



import argparse

import requests
import torch
from PIL import Image

from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel






def 							A							(   _lowerCamelCase    ):





							'''simple docstring'''




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

							return name






def 							A							(   _lowerCamelCase							,					_lowerCamelCase    ):





							'''simple docstring'''




							for key in orig_state_dict.copy().keys():
														_lowerCAmelCase	:				int					  =       orig_state_dict.pop(SCREAMING_SNAKE_CASE_    )

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

							return orig_state_dict






def 							A							(   ):





							'''simple docstring'''




							_lowerCAmelCase	:				Optional[int]					  =       "http://images.cocodataset.org/val2017/000000039769.jpg"
							_lowerCAmelCase	:				List[Any]					  =       Image.open(requests.get(SCREAMING_SNAKE_CASE_							,					stream=SCREAMING_SNAKE_CASE_    ).raw    )
							return im






@torch.no_grad()
def 							A							(   _lowerCamelCase							,					_lowerCamelCase							,					_lowerCamelCase="groupvit-gcc-yfcc"							,					_lowerCamelCase=False    ):





							'''simple docstring'''




							_lowerCAmelCase	:				Optional[int]					  =       GroupViTConfig()
							_lowerCAmelCase	:				Union[str, Any]					  =       GroupViTModel(SCREAMING_SNAKE_CASE_    ).eval()

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

							# verify result
							_lowerCAmelCase	:				List[Any]					  =       CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32"    )
							_lowerCAmelCase	:				Dict					  =       prepare_img()
							_lowerCAmelCase	:				Tuple					  =       processor(text=["a photo of a cat", "a photo of a dog"]							,					images=SCREAMING_SNAKE_CASE_							,					padding=SCREAMING_SNAKE_CASE_							,					return_tensors="pt"    )

							with torch.no_grad():
														_lowerCAmelCase	:				List[Any]					  =       model(**SCREAMING_SNAKE_CASE_    )

							if model_name == "groupvit-gcc-yfcc":
														_lowerCAmelCase	:				Any					  =       torch.tensor([[13.35_23, 6.36_29]]    )
							elif model_name == "groupvit-gcc-redcaps":
														_lowerCAmelCase	:				int					  =       torch.tensor([[16.18_73, 8.62_30]]    )
							else:
														raise ValueError(F"Model name {model_name} not supported."    )
							assert torch.allclose(outputs.logits_per_image							,					SCREAMING_SNAKE_CASE_							,					atol=1e-3    )

							processor.save_pretrained(SCREAMING_SNAKE_CASE_    )
							model.save_pretrained(SCREAMING_SNAKE_CASE_    )
							print("Successfully saved processor and model to"							,					SCREAMING_SNAKE_CASE_    )

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


if __name__ == "__main__":
						_snake_case						=    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`.",
						)
						_snake_case						=    parser.parse_args()

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

'''simple docstring'''


from typing import Dict, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import flip_channel_order, resize, to_channel_dimension_format, to_pil_image
from ...image_utils import (
    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

__snake_case						  =		logging.get_logger(__name__)

def   A_      (       SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						)			->Tuple:
  return [
      int(10_00 * (box[0] / width)						),
      int(10_00 * (box[1] / height)						),
      int(10_00 * (box[2] / width)						),
      int(10_00 * (box[3] / height)						),
  ]

def   A_      (       SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ = None						)			->List[Any]:
  lowercase_     =       tesseract_config if tesseract_config is not None else """"""

  # apply OCR
  lowercase_     =       to_pil_image(SCREAMING_SNAKE_CASE_						)
  lowercase_     ,	lowercase_     =       pil_image.size
  lowercase_     =       pytesseract.image_to_data(SCREAMING_SNAKE_CASE_ ,	lang=SCREAMING_SNAKE_CASE_ ,	output_type="""dict""" ,	config=SCREAMING_SNAKE_CASE_						)
  lowercase_     ,	lowercase_     ,	lowercase_     ,	lowercase_     ,	lowercase_     =       data["""text"""], data["""left"""], data["""top"""], data["""width"""], data["""height"""]

