
'''simple docstring'''






import unittest
from typing import Dict, List, Optional, Union

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
 import torch

if is_vision_available():
 from PIL import Image

 from transformers import BridgeTowerImageProcessor

class  UpperCAmelCase__						(       unittest.TestCase ):



    def __init__(	self      :       Union[str, Any],__A      :       Optional[Any],__A      :       bool = True,__A      :       Dict[str, int] = None,__A      :       int = 3_2,__A      :       bool = True,__A      :       Union[int, float] = 1 / 2_5_5,__A      :       bool = True,__A      :       bool = True,__A      :       Optional[Union[float, List[float]]] = [0.48145466, 0.4578275, 0.40821073],__A      :       Optional[Union[float, List[float]]] = [0.26862954, 0.26130258, 0.27577711],__A      :       bool = True,__A      :       Optional[Any]=7,__A      :       Union[str, Any]=3_0,__A      :       Any=4_0_0,__A      :       Optional[Any]=3,):
          _lowerCamelCase				:   List[Any]								=   parent
          _lowerCamelCase				:   Union[str, Any]								=   do_resize
          _lowerCamelCase				:   Tuple								=   size if size is not None else {"shortest_edge": 2_8_8}
          _lowerCamelCase				:   int								=   size_divisor
          _lowerCamelCase				:   List[str]								=   do_rescale
          _lowerCamelCase				:   Tuple								=   rescale_factor
          _lowerCamelCase				:   int								=   do_normalize
          _lowerCamelCase				:   str								=   do_center_crop
          _lowerCamelCase				:   Optional[Any]								=   image_mean
          _lowerCamelCase				:   Any								=   image_std
          _lowerCamelCase				:   Tuple								=   do_pad
          _lowerCamelCase				:   Tuple								=   batch_size
          _lowerCamelCase				:   int								=   num_channels
          _lowerCamelCase				:   List[Any]								=   min_resolution
          _lowerCamelCase				:   List[Any]								=   max_resolution



    def   lowerCamelCase_  (	self      :       List[Any]     ):
          return {
              "image_mean": self.image_mean,
              "image_std": self.image_std,
              "do_normalize": self.do_normalize,
              "do_resize": self.do_resize,
              "size": self.size,
              "size_divisor": self.size_divisor,
          }



    def   lowerCamelCase_  (	self      :       Any,__A      :       Optional[Any],__A      :       Optional[Any]=False     ):
          if not batched:
                _lowerCamelCase				:   Any								=   self.size["shortest_edge"]
                _lowerCamelCase				:   List[str]								=   image_inputs[0]
                if isinstance(__A,Image.Image     ):
                      _lowerCamelCase						,		_lowerCamelCase				:   List[str]								=   image.size
                else:
                      _lowerCamelCase						,		_lowerCamelCase				:   int								=   image.shape[1], image.shape[2]
                _lowerCamelCase				:   List[str]								=   size / min(__A,__A     )
                if h < w:
                      _lowerCamelCase						,		_lowerCamelCase				:   Union[str, Any]								=   size, scale * w
                else:
                      _lowerCamelCase						,		_lowerCamelCase				:   Optional[int]								=   scale * h, size

                _lowerCamelCase				:   List[Any]								=   int((1_3_3_3 / 8_0_0) * size     )
                if max(__A,__A     ) > max_size:
                      _lowerCamelCase				:   int								=   max_size / max(__A,__A     )
                      _lowerCamelCase				:   int								=   newh * scale
                      _lowerCamelCase				:   Optional[int]								=   neww * scale

                _lowerCamelCase						,		_lowerCamelCase				:   List[str]								=   int(newh + 0.5     ), int(neww + 0.5     )
                _lowerCamelCase						,		_lowerCamelCase				:   Union[str, Any]								=   (
                    newh // self.size_divisor * self.size_divisor,
                    neww // self.size_divisor * self.size_divisor,
                )

          else:
                _lowerCamelCase				:   Union[str, Any]								=   []
                for image in image_inputs:
                      _lowerCamelCase						,		_lowerCamelCase				:   Optional[Any]								=   self.get_expected_values([image]     )
                      expected_values.append((expected_height, expected_width)     )
                _lowerCamelCase				:   Any								=   max(__A,key=lambda __A     : item[0]     )[0]
                _lowerCamelCase				:   Dict								=   max(__A,key=lambda __A     : item[1]     )[1]

          return expected_height, expected_width


@require_torch
@require_vision
class  UpperCAmelCase__						(       A						,	unittest.TestCase ):
    lowerCAmelCase_				    =		BridgeTowerImageProcessor if is_vision_available() else None



    def   lowerCamelCase_  (	self      :       Tuple     ):
          _lowerCamelCase				:   Optional[int]								=   BridgeTowerImageProcessingTester(self     )



