


import argparse
import os
from pathlib import Path

import fairseq
import torch
from packaging import version
from torch import nn

from transformers import (
    BartConfig,
    BartForConditionalGeneration,
    BartForSequenceClassification,
    BartModel,
    BartTokenizer,
)
from transformers.utils import logging


_lowercase       :      List[Any]	=	["bart.large", "bart.large.mnli", "bart.large.cnn", "bart_xsum/model.pt"]
_lowercase       :      List[Any]	=	{"bart.large": BartModel, "bart.large.mnli": BartForSequenceClassification}
if version.parse(fairseq.__version__) < version.parse("0.9.0"):
				raise Exception("requires fairseq >= 0.9.0")


logging.set_verbosity_info()
_lowercase       :      Dict	=	logging.get_logger(__name__)

_lowercase       :      Union[str, Any]	=	" Hello world! cécé herlolip"

_lowercase       :      List[str]	=	[
    ("model.classification_heads.mnli.dense.weight", "classification_head.dense.weight"),
    ("model.classification_heads.mnli.dense.bias", "classification_head.dense.bias"),
    ("model.classification_heads.mnli.out_proj.weight", "classification_head.out_proj.weight"),
    ("model.classification_heads.mnli.out_proj.bias", "classification_head.out_proj.bias"),
]





def   _lowerCAmelCase				(   UpperCamelCase__:     Any						) ->   str:





	"""simple docstring"""







	A								=				[
	    """encoder.version""",
	    """decoder.version""",
	    """model.encoder.version""",
	    """model.decoder.version""",
	    """_float_tensor""",
	]
	for k in ignore_keys:
		state_dict.pop(UpperCamelCase__							,					UpperCamelCase__						)





def   _lowerCAmelCase				(   UpperCamelCase__:     Tuple							,					UpperCamelCase__:     Optional[Any]							,					UpperCamelCase__:     int						) ->   int:





	"""simple docstring"""







	A								=				dct.pop(UpperCamelCase__						)
	A								=				val





def   _lowerCAmelCase				(   UpperCamelCase__:     Any						) ->   Any:





	"""simple docstring"""







	A								=				torch.load(UpperCamelCase__							,					map_location="""cpu"""						)
	A								=				torch.hub.load("""pytorch/fairseq"""							,					"""bart.large.cnn"""						).eval()
	hub_interface.model.load_state_dict(sd["""model"""]						)
	return hub_interface





def   _lowerCAmelCase				(   UpperCamelCase__:     List[Any]						) ->   Optional[int]:





	"""simple docstring"""







	A					,			A								=				emb.weight.shape
	A								=				nn.Linear(UpperCamelCase__							,					UpperCamelCase__							,					bias=UpperCamelCase__						)
	A								=				emb.weight.data
	return lin_layer





@torch.no_grad()
def   _lowerCAmelCase				(   UpperCamelCase__:     Optional[Any]							,					UpperCamelCase__:     int							,					UpperCamelCase__:     Optional[Any]=None						) ->   Union[str, Any]:





	"""simple docstring"""







	if not os.path.exists(UpperCamelCase__						):
		A								=				torch.hub.load("""pytorch/fairseq"""							,					UpperCamelCase__						).eval()
	else:
		A								=				load_xsum_checkpoint(UpperCamelCase__						)

	bart.model.upgrade_state_dict(bart.model.state_dict()						)
	if hf_checkpoint_name is None:
		A								=				checkpoint_path.replace("""."""							,					"""-"""						)
	A								=				BartConfig.from_pretrained(UpperCamelCase__						)
	A								=				bart.encode(UpperCamelCase__						).unsqueeze(0						)
	A								=				BartTokenizer.from_pretrained(UpperCamelCase__						).encode(UpperCamelCase__							,					return_tensors="""pt"""						).unsqueeze(0						)
	if not torch.eq(UpperCamelCase__							,					UpperCamelCase__						).all():
		raise ValueError(
		    f'converted tokenizer and pretrained tokenizer returned different output: {tokens} != {tokensa}'						)

