


"""simple docstring"""
import json
import pathlib
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision, slow
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 ConditionalDetrImageProcessor




class 	lowercase__				(					unittest.TestCase				):





   def __init__(							self      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE=7      ,   SCREAMING_SNAKE_CASE=3      ,   SCREAMING_SNAKE_CASE=30      ,   SCREAMING_SNAKE_CASE=400      ,   SCREAMING_SNAKE_CASE=True      ,   SCREAMING_SNAKE_CASE=None      ,   SCREAMING_SNAKE_CASE=True      ,   SCREAMING_SNAKE_CASE=[0.5, 0.5, 0.5]      ,   SCREAMING_SNAKE_CASE=[0.5, 0.5, 0.5]      ,   SCREAMING_SNAKE_CASE=True      ,   SCREAMING_SNAKE_CASE=1 / 255      ,   SCREAMING_SNAKE_CASE=True      ,   )  ->  Dict:
      # by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
      _lowerCamelCase	:					Union[str, Any]   =		size if size is not None else {"""shortest_edge""": 18, """longest_edge""": 1333}
      _lowerCamelCase	:					Union[str, Any]   =		parent
      _lowerCamelCase	:					Optional[Any]   =		batch_size
      _lowerCamelCase	:					Optional[int]   =		num_channels
      _lowerCamelCase	:					Union[str, Any]   =		min_resolution
      _lowerCamelCase	:					Optional[int]   =		max_resolution
      _lowerCamelCase	:					List[Any]   =		do_resize
      _lowerCamelCase	:					str   =		size
      _lowerCamelCase	:					Union[str, Any]   =		do_normalize
      _lowerCamelCase	:					Union[str, Any]   =		image_mean
      _lowerCamelCase	:					Tuple   =		image_std
      _lowerCamelCase	:					List[Any]   =		do_rescale
      _lowerCamelCase	:					Dict   =		rescale_factor
      _lowerCamelCase	:					Any   =		do_pad





   def 		UpperCamelCase_  (							self)  ->  int:
      return {
          "do_resize": self.do_resize,
          "size": self.size,
          "do_normalize": self.do_normalize,
          "image_mean": self.image_mean,
          "image_std": self.image_std,
          "do_rescale": self.do_rescale,
          "rescale_factor": self.rescale_factor,
          "do_pad": self.do_pad,
      }





   def 		UpperCamelCase_  (							self      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE=False)  ->  Any:
      if not batched:
         _lowerCamelCase	:					Tuple   =		image_inputs[0]
         if isinstance(SCREAMING_SNAKE_CASE      ,   Image.Image):
            _lowerCamelCase   ,    _lowerCamelCase	:					Any   =		image.size
         else:
            _lowerCamelCase   ,    _lowerCamelCase	:					int   =		image.shape[1], image.shape[2]
         if w < h:
            _lowerCamelCase	:					Optional[int]   =		int(self.size["""shortest_edge"""] * h / w)
            _lowerCamelCase	:					Optional[int]   =		self.size["""shortest_edge"""]
         elif w > h:
            _lowerCamelCase	:					Any   =		self.size["""shortest_edge"""]
            _lowerCamelCase	:					Union[str, Any]   =		int(self.size["""shortest_edge"""] * w / h)
         else:
            _lowerCamelCase	:					Optional[Any]   =		self.size["""shortest_edge"""]
            _lowerCamelCase	:					Optional[Any]   =		self.size["""shortest_edge"""]

      else:
         _lowerCamelCase	:					Any   =		[]
         for image in image_inputs:
            _lowerCamelCase   ,    _lowerCamelCase	:					List[str]   =		self.get_expected_values([image])
            expected_values.append((expected_height, expected_width))
         _lowerCamelCase	:					List[Any]   =		max(SCREAMING_SNAKE_CASE      ,   key=lambda SCREAMING_SNAKE_CASE: item[0])[0]
         _lowerCamelCase	:					int   =		max(SCREAMING_SNAKE_CASE      ,   key=lambda SCREAMING_SNAKE_CASE: item[1])[1]

      return expected_height, expected_width







@require_torch
@require_vision
class 	lowercase__				(					A_ ,unittest.TestCase				):
   __UpperCAmelCase							       =					ConditionalDetrImageProcessor if is_vision_available() else None





   def 		UpperCamelCase_  (							self)  ->  Optional[Any]:
      _lowerCamelCase	:					Dict   =		ConditionalDetrImageProcessingTester(self)





