





'''simple docstring'''



import inspect
import unittest

import numpy as np

from tests.test_modeling_common import floats_tensor
from transformers import MaskaFormerConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
	import torch

	from transformers import MaskaFormerForUniversalSegmentation, MaskaFormerModel

	if is_vision_available():
		from transformers import MaskaFormerImageProcessor

if is_vision_available():
	from PIL import Image






class 							__lowercase    :







	'''simple docstring'''

	def __init__(self       ,_lowerCamelCase       ,_lowerCamelCase=2       ,_lowerCamelCase=True       ,_lowerCamelCase=False       ,_lowerCamelCase=10       ,_lowerCamelCase=3       ,_lowerCamelCase=32 * 8       ,_lowerCamelCase=32 * 8       ,_lowerCamelCase=4       ,_lowerCamelCase=64       ,)							->							Any:







				'''simple docstring'''





				__lowercase			  =							parent
				__lowercase			  =							batch_size
				__lowercase			  =							is_training
				__lowercase			  =							use_auxiliary_loss
				__lowercase			  =							num_queries
				__lowercase			  =							num_channels
				__lowercase			  =							min_size
				__lowercase			  =							max_size
				__lowercase			  =							num_labels
				__lowercase			  =							hidden_dim
				__lowercase			  =							hidden_dim


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







				'''simple docstring'''





				__lowercase			  =							floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size]	).to(
				    _lowerCamelCase	)

				__lowercase			  =							torch.ones([self.batch_size, self.min_size, self.max_size]       ,device=_lowerCamelCase	)

				__lowercase			  =							(
				    torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size]       ,device=_lowerCamelCase	) > 0.5
				).float()
				__lowercase			  =							(torch.rand((self.batch_size, self.num_labels)       ,device=_lowerCamelCase	) > 0.5).long()

				__lowercase			  =							self.get_config()
				return config, pixel_values, pixel_mask, mask_labels, class_labels


	def   _UpperCAmelCase       (self	)							->							str:







				'''simple docstring'''





				__lowercase			  =							MaskaFormerConfig(
				    hidden_size=self.hidden_dim       ,)
				__lowercase			  =							self.num_queries
				__lowercase			  =							self.num_labels

				__lowercase			  =							[1, 1, 1, 1]
				__lowercase			  =							self.num_channels

				__lowercase			  =							64
				__lowercase			  =							128
				__lowercase			  =							self.hidden_dim
				__lowercase			  =							self.hidden_dim
				__lowercase			  =							self.hidden_dim
				return config


	def   _UpperCAmelCase       (self	)							->							str:







				'''simple docstring'''





				__lowercase  ,						__lowercase  ,						__lowercase  ,						__lowercase  ,						__lowercase			  =							self.prepare_config_and_inputs()
				__lowercase			  =							{'''pixel_values''': pixel_values, '''pixel_mask''': pixel_mask}
				return config, inputs_dict


	def   _UpperCAmelCase       (self       ,_lowerCamelCase       ,_lowerCamelCase	)							->							List[str]:







				'''simple docstring'''





				__lowercase			  =							output.encoder_hidden_states
				__lowercase			  =							output.pixel_decoder_hidden_states
				__lowercase			  =							output.transformer_decoder_hidden_states

				self.parent.assertTrue(len(_lowerCamelCase	)       ,len(config.backbone_config.depths	)	)
				self.parent.assertTrue(len(_lowerCamelCase	)       ,len(config.backbone_config.depths	)	)
				self.parent.assertTrue(len(_lowerCamelCase	)       ,config.decoder_layers	)


	def   _UpperCAmelCase       (self       ,_lowerCamelCase       ,_lowerCamelCase       ,_lowerCamelCase       ,_lowerCamelCase=False	)							->							int:







				'''simple docstring'''





				with torch.no_grad():
							__lowercase			  =							MaskaFormerModel(config=_lowerCamelCase	)
							model.to(_lowerCamelCase	)
							model.eval()

							__lowercase			  =							model(pixel_values=_lowerCamelCase       ,pixel_mask=_lowerCamelCase	)
							__lowercase			  =							model(_lowerCamelCase       ,output_hidden_states=_lowerCamelCase	)

				self.parent.assertEqual(
				    output.transformer_decoder_last_hidden_state.shape       ,(self.batch_size, self.num_queries, self.hidden_dim)       ,)
				# let's ensure the other two hidden state exists
				self.parent.assertTrue(output.pixel_decoder_last_hidden_state is not None	)
				self.parent.assertTrue(output.encoder_last_hidden_state is not None	)

				if output_hidden_states:
							self.check_output_hidden_state(_lowerCamelCase       ,_lowerCamelCase	)


	def   _UpperCAmelCase       (self       ,_lowerCamelCase       ,_lowerCamelCase       ,_lowerCamelCase       ,_lowerCamelCase       ,_lowerCamelCase	)							->							str:







				'''simple docstring'''





				__lowercase			  =							MaskaFormerForUniversalSegmentation(config=_lowerCamelCase	)
				model.to(_lowerCamelCase	)
				model.eval()

				def comm_check_on_output(_lowerCamelCase	):
							# let's still check that all the required stuff is there
							self.parent.assertTrue(result.transformer_decoder_last_hidden_state is not None	)
							self.parent.assertTrue(result.pixel_decoder_last_hidden_state is not None	)
							self.parent.assertTrue(result.encoder_last_hidden_state is not None	)
							# okay, now we need to check the logits shape
							# due to the encoder compression, masks have a //4 spatial size
							self.parent.assertEqual(
							    result.masks_queries_logits.shape       ,(self.batch_size, self.num_queries, self.min_size // 4, self.max_size // 4)       ,)
							# + 1 for null class
							self.parent.assertEqual(
							    result.class_queries_logits.shape       ,(self.batch_size, self.num_queries, self.num_labels + 1)	)

				with torch.no_grad():
							__lowercase			  =							model(pixel_values=_lowerCamelCase       ,pixel_mask=_lowerCamelCase	)
							__lowercase			  =							model(_lowerCamelCase	)

							comm_check_on_output(_lowerCamelCase	)

							__lowercase			  =							model(
							    pixel_values=_lowerCamelCase       ,pixel_mask=_lowerCamelCase       ,mask_labels=_lowerCamelCase       ,class_labels=_lowerCamelCase	)

				comm_check_on_output(_lowerCamelCase	)

				self.parent.assertTrue(result.loss is not None	)
				self.parent.assertEqual(result.loss.shape       ,torch.Size([1]	)	)






@require_torch
class 							__lowercase    ( lowerCAmelCase__	, lowerCAmelCase__	, unittest.TestCase  ):







	'''simple docstring'''

	a      :			Union[str, Any]     =    (MaskaFormerModel, MaskaFormerForUniversalSegmentation) if is_torch_available() else ()
	a      :			int     =    {"feature-extraction": MaskaFormerModel} if is_torch_available() else {}

	a      :			List[Any]     =    False
	a      :			Optional[Any]     =    False
	a      :			Any     =    False
	a      :			Optional[int]     =    False


	def   _UpperCAmelCase       (self	)							->							Optional[Any]:







				'''simple docstring'''





				__lowercase			  =							MaskaFormerModelTester(self	)
				__lowercase			  =							ConfigTester(self       ,config_class=_lowerCamelCase       ,has_text_modality=_lowerCamelCase	)


	def   _UpperCAmelCase       (self	)							->							str:







				'''simple docstring'''





				self.config_tester.run_common_tests()


	def   _UpperCAmelCase       (self	)							->							Any:







				'''simple docstring'''





				__lowercase  ,						__lowercase			  =							self.model_tester.prepare_config_and_inputs_for_common()
				self.model_tester.create_and_check_maskaformer_model(_lowerCamelCase       ,**_lowerCamelCase       ,output_hidden_states=_lowerCamelCase	)


	def   _UpperCAmelCase       (self	)							->							int:







				'''simple docstring'''





				__lowercase			  =							self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_maskaformer_instance_segmentation_head_model(*_lowerCamelCase	)


	@unittest.skip(reason='''Mask2Former does not use inputs_embeds'''	)
	def   _UpperCAmelCase       (self	)							->							int:







				'''simple docstring'''





				pass


	@unittest.skip(reason='''Mask2Former does not have a get_input_embeddings method'''	)
	def   _UpperCAmelCase       (self	)							->							Tuple:







				'''simple docstring'''





				pass


	@unittest.skip(reason='''Mask2Former is not a generative model'''	)
	def   _UpperCAmelCase       (self	)							->							List[Any]:







				'''simple docstring'''





				pass


	@unittest.skip(reason='''Mask2Former does not use token embeddings'''	)
	def   _UpperCAmelCase       (self	)							->							str:







				'''simple docstring'''





				pass


	@require_torch_multi_gpu
	@unittest.skip(
	    reason='''Mask2Former has some layers using `add_module` which doesn\'t work well with `nn.DataParallel`'''	)
	def   _UpperCAmelCase       (self	)							->							str:







				'''simple docstring'''





				pass


	@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.'''	)
	def   _UpperCAmelCase       (self	)							->							Optional[Any]:







				'''simple docstring'''





				pass


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







				'''simple docstring'''





				__lowercase  ,						__lowercase			  =							self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
							__lowercase			  =							model_class(_lowerCamelCase	)
							__lowercase			  =							inspect.signature(model.forward	)
							# signature.parameters is an OrderedDict => so arg_names order is deterministic
							__lowercase			  =							[*signature.parameters.keys()]

							__lowercase			  =							['''pixel_values''']
							self.assertListEqual(arg_names[:1]       ,_lowerCamelCase	)


	@slow
	def   _UpperCAmelCase       (self	)							->							Optional[int]:







				'''simple docstring'''





				for model_name in ["facebook/mask2former-swin-small-coco-instance"]:
							__lowercase			  =							MaskaFormerModel.from_pretrained(_lowerCamelCase	)
							self.assertIsNotNone(_lowerCamelCase	)


	def   _UpperCAmelCase       (self	)							->							Union[str, Any]:







				'''simple docstring'''





				__lowercase			  =							(self.model_tester.min_size,) * 2
				__lowercase			  =							{
				    '''pixel_values''': torch.randn((2, 3, *size)       ,device=_lowerCamelCase	),
				    '''mask_labels''': torch.randn((2, 10, *size)       ,device=_lowerCamelCase	),
				    '''class_labels''': torch.zeros(2       ,10       ,device=_lowerCamelCase	).long(),
				}
				__lowercase			  =							self.model_tester.get_config()

				__lowercase			  =							MaskaFormerForUniversalSegmentation(_lowerCamelCase	).to(_lowerCamelCase	)
				__lowercase			  =							model(**_lowerCamelCase	)
				self.assertTrue(outputs.loss is not None	)


	def   _UpperCAmelCase       (self	)							->							Any:







				'''simple docstring'''





				__lowercase  ,						__lowercase			  =							self.model_tester.prepare_config_and_inputs_for_common()
				self.model_tester.create_and_check_maskaformer_model(_lowerCamelCase       ,**_lowerCamelCase       ,output_hidden_states=_lowerCamelCase	)


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







				'''simple docstring'''





				__lowercase  ,						__lowercase			  =							self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
							__lowercase			  =							model_class(_lowerCamelCase	).to(_lowerCamelCase	)
							__lowercase			  =							model(**_lowerCamelCase       ,output_attentions=_lowerCamelCase	)
							self.assertTrue(outputs.attentions is not None	)


	def   _UpperCAmelCase       (self	)							->							str:







				'''simple docstring'''





				if not self.model_tester.is_training:
							return

				__lowercase			  =							self.all_model_classes[1]
				__lowercase  ,						__lowercase  ,						__lowercase  ,						__lowercase  ,						__lowercase			  =							self.model_tester.prepare_config_and_inputs()

				__lowercase			  =							model_class(_lowerCamelCase	)
				model.to(_lowerCamelCase	)
				model.train()

				__lowercase			  =							model(_lowerCamelCase       ,mask_labels=_lowerCamelCase       ,class_labels=_lowerCamelCase	).loss
				loss.backward()


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







				'''simple docstring'''





				__lowercase			  =							self.all_model_classes[1]
				__lowercase  ,						__lowercase  ,						__lowercase  ,						__lowercase  ,						__lowercase			  =							self.model_tester.prepare_config_and_inputs()
				__lowercase			  =							True
				__lowercase			  =							True

				__lowercase			  =							model_class(_lowerCamelCase	).to(_lowerCamelCase	)
				model.train()

				__lowercase			  =							model(_lowerCamelCase       ,mask_labels=_lowerCamelCase       ,class_labels=_lowerCamelCase	)

				__lowercase			  =							outputs.encoder_hidden_states[0]
				encoder_hidden_states.retain_grad()

				__lowercase			  =							outputs.pixel_decoder_hidden_states[0]
				pixel_decoder_hidden_states.retain_grad()

				__lowercase			  =							outputs.transformer_decoder_hidden_states[0]
				transformer_decoder_hidden_states.retain_grad()

				__lowercase			  =							outputs.attentions[0]
				attentions.retain_grad()

				outputs.loss.backward(retain_graph=_lowerCamelCase	)

				self.assertIsNotNone(encoder_hidden_states.grad	)
				self.assertIsNotNone(pixel_decoder_hidden_states.grad	)
				self.assertIsNotNone(transformer_decoder_hidden_states.grad	)
				self.assertIsNotNone(attentions.grad	)





