
from __future__ import annotations

import matplotlib.pyplot as plt  # type: ignore
import numpy

# initial triangle of Koch snowflake
A		: Union[str, Any]								=     numpy.array([0, 0])
A		: List[str]								=     numpy.array([0.5, 0.8_66_02_54])
A		: Any								=     numpy.array([1, 0])
A		: Optional[Any]								=     [VECTOR_1, VECTOR_2, VECTOR_3, VECTOR_1]




def 	UpperCamelCase__  (     SCREAMING_SNAKE_CASE_	:	list[numpy.ndarray]		,   SCREAMING_SNAKE_CASE_	:	int       )  ->     list[numpy.ndarray]:
						_lowercase					       =					initial_vectors
						for _ in range(SCREAMING_SNAKE_CASE_       ):
												_lowercase					       =					iteration_step(SCREAMING_SNAKE_CASE_       )
						return vectors




def 	UpperCamelCase__  (     SCREAMING_SNAKE_CASE_	:	list[numpy.ndarray]       )  ->     list[numpy.ndarray]:
						_lowercase					       =					[]
						for i, start_vector in enumerate(vectors[:-1]       ):
												_lowercase					       =					vectors[i + 1]
												new_vectors.append(SCREAMING_SNAKE_CASE_       )
												_lowercase					       =					end_vector - start_vector
												new_vectors.append(start_vector + difference_vector / 3       )
												new_vectors.append(
												    start_vector + difference_vector / 3 + rotate(difference_vector / 3		,   60       )       )
												new_vectors.append(start_vector + difference_vector * 2 / 3       )
						new_vectors.append(vectors[-1]       )
						return new_vectors




def 	UpperCamelCase__  (     SCREAMING_SNAKE_CASE_	:	numpy.ndarray		,   SCREAMING_SNAKE_CASE_	:	float       )  ->     numpy.ndarray:
						_lowercase					       =					numpy.radians(SCREAMING_SNAKE_CASE_       )
						_lowercase       ,						_lowercase					       =					numpy.cos(SCREAMING_SNAKE_CASE_       ), numpy.sin(SCREAMING_SNAKE_CASE_       )
						_lowercase					       =					numpy.array(((c, -s), (s, c))       )
						return numpy.dot(SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_       )




def 	UpperCamelCase__  (     SCREAMING_SNAKE_CASE_	:	list[numpy.ndarray]       )  ->     None:
						_lowercase					       =					plt.gca()
						axes.set_aspect("""equal"""       )

						# matplotlib.pyplot.plot takes a list of all x-coordinates and a list of all
						# y-coordinates as inputs, which are constructed from the vector-list using
						# zip()
						_lowercase       ,						_lowercase					       =					zip(*SCREAMING_SNAKE_CASE_       )
						plt.plot(SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_       )
						plt.show()


if __name__ == "__main__":
		import doctest

		doctest.testmod()

		A		: Union[str, Any]								=     iterate(INITIAL_VECTORS, 5)
		plot(processed_vectors)

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		: int								=     ['''bart.large''', '''bart.large.mnli''', '''bart.large.cnn''', '''bart_xsum/model.pt''']
A		: 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()
A		: Optional[int]								=     logging.get_logger(__name__)

A		: int								=     ''' Hello world! cécé herlolip'''

A		: 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 	UpperCamelCase__  (     SCREAMING_SNAKE_CASE_	:	Union[str, Any]       )  ->     Tuple:
						_lowercase					       =					[
						    """encoder.version""",
						    """decoder.version""",
						    """model.encoder.version""",
						    """model.decoder.version""",
						    """_float_tensor""",
						]
						for k in ignore_keys:
												state_dict.pop(SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_       )




def 	UpperCamelCase__  (     SCREAMING_SNAKE_CASE_	:	Union[str, Any]		,   SCREAMING_SNAKE_CASE_	:	Optional[int]		,   SCREAMING_SNAKE_CASE_	:	List[Any]       )  ->     List[str]:
						_lowercase					       =					dct.pop(SCREAMING_SNAKE_CASE_       )
						_lowercase					       =					val




def 	UpperCamelCase__  (     SCREAMING_SNAKE_CASE_	:	Any       )  ->     Any:
						_lowercase					       =					torch.load(SCREAMING_SNAKE_CASE_		,   map_location="""cpu"""       )
						_lowercase					       =					torch.hub.load("""pytorch/fairseq"""		,   """bart.large.cnn"""       ).eval()
						hub_interface.model.load_state_dict(sd["""model"""]       )
						return hub_interface




def 	UpperCamelCase__  (     SCREAMING_SNAKE_CASE_	:	List[str]       )  ->     Optional[int]:
						_lowercase       ,						_lowercase					       =					emb.weight.shape
						_lowercase					       =					nn.Linear(SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_		,   bias=SCREAMING_SNAKE_CASE_       )
						_lowercase					       =					emb.weight.data
						return lin_layer




@torch.no_grad()
def 	UpperCamelCase__  (     SCREAMING_SNAKE_CASE_	:	Any		,   SCREAMING_SNAKE_CASE_	:	Tuple		,   SCREAMING_SNAKE_CASE_	:	int=None       )  ->     int:
						if not os.path.exists(SCREAMING_SNAKE_CASE_       ):
												_lowercase					       =					torch.hub.load("""pytorch/fairseq"""		,   SCREAMING_SNAKE_CASE_       ).eval()
						else:
												_lowercase					       =					load_xsum_checkpoint(SCREAMING_SNAKE_CASE_       )

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

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


if __name__ == "__main__":
		A		: 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'''
		)
		A		: Any								=     parser.parse_args()
		convert_bart_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, hf_checkpoint_name=args.hf_config)