   @property
   def 		UpperCamelCase_  (							self)  ->  str:
      return self.image_processor_tester.prepare_image_processor_dict()





   def 		UpperCamelCase_  (							self)  ->  Dict:
      _lowerCamelCase	:					Any   =		self.image_processing_class(**self.image_processor_dict)
      self.assertTrue(hasattr(SCREAMING_SNAKE_CASE      ,   """image_mean"""))
      self.assertTrue(hasattr(SCREAMING_SNAKE_CASE      ,   """image_std"""))
      self.assertTrue(hasattr(SCREAMING_SNAKE_CASE      ,   """do_normalize"""))
      self.assertTrue(hasattr(SCREAMING_SNAKE_CASE      ,   """do_resize"""))
      self.assertTrue(hasattr(SCREAMING_SNAKE_CASE      ,   """size"""))





   def 		UpperCamelCase_  (							self)  ->  int:
      _lowerCamelCase	:					int   =		self.image_processing_class.from_dict(self.image_processor_dict)
      self.assertEqual(image_processor.size      ,   {"""shortest_edge""": 18, """longest_edge""": 1333})
      self.assertEqual(image_processor.do_pad      ,   SCREAMING_SNAKE_CASE)

      _lowerCamelCase	:					Optional[int]   =		self.image_processing_class.from_dict(
          self.image_processor_dict      ,   size=42      ,   max_size=84      ,   pad_and_return_pixel_mask=SCREAMING_SNAKE_CASE)
      self.assertEqual(image_processor.size      ,   {"""shortest_edge""": 42, """longest_edge""": 84})
      self.assertEqual(image_processor.do_pad      ,   SCREAMING_SNAKE_CASE)





   def 		UpperCamelCase_  (							self)  ->  Tuple:
      pass





   def 		UpperCamelCase_  (							self)  ->  Any:
      # Initialize image_processing
      _lowerCamelCase	:					Tuple   =		self.image_processing_class(**self.image_processor_dict)
      # create random PIL images
      _lowerCamelCase	:					Optional[Any]   =		prepare_image_inputs(self.image_processor_tester      ,   equal_resolution=SCREAMING_SNAKE_CASE)
      for image in image_inputs:
         self.assertIsInstance(SCREAMING_SNAKE_CASE      ,   Image.Image)

      # Test not batched input
      _lowerCamelCase	:					Optional[Any]   =		image_processing(image_inputs[0]      ,   return_tensors="""pt""").pixel_values

      _lowerCamelCase   ,    _lowerCamelCase	:					Optional[Any]   =		self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE)

      self.assertEqual(
          encoded_images.shape      ,   (1, self.image_processor_tester.num_channels, expected_height, expected_width)      ,   )

      # Test batched
      _lowerCamelCase   ,    _lowerCamelCase	:					Union[str, Any]   =		self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE      ,   batched=SCREAMING_SNAKE_CASE)

      _lowerCamelCase	:					Optional[Any]   =		image_processing(SCREAMING_SNAKE_CASE      ,   return_tensors="""pt""").pixel_values
      self.assertEqual(
          encoded_images.shape      ,   (
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              expected_height,
              expected_width,
          )      ,   )





   def 		UpperCamelCase_  (							self)  ->  int:
      # Initialize image_processing
      _lowerCamelCase	:					Dict   =		self.image_processing_class(**self.image_processor_dict)
      # create random numpy tensors
      _lowerCamelCase	:					Dict   =		prepare_image_inputs(self.image_processor_tester      ,   equal_resolution=SCREAMING_SNAKE_CASE      ,   numpify=SCREAMING_SNAKE_CASE)
      for image in image_inputs:
         self.assertIsInstance(SCREAMING_SNAKE_CASE      ,   np.ndarray)