    @property
    def   lowerCamelCase_  (	self      :       int     ):
          return self.image_processor_tester.prepare_image_processor_dict()



    def   lowerCamelCase_  (	self      :       List[Any]     ):
          _lowerCamelCase				:   Tuple								=   self.image_processing_class(**self.image_processor_dict     )
          self.assertTrue(hasattr(__A,"image_mean"     )     )
          self.assertTrue(hasattr(__A,"image_std"     )     )
          self.assertTrue(hasattr(__A,"do_normalize"     )     )
          self.assertTrue(hasattr(__A,"do_resize"     )     )
          self.assertTrue(hasattr(__A,"size"     )     )
          self.assertTrue(hasattr(__A,"size_divisor"     )     )



    def   lowerCamelCase_  (	self      :       int     ):
          pass



    def   lowerCamelCase_  (	self      :       Optional[Any]     ):
          # Initialize image processor
          _lowerCamelCase				:   Dict								=   self.image_processing_class(**self.image_processor_dict     )
          # create random PIL images
          _lowerCamelCase				:   List[Any]								=   prepare_image_inputs(self.image_processor_tester,equal_resolution=__A     )
          for image in image_inputs:
                self.assertIsInstance(__A,Image.Image     )

          # Test not batched input
          _lowerCamelCase				:   List[Any]								=   image_processing(image_inputs[0],return_tensors="pt"     ).pixel_values

          _lowerCamelCase						,		_lowerCamelCase				:   Union[str, Any]								=   self.image_processor_tester.get_expected_values(__A     )
          self.assertEqual(
              encoded_images.shape,(1, self.image_processor_tester.num_channels, expected_height, expected_width),)

          # Test batched
          _lowerCamelCase				:   Tuple								=   image_processing(__A,return_tensors="pt"     ).pixel_values

          _lowerCamelCase						,		_lowerCamelCase				:   List[Any]								=   self.image_processor_tester.get_expected_values(__A,batched=__A     )
          self.assertEqual(
              encoded_images.shape,(
                  self.image_processor_tester.batch_size,
                  self.image_processor_tester.num_channels,
                  expected_height,
                  expected_width,
              ),)



    def   lowerCamelCase_  (	self      :       Dict     ):
          # Initialize image processor
          _lowerCamelCase				:   Optional[int]								=   self.image_processing_class(**self.image_processor_dict     )
          # create random numpy tensors
          _lowerCamelCase				:   Union[str, Any]								=   prepare_image_inputs(self.image_processor_tester,equal_resolution=__A,numpify=__A     )
          for image in image_inputs:
                self.assertIsInstance(__A,np.ndarray     )

          # Test not batched input
          _lowerCamelCase				:   List[str]								=   image_processing(image_inputs[0],return_tensors="pt"     ).pixel_values

          _lowerCamelCase						,		_lowerCamelCase				:   str								=   self.image_processor_tester.get_expected_values(__A     )
          self.assertEqual(
              encoded_images.shape,(1, self.image_processor_tester.num_channels, expected_height, expected_width),)

          # Test batched
          _lowerCamelCase				:   Optional[int]								=   image_processing(__A,return_tensors="pt"     ).pixel_values

          _lowerCamelCase						,		_lowerCamelCase				:   Optional[Any]								=   self.image_processor_tester.get_expected_values(__A,batched=__A     )
          self.assertEqual(
              encoded_images.shape,(
                  self.image_processor_tester.batch_size,
                  self.image_processor_tester.num_channels,
                  expected_height,
                  expected_width,
              ),)



    def   lowerCamelCase_  (	self      :       Optional[Any]     ):
          # Initialize image processor
          _lowerCamelCase				:   int								=   self.image_processing_class(**self.image_processor_dict     )
          # create random PyTorch tensors
          _lowerCamelCase				:   Tuple								=   prepare_image_inputs(self.image_processor_tester,equal_resolution=__A,torchify=__A     )
          for image in image_inputs:
                self.assertIsInstance(__A,torch.Tensor     )

          # Test not batched input
          _lowerCamelCase				:   Tuple								=   image_processing(image_inputs[0],return_tensors="pt"     ).pixel_values

          _lowerCamelCase						,		_lowerCamelCase				:   Optional[int]								=   self.image_processor_tester.get_expected_values(__A     )
          self.assertEqual(
              encoded_images.shape,(1, self.image_processor_tester.num_channels, expected_height, expected_width),)

