
'''simple docstring'''

from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
     import PIL


snake_case									=    logging.get_logger(__name__)




class    lowerCAmelCase       (     UpperCamelCase_  ):
 A_				:				Dict	=			["""pixel_values"""]


 def __init__(    self  : Union[str, Any]			,							a__  : bool = True			,							a__  : Dict[str, int] = None			,							a__  : int = 0.9			,							a__  : PILImageResampling = PILImageResampling.BICUBIC			,							a__  : bool = True			,							a__  : Dict[str, int] = None			,							a__  : Union[int, float] = 1 / 255			,							a__  : bool = True			,							a__  : bool = True			,							a__  : Optional[Union[float, List[float]]] = None			,							a__  : Optional[Union[float, List[float]]] = None			,							**a__  : int			,							):
  '''simple docstring'''

  super().__init__(**a__    )
  lowerCAmelCase__     :   int     		=	size if size is not None else {"shortest_edge": 224}
  lowerCAmelCase__     :   Union[str, Any]     		=	get_size_dict(a__			,							default_to_square=a__    )
  lowerCAmelCase__     :   Optional[int]     		=	crop_size if crop_size is not None else {"height": 224, "width": 224}
  lowerCAmelCase__     :   int     		=	get_size_dict(a__			,							param_name="crop_size"    )

  lowerCAmelCase__     :   List[str]     		=	do_resize
  lowerCAmelCase__     :   Tuple     		=	size
  lowerCAmelCase__     :   Optional[int]     		=	crop_pct
  lowerCAmelCase__     :   Tuple     		=	resample
  lowerCAmelCase__     :   Optional[Any]     		=	do_center_crop
  lowerCAmelCase__     :   str     		=	crop_size
  lowerCAmelCase__     :   List[Any]     		=	do_rescale
  lowerCAmelCase__     :   Any     		=	rescale_factor
  lowerCAmelCase__     :   Tuple     		=	do_normalize
  lowerCAmelCase__     :   Optional[int]     		=	image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
  lowerCAmelCase__     :   List[Any]     		=	image_std if image_std is not None else IMAGENET_DEFAULT_STD


 def    _A	(    self  : List[Any]			,							a__  : np.ndarray			,							a__  : Dict[str, int]			,							a__  : Optional[float] = None			,							a__  : PILImageResampling = PILImageResampling.BICUBIC			,							a__  : Optional[Union[str, ChannelDimension]] = None			,							**a__  : Tuple			,							):
  '''simple docstring'''

  lowerCAmelCase__     :   int     		=	get_size_dict(a__			,							default_to_square=a__    )
  if "shortest_edge" not in size and ("height" not in size or "width" not in size):
   raise ValueError(F'''size must contain \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}'''    )
  if crop_pct is not None:
   if "shortest_edge" in size:
    lowerCAmelCase__     :   str     		=	int(size["shortest_edge"] / crop_pct    )
   elif "height" in size and "width" in size:
    if size["height"] == size["width"]:
     lowerCAmelCase__     :   Union[str, Any]     		=	int(size["height"] / crop_pct    )
    else:
     lowerCAmelCase__     :   Optional[int]     		=	(int(size["height"] / crop_pct    ), int(size["width"] / crop_pct    ))
   else:
    raise ValueError("Invalid size for resize: {}".format(a__    )    )

   lowerCAmelCase__     :   str     		=	get_resize_output_image_size(a__			,							size=a__			,							default_to_square=a__    )
  else:
   if "shortest_edge" in size:
    lowerCAmelCase__     :   Optional[Any]     		=	get_resize_output_image_size(a__			,							size=size["shortest_edge"]			,							default_to_square=a__    )
   elif "height" in size and "width" in size:
    lowerCAmelCase__     :   Dict     		=	(size["height"], size["width"])
   else:
    raise ValueError("Invalid size for resize: {}".format(a__    )    )

  return resize(a__			,							size=a__			,							resample=a__			,							data_format=a__			,							**a__    )


 def    _A	(    self  : str			,							a__  : np.ndarray			,							a__  : Dict[str, int]			,							a__  : Optional[Union[str, ChannelDimension]] = None			,							**a__  : Dict			,							):
  '''simple docstring'''

  lowerCAmelCase__     :   Optional[int]     		=	get_size_dict(a__    )
  if "height" not in size or "width" not in size:
   raise ValueError(F'''size must contain \'height\' and \'width\' as keys. Got {size.keys()}'''    )
  return center_crop(a__			,							size=(size["height"], size["width"])			,							data_format=a__			,							**a__    )


 def    _A	(    self  : Dict			,							a__  : np.ndarray			,							a__  : Union[int, float]			,							a__  : Optional[Union[str, ChannelDimension]] = None			,							**a__  : Union[str, Any]			,							):
  '''simple docstring'''

  return rescale(a__			,							scale=a__			,							data_format=a__			,							**a__    )


 def    _A	(    self  : int			,							a__  : np.ndarray			,							a__  : Union[float, List[float]]			,							a__  : Union[float, List[float]]			,							a__  : Optional[Union[str, ChannelDimension]] = None			,							**a__  : Any			,							):
  '''simple docstring'''

  return normalize(a__			,							mean=a__			,							std=a__			,							data_format=a__			,							**a__    )


 def    _A	(    self  : Union[str, Any]			,							a__  : ImageInput			,							a__  : bool = None			,							a__  : Dict[str, int] = None			,							a__  : int = None			,							a__  : PILImageResampling = None			,							a__  : bool = None			,							a__  : Dict[str, int] = None			,							a__  : bool = None			,							a__  : float = None			,							a__  : bool = None			,							a__  : Optional[Union[float, List[float]]] = None			,							a__  : Optional[Union[float, List[float]]] = None			,							a__  : Optional[Union[str, TensorType]] = None			,							a__  : ChannelDimension = ChannelDimension.FIRST			,							**a__  : Dict			,							):
  '''simple docstring'''

