

"""simple docstring"""


import argparse
import os

import torch

from transformers import FlavaImageCodebook, FlavaImageCodebookConfig




def  _lowercase					(    __lowerCAmelCase  ,					__lowerCAmelCase  ,					__lowerCAmelCase  ,					__lowerCAmelCase							)       ->				Tuple:
      SCREAMING_SNAKE_CASE__   :      str      		=	s.rsplit(__lowerCAmelCase  ,					__lowerCAmelCase							)
      return new.join(__lowerCAmelCase							)




def  _lowercase					(    __lowerCAmelCase							)       ->				Dict:
      # encoder.embeddings are double copied in original FLAVA
      return sum(param.float().sum() if """encoder.embeddings""" not in key else 0 for key, param in state_dict.items()							)




def  _lowercase					(    __lowerCAmelCase							)       ->				Any:
      SCREAMING_SNAKE_CASE__   :      Dict      		=	{}

      SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	["""group_1""", """group_2""", """group_3""", """group_4"""]
      for key, value in state_dict.items():
            for group_key in group_keys:
                  if group_key in key:
                        SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	key.replace(F'''{group_key}.'''  ,					F'''{group_key}.group.'''							)

            if "res_path" in key:
                  SCREAMING_SNAKE_CASE__   :      Tuple      		=	key.replace("""res_path."""  ,					"""res_path.path."""							)

            if key.endswith(""".w"""							):
                  SCREAMING_SNAKE_CASE__   :      Any      		=	rreplace(__lowerCAmelCase  ,					""".w"""  ,					""".weight"""  ,					1							)
            if key.endswith(""".b"""							):
                  SCREAMING_SNAKE_CASE__   :      List[Any]      		=	rreplace(__lowerCAmelCase  ,					""".b"""  ,					""".bias"""  ,					1							)

            SCREAMING_SNAKE_CASE__   :      Any      		=	value.float()

      return upgrade




@torch.no_grad()
def  _lowercase					(    __lowerCAmelCase  ,					__lowerCAmelCase  ,					__lowerCAmelCase=None  ,					__lowerCAmelCase=True							)       ->				List[str]:
      from dall_e import Encoder

      SCREAMING_SNAKE_CASE__   :      int      		=	Encoder()
      if os.path.exists(__lowerCAmelCase							):
            SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	torch.load(__lowerCAmelCase							)
      else:
            SCREAMING_SNAKE_CASE__   :      int      		=	torch.hub.load_state_dict_from_url(__lowerCAmelCase							)

      if isinstance(__lowerCAmelCase  ,					__lowerCAmelCase							):
            SCREAMING_SNAKE_CASE__   :      List[str]      		=	ckpt.state_dict()
      encoder.load_state_dict(__lowerCAmelCase							)

      if config_path is not None:
            SCREAMING_SNAKE_CASE__   :      str      		=	FlavaImageCodebookConfig.from_pretrained(__lowerCAmelCase							)
      else:
            SCREAMING_SNAKE_CASE__   :      List[Any]      		=	FlavaImageCodebookConfig()

      SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	FlavaImageCodebook(__lowerCAmelCase							).eval()
      SCREAMING_SNAKE_CASE__   :      Any      		=	encoder.state_dict()

      SCREAMING_SNAKE_CASE__   :      int      		=	upgrade_state_dict(__lowerCAmelCase							)
      hf_model.load_state_dict(__lowerCAmelCase							)
      SCREAMING_SNAKE_CASE__   :      List[Any]      		=	hf_model.state_dict()
      SCREAMING_SNAKE_CASE__   :      int      		=	count_parameters(__lowerCAmelCase							)
      SCREAMING_SNAKE_CASE__   :      List[str]      		=	count_parameters(__lowerCAmelCase							)

      assert torch.allclose(__lowerCAmelCase  ,					__lowerCAmelCase  ,					atol=1E-3							)

      if save_checkpoint:
            hf_model.save_pretrained(__lowerCAmelCase							)
      else:
            return hf_state_dict


if __name__ == "__main__":
  a					:Any    = argparse.ArgumentParser()
  parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
  parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to flava checkpoint")
  parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
  a					:List[Any]    = parser.parse_args()

  convert_dalle_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)