_SCREAMING_SNAKE_CASE   =      1e-4




def 				_lowerCAmelCase  (			):
			__lowercase			  =							Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''			)
			return image






@require_vision
@slow
class 							__lowercase    ( unittest.TestCase  ):







	'''simple docstring'''

	@cached_property
	def   _UpperCAmelCase       (self	)							->							Tuple:







				'''simple docstring'''





				return "facebook/mask2former-swin-small-coco-instance"


	@cached_property
	def   _UpperCAmelCase       (self	)							->							str:







				'''simple docstring'''





				return MaskaFormerImageProcessor.from_pretrained(self.model_checkpoints	) if is_vision_available() else None


	def   _UpperCAmelCase       (self	)							->							Union[str, Any]:







				'''simple docstring'''





				__lowercase			  =							MaskaFormerModel.from_pretrained(self.model_checkpoints	).to(_lowerCamelCase	)
				__lowercase			  =							self.default_image_processor
				__lowercase			  =							prepare_img()
				__lowercase			  =							image_processor(_lowerCamelCase       ,return_tensors='''pt'''	).to(_lowerCamelCase	)
				__lowercase			  =							inputs['''pixel_values'''].shape
				# check size is divisible by 32
				self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0	)
				# check size
				self.assertEqual(_lowerCamelCase       ,(1, 3, 384, 384)	)

				with torch.no_grad():
							__lowercase			  =							model(**_lowerCamelCase	)

				__lowercase			  =							torch.tensor(
				    [[-0.2_7_9_0, -1.0_7_1_7, -1.1_6_6_8], [-0.5_1_2_8, -0.3_1_2_8, -0.4_9_8_7], [-0.5_8_3_2, 0.1_9_7_1, -0.0_1_9_7]]	).to(_lowerCamelCase	)
				self.assertTrue(
				    torch.allclose(
				        outputs.encoder_last_hidden_state[0, 0, :3, :3]       ,_lowerCamelCase       ,atol=_lowerCamelCase	)	)

				__lowercase			  =							torch.tensor(
				    [[0.8_9_7_3, 1.1_8_4_7, 1.1_7_7_6], [1.1_9_3_4, 1.5_0_4_0, 1.5_1_2_8], [1.1_1_5_3, 1.4_4_8_6, 1.4_9_5_1]]	).to(_lowerCamelCase	)
				self.assertTrue(
				    torch.allclose(
				        outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3]       ,_lowerCamelCase       ,atol=_lowerCamelCase	)	)

				__lowercase			  =							torch.tensor(
				    [[2.1_1_5_2, 1.7_0_0_0, -0.8_6_0_3], [1.5_8_0_8, 1.8_0_0_4, -0.9_3_5_3], [1.6_0_4_3, 1.7_4_9_5, -0.5_9_9_9]]	).to(_lowerCamelCase	)
				self.assertTrue(
				    torch.allclose(
				        outputs.transformer_decoder_last_hidden_state[0, :3, :3]       ,_lowerCamelCase       ,atol=_lowerCamelCase	)	)


	def   _UpperCAmelCase       (self	)							->							List[str]:







				'''simple docstring'''





				__lowercase			  =							MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints	).to(_lowerCamelCase	).eval()
				__lowercase			  =							self.default_image_processor
				__lowercase			  =							prepare_img()
				__lowercase			  =							image_processor(_lowerCamelCase       ,return_tensors='''pt'''	).to(_lowerCamelCase	)
				__lowercase			  =							inputs['''pixel_values'''].shape
				# check size is divisible by 32
				self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0	)
				# check size
				self.assertEqual(_lowerCamelCase       ,(1, 3, 384, 384)	)

