





'''simple docstring'''



import unittest

import numpy as np
from datasets import load_dataset

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 BeitImageProcessor






class 							__lowercase    ( unittest.TestCase  ):







	'''simple docstring'''

	def __init__(self       ,_lowerCamelCase       ,_lowerCamelCase=7       ,_lowerCamelCase=3       ,_lowerCamelCase=18       ,_lowerCamelCase=30       ,_lowerCamelCase=400       ,_lowerCamelCase=True       ,_lowerCamelCase=None       ,_lowerCamelCase=True       ,_lowerCamelCase=None       ,_lowerCamelCase=True       ,_lowerCamelCase=[0.5, 0.5, 0.5]       ,_lowerCamelCase=[0.5, 0.5, 0.5]       ,_lowerCamelCase=False       ,)							->							int:







				'''simple docstring'''





				__lowercase			  =							size if size is not None else {'''height''': 20, '''width''': 20}
				__lowercase			  =							crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}
				__lowercase			  =							parent
				__lowercase			  =							batch_size
				__lowercase			  =							num_channels
				__lowercase			  =							image_size
				__lowercase			  =							min_resolution
				__lowercase			  =							max_resolution
				__lowercase			  =							do_resize
				__lowercase			  =							size
				__lowercase			  =							do_center_crop
				__lowercase			  =							crop_size
				__lowercase			  =							do_normalize
				__lowercase			  =							image_mean
				__lowercase			  =							image_std
				__lowercase			  =							do_reduce_labels


	def   _UpperCAmelCase       (self	)							->							str:







				'''simple docstring'''





				return {
				    "do_resize": self.do_resize,
				    "size": self.size,
				    "do_center_crop": self.do_center_crop,
				    "crop_size": self.crop_size,
				    "do_normalize": self.do_normalize,
				    "image_mean": self.image_mean,
				    "image_std": self.image_std,
				    "do_reduce_labels": self.do_reduce_labels,
				}





def 				_lowerCAmelCase  (			):
			__lowercase			  =							load_dataset('''hf-internal-testing/fixtures_ade20k'''						,    split='''test'''			)

			__lowercase			  =							Image.open(dataset[0]['''file''']			)
			__lowercase			  =							Image.open(dataset[1]['''file''']			)

			return image, map




def 				_lowerCAmelCase  (			):
			__lowercase			  =							load_dataset('''hf-internal-testing/fixtures_ade20k'''						,    split='''test'''			)

			__lowercase			  =							Image.open(ds[0]['''file''']			)
			__lowercase			  =							Image.open(ds[1]['''file''']			)
			__lowercase			  =							Image.open(ds[2]['''file''']			)
			__lowercase			  =							Image.open(ds[3]['''file''']			)

			return [imagea, imagea], [mapa, mapa]






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







	'''simple docstring'''

	a      :			Dict     =    BeitImageProcessor if is_vision_available() else None


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







				'''simple docstring'''





				__lowercase			  =							BeitImageProcessingTester(self	)


	@property
	def   _UpperCAmelCase       (self	)							->							Any:







				'''simple docstring'''





				return self.image_processor_tester.prepare_image_processor_dict()


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







				'''simple docstring'''





				__lowercase			  =							self.image_processing_class(**self.image_processor_dict	)
				self.assertTrue(hasattr(_lowerCamelCase       ,'''do_resize'''	)	)
				self.assertTrue(hasattr(_lowerCamelCase       ,'''size'''	)	)
				self.assertTrue(hasattr(_lowerCamelCase       ,'''do_center_crop'''	)	)
				self.assertTrue(hasattr(_lowerCamelCase       ,'''center_crop'''	)	)
				self.assertTrue(hasattr(_lowerCamelCase       ,'''do_normalize'''	)	)
				self.assertTrue(hasattr(_lowerCamelCase       ,'''image_mean'''	)	)
				self.assertTrue(hasattr(_lowerCamelCase       ,'''image_std'''	)	)


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







				'''simple docstring'''





				__lowercase			  =							self.image_processing_class.from_dict(self.image_processor_dict	)
				self.assertEqual(image_processor.size       ,{'''height''': 20, '''width''': 20}	)
				self.assertEqual(image_processor.crop_size       ,{'''height''': 18, '''width''': 18}	)
				self.assertEqual(image_processor.do_reduce_labels       ,_lowerCamelCase	)

				__lowercase			  =							self.image_processing_class.from_dict(
				    self.image_processor_dict       ,size=42       ,crop_size=84       ,reduce_labels=_lowerCamelCase	)
				self.assertEqual(image_processor.size       ,{'''height''': 42, '''width''': 42}	)
				self.assertEqual(image_processor.crop_size       ,{'''height''': 84, '''width''': 84}	)
				self.assertEqual(image_processor.do_reduce_labels       ,_lowerCamelCase	)


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







				'''simple docstring'''





				pass


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







				'''simple docstring'''





				__lowercase			  =							self.image_processing_class(**self.image_processor_dict	)
				# create random PIL images
				__lowercase			  =							prepare_image_inputs(self.image_processor_tester       ,equal_resolution=_lowerCamelCase	)
				for image in image_inputs:
							self.assertIsInstance(_lowerCamelCase       ,Image.Image	)

				# Test not batched input
				__lowercase			  =							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
				__lowercase			  =							image_processing(_lowerCamelCase       ,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   _UpperCAmelCase       (self	)							->							Dict:







				'''simple docstring'''





				__lowercase			  =							self.image_processing_class(**self.image_processor_dict	)
				# create random numpy tensors
				__lowercase			  =							prepare_image_inputs(self.image_processor_tester       ,equal_resolution=_lowerCamelCase       ,numpify=_lowerCamelCase	)
				for image in image_inputs:
							self.assertIsInstance(_lowerCamelCase       ,np.ndarray	)