      # Test not batched input
      _lowerCamelCase	:					Optional[int]   =		image_processing(image_inputs[0]      ,   return_tensors="""pt""").pixel_values

      _lowerCamelCase   ,    _lowerCamelCase	:					int   =		self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE)

      self.assertEqual(
          encoded_images.shape      ,   (1, self.image_processor_tester.num_channels, expected_height, expected_width)      ,   )

      # Test batched
      _lowerCamelCase	:					int   =		image_processing(SCREAMING_SNAKE_CASE      ,   return_tensors="""pt""").pixel_values

      _lowerCamelCase   ,    _lowerCamelCase	:					Any   =		self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE      ,   batched=SCREAMING_SNAKE_CASE)

      self.assertEqual(
          encoded_images.shape      ,   (
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              expected_height,
              expected_width,
          )      ,   )





   def 		UpperCamelCase_  (							self)  ->  Optional[Any]:
      # Initialize image_processing
      _lowerCamelCase	:					Tuple   =		self.image_processing_class(**self.image_processor_dict)
      # create random PyTorch tensors
      _lowerCamelCase	:					Union[str, Any]   =		prepare_image_inputs(self.image_processor_tester      ,   equal_resolution=SCREAMING_SNAKE_CASE      ,   torchify=SCREAMING_SNAKE_CASE)
      for image in image_inputs:
         self.assertIsInstance(SCREAMING_SNAKE_CASE      ,   torch.Tensor)

      # Test not batched input
      _lowerCamelCase	:					Optional[Any]   =		image_processing(image_inputs[0]      ,   return_tensors="""pt""").pixel_values

      _lowerCamelCase   ,    _lowerCamelCase	:					Optional[int]   =		self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE)

      self.assertEqual(
          encoded_images.shape      ,   (1, self.image_processor_tester.num_channels, expected_height, expected_width)      ,   )

      # Test batched
      _lowerCamelCase	:					Optional[Any]   =		image_processing(SCREAMING_SNAKE_CASE      ,   return_tensors="""pt""").pixel_values

      _lowerCamelCase   ,    _lowerCamelCase	:					Tuple   =		self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE      ,   batched=SCREAMING_SNAKE_CASE)

      self.assertEqual(
          encoded_images.shape      ,   (
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              expected_height,
              expected_width,
          )      ,   )





   @slow
   def 		UpperCamelCase_  (							self)  ->  str:
      # prepare image and target
      _lowerCamelCase	:					Union[str, Any]   =		Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""")
      with open("""./tests/fixtures/tests_samples/COCO/coco_annotations.txt"""      ,   """r""") as f:
         _lowerCamelCase	:					List[Any]   =		json.loads(f.read())

      _lowerCamelCase	:					Union[str, Any]   =		{"""image_id""": 3_9769, """annotations""": target}

      # encode them
      _lowerCamelCase	:					Any   =		ConditionalDetrImageProcessor.from_pretrained("""microsoft/conditional-detr-resnet-50""")
      _lowerCamelCase	:					Optional[Any]   =		image_processing(images=SCREAMING_SNAKE_CASE      ,   annotations=SCREAMING_SNAKE_CASE      ,   return_tensors="""pt""")

      # verify pixel values
      _lowerCamelCase	:					Dict   =		torch.Size([1, 3, 800, 1066])
      self.assertEqual(encoding["""pixel_values"""].shape      ,   SCREAMING_SNAKE_CASE)

