


"""simple docstring"""






import unittest

from transformers import AutoConfig, AutoTokenizer, BertConfig, TensorType, is_flax_available
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, require_flax, slow


if is_flax_available():
   import jax

   from transformers.models.auto.modeling_flax_auto import FlaxAutoModel
   from transformers.models.bert.modeling_flax_bert import FlaxBertModel
   from transformers.models.roberta.modeling_flax_roberta import FlaxRobertaModel


@require_flax
class     __lowerCamelCase  (					unittest.TestCase				):

     '''simple docstring'''


     @slow
     def 							_UpperCAmelCase      (      self							)       ->							Optional[int]:
          for model_name in ["bert-base-cased", "bert-large-uncased"]:
               with self.subTest(__UpperCAmelCase							):
                    _a    	=				AutoConfig.from_pretrained(__UpperCAmelCase							)
                    self.assertIsNotNone(__UpperCAmelCase							)
                    self.assertIsInstance(__UpperCAmelCase    ,				__UpperCAmelCase							)

                    _a    	=				FlaxAutoModel.from_pretrained(__UpperCAmelCase							)
                    self.assertIsNotNone(__UpperCAmelCase							)
                    self.assertIsInstance(__UpperCAmelCase    ,				__UpperCAmelCase							)


     @slow
     def 							_UpperCAmelCase      (      self							)       ->							Union[str, Any]:
          for model_name in ["roberta-base", "roberta-large"]:
               with self.subTest(__UpperCAmelCase							):
                    _a    	=				AutoConfig.from_pretrained(__UpperCAmelCase							)
                    self.assertIsNotNone(__UpperCAmelCase							)
                    self.assertIsInstance(__UpperCAmelCase    ,				__UpperCAmelCase							)

                    _a    	=				FlaxAutoModel.from_pretrained(__UpperCAmelCase							)
                    self.assertIsNotNone(__UpperCAmelCase							)
                    self.assertIsInstance(__UpperCAmelCase    ,				__UpperCAmelCase							)


     @slow
     def 							_UpperCAmelCase      (      self							)       ->							Any:
          for model_name in ["bert-base-cased", "bert-large-uncased"]:
               _a    	=				AutoTokenizer.from_pretrained(__UpperCAmelCase							)
               _a    	=				FlaxBertModel.from_pretrained(__UpperCAmelCase							)
               _a    	=				tokenizer('''Do you support jax jitted function?'''    ,				return_tensors=TensorType.JAX							)

               @jax.jit
               def eval(**__UpperCAmelCase							):
                    return model(**__UpperCAmelCase							)

               eval(**__UpperCAmelCase							).block_until_ready()


     @slow
     def 							_UpperCAmelCase      (      self							)       ->							Tuple:
          for model_name in ["roberta-base", "roberta-large"]:
               _a    	=				AutoTokenizer.from_pretrained(__UpperCAmelCase							)
               _a    	=				FlaxRobertaModel.from_pretrained(__UpperCAmelCase							)
               _a    	=				tokenizer('''Do you support jax jitted function?'''    ,				return_tensors=TensorType.JAX							)

               @jax.jit
               def eval(**__UpperCAmelCase							):
                    return model(**__UpperCAmelCase							)

               eval(**__UpperCAmelCase							).block_until_ready()


     def 							_UpperCAmelCase      (      self							)       ->							Optional[Any]:
          with self.assertRaisesRegex(
              __UpperCAmelCase    ,				'''bert-base is not a local folder and is not a valid model identifier'''							):
               _a    	=				FlaxAutoModel.from_pretrained('''bert-base'''							)


     def 							_UpperCAmelCase      (      self							)       ->							str:
          with self.assertRaisesRegex(
              __UpperCAmelCase    ,				r'''aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)'''							):
               _a    	=				FlaxAutoModel.from_pretrained(__UpperCAmelCase    ,				revision='''aaaaaa'''							)


     def 							_UpperCAmelCase      (      self							)       ->							Any:
          with self.assertRaisesRegex(
              __UpperCAmelCase    ,				'''hf-internal-testing/config-no-model does not appear to have a file named flax_model.msgpack'''    ,				):
               _a    	=				FlaxAutoModel.from_pretrained('''hf-internal-testing/config-no-model'''							)





     def 							_UpperCAmelCase      (      self							)       ->							List[Any]:
          with self.assertRaisesRegex(__UpperCAmelCase    ,				'''Use `from_pt=True` to load this model'''							):
               _a    	=				FlaxAutoModel.from_pretrained('''hf-internal-testing/tiny-bert-pt-only'''							)



"""simple docstring"""






import argparse

import requests
import torch
from PIL import Image

from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel
def       A_   (   _lowerCAmelCase     :					Dict       ):






     """simple docstring"""

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

     return name
def       A_   (   _lowerCAmelCase     :					List[str],     _lowerCAmelCase     :					int       ):






     """simple docstring"""

     for key in orig_state_dict.copy().keys():
          _a    	=				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
               _a    	=				key.split('''.'''       )
               _a	,      _a    	=				int(key_split[2]       ), int(key_split[4]       )
               _a    	=				config.vision_config.hidden_size
               if "weight" in key:
                    _a    	=				val[:dim, :]
                    _a    	=				val[dim : dim * 2, :]
                    _a    	=				val[-dim:, :]
               else:
                    _a    	=				val[:dim]
                    _a    	=				val[dim : dim * 2]
                    _a    	=				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
               _a    	=				key.split('''.'''       )
               _a    	=				int(key_split[3]       )
               _a    	=				config.text_config.hidden_size
               if "weight" in key:
                    _a    	=				val[:dim, :]
                    _a    	=				val[
                        dim : dim * 2, :
                    ]
                    _a    	=				val[-dim:, :]
               else:
                    _a    	=				val[:dim]
                    _a    	=				val[dim : dim * 2]
                    _a    	=				val[-dim:]
          else:
               _a    	=				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
               ):
                    _a    	=				val.squeeze_()
               else:
                    _a    	=				val

     return orig_state_dict
def       A_   (   ):






     """simple docstring"""

     _a    	=				'''http://images.cocodataset.org/val2017/000000039769.jpg'''
     _a    	=				Image.open(requests.get(_lowerCAmelCase,     stream=_lowerCAmelCase       ).raw       )
     return im
@torch.no_grad()
def       A_   (   _lowerCAmelCase     :					Dict,     _lowerCAmelCase     :					List[str],     _lowerCAmelCase     :					Dict="groupvit-gcc-yfcc",     _lowerCAmelCase     :					str=False       ):






     """simple docstring"""

     _a    	=				GroupViTConfig()
     _a    	=				GroupViTModel(_lowerCAmelCase       ).eval()

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

     with torch.no_grad():
          _a    	=				model(**_lowerCAmelCase       )

     if model_name == "groupvit-gcc-yfcc":
          _a    	=				torch.tensor([[1_3.3_5_2_3, 6.3_6_2_9]]       )
     elif model_name == "groupvit-gcc-redcaps":
          _a    	=				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__":
   __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)