				# Test not batched input
				__lowercase			  =							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
				__lowercase			  =							image_processing(_lowerCamelCase       ,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   _UpperCAmelCase       (self	)							->							Optional[Any]:







				'''simple docstring'''





				__lowercase			  =							self.image_processing_class(**self.image_processor_dict	)
				# create random PyTorch tensors
				__lowercase			  =							prepare_image_inputs(self.image_processor_tester       ,equal_resolution=_lowerCamelCase       ,torchify=_lowerCamelCase	)
				for image in image_inputs:
							self.assertIsInstance(_lowerCamelCase       ,torch.Tensor	)

				# Test not batched input
				__lowercase			  =							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
				__lowercase			  =							image_processing(_lowerCamelCase       ,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   _UpperCAmelCase       (self	)							->							Union[str, Any]:







				'''simple docstring'''





				__lowercase			  =							self.image_processing_class(**self.image_processor_dict	)
				# create random PyTorch tensors
				__lowercase			  =							prepare_image_inputs(self.image_processor_tester       ,equal_resolution=_lowerCamelCase       ,torchify=_lowerCamelCase	)
				__lowercase			  =							[]
				for image in image_inputs:
							self.assertIsInstance(_lowerCamelCase       ,torch.Tensor	)
							maps.append(torch.zeros(image.shape[-2:]	).long()	)

				# Test not batched input
				__lowercase			  =							image_processing(image_inputs[0]       ,maps[0]       ,return_tensors='''pt'''	)
				self.assertEqual(
				    encoding['''pixel_values'''].shape       ,(
				        1,
				        self.image_processor_tester.num_channels,
				        self.image_processor_tester.crop_size['''height'''],
				        self.image_processor_tester.crop_size['''width'''],
				    )       ,)
				self.assertEqual(
				    encoding['''labels'''].shape       ,(
				        1,
				        self.image_processor_tester.crop_size['''height'''],
				        self.image_processor_tester.crop_size['''width'''],
				    )       ,)
				self.assertEqual(encoding['''labels'''].dtype       ,torch.long	)
				self.assertTrue(encoding['''labels'''].min().item() >= 0	)
				self.assertTrue(encoding['''labels'''].max().item() <= 255	)

				# Test batched
				__lowercase			  =							image_processing(_lowerCamelCase       ,_lowerCamelCase       ,return_tensors='''pt'''	)
				self.assertEqual(
				    encoding['''pixel_values'''].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'''],
				    )       ,)
				self.assertEqual(
				    encoding['''labels'''].shape       ,(
				        self.image_processor_tester.batch_size,
				        self.image_processor_tester.crop_size['''height'''],
				        self.image_processor_tester.crop_size['''width'''],
				    )       ,)
				self.assertEqual(encoding['''labels'''].dtype       ,torch.long	)
				self.assertTrue(encoding['''labels'''].min().item() >= 0	)
				self.assertTrue(encoding['''labels'''].max().item() <= 255	)

				# Test not batched input (PIL images)
				__lowercase			  =							prepare_semantic_single_inputs()

				__lowercase			  =							image_processing(_lowerCamelCase       ,_lowerCamelCase       ,return_tensors='''pt'''	)
				self.assertEqual(
				    encoding['''pixel_values'''].shape       ,(
				        1,
				        self.image_processor_tester.num_channels,
				        self.image_processor_tester.crop_size['''height'''],
				        self.image_processor_tester.crop_size['''width'''],
				    )       ,)
				self.assertEqual(
				    encoding['''labels'''].shape       ,(
				        1,
				        self.image_processor_tester.crop_size['''height'''],
				        self.image_processor_tester.crop_size['''width'''],
				    )       ,)
				self.assertEqual(encoding['''labels'''].dtype       ,torch.long	)
				self.assertTrue(encoding['''labels'''].min().item() >= 0	)
				self.assertTrue(encoding['''labels'''].max().item() <= 255	)

				# Test batched input (PIL images)
				__lowercase			  =							prepare_semantic_batch_inputs()

				__lowercase			  =							image_processing(_lowerCamelCase       ,_lowerCamelCase       ,return_tensors='''pt'''	)
				self.assertEqual(
				    encoding['''pixel_values'''].shape       ,(
				        2,
				        self.image_processor_tester.num_channels,
				        self.image_processor_tester.crop_size['''height'''],
				        self.image_processor_tester.crop_size['''width'''],
				    )       ,)
				self.assertEqual(
				    encoding['''labels'''].shape       ,(
				        2,
				        self.image_processor_tester.crop_size['''height'''],
				        self.image_processor_tester.crop_size['''width'''],
				    )       ,)
				self.assertEqual(encoding['''labels'''].dtype       ,torch.long	)
				self.assertTrue(encoding['''labels'''].min().item() >= 0	)
				self.assertTrue(encoding['''labels'''].max().item() <= 255	)


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







				'''simple docstring'''





				__lowercase			  =							self.image_processing_class(**self.image_processor_dict	)

				# ADE20k has 150 classes, and the background is included, so labels should be between 0 and 150
				__lowercase			  =							prepare_semantic_single_inputs()
				__lowercase			  =							image_processing(_lowerCamelCase       ,_lowerCamelCase       ,return_tensors='''pt'''	)
				self.assertTrue(encoding['''labels'''].min().item() >= 0	)
				self.assertTrue(encoding['''labels'''].max().item() <= 150	)

				__lowercase			  =							True
				__lowercase			  =							image_processing(_lowerCamelCase       ,_lowerCamelCase       ,return_tensors='''pt'''	)
				self.assertTrue(encoding['''labels'''].min().item() >= 0	)
				self.assertTrue(encoding['''labels'''].max().item() <= 255	)












'''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)





