





'''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 YolosImageProcessor






class 							__lowercase    ( unittest.TestCase  ):







	'''simple docstring'''

	def __init__(self       ,_lowerCamelCase       ,_lowerCamelCase=7       ,_lowerCamelCase=3       ,_lowerCamelCase=30       ,_lowerCamelCase=400       ,_lowerCamelCase=True       ,_lowerCamelCase=None       ,_lowerCamelCase=True       ,_lowerCamelCase=[0.5, 0.5, 0.5]       ,_lowerCamelCase=[0.5, 0.5, 0.5]       ,_lowerCamelCase=True       ,_lowerCamelCase=1 / 255       ,_lowerCamelCase=True       ,)							->							Dict:







				'''simple docstring'''





				__lowercase			  =							size if size is not None else {'''shortest_edge''': 18, '''longest_edge''': 1333}
				__lowercase			  =							parent
				__lowercase			  =							batch_size
				__lowercase			  =							num_channels
				__lowercase			  =							min_resolution
				__lowercase			  =							max_resolution
				__lowercase			  =							do_resize
				__lowercase			  =							size
				__lowercase			  =							do_normalize
				__lowercase			  =							image_mean
				__lowercase			  =							image_std
				__lowercase			  =							do_rescale
				__lowercase			  =							rescale_factor
				__lowercase			  =							do_pad


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







				'''simple docstring'''





				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       ,_lowerCamelCase       ,_lowerCamelCase=False	)							->							List[Any]:







				'''simple docstring'''





				if not batched:
							__lowercase			  =							image_inputs[0]
							if isinstance(_lowerCamelCase       ,Image.Image	):
										__lowercase  ,						__lowercase			  =							image.size
							else:
										__lowercase  ,						__lowercase			  =							image.shape[1], image.shape[2]
							if w < h:
										__lowercase			  =							int(self.size['''shortest_edge'''] * h / w	)
										__lowercase			  =							self.size['''shortest_edge''']
							elif w > h:
										__lowercase			  =							self.size['''shortest_edge''']
										__lowercase			  =							int(self.size['''shortest_edge'''] * w / h	)
							else:
										__lowercase			  =							self.size['''shortest_edge''']
										__lowercase			  =							self.size['''shortest_edge''']

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

				return expected_height, expected_width






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







	'''simple docstring'''

	a      :			List[str]     =    YolosImageProcessor if is_vision_available() else None


	def   _UpperCAmelCase       (self	)							->							Any:







				'''simple docstring'''





				__lowercase			  =							YolosImageProcessingTester(self	)


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







				'''simple docstring'''





				return self.image_processor_tester.prepare_image_processor_dict()


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







				'''simple docstring'''





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


	def   _UpperCAmelCase       (self	)							->							Any:







				'''simple docstring'''





				__lowercase			  =							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       ,_lowerCamelCase	)

				__lowercase			  =							self.image_processing_class.from_dict(
				    self.image_processor_dict       ,size=42       ,max_size=84       ,pad_and_return_pixel_mask=_lowerCamelCase	)
				self.assertEqual(image_processor.size       ,{'''shortest_edge''': 42, '''longest_edge''': 84}	)
				self.assertEqual(image_processor.do_pad       ,_lowerCamelCase	)


	def   _UpperCAmelCase       (self	)							->							int:







				'''simple docstring'''





				pass


	def   _UpperCAmelCase       (self	)							->							Any:







				'''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

				__lowercase  ,						__lowercase			  =							self.image_processor_tester.get_expected_values(_lowerCamelCase	)

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

				# Test batched
				__lowercase  ,						__lowercase			  =							self.image_processor_tester.get_expected_values(_lowerCamelCase       ,batched=_lowerCamelCase	)

				__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,
				        expected_height,
				        expected_width,
				    )       ,)


	def   _UpperCAmelCase       (self	)							->							str:







				'''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

				__lowercase  ,						__lowercase			  =							self.image_processor_tester.get_expected_values(_lowerCamelCase	)

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

				# Test batched
				__lowercase			  =							image_processing(_lowerCamelCase       ,return_tensors='''pt'''	).pixel_values

				__lowercase  ,						__lowercase			  =							self.image_processor_tester.get_expected_values(_lowerCamelCase       ,batched=_lowerCamelCase	)

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


	def   _UpperCAmelCase       (self	)							->							Tuple:







				'''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

				__lowercase  ,						__lowercase			  =							self.image_processor_tester.get_expected_values(_lowerCamelCase	)

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

				# Test batched
				__lowercase			  =							image_processing(_lowerCamelCase       ,return_tensors='''pt'''	).pixel_values

				__lowercase  ,						__lowercase			  =							self.image_processor_tester.get_expected_values(_lowerCamelCase       ,batched=_lowerCamelCase	)

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


	def   _UpperCAmelCase       (self	)							->							Any:







				'''simple docstring'''





				__lowercase			  =							self.image_processing_class(**self.image_processor_dict	)
				__lowercase			  =							self.image_processing_class(do_resize=_lowerCamelCase       ,do_normalize=_lowerCamelCase       ,do_rescale=_lowerCamelCase	)
				# 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 whether the method "pad" and calling the image processor return the same tensors
				__lowercase			  =							image_processing_a.pad(_lowerCamelCase       ,return_tensors='''pt'''	)
				__lowercase			  =							image_processing_a(_lowerCamelCase       ,return_tensors='''pt'''	)

				self.assertTrue(
				    torch.allclose(encoded_images_with_method['''pixel_values''']       ,encoded_images['''pixel_values''']       ,atol=1E-4	)	)


