





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

UpperCAmelCase_      							=     """ Hello world! cécé herlolip"""

UpperCAmelCase_      							=     [
    ("""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__			:       List[str]						)			-> Optional[int]:



					'''simple docstring'''




					_snake_case									=				[
					    '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__			:       Optional[int]       ,    UpperCamelCase__			:       List[str]       ,    UpperCamelCase__			:       str						)			-> Union[str, Any]:



					'''simple docstring'''




					_snake_case									=				dct.pop(UpperCamelCase__						)
					_snake_case									=				val





def 						lowerCamelCase__     (     UpperCamelCase__			:       Any						)			-> List[str]:



					'''simple docstring'''




					_snake_case									=				torch.load(UpperCamelCase__       ,    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__			:       str						)			-> Optional[Any]:



					'''simple docstring'''




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





@torch.no_grad()
def 						lowerCamelCase__     (     UpperCamelCase__			:       Optional[Any]       ,    UpperCamelCase__			:       Tuple       ,    UpperCamelCase__			:       List[Any]=None						)			-> List[Any]:



					'''simple docstring'''




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

					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(UpperCamelCase__						)
					_snake_case									=				bart.encode(UpperCamelCase__						).unsqueeze(0						)
					_snake_case									=				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":
										_snake_case									=				bart.state_dict()
										remove_ignore_keys_(UpperCamelCase__						)
										_snake_case									=				state_dict['model.decoder.embed_tokens.weight']
										for src, dest in mnli_rename_keys:
															rename_key(UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__						)
										_snake_case									=				BartForSequenceClassification(UpperCamelCase__						).eval()
										model.load_state_dict(UpperCamelCase__						)
										_snake_case									=				bart.predict('mnli'       ,    UpperCamelCase__       ,    return_logits=UpperCamelCase__						)
										_snake_case									=				model(UpperCamelCase__						)[0]  # logits
					else:  # no classification heads to worry about
										_snake_case									=				bart.model.state_dict()
										remove_ignore_keys_(UpperCamelCase__						)
										_snake_case									=				state_dict['decoder.embed_tokens.weight']
										_snake_case									=				bart.extract_features(UpperCamelCase__						)
										if hf_checkpoint_name == "facebook/bart-large":
															_snake_case									=				BartModel(UpperCamelCase__						).eval()
															model.load_state_dict(UpperCamelCase__						)
															_snake_case									=				model(UpperCamelCase__						).model[0]
										else:
															_snake_case									=				BartForConditionalGeneration(UpperCamelCase__						).eval()  # an existing summarization ckpt
															model.model.load_state_dict(UpperCamelCase__						)
															if hasattr(UpperCamelCase__       ,    'lm_head'						):
																				_snake_case									=				make_linear_from_emb(model.model.shared						)
															_snake_case									=				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__":
					UpperCAmelCase_      							=     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_      							=     parser.parse_args()
					convert_bart_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, hf_checkpoint_name=args.hf_config)










UpperCAmelCase_      							=     range(2, 20 + 1)
UpperCAmelCase_      							=     [10**k for k in range(ks[-1] + 1)]
UpperCAmelCase_      							=     {}





def 						lowerCamelCase__     (     UpperCamelCase__			:       Optional[Any]       ,    UpperCamelCase__			:       List[Any]       ,    UpperCamelCase__			:       Union[str, Any]       ,    UpperCamelCase__			:       str						)			-> List[str]:



					'''simple docstring'''




					_snake_case									=				sum(a_i[j] for j in range(UpperCamelCase__       ,    len(UpperCamelCase__						)						)						)
					_snake_case									=				sum(a_i[j] * base[j] for j in range(min(len(UpperCamelCase__						)       ,    UpperCamelCase__						)						)						)

					_snake_case							,     _snake_case									=				0, 0
					_snake_case									=				n - i

