






"""simple docstring"""



import argparse

from transformers import (
    TapasConfig,
    TapasForMaskedLM,
    TapasForQuestionAnswering,
    TapasForSequenceClassification,
    TapasModel,
    TapasTokenizer,
    load_tf_weights_in_tapas,
)
from transformers.utils import logging


logging.set_verbosity_info()



def 						lowercase    (				__snake_case				: List[Any]		,					__snake_case				: Any		,					__snake_case				: Dict		,					__snake_case				: Optional[Any]		,					__snake_case				: Dict	):
		# Initialise PyTorch model.
		# If you want to convert a checkpoint that uses absolute position embeddings, make sure to set reset_position_index_per_cell of
		# TapasConfig to False.

		# initialize configuration from json file
		lowercase_ : Optional[Any]							       =   TapasConfig.from_json_file(__snake_case	)
		# set absolute/relative position embeddings parameter
		lowercase_ : Any							       =   reset_position_index_per_cell

		# set remaining parameters of TapasConfig as well as the model based on the task
		if task == "SQA":
				lowercase_ : List[Any]							       =   TapasForQuestionAnswering(config=__snake_case	)
		elif task == "WTQ":
				# run_task_main.py hparams
				lowercase_ : int							       =   4
				lowercase_ : str							       =   True
				# hparam_utils.py hparams
				lowercase_ : int							       =   0.664694
				lowercase_ : Any							       =   0.207951
				lowercase_ : List[Any]							       =   0.121194
				lowercase_ : Optional[int]							       =   True
				lowercase_ : List[Any]							       =   True
				lowercase_ : Optional[Any]							       =   False
				lowercase_ : Optional[int]							       =   0.0352513

				lowercase_ : str							       =   TapasForQuestionAnswering(config=__snake_case	)
		elif task == "WIKISQL_SUPERVISED":
				# run_task_main.py hparams
				lowercase_ : Tuple							       =   4
				lowercase_ : Optional[int]							       =   False
				# hparam_utils.py hparams
				lowercase_ : Dict							       =   36.4519
				lowercase_ : Tuple							       =   0.903421
				lowercase_ : int							       =   222.088
				lowercase_ : int							       =   True
				lowercase_ : List[str]							       =   True
				lowercase_ : Optional[Any]							       =   True
				lowercase_ : List[str]							       =   0.763141

				lowercase_ : Any							       =   TapasForQuestionAnswering(config=__snake_case	)
		elif task == "TABFACT":
				lowercase_ : Dict							       =   TapasForSequenceClassification(config=__snake_case	)
		elif task == "MLM":
				lowercase_ : List[Any]							       =   TapasForMaskedLM(config=__snake_case	)
		elif task == "INTERMEDIATE_PRETRAINING":
				lowercase_ : Optional[int]							       =   TapasModel(config=__snake_case	)
		else:
				raise ValueError(F'''Task {task} not supported.'''	)

		print(F'''Building PyTorch model from configuration: {config}'''	)
		# Load weights from tf checkpoint
		load_tf_weights_in_tapas(__snake_case		,					__snake_case		,					__snake_case	)

		# Save pytorch-model (weights and configuration)
		print(F'''Save PyTorch model to {pytorch_dump_path}'''	)
		model.save_pretrained(__snake_case	)

		# Save tokenizer files
		print(F'''Save tokenizer files to {pytorch_dump_path}'''	)
		lowercase_ : List[str]							       =   TapasTokenizer(vocab_file=tf_checkpoint_path[:-1_0] + '''vocab.txt'''		,					model_max_length=5_1_2	)
		tokenizer.save_pretrained(__snake_case	)

		print('''Used relative position embeddings:'''		,					model.config.reset_position_index_per_cell	)


if __name__ == "__main__":
						__A		:       List[str]  				=		argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    '''--task''', default='''SQA''', type=str, help='''Model task for which to convert a checkpoint. Defaults to SQA.'''
						)
						parser.add_argument(
						    '''--reset_position_index_per_cell''',
						    default=False,
						    action='''store_true''',
						    help='''Whether to use relative position embeddings or not. Defaults to True.''',
						)
						parser.add_argument(
						    '''--tf_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
						)
						parser.add_argument(
						    '''--tapas_config_file''',
						    default=None,
						    type=str,
						    required=True,
						    help=(
						        '''The config json file corresponding to the pre-trained TAPAS model. \n'''
						        '''This specifies the model architecture.'''
						    ),
						)
						parser.add_argument(
						    '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
						)
						__A		:       List[str]  				=		parser.parse_args()
						convert_tf_checkpoint_to_pytorch(
						    args.task,
						    args.reset_position_index_per_cell,
						    args.tf_checkpoint_path,
						    args.tapas_config_file,
						    args.pytorch_dump_path,
						)




'''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)






