


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    =					["""bart.large""", """bart.large.mnli""", """bart.large.cnn""", """bart_xsum/model.pt"""]
UpperCAmelCase    =					{"""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    =					logging.get_logger(__name__)

UpperCAmelCase    =					""" Hello world! cécé herlolip"""

UpperCAmelCase    =					[
    ("""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  __lowerCAmelCase					(SCREAMING_SNAKE_CASE      )->						int:




						"""simple docstring"""

						snake_case_ =   [
						    '''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  __lowerCAmelCase					(SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      )->						List[str]:




						"""simple docstring"""

						snake_case_ =   dct.pop(SCREAMING_SNAKE_CASE      )
						snake_case_ =   val






def  __lowerCAmelCase					(SCREAMING_SNAKE_CASE      )->						int:




						"""simple docstring"""

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






def  __lowerCAmelCase					(SCREAMING_SNAKE_CASE      )->						Any:




						"""simple docstring"""

						snake_case_ ,					snake_case_ =   emb.weight.shape
						snake_case_ =   nn.Linear(SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      ,      bias=SCREAMING_SNAKE_CASE      )
						snake_case_ =   emb.weight.data
						return lin_layer






@torch.no_grad()
def  __lowerCAmelCase					(SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE=None      )->						Any:




						"""simple docstring"""

						if not os.path.exists(SCREAMING_SNAKE_CASE      ):
												snake_case_ =   torch.hub.load('''pytorch/fairseq'''      ,      SCREAMING_SNAKE_CASE      ).eval()
						else:
												snake_case_ =   load_xsum_checkpoint(SCREAMING_SNAKE_CASE      )

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



# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING, Dict, Optional

import numpy as np
import pyarrow as pa

from .. import config
from ..utils.logging import get_logger
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter


if TYPE_CHECKING:
   import jax
   import jaxlib

UpperCAmelCase    =					get_logger()

UpperCAmelCase    =					None







class lowerCAmelCase_		(				TensorFormatter[Mapping, "jax.Array", Mapping] ):

 '''simple docstring'''

 def __init__(			self	,   _UpperCAmelCase=None	,   _UpperCAmelCase=None	,   **_UpperCAmelCase      ):
       super().__init__(features=_UpperCAmelCase      )
       import jax
       from jaxlib.xla_client import Device

       if isinstance(_UpperCAmelCase	,   _UpperCAmelCase      ):
             raise ValueError(
                 F'''Expected {device} to be a `str` not {type(_UpperCAmelCase      )}, as `jaxlib.xla_extension.Device` '''
                 '''is not serializable neither with `pickle` nor with `dill`. Instead you can surround '''
                 '''the device with `str()` to get its string identifier that will be internally mapped '''
                 '''to the actual `jaxlib.xla_extension.Device`.'''      )
       snake_case_ =   device if isinstance(_UpperCAmelCase	,   _UpperCAmelCase      ) else str(jax.devices()[0]      )
       # using global variable since `jaxlib.xla_extension.Device` is not serializable neither
       # with `pickle` nor with `dill`, so we need to use a global variable instead
       global DEVICE_MAPPING
       if DEVICE_MAPPING is None:
             snake_case_ =   self._map_devices_to_str()
       if self.device not in list(DEVICE_MAPPING.keys()      ):
             logger.warning(
                 F'''Device with string identifier {self.device} not listed among the available '''
                 F'''devices: {list(DEVICE_MAPPING.keys()      )}, so falling back to the default '''
                 F'''device: {str(jax.devices()[0]      )}.'''      )
             snake_case_ =   str(jax.devices()[0]      )
       snake_case_ =   jnp_array_kwargs



 @staticmethod
 def 		UpperCamelCase__  (			):
       import jax

       return {str(_UpperCAmelCase      ): device for device in jax.devices()}



 def 		UpperCamelCase__  (			self	,   _UpperCAmelCase      ):
       import jax
       import jax.numpy as jnp

       if isinstance(_UpperCAmelCase	,   _UpperCAmelCase      ) and column:
             if all(
                 isinstance(_UpperCAmelCase	,   jax.Array      ) and x.shape == column[0].shape and x.dtype == column[0].dtype for x in column      ):
                   return jnp.stack(_UpperCAmelCase	,   axis=0      )
       return column



 def 		UpperCamelCase__  (			self	,   _UpperCAmelCase      ):
       import jax
       import jax.numpy as jnp

