

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


_UpperCAmelCase	:     int		       = ["bart.large", "bart.large.mnli", "bart.large.cnn", "bart_xsum/model.pt"]
_UpperCAmelCase	:     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()
_UpperCAmelCase	:     Dict		       = logging.get_logger(__name__)

_UpperCAmelCase	:     Dict		       = " Hello world! cécé herlolip"

_UpperCAmelCase	:     List[Any]		       = [
    ("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__   ):







							'''simple docstring'''






							snake_case_           =					[
							    'encoder.version',
							    'decoder.version',
							    'model.encoder.version',
							    'model.decoder.version',
							    '_float_tensor',
							]
							for k in ignore_keys:
														state_dict.pop(__lowerCAmelCase     ,							__lowerCAmelCase   )





def       __lowerCamelCase			(				UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__   ):







							'''simple docstring'''






							snake_case_           =					dct.pop(__lowerCAmelCase   )
							snake_case_           =					val





def       __lowerCamelCase			(				UpperCamelCase__   ):







							'''simple docstring'''






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





def       __lowerCamelCase			(				UpperCamelCase__   ):







							'''simple docstring'''






							snake_case_       ,					snake_case_           =					emb.weight.shape
							snake_case_           =					nn.Linear(__lowerCAmelCase     ,							__lowerCAmelCase     ,							bias=__lowerCAmelCase   )
							snake_case_           =					emb.weight.data
							return lin_layer





@torch.no_grad()
def       __lowerCamelCase			(				UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__=None   ):







							'''simple docstring'''






							if not os.path.exists(__lowerCAmelCase   ):
														snake_case_           =					torch.hub.load('pytorch/fairseq'     ,							__lowerCAmelCase   ).eval()
							else:
														snake_case_           =					load_xsum_checkpoint(__lowerCAmelCase   )

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

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


if __name__ == "__main__":
				_UpperCAmelCase	:     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"""
				)
				_UpperCAmelCase	:     Optional[Any]		       = parser.parse_args()
				convert_bart_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, hf_checkpoint_name=args.hf_config)







def       __lowerCamelCase			(				UpperCamelCase__     ,							UpperCamelCase__   ):







							'''simple docstring'''






							snake_case_           =					len(UpperCamelCase__   )
							snake_case_           =					len(UpperCamelCase__   )
							snake_case_           =					(
							    first_str_length if first_str_length > second_str_length else second_str_length
							)
							snake_case_           =					[]
							for char_count in range(UpperCamelCase__   ):
														if char_count < first_str_length:
																					output_list.append(first_str[char_count]   )
														if char_count < second_str_length:
																					output_list.append(second_str[char_count]   )
							return "".join(UpperCamelCase__   )


if __name__ == "__main__":
					print(alternative_string_arrange("""AB""", """XYZ"""), end=""" """)