				with torch.no_grad():
							__lowercase			  =							model(**_lowerCamelCase	)
				# masks_queries_logits
				__lowercase			  =							outputs.masks_queries_logits
				self.assertEqual(
				    masks_queries_logits.shape       ,(1, model.config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4)	)
				__lowercase			  =							[
				    [-8.7_8_3_9, -9.0_0_5_6, -8.8_1_2_1],
				    [-7.4_1_0_4, -7.0_3_1_3, -6.5_4_0_1],
				    [-6.6_1_0_5, -6.3_4_2_7, -6.4_6_7_5],
				]
				__lowercase			  =							torch.tensor(_lowerCamelCase	).to(_lowerCamelCase	)
				self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3]       ,_lowerCamelCase       ,atol=_lowerCamelCase	)	)
				# class_queries_logits
				__lowercase			  =							outputs.class_queries_logits
				self.assertEqual(class_queries_logits.shape       ,(1, model.config.num_queries, model.config.num_labels + 1)	)
				__lowercase			  =							torch.tensor(
				    [
				        [1.8_3_2_4, -8.0_8_3_5, -4.1_9_2_2],
				        [0.8_4_5_0, -9.0_0_5_0, -3.6_0_5_3],
				        [0.3_0_4_5, -7.7_2_9_3, -3.0_2_7_5],
				    ]	).to(_lowerCamelCase	)
				self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3]       ,_lowerCamelCase       ,atol=_lowerCamelCase	)	)


	def   _UpperCAmelCase       (self	)							->							str:







				'''simple docstring'''





				__lowercase			  =							MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints	).to(_lowerCamelCase	).eval()
				__lowercase			  =							self.default_image_processor

				__lowercase			  =							image_processor(
				    [np.zeros((3, 800, 1333)	), np.zeros((3, 800, 1333)	)]       ,segmentation_maps=[np.zeros((384, 384)	).astype(np.floataa	), np.zeros((384, 384)	).astype(np.floataa	)]       ,return_tensors='''pt'''       ,)

				__lowercase			  =							inputs['''pixel_values'''].to(_lowerCamelCase	)
				__lowercase			  =							[el.to(_lowerCamelCase	) for el in inputs['''mask_labels''']]
				__lowercase			  =							[el.to(_lowerCamelCase	) for el in inputs['''class_labels''']]

				with torch.no_grad():
							__lowercase			  =							model(**_lowerCamelCase	)

				self.assertTrue(outputs.loss is not None	)












'''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


_SCREAMING_SNAKE_CASE   =      ['''bart.large''', '''bart.large.mnli''', '''bart.large.cnn''', '''bart_xsum/model.pt''']
_SCREAMING_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()
_SCREAMING_SNAKE_CASE   =      logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE   =      ''' Hello world! cécé herlolip'''

_SCREAMING_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 				_lowerCAmelCase  (			lowerCamelCase_     :					int			):
			__lowercase			  =							[
			    '''encoder.version''',
			    '''decoder.version''',
			    '''model.encoder.version''',
			    '''model.decoder.version''',
			    '''_float_tensor''',
			]
			for k in ignore_keys:
						state_dict.pop(lowerCamelCase_						,    lowerCamelCase_			)




def 				_lowerCAmelCase  (			lowerCamelCase_     :					Tuple						,    lowerCamelCase_     :					str						,    lowerCamelCase_     :					Union[str, Any]			):
			__lowercase			  =							dct.pop(lowerCamelCase_			)
			__lowercase			  =							val




def 				_lowerCAmelCase  (			lowerCamelCase_     :					Any			):
			__lowercase			  =							torch.load(lowerCamelCase_						,    map_location='''cpu'''			)
			__lowercase			  =							torch.hub.load('''pytorch/fairseq'''						,    '''bart.large.cnn'''			).eval()
			hub_interface.model.load_state_dict(sd['''model''']			)
			return hub_interface




def 				_lowerCAmelCase  (			lowerCamelCase_     :					List[str]			):
			__lowercase  ,						__lowercase			  =							emb.weight.shape
			__lowercase			  =							nn.Linear(lowerCamelCase_						,    lowerCamelCase_						,    bias=lowerCamelCase_			)
			__lowercase			  =							emb.weight.data
			return lin_layer




@torch.no_grad()
def 				_lowerCAmelCase  (			lowerCamelCase_     :					Union[str, Any]						,    lowerCamelCase_     :					Optional[Any]						,    lowerCamelCase_     :					List[Any]=None			):
			if not os.path.exists(lowerCamelCase_			):
						__lowercase			  =							torch.hub.load('''pytorch/fairseq'''						,    lowerCamelCase_			).eval()
			else:
						__lowercase			  =							load_xsum_checkpoint(lowerCamelCase_			)

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