       if isinstance(_UpperCAmelCase	,   (str, bytes, type(_UpperCAmelCase      ))      ):
             return value
       elif isinstance(_UpperCAmelCase	,   (np.character, np.ndarray)      ) and np.issubdtype(value.dtype	,   np.character      ):
             return value.tolist()

       snake_case_ =   {}

       if isinstance(_UpperCAmelCase	,   (np.number, np.ndarray)      ) and np.issubdtype(value.dtype	,   np.integer      ):
             # the default int precision depends on the jax config
             # see https://jax.readthedocs.io/en/latest/notebooks/Common_Gotchas_in_JAX.html#double-64bit-precision
             if jax.config.jax_enable_xaa:
                   snake_case_ =   {'''dtype''': jnp.intaa}
             else:
                   snake_case_ =   {'''dtype''': jnp.intaa}
       elif isinstance(_UpperCAmelCase	,   (np.number, np.ndarray)      ) and np.issubdtype(value.dtype	,   np.floating      ):
             snake_case_ =   {'''dtype''': jnp.floataa}
       elif config.PIL_AVAILABLE and "PIL" in sys.modules:
             import PIL.Image

             if isinstance(_UpperCAmelCase	,   PIL.Image.Image      ):
                   snake_case_ =   np.asarray(_UpperCAmelCase      )

        # using global variable since `jaxlib.xla_extension.Device` is not serializable neither
        # with `pickle` nor with `dill`, so we need to use a global variable instead
       global DEVICE_MAPPING
       if DEVICE_MAPPING is None:
             snake_case_ =   self._map_devices_to_str()

       with jax.default_device(DEVICE_MAPPING[self.device]      ):
             # calling jnp.array on a np.ndarray does copy the data
             # see https://github.com/google/jax/issues/4486
             return jnp.array(_UpperCAmelCase	,   **{**default_dtype, **self.jnp_array_kwargs}      )



 def 		UpperCamelCase__  (			self	,   _UpperCAmelCase      ):
       import jax

       # support for torch, tf, jax etc.
       if config.TORCH_AVAILABLE and "torch" in sys.modules:
             import torch

             if isinstance(_UpperCAmelCase	,   torch.Tensor      ):
                   return self._tensorize(data_struct.detach().cpu().numpy()[()]      )
       if hasattr(_UpperCAmelCase	,   '''__array__'''      ) and not isinstance(_UpperCAmelCase	,   jax.Array      ):
             snake_case_ =   data_struct.__array__()
       # support for nested types like struct of list of struct
       if isinstance(_UpperCAmelCase	,   np.ndarray      ):
             if data_struct.dtype == object:  # jax arrays cannot be instantied from an array of objects
                   return self._consolidate([self.recursive_tensorize(_UpperCAmelCase      ) for substruct in data_struct]      )
       elif isinstance(_UpperCAmelCase	,   (list, tuple)      ):
             return self._consolidate([self.recursive_tensorize(_UpperCAmelCase      ) for substruct in data_struct]      )
       return self._tensorize(_UpperCAmelCase      )



 def 		UpperCamelCase__  (			self	,   _UpperCAmelCase      ):
       return map_nested(self._recursive_tensorize	,   _UpperCAmelCase	,   map_list=_UpperCAmelCase      )



 def 		UpperCamelCase__  (			self	,   _UpperCAmelCase      ):
       snake_case_ =   self.numpy_arrow_extractor().extract_row(_UpperCAmelCase      )
       snake_case_ =   self.python_features_decoder.decode_row(_UpperCAmelCase      )
       return self.recursive_tensorize(_UpperCAmelCase      )



 def 		UpperCamelCase__  (			self	,   _UpperCAmelCase      ):
       snake_case_ =   self.numpy_arrow_extractor().extract_column(_UpperCAmelCase      )
       snake_case_ =   self.python_features_decoder.decode_column(_UpperCAmelCase	,   pa_table.column_names[0]      )
       snake_case_ =   self.recursive_tensorize(_UpperCAmelCase      )
       snake_case_ =   self._consolidate(_UpperCAmelCase      )
       return column





 def 		UpperCamelCase__  (			self	,   _UpperCAmelCase      ):
       snake_case_ =   self.numpy_arrow_extractor().extract_batch(_UpperCAmelCase      )
       snake_case_ =   self.python_features_decoder.decode_batch(_UpperCAmelCase      )
       snake_case_ =   self.recursive_tensorize(_UpperCAmelCase      )
       for column_name in batch:
             snake_case_ =   self._consolidate(batch[column_name]      )
       return batch