
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 __snake_case		(				unittest.TestCase					):
    def __init__(  self		,snake_case		,snake_case = True		,snake_case = None		,snake_case = 32		,snake_case = True		,snake_case = 1 / 255		,snake_case = True		,snake_case = True		,snake_case = [0.48_145_466, 0.4_578_275, 0.40_821_073]		,snake_case = [0.26_862_954, 0.26_130_258, 0.27_577_711]		,snake_case = True		,snake_case=7		,snake_case=30		,snake_case=400		,snake_case=3		,):



           '''simple docstring'''







           lowercase							:					str	     =				parent
           lowercase							:					Dict	     =				do_resize
           lowercase							:					Union[str, Any]	     =				size if size is not None else {"""shortest_edge""": 288}
           lowercase							:					str	     =				size_divisor
           lowercase							:					Optional[Any]	     =				do_rescale
           lowercase							:					Optional[Any]	     =				rescale_factor
           lowercase							:					List[Any]	     =				do_normalize
           lowercase							:					Dict	     =				do_center_crop
           lowercase							:					Optional[int]	     =				image_mean
           lowercase							:					Dict	     =				image_std
           lowercase							:					int	     =				do_pad
           lowercase							:					Union[str, Any]	     =				batch_size
           lowercase							:					Tuple	     =				num_channels
           lowercase							:					Dict	     =				min_resolution
           lowercase							:					Any	     =				max_resolution
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           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 				_SCREAMING_SNAKE_CASE	(  self		,snake_case		,snake_case=False				):



           '''simple docstring'''







           if not batched:
                  lowercase							:					str	     =				self.size["""shortest_edge"""]
                  lowercase							:					Tuple	     =				image_inputs[0]
                  if isinstance(snake_case		,Image.Image				):
                         lowercase     ,      lowercase							:					List[str]	     =				image.size
                  else:
                         lowercase     ,      lowercase							:					str	     =				image.shape[1], image.shape[2]
                  lowercase							:					Tuple	     =				size / min(snake_case		,snake_case				)
                  if h < w:
                         lowercase     ,      lowercase							:					Tuple	     =				size, scale * w
                  else:
                         lowercase     ,      lowercase							:					Tuple	     =				scale * h, size

                  lowercase							:					Union[str, Any]	     =				int((1333 / 800) * size				)
                  if max(snake_case		,snake_case				) > max_size:
                         lowercase							:					Union[str, Any]	     =				max_size / max(snake_case		,snake_case				)
                         lowercase							:					Optional[int]	     =				newh * scale
                         lowercase							:					List[str]	     =				neww * scale

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

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

           return expected_height, expected_width






@require_torch
@require_vision
class __snake_case		(				lowerCAmelCase ,     unittest.TestCase					):
    _a		:						Union[str, Any]=    BridgeTowerImageProcessor if is_vision_available() else None
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           lowercase							:					int	     =				BridgeTowerImageProcessingTester(self				)
    @property
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           return self.image_processor_tester.prepare_image_processor_dict()
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           lowercase							:					int	     =				self.image_processing_class(**self.image_processor_dict				)
           self.assertTrue(hasattr(snake_case		,"""image_mean"""				)				)
           self.assertTrue(hasattr(snake_case		,"""image_std"""				)				)
           self.assertTrue(hasattr(snake_case		,"""do_normalize"""				)				)
           self.assertTrue(hasattr(snake_case		,"""do_resize"""				)				)
           self.assertTrue(hasattr(snake_case		,"""size"""				)				)
           self.assertTrue(hasattr(snake_case		,"""size_divisor"""				)				)
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           pass
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           lowercase							:					Union[str, Any]	     =				self.image_processing_class(**self.image_processor_dict				)
           # create random PIL images
           lowercase							:					Any	     =				prepare_image_inputs(self.image_processor_tester		,equal_resolution=snake_case				)
           for image in image_inputs:
                  self.assertIsInstance(snake_case		,Image.Image				)

