

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


a		=["""bart.large""", """bart.large.mnli""", """bart.large.cnn""", """bart_xsum/model.pt"""]
a		={"""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()
a		=logging.get_logger(__name__)

a		=""" Hello world! cécé herlolip"""

a		=[
    ("""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    SCREAMING_SNAKE_CASE__       (  lowerCamelCase__			)  ->     Optional[int]:
	__lowerCamelCase				:				Union[str, Any]					       = [
	    'encoder.version',
	    'decoder.version',
	    'model.encoder.version',
	    'model.decoder.version',
	    '_float_tensor',
	]
	for k in ignore_keys:
		state_dict.pop(SCREAMING_SNAKE_CASE__	, SCREAMING_SNAKE_CASE__			)






def    SCREAMING_SNAKE_CASE__       (  lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__			)  ->     Optional[int]:
	__lowerCamelCase				:				Tuple					       = dct.pop(SCREAMING_SNAKE_CASE__			)
	__lowerCamelCase				:				Tuple					       = val






def    SCREAMING_SNAKE_CASE__       (  lowerCamelCase__			)  ->     Union[str, Any]:
	__lowerCamelCase				:				Optional[int]					       = torch.load(SCREAMING_SNAKE_CASE__	, map_location='cpu'			)
	__lowerCamelCase				:				Tuple					       = torch.hub.load('pytorch/fairseq'	, 'bart.large.cnn'			).eval()
	hub_interface.model.load_state_dict(sd['model']			)
	return hub_interface






def    SCREAMING_SNAKE_CASE__       (  lowerCamelCase__			)  ->     int:
	__lowerCamelCase       , __lowerCamelCase				:				List[Any]					       = emb.weight.shape
	__lowerCamelCase				:				Any					       = nn.Linear(SCREAMING_SNAKE_CASE__	, SCREAMING_SNAKE_CASE__	, bias=SCREAMING_SNAKE_CASE__			)
	__lowerCamelCase				:				List[Any]					       = emb.weight.data
	return lin_layer






@torch.no_grad()
def    SCREAMING_SNAKE_CASE__       (  lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__=None			)  ->     Union[str, Any]:
	if not os.path.exists(SCREAMING_SNAKE_CASE__			):
		__lowerCamelCase				:				str					       = torch.hub.load('pytorch/fairseq'	, SCREAMING_SNAKE_CASE__			).eval()
	else:
		__lowerCamelCase				:				Optional[Any]					       = load_xsum_checkpoint(SCREAMING_SNAKE_CASE__			)

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

	if checkpoint_path == "bart.large.mnli":
		__lowerCamelCase				:				Dict					       = bart.state_dict()
		remove_ignore_keys_(SCREAMING_SNAKE_CASE__			)
		__lowerCamelCase				:				Any					       = state_dict['model.decoder.embed_tokens.weight']
		for src, dest in mnli_rename_keys:
			rename_key(SCREAMING_SNAKE_CASE__	, SCREAMING_SNAKE_CASE__	, SCREAMING_SNAKE_CASE__			)
		__lowerCamelCase				:				Any					       = BartForSequenceClassification(SCREAMING_SNAKE_CASE__			).eval()
		model.load_state_dict(SCREAMING_SNAKE_CASE__			)
		__lowerCamelCase				:				Tuple					       = bart.predict('mnli'	, SCREAMING_SNAKE_CASE__	, return_logits=SCREAMING_SNAKE_CASE__			)
		__lowerCamelCase				:				str					       = model(SCREAMING_SNAKE_CASE__			)[0]  # logits
	else:  # no classification heads to worry about
		__lowerCamelCase				:				Optional[Any]					       = bart.model.state_dict()
		remove_ignore_keys_(SCREAMING_SNAKE_CASE__			)
		__lowerCamelCase				:				List[Any]					       = state_dict['decoder.embed_tokens.weight']
		__lowerCamelCase				:				List[Any]					       = bart.extract_features(SCREAMING_SNAKE_CASE__			)
		if hf_checkpoint_name == "facebook/bart-large":
			__lowerCamelCase				:				int					       = BartModel(SCREAMING_SNAKE_CASE__			).eval()
			model.load_state_dict(SCREAMING_SNAKE_CASE__			)
			__lowerCamelCase				:				Optional[int]					       = model(SCREAMING_SNAKE_CASE__			).model[0]
		else:
			__lowerCamelCase				:				int					       = BartForConditionalGeneration(SCREAMING_SNAKE_CASE__			).eval()  # an existing summarization ckpt
			model.model.load_state_dict(SCREAMING_SNAKE_CASE__			)
			if hasattr(SCREAMING_SNAKE_CASE__	, 'lm_head'			):
				__lowerCamelCase				:				Any					       = make_linear_from_emb(model.model.shared			)
			__lowerCamelCase				:				Optional[Any]					       = model.model(SCREAMING_SNAKE_CASE__			)[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(SCREAMING_SNAKE_CASE__			).mkdir(exist_ok=SCREAMING_SNAKE_CASE__			)
	model.save_pretrained(SCREAMING_SNAKE_CASE__			)


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=None, type=str, help="""Which huggingface architecture to use: bart-large-xsum"""
					)
					a		=parser.parse_args()
					convert_bart_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, hf_checkpoint_name=args.hf_config)







from ..utils import DummyObject, requires_backends





class 	snake_case_   (		metaclass=__A					):







			'''simple docstring'''

			SCREAMING_SNAKE_CASE     :   Union[str, Any]									=				["note_seq"]







			def __init__(     self :				Optional[int]							,   *_UpperCamelCase :				str							,   **_UpperCamelCase :				Optional[int]							)      ->Any:
									requires_backends(self							,   ['''note_seq''']							)







			@classmethod
			def 				snake_case__(     cls :				int							,   *_UpperCamelCase :				Any							,   **_UpperCamelCase :				List[Any]							)      ->int:
									requires_backends(cls							,   ['''note_seq''']							)







			@classmethod
			def 				snake_case__(     cls :				Dict							,   *_UpperCamelCase :				Optional[int]							,   **_UpperCamelCase :				Union[str, Any]							)      ->List[str]:
									requires_backends(cls							,   ['''note_seq''']							)