	if checkpoint_path == "bart.large.mnli":
		A								=				bart.state_dict()
		remove_ignore_keys_(UpperCamelCase__						)
		A								=				state_dict["""model.decoder.embed_tokens.weight"""]
		for src, dest in mnli_rename_keys:
			rename_key(UpperCamelCase__							,					UpperCamelCase__							,					UpperCamelCase__						)
		A								=				BartForSequenceClassification(UpperCamelCase__						).eval()
		model.load_state_dict(UpperCamelCase__						)
		A								=				bart.predict("""mnli"""							,					UpperCamelCase__							,					return_logits=UpperCamelCase__						)
		A								=				model(UpperCamelCase__						)[0]  # logits
	else:  # no classification heads to worry about
		A								=				bart.model.state_dict()
		remove_ignore_keys_(UpperCamelCase__						)
		A								=				state_dict["""decoder.embed_tokens.weight"""]
		A								=				bart.extract_features(UpperCamelCase__						)
		if hf_checkpoint_name == "facebook/bart-large":
			A								=				BartModel(UpperCamelCase__						).eval()
			model.load_state_dict(UpperCamelCase__						)
			A								=				model(UpperCamelCase__						).model[0]
		else:
			A								=				BartForConditionalGeneration(UpperCamelCase__						).eval()  # an existing summarization ckpt
			model.model.load_state_dict(UpperCamelCase__						)
			if hasattr(UpperCamelCase__							,					"""lm_head"""						):
				A								=				make_linear_from_emb(model.model.shared						)
			A								=				model.model(UpperCamelCase__						)[0]

    # Check results
	if fairseq_output.shape != new_model_outputs.shape:
		raise ValueError(
		    f'`fairseq_output` shape and `new_model_output` shape are different: {fairseq_output.shape=}, {new_model_outputs.shape}'						)
	if (fairseq_output != new_model_outputs).any().item():
		raise ValueError("""Some values in `fairseq_output` are different from `new_model_outputs`"""						)
	Path(UpperCamelCase__						).mkdir(exist_ok=UpperCamelCase__						)
	model.save_pretrained(UpperCamelCase__						)


if __name__ == "__main__":
				_lowercase       :      str	=	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=None, type=str, help="Which huggingface architecture to use: bart-large-xsum"
				)
				_lowercase       :      Optional[int]	=	parser.parse_args()
				convert_bart_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, hf_checkpoint_name=args.hf_config)







import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import CONFIG_MAPPING


_lowercase       :      int	=	logging.get_logger(__name__)






class       _UpperCamelCase    (					__snake_case				):

				"""simple docstring"""







				lowerCAmelCase 		= 'upernet'

				def __init__(						self				,   a__=None				,   a__=512				,   a__=0.02				,   a__=[1, 2, 3, 6]				,   a__=True				,   a__=0.4				,   a__=384				,   a__=256				,   a__=1				,   a__=False				,   a__=255				,   **a__				,   )	->				Tuple:
					super().__init__(**a__   )

					if backbone_config is None:
						logger.info("""`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone."""   )
						A								=				CONFIG_MAPPING["""resnet"""](out_features=["""stage1""", """stage2""", """stage3""", """stage4"""]   )
					elif isinstance(a__				,   a__   ):
						A								=				backbone_config.get("""model_type"""   )
						A								=				CONFIG_MAPPING[backbone_model_type]
						A								=				config_class.from_dict(a__   )

					A								=				backbone_config
					A								=				hidden_size
					A								=				initializer_range
					A								=				pool_scales
					A								=				use_auxiliary_head
					A								=				auxiliary_loss_weight
					A								=				auxiliary_in_channels
					A								=				auxiliary_channels
					A								=				auxiliary_num_convs
					A								=				auxiliary_concat_input
					A								=				loss_ignore_index

				def      _UpperCAmelCase					(						self   )	->				Dict:
					A								=				copy.deepcopy(self.__dict__   )
					A								=				self.backbone_config.to_dict()
					A								=				self.__class__.model_type
					return output



