





import collections
import inspect
import unittest

from transformers import SwinvaConfig
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, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
						import torch
						from torch import nn

						from transformers import SwinvaForImageClassification, SwinvaForMaskedImageModeling, SwinvaModel
						from transformers.models.swinva.modeling_swinva import SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
						from PIL import Image

						from transformers import AutoImageProcessor
class SCREAMING_SNAKE_CASE_						:

						'''simple docstring'''





						def __init__(      self      :      int		,   _A      :      Union[str, Any]		,   _A      :      Union[str, Any]=13		,   _A      :      Optional[Any]=32		,   _A      :      Optional[Any]=2		,   _A      :      str=3		,   _A      :      int=16		,   _A      :      List[str]=[1, 2, 1]		,   _A      :      Dict=[2, 2, 4]		,   _A      :      List[Any]=2		,   _A      :      int=2.0		,   _A      :      List[Any]=True		,   _A      :      Union[str, Any]=0.0		,   _A      :      Dict=0.0		,   _A      :      Dict=0.1		,   _A      :      List[Any]="gelu"		,   _A      :      str=False		,   _A      :      List[str]=True		,   _A      :      int=0.0_2		,   _A      :      Optional[Any]=1E-5		,   _A      :      Union[str, Any]=True		,   _A      :      Any=None		,   _A      :      Dict=True		,   _A      :      str=10		,   _A      :      Optional[Any]=8		,   )							->    Tuple:







							"""simple docstring"""


							snake_case_		:			Dict											=	parent
							snake_case_		:			List[str]											=	batch_size
							snake_case_		:			int											=	image_size
							snake_case_		:			Any											=	patch_size
							snake_case_		:			Optional[int]											=	num_channels
							snake_case_		:			str											=	embed_dim
							snake_case_		:			Union[str, Any]											=	depths
							snake_case_		:			Tuple											=	num_heads
							snake_case_		:			str											=	window_size
							snake_case_		:			Any											=	mlp_ratio
							snake_case_		:			Optional[int]											=	qkv_bias
							snake_case_		:			Optional[Any]											=	hidden_dropout_prob
							snake_case_		:			Dict											=	attention_probs_dropout_prob
							snake_case_		:			Any											=	drop_path_rate
							snake_case_		:			str											=	hidden_act
							snake_case_		:			Tuple											=	use_absolute_embeddings
							snake_case_		:			Tuple											=	patch_norm
							snake_case_		:			int											=	layer_norm_eps
							snake_case_		:			Union[str, Any]											=	initializer_range
							snake_case_		:			List[str]											=	is_training
							snake_case_		:			List[str]											=	scope
							snake_case_		:			List[Any]											=	use_labels
							snake_case_		:			Tuple											=	type_sequence_label_size
							snake_case_		:			Optional[Any]											=	encoder_stride






						def UpperCAmelCase_				(      self      :      str	)							->    Optional[Any]:







							"""simple docstring"""


							snake_case_		:			Dict											=	floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]	)

							snake_case_		:			Any											=	None
							if self.use_labels:
								snake_case_		:			Dict											=	ids_tensor([self.batch_size]		,   self.type_sequence_label_size	)

							snake_case_		:			List[Any]											=	self.get_config()

							return config, pixel_values, labels






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







							"""simple docstring"""


							return SwinvaConfig(
							    image_size=self.image_size		,   patch_size=self.patch_size		,   num_channels=self.num_channels		,   embed_dim=self.embed_dim		,   depths=self.depths		,   num_heads=self.num_heads		,   window_size=self.window_size		,   mlp_ratio=self.mlp_ratio		,   qkv_bias=self.qkv_bias		,   hidden_dropout_prob=self.hidden_dropout_prob		,   attention_probs_dropout_prob=self.attention_probs_dropout_prob		,   drop_path_rate=self.drop_path_rate		,   hidden_act=self.hidden_act		,   use_absolute_embeddings=self.use_absolute_embeddings		,   path_norm=self.patch_norm		,   layer_norm_eps=self.layer_norm_eps		,   initializer_range=self.initializer_range		,   encoder_stride=self.encoder_stride		,   )






						def UpperCAmelCase_				(      self      :      List[Any]		,   _A      :      Optional[Any]		,   _A      :      Dict		,   _A      :      Dict	)							->    Optional[int]:







							"""simple docstring"""


							snake_case_		:			Optional[Any]											=	SwinvaModel(config=snake_case__	)
							model.to(snake_case__	)
							model.eval()
							snake_case_		:			Union[str, Any]											=	model(snake_case__	)

							snake_case_		:			str											=	((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths	) - 1))
							snake_case_		:			Dict											=	int(config.embed_dim * 2 ** (len(config.depths	) - 1)	)

							self.parent.assertEqual(result.last_hidden_state.shape		,   (self.batch_size, expected_seq_len, expected_dim)	)






						def UpperCAmelCase_				(      self      :      int		,   _A      :      Optional[Any]		,   _A      :      Optional[int]		,   _A      :      Any	)							->    Any:







							"""simple docstring"""


							snake_case_		:			Dict											=	SwinvaForMaskedImageModeling(config=snake_case__	)
							model.to(snake_case__	)
							model.eval()
							snake_case_		:			str											=	model(snake_case__	)
							self.parent.assertEqual(
							    result.logits.shape		,   (self.batch_size, self.num_channels, self.image_size, self.image_size)	)

							# test greyscale images
							snake_case_		:			int											=	1
							snake_case_		:			List[str]											=	SwinvaForMaskedImageModeling(snake_case__	)
							model.to(snake_case__	)
							model.eval()

							snake_case_		:			List[str]											=	floats_tensor([self.batch_size, 1, self.image_size, self.image_size]	)
							snake_case_		:			Optional[int]											=	model(snake_case__	)
							self.parent.assertEqual(result.logits.shape		,   (self.batch_size, 1, self.image_size, self.image_size)	)






						def UpperCAmelCase_				(      self      :      List[str]		,   _A      :      Optional[int]		,   _A      :      str		,   _A      :      Any	)							->    List[str]:







							"""simple docstring"""


							snake_case_		:			str											=	self.type_sequence_label_size
							snake_case_		:			Tuple											=	SwinvaForImageClassification(snake_case__	)
							model.to(snake_case__	)
							model.eval()
							snake_case_		:			str											=	model(snake_case__		,   labels=snake_case__	)
							self.parent.assertEqual(result.logits.shape		,   (self.batch_size, self.type_sequence_label_size)	)






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







							"""simple docstring"""


							snake_case_		:			Dict											=	self.prepare_config_and_inputs()
							snake_case_						,snake_case_						,snake_case_		:			Any											=	config_and_inputs
							snake_case_		:			int											=	{'pixel_values': pixel_values}
							return config, inputs_dict
@require_torch
class SCREAMING_SNAKE_CASE_						(					__a       ,				__a       ,				unittest.TestCase					):

						'''simple docstring'''





						__magic_name__:			Any       =       (
						    (SwinvaModel, SwinvaForImageClassification, SwinvaForMaskedImageModeling) if is_torch_available() else ()
						)
						__magic_name__:			Any       =       (
						    {'''feature-extraction''': SwinvaModel, '''image-classification''': SwinvaForImageClassification}
						    if is_torch_available()
						    else {}
						)

						__magic_name__:			Union[str, Any]       =       False
						__magic_name__:			List[str]       =       False
						__magic_name__:			Any       =       False
						__magic_name__:			Optional[Any]       =       False






						def UpperCAmelCase_				(      self      :      Dict	)							->    List[str]:







							"""simple docstring"""


							snake_case_		:			str											=	SwinvaModelTester(self	)
							snake_case_		:			str											=	ConfigTester(self		,   config_class=snake_case__		,   embed_dim=37	)






						def UpperCAmelCase_				(      self      :      Tuple	)							->    List[str]:







							"""simple docstring"""


							self.config_tester.create_and_test_config_to_json_string()
							self.config_tester.create_and_test_config_to_json_file()
							self.config_tester.create_and_test_config_from_and_save_pretrained()
							self.config_tester.create_and_test_config_with_num_labels()
							self.config_tester.check_config_can_be_init_without_params()
							self.config_tester.check_config_arguments_init()






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







							"""simple docstring"""


							snake_case_		:			int											=	self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*snake_case__	)






						@unittest.skip(reason='Got `CUDA error: misaligned address` with PyTorch 2.0.0.'	)
						def UpperCAmelCase_				(      self      :      List[Any]	)							->    Any:







							"""simple docstring"""


							pass






						@unittest.skip(reason='Swinv2 does not use inputs_embeds'	)
						def UpperCAmelCase_				(      self      :      List[str]	)							->    int:







							"""simple docstring"""


							pass






						def UpperCAmelCase_				(      self      :      Optional[Any]	)							->    Dict:







							"""simple docstring"""


							snake_case_						,snake_case_		:			List[Any]											=	self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
								snake_case_		:			List[str]											=	model_class(snake_case__	)
								self.assertIsInstance(model.get_input_embeddings()		,   (nn.Module)	)
								snake_case_		:			Tuple											=	model.get_output_embeddings()
								self.assertTrue(x is None or isinstance(snake_case__		,   nn.Linear	)	)






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







							"""simple docstring"""


							snake_case_						,snake_case_		:			List[str]											=	self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
								snake_case_		:			Union[str, Any]											=	model_class(snake_case__	)
								snake_case_		:			Optional[Any]											=	inspect.signature(model.forward	)
								# signature.parameters is an OrderedDict => so arg_names order is deterministic
								snake_case_		:			Optional[Any]											=	[*signature.parameters.keys()]

								snake_case_		:			List[str]											=	['pixel_values']
								self.assertListEqual(arg_names[:1]		,   snake_case__	)






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







							"""simple docstring"""


							snake_case_						,snake_case_		:			int											=	self.model_tester.prepare_config_and_inputs_for_common()
							snake_case_		:			Optional[int]											=	True

							for model_class in self.all_model_classes:
								snake_case_		:			Dict											=	True
								snake_case_		:			List[Any]											=	False
								snake_case_		:			List[Any]											=	True
								snake_case_		:			str											=	model_class(snake_case__	)
								model.to(snake_case__	)
								model.eval()
								with torch.no_grad():
									snake_case_		:			Tuple											=	model(**self._prepare_for_class(snake_case__		,   snake_case__	)	)
								snake_case_		:			str											=	outputs.attentions
								snake_case_		:			Optional[int]											=	len(self.model_tester.depths	)
								self.assertEqual(len(snake_case__	)		,   snake_case__	)

								# check that output_attentions also work using config
								del inputs_dict["output_attentions"]
								snake_case_		:			List[Any]											=	True
								snake_case_		:			Optional[int]											=	config.window_size**2
								snake_case_		:			str											=	model_class(snake_case__	)
								model.to(snake_case__	)
								model.eval()
								with torch.no_grad():
									snake_case_		:			Optional[Any]											=	model(**self._prepare_for_class(snake_case__		,   snake_case__	)	)
								snake_case_		:			Tuple											=	outputs.attentions
								self.assertEqual(len(snake_case__	)		,   snake_case__	)

								self.assertListEqual(
								    list(attentions[0].shape[-3:]	)		,   [self.model_tester.num_heads[0], window_size_squared, window_size_squared]		,   )
								snake_case_		:			List[Any]											=	len(snake_case__	)

								# Check attention is always last and order is fine
								snake_case_		:			int											=	True
								snake_case_		:			Any											=	True
								snake_case_		:			List[str]											=	model_class(snake_case__	)
								model.to(snake_case__	)
								model.eval()
								with torch.no_grad():
									snake_case_		:			Union[str, Any]											=	model(**self._prepare_for_class(snake_case__		,   snake_case__	)	)

								if hasattr(self.model_tester		,   'num_hidden_states_types'	):
									snake_case_		:			List[str]											=	self.model_tester.num_hidden_states_types
								else:
									# also another +1 for reshaped_hidden_states
									snake_case_		:			List[str]											=	2
								self.assertEqual(out_len + added_hidden_states		,   len(snake_case__	)	)

								snake_case_		:			Tuple											=	outputs.attentions

								self.assertEqual(len(snake_case__	)		,   snake_case__	)

								self.assertListEqual(
								    list(self_attentions[0].shape[-3:]	)		,   [self.model_tester.num_heads[0], window_size_squared, window_size_squared]		,   )






						def UpperCAmelCase_				(      self      :      Dict		,   _A      :      Any		,   _A      :      Union[str, Any]		,   _A      :      Tuple		,   _A      :      Tuple	)							->    List[Any]:







							"""simple docstring"""


							snake_case_		:			Tuple											=	model_class(snake_case__	)
							model.to(snake_case__	)
							model.eval()

							with torch.no_grad():
								snake_case_		:			str											=	model(**self._prepare_for_class(snake_case__		,   snake_case__	)	)

							snake_case_		:			Any											=	outputs.hidden_states

							snake_case_		:			Optional[Any]											=	getattr(
							    self.model_tester		,   'expected_num_hidden_layers'		,   len(self.model_tester.depths	) + 1	)
							self.assertEqual(len(snake_case__	)		,   snake_case__	)

							# Swinv2 has a different seq_length
							snake_case_		:			int											=	(
							    config.patch_size
							    if isinstance(config.patch_size		,   collections.abc.Iterable	)
							    else (config.patch_size, config.patch_size)
							)

							snake_case_		:			Any											=	(image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])

							self.assertListEqual(
							    list(hidden_states[0].shape[-2:]	)		,   [num_patches, self.model_tester.embed_dim]		,   )

							snake_case_		:			int											=	outputs.reshaped_hidden_states
							self.assertEqual(len(snake_case__	)		,   snake_case__	)

							snake_case_						,snake_case_						,snake_case_						,snake_case_		:			Optional[Any]											=	reshaped_hidden_states[0].shape
							snake_case_		:			Union[str, Any]											=	(
							    reshaped_hidden_states[0].view(snake_case__		,   snake_case__		,   height * width	).permute(0		,   2		,   1	)
							)
							self.assertListEqual(
							    list(reshaped_hidden_states.shape[-2:]	)		,   [num_patches, self.model_tester.embed_dim]		,   )






						def UpperCAmelCase_				(      self      :      List[Any]	)							->    Tuple:







							"""simple docstring"""


							snake_case_						,snake_case_		:			Any											=	self.model_tester.prepare_config_and_inputs_for_common()

							snake_case_		:			List[str]											=	(
							    self.model_tester.image_size
							    if isinstance(self.model_tester.image_size		,   collections.abc.Iterable	)
							    else (self.model_tester.image_size, self.model_tester.image_size)
							)

							for model_class in self.all_model_classes:
								snake_case_		:			List[Any]											=	True
								self.check_hidden_states_output(snake_case__		,   snake_case__		,   snake_case__		,   snake_case__	)

								# check that output_hidden_states also work using config
								del inputs_dict["output_hidden_states"]
								snake_case_		:			Union[str, Any]											=	True

								self.check_hidden_states_output(snake_case__		,   snake_case__		,   snake_case__		,   snake_case__	)






						def UpperCAmelCase_				(      self      :      Dict	)							->    List[str]:







							"""simple docstring"""


							snake_case_						,snake_case_		:			Dict											=	self.model_tester.prepare_config_and_inputs_for_common()
							snake_case_		:			Union[str, Any]											=	3

							snake_case_		:			Optional[Any]											=	(
							    self.model_tester.image_size
							    if isinstance(self.model_tester.image_size		,   collections.abc.Iterable	)
							    else (self.model_tester.image_size, self.model_tester.image_size)
							)
							snake_case_		:			Optional[int]											=	(
							    config.patch_size
							    if isinstance(config.patch_size		,   collections.abc.Iterable	)
							    else (config.patch_size, config.patch_size)
							)

							snake_case_		:			Tuple											=	image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
							snake_case_		:			List[str]											=	image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

							for model_class in self.all_model_classes:
								snake_case_		:			Tuple											=	True
								self.check_hidden_states_output(snake_case__		,   snake_case__		,   snake_case__		,   (padded_height, padded_width)	)

								# check that output_hidden_states also work using config
								del inputs_dict["output_hidden_states"]
								snake_case_		:			List[Any]											=	True
								self.check_hidden_states_output(snake_case__		,   snake_case__		,   snake_case__		,   (padded_height, padded_width)	)






						def UpperCAmelCase_				(      self      :      Optional[int]	)							->    str:







							"""simple docstring"""


							snake_case_		:			List[str]											=	self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_masked_image_modeling(*snake_case__	)






						def UpperCAmelCase_				(      self      :      List[str]	)							->    Optional[int]:







							"""simple docstring"""


							snake_case_		:			Tuple											=	self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_image_classification(*snake_case__	)






						@slow
						def UpperCAmelCase_				(      self      :      Union[str, Any]	)							->    Dict:







							"""simple docstring"""


							for model_name in SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
								snake_case_		:			Any											=	SwinvaModel.from_pretrained(snake_case__	)
								self.assertIsNotNone(snake_case__	)






						def UpperCAmelCase_				(      self      :      Dict	)							->    int:







							"""simple docstring"""


							snake_case_						,snake_case_		:			Dict											=	self.model_tester.prepare_config_and_inputs_for_common()

							snake_case_		:			Any											=	_config_zero_init(snake_case__	)
							for model_class in self.all_model_classes:
								snake_case_		:			str											=	model_class(config=snake_case__	)
								for name, param in model.named_parameters():
									if "embeddings" not in name and "logit_scale" not in name and param.requires_grad:
										self.assertIn(
										    ((param.data.mean() * 1E9).round() / 1E9).item()		,   [0.0, 1.0]		,   msg=F"""Parameter {name} of model {model_class} seems not properly initialized"""		,   )
@require_vision
@require_torch
class SCREAMING_SNAKE_CASE_						(					unittest.TestCase					):

						'''simple docstring'''





						@cached_property
						def UpperCAmelCase_				(      self      :      Optional[Any]	)							->    int:







							"""simple docstring"""


							return (
							    AutoImageProcessor.from_pretrained('microsoft/swinv2-tiny-patch4-window8-256'	)
							    if is_vision_available()
							    else None
							)






						@slow
						def UpperCAmelCase_				(      self      :      Dict	)							->    Union[str, Any]:







							"""simple docstring"""


							snake_case_		:			Tuple											=	SwinvaForImageClassification.from_pretrained('microsoft/swinv2-tiny-patch4-window8-256'	).to(
							    snake_case__	)
							snake_case_		:			Union[str, Any]											=	self.default_image_processor

							snake_case_		:			List[Any]											=	Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'	)
							snake_case_		:			Any											=	image_processor(images=snake_case__		,   return_tensors='pt'	).to(snake_case__	)

							# forward pass
							with torch.no_grad():
								snake_case_		:			Tuple											=	model(**snake_case__	)

							# verify the logits
							snake_case_		:			Optional[int]											=	torch.Size((1, 1000)	)
							self.assertEqual(outputs.logits.shape		,   snake_case__	)
							snake_case_		:			Optional[int]											=	torch.tensor([-0.3_9_4_7, -0.4_3_0_6, 0.0_0_2_6]	).to(snake_case__	)
							self.assertTrue(torch.allclose(outputs.logits[0, :3]		,   snake_case__		,   atol=1E-4	)	)








import argparse

from torch import nn

# transformers_old should correspond to branch `save_old_prophetnet_model_structure` here
# original prophetnet_checkpoints are saved under `patrickvonplaten/..._old` respectively
from transformers_old.modeling_prophetnet import (
    ProphetNetForConditionalGeneration as ProphetNetForConditionalGenerationOld,
)
from transformers_old.modeling_xlm_prophetnet import (
    XLMProphetNetForConditionalGeneration as XLMProphetNetForConditionalGenerationOld,
)

from transformers import ProphetNetForConditionalGeneration, XLMProphetNetForConditionalGeneration, logging


_SCREAMING_SNAKE_CASE      	=		logging.get_logger(__name__)
logging.set_verbosity_info()


def 							SCREAMING_SNAKE_CASE__						(    __a   , __a   ):
	if "xprophetnet" in prophetnet_checkpoint_path:
		snake_case_		:			int											=	XLMProphetNetForConditionalGenerationOld.from_pretrained(__a   )
		snake_case_						,snake_case_		:			List[Any]											=	XLMProphetNetForConditionalGeneration.from_pretrained(
		    __a   , output_loading_info=__a   )
	else:
		snake_case_		:			List[str]											=	ProphetNetForConditionalGenerationOld.from_pretrained(__a   )
		snake_case_						,snake_case_		:			Dict											=	ProphetNetForConditionalGeneration.from_pretrained(
		    __a   , output_loading_info=__a   )

	snake_case_		:			str											=	['key_proj', 'value_proj', 'query_proj']

	snake_case_		:			List[str]											=	{
	    'self_attn': 'ngram_self_attn',
	    'cross_attn': 'encoder_attn',
	    'cross_attn_layer_norm': 'encoder_attn_layer_norm',
	    'feed_forward_layer_norm': 'final_layer_norm',
	    'feed_forward': '',
	    'intermediate': 'fc1',
	    'output': 'fc2',
	    'key_proj': 'k_proj',
	    'query_proj': 'q_proj',
	    'value_proj': 'v_proj',
	    'word_embeddings': 'embed_tokens',
	    'embeddings_layer_norm': 'emb_layer_norm',
	    'relative_pos_embeddings': 'relative_linear',
	    'ngram_embeddings': 'ngram_input_embed',
	    'position_embeddings': 'embed_positions',
	}

	for key in loading_info["missing_keys"]:
		snake_case_		:			Union[str, Any]											=	key.split('.'   )

		if attributes[0] == "lm_head":
			snake_case_		:			Optional[Any]											=	prophet
			snake_case_		:			Any											=	prophet_old
		else:
			snake_case_		:			Optional[int]											=	prophet.prophetnet
			snake_case_		:			str											=	prophet_old.model

		snake_case_		:			Union[str, Any]											=	False
		for attribute in attributes:
			if attribute in mapping:
				snake_case_		:			Optional[Any]											=	mapping[attribute]
				if not hasattr(__a   , __a   ) and len(__a   ) > 0:
					snake_case_		:			List[Any]											=	attribute
			elif hasattr(__a   , __a   ):
				snake_case_		:			List[Any]											=	attribute

			if attribute == "weight":
				assert old_model.weight.shape == model.weight.shape, "Shapes have to match!"
				snake_case_		:			int											=	old_model.weight
				logger.info(f"""{attribute} is initialized."""   )
				snake_case_		:			Optional[int]											=	True
				break
			elif attribute == "bias":
				assert old_model.bias.shape == model.bias.shape, "Shapes have to match!"
				snake_case_		:			List[str]											=	old_model.bias
				logger.info(f"""{attribute} is initialized"""   )
				snake_case_		:			int											=	True
				break
			elif attribute in special_keys and hasattr(__a   , 'in_proj_weight'   ):
				snake_case_		:			Optional[Any]											=	old_model.in_proj_weight.shape[0] // 3
				snake_case_		:			List[Any]											=	getattr(__a   , __a   )
				param.weight.shape == old_model.in_proj_weight[:embed_dim, :].shape, "Shapes have to match"
				param.bias.shape == old_model.in_proj_bias[:embed_dim].shape, "Shapes have to match"
				if attribute == "query_proj":
					snake_case_		:			Tuple											=	nn.Parameter(old_model.in_proj_weight[:embed_dim, :]   )
					snake_case_		:			Optional[int]											=	nn.Parameter(old_model.in_proj_bias[:embed_dim]   )

				elif attribute == "key_proj":
					snake_case_		:			int											=	nn.Parameter(old_model.in_proj_weight[embed_dim : 2 * embed_dim, :]   )
					snake_case_		:			Any											=	nn.Parameter(old_model.in_proj_bias[embed_dim : 2 * embed_dim]   )
				elif attribute == "value_proj":
					snake_case_		:			List[Any]											=	nn.Parameter(old_model.in_proj_weight[2 * embed_dim :, :]   )
					snake_case_		:			str											=	nn.Parameter(old_model.in_proj_bias[2 * embed_dim :]   )
				snake_case_		:			Any											=	True
				break
			elif attribute == "position_embeddings":
				assert (
				    model.position_embeddings.weight.shape[-1] == old_model.embed_positions.weight.shape[-1]
				), "Hidden size has to match"
				assert model.position_embeddings.weight.shape[0] == 5_12, "We want 512 position_embeddings."
				snake_case_		:			Any											=	nn.Parameter(old_model.embed_positions.weight[:5_12, :]   )
				snake_case_		:			List[Any]											=	True
				break

			if attribute.isdigit():
				snake_case_		:			Any											=	model[int(__a   )]
				snake_case_		:			Any											=	old_model[int(__a   )]
			else:
				snake_case_		:			str											=	getattr(__a   , __a   )

				if old_attribute == "":
					snake_case_		:			Tuple											=	old_model
				else:
					if not hasattr(__a   , __a   ):
						raise ValueError(f"""{old_model} does not have {old_attribute}"""   )
					snake_case_		:			List[str]											=	getattr(__a   , __a   )

		if not is_key_init:
			raise ValueError(f"""{key} was not correctly initialized!"""   )

	print(f"""Saving model to {pytorch_dump_folder_path}"""   )
	prophet.save_pretrained(__a   )


if __name__ == "__main__":
						_SCREAMING_SNAKE_CASE      	=		argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    """--prophetnet_checkpoint_path""", default=None, type=str, required=True, help="""Path the official PyTorch dump."""
						)
						parser.add_argument(
						    """--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
						)
						_SCREAMING_SNAKE_CASE      	=		parser.parse_args()
						convert_prophetnet_checkpoint_to_pytorch(args.prophetnet_checkpoint_path, args.pytorch_dump_folder_path)

