

'''simple docstring'''




import argparse

import torch
from torch import nn

from transformers import PLBartConfig, PLBartForConditionalGeneration, PLBartForSequenceClassification


def       lowerCamelCase__		(						SCREAMING_SNAKE_CASE      :   Optional[int]						):
  UpperCAmelCase													=	[
      "encoder.version",
      "decoder.version",
      "model.encoder.version",
      "model.decoder.version",
      "_float_tensor",
      "decoder.output_projection.weight",
  ]
  for k in ignore_keys:
    state_dict.pop(__lowerCAmelCase       ,   __lowerCAmelCase						)


def       lowerCamelCase__		(						SCREAMING_SNAKE_CASE      :   Any						):
  UpperCAmelCase													=	emb.weight.shape
  UpperCAmelCase													=	nn.Linear(__lowerCAmelCase       ,   __lowerCAmelCase       ,   bias=__lowerCAmelCase						)
  UpperCAmelCase													=	emb.weight.data
  return lin_layer


def       lowerCamelCase__		(						SCREAMING_SNAKE_CASE      :   Dict       ,   SCREAMING_SNAKE_CASE      :   Optional[int]="uclanlp/plbart-base"       ,   SCREAMING_SNAKE_CASE      :   Optional[Any]=False       ,   SCREAMING_SNAKE_CASE      :   Optional[Any]=False						):
  UpperCAmelCase													=	torch.load(__lowerCAmelCase       ,   map_location='cpu'						)["model"]
  remove_ignore_keys_(__lowerCAmelCase						)
  UpperCAmelCase													=	state_dict["encoder.embed_tokens.weight"].shape[0]

  UpperCAmelCase													=	PLBartConfig.from_pretrained(__lowerCAmelCase       ,   vocab_size=__lowerCAmelCase						)

  UpperCAmelCase													=	state_dict["decoder.embed_tokens.weight"]
  if not classification:
    UpperCAmelCase													=	PLBartForConditionalGeneration(__lowerCAmelCase						)
    model.model.load_state_dict(__lowerCAmelCase						)
    if finetuned:
      UpperCAmelCase													=	make_linear_from_emb(model.model.shared						)

  else:
    UpperCAmelCase													=	{}
    for key, value in state_dict.copy().items():
      if key.startswith('classification_heads.sentence_classification_head'						):
        UpperCAmelCase													=	value
        state_dict.pop(__lowerCAmelCase						)
    UpperCAmelCase													=	PLBartForSequenceClassification(__lowerCAmelCase						)
    model.model.load_state_dict(__lowerCAmelCase						)
    model.classification_head.load_state_dict(__lowerCAmelCase						)

  return model


if __name__ == "__main__":
  _a				: Union[str, Any] 							=						argparse.ArgumentParser()
  # Required parameters
  parser.add_argument('fairseq_path', type=str, help='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='uclanlp/plbart-base',
      type=str,
      help='Which huggingface architecture to use: plbart-base',
  )
  parser.add_argument('--finetuned', action='store_true', help='whether the model is a fine-tuned checkpoint')
  parser.add_argument(
      '--classification', action='store_true', help='whether the model is a classification checkpoint'
  )
  _a				: Optional[Any] 							=						parser.parse_args()
  _a				: Tuple 							=						convert_fairseq_plbart_checkpoint_from_disk(
      args.fairseq_path,
      hf_config_path=args.hf_config,
      finetuned=args.finetuned,
      classification=args.classification,
  )
  model.save_pretrained(args.pytorch_dump_folder_path)








'''simple docstring'''




import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
  import torch

if is_vision_available():
  from PIL import Image

  from transformers import MobileNetVaImageProcessor



class        lowercase_	(		unittest.TestCase ):

  '''simple docstring'''





  def __init__(     self   , a_   , a_=7   , a_=3   , a_=1_8   , a_=3_0   , a_=4_0_0   , a_=True   , a_=None   , a_=True   , a_=None   , )							->				List[str]:


    """simple docstring"""
    UpperCAmelCase													=	size if size is not None else {'shortest_edge': 2_0}
    UpperCAmelCase													=	crop_size if crop_size is not None else {'height': 1_8, 'width': 1_8}
    UpperCAmelCase													=	parent
    UpperCAmelCase													=	batch_size
    UpperCAmelCase													=	num_channels
    UpperCAmelCase													=	image_size
    UpperCAmelCase													=	min_resolution
    UpperCAmelCase													=	max_resolution
    UpperCAmelCase													=	do_resize
    UpperCAmelCase													=	size
    UpperCAmelCase													=	do_center_crop
    UpperCAmelCase													=	crop_size





  def 						snake_case_			(     self     )							->				Optional[int]:


    """simple docstring"""
    return {
        "do_resize": self.do_resize,
        "size": self.size,
        "do_center_crop": self.do_center_crop,
        "crop_size": self.crop_size,
    }




@require_torch
@require_vision
class        lowercase_	(		a ,       unittest.TestCase ):

  '''simple docstring'''





  __lowerCAmelCase					: Union[str, Any]      =   MobileNetVaImageProcessor if is_vision_available() else None
  def 						snake_case_			(     self     )							->				Dict:


    """simple docstring"""
    UpperCAmelCase													=	MobileNetVaImageProcessingTester(self     )
  @property
  def 						snake_case_			(     self     )							->				List[str]:


    """simple docstring"""
    return self.image_processor_tester.prepare_image_processor_dict()
  def 						snake_case_			(     self     )							->				Optional[int]:


    """simple docstring"""
    UpperCAmelCase													=	self.image_processing_class(**self.image_processor_dict     )
    self.assertTrue(hasattr(a_   , 'do_resize'     )     )
    self.assertTrue(hasattr(a_   , 'size'     )     )
    self.assertTrue(hasattr(a_   , 'do_center_crop'     )     )
    self.assertTrue(hasattr(a_   , 'center_crop'     )     )
  def 						snake_case_			(     self     )							->				str:


    """simple docstring"""
    UpperCAmelCase													=	self.image_processing_class.from_dict(self.image_processor_dict     )
    self.assertEqual(image_processor.size   , {'shortest_edge': 2_0}     )
    self.assertEqual(image_processor.crop_size   , {'height': 1_8, 'width': 1_8}     )

    UpperCAmelCase													=	self.image_processing_class.from_dict(self.image_processor_dict   , size=4_2   , crop_size=8_4     )
    self.assertEqual(image_processor.size   , {'shortest_edge': 4_2}     )
    self.assertEqual(image_processor.crop_size   , {'height': 8_4, 'width': 8_4}     )
  def 						snake_case_			(     self     )							->				List[str]:


    """simple docstring"""
    pass
  def 						snake_case_			(     self     )							->				List[Any]:


    """simple docstring"""
    # Initialize image_processing
    UpperCAmelCase													=	self.image_processing_class(**self.image_processor_dict     )
    # create random PIL images
    UpperCAmelCase													=	prepare_image_inputs(self.image_processor_tester   , equal_resolution=a_     )
    for image in image_inputs:
      self.assertIsInstance(a_   , Image.Image     )

    # Test not batched input
    UpperCAmelCase													=	image_processing(image_inputs[0]   , return_tensors='pt'     ).pixel_values
    self.assertEqual(
        encoded_images.shape   , (
            1,
            self.image_processor_tester.num_channels,
            self.image_processor_tester.crop_size['height'],
            self.image_processor_tester.crop_size['width'],
        )   , )

    # Test batched
    UpperCAmelCase													=	image_processing(a_   , return_tensors='pt'     ).pixel_values
    self.assertEqual(
        encoded_images.shape   , (
            self.image_processor_tester.batch_size,
            self.image_processor_tester.num_channels,
            self.image_processor_tester.crop_size['height'],
            self.image_processor_tester.crop_size['width'],
        )   , )
  def 						snake_case_			(     self     )							->				str:


    """simple docstring"""
    # Initialize image_processing
    UpperCAmelCase													=	self.image_processing_class(**self.image_processor_dict     )
    # create random numpy tensors
    UpperCAmelCase													=	prepare_image_inputs(self.image_processor_tester   , equal_resolution=a_   , numpify=a_     )
    for image in image_inputs:
      self.assertIsInstance(a_   , np.ndarray     )

    # Test not batched input
    UpperCAmelCase													=	image_processing(image_inputs[0]   , return_tensors='pt'     ).pixel_values
    self.assertEqual(
        encoded_images.shape   , (
            1,
            self.image_processor_tester.num_channels,
            self.image_processor_tester.crop_size['height'],
            self.image_processor_tester.crop_size['width'],
        )   , )

    # Test batched
    UpperCAmelCase													=	image_processing(a_   , return_tensors='pt'     ).pixel_values
    self.assertEqual(
        encoded_images.shape   , (
            self.image_processor_tester.batch_size,
            self.image_processor_tester.num_channels,
            self.image_processor_tester.crop_size['height'],
            self.image_processor_tester.crop_size['width'],
        )   , )





  def 						snake_case_			(     self     )							->				List[Any]:


    """simple docstring"""
    # Initialize image_processing
    UpperCAmelCase													=	self.image_processing_class(**self.image_processor_dict     )
    # create random PyTorch tensors
    UpperCAmelCase													=	prepare_image_inputs(self.image_processor_tester   , equal_resolution=a_   , torchify=a_     )
    for image in image_inputs:
      self.assertIsInstance(a_   , torch.Tensor     )

    # Test not batched input
    UpperCAmelCase													=	image_processing(image_inputs[0]   , return_tensors='pt'     ).pixel_values
    self.assertEqual(
        encoded_images.shape   , (
            1,
            self.image_processor_tester.num_channels,
            self.image_processor_tester.crop_size['height'],
            self.image_processor_tester.crop_size['width'],
        )   , )

    # Test batched
    UpperCAmelCase													=	image_processing(a_   , return_tensors='pt'     ).pixel_values
    self.assertEqual(
        encoded_images.shape   , (
            self.image_processor_tester.batch_size,
            self.image_processor_tester.num_channels,
            self.image_processor_tester.crop_size['height'],
            self.image_processor_tester.crop_size['width'],
        )   , )