"""simple docstring"""


from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging


a					:Optional[Any]    = logging.get_logger(__name__)

a					:Union[str, Any]    = {
    "t5-small": "https://huggingface.co/t5-small/resolve/main/config.json",
    "t5-base": "https://huggingface.co/t5-base/resolve/main/config.json",
    "t5-large": "https://huggingface.co/t5-large/resolve/main/config.json",
    "t5-3b": "https://huggingface.co/t5-3b/resolve/main/config.json",
    "t5-11b": "https://huggingface.co/t5-11b/resolve/main/config.json",
}

class      __a							(UpperCamelCase_):






     '''simple docstring'''
     _SCREAMING_SNAKE_CASE						:List[Any]       = """t5"""
     _SCREAMING_SNAKE_CASE						:List[str]       = ["""past_key_values"""]
     _SCREAMING_SNAKE_CASE						:Any       = {"""hidden_size""": """d_model""", """num_attention_heads""": """num_heads""", """num_hidden_layers""": """num_layers"""}

     def __init__(						self	,    _a=32_128	,    _a=512	,    _a=64	,    _a=2_048	,    _a=6	,    _a=None	,    _a=8	,    _a=32	,    _a=128	,    _a=0.1	,    _a=1E-6	,    _a=1.0	,    _a="relu"	,    _a=True	,    _a=True	,    _a=0	,    _a=1	,    **_a	,    )  ->      Optional[int]:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	vocab_size
           SCREAMING_SNAKE_CASE__   :      Tuple      		=	d_model
           SCREAMING_SNAKE_CASE__   :      int      		=	d_kv
           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	d_ff
           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	num_layers
           SCREAMING_SNAKE_CASE__   :      int      		=	(
               num_decoder_layers if num_decoder_layers is not None else self.num_layers
           )  # default = symmetry
           SCREAMING_SNAKE_CASE__   :      Tuple      		=	num_heads
           SCREAMING_SNAKE_CASE__   :      Dict      		=	relative_attention_num_buckets
           SCREAMING_SNAKE_CASE__   :      str      		=	relative_attention_max_distance
           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	dropout_rate
           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	layer_norm_epsilon
           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	initializer_factor
           SCREAMING_SNAKE_CASE__   :      Tuple      		=	feed_forward_proj
           SCREAMING_SNAKE_CASE__   :      str      		=	use_cache

           SCREAMING_SNAKE_CASE__   :      List[str]      		=	self.feed_forward_proj.split("""-"""  )
           SCREAMING_SNAKE_CASE__   :      Dict      		=	act_info[-1]
           SCREAMING_SNAKE_CASE__   :      str      		=	act_info[0] == """gated"""

           if len(_a  ) > 1 and act_info[0] != "gated" or len(_a  ) > 2:
                 raise ValueError(
                     f'''`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer.'''
                     """Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. """
                     """'gated-gelu' or 'relu'"""  )

           # for backwards compatibility
           if feed_forward_proj == "gated-gelu":
                 SCREAMING_SNAKE_CASE__   :      List[Any]      		=	"""gelu_new"""

           super().__init__(
               pad_token_id=_a	,    eos_token_id=_a	,    is_encoder_decoder=_a	,    **_a	,    )





class      __a							(UpperCamelCase_):






     '''simple docstring'''

     @property
     def 					_a			(						self  )  ->      Mapping[str, Mapping[int, str]]:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      int      		=	{
               """input_ids""": {0: """batch""", 1: """encoder_sequence"""},
               """attention_mask""": {0: """batch""", 1: """encoder_sequence"""},
           }
           if self.use_past:
                 SCREAMING_SNAKE_CASE__   :      Tuple      		=	"""past_encoder_sequence + sequence"""
                 SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	{0: """batch"""}
                 SCREAMING_SNAKE_CASE__   :      Tuple      		=	{0: """batch""", 1: """past_decoder_sequence + sequence"""}
           else:
                 SCREAMING_SNAKE_CASE__   :      str      		=	{0: """batch""", 1: """decoder_sequence"""}
                 SCREAMING_SNAKE_CASE__   :      Dict      		=	{0: """batch""", 1: """decoder_sequence"""}

           if self.use_past:
                 self.fill_with_past_key_values_(_a	,    direction="""inputs"""  )

           return common_inputs





     @property
     def 					_a			(						self  )  ->      int:


           """simple docstring"""


           return 13




