





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[int]							=	{"""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     :     Union[str, Any]							=	logging.get_logger(__name__)

UpperCAmelCase     :     List[str]							=	""" 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 					_A	(		SCREAMING_SNAKE_CASE					:       List[Any]						):





  """simple docstring"""



  a__     : Optional[int]  =[
      "encoder.version",
      "decoder.version",
      "model.encoder.version",
      "model.decoder.version",
      "_float_tensor",
  ]
  for k in ignore_keys:
    state_dict.pop(__snake_case	,	__snake_case						)
def 					_A	(		SCREAMING_SNAKE_CASE					:       Optional[Any]	,	SCREAMING_SNAKE_CASE					:       Union[str, Any]	,	SCREAMING_SNAKE_CASE					:       Any						):





  """simple docstring"""



  a__     : Any  =dct.pop(__snake_case						)
  a__     : Dict  =val
def 					_A	(		SCREAMING_SNAKE_CASE					:       Dict						):





  """simple docstring"""



  a__     : str  =torch.load(__snake_case	,	map_location="cpu"						)
  a__     : Tuple  =torch.hub.load("pytorch/fairseq"	,	"bart.large.cnn"						).eval()
  hub_interface.model.load_state_dict(sd["model"]						)
  return hub_interface
def 					_A	(		SCREAMING_SNAKE_CASE					:       Union[str, Any]						):





  """simple docstring"""



  a__ ,							a__     : List[Any]  =emb.weight.shape
  a__     : Any  =nn.Linear(__snake_case	,	__snake_case	,	bias=__snake_case						)
  a__     : List[str]  =emb.weight.data
  return lin_layer
@torch.no_grad()
def 					_A	(		SCREAMING_SNAKE_CASE					:       Union[str, Any]	,	SCREAMING_SNAKE_CASE					:       Any	,	SCREAMING_SNAKE_CASE					:       Tuple=None						):





  """simple docstring"""



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

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

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


if __name__ == "__main__":
     UpperCAmelCase     :     Union[str, 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"""
     )
     UpperCAmelCase     :     List[str]							=	parser.parse_args()
     convert_bart_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, hf_checkpoint_name=args.hf_config)













from __future__ import annotations







class     __lowerCAmelCase	:







      def __init__(   self		,				lowerCAmelCase__							)       ->						str:



        '''simple docstring'''




        a__     : int  =TypeError(
            "Matrices must be formed from a list of zero or more lists containing at "
            "least one and the same number of values, each of which must be of type "
            "int or float."							)
        if len(lowerCAmelCase__							) != 0:
          a__     : List[str]  =len(rows[0]							)
          if cols == 0:
            raise error
          for row in rows:
            if len(lowerCAmelCase__							) != cols:
              raise error
            for value in row:
              if not isinstance(lowerCAmelCase__		,				(int, float)							):
                raise error
          a__     : List[Any]  =rows
        else:
          a__     : str  =[]







      def        _lowercase  (   self							)       ->						list[list[int]]:



        '''simple docstring'''




        return [[row[i] for row in self.rows] for i in range(len(self.rows[0]							)							)]







      @property
      def        _lowercase  (   self							)       ->						int:



        '''simple docstring'''




        return len(self.rows							)







      @property
      def        _lowercase  (   self							)       ->						int:



        '''simple docstring'''




        return len(self.rows[0]							)







      @property
      def        _lowercase  (   self							)       ->						tuple[int, int]:



        '''simple docstring'''




        return (self.num_rows, self.num_columns)







      @property
      def        _lowercase  (   self							)       ->						bool:



        '''simple docstring'''




        return self.order[0] == self.order[1]







      def        _lowercase  (   self							)       ->						Matrix:



        '''simple docstring'''




        a__     : str  =[
            [0 if column_num != row_num else 1 for column_num in range(self.num_rows							)]
            for row_num in range(self.num_rows							)
        ]
        return Matrix(lowerCAmelCase__							)







      def        _lowercase  (   self							)       ->						int:



        '''simple docstring'''




        if not self.is_square:
          return 0
        if self.order == (0, 0):
          return 1
        if self.order == (1, 1):
          return int(self.rows[0][0]							)
        if self.order == (2, 2):
          return int(
              (self.rows[0][0] * self.rows[1][1])
              - (self.rows[0][1] * self.rows[1][0])							)
        else:
          return sum(
              self.rows[0][column] * self.cofactors().rows[0][column]
              for column in range(self.num_columns							)							)







      def        _lowercase  (   self							)       ->						bool:



        '''simple docstring'''




        return bool(self.determinant()							)







      def        _lowercase  (   self		,				lowerCAmelCase__		,				lowerCAmelCase__							)       ->						int:



        '''simple docstring'''




        a__     : List[str]  =[
            [
                self.rows[other_row][other_column]
                for other_column in range(self.num_columns							)
                if other_column != column
            ]
            for other_row in range(self.num_rows							)
            if other_row != row
        ]
        return Matrix(lowerCAmelCase__							).determinant()







      def        _lowercase  (   self		,				lowerCAmelCase__		,				lowerCAmelCase__							)       ->						int:



        '''simple docstring'''




        if (row + column) % 2 == 0:
          return self.get_minor(lowerCAmelCase__		,				lowerCAmelCase__							)
        return -1 * self.get_minor(lowerCAmelCase__		,				lowerCAmelCase__							)







      def        _lowercase  (   self							)       ->						Matrix:



        '''simple docstring'''




        return Matrix(
            [
                [self.get_minor(lowerCAmelCase__		,				lowerCAmelCase__							) for column in range(self.num_columns							)]
                for row in range(self.num_rows							)
            ]							)







      def        _lowercase  (   self							)       ->						Matrix:



        '''simple docstring'''




        return Matrix(
            [
                [
                    self.minors().rows[row][column]
                    if (row + column) % 2 == 0
                    else self.minors().rows[row][column] * -1
                    for column in range(self.minors().num_columns							)
                ]
                for row in range(self.minors().num_rows							)
            ]							)







      def        _lowercase  (   self							)       ->						Matrix:



        '''simple docstring'''




        a__     : Dict  =[
            [self.cofactors().rows[column][row] for column in range(self.num_columns							)]
            for row in range(self.num_rows							)
        ]
        return Matrix(lowerCAmelCase__							)







      def        _lowercase  (   self							)       ->						Matrix:



        '''simple docstring'''




        a__     : Union[str, Any]  =self.determinant()
        if not determinant:
          raise TypeError("Only matrices with a non-zero determinant have an inverse"							)
        return self.adjugate() * (1 / determinant)







      def __repr__(   self							)       ->						str:



        '''simple docstring'''




        return str(self.rows							)







      def __str__(   self							)       ->						str:



        '''simple docstring'''




        if self.num_rows == 0:
          return "[]"
        if self.num_rows == 1:
          return "[[" + ". ".join(str(self.rows[0]							)							) + "]]"
        return (
            "["
            + "\n ".join(
                [
                    "[" + ". ".join([str(lowerCAmelCase__							) for value in row]							) + ".]"
                    for row in self.rows
                ]							)
            + "]"
        )







      def        _lowercase  (   self		,				lowerCAmelCase__		,				lowerCAmelCase__ = None							)       ->						None:



        '''simple docstring'''




        a__     : List[str]  =TypeError("Row must be a list containing all ints and/or floats"							)
        if not isinstance(lowerCAmelCase__		,				lowerCAmelCase__							):
          raise type_error
        for value in row:
          if not isinstance(lowerCAmelCase__		,				(int, float)							):
            raise type_error
        if len(lowerCAmelCase__							) != self.num_columns:
          raise ValueError(
              "Row must be equal in length to the other rows in the matrix"							)
        if position is None:
          self.rows.append(lowerCAmelCase__							)
        else:
          a__     : Tuple  =self.rows[0:position] + [row] + self.rows[position:]







      def        _lowercase  (   self		,				lowerCAmelCase__		,				lowerCAmelCase__ = None							)       ->						None:



