




import copy
import json
import os
import tempfile

from transformers import is_torch_available

from .test_configuration_utils import config_common_kwargs





class 	lowerCamelCase_							(				_SCREAMING_SNAKE_CASE ):


   '''simple docstring'''







   def __init__(							self  :		Dict    ,    _lowerCAmelCase  :		List[Any]    ,    _lowerCAmelCase  :		int=None    ,    _lowerCAmelCase  :		Dict=True    ,    _lowerCAmelCase  :		int=None    ,    **_lowerCAmelCase  :		str						):
         SCREAMING_SNAKE_CASE_     						=      parent
         SCREAMING_SNAKE_CASE_     						=      config_class
         SCREAMING_SNAKE_CASE_     						=      has_text_modality
         SCREAMING_SNAKE_CASE_     						=      kwargs
         SCREAMING_SNAKE_CASE_     						=      common_properties



   def 				lowerCAmelCase_  (							self  :		Any						):
         SCREAMING_SNAKE_CASE_     						=      self.config_class(**self.inputs_dict						)
         SCREAMING_SNAKE_CASE_     						=      (
             ['hidden_size', 'num_attention_heads', 'num_hidden_layers']
             if self.common_properties is None
             else self.common_properties
         )

         # Add common fields for text models
         if self.has_text_modality:
               common_properties.extend(['vocab_size']						)

         # Test that config has the common properties as getters
         for prop in common_properties:
               self.parent.assertTrue(hasattr(_lowerCAmelCase    ,    _lowerCAmelCase						)    ,    msg=F"`{prop}` does not exist"						)

         # Test that config has the common properties as setter
         for idx, name in enumerate(_lowerCAmelCase						):
               try:
                     setattr(_lowerCAmelCase    ,    _lowerCAmelCase    ,    _lowerCAmelCase						)
                     self.parent.assertEqual(
                         getattr(_lowerCAmelCase    ,    _lowerCAmelCase						)    ,    _lowerCAmelCase    ,    msg=F"`{name} value {idx} expected, but was {getattr(_lowerCAmelCase    ,    _lowerCAmelCase						)}"						)
               except NotImplementedError:
                     # Some models might not be able to implement setters for common_properties
                     # In that case, a NotImplementedError is raised
                     pass

        # Test if config class can be called with Config(prop_name=..)
         for idx, name in enumerate(_lowerCAmelCase						):
               try:
                     SCREAMING_SNAKE_CASE_     						=      self.config_class(**{name: idx}						)
                     self.parent.assertEqual(
                         getattr(_lowerCAmelCase    ,    _lowerCAmelCase						)    ,    _lowerCAmelCase    ,    msg=F"`{name} value {idx} expected, but was {getattr(_lowerCAmelCase    ,    _lowerCAmelCase						)}"						)
               except NotImplementedError:
                     # Some models might not be able to implement setters for common_properties
                     # In that case, a NotImplementedError is raised
                     pass



   def 				lowerCAmelCase_  (							self  :		List[Any]						):
         SCREAMING_SNAKE_CASE_     						=      self.config_class(**self.inputs_dict						)
         SCREAMING_SNAKE_CASE_     						=      json.loads(config.to_json_string()						)
         for key, value in self.inputs_dict.items():
               self.parent.assertEqual(obj[key]    ,    _lowerCAmelCase						)



   def 				lowerCAmelCase_  (							self  :		int						):
         SCREAMING_SNAKE_CASE_     						=      self.config_class(**self.inputs_dict						)

         with tempfile.TemporaryDirectory() as tmpdirname:
               SCREAMING_SNAKE_CASE_     						=      os.path.join(_lowerCAmelCase    ,    'config.json'						)
               config_first.to_json_file(_lowerCAmelCase						)
               SCREAMING_SNAKE_CASE_     						=      self.config_class.from_json_file(_lowerCAmelCase						)

         self.parent.assertEqual(config_second.to_dict()    ,    config_first.to_dict()						)



   def 				lowerCAmelCase_  (							self  :		str						):
         SCREAMING_SNAKE_CASE_     						=      self.config_class(**self.inputs_dict						)

         with tempfile.TemporaryDirectory() as tmpdirname:
               config_first.save_pretrained(_lowerCAmelCase						)
               SCREAMING_SNAKE_CASE_     						=      self.config_class.from_pretrained(_lowerCAmelCase						)

         self.parent.assertEqual(config_second.to_dict()    ,    config_first.to_dict()						)



   def 				lowerCAmelCase_  (							self  :		List[str]						):
         SCREAMING_SNAKE_CASE_     						=      self.config_class(**self.inputs_dict						)

