1 / 2_5_5						,						__SCREAMING_SNAKE_CASE = True						,						__SCREAMING_SNAKE_CASE = None						,						__SCREAMING_SNAKE_CASE = None						,						__SCREAMING_SNAKE_CASE = True						,						__SCREAMING_SNAKE_CASE = None						,						__SCREAMING_SNAKE_CASE = ""						,						**__SCREAMING_SNAKE_CASE						,						)  ->   None:




           """simple docstring"""







           super().__init__(**__SCREAMING_SNAKE_CASE  )
           UpperCamelCase__    :   Optional[Any]					=				size if size is not None else {'''height''': 2_2_4, '''width''': 2_2_4}
           UpperCamelCase__    :   Optional[int]					=				get_size_dict(__SCREAMING_SNAKE_CASE  )

           UpperCamelCase__    :   Dict					=				do_resize
           UpperCamelCase__    :   Optional[int]					=				size
           UpperCamelCase__    :   Optional[Any]					=				resample
           UpperCamelCase__    :   Optional[Any]					=				do_rescale
           UpperCamelCase__    :   str					=				rescale_value
           UpperCamelCase__    :   Any					=				do_normalize
           UpperCamelCase__    :   List[Any]					=				image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
           UpperCamelCase__    :   Optional[int]					=				image_std if image_std is not None else IMAGENET_STANDARD_STD
           UpperCamelCase__    :   Dict					=				apply_ocr
           UpperCamelCase__    :   List[Any]					=				ocr_lang
           UpperCamelCase__    :   str					=				tesseract_config






     def    __SCREAMING_SNAKE_CASE			(							self						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE = PILImageResampling.BILINEAR						,						__SCREAMING_SNAKE_CASE = None						,						**__SCREAMING_SNAKE_CASE						,						)  ->   np.ndarray:




           """simple docstring"""







           UpperCamelCase__    :   Union[str, Any]					=				get_size_dict(__SCREAMING_SNAKE_CASE  )
           if "height" not in size or "width" not in size:
                 raise ValueError(F'''The size dictionary must contain the keys \'height\' and \'width\'. Got {size.keys()}'''  )
           UpperCamelCase__    :   int					=				(size['''height'''], size['''width'''])
           return resize(__SCREAMING_SNAKE_CASE						,						size=__SCREAMING_SNAKE_CASE						,						resample=__SCREAMING_SNAKE_CASE						,						data_format=__SCREAMING_SNAKE_CASE						,						**__SCREAMING_SNAKE_CASE  )






     def    __SCREAMING_SNAKE_CASE			(							self						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE = None						,						**__SCREAMING_SNAKE_CASE						,						)  ->   np.ndarray:




           """simple docstring"""







           return rescale(__SCREAMING_SNAKE_CASE						,						scale=__SCREAMING_SNAKE_CASE						,						data_format=__SCREAMING_SNAKE_CASE						,						**__SCREAMING_SNAKE_CASE  )






     def    __SCREAMING_SNAKE_CASE			(							self						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE = None						,						**__SCREAMING_SNAKE_CASE						,						)  ->   np.ndarray:




           """simple docstring"""







           return normalize(__SCREAMING_SNAKE_CASE						,						mean=__SCREAMING_SNAKE_CASE						,						std=__SCREAMING_SNAKE_CASE						,						data_format=__SCREAMING_SNAKE_CASE						,						**__SCREAMING_SNAKE_CASE  )






     def    __SCREAMING_SNAKE_CASE			(							self						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE = None						,						__SCREAMING_SNAKE_CASE = None						,						__SCREAMING_SNAKE_CASE=None						,						__SCREAMING_SNAKE_CASE = None						,						__SCREAMING_SNAKE_CASE = None						,						__SCREAMING_SNAKE_CASE = None						,						__SCREAMING_SNAKE_CASE = None						,						__SCREAMING_SNAKE_CASE = None						,						__SCREAMING_SNAKE_CASE = None						,						__SCREAMING_SNAKE_CASE = None						,						__SCREAMING_SNAKE_CASE = None						,						__SCREAMING_SNAKE_CASE = None						,						__SCREAMING_SNAKE_CASE = ChannelDimension.FIRST						,						**__SCREAMING_SNAKE_CASE						,						)  ->   PIL.Image.Image:




           """simple docstring"""







           UpperCamelCase__    :   Union[str, Any]					=				do_resize if do_resize is not None else self.do_resize
           UpperCamelCase__    :   List[str]					=				size if size is not None else self.size
           UpperCamelCase__    :   Dict					=				get_size_dict(__SCREAMING_SNAKE_CASE  )
           UpperCamelCase__    :   List[str]					=				resample if resample is not None else self.resample
           UpperCamelCase__    :   int					=				do_rescale if do_rescale is not None else self.do_rescale
           UpperCamelCase__    :   List[str]					=				rescale_factor if rescale_factor is not None else self.rescale_factor
           UpperCamelCase__    :   str					=				do_normalize if do_normalize is not None else self.do_normalize
           UpperCamelCase__    :   Union[str, Any]					=				image_mean if image_mean is not None else self.image_mean
           UpperCamelCase__    :   Tuple					=				image_std if image_std is not None else self.image_std
           UpperCamelCase__    :   Optional[Any]					=				apply_ocr if apply_ocr is not None else self.apply_ocr
           UpperCamelCase__    :   List[str]					=				ocr_lang if ocr_lang is not None else self.ocr_lang
           UpperCamelCase__    :   List[Any]					=				tesseract_config if tesseract_config is not None else self.tesseract_config

           UpperCamelCase__    :   List[Any]					=				make_list_of_images(__SCREAMING_SNAKE_CASE  )

           if not valid_images(__SCREAMING_SNAKE_CASE  ):
                 raise ValueError(
                     '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
                     '''torch.Tensor, tf.Tensor or jax.ndarray.'''  )

           if do_resize and size is None:
                 raise ValueError('''Size must be specified if do_resize is True.'''  )

           if do_rescale and rescale_factor is None:
                 raise ValueError('''Rescale factor must be specified if do_rescale is True.'''  )

           if do_normalize and (image_mean is None or image_std is None):
                 raise ValueError('''If do_normalize is True, image_mean and image_std must be specified.'''  )

           # All transformations expect numpy arrays.
           UpperCamelCase__    :   List[Any]					=				[to_numpy_array(__SCREAMING_SNAKE_CASE  ) for image in images]

           # Tesseract OCR to get words + normalized bounding boxes
           if apply_ocr:
                 requires_backends(self						,						'''pytesseract'''  )
                 UpperCamelCase__    :   Optional[int]					=				[]
                 UpperCamelCase__    :   List[Any]					=				[]
                 for image in images:
                       UpperCamelCase__				,UpperCamelCase__    :   Tuple					=				apply_tesseract(__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE  )
                       words_batch.append(__SCREAMING_SNAKE_CASE  )
                       boxes_batch.append(__SCREAMING_SNAKE_CASE  )

           if do_resize:
                 UpperCamelCase__    :   str					=				[self.resize(image=__SCREAMING_SNAKE_CASE						,						size=__SCREAMING_SNAKE_CASE						,						resample=__SCREAMING_SNAKE_CASE  ) for image in images]

           if do_rescale:
                 UpperCamelCase__    :   List[Any]					=				[self.rescale(image=__SCREAMING_SNAKE_CASE						,						scale=__SCREAMING_SNAKE_CASE  ) for image in images]

           if do_normalize:
                 UpperCamelCase__    :   str					=				[self.normalize(image=__SCREAMING_SNAKE_CASE						,						mean=__SCREAMING_SNAKE_CASE						,						std=__SCREAMING_SNAKE_CASE  ) for image in images]

           UpperCamelCase__    :   int					=				[to_channel_dimension_format(__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE  ) for image in images]

           UpperCamelCase__    :   List[Any]					=				BatchFeature(data={'''pixel_values''': images}						,						tensor_type=__SCREAMING_SNAKE_CASE  )

           if apply_ocr:
                 UpperCamelCase__    :   str					=				words_batch
                 UpperCamelCase__    :   Union[str, Any]					=				boxes_batch
           return data