      _lowerCamelCase	:					Tuple   =		torch.tensor([0.27_96, 0.31_38, 0.34_81])
      self.assertTrue(torch.allclose(encoding["""pixel_values"""][0, 0, 0, :3]      ,   SCREAMING_SNAKE_CASE      ,   atol=1e-4))

      # verify area
      _lowerCamelCase	:					List[str]   =		torch.tensor([58_87.96_00, 1_12_50.20_61, 48_93_53.84_38, 83_71_22.75_00, 14_79_67.51_56, 16_57_32.34_38])
      self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""]      ,   SCREAMING_SNAKE_CASE))
      # verify boxes
      _lowerCamelCase	:					Union[str, Any]   =		torch.Size([6, 4])
      self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape      ,   SCREAMING_SNAKE_CASE)
      _lowerCamelCase	:					List[str]   =		torch.tensor([0.55_03, 0.27_65, 0.06_04, 0.22_15])
      self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0]      ,   SCREAMING_SNAKE_CASE      ,   atol=1e-3))
      # verify image_id
      _lowerCamelCase	:					Union[str, Any]   =		torch.tensor([3_9769])
      self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]      ,   SCREAMING_SNAKE_CASE))
      # verify is_crowd
      _lowerCamelCase	:					Any   =		torch.tensor([0, 0, 0, 0, 0, 0])
      self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]      ,   SCREAMING_SNAKE_CASE))
      # verify class_labels
      _lowerCamelCase	:					Any   =		torch.tensor([75, 75, 63, 65, 17, 17])
      self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]      ,   SCREAMING_SNAKE_CASE))
      # verify orig_size
      _lowerCamelCase	:					List[str]   =		torch.tensor([480, 640])
      self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]      ,   SCREAMING_SNAKE_CASE))
      # verify size
      _lowerCamelCase	:					Tuple   =		torch.tensor([800, 1066])
      self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]      ,   SCREAMING_SNAKE_CASE))





   @slow
   def 		UpperCamelCase_  (							self)  ->  Optional[Any]:
      # prepare image, target and masks_path
      _lowerCamelCase	:					Union[str, Any]   =		Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""")
      with open("""./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt"""      ,   """r""") as f:
         _lowerCamelCase	:					Optional[int]   =		json.loads(f.read())

      _lowerCamelCase	:					Optional[Any]   =		{"""file_name""": """000000039769.png""", """image_id""": 3_9769, """segments_info""": target}

      _lowerCamelCase	:					List[str]   =		pathlib.Path("""./tests/fixtures/tests_samples/COCO/coco_panoptic""")

      # encode them
      _lowerCamelCase	:					List[str]   =		ConditionalDetrImageProcessor(format="""coco_panoptic""")
      _lowerCamelCase	:					Dict   =		image_processing(images=SCREAMING_SNAKE_CASE      ,   annotations=SCREAMING_SNAKE_CASE      ,   masks_path=SCREAMING_SNAKE_CASE      ,   return_tensors="""pt""")

      # verify pixel values
      _lowerCamelCase	:					int   =		torch.Size([1, 3, 800, 1066])
      self.assertEqual(encoding["""pixel_values"""].shape      ,   SCREAMING_SNAKE_CASE)

      _lowerCamelCase	:					Union[str, Any]   =		torch.tensor([0.27_96, 0.31_38, 0.34_81])
      self.assertTrue(torch.allclose(encoding["""pixel_values"""][0, 0, 0, :3]      ,   SCREAMING_SNAKE_CASE      ,   atol=1e-4))

      # verify area
      _lowerCamelCase	:					Union[str, Any]   =		torch.tensor([14_79_79.68_75, 16_55_27.04_69, 48_46_38.59_38, 1_12_92.93_75, 58_79.65_62, 76_34.11_47])
      self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""]      ,   SCREAMING_SNAKE_CASE))
      # verify boxes
      _lowerCamelCase	:					int   =		torch.Size([6, 4])
      self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape      ,   SCREAMING_SNAKE_CASE)
      _lowerCamelCase	:					List[Any]   =		torch.tensor([0.26_25, 0.54_37, 0.46_88, 0.86_25])
      self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0]      ,   SCREAMING_SNAKE_CASE      ,   atol=1e-3))
      # verify image_id
      _lowerCamelCase	:					List[str]   =		torch.tensor([3_9769])
      self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]      ,   SCREAMING_SNAKE_CASE))
      # verify is_crowd
      _lowerCamelCase	:					Dict   =		torch.tensor([0, 0, 0, 0, 0, 0])
      self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]      ,   SCREAMING_SNAKE_CASE))
      # verify class_labels
      _lowerCamelCase	:					str   =		torch.tensor([17, 17, 63, 75, 75, 93])
      self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]      ,   SCREAMING_SNAKE_CASE))
      # verify masks
      _lowerCamelCase	:					Any   =		82_2873
      self.assertEqual(encoding["""labels"""][0]["""masks"""].sum().item()      ,   SCREAMING_SNAKE_CASE)
      # verify orig_size
      _lowerCamelCase	:					List[Any]   =		torch.tensor([480, 640])
      self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]      ,   SCREAMING_SNAKE_CASE))
      # verify size
      _lowerCamelCase	:					Optional[Any]   =		torch.tensor([800, 1066])
      self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]      ,   SCREAMING_SNAKE_CASE))







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


SCREAMING_SNAKE_CASE:     int									= ['''bart.large''', '''bart.large.mnli''', '''bart.large.cnn''', '''bart_xsum/model.pt''']
SCREAMING_SNAKE_CASE:     Tuple									= {'''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()
SCREAMING_SNAKE_CASE:     int									= logging.get_logger(__name__)

SCREAMING_SNAKE_CASE:     Any									= ''' Hello world! cécé herlolip'''

SCREAMING_SNAKE_CASE:     Dict									= [
    ('''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							(					lowerCAmelCase )->	Any:
		SCREAMING_SNAKE_CASE_					      =							[
		    'encoder.version',
		    'decoder.version',
		    'model.encoder.version',
		    'model.decoder.version',
		    '_float_tensor',
		]
		for k in ignore_keys:
				state_dict.pop(lowerCAmelCase    ,						lowerCAmelCase )


def 					_a							(					lowerCAmelCase    ,						lowerCAmelCase    ,						lowerCAmelCase )->	str:
		SCREAMING_SNAKE_CASE_					      =							dct.pop(lowerCAmelCase )
		SCREAMING_SNAKE_CASE_					      =							val


def 					_a							(					lowerCAmelCase )->	List[str]:
		SCREAMING_SNAKE_CASE_					      =							torch.load(lowerCAmelCase    ,						map_location='cpu' )
		SCREAMING_SNAKE_CASE_					      =							torch.hub.load('pytorch/fairseq'    ,						'bart.large.cnn' ).eval()
		hub_interface.model.load_state_dict(sd['model'] )
		return hub_interface


def 					_a							(					lowerCAmelCase )->	Optional[Any]:
		SCREAMING_SNAKE_CASE_      ,				SCREAMING_SNAKE_CASE_					      =							emb.weight.shape
		SCREAMING_SNAKE_CASE_					      =							nn.Linear(lowerCAmelCase    ,						lowerCAmelCase    ,						bias=lowerCAmelCase )
		SCREAMING_SNAKE_CASE_					      =							emb.weight.data
		return lin_layer


@torch.no_grad()
def 					_a							(					lowerCAmelCase    ,						lowerCAmelCase    ,						lowerCAmelCase=None )->	Tuple:
		if not os.path.exists(lowerCAmelCase ):
				SCREAMING_SNAKE_CASE_					      =							torch.hub.load('pytorch/fairseq'    ,						lowerCAmelCase ).eval()
		else:
				SCREAMING_SNAKE_CASE_					      =							load_xsum_checkpoint(lowerCAmelCase )

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