'''simple docstring'''

from typing import Mapping

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


_A			:      Union[str, Any]					      =				logging.get_logger(__name__)

_A			:      Optional[Any]					      =				{
    '''google/umt5-small''': '''https://huggingface.co/google/umt5-small/resolve/main/config.json''',
    # See all umt5 models at https://huggingface.co/models?filter=umt5
}







class 				_lowercase     (				UpperCAmelCase__			):


 '''simple docstring'''






 _SCREAMING_SNAKE_CASE     :       Any      	=					"""umt5"""
 _SCREAMING_SNAKE_CASE     :       Any      	=					["""past_key_values"""]
 def __init__(      self   :	Tuple      ,       SCREAMING_SNAKE_CASE__   :	Dict=25_01_12      ,       SCREAMING_SNAKE_CASE__   :	Optional[int]=5_12      ,       SCREAMING_SNAKE_CASE__   :	List[Any]=64      ,       SCREAMING_SNAKE_CASE__   :	Dict=10_24      ,       SCREAMING_SNAKE_CASE__   :	Tuple=8      ,       SCREAMING_SNAKE_CASE__   :	Any=None      ,       SCREAMING_SNAKE_CASE__   :	Any=6      ,       SCREAMING_SNAKE_CASE__   :	Tuple=32      ,       SCREAMING_SNAKE_CASE__   :	List[str]=1_28      ,       SCREAMING_SNAKE_CASE__   :	List[Any]=0.1      ,       SCREAMING_SNAKE_CASE__   :	Any=1e-6      ,       SCREAMING_SNAKE_CASE__   :	Any=1.0      ,       SCREAMING_SNAKE_CASE__   :	Optional[int]="gated-gelu"      ,       SCREAMING_SNAKE_CASE__   :	str=True      ,       SCREAMING_SNAKE_CASE__   :	Dict=True      ,       SCREAMING_SNAKE_CASE__   :	int="T5Tokenizer"      ,       SCREAMING_SNAKE_CASE__   :	str=True      ,       SCREAMING_SNAKE_CASE__   :	Optional[int]=0      ,       SCREAMING_SNAKE_CASE__   :	Optional[int]=1      ,       SCREAMING_SNAKE_CASE__   :	Any=0      ,       **SCREAMING_SNAKE_CASE__   :	Optional[Any]      ,       )  ->    str:
  super().__init__(
      is_encoder_decoder=SCREAMING_SNAKE_CASE__      ,       tokenizer_class=SCREAMING_SNAKE_CASE__      ,       tie_word_embeddings=SCREAMING_SNAKE_CASE__      ,       pad_token_id=SCREAMING_SNAKE_CASE__      ,       eos_token_id=SCREAMING_SNAKE_CASE__      ,       decoder_start_token_id=SCREAMING_SNAKE_CASE__      ,       **SCREAMING_SNAKE_CASE__      ,       )
  __lowerCAmelCase											=						vocab_size
  __lowerCAmelCase											=						d_model
  __lowerCAmelCase											=						d_kv
  __lowerCAmelCase											=						d_ff
  __lowerCAmelCase											=						num_layers
  __lowerCAmelCase											=						(
      num_decoder_layers if num_decoder_layers is not None else self.num_layers
  )  # default = symmetry
  __lowerCAmelCase											=						num_heads
  __lowerCAmelCase											=						relative_attention_num_buckets
  __lowerCAmelCase											=						relative_attention_max_distance
  __lowerCAmelCase											=						dropout_rate
  __lowerCAmelCase											=						layer_norm_epsilon
  __lowerCAmelCase											=						initializer_factor
  __lowerCAmelCase											=						feed_forward_proj
  __lowerCAmelCase											=						use_cache

  __lowerCAmelCase											=						self.feed_forward_proj.split("""-"""							)
  __lowerCAmelCase											=						act_info[-1]
  __lowerCAmelCase											=						act_info[0] == """gated"""

  if len(SCREAMING_SNAKE_CASE__							) > 1 and act_info[0] != "gated" or len(SCREAMING_SNAKE_CASE__							) > 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'"""							)

  if feed_forward_proj == "gated-gelu":
   __lowerCAmelCase											=						"""gelu_new"""
 @property
 def a					(      self   :	str							)  ->    Tuple:
  return self.d_model
 @property
 def a					(      self   :	List[Any]							)  ->    Optional[int]:
  return self.num_heads





 @property
 def a					(      self   :	str							)  ->    Optional[Any]:
  return self.num_layers







class 				_lowercase     (				UpperCAmelCase__			):


 '''simple docstring'''






 @property
 # Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
 def a					(      self   :	Dict							)  ->    Mapping[str, Mapping[int, str]]:
  __lowerCAmelCase											=						{
      """input_ids""": {0: """batch""", 1: """encoder_sequence"""},
      """attention_mask""": {0: """batch""", 1: """encoder_sequence"""},
  }
  if self.use_past:
   __lowerCAmelCase											=						"""past_encoder_sequence + sequence"""
   __lowerCAmelCase											=						{0: """batch"""}
   __lowerCAmelCase											=						{0: """batch""", 1: """past_decoder_sequence + sequence"""}
  else:
   __lowerCAmelCase											=						{0: """batch""", 1: """decoder_sequence"""}
   __lowerCAmelCase											=						{0: """batch""", 1: """decoder_sequence"""}

