





"""simple docstring"""






import inspect
import unittest

from transformers import ViTConfig
from transformers.testing_utils import (
    require_accelerate,
    require_torch,
    require_torch_gpu,
    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 ViTForImageClassification, ViTForMaskedImageModeling, ViTModel
		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 				__lowercase			:







					'''simple docstring'''




					def __init__(						self	,			_UpperCAmelCase	,			_UpperCAmelCase=13	,			_UpperCAmelCase=30	,			_UpperCAmelCase=2	,			_UpperCAmelCase=3	,			_UpperCAmelCase=True	,			_UpperCAmelCase=True	,			_UpperCAmelCase=32	,			_UpperCAmelCase=5	,			_UpperCAmelCase=4	,			_UpperCAmelCase=37	,			_UpperCAmelCase="gelu"	,			_UpperCAmelCase=0.1	,			_UpperCAmelCase=0.1	,			_UpperCAmelCase=10	,			_UpperCAmelCase=0.0_2	,			_UpperCAmelCase=None	,			_UpperCAmelCase=2	,			):
									__a :						Union[str, Any]			       =  parent
									__a :						List[Any]			       =  batch_size
									__a :						Dict			       =  image_size
									__a :						List[Any]			       =  patch_size
									__a :						Tuple			       =  num_channels
									__a :						Tuple			       =  is_training
									__a :						List[Any]			       =  use_labels
									__a :						Any			       =  hidden_size
									__a :						Tuple			       =  num_hidden_layers
									__a :						Dict			       =  num_attention_heads
									__a :						List[Any]			       =  intermediate_size
									__a :						Optional[Any]			       =  hidden_act
									__a :						Union[str, Any]			       =  hidden_dropout_prob
									__a :						Optional[int]			       =  attention_probs_dropout_prob
									__a :						Tuple			       =  type_sequence_label_size
									__a :						Dict			       =  initializer_range
									__a :						Union[str, Any]			       =  scope
									__a :						Optional[Any]			       =  encoder_stride

									# in ViT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
									__a :						Any			       =  (image_size // patch_size) ** 2
									__a :						Tuple			       =  num_patches + 1







					def  _lowerCamelCase		(						self     ):
									__a :						Any			       =  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]     )

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

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

									return config, pixel_values, labels







					def  _lowerCamelCase		(						self     ):
									return ViTConfig(
									    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=_UpperCAmelCase	,			initializer_range=self.initializer_range	,			encoder_stride=self.encoder_stride	,			)







					def  _lowerCamelCase		(						self	,			_UpperCAmelCase	,			_UpperCAmelCase	,			_UpperCAmelCase     ):
									__a :						Optional[Any]			       =  ViTModel(config=_UpperCAmelCase     )
									model.to(_UpperCAmelCase     )
									model.eval()
									__a :						Optional[Any]			       =  model(_UpperCAmelCase     )
									self.parent.assertEqual(result.last_hidden_state.shape	,			(self.batch_size, self.seq_length, self.hidden_size)     )







					def  _lowerCamelCase		(						self	,			_UpperCAmelCase	,			_UpperCAmelCase	,			_UpperCAmelCase     ):
									__a :						str			       =  ViTForMaskedImageModeling(config=_UpperCAmelCase     )
									model.to(_UpperCAmelCase     )
									model.eval()
									__a :						Dict			       =  model(_UpperCAmelCase     )
									self.parent.assertEqual(
									    result.reconstruction.shape	,			(self.batch_size, self.num_channels, self.image_size, self.image_size)     )

									# test greyscale images
									__a :						List[str]			       =  1
									__a :						Optional[Any]			       =  ViTForMaskedImageModeling(_UpperCAmelCase     )
									model.to(_UpperCAmelCase     )
									model.eval()

									__a :						str			       =  floats_tensor([self.batch_size, 1, self.image_size, self.image_size]     )
									__a :						Any			       =  model(_UpperCAmelCase     )
									self.parent.assertEqual(result.reconstruction.shape	,			(self.batch_size, 1, self.image_size, self.image_size)     )







					def  _lowerCamelCase		(						self	,			_UpperCAmelCase	,			_UpperCAmelCase	,			_UpperCAmelCase     ):
									__a :						str			       =  self.type_sequence_label_size
									__a :						List[Any]			       =  ViTForImageClassification(_UpperCAmelCase     )
									model.to(_UpperCAmelCase     )
									model.eval()
									__a :						Optional[int]			       =  model(_UpperCAmelCase	,			labels=_UpperCAmelCase     )
									self.parent.assertEqual(result.logits.shape	,			(self.batch_size, self.type_sequence_label_size)     )

									# test greyscale images
									__a :						Dict			       =  1
									__a :						Optional[Any]			       =  ViTForImageClassification(_UpperCAmelCase     )
									model.to(_UpperCAmelCase     )
									model.eval()

									__a :						Optional[Any]			       =  floats_tensor([self.batch_size, 1, self.image_size, self.image_size]     )
									__a :						Optional[int]			       =  model(_UpperCAmelCase     )
									self.parent.assertEqual(result.logits.shape	,			(self.batch_size, self.type_sequence_label_size)     )







					def  _lowerCamelCase		(						self     ):
									__a :						Optional[int]			       =  self.prepare_config_and_inