






import inspect
import math
import tempfile
import unittest

import numpy as np

from transformers import ViTMAEConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
	import torch
	from torch import nn

	from transformers import ViTMAEForPreTraining, ViTMAEModel
	from transformers.models.vit.modeling_vit import VIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
	from PIL import Image

	from transformers import ViTImageProcessor



class 							SCREAMING_SNAKE_CASE__						:





					"""simple docstring"""






					def __init__( self					:  Dict							,		__A					:  Tuple							,		__A					:  str=1_3							,		__A					:  List[str]=3_0							,		__A					:  Any=2							,		__A					:  Tuple=3							,		__A					:  Dict=True							,		__A					:  str=True							,		__A					:  Dict=3_2							,		__A					:  str=5							,		__A					:  Tuple=4							,		__A					:  List[str]=3_7							,		__A					:  List[str]="gelu"							,		__A					:  List[str]=0.1							,		__A					:  Tuple=0.1							,		__A					:  str=1_0							,		__A					:  List[str]=0.0_2							,		__A					:  Union[str, Any]=3							,		__A					:  Optional[Any]=0.6							,		__A					:  List[str]=None							,		):
						snake_case__			:				int       =  parent
						snake_case__			:				Union[str, Any]       =  batch_size
						snake_case__			:				List[Any]       =  image_size
						snake_case__			:				Optional[Any]       =  patch_size
						snake_case__			:				Tuple       =  num_channels
						snake_case__			:				List[str]       =  is_training
						snake_case__			:				Optional[Any]       =  use_labels
						snake_case__			:				str       =  hidden_size
						snake_case__			:				int       =  num_hidden_layers
						snake_case__			:				Any       =  num_attention_heads
						snake_case__			:				Optional[Any]       =  intermediate_size
						snake_case__			:				Optional[int]       =  hidden_act
						snake_case__			:				Tuple       =  hidden_dropout_prob
						snake_case__			:				Union[str, Any]       =  attention_probs_dropout_prob
						snake_case__			:				Dict       =  type_sequence_label_size
						snake_case__			:				Optional[Any]       =  initializer_range
						snake_case__			:				Union[str, Any]       =  mask_ratio
						snake_case__			:				int       =  scope

						# in ViTMAE, the expected sequence length = (num_patches + 1) * (1 - config.mask_ratio), rounded above
						# (we add 1 for the [CLS] token)
						snake_case__			:				Tuple       =  (image_size // patch_size) ** 2
						snake_case__			:				Tuple       =  int(math.ceil((1 - mask_ratio) * (num_patches + 1)	)	)



					def 	_lowercase					( self					:  Optional[int]	):
						snake_case__			:				Optional[Any]       =  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]	)

						snake_case__			:				int       =  None
						if self.use_labels:
							snake_case__			:				Tuple       =  ids_tensor([self.batch_size]							,		self.type_sequence_label_size	)

						snake_case__			:				List[str]       =  self.get_config()

						return config, pixel_values, labels



					def 	_lowercase					( self					:  Any	):
						return ViTMAEConfig(
						    image_size=self.image_size							,		patch_size=self.patch_size							,		num_channels=self.num_channels							,		hidden_size=self.hidden_size							,		num_hidden_layers=self.num_hidden_layers							,		num_attention_heads=self.num_attention_heads							,		intermediate_size=self.intermediate_size							,		hidden_act=self.hidden_act							,		hidden_dropout_prob=self.hidden_dropout_prob							,		attention_probs_dropout_prob=self.attention_probs_dropout_prob							,		is_decoder=__A							,		initializer_range=self.initializer_range							,		mask_ratio=self.mask_ratio							,		)



					def 	_lowercase					( self					:  Optional[Any]							,		__A					:  Optional[int]							,		__A					:  Optional[Any]							,		__A					:  Dict	):
						snake_case__			:				Dict       =  ViTMAEModel(config=__A	)
						model.to(__A	)
						model.eval()
						snake_case__			:				List[str]       =  model(__A	)
						self.parent.assertEqual(result.last_hidden_state.shape							,		(self.batch_size, self.seq_length, self.hidden_size)	)



					def 	_lowercase					( self					:  Dict							,		__A					:  Any							,		__A					:  Tuple							,		__A					:  str	):
						snake_case__			:				int       =  ViTMAEForPreTraining(__A	)
						model.to(__A	)
						model.eval()
						snake_case__			:				Any       =  model(__A	)
						snake_case__			:				Union[str, Any]       =  (self.image_size // self.patch_size) ** 2
						snake_case__			:				Union[str, Any]       =  self.patch_size**2 * self.num_channels
						self.parent.assertEqual(result.logits.shape							,		(self.batch_size, num_patches, expected_num_channels)	)

						# test greyscale images
						snake_case__			:				Any       =  1
						snake_case__			:				str       =  ViTMAEForPreTraining(__A	)
						model.to(__A	)
						model.eval()
						snake_case__			:				str       =  floats_tensor([self.batch_size, 1, self.image_size, self.image_size]	)
						snake_case__			:				Dict       =  model(__A	)
						snake_case__			:				int       =  self.patch_size**2
						self.parent.assertEqual(result.logits.shape							,		(self.batch_size, num_patches, expected_num_channels)	)






					def 	_lowercase					( self					:  Dict	):
						snake_case__			:				Tuple       =  self.prepare_config_and_inputs()
						snake_case__			:				Union[str, Any]       =  config_and_inputs
						snake_case__			:				List[str]       =  {"pixel_values": pixel_values}
						return config, inputs_dict



@require_torch
class 							SCREAMING_SNAKE_CASE__						(    UpperCamelCase_			,       UpperCamelCase_			,       unittest.TestCase						):





					"""simple docstring"""






					a_     =		(ViTMAEModel, ViTMAEForPreTraining) if is_torch_available() else ()
					a_     =		{"feature-extraction": ViTMAEModel} if is_torch_available() else {}

					a_     =		False
					a_     =		False
					a_     =		False
					a_     =		False



					def 	_lowercase					( self					:  Optional[int]	):
						snake_case__			:				List[str]       =  ViTMAEModelTester(self	)
						snake_case__			:				List[Any]       =  ConfigTester(self							,		config_class=__A							,		has_text_modality=__A							,		hidden_size=3_7	)



					def 	_lowercase					( self					:  int	):
						self.config_tester.run_common_tests()



					@unittest.skip(reason="ViTMAE does not use inputs_embeds"	)
					def 	_lowercase					( self					:  Dict	):
						pass



					def 	_lowercase					( self					:  List[Any]	):
						snake_case__			:				List[Any]       =  self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
							snake_case__			:				int       =  model_class(__A	)
							self.assertIsInstance(model.get_input_embeddings()							,		(nn.Module)	)
							snake_case__			:				str       =  model.get_output_embeddings()
							self.assertTrue(x is None or isinstance(__A							,		nn.Linear	)	)



					def 	_lowercase					( self					:  Union[str, Any]	):
						snake_case__			:				Tuple       =  self.model_tester.prepare_config_and_inputs_for_common()

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

							snake_case__			:				Union[str, Any]       =  ["pixel_values"]
							self.assertListEqual(arg_names[:1]							,		__A	)



					def 	_lowercase					( self					:  Optional[int]	):
						snake_case__			:				Optional[int]       =  self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*__A	)



					def 	_lowercase					( self					:  Dict	):
						snake_case__			:				List[Any]       =  self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_pretraining(*__A	)



					def 	_lowercase					( self					:  List[Any]							,		__A					:  Union[str, Any]							,		__A					:  List[str]							,		__A					:  Union[str, Any]	):
						# make masks reproducible
						np.random.seed(2	)

						snake_case__			:				Optional[int]       =  int((pt_model.config.image_size // pt_model.config.patch_size) ** 2	)
						snake_case__			:				Union[str, Any]       =  np.random.uniform(size=(self.model_tester.batch_size, num_patches)	)
						snake_case__			:				Optional[Any]       =  torch.from_numpy(__A	)

						# Add `noise` argument.
						# PT inputs will be prepared in `super().check_pt_tf_models()` with this added `noise` argument
						snake_case__			:				Any       =  pt_noise

						super().check_pt_tf_models(__A							,		__A							,		__A	)



					def 	_lowercase					( self					:  int	):
						snake_case__			:				str       =  self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
							snake_case__			:				List[Any]       =  model_class(__A	)
							model.to(__A	)
							model.eval()
							# make random mask reproducible
							torch.manual_seed(2	)
							with torch.no_grad():
								snake_case__			:				str       =  model(**self._prepare_for_class(__A							,		__A	)	)

							snake_case__			:				List[Any]       =  outputs[0].cpu().numpy()
							snake_case__			:				Tuple       =  0

							with tempfile.TemporaryDirectory() as tmpdirname:
								model.save_pretrained(__A	)
								snake_case__			:				List[str]       =  model_class.from_pretrained(__A	)
								model.to(__A	)
								# make random mask reproducible
								torch.manual_seed(2	)
								with torch.no_grad():
									snake_case__			:				List[Any]       =  model(**self._prepare_for_class(__A							,		__A	)	)

								# Make sure we don't have nans
								snake_case__			:				Tuple       =  after_outputs[0].cpu().numpy()
								snake_case__			:				int       =  0
								snake_case__			:				Dict       =  np.amax(np.abs(out_a - out_a	)	)
								self.assertLessEqual(__A							,		1e-5	)



					@unittest.skip(
					    reason="ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load\n    to get deterministic results."	)
					def 	_lowercase					( self					:  Optional[int]	):
						pass



					@unittest.skip(
					    reason="ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load\n    to get deterministic results."	)
					def 	_lowercase					( self					:  int	):
						pass



					@unittest.skip(
					    reason="ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load\n    to get deterministic results."	)
					def 	_lowercase					( self					:  Tuple	):
						pass



					@unittest.skip(reason="ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load"	)
					def 	_lowercase					( self					:  Dict	):
						pass



					@unittest.skip("Will be fixed soon by reducing the size of the model used for common tests."	)
					def 	_lowercase					( self					:  Union[str, Any]	):
						pass






					@slow
					def 	_lowercase					( self					:  List[str]	):
						for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
							snake_case__			:				Dict       =  ViTMAEModel.from_pretrained(__A	)
							self.assertIsNotNone(__A	)







def 					SCREAMING_SNAKE_CASE    (			):
	snake_case__			:				List[Any]       =  Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"       )
	return image





@require_torch
@require_vision
class 							SCREAMING_SNAKE_CASE__						(    unittest.TestCase						):





					"""simple docstring"""






					@cached_property
					def 	_lowercase					( self					:  List[str]	):
						return ViTImageProcessor.from_pretrained("facebook/vit-mae-base"	) if is_vision_available() else None






					@slow
					def 	_lowercase					( self					:  List[Any]	):
						# make random mask reproducible across the PT and TF model
						np.random.seed(2	)

						snake_case__			:				Union[str, Any]       =  ViTMAEForPreTraining.from_pretrained("facebook/vit-mae-base"	).to(__A	)

						snake_case__			:				Dict       =  self.default_image_processor
						snake_case__			:				Optional[Any]       =  prepare_img()
						snake_case__			:				List[str]       =  image_processor(images=__A							,		return_tensors="pt"	).to(__A	)

						# prepare a noise vector that will be also used for testing the TF model
						# (this way we can ensure that the PT and TF models operate on the same inputs)
						snake_case__			:				List[str]       =  ViTMAEConfig()
						snake_case__			:				List[str]       =  int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2	)
						snake_case__			:				Tuple       =  np.random.uniform(size=(1, num_patches)	)

						# forward pass
						with torch.no_grad():
							snake_case__			:				Optional[Any]       =  model(**__A							,		noise=torch.from_numpy(__A	).to(device=__A	)	)

						# verify the logits
						snake_case__			:				Optional[int]       =  torch.Size((1, 1_9_6, 7_6_8)	)
						self.assertEqual(outputs.logits.shape							,		__A	)

						snake_case__			:				Any       =  torch.tensor(
						    [[-0.0_5_4_8, -1.7_0_2_3, -0.9_3_2_5], [0.3_7_2_1, -0.5_6_7_0, -0.2_2_3_3], [0.8_2_3_5, -1.3_8_7_8, -0.3_5_2_4]]	)

						self.assertTrue(torch.allclose(outputs.logits[0, :3, :3]							,		expected_slice.to(__A	)							,		atol=1e-4	)	)









import argparse

import torch
from torch import nn

from transformers import MBartConfig, MBartForConditionalGeneration







def 					SCREAMING_SNAKE_CASE    (			snake_case_ :							str       ):
	snake_case__			:				Optional[Any]       =  [
	    "encoder.version",
	    "decoder.version",
	    "model.encoder.version",
	    "model.decoder.version",
	    "_float_tensor",
	    "decoder.output_projection.weight",
	]
	for k in ignore_keys:
		state_dict.pop(snake_case_					,  snake_case_       )







def 					SCREAMING_SNAKE_CASE    (			snake_case_ :							int       ):
	snake_case__,						snake_case__			:				Optional[Any]       =  emb.weight.shape
	snake_case__			:				Tuple       =  nn.Linear(snake_case_					,  snake_case_					,  bias=snake_case_       )
	snake_case__			:				Optional[int]       =  emb.weight.data
	return lin_layer







def 					SCREAMING_SNAKE_CASE    (			snake_case_ :							List[Any]					,  snake_case_ :							Optional[Any]="facebook/mbart-large-en-ro"					,  snake_case_ :							Optional[int]=False					,  snake_case_ :							List[Any]=False       ):
	snake_case__			:				Tuple       =  torch.load(snake_case_					,  map_location="cpu"       )["model"]
	remove_ignore_keys_(snake_case_       )
	snake_case__			:				Any       =  state_dict["encoder.embed_tokens.weight"].shape[0]

	snake_case__			:				List[Any]       =  MBartConfig.from_pretrained(snake_case_					,  vocab_size=snake_case_       )
	if mbart_aa and finetuned:
		snake_case__			:				int       =  "relu"

	snake_case__			:				List[str]       =  state_dict["decoder.embed_tokens.weight"]
	snake_case__			:				Tuple       =  MBartForConditionalGeneration(snake_case_       )
	model.model.load_state_dict(snake_case_       )

	if finetuned:
		snake_case__			:				Any       =  make_linear_from_emb(model.model.shared       )

	return model


if __name__ == "__main__":
	__lowerCamelCase :      int										=				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="""facebook/mbart-large-cc25""",
	    type=str,
	    help="""Which huggingface architecture to use: mbart-large""",
	)
	parser.add_argument("""--mbart_50""", action="""store_true""", help="""whether the model is mMART-50 checkpoint""")
	parser.add_argument("""--finetuned""", action="""store_true""", help="""whether the model is a fine-tuned checkpoint""")
	__lowerCamelCase :      Optional[Any]										=				parser.parse_args()
	__lowerCamelCase :      str										=				convert_fairseq_mbart_checkpoint_from_disk(
	    args.fairseq_path, hf_config_path=args.hf_config, finetuned=args.finetuned, mbart_aa=args.mbart_aa
	)
	model.save_pretrained(args.pytorch_dump_folder_path)



