





import argparse

import torch
from torch import nn

from transformers import MBartConfig, MBartForConditionalGeneration





def lowerCAmelCase__(__snake_case							)       ->   List[Any]:







	'''simple docstring'''







	lowerCamelCase__              =	[
	    '''encoder.version''',
	    '''decoder.version''',
	    '''model.encoder.version''',
	    '''model.decoder.version''',
	    '''_float_tensor''',
	    '''decoder.output_projection.weight''',
	]
	for k in ignore_keys:
		state_dict.pop(__snake_case ,__snake_case							)





def lowerCAmelCase__(__snake_case							)       ->   Any:







	'''simple docstring'''







	lowerCamelCase__       ,			lowerCamelCase__              =	emb.weight.shape
	lowerCamelCase__              =	nn.Linear(__snake_case ,__snake_case ,bias=__snake_case							)
	lowerCamelCase__              =	emb.weight.data
	return lin_layer





def lowerCAmelCase__(__snake_case ,__snake_case="facebook/mbart-large-en-ro" ,__snake_case=False ,__snake_case=False							)       ->   List[Any]:







	'''simple docstring'''







	lowerCamelCase__              =	torch.load(__snake_case ,map_location='''cpu'''							)['''model''']
	remove_ignore_keys_(__snake_case							)
	lowerCamelCase__              =	state_dict['''encoder.embed_tokens.weight'''].shape[0]

	lowerCamelCase__              =	MBartConfig.from_pretrained(__snake_case ,vocab_size=__snake_case							)
	if mbart_aa and finetuned:
		lowerCamelCase__              =	'''relu'''

	lowerCamelCase__              =	state_dict['''decoder.embed_tokens.weight''']
	lowerCamelCase__              =	MBartForConditionalGeneration(__snake_case							)
	model.model.load_state_dict(__snake_case							)

	if finetuned:
		lowerCamelCase__              =	make_linear_from_emb(model.model.shared							)

	return model


if __name__ == "__main__":
			_a						=   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")
			_a						=   parser.parse_args()
			_a						=   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)










from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_a						=   {
    "configuration_funnel": ["FUNNEL_PRETRAINED_CONFIG_ARCHIVE_MAP", "FunnelConfig"],
    "convert_funnel_original_tf_checkpoint_to_pytorch": [],
    "tokenization_funnel": ["FunnelTokenizer"],
}

try:
			if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_a						=   ["FunnelTokenizerFast"]

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_a						=   [
			    "FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST",
			    "FunnelBaseModel",
			    "FunnelForMaskedLM",
			    "FunnelForMultipleChoice",
			    "FunnelForPreTraining",
			    "FunnelForQuestionAnswering",
			    "FunnelForSequenceClassification",
			    "FunnelForTokenClassification",
			    "FunnelModel",
			    "FunnelPreTrainedModel",
			    "load_tf_weights_in_funnel",
			]

try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_a						=   [
			    "TF_FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST",
			    "TFFunnelBaseModel",
			    "TFFunnelForMaskedLM",
			    "TFFunnelForMultipleChoice",
			    "TFFunnelForPreTraining",
			    "TFFunnelForQuestionAnswering",
			    "TFFunnelForSequenceClassification",
			    "TFFunnelForTokenClassification",
			    "TFFunnelModel",
			    "TFFunnelPreTrainedModel",
			]


if TYPE_CHECKING:
			from .configuration_funnel import FUNNEL_PRETRAINED_CONFIG_ARCHIVE_MAP, FunnelConfig
			from .tokenization_funnel import FunnelTokenizer

			try:
						if not is_tokenizers_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_funnel_fast import FunnelTokenizerFast

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_funnel import (
						    FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST,
						    FunnelBaseModel,
						    FunnelForMaskedLM,
						    FunnelForMultipleChoice,
						    FunnelForPreTraining,
						    FunnelForQuestionAnswering,
						    FunnelForSequenceClassification,
						    FunnelForTokenClassification,
						    FunnelModel,
						    FunnelPreTrainedModel,
						    load_tf_weights_in_funnel,
						)

			try:
						if not is_tf_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_funnel import (
						    TF_FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST,
						    TFFunnelBaseModel,
						    TFFunnelForMaskedLM,
						    TFFunnelForMultipleChoice,
						    TFFunnelForPreTraining,
						    TFFunnelForQuestionAnswering,
						    TFFunnelForSequenceClassification,
						    TFFunnelForTokenClassification,
						    TFFunnelModel,
						    TFFunnelPreTrainedModel,
						)

else:
			import sys

			_a						=   _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



