

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[Any]:
	__lowerCamelCase				:				Union[str, Any]					       = [
	    'encoder.version',
	    'decoder.version',
	    'model.encoder.version',
	    'model.decoder.version',
	    '_float_tensor',
	]
	for k in ignore_keys:
		state_dict.pop(a__	, a__			)






def    SCREAMING_SNAKE_CASE__       (  lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__			)  ->     Optional[Any]:
	__lowerCamelCase				:				List[str]					       = dct.pop(a__			)
	__lowerCamelCase				:				Dict					       = val






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






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

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

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


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 __future__ import annotations

import time
from collections.abc import Sequence
from random import randint

from matplotlib import pyplot as plt


def     __lowerCAmelCase			(			a__						,    a__						,    a__  )       ->  tuple[int | None, int | None, float]:
				if not arr:
								return None, None, 0
				if low == high:
								return low, high, arr[low]

				__a										=    (low + high) // 2
				__a							,     __a							,     __a										=    max_subarray(a__						,    a__						,    a__  )
				__a							,     __a							,     __a										=    max_subarray(a__						,    mid + 1						,    a__  )
				__a							,     __a							,     __a										=    max_cross_sum(a__						,    a__						,    a__						,    a__  )
				if left_sum >= right_sum and left_sum >= cross_sum:
								return left_low, left_high, left_sum
				elif right_sum >= left_sum and right_sum >= cross_sum:
								return right_low, right_high, right_sum
				return cross_left, cross_right, cross_sum


def     __lowerCAmelCase			(			a__						,    a__						,    a__						,    a__  )       ->  tuple[int, int, float]:
				__a							,     __a										=    float('''-inf'''  ), -1
				__a							,     __a										=    float('''-inf'''  ), -1

				__a										=    0
				for i in range(a__						,    low - 1						,    -1  ):
								summ += arr[i]
								if summ > left_sum:
												__a										=    summ
												__a										=    i

				__a										=    0
				for i in range(mid + 1						,    high + 1  ):
								summ += arr[i]
								if summ > right_sum:
												__a										=    summ
												__a										=    i

				return max_left, max_right, (left_sum + right_sum)


def     __lowerCAmelCase			(			a__  )       ->  float:
				__a										=    [randint(1						,    a__  ) for _ in range(a__  )]
				__a										=    time.time()
				max_subarray(a__						,    0						,    input_size - 1  )
				__a										=    time.time()
				return end - start


def     __lowerCAmelCase			(			)       ->  None:
				__a										=    [10, 100, 1000, 1_0000, 5_0000, 10_0000, 20_0000, 30_0000, 40_0000, 50_0000]
				__a										=    [time_max_subarray(a__  ) for input_size in input_sizes]
				print('''No of Inputs\t\tTime Taken'''  )
				for input_size, runtime in zip(a__						,    a__  ):
								print(a__						,    '''\t\t'''						,    a__  )
				plt.plot(a__						,    a__  )
				plt.xlabel('''Number of Inputs'''  )
				plt.ylabel('''Time taken in seconds'''  )
				plt.show()


if __name__ == "__main__":
					from doctest import testmod

					testmod()