         SCREAMING_SNAKE_CASE_     						=      'test'
         with tempfile.TemporaryDirectory() as tmpdirname:
               SCREAMING_SNAKE_CASE_     						=      os.path.join(_lowerCAmelCase    ,    _lowerCAmelCase						)
               config_first.save_pretrained(_lowerCAmelCase						)
               SCREAMING_SNAKE_CASE_     						=      self.config_class.from_pretrained(_lowerCAmelCase    ,    subfolder=_lowerCAmelCase						)

         self.parent.assertEqual(config_second.to_dict()    ,    config_first.to_dict()						)



   def 				lowerCAmelCase_  (							self  :		int						):
         SCREAMING_SNAKE_CASE_     						=      self.config_class(**self.inputs_dict    ,    num_labels=5						)
         self.parent.assertEqual(len(config.idalabel						)    ,    5						)
         self.parent.assertEqual(len(config.labelaid						)    ,    5						)

         SCREAMING_SNAKE_CASE_     						=      3
         self.parent.assertEqual(len(config.idalabel						)    ,    3						)
         self.parent.assertEqual(len(config.labelaid						)    ,    3						)



   def 				lowerCAmelCase_  (							self  :		Any						):
         if self.config_class.is_composition:
               return
         SCREAMING_SNAKE_CASE_     						=      self.config_class()
         self.parent.assertIsNotNone(_lowerCAmelCase						)



   def 				lowerCAmelCase_  (							self  :		Dict						):
         SCREAMING_SNAKE_CASE_     						=      copy.deepcopy(_lowerCAmelCase						)
         SCREAMING_SNAKE_CASE_     						=      self.config_class(**_lowerCAmelCase						)
         SCREAMING_SNAKE_CASE_     						=      []
         for key, value in config_common_kwargs.items():
               if key == "torch_dtype":
                     if not is_torch_available():
                           continue
                     else:
                           import torch

                           if config.torch_dtype != torch.floataa:
                                 wrong_values.append(('torch_dtype', config.torch_dtype, torch.floataa)						)
               elif getattr(_lowerCAmelCase    ,    _lowerCAmelCase						) != value:
                     wrong_values.append((key, getattr(_lowerCAmelCase    ,    _lowerCAmelCase						), value)						)

         if len(_lowerCAmelCase						) > 0:
               SCREAMING_SNAKE_CASE_     						=      '\n'.join([F"- {v[0]}: got {v[1]} instead of {v[2]}" for v in wrong_values]						)
               raise ValueError(F"The following keys were not properly set in the config:\n{errors}"						)




   def 				lowerCAmelCase_  (							self  :		str						):
         self.create_and_test_config_common_properties()
         self.create_and_test_config_to_json_string()
         self.create_and_test_config_to_json_file()
         self.create_and_test_config_from_and_save_pretrained()
         self.create_and_test_config_from_and_save_pretrained_subfolder()
         self.create_and_test_config_with_num_labels()
         self.check_config_can_be_init_without_params()
         self.check_config_arguments_init()





import argparse

import requests
import torch
from PIL import Image

from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel

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

      return name

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

      return orig_state_dict

def  UpperCAmelCase_							(				) ->			List[Any]:
      SCREAMING_SNAKE_CASE_     						=      'http://images.cocodataset.org/val2017/000000039769.jpg'
      SCREAMING_SNAKE_CASE_     						=      Image.open(requests.get(__UpperCAmelCase      ,      stream=__UpperCAmelCase							).raw							)
      return im

@torch.no_grad()
def  UpperCAmelCase_							(				__UpperCAmelCase						:     int      ,      __UpperCAmelCase						:     str      ,      __UpperCAmelCase						:     Dict="groupvit-gcc-yfcc"      ,      __UpperCAmelCase						:     Any=False							) ->			Optional[int]:
      SCREAMING_SNAKE_CASE_     						=      GroupViTConfig()
      SCREAMING_SNAKE_CASE_     						=      GroupViTModel(__UpperCAmelCase							).eval()

      SCREAMING_SNAKE_CASE_     						=      torch.load(__UpperCAmelCase      ,      map_location='cpu'							)['model']
      SCREAMING_SNAKE_CASE_     						=      convert_state_dict(__UpperCAmelCase      ,      __UpperCAmelCase							)
      SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_     						=      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
      SCREAMING_SNAKE_CASE_     						=      CLIPProcessor.from_pretrained('openai/clip-vit-base-patch32'							)
      SCREAMING_SNAKE_CASE_     						=      prepare_img()
      SCREAMING_SNAKE_CASE_     						=      processor(text=['a photo of a cat', 'a photo of a dog']      ,      images=__UpperCAmelCase      ,      padding=__UpperCAmelCase      ,      return_tensors='pt'							)

      with torch.no_grad():
            SCREAMING_SNAKE_CASE_     						=      model(**__UpperCAmelCase							)

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

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