  # filter empty words and corresponding coordinates
  lowercase_     =       [idx for idx, word in enumerate(SCREAMING_SNAKE_CASE_						) if not word.strip()]
  lowercase_     =       [word for idx, word in enumerate(SCREAMING_SNAKE_CASE_						) if idx not in irrelevant_indices]
  lowercase_     =       [coord for idx, coord in enumerate(SCREAMING_SNAKE_CASE_						) if idx not in irrelevant_indices]
  lowercase_     =       [coord for idx, coord in enumerate(SCREAMING_SNAKE_CASE_						) if idx not in irrelevant_indices]
  lowercase_     =       [coord for idx, coord in enumerate(SCREAMING_SNAKE_CASE_						) if idx not in irrelevant_indices]
  lowercase_     =       [coord for idx, coord in enumerate(SCREAMING_SNAKE_CASE_						) if idx not in irrelevant_indices]

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

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

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

  return words, normalized_boxes





class       _a    (		__a      ):


  """simple docstring"""

  A_    							=						['''pixel_values''']




  def __init__(		self      :       Tuple   ,				lowercase_      :       bool = True   ,				lowercase_      :       Dict[str, int] = None   ,				lowercase_      :       PILImageResampling = PILImageResampling.BILINEAR   ,				lowercase_      :       bool = True   ,				lowercase_      :       Optional[str] = None   ,				lowercase_      :       Optional[str] = ""   ,				**lowercase_      :       Optional[int]   ,				):







    '''simple docstring'''


    super().__init__(**lowercase_      )
    lowercase_     =       size if size is not None else {"""height""": 224, """width""": 224}
    lowercase_     =       get_size_dict(lowercase_      )

    lowercase_     =       do_resize
    lowercase_     =       size
    lowercase_     =       resample
    lowercase_     =       apply_ocr
    lowercase_     =       ocr_lang
    lowercase_     =       tesseract_config




  def 		lowerCamelCase__  (		self      :       Optional[int]   ,				lowercase_      :       np.ndarray   ,				lowercase_      :       Dict[str, int]   ,				lowercase_      :       PILImageResampling = PILImageResampling.BILINEAR   ,				lowercase_      :       Optional[Union[str, ChannelDimension]] = None   ,				**lowercase_      :       int   ,				):







    '''simple docstring'''


    lowercase_     =       get_size_dict(lowercase_      )
    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()}"""      )
    lowercase_     =       (size["""height"""], size["""width"""])
    return resize(lowercase_   ,				size=lowercase_   ,				resample=lowercase_   ,				data_format=lowercase_   ,				**lowercase_      )





  def 		lowerCamelCase__  (		self      :       List[Any]   ,				lowercase_      :       ImageInput   ,				lowercase_      :       bool = None   ,				lowercase_      :       Dict[str, int] = None   ,				lowercase_      :       PILImageResampling = None   ,				lowercase_      :       bool = None   ,				lowercase_      :       Optional[str] = None   ,				lowercase_      :       Optional[str] = None   ,				lowercase_      :       Optional[Union[str, TensorType]] = None   ,				lowercase_      :       ChannelDimension = ChannelDimension.FIRST   ,				**lowercase_      :       Dict   ,				):







    '''simple docstring'''


    lowercase_     =       do_resize if do_resize is not None else self.do_resize
    lowercase_     =       size if size is not None else self.size
    lowercase_     =       get_size_dict(lowercase_      )
    lowercase_     =       resample if resample is not None else self.resample
    lowercase_     =       apply_ocr if apply_ocr is not None else self.apply_ocr
    lowercase_     =       ocr_lang if ocr_lang is not None else self.ocr_lang
    lowercase_     =       tesseract_config if tesseract_config is not None else self.tesseract_config

    lowercase_     =       make_list_of_images(lowercase_      )

    if not valid_images(lowercase_      ):
      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."""      )

    # All transformations expect numpy arrays.
    lowercase_     =       [to_numpy_array(lowercase_      ) for image in images]

    if apply_ocr:
      requires_backends(self   ,				"""pytesseract"""      )
      lowercase_     =       []
      lowercase_     =       []
      for image in images:
        lowercase_     ,	lowercase_     =       apply_tesseract(lowercase_   ,				lowercase_   ,				lowercase_      )
        words_batch.append(lowercase_      )
        boxes_batch.append(lowercase_      )

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

    # flip color channels from RGB to BGR (as Detectron2 requires this)
    lowercase_     =       [flip_channel_order(lowercase_      ) for image in images]
    lowercase_     =       [to_channel_dimension_format(lowercase_   ,				lowercase_      ) for image in images]

    lowercase_     =       BatchFeature(data={"""pixel_values""": images}   ,				tensor_type=lowercase_      )

    if apply_ocr:
      lowercase_     =       words_batch
      lowercase_     =       boxes_batch
    return data

