



'''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


UpperCamelCase__:   Optional[Any]								=			["bart.large", "bart.large.mnli", "bart.large.cnn", "bart_xsum/model.pt"]
UpperCamelCase__:   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()
UpperCamelCase__:   Optional[Any]								=			logging.get_logger(__name__)

UpperCamelCase__:   Optional[int]								=			" Hello world! cécé herlolip"

UpperCamelCase__:   int								=			[
    ("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  snake_case_     (					_lowerCAmelCase  :						Tuple  )      ->   List[Any]:
							UpperCAmelCase :       Union[str, Any] 							=		[
							    '''encoder.version''',
							    '''decoder.version''',
							    '''model.encoder.version''',
							    '''model.decoder.version''',
							    '''_float_tensor''',
							]
							for k in ignore_keys:
														state_dict.pop(_lowerCAmelCase   ,      _lowerCAmelCase  )


def  snake_case_     (					_lowerCAmelCase  :						Optional[int]   ,      _lowerCAmelCase  :						Optional[int]   ,      _lowerCAmelCase  :						Dict  )      ->   Tuple:
							UpperCAmelCase :       Tuple 							=		dct.pop(_lowerCAmelCase  )
							UpperCAmelCase :       Dict 							=		val


def  snake_case_     (					_lowerCAmelCase  :						Union[str, Any]  )      ->   Union[str, Any]:
							UpperCAmelCase :       Dict 							=		torch.load(_lowerCAmelCase   ,      map_location='''cpu'''  )
							UpperCAmelCase :       Any 							=		torch.hub.load('''pytorch/fairseq'''   ,      '''bart.large.cnn'''  ).eval()
							hub_interface.model.load_state_dict(sd['''model''']  )
							return hub_interface


def  snake_case_     (					_lowerCAmelCase  :						Any  )      ->   Union[str, Any]:
							UpperCAmelCase							,		UpperCAmelCase :       List[str] 							=		emb.weight.shape
							UpperCAmelCase :       Dict 							=		nn.Linear(_lowerCAmelCase   ,      _lowerCAmelCase   ,      bias=_lowerCAmelCase  )
							UpperCAmelCase :       List[str] 							=		emb.weight.data
							return lin_layer


@torch.no_grad()
def  snake_case_     (					_lowerCAmelCase  :						List[Any]   ,      _lowerCAmelCase  :						Dict   ,      _lowerCAmelCase  :						Optional[Any]=None  )      ->   List[Any]:
							if not os.path.exists(_lowerCAmelCase  ):
														UpperCAmelCase :       Union[str, Any] 							=		torch.hub.load('''pytorch/fairseq'''   ,      _lowerCAmelCase  ).eval()
							else:
														UpperCAmelCase :       List[Any] 							=		load_xsum_checkpoint(_lowerCAmelCase  )

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










'''simple docstring'''






from math import isclose, sqrt


def  snake_case_     (					_lowerCAmelCase  :						float   ,      _lowerCAmelCase  :						float   ,      _lowerCAmelCase  :						float  )      ->   tuple[float, float, float]:
							UpperCAmelCase :       Optional[int] 							=		point_y / 4 / point_x
							UpperCAmelCase :       str 							=		2 * normal_gradient / (1 + normal_gradient * normal_gradient)
							UpperCAmelCase :       Any 							=		(1 - normal_gradient * normal_gradient) / (
							    1 + normal_gradient * normal_gradient
							)
							UpperCAmelCase :       Union[str, Any] 							=		(sa - ca * incoming_gradient) / (ca + sa * incoming_gradient)

							# to find the next point, solve the simultaeneous equations:
							# y^2 + 4x^2 = 100
							# y - b = m * (x - a)
							# ==> A x^2 + B x + C = 0
							UpperCAmelCase :       Union[str, Any] 							=		outgoing_gradient**2 + 4
							UpperCAmelCase :       Dict 							=		2 * outgoing_gradient * (point_y - outgoing_gradient * point_x)
							UpperCAmelCase :       List[Any] 							=		(point_y - outgoing_gradient * point_x) ** 2 - 100

							UpperCAmelCase :       List[str] 							=		(
							    -linear_term - sqrt(linear_term**2 - 4 * quadratic_term * constant_term  )
							) / (2 * quadratic_term)
							UpperCAmelCase :       Optional[int] 							=		(
							    -linear_term + sqrt(linear_term**2 - 4 * quadratic_term * constant_term  )
							) / (2 * quadratic_term)

							# two solutions, one of which is our input point
							UpperCAmelCase :       Optional[Any] 							=		x_minus if isclose(_lowerCAmelCase   ,      _lowerCAmelCase  ) else x_plus
							UpperCAmelCase :       Union[str, Any] 							=		point_y + outgoing_gradient * (next_x - point_x)

							return next_x, next_y, outgoing_gradient


def  snake_case_     (					_lowerCAmelCase  :						float = 1.4   ,      _lowerCAmelCase  :						float = -9.6  )      ->   int:
							UpperCAmelCase :       int 							=		0
							UpperCAmelCase :       float 							=		first_x_coord
							UpperCAmelCase :       float 							=		first_y_coord
							UpperCAmelCase :       float 							=		(1_0.1 - point_y) / (0.0 - point_x)

							while not (-0.0_1 <= point_x <= 0.0_1 and point_y > 0):
														UpperCAmelCase							,		UpperCAmelCase							,		UpperCAmelCase :       str 							=		next_point(_lowerCAmelCase   ,      _lowerCAmelCase   ,      _lowerCAmelCase  )
														num_reflections += 1

							return num_reflections


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