					_snake_case									=				memo.get(UpperCamelCase__						)

					if sub_memo is not None:
										_snake_case									=				sub_memo.get(UpperCamelCase__						)

										if jumps is not None and len(UpperCamelCase__						) > 0:
															# find and make the largest jump without going over
															_snake_case									=				-1
															for _k in range(len(UpperCamelCase__						) - 1       ,    -1       ,    -1						):
																				if jumps[_k][2] <= k and jumps[_k][1] <= max_dn:
																									_snake_case									=				_k
																									break

															if max_jump >= 0:
																				_snake_case							,     _snake_case							,     _snake_case									=				jumps[max_jump]
																				# since the difference between jumps is cached, add c
																				_snake_case									=				diff + c
																				for j in range(min(UpperCamelCase__       ,    len(UpperCamelCase__						)						)						):
																									_snake_case							,     _snake_case									=				divmod(UpperCamelCase__       ,    10						)
																				if new_c > 0:
																									add(UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__						)

										else:
															_snake_case									=				[]
					else:
										_snake_case									=				{c: []}
										_snake_case									=				sub_memo

					if dn >= max_dn or c + diff >= base[k]:
										return diff, dn

					if k > ks[0]:
										while True:
															# keep doing smaller jumps
															_snake_case							,     _snake_case									=				next_term(UpperCamelCase__       ,    k - 1       ,    i + dn       ,    UpperCamelCase__						)
															diff += _diff
															dn += terms_jumped

															if dn >= max_dn or c + diff >= base[k]:
																				break
					else:
										# would be too small a jump, just compute sequential terms instead
										_snake_case							,     _snake_case									=				compute(UpperCamelCase__       ,    UpperCamelCase__       ,    i + dn       ,    UpperCamelCase__						)
										diff += _diff
										dn += terms_jumped

					_snake_case									=				sub_memo[c]

					# keep jumps sorted by # of terms skipped
					_snake_case									=				0
					while j < len(UpperCamelCase__						):
										if jumps[j][1] > dn:
															break
										j += 1

					# cache the jump for this value digitsum(b) and c
					sub_memo[c].insert(UpperCamelCase__       ,    (diff, dn, k)						)
					return (diff, dn)





def 						lowerCamelCase__     (     UpperCamelCase__			:       str       ,    UpperCamelCase__			:       str       ,    UpperCamelCase__			:       List[str]       ,    UpperCamelCase__			:       Union[str, Any]						)			-> Optional[Any]:



					'''simple docstring'''




					if i >= n:
										return 0, i
					if k > len(UpperCamelCase__						):
										a_i.extend([0 for _ in range(k - len(UpperCamelCase__						)						)]						)

					# note: a_i -> b * 10^k + c
					# ds_b -> digitsum(b)
					# ds_c -> digitsum(c)
					_snake_case									=				i
					_snake_case							,     _snake_case							,     _snake_case									=				0, 0, 0
					for j in range(len(UpperCamelCase__						)						):
										if j >= k:
															ds_b += a_i[j]
										else:
															ds_c += a_i[j]

					while i < n:
										i += 1
										_snake_case									=				ds_c + ds_b
										diff += addend
										_snake_case									=				0
										for j in range(UpperCamelCase__						):
															_snake_case									=				a_i[j] + addend
															_snake_case							,     _snake_case									=				divmod(UpperCamelCase__       ,    10						)

															ds_c += a_i[j]

										if addend > 0:
															break

					if addend > 0:
										add(UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__						)
					return diff, i - start_i





def 						lowerCamelCase__     (     UpperCamelCase__			:       Any       ,    UpperCamelCase__			:       Union[str, Any]       ,    UpperCamelCase__			:       Optional[Any]						)			-> Any:



					'''simple docstring'''




					for j in range(UpperCamelCase__       ,    len(UpperCamelCase__						)						):
										_snake_case									=				digits[j] + addend
										if s >= 10:
															_snake_case							,     _snake_case									=				divmod(UpperCamelCase__       ,    10						)
															_snake_case									=				addend // 10 + quotient
										else:
															_snake_case									=				s
															_snake_case									=				addend // 10

										if addend == 0:
															break

					while addend > 0:
										_snake_case							,     _snake_case									=				divmod(UpperCamelCase__       ,    10						)
										digits.append(UpperCamelCase__						)





def 						lowerCamelCase__     (     UpperCamelCase__			:       int = 10**15						)			-> int:



					'''simple docstring'''




					_snake_case									=				[1]
					_snake_case									=				1
					_snake_case									=				0
					while True:
										_snake_case							,     _snake_case									=				next_term(UpperCamelCase__       ,    20       ,    i + dn       ,    UpperCamelCase__						)
										dn += terms_jumped
										if dn == n - i:
															break

					_snake_case									=				0
					for j in range(len(UpperCamelCase__						)						):
										a_n += digits[j] * 10**j
					return a_n


if __name__ == "__main__":
					print(F"{solution() = }")



