import argparse

import requests
import torch
from PIL import Image

from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel



def      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	List[Any]     )							-> List[Any]:







  """simple docstring"""





  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      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	List[str]			,					__UpperCamelCase   :	List[Any]     )							-> List[Any]:







  """simple docstring"""





  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      __SCREAMING_SNAKE_CASE  ( )							-> str:







  """simple docstring"""





  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      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	str			,					__UpperCamelCase   :	List[Any]			,					__UpperCamelCase   :	Tuple="groupvit-gcc-yfcc"			,					__UpperCamelCase   :	Optional[int]=False     )							-> Any:







  """simple docstring"""





  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([[13.3523, 6.3629]]     )
  elif model_name == "groupvit-gcc-redcaps":
    SCREAMING_SNAKE_CASE__      							=				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      :			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`.''',
   )
   __lowerCamelCase      :			str							       =		parser.parse_args()

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

import json
import sys
import tempfile
import unittest
from pathlib import Path

import transformers
from transformers import (
    CONFIG_MAPPING,
    FEATURE_EXTRACTOR_MAPPING,
    AutoConfig,
    AutoFeatureExtractor,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
)
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, get_tests_dir


sys.path.append(str(Path(__file__).parent.parent.parent.parent / '''utils'''))

from test_module.custom_configuration import CustomConfig  # noqa E402
from test_module.custom_feature_extraction import CustomFeatureExtractor  # noqa E402


__lowerCamelCase      :			List[Any]							       =		get_tests_dir('''fixtures''')
__lowerCamelCase      :			Optional[int]							       =		get_tests_dir('''fixtures/dummy_feature_extractor_config.json''')
__lowerCamelCase      :			Any							       =		get_tests_dir('''fixtures/dummy-config.json''')







class __snake_case    (						unittest.TestCase		):




   def      __a						(   self      :    Optional[Any]				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				0




   def      __a						(   self      :    List[Any]				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				AutoFeatureExtractor.from_pretrained("""facebook/wav2vec2-base-960h"""				)
     self.assertIsInstance(_lowercase	,     _lowercase				)




   def      __a						(   self      :    Union[str, Any]				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				AutoFeatureExtractor.from_pretrained(_lowercase				)
     self.assertIsInstance(_lowercase	,     _lowercase				)




   def      __a						(   self      :    Optional[int]				):



     """simple docstring"""






     with tempfile.TemporaryDirectory() as tmpdirname:
       SCREAMING_SNAKE_CASE__      							=				WavaVecaConfig()

       # remove feature_extractor_type to make sure config.json alone is enough to load feature processor locally
       SCREAMING_SNAKE_CASE__      							=				AutoFeatureExtractor.from_pretrained(_lowercase				).to_dict()

       config_dict.pop("""feature_extractor_type"""				)
       SCREAMING_SNAKE_CASE__      							=				WavaVecaFeatureExtractor(**_lowercase				)

       # save in new folder
       model_config.save_pretrained(_lowercase				)
       config.save_pretrained(_lowercase				)

       SCREAMING_SNAKE_CASE__      							=				AutoFeatureExtractor.from_pretrained(_lowercase				)

       # make sure private variable is not incorrectly saved
       SCREAMING_SNAKE_CASE__      							=				json.loads(config.to_json_string()				)
       self.assertTrue("""_processor_class""" not in dict_as_saved				)

     self.assertIsInstance(_lowercase	,     _lowercase				)




   def      __a						(   self      :    Optional[Any]				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				AutoFeatureExtractor.from_pretrained(_lowercase				)
     self.assertIsInstance(_lowercase	,     _lowercase				)




   def      __a						(   self      :    Dict				):



     """simple docstring"""






     with self.assertRaisesRegex(
         _lowercase	,     """bert-base is not a local folder and is not a valid model identifier"""				):
       SCREAMING_SNAKE_CASE__      							=				AutoFeatureExtractor.from_pretrained("""bert-base"""				)




   def      __a						(   self      :    Union[str, Any]				):



     """simple docstring"""






     with self.assertRaisesRegex(
         _lowercase	,     R"""aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)"""				):
       SCREAMING_SNAKE_CASE__      							=				AutoFeatureExtractor.from_pretrained(_lowercase	,     revision="""aaaaaa"""				)




   def      __a						(   self      :    List[Any]				):



     """simple docstring"""






     with self.assertRaisesRegex(
         _lowercase	,     """hf-internal-testing/config-no-model does not appear to have a file named preprocessor_config.json."""	,     ):
       SCREAMING_SNAKE_CASE__      							=				AutoFeatureExtractor.from_pretrained("""hf-internal-testing/config-no-model"""				)




   def      __a						(   self      :    str				):