  lowerCAmelCase__     :   Dict     		=	do_resize if do_resize is not None else self.do_resize
  lowerCAmelCase__     :   Optional[Any]     		=	crop_pct if crop_pct is not None else self.crop_pct
  lowerCAmelCase__     :   List[str]     		=	resample if resample is not None else self.resample
  lowerCAmelCase__     :   Dict     		=	do_center_crop if do_center_crop is not None else self.do_center_crop
  lowerCAmelCase__     :   Optional[Any]     		=	do_rescale if do_rescale is not None else self.do_rescale
  lowerCAmelCase__     :   str     		=	rescale_factor if rescale_factor is not None else self.rescale_factor
  lowerCAmelCase__     :   str     		=	do_normalize if do_normalize is not None else self.do_normalize
  lowerCAmelCase__     :   Optional[int]     		=	image_mean if image_mean is not None else self.image_mean
  lowerCAmelCase__     :   str     		=	image_std if image_std is not None else self.image_std

  lowerCAmelCase__     :   str     		=	size if size is not None else self.size
  lowerCAmelCase__     :   List[str]     		=	get_size_dict(a__			,							default_to_square=a__    )
  lowerCAmelCase__     :   Optional[int]     		=	crop_size if crop_size is not None else self.crop_size
  lowerCAmelCase__     :   Optional[Any]     		=	get_size_dict(a__			,							param_name="crop_size"    )

  lowerCAmelCase__     :   List[Any]     		=	make_list_of_images(a__    )

  if not valid_images(a__    ):
   raise ValueError(
       "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
       "torch.Tensor, tf.Tensor or jax.ndarray."    )

  if do_resize and size is None or resample is None:
   raise ValueError("Size and resample must be specified if do_resize is True."    )

  if do_center_crop and crop_pct is None:
   raise ValueError("Crop_pct must be specified if do_center_crop is True."    )

  if do_rescale and rescale_factor is None:
   raise ValueError("Rescale factor must be specified if do_rescale is True."    )

  if do_normalize and (image_mean is None or image_std is None):
   raise ValueError("Image mean and std must be specified if do_normalize is True."    )

  # All transformations expect numpy arrays.
  lowerCAmelCase__     :   List[Any]     		=	[to_numpy_array(a__    ) for image in images]

  if do_resize:
   lowerCAmelCase__     :   Any     		=	[self.resize(image=a__			,							size=a__			,							crop_pct=a__			,							resample=a__    ) for image in images]

  if do_center_crop:
   lowerCAmelCase__     :   Optional[int]     		=	[self.center_crop(image=a__			,							size=a__    ) for image in images]

  if do_rescale:
   lowerCAmelCase__     :   Optional[Any]     		=	[self.rescale(image=a__			,							scale=a__    ) for image in images]

  if do_normalize:
   lowerCAmelCase__     :   int     		=	[self.normalize(image=a__			,							mean=a__			,							std=a__    ) for image in images]

  lowerCAmelCase__     :   List[str]     		=	[to_channel_dimension_format(a__			,							a__    ) for image in images]

  lowerCAmelCase__     :   Tuple     		=	{"pixel_values": images}
  return BatchFeature(data=a__			,							tensor_type=a__    )







'''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


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

snake_case									=    """ Hello world! cécé herlolip"""

snake_case									=    [
    ("""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_ (  lowerCamelCase_			):







 """simple docstring"""


 lowerCAmelCase__     :   Dict     		=	[
     "encoder.version",
     "decoder.version",
     "model.encoder.version",
     "model.decoder.version",
     "_float_tensor",
 ]
 for k in ignore_keys:
  state_dict.pop(lowerCamelCase_      ,			lowerCamelCase_			)


def        UpperCAmelCase_ (  lowerCamelCase_      ,			lowerCamelCase_      ,			lowerCamelCase_			):







 """simple docstring"""


 lowerCAmelCase__     :   List[Any]     		=	dct.pop(lowerCamelCase_			)
 lowerCAmelCase__     :   List[str]     		=	val


def        UpperCAmelCase_ (  lowerCamelCase_			):







 """simple docstring"""


 lowerCAmelCase__     :   Optional[int]     		=	torch.load(lowerCamelCase_      ,			map_location="cpu"			)
 lowerCAmelCase__     :   Union[str, Any]     		=	torch.hub.load("pytorch/fairseq"      ,			"bart.large.cnn"			).eval()
 hub_interface.model.load_state_dict(sd["model"]			)
 return hub_interface


def        UpperCAmelCase_ (  lowerCamelCase_			):







 """simple docstring"""


 lowerCAmelCase__		,     lowerCAmelCase__     :   Optional[int]     		=	emb.weight.shape
 lowerCAmelCase__     :   List[Any]     		=	nn.Linear(lowerCamelCase_      ,			lowerCamelCase_      ,			bias=lowerCamelCase_			)
 lowerCAmelCase__     :   Optional[int]     		=	emb.weight.data
 return lin_layer


@torch.no_grad()
def        UpperCAmelCase_ (  lowerCamelCase_      ,			lowerCamelCase_      ,			lowerCamelCase_=None			):







 """simple docstring"""


 if not os.path.exists(lowerCamelCase_			):
  lowerCAmelCase__     :   Union[str, Any]     		=	torch.hub.load("pytorch/fairseq"      ,			lowerCamelCase_			).eval()
 else:
  lowerCAmelCase__     :   Tuple     		=	load_xsum_checkpoint(lowerCamelCase_			)

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