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

  return common_inputs
 @property
 # Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.default_onnx_opset
 def a					(      self   :	Any							)  ->    int:
  return 13





 @property
 def a					(      self   :	Dict							)  ->    float:
  return 5e-4







'''simple docstring'''

import argparse

import torch

from transformers import BlenderbotConfig, BlenderbotForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()
_A			:      str					      =				logging.get_logger(__name__)

_A			:      str					      =				[
    ['''attention''', '''attn'''],
    ['''encoder_attention''', '''encoder_attn'''],
    ['''q_lin''', '''q_proj'''],
    ['''k_lin''', '''k_proj'''],
    ['''v_lin''', '''v_proj'''],
    ['''out_lin''', '''out_proj'''],
    ['''norm_embeddings''', '''layernorm_embedding'''],
    ['''position_embeddings''', '''embed_positions'''],
    ['''embeddings''', '''embed_tokens'''],
    ['''ffn.lin''', '''fc'''],
]







def      UpperCamelCase_						(					snake_case_       :       Tuple      )					->     Optional[int]:






 '''simple docstring'''

 if k == "embeddings.weight":
  return "shared.weight"

 for parlai_name, hf_name in PATTERNS:
  __lowerCAmelCase											=						k.replace(snake_case_						,		snake_case_      )

 if k.startswith("""encoder"""      ):
  __lowerCAmelCase											=						k.replace(""".attn"""						,		""".self_attn"""      )
  __lowerCAmelCase											=						k.replace("""norm1"""						,		"""self_attn_layer_norm"""      )
  __lowerCAmelCase											=						k.replace("""norm2"""						,		"""final_layer_norm"""      )
 elif k.startswith("""decoder"""      ):
  __lowerCAmelCase											=						k.replace("""norm1"""						,		"""self_attn_layer_norm"""      )
  __lowerCAmelCase											=						k.replace("""norm2"""						,		"""encoder_attn_layer_norm"""      )
  __lowerCAmelCase											=						k.replace("""norm3"""						,		"""final_layer_norm"""      )
 return k







def      UpperCamelCase_						(					snake_case_       :       List[str]      )					->     Dict:






 '''simple docstring'''

 __lowerCAmelCase											=						[
     """model.encoder.layernorm_embedding.weight""",
     """model.encoder.layernorm_embedding.bias""",
     """model.decoder.layernorm_embedding.weight""",
     """model.decoder.layernorm_embedding.bias""",
 ]
 for k in keys:
  __lowerCAmelCase											=						sd.pop(snake_case_      )
  __lowerCAmelCase											=						k.replace("""layernorm_embedding"""						,		"""layer_norm"""      )
  assert new_k not in sd
  __lowerCAmelCase											=						v


_A			:      int					      =				['''START''']







@torch.no_grad()
def      UpperCamelCase_						(					snake_case_       :       List[Any]						,		snake_case_       :       Any						,		snake_case_       :       Optional[int]      )					->     Optional[Any]:






 '''simple docstring'''

 __lowerCAmelCase											=						torch.load(snake_case_						,		map_location="""cpu"""      )
 __lowerCAmelCase											=						model["""model"""]
 __lowerCAmelCase											=						BlenderbotConfig.from_json_file(snake_case_      )
 __lowerCAmelCase											=						BlenderbotForConditionalGeneration(snake_case_      )
 __lowerCAmelCase											=						m.model.state_dict().keys()
 __lowerCAmelCase											=						[]
 __lowerCAmelCase											=						{}
 for k, v in sd.items():
  if k in IGNORE_KEYS:
   continue

  __lowerCAmelCase											=						rename_state_dict_key(snake_case_      )
  if new_k not in valid_keys:
   failures.append([k, new_k]      )
  else:
   __lowerCAmelCase											=						v
 if cfg.normalize_before:  # Blenderbot-3B checkpoints. Rename layernorm_embedding -> layer_norm
  rename_layernorm_keys(snake_case_      )
 m.model.load_state_dict(snake_case_						,		strict=snake_case_      )
 m.half()
 m.save_pretrained(snake_case_      )


if __name__ == "__main__":
 _A			:      Optional[Any]					      =				argparse.ArgumentParser()
 # Required parameters
 parser.add_argument('''--src_path''', type=str, help='''like blenderbot-model.bin''')
 parser.add_argument('''--save_dir''', default='''hf_blenderbot''', type=str, help='''Where to save converted model.''')
 parser.add_argument(
     '''--hf_config_json''', default='''blenderbot-3b-config.json''', type=str, help='''Path to config to use'''
 )
 _A			:      Tuple					      =				parser.parse_args()
 convert_parlai_checkpoint(args.src_path, args.save_dir, args.hf_config_json)






