


'''simple docstring'''


import argparse

import torch
from torch import nn

from transformers import MBartConfig, MBartForConditionalGeneration







def 			snake_case_   (  SCREAMING_SNAKE_CASE__     ):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      [
				    """encoder.version""",
				    """decoder.version""",
				    """model.encoder.version""",
				    """model.decoder.version""",
				    """_float_tensor""",
				    """decoder.output_projection.weight""",
				]
				for k in ignore_keys:
								state_dict.pop(SCREAMING_SNAKE_CASE__							,      SCREAMING_SNAKE_CASE__     )







def 			snake_case_   (  SCREAMING_SNAKE_CASE__     ):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE		,       _SCREAMING_SNAKE_CASE :       List[Any]     		=      emb.weight.shape
				_SCREAMING_SNAKE_CASE :       List[str]     		=      nn.Linear(SCREAMING_SNAKE_CASE__							,      SCREAMING_SNAKE_CASE__							,      bias=SCREAMING_SNAKE_CASE__     )
				_SCREAMING_SNAKE_CASE :       Tuple     		=      emb.weight.data
				return lin_layer







def 			snake_case_   (  SCREAMING_SNAKE_CASE__							,      SCREAMING_SNAKE_CASE__="facebook/mbart-large-en-ro"							,      SCREAMING_SNAKE_CASE__=False							,      SCREAMING_SNAKE_CASE__=False     ):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      torch.load(SCREAMING_SNAKE_CASE__							,      map_location="""cpu"""     )["""model"""]
				remove_ignore_keys_(SCREAMING_SNAKE_CASE__     )
				_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      state_dict["""encoder.embed_tokens.weight"""].shape[0]

				_SCREAMING_SNAKE_CASE :       Tuple     		=      MBartConfig.from_pretrained(SCREAMING_SNAKE_CASE__							,      vocab_size=SCREAMING_SNAKE_CASE__     )
				if mbart_aa and finetuned:
								_SCREAMING_SNAKE_CASE :       Dict     		=      """relu"""

				_SCREAMING_SNAKE_CASE :       Dict     		=      state_dict["""decoder.embed_tokens.weight"""]
				_SCREAMING_SNAKE_CASE :       int     		=      MBartForConditionalGeneration(SCREAMING_SNAKE_CASE__     )
				model.model.load_state_dict(SCREAMING_SNAKE_CASE__     )

				if finetuned:
								_SCREAMING_SNAKE_CASE :       int     		=      make_linear_from_emb(model.model.shared     )

				return model


if __name__ == "__main__":
				UpperCAmelCase_		:   Union[str, Any]							   =		argparse.ArgumentParser()
				# Required parameters
				parser.add_argument(
				    'fairseq_path', type=str, help='bart.large, bart.large.cnn or a path to a model.pt on local filesystem.'
				)
				parser.add_argument('pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
				parser.add_argument(
				    '--hf_config',
				    default='facebook/mbart-large-cc25',
				    type=str,
				    help='Which huggingface architecture to use: mbart-large',
				)
				parser.add_argument('--mbart_50', action='store_true', help='whether the model is mMART-50 checkpoint')
				parser.add_argument('--finetuned', action='store_true', help='whether the model is a fine-tuned checkpoint')
				UpperCAmelCase_		:   Optional[int]							   =		parser.parse_args()
				UpperCAmelCase_		:   Optional[Any]							   =		convert_fairseq_mbart_checkpoint_from_disk(
				    args.fairseq_path, hf_config_path=args.hf_config, finetuned=args.finetuned, mbart_aa=args.mbart_aa
				)
				model.save_pretrained(args.pytorch_dump_folder_path)








'''simple docstring'''


from collections import OrderedDict
from typing import Mapping

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


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

UpperCAmelCase_		:   Optional[int]							   =		{
    'google/bigbird-roberta-base': 'https://huggingface.co/google/bigbird-roberta-base/resolve/main/config.json',
    'google/bigbird-roberta-large': 'https://huggingface.co/google/bigbird-roberta-large/resolve/main/config.json',
    'google/bigbird-base-trivia-itc': 'https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/config.json',
    # See all BigBird models at https://huggingface.co/models?filter=big_bird
}





class     lowercase__  (						_snake_case					):





							'''simple docstring'''


							A_							:      str      	=  """big_bird"""






							def __init__(    self    ,					__snake_case=5_0358    ,					__snake_case=768    ,					__snake_case=12    ,					__snake_case=12    ,					__snake_case=3072    ,					__snake_case="gelu_new"    ,					__snake_case=0.1    ,					__snake_case=0.1    ,					__snake_case=4096    ,					__snake_case=2    ,					__snake_case=0.02    ,					__snake_case=1e-12    ,					__snake_case=True    ,					__snake_case=0    ,					__snake_case=1    ,					__snake_case=2    ,					__snake_case=66    ,					__snake_case="block_sparse"    ,					__snake_case=True    ,					__snake_case=False    ,					__snake_case=64    ,					__snake_case=3    ,					__snake_case=None    ,					**__snake_case    ,					):
											super().__init__(
											    pad_token_id=__snake_case    ,					bos_token_id=__snake_case    ,					eos_token_id=__snake_case    ,					sep_token_id=__snake_case    ,					**__snake_case    ,					)

											_SCREAMING_SNAKE_CASE :       str     		=      vocab_size
											_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      max_position_embeddings
											_SCREAMING_SNAKE_CASE :       List[str]     		=      hidden_size
											_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      num_hidden_layers
											_SCREAMING_SNAKE_CASE :       Any     		=      num_attention_heads
											_SCREAMING_SNAKE_CASE :       Optional[int]     		=      intermediate_size
											_SCREAMING_SNAKE_CASE :       List[Any]     		=      hidden_act
											_SCREAMING_SNAKE_CASE :       Optional[int]     		=      hidden_dropout_prob
											_SCREAMING_SNAKE_CASE :       Optional[int]     		=      attention_probs_dropout_prob
											_SCREAMING_SNAKE_CASE :       Tuple     		=      initializer_range
											_SCREAMING_SNAKE_CASE :       Any     		=      type_vocab_size
											_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      layer_norm_eps
											_SCREAMING_SNAKE_CASE :       List[Any]     		=      use_cache

											_SCREAMING_SNAKE_CASE :       List[Any]     		=      rescale_embeddings
											_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      attention_type
											_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      use_bias
											_SCREAMING_SNAKE_CASE :       int     		=      block_size
											_SCREAMING_SNAKE_CASE :       Any     		=      num_random_blocks
											_SCREAMING_SNAKE_CASE :       List[str]     		=      classifier_dropout





class     lowercase__  (						_snake_case					):





							'''simple docstring'''


							@property
							def UpperCAmelCase_	(    self  ):
											if self.task == "multiple-choice":
															_SCREAMING_SNAKE_CASE :       int     		=      {0: """batch""", 1: """choice""", 2: """sequence"""}
											else:
															_SCREAMING_SNAKE_CASE :       Optional[int]     		=      {0: """batch""", 1: """sequence"""}
											return OrderedDict(
											    [
											        ("""input_ids""", dynamic_axis),
											        ("""attention_mask""", dynamic_axis),
											    ]  )