          # Test batched
          _lowerCamelCase				:   Dict								=   image_processing(__A,return_tensors="pt"     ).pixel_values

          _lowerCamelCase						,		_lowerCamelCase				:   Union[str, Any]								=   self.image_processor_tester.get_expected_values(__A,batched=__A     )
          self.assertEqual(
              encoded_images.shape,(
                  self.image_processor_tester.batch_size,
                  self.image_processor_tester.num_channels,
                  expected_height,
                  expected_width,
              ),)

'''simple docstring'''






import argparse

import requests
import torch
from PIL import Image

from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel







def 		A_       (	_lowerCAmelCase   :    Union[str, Any]							):



      """simple docstring"""




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

      return name







def 		A_       (	_lowerCAmelCase   :    List[str]				,	_lowerCAmelCase   :    str							):



      """simple docstring"""




      for key in orig_state_dict.copy().keys():
            _lowerCamelCase				:   Optional[int]								=   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				:   Any								=   key.split("."							)
                  _lowerCamelCase						,		_lowerCamelCase				:   List[str]								=   int(key_split[2]							), int(key_split[4]							)
                  _lowerCamelCase				:   List[Any]								=   config.vision_config.hidden_size
                  if "weight" in key:
                        _lowerCamelCase				:   List[Any]								=   val[:dim, :]
                        _lowerCamelCase				:   int								=   val[dim : dim * 2, :]
                        _lowerCamelCase				:   str								=   val[-dim:, :]
                  else:
                        _lowerCamelCase				:   Optional[int]								=   val[:dim]
                        _lowerCamelCase				:   str								=   val[dim : dim * 2]
                        _lowerCamelCase				:   Tuple								=   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				:   Optional[Any]								=   key.split("."							)
                  _lowerCamelCase				:   int								=   int(key_split[3]							)
                  _lowerCamelCase				:   Tuple								=   config.text_config.hidden_size
                  if "weight" in key:
                        _lowerCamelCase				:   List[Any]								=   val[:dim, :]
                        _lowerCamelCase				:   str								=   val[
                            dim : dim * 2, :
                        ]
                        _lowerCamelCase				:   Optional[int]								=   val[-dim:, :]
                  else:
                        _lowerCamelCase				:   str								=   val[:dim]
                        _lowerCamelCase				:   Optional[Any]								=   val[dim : dim * 2]
                        _lowerCamelCase				:   Optional[int]								=   val[-dim:]
            else:
                  _lowerCamelCase				:   int								=   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				:   Any								=   val.squeeze_()
                  else:
                        _lowerCamelCase				:   Union[str, Any]								=   val

      return orig_state_dict







def 		A_       (	):



      """simple docstring"""




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







@torch.no_grad()
def 		A_       (	_lowerCAmelCase   :    Optional[int]				,	_lowerCAmelCase   :    Union[str, Any]				,	_lowerCAmelCase   :    str="groupvit-gcc-yfcc"				,	_lowerCAmelCase   :    List[str]=False							):



      """simple docstring"""




      _lowerCamelCase				:   List[str]								=   GroupViTConfig()
      _lowerCamelCase				:   Union[str, Any]								=   GroupViTModel(_lowerCAmelCase							).eval()

      _lowerCamelCase				:   Any								=   torch.load(_lowerCAmelCase				,	map_location="cpu"							)["model"]
      _lowerCamelCase				:   Union[str, Any]								=   convert_state_dict(_lowerCAmelCase				,	_lowerCAmelCase							)
      _lowerCamelCase						,		_lowerCamelCase				:   Tuple								=   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				:   List[Any]								=   CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32"							)
      _lowerCamelCase				:   List[str]								=   prepare_img()
      _lowerCamelCase				:   Any								=   processor(text=["a photo of a cat", "a photo of a dog"]				,	images=_lowerCAmelCase				,	padding=_lowerCAmelCase				,	return_tensors="pt"							)

      with torch.no_grad():
            _lowerCamelCase				:   List[str]								=   model(**_lowerCAmelCase							)

      if model_name == "groupvit-gcc-yfcc":
            _lowerCamelCase				:   List[Any]								=   torch.tensor([[1_3.3_5_2_3, 6.3_6_2_9]]							)
      elif model_name == "groupvit-gcc-redcaps":
            _lowerCamelCase				:   Optional[Any]								=   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__":
 UpperCAmelCase_       :   Optional[int]   =       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`.',
 )
 UpperCAmelCase_       :   Union[str, Any]   =       parser.parse_args()

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