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

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

           # Test batched
           lowercase							:					Tuple	     =				image_processing(snake_case		,return_tensors="""pt"""				).pixel_values

           lowercase     ,      lowercase							:					int	     =				self.image_processor_tester.get_expected_values(snake_case		,batched=snake_case				)
           self.assertEqual(
               encoded_images.shape		,(
                   self.image_processor_tester.batch_size,
                   self.image_processor_tester.num_channels,
                   expected_height,
                   expected_width,
               )		,)
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           lowercase							:					Union[str, Any]	     =				self.image_processing_class(**self.image_processor_dict				)
           # create random numpy tensors
           lowercase							:					str	     =				prepare_image_inputs(self.image_processor_tester		,equal_resolution=snake_case		,numpify=snake_case				)
           for image in image_inputs:
                  self.assertIsInstance(snake_case		,np.ndarray				)

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

           lowercase     ,      lowercase							:					int	     =				self.image_processor_tester.get_expected_values(snake_case				)
           self.assertEqual(
               encoded_images.shape		,(1, self.image_processor_tester.num_channels, expected_height, expected_width)		,)

           # Test batched
           lowercase							:					Any	     =				image_processing(snake_case		,return_tensors="""pt"""				).pixel_values

           lowercase     ,      lowercase							:					Dict	     =				self.image_processor_tester.get_expected_values(snake_case		,batched=snake_case				)
           self.assertEqual(
               encoded_images.shape		,(
                   self.image_processor_tester.batch_size,
                   self.image_processor_tester.num_channels,
                   expected_height,
                   expected_width,
               )		,)
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           lowercase							:					Tuple	     =				self.image_processing_class(**self.image_processor_dict				)
           # create random PyTorch tensors
           lowercase							:					Union[str, Any]	     =				prepare_image_inputs(self.image_processor_tester		,equal_resolution=snake_case		,torchify=snake_case				)
           for image in image_inputs:
                  self.assertIsInstance(snake_case		,torch.Tensor				)

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

           lowercase     ,      lowercase							:					List[str]	     =				self.image_processor_tester.get_expected_values(snake_case				)
           self.assertEqual(
               encoded_images.shape		,(1, self.image_processor_tester.num_channels, expected_height, expected_width)		,)

           # Test batched
           lowercase							:					List[Any]	     =				image_processing(snake_case		,return_tensors="""pt"""				).pixel_values

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


import argparse

import requests
import torch
from PIL import Image

from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel







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

       return name







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

       return orig_state_dict







def 						_snake_case(  )			->   List[Any]:
       lowercase							:					Tuple	     =				"""http://images.cocodataset.org/val2017/000000039769.jpg"""
       lowercase							:					Any	     =				Image.open(requests.get(SCREAMING_SNAKE_CASE__ ,     stream=SCREAMING_SNAKE_CASE__    ).raw    )
       return im







@torch.no_grad()
def 						_snake_case(  SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__="groupvit-gcc-yfcc" ,     SCREAMING_SNAKE_CASE__=False    )			->   str:
       lowercase							:					Dict	     =				GroupViTConfig()
       lowercase							:					Tuple	     =				GroupViTModel(SCREAMING_SNAKE_CASE__    ).eval()

       lowercase							:					List[str]	     =				torch.load(SCREAMING_SNAKE_CASE__ ,     map_location="""cpu"""    )["""model"""]
       lowercase							:					Optional[Any]	     =				convert_state_dict(SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    )
       lowercase     ,      lowercase							:					int	     =				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
       lowercase							:					Tuple	     =				CLIPProcessor.from_pretrained("""openai/clip-vit-base-patch32"""    )
       lowercase							:					Optional[Any]	     =				prepare_img()
       lowercase							:					List[Any]	     =				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():
              lowercase							:					Optional[Any]	     =				model(**SCREAMING_SNAKE_CASE__    )

       if model_name == "groupvit-gcc-yfcc":
              lowercase							:					Optional[int]	     =				torch.tensor([[13.3523, 6.3629]]    )
       elif model_name == "groupvit-gcc-redcaps":
              lowercase							:					int	     =				torch.tensor([[16.1873, 8.6230]]    )
       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__":
  lowercase  : List[Any]						   =							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`.""",
  )
  lowercase  : Union[str, Any]						   =							parser.parse_args()

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