




from ...configuration_utils import PretrainedConfig
from ...utils import logging


_lowerCamelCase					:     int					  =  logging.get_logger(__name__)

_lowerCamelCase					:     Optional[Any]					  =  {
    "google/pegasus-x-base": "https://huggingface.co/google/pegasus-x-base/resolve/main/config.json",
    "google/pegasus-x-large": "https://huggingface.co/google/pegasus-x-large/resolve/main/config.json",
    # See all PEGASUS-X models at https://huggingface.co/models?filter=pegasus-x
}
class 				__snake_case     (_a						):
				lowerCAmelCase__			=   "pegasus_x"
				lowerCAmelCase__			=   ["past_key_values"]
				lowerCAmelCase__			=   {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}



				def __init__(					self					:						List[Any]						,		_UpperCAmelCase					:						List[str]=9_6103						,		_UpperCAmelCase					:						Optional[Any]=1_6384						,		_UpperCAmelCase					:						Union[str, Any]=16						,		_UpperCAmelCase					:						Tuple=4096						,		_UpperCAmelCase					:						Any=16						,		_UpperCAmelCase					:						Optional[int]=16						,		_UpperCAmelCase					:						Optional[Any]=4096						,		_UpperCAmelCase					:						Optional[int]=16						,		_UpperCAmelCase					:						List[str]=0.0						,		_UpperCAmelCase					:						Union[str, Any]=0.0						,		_UpperCAmelCase					:						Any=True						,		_UpperCAmelCase					:						List[Any]=True						,		_UpperCAmelCase					:						Tuple="gelu"						,		_UpperCAmelCase					:						Any=1024						,		_UpperCAmelCase					:						Any=0.1						,		_UpperCAmelCase					:						Tuple=0.0						,		_UpperCAmelCase					:						Optional[Any]=0.0						,		_UpperCAmelCase					:						Dict=0.02						,		_UpperCAmelCase					:						Dict=0						,		_UpperCAmelCase					:						List[str]=True						,		_UpperCAmelCase					:						str=0						,		_UpperCAmelCase					:						List[str]=1						,		_UpperCAmelCase					:						str=1						,		_UpperCAmelCase					:						str=32						,		_UpperCAmelCase					:						Union[str, Any]=512						,		_UpperCAmelCase					:						List[str]=True						,		**_UpperCAmelCase					:						Optional[int]						,		)     ->			Tuple:





								'''simple docstring'''




								_lowerCAmelCase			:			Any        =					vocab_size
								_lowerCAmelCase			:			Dict        =					max_position_embeddings
								_lowerCAmelCase			:			int        =					d_model
								_lowerCAmelCase			:			Any        =					encoder_ffn_dim
								_lowerCAmelCase			:			List[Any]        =					encoder_layers
								_lowerCAmelCase			:			Any        =					encoder_attention_heads
								_lowerCAmelCase			:			List[str]        =					decoder_ffn_dim
								_lowerCAmelCase			:			Optional[int]        =					decoder_layers
								_lowerCAmelCase			:			Optional[Any]        =					decoder_attention_heads
								_lowerCAmelCase			:			Optional[Any]        =					dropout
								_lowerCAmelCase			:			str        =					attention_dropout
								_lowerCAmelCase			:			str        =					activation_dropout
								_lowerCAmelCase			:			Optional[int]        =					activation_function
								_lowerCAmelCase			:			List[Any]        =					init_std
								_lowerCAmelCase			:			Dict        =					encoder_layerdrop
								_lowerCAmelCase			:			Union[str, Any]        =					decoder_layerdrop
								_lowerCAmelCase			:			Optional[Any]        =					use_cache
								_lowerCAmelCase			:			Optional[Any]        =					encoder_layers
								_lowerCAmelCase			:			Dict        =					scale_embedding  # scale factor will be sqrt(d_model) if True

								_lowerCAmelCase			:			Optional[int]        =					num_global_tokens
								_lowerCAmelCase			:			Tuple        =					block_size
								_lowerCAmelCase			:			Optional[int]        =					stagger_local_blocks

								super().__init__(
								    pad_token_id=_UpperCAmelCase						,		eos_token_id=_UpperCAmelCase						,		is_encoder_decoder=_UpperCAmelCase						,		decoder_start_token_id=_UpperCAmelCase						,		forced_eos_token_id=_UpperCAmelCase						,		**_UpperCAmelCase						,		)



				@property
				def 					SCREAMING_SNAKE_CASE				(					self					:						Optional[Any]					)     ->			int:





								'''simple docstring'''




								return self.encoder_attention_heads



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





								'''simple docstring'''




								return self.d_model







import argparse

from transformers import CanineConfig, CanineModel, CanineTokenizer, load_tf_weights_in_canine
from transformers.utils import logging


logging.set_verbosity_info()



def 							_A   (						lowerCAmelCase_     :					Optional[Any]   ,				lowerCAmelCase_     :					Tuple			):
     """simple docstring"""

     lowerCAmelCase__		= CanineConfig()
     lowerCAmelCase__		= CanineModel(lowerCAmelCase_			)
     model.eval()

     print(F'Building PyTorch model from configuration: {config}'			)

     # Load weights from tf checkpoint
     load_tf_weights_in_canine(lowerCAmelCase_   ,				lowerCAmelCase_   ,				lowerCAmelCase_			)

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

     # Save tokenizer files
     lowerCAmelCase__		= CanineTokenizer()
     print(F'Save tokenizer files to {pytorch_dump_path}'			)
     tokenizer.save_pretrained(lowerCAmelCase_			)


if __name__ == "__main__":
     UpperCamelCase								=     argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         '--tf_checkpoint_path',
         default=None,
         type=str,
         required=True,
         help='Path to the TensorFlow checkpoint. Should end with model.ckpt',
     )
     parser.add_argument(
         '--pytorch_dump_path',
         default=None,
         type=str,
         required=True,
         help='Path to a folder where the PyTorch model will be placed.',
     )
     UpperCamelCase								=     parser.parse_args()
     convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.pytorch_dump_path)