	@slow
	def   _UpperCAmelCase       (self	)							->							int:







				'''simple docstring'''





				__lowercase			  =							Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''	)
				with open('''./tests/fixtures/tests_samples/COCO/coco_annotations.txt'''       ,'''r'''	) as f:
							__lowercase			  =							json.loads(f.read()	)

				__lowercase			  =							{'''image_id''': 39769, '''annotations''': target}

				# encode them
				__lowercase			  =							YolosImageProcessor.from_pretrained('''hustvl/yolos-small'''	)
				__lowercase			  =							image_processing(images=_lowerCamelCase       ,annotations=_lowerCamelCase       ,return_tensors='''pt'''	)

				# verify pixel values
				__lowercase			  =							torch.Size([1, 3, 800, 1066]	)
				self.assertEqual(encoding['''pixel_values'''].shape       ,_lowerCamelCase	)

				__lowercase			  =							torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1]	)
				self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3]       ,_lowerCamelCase       ,atol=1E-4	)	)

				# verify area
				__lowercase			  =							torch.tensor([5887.9600, 11250.2061, 489353.8438, 837122.7500, 147967.5156, 165732.3438]	)
				self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']       ,_lowerCamelCase	)	)
				# verify boxes
				__lowercase			  =							torch.Size([6, 4]	)
				self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape       ,_lowerCamelCase	)
				__lowercase			  =							torch.tensor([0.5_5_0_3, 0.2_7_6_5, 0.0_6_0_4, 0.2_2_1_5]	)
				self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]       ,_lowerCamelCase       ,atol=1E-3	)	)
				# verify image_id
				__lowercase			  =							torch.tensor([39769]	)
				self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']       ,_lowerCamelCase	)	)
				# verify is_crowd
				__lowercase			  =							torch.tensor([0, 0, 0, 0, 0, 0]	)
				self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']       ,_lowerCamelCase	)	)
				# verify class_labels
				__lowercase			  =							torch.tensor([75, 75, 63, 65, 17, 17]	)
				self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']       ,_lowerCamelCase	)	)
				# verify orig_size
				__lowercase			  =							torch.tensor([480, 640]	)
				self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']       ,_lowerCamelCase	)	)
				# verify size
				__lowercase			  =							torch.tensor([800, 1066]	)
				self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']       ,_lowerCamelCase	)	)


	@slow
	def   _UpperCAmelCase       (self	)							->							Any:







				'''simple docstring'''





				__lowercase			  =							Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''	)
				with open('''./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt'''       ,'''r'''	) as f:
							__lowercase			  =							json.loads(f.read()	)

				__lowercase			  =							{'''file_name''': '''000000039769.png''', '''image_id''': 39769, '''segments_info''': target}

				__lowercase			  =							pathlib.Path('''./tests/fixtures/tests_samples/COCO/coco_panoptic'''	)

				# encode them
				__lowercase			  =							YolosImageProcessor(format='''coco_panoptic'''	)
				__lowercase			  =							image_processing(images=_lowerCamelCase       ,annotations=_lowerCamelCase       ,masks_path=_lowerCamelCase       ,return_tensors='''pt'''	)

				# verify pixel values
				__lowercase			  =							torch.Size([1, 3, 800, 1066]	)
				self.assertEqual(encoding['''pixel_values'''].shape       ,_lowerCamelCase	)

				__lowercase			  =							torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1]	)
				self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3]       ,_lowerCamelCase       ,atol=1E-4	)	)

				# verify area
				__lowercase			  =							torch.tensor([147979.6875, 165527.0469, 484638.5938, 11292.9375, 5879.6562, 7634.1147]	)
				self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']       ,_lowerCamelCase	)	)
				# verify boxes
				__lowercase			  =							torch.Size([6, 4]	)
				self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape       ,_lowerCamelCase	)
				__lowercase			  =							torch.tensor([0.2_6_2_5, 0.5_4_3_7, 0.4_6_8_8, 0.8_6_2_5]	)
				self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]       ,_lowerCamelCase       ,atol=1E-3	)	)
				# verify image_id
				__lowercase			  =							torch.tensor([39769]	)
				self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']       ,_lowerCamelCase	)	)
				# verify is_crowd
				__lowercase			  =							torch.tensor([0, 0, 0, 0, 0, 0]	)
				self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']       ,_lowerCamelCase	)	)
				# verify class_labels
				__lowercase			  =							torch.tensor([17, 17, 63, 75, 75, 93]	)
				self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']       ,_lowerCamelCase	)	)
				# verify masks
				__lowercase			  =							822873
				self.assertEqual(encoding['''labels'''][0]['''masks'''].sum().item()       ,_lowerCamelCase	)
				# verify orig_size
				__lowercase			  =							torch.tensor([480, 640]	)
				self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']       ,_lowerCamelCase	)	)
				# verify size
				__lowercase			  =							torch.tensor([800, 1066]	)
				self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']       ,_lowerCamelCase	)	)












'''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)





