



from collections import OrderedDict
from typing import Mapping

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


UpperCAmelCase :				Optional[int]							=logging.get_logger(__name__)

UpperCAmelCase :				int							={
    """uclanlp/plbart-base""": """https://huggingface.co/uclanlp/plbart-base/resolve/main/config.json""",
    # See all PLBART models at https://huggingface.co/models?filter=plbart
}


class    _lowercase (a_   ):
				'''simple docstring'''







				lowercase__     =    """plbart"""
				lowercase__     =    ["""past_key_values"""]
				lowercase__     =    {"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}

				def __init__(			self  ,			snake_case__=5_0005  ,			snake_case__=1024  ,			snake_case__=6  ,			snake_case__=3072  ,			snake_case__=12  ,			snake_case__=6  ,			snake_case__=3072  ,			snake_case__=12  ,			snake_case__=0.0  ,			snake_case__=0.0  ,			snake_case__=True  ,			snake_case__=True  ,			snake_case__="gelu"  ,			snake_case__=768  ,			snake_case__=0.1  ,			snake_case__=0.1  ,			snake_case__=0.0  ,			snake_case__=0.02  ,			snake_case__=0.0  ,			snake_case__=True  ,			snake_case__=1  ,			snake_case__=0  ,			snake_case__=2  ,			snake_case__=2  ,			**snake_case__  ,			):

									'''simple docstring'''


									UpperCamelCase_			= vocab_size
									UpperCamelCase_			= max_position_embeddings
									UpperCamelCase_			= d_model
									UpperCamelCase_			= encoder_ffn_dim
									UpperCamelCase_			= encoder_layers
									UpperCamelCase_			= encoder_attention_heads
									UpperCamelCase_			= decoder_ffn_dim
									UpperCamelCase_			= decoder_layers
									UpperCamelCase_			= decoder_attention_heads
									UpperCamelCase_			= dropout
									UpperCamelCase_			= attention_dropout
									UpperCamelCase_			= activation_dropout
									UpperCamelCase_			= activation_function
									UpperCamelCase_			= init_std
									UpperCamelCase_			= encoder_layerdrop
									UpperCamelCase_			= decoder_layerdrop
									UpperCamelCase_			= classifier_dropout
									UpperCamelCase_			= use_cache
									UpperCamelCase_			= encoder_layers
									UpperCamelCase_			= scale_embedding  # scale factor will be sqrt(d_model) if True
									super().__init__(
									    pad_token_id=snake_case__  ,			bos_token_id=snake_case__  ,			eos_token_id=snake_case__  ,			is_encoder_decoder=snake_case__  ,			forced_eos_token_id=snake_case__  ,			**snake_case__  ,			)




class    _lowercase (a_   ):
				'''simple docstring'''







				@property
				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									return OrderedDict(
									    [
									        ("input_ids", {0: "batch", 1: "sequence"}),
									        ("attention_mask", {0: "batch", 1: "sequence"}),
									    ]     )

				@property
				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									if self.use_past:
														return OrderedDict(
														    [
														        ("last_hidden_state", {0: "batch", 1: "sequence"}),
														        ("past_keys", {0: "batch", 2: "sequence"}),
														        ("encoder_last_hidden_state", {0: "batch", 1: "sequence"}),
														    ]     )
									else:
														return OrderedDict(
														    [
														        ("last_hidden_state", {0: "batch", 1: "sequence"}),
														        ("encoder_last_hidden_state", {0: "batch", 1: "sequence"}),
														    ]     )






import argparse

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


logging.set_verbosity_info()



def    UpperCAmelCase							(  _snake_case    ,			_snake_case				):
							# Initialize PyTorch model
							lowerCAmelCase =		CanineConfig()
							lowerCAmelCase =		CanineModel(_snake_case				)
							model.eval()

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

							# Load weights from tf checkpoint
							load_tf_weights_in_canine(_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
							lowerCAmelCase =		CanineTokenizer()
							print(F"""Save tokenizer files to {pytorch_dump_path}"""				)
							tokenizer.save_pretrained(_snake_case				)


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)



