
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						:      Optional[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						:      Optional[Any]									= logging.get_logger(__name__)

lowercase						:      Any									= ' Hello world! cécé herlolip'

lowercase						:      Optional[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 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    Union[str, Any])      ->					Dict:






							'''simple docstring'''





							__UpperCamelCase			:     List[str]         =      [
							    "encoder.version",
							    "decoder.version",
							    "model.encoder.version",
							    "model.decoder.version",
							    "_float_tensor",
							]
							for k in ignore_keys:
														state_dict.pop(_lowerCamelCase		,						_lowerCamelCase)




def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    Optional[int]		,						_lowerCamelCase				:    Any		,						_lowerCamelCase				:    Union[str, Any])      ->					Dict:






							'''simple docstring'''





							__UpperCamelCase			:     Optional[int]         =      dct.pop(_lowerCamelCase)
							__UpperCamelCase			:     Any         =      val




def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    List[Any])      ->					Any:






							'''simple docstring'''





							__UpperCamelCase			:     Any         =      torch.load(_lowerCamelCase		,						map_location="cpu")
							__UpperCamelCase			:     List[str]         =      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				:    Optional[int])      ->					Optional[int]:






							'''simple docstring'''





							__UpperCamelCase			:     Optional[int]         =      emb.weight.shape
							__UpperCamelCase			:     Tuple         =      nn.Linear(_lowerCamelCase		,						_lowerCamelCase		,						bias=_lowerCamelCase)
							__UpperCamelCase			:     Optional[Any]         =      emb.weight.data
							return lin_layer




@torch.no_grad()
def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    Any		,						_lowerCamelCase				:    int		,						_lowerCamelCase				:    Optional[int]=None)      ->					Dict:






							'''simple docstring'''





							if not os.path.exists(_lowerCamelCase):
														__UpperCamelCase			:     List[str]         =      torch.hub.load("pytorch/fairseq"		,						_lowerCamelCase).eval()
							else:
														__UpperCamelCase			:     int         =      load_xsum_checkpoint(_lowerCamelCase)

							bart.model.upgrade_state_dict(bart.model.state_dict())
							if hf_checkpoint_name is None:
														__UpperCamelCase			:     int         =      checkpoint_path.replace("."		,						"-")
							__UpperCamelCase			:     Union[str, Any]         =      BartConfig.from_pretrained(_lowerCamelCase)
							__UpperCamelCase			:     Optional[Any]         =      bart.encode(_lowerCamelCase).unsqueeze(0)
							__UpperCamelCase			:     Union[str, Any]         =      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":
														__UpperCamelCase			:     str         =      bart.state_dict()
														remove_ignore_keys_(_lowerCamelCase)
														__UpperCamelCase			:     Optional[Any]         =      state_dict["model.decoder.embed_tokens.weight"]
														for src, dest in mnli_rename_keys:
																					rename_key(_lowerCamelCase		,						_lowerCamelCase		,						_lowerCamelCase)
														__UpperCamelCase			:     Any         =      BartForSequenceClassification(_lowerCamelCase).eval()
														model.load_state_dict(_lowerCamelCase)
														__UpperCamelCase			:     Dict         =      bart.predict("mnli"		,						_lowerCamelCase		,						return_logits=_lowerCamelCase)
														__UpperCamelCase			:     Tuple         =      model(_lowerCamelCase)[0]  # logits
							else:  # no classification heads to worry about
														__UpperCamelCase			:     Optional[Any]         =      bart.model.state_dict()
														remove_ignore_keys_(_lowerCamelCase)
														__UpperCamelCase			:     List[Any]         =      state_dict["decoder.embed_tokens.weight"]
														__UpperCamelCase			:     Union[str, Any]         =      bart.extract_features(_lowerCamelCase)
														if hf_checkpoint_name == "facebook/bart-large":
																					__UpperCamelCase			:     Union[str, Any]         =      BartModel(_lowerCamelCase).eval()
																					model.load_state_dict(_lowerCamelCase)
																					__UpperCamelCase			:     int         =      model(_lowerCamelCase).model[0]
														else:
																					__UpperCamelCase			:     int         =      BartForConditionalGeneration(_lowerCamelCase).eval()  # an existing summarization ckpt
																					model.model.load_state_dict(_lowerCamelCase)
																					if hasattr(_lowerCamelCase		,						"lm_head"):
																												__UpperCamelCase			:     List[str]         =      make_linear_from_emb(model.model.shared)
																					__UpperCamelCase			:     Any         =      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__":
		lowercase						:      List[Any]									= 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						:      List[Any]									= parser.parse_args()
		convert_bart_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, hf_checkpoint_name=args.hf_config)

def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    int = 200)      ->					int:






							'''simple docstring'''





							__UpperCamelCase			:     Any         =      [1, 2, 5, 10, 20, 50, 100, 200]
							__UpperCamelCase			:     Any         =      [0] * (pence + 1)
							__UpperCamelCase			:     Union[str, Any]         =      1  # base case: 1 way to make 0 pence

							for coin in coins:
														for i in range(_lowerCamelCase		,						pence + 1		,						1):
																					number_of_ways[i] += number_of_ways[i - coin]
							return number_of_ways[pence]


if __name__ == "__main__":
		assert solution(200) == 73682