        '''simple docstring'''




        a__     : str  =TypeError(
            "Column must be a list containing all ints and/or floats"							)
        if not isinstance(lowerCAmelCase__		,				lowerCAmelCase__							):
          raise type_error
        for value in column:
          if not isinstance(lowerCAmelCase__		,				(int, float)							):
            raise type_error
        if len(lowerCAmelCase__							) != self.num_rows:
          raise ValueError(
              "Column must be equal in length to the other columns in the matrix"							)
        if position is None:
          a__     : Optional[Any]  =[self.rows[i] + [column[i]] for i in range(self.num_rows							)]
        else:
          a__     : Any  =[
              self.rows[i][0:position] + [column[i]] + self.rows[i][position:]
              for i in range(self.num_rows							)
          ]







      def __eq__(   self		,				lowerCAmelCase__							)       ->						bool:



        '''simple docstring'''




        if not isinstance(lowerCAmelCase__		,				lowerCAmelCase__							):
          return NotImplemented
        return self.rows == other.rows







      def __ne__(   self		,				lowerCAmelCase__							)       ->						bool:



        '''simple docstring'''




        return not self == other







      def __neg__(   self							)       ->						Matrix:



        '''simple docstring'''




        return self * -1







      def __add__(   self		,				lowerCAmelCase__							)       ->						Matrix:



        '''simple docstring'''




        if self.order != other.order:
          raise ValueError("Addition requires matrices of the same order"							)
        return Matrix(
            [
                [self.rows[i][j] + other.rows[i][j] for j in range(self.num_columns							)]
                for i in range(self.num_rows							)
            ]							)







      def __sub__(   self		,				lowerCAmelCase__							)       ->						Matrix:



        '''simple docstring'''




        if self.order != other.order:
          raise ValueError("Subtraction requires matrices of the same order"							)
        return Matrix(
            [
                [self.rows[i][j] - other.rows[i][j] for j in range(self.num_columns							)]
                for i in range(self.num_rows							)
            ]							)







      def __mul__(   self		,				lowerCAmelCase__							)       ->						Matrix:



        '''simple docstring'''




        if isinstance(lowerCAmelCase__		,				(int, float)							):
          return Matrix(
              [[int(element * other							) for element in row] for row in self.rows]							)
        elif isinstance(lowerCAmelCase__		,				lowerCAmelCase__							):
          if self.num_columns != other.num_rows:
            raise ValueError(
                "The number of columns in the first matrix must "
                "be equal to the number of rows in the second"							)
          return Matrix(
              [
                  [Matrix.dot_product(lowerCAmelCase__		,				lowerCAmelCase__							) for column in other.columns()]
                  for row in self.rows
              ]							)
        else:
          raise TypeError(
              "A Matrix can only be multiplied by an int, float, or another matrix"							)







      def __pow__(   self		,				lowerCAmelCase__							)       ->						Matrix:



        '''simple docstring'''




        if not isinstance(lowerCAmelCase__		,				lowerCAmelCase__							):
          raise TypeError("A Matrix can only be raised to the power of an int"							)
        if not self.is_square:
          raise ValueError("Only square matrices can be raised to a power"							)
        if other == 0:
          return self.identity()
        if other < 0:
          if self.is_invertable():
            return self.inverse() ** (-other)
          raise ValueError(
              "Only invertable matrices can be raised to a negative power"							)
        a__     : Tuple  =self
        for _ in range(other - 1							):
          result *= self
        return result







      @classmethod
      def        _lowercase  (   cls		,				lowerCAmelCase__		,				lowerCAmelCase__							)       ->						int:



        '''simple docstring'''




        return sum(row[i] * column[i] for i in range(len(lowerCAmelCase__							)							)							)


if __name__ == "__main__":
     import doctest

     doctest.testmod()