     """simple docstring"""






     with self.assertRaises(_lowercase				):
       SCREAMING_SNAKE_CASE__      							=				AutoFeatureExtractor.from_pretrained(
           """hf-internal-testing/test_dynamic_feature_extractor"""				)
     # If remote code is disabled, we can't load this config.
     with self.assertRaises(_lowercase				):
       SCREAMING_SNAKE_CASE__      							=				AutoFeatureExtractor.from_pretrained(
           """hf-internal-testing/test_dynamic_feature_extractor"""	,     trust_remote_code=_lowercase				)

     SCREAMING_SNAKE_CASE__      							=				AutoFeatureExtractor.from_pretrained(
         """hf-internal-testing/test_dynamic_feature_extractor"""	,     trust_remote_code=_lowercase				)
     self.assertEqual(feature_extractor.__class__.__name__	,     """NewFeatureExtractor"""				)

     # Test feature extractor can be reloaded.
     with tempfile.TemporaryDirectory() as tmp_dir:
       feature_extractor.save_pretrained(_lowercase				)
       SCREAMING_SNAKE_CASE__      							=				AutoFeatureExtractor.from_pretrained(_lowercase	,     trust_remote_code=_lowercase				)
     self.assertEqual(reloaded_feature_extractor.__class__.__name__	,     """NewFeatureExtractor"""				)




   def      __a						(   self      :    Union[str, Any]				):



     """simple docstring"""






     try:
       AutoConfig.register("""custom"""	,     _lowercase				)
       AutoFeatureExtractor.register(_lowercase	,     _lowercase				)
       # Trying to register something existing in the Transformers library will raise an error
       with self.assertRaises(_lowercase				):
         AutoFeatureExtractor.register(_lowercase	,     _lowercase				)

       # Now that the config is registered, it can be used as any other config with the auto-API
       SCREAMING_SNAKE_CASE__      							=				CustomFeatureExtractor.from_pretrained(_lowercase				)
       with tempfile.TemporaryDirectory() as tmp_dir:
         feature_extractor.save_pretrained(_lowercase				)
         SCREAMING_SNAKE_CASE__      							=				AutoFeatureExtractor.from_pretrained(_lowercase				)
         self.assertIsInstance(_lowercase	,     _lowercase				)

     finally:
       if "custom" in CONFIG_MAPPING._extra_content:
         del CONFIG_MAPPING._extra_content["custom"]
       if CustomConfig in FEATURE_EXTRACTOR_MAPPING._extra_content:
         del FEATURE_EXTRACTOR_MAPPING._extra_content[CustomConfig]




   def      __a						(   self      :    Any				):



     """simple docstring"""






     class __snake_case    (						lowerCamelCase_		):
        lowerCAmelCase_														=							True

     try:
       AutoConfig.register("""custom"""	,     _lowercase				)
       AutoFeatureExtractor.register(_lowercase	,     _lowercase				)
       # If remote code is not set, the default is to use local
       SCREAMING_SNAKE_CASE__      							=				AutoFeatureExtractor.from_pretrained(
           """hf-internal-testing/test_dynamic_feature_extractor"""				)
       self.assertEqual(feature_extractor.__class__.__name__	,     """NewFeatureExtractor"""				)
       self.assertTrue(feature_extractor.is_local				)

       # If remote code is disabled, we load the local one.
       SCREAMING_SNAKE_CASE__      							=				AutoFeatureExtractor.from_pretrained(
           """hf-internal-testing/test_dynamic_feature_extractor"""	,     trust_remote_code=_lowercase				)
       self.assertEqual(feature_extractor.__class__.__name__	,     """NewFeatureExtractor"""				)
       self.assertTrue(feature_extractor.is_local				)

       # If remote is enabled, we load from the Hub
       SCREAMING_SNAKE_CASE__      							=				AutoFeatureExtractor.from_pretrained(
           """hf-internal-testing/test_dynamic_feature_extractor"""	,     trust_remote_code=_lowercase				)
       self.assertEqual(feature_extractor.__class__.__name__	,     """NewFeatureExtractor"""				)
       self.assertTrue(not hasattr(_lowercase	,     """is_local"""				)				)

     finally:
       if "custom" in CONFIG_MAPPING._extra_content:
         del CONFIG_MAPPING._extra_content["custom"]
       if CustomConfig in FEATURE_EXTRACTOR_MAPPING._extra_content:
         del FEATURE_EXTRACTOR_MAPPING._extra_content[CustomConfig]
