



"""simple docstring"""
from collections import OrderedDict
from typing import Any, List, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging


_UpperCAmelCase     =			logging.get_logger(__name__)


_UpperCAmelCase     =			{
    """Salesforce/codegen-350M-nl""": """https://huggingface.co/Salesforce/codegen-350M-nl/resolve/main/config.json""",
    """Salesforce/codegen-350M-multi""": """https://huggingface.co/Salesforce/codegen-350M-multi/resolve/main/config.json""",
    """Salesforce/codegen-350M-mono""": """https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/config.json""",
    """Salesforce/codegen-2B-nl""": """https://huggingface.co/Salesforce/codegen-2B-nl/resolve/main/config.json""",
    """Salesforce/codegen-2B-multi""": """https://huggingface.co/Salesforce/codegen-2B-multi/resolve/main/config.json""",
    """Salesforce/codegen-2B-mono""": """https://huggingface.co/Salesforce/codegen-2B-mono/resolve/main/config.json""",
    """Salesforce/codegen-6B-nl""": """https://huggingface.co/Salesforce/codegen-6B-nl/resolve/main/config.json""",
    """Salesforce/codegen-6B-multi""": """https://huggingface.co/Salesforce/codegen-6B-multi/resolve/main/config.json""",
    """Salesforce/codegen-6B-mono""": """https://huggingface.co/Salesforce/codegen-6B-mono/resolve/main/config.json""",
    """Salesforce/codegen-16B-nl""": """https://huggingface.co/Salesforce/codegen-16B-nl/resolve/main/config.json""",
    """Salesforce/codegen-16B-multi""": """https://huggingface.co/Salesforce/codegen-16B-multi/resolve/main/config.json""",
    """Salesforce/codegen-16B-mono""": """https://huggingface.co/Salesforce/codegen-16B-mono/resolve/main/config.json""",
}
class 					a	(  UpperCAmelCase__       ):
  UpperCamelCase	:				Optional[int]     						=					'codegen'
  UpperCamelCase	:				List[str]     						=					{
      'max_position_embeddings': 'n_positions',
      'hidden_size': 'n_embd',
      'num_attention_heads': 'n_head',
      'num_hidden_layers': 'n_layer',
  }



  def __init__(						self :					List[Any]       ,		lowerCAmelCase :					Union[str, Any]=5_0400       ,		lowerCAmelCase :					Tuple=2048       ,		lowerCAmelCase :					Dict=2048       ,		lowerCAmelCase :					List[Any]=4096       ,		lowerCAmelCase :					str=28       ,		lowerCAmelCase :					Union[str, Any]=16       ,		lowerCAmelCase :					Tuple=64       ,		lowerCAmelCase :					Optional[int]=None       ,		lowerCAmelCase :					List[str]="gelu_new"       ,		lowerCAmelCase :					int=0.0       ,		lowerCAmelCase :					Optional[int]=0.0       ,		lowerCAmelCase :					int=0.0       ,		lowerCAmelCase :					int=1E-5       ,		lowerCAmelCase :					Dict=0.0_2       ,		lowerCAmelCase :					Optional[Any]=True       ,		lowerCAmelCase :					List[str]=5_0256       ,		lowerCAmelCase :					Dict=5_0256       ,		lowerCAmelCase :					int=False       ,		**lowerCAmelCase :					str       ,		)     ->					str:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: Optional[Any]     			=vocab_size
     SCREAMING_SNAKE_CASE_: Any     			=n_ctx
     SCREAMING_SNAKE_CASE_: str     			=n_positions
     SCREAMING_SNAKE_CASE_: Any     			=n_embd
     SCREAMING_SNAKE_CASE_: List[Any]     			=n_layer
     SCREAMING_SNAKE_CASE_: Tuple     			=n_head
     SCREAMING_SNAKE_CASE_: List[Any]     			=n_inner
     SCREAMING_SNAKE_CASE_: List[Any]     			=rotary_dim
     SCREAMING_SNAKE_CASE_: Tuple     			=activation_function
     SCREAMING_SNAKE_CASE_: Any     			=resid_pdrop
     SCREAMING_SNAKE_CASE_: List[str]     			=embd_pdrop
     SCREAMING_SNAKE_CASE_: List[Any]     			=attn_pdrop
     SCREAMING_SNAKE_CASE_: Optional[Any]     			=layer_norm_epsilon
     SCREAMING_SNAKE_CASE_: Optional[Any]     			=initializer_range
     SCREAMING_SNAKE_CASE_: Tuple     			=use_cache

