


"""simple docstring"""







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__				:							List[str]						=     ['''bart.large''', '''bart.large.mnli''', '''bart.large.cnn''', '''bart_xsum/model.pt''']
a__				:							str						=     {'''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__				:							List[str]						=     logging.get_logger(__name__)

a__				:							Any						=     ''' Hello world! cécé herlolip'''

a__				:							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    UpperCAmelCase__       (lowerCAmelCase_   ):




					'''simple docstring'''


					__SCREAMING_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    UpperCAmelCase__       (lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_   ):




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE						   =				dct.pop(lowerCAmelCase_   )
					__SCREAMING_SNAKE_CASE						   =				val




def    UpperCAmelCase__       (lowerCAmelCase_   ):




					'''simple docstring'''


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




def    UpperCAmelCase__       (lowerCAmelCase_   ):




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE   ,  __SCREAMING_SNAKE_CASE						   =				emb.weight.shape
					__SCREAMING_SNAKE_CASE						   =				nn.Linear(lowerCAmelCase_  ,						lowerCAmelCase_  ,						bias=lowerCAmelCase_   )
					__SCREAMING_SNAKE_CASE						   =				emb.weight.data
					return lin_layer




@torch.no_grad()
def    UpperCAmelCase__       (lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_=None   ):




					'''simple docstring'''


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

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




"""simple docstring"""







from PIL import Image




def    UpperCAmelCase__       (lowerCAmelCase_   ):




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE   ,  __SCREAMING_SNAKE_CASE						   =				image.size
					__SCREAMING_SNAKE_CASE						   =				0
					__SCREAMING_SNAKE_CASE						   =				image.load()
					for i in range(lowerCAmelCase_   ):
										for j in range(lowerCAmelCase_   ):
															__SCREAMING_SNAKE_CASE						   =				pixels[j, i]
															mean += pixel
					mean //= width * height

					for j in range(lowerCAmelCase_   ):
										for i in range(lowerCAmelCase_   ):
															__SCREAMING_SNAKE_CASE						   =				255 if pixels[i, j] > mean else 0
					return image


if __name__ == "__main__":
		a__				:							List[str]						=     mean_threshold(Image.open('''path_to_image''').convert('''L'''))
		image.save('''output_image_path''')