     SCREAMING_SNAKE_CASE_: Any     			=bos_token_id
     SCREAMING_SNAKE_CASE_: str     			=eos_token_id

     super().__init__(
         bos_token_id=lowerCAmelCase       ,		eos_token_id=lowerCAmelCase       ,		tie_word_embeddings=lowerCAmelCase       ,		**lowerCAmelCase		)







class 					a	(  UpperCAmelCase__       ):



  def __init__(						self :					Union[str, Any]       ,		lowerCAmelCase :					PretrainedConfig       ,		lowerCAmelCase :					str = "default"       ,		lowerCAmelCase :					List[PatchingSpec] = None       ,		lowerCAmelCase :					bool = False       ,		)     ->					Optional[Any]:


     '''simple docstring'''




     super().__init__(lowerCAmelCase       ,		task=lowerCAmelCase       ,		patching_specs=lowerCAmelCase       ,		use_past=lowerCAmelCase		)
     if not getattr(self._config       ,		"""pad_token_id"""       ,		lowerCAmelCase		):
        # TODO: how to do that better?
        SCREAMING_SNAKE_CASE_: Dict     			=0



  @property
  def 	lowerCamelCase__       (						self :					List[Any]		)     ->					Mapping[str, Mapping[int, str]]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: Optional[int]     			=OrderedDict({"""input_ids""": {0: """batch""", 1: """sequence"""}}		)
     if self.use_past:
        self.fill_with_past_key_values_(lowerCAmelCase       ,		direction="""inputs"""		)
        SCREAMING_SNAKE_CASE_: Any     			={0: """batch""", 1: """past_sequence + sequence"""}
     else:
        SCREAMING_SNAKE_CASE_: Optional[Any]     			={0: """batch""", 1: """sequence"""}

     return common_inputs



  @property
  def 	lowerCamelCase__       (						self :					int		)     ->					int:


     '''simple docstring'''




     return self._config.n_layer



  @property
  def 	lowerCamelCase__       (						self :					Union[str, Any]		)     ->					int:


     '''simple docstring'''




     return self._config.n_head



  def 	lowerCamelCase__       (						self :					List[str]       ,		lowerCAmelCase :					PreTrainedTokenizer       ,		lowerCAmelCase :					int = -1       ,		lowerCAmelCase :					int = -1       ,		lowerCAmelCase :					bool = False       ,		lowerCAmelCase :					Optional[TensorType] = None       ,		)     ->					Mapping[str, Any]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: Union[str, Any]     			=super(lowerCAmelCase       ,		self		).generate_dummy_inputs(
         lowerCAmelCase       ,		batch_size=lowerCAmelCase       ,		seq_length=lowerCAmelCase       ,		is_pair=lowerCAmelCase       ,		framework=lowerCAmelCase		)

     # We need to order the input in the way they appears in the forward()
     SCREAMING_SNAKE_CASE_: int     			=OrderedDict({"""input_ids""": common_inputs["""input_ids"""]}		)

     # Need to add the past_keys
     if self.use_past:
        if not is_torch_available():
           raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed."""		)
        else:
           import torch

           SCREAMING_SNAKE_CASE_: int     			=common_inputs["""input_ids"""].shape
           # Not using the same length for past_key_values
           SCREAMING_SNAKE_CASE_: Dict     			=seqlen + 2
           SCREAMING_SNAKE_CASE_: List[str]     			=(
               batch,
               self.num_attention_heads,
               past_key_values_length,
               self._config.hidden_size // self.num_attention_heads,
           )
           SCREAMING_SNAKE_CASE_: Any     			=[
               (torch.zeros(lowerCAmelCase		), torch.zeros(lowerCAmelCase		)) for _ in range(self.num_layers		)
           ]

     SCREAMING_SNAKE_CASE_: Dict     			=common_inputs["""attention_mask"""]
     if self.use_past:
        SCREAMING_SNAKE_CASE_: int     			=ordered_inputs["""attention_mask"""].dtype
        SCREAMING_SNAKE_CASE_: Any     			=torch.cat(
            [ordered_inputs["""attention_mask"""], torch.ones(lowerCAmelCase       ,		lowerCAmelCase       ,		dtype=lowerCAmelCase		)]       ,		dim=1		)

     return ordered_inputs





  @property
  def 	lowerCamelCase__       (						self :					Dict		)     ->					int:


     '''simple docstring'''




     return 13








"""simple docstring"""
import argparse

import requests
import torch
from PIL import Image

from transformers import ViTMAEConfig, ViTMAEForPreTraining, ViTMAEImageProcessor





def      __magic_name__				(							lowercase ):
   if "cls_token" in name:
      SCREAMING_SNAKE_CASE_: Optional[int]     			=name.replace("""cls_token"""							,			"""vit.embeddings.cls_token""" )
   if "mask_token" in name:
      SCREAMING_SNAKE_CASE_: Optional[int]     			=name.replace("""mask_token"""							,			"""decoder.mask_token""" )
   if "decoder_pos_embed" in name:
      SCREAMING_SNAKE_CASE_: int     			=name.replace("""decoder_pos_embed"""							,			"""decoder.decoder_pos_embed""" )
   if "pos_embed" in name and "decoder" not in name:
      SCREAMING_SNAKE_CASE_: str     			=name.replace("""pos_embed"""							,			"""vit.embeddings.position_embeddings""" )
   if "patch_embed.proj" in name:
      SCREAMING_SNAKE_CASE_: str     			=name.replace("""patch_embed.proj"""							,			"""vit.embeddings.patch_embeddings.projection""" )
   if "patch_embed.norm" in name:
      SCREAMING_SNAKE_CASE_: int     			=name.replace("""patch_embed.norm"""							,			"""vit.embeddings.norm""" )
   if "decoder_blocks" in name:
      SCREAMING_SNAKE_CASE_: List[Any]     			=name.replace("""decoder_blocks"""							,			"""decoder.decoder_layers""" )
   if "blocks" in name:
      SCREAMING_SNAKE_CASE_: str     			=name.replace("""blocks"""							,			"""vit.encoder.layer""" )
   if "attn.proj" in name:
      SCREAMING_SNAKE_CASE_: Optional[Any]     			=name.replace("""attn.proj"""							,			"""attention.output.dense""" )
   if "attn" in name:
      SCREAMING_SNAKE_CASE_: Union[str, Any]     			=name.replace("""attn"""							,			"""attention.self""" )
   if "norm1" in name:
      SCREAMING_SNAKE_CASE_: Optional[Any]     			=name.replace("""norm1"""							,			"""layernorm_before""" )
   if "norm2" in name:
      SCREAMING_SNAKE_CASE_: int     			=name.replace("""norm2"""							,			"""layernorm_after""" )
   if "mlp.fc1" in name:
      SCREAMING_SNAKE_CASE_: int     			=name.replace("""mlp.fc1"""							,			"""intermediate.dense""" )
   if "mlp.fc2" in name:
      SCREAMING_SNAKE_CASE_: Dict     			=name.replace("""mlp.fc2"""							,			"""output.dense""" )
   if "decoder_embed" in name:
      SCREAMING_SNAKE_CASE_: List[str]     			=name.replace("""decoder_embed"""							,			"""decoder.decoder_embed""" )
   if "decoder_norm" in name:
      SCREAMING_SNAKE_CASE_: Tuple     			=name.replace("""decoder_norm"""							,			"""decoder.decoder_norm""" )
   if "decoder_pred" in name:
      SCREAMING_SNAKE_CASE_: Any     			=name.replace("""decoder_pred"""							,			"""decoder.decoder_pred""" )
   if "norm.weight" in name and "decoder" not in name:
      SCREAMING_SNAKE_CASE_: List[str]     			=name.replace("""norm.weight"""							,			"""vit.layernorm.weight""" )
   if "norm.bias" in name and "decoder" not in name:
      SCREAMING_SNAKE_CASE_: List[str]     			=name.replace("""norm.bias"""							,			"""vit.layernorm.bias""" )

   return name





def      __magic_name__				(							lowercase							,			lowercase ):
   for key in orig_state_dict.copy().keys():
      SCREAMING_SNAKE_CASE_: Optional[int]     			=orig_state_dict.pop(lowercase )

      if "qkv" in key:
         SCREAMING_SNAKE_CASE_: Dict     			=key.split(""".""" )
         SCREAMING_SNAKE_CASE_: Optional[Any]     			=int(key_split[1] )
         if "decoder_blocks" in key:
            SCREAMING_SNAKE_CASE_: int     			=config.decoder_hidden_size
            SCREAMING_SNAKE_CASE_: Optional[int]     			="""decoder.decoder_layers."""
            if "weight" in key:
               SCREAMING_SNAKE_CASE_: Dict     			=val[:dim, :]
               SCREAMING_SNAKE_CASE_: Tuple     			=val[dim : dim * 2, :]
               SCREAMING_SNAKE_CASE_: str     			=val[-dim:, :]
            elif "bias" in key:
               SCREAMING_SNAKE_CASE_: List[Any]     			=val[:dim]
               SCREAMING_SNAKE_CASE_: Tuple     			=val[dim : dim * 2]
               SCREAMING_SNAKE_CASE_: List[Any]     			=val[-dim:]
         else:
            SCREAMING_SNAKE_CASE_: Any     			=config.hidden_size
            SCREAMING_SNAKE_CASE_: Union[str, Any]     			="""vit.encoder.layer."""
            if "weight" in key:
               SCREAMING_SNAKE_CASE_: Optional[Any]     			=val[:dim, :]
               SCREAMING_SNAKE_CASE_: Optional[Any]     			=val[dim : dim * 2, :]
               SCREAMING_SNAKE_CASE_: Dict     			=val[-dim:, :]
            elif "bias" in key:
               SCREAMING_SNAKE_CASE_: Optional[Any]     			=val[:dim]
               SCREAMING_SNAKE_CASE_: Any     			=val[dim : dim * 2]
               SCREAMING_SNAKE_CASE_: Optional[Any]     			=val[-dim:]

      else:
         SCREAMING_SNAKE_CASE_: Tuple     			=val

   return orig_state_dict





def      __magic_name__				(							lowercase							,			lowercase ):
   SCREAMING_SNAKE_CASE_: Dict     			=ViTMAEConfig()
   if "large" in checkpoint_url:
      SCREAMING_SNAKE_CASE_: List[Any]     			=1024
      SCREAMING_SNAKE_CASE_: Dict     			=4096
      SCREAMING_SNAKE_CASE_: Tuple     			=24
      SCREAMING_SNAKE_CASE_: int     			=16
   elif "huge" in checkpoint_url:
      SCREAMING_SNAKE_CASE_: Union[str, Any]     			=14
      SCREAMING_SNAKE_CASE_: Any     			=1280
      SCREAMING_SNAKE_CASE_: Dict     			=5120
      SCREAMING_SNAKE_CASE_: Optional[int]     			=32
      SCREAMING_SNAKE_CASE_: Optional[Any]     			=16

   SCREAMING_SNAKE_CASE_: Tuple     			=ViTMAEForPreTraining(lowercase )

   SCREAMING_SNAKE_CASE_: Optional[int]     			=torch.hub.load_state_dict_from_url(lowercase							,			map_location="""cpu""" )["""model"""]

   SCREAMING_SNAKE_CASE_: Optional[Any]     			=ViTMAEImageProcessor(size=config.image_size )

   SCREAMING_SNAKE_CASE_: str     			=convert_state_dict(lowercase							,			lowercase )

   model.load_state_dict(lowercase )
   model.eval()

   SCREAMING_SNAKE_CASE_: Tuple     			="""https://user-images.githubusercontent.com/11435359/147738734-196fd92f-9260-48d5-ba7e-bf103d29364d.jpg"""

   SCREAMING_SNAKE_CASE_: List[Any]     			=Image.open(requests.get(lowercase							,			stream=lowercase ).raw )
   SCREAMING_SNAKE_CASE_: int     			=ViTMAEImageProcessor(size=config.image_size )
   SCREAMING_SNAKE_CASE_: int     			=image_processor(images=lowercase							,			return_tensors="""pt""" )

   # forward pass
   torch.manual_seed(2 )
   SCREAMING_SNAKE_CASE_: Optional[Any]     			=model(**lowercase )
   SCREAMING_SNAKE_CASE_: Optional[int]     			=outputs.logits

   if "large" in checkpoint_url:
      SCREAMING_SNAKE_CASE_: Dict     			=torch.tensor(
          [[-0.7_309, -0.7_128, -1.0_169], [-1.0_161, -0.9_058, -1.1_878], [-1.0_478, -0.9_411, -1.1_911]] )
   elif "huge" in checkpoint_url:
      SCREAMING_SNAKE_CASE_: Tuple     			=torch.tensor(
          [[-1.1_599, -0.9_199, -1.2_221], [-1.1_952, -0.9_269, -1.2_307], [-1.2_143, -0.9_337, -1.2_262]] )
   else:
      SCREAMING_SNAKE_CASE_: Any     			=torch.tensor(
          [[-0.9_192, -0.8_481, -1.1_259], [-1.1_349, -1.0_034, -1.2_599], [-1.1_757, -1.0_429, -1.2_726]] )

   # verify logits
   assert torch.allclose(logits[0, :3, :3]							,			lowercase							,			atol=1e-4 )

   print(f'''Saving model to {pytorch_dump_folder_path}''' )
   model.save_pretrained(lowercase )

   print(f'''Saving image processor to {pytorch_dump_folder_path}''' )
   image_processor.save_pretrained(lowercase )


if __name__ == "__main__":
   _UpperCAmelCase     =			argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       """--checkpoint_url""",
       default="""https://dl.fbaipublicfiles.com/mae/visualize/mae_visualize_vit_base.pth""",
       type=str,
       help="""URL of the checkpoint you'd like to convert.""",
   )
   parser.add_argument(
       """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
   )

   _UpperCAmelCase     =			parser.parse_args()
   convert_vit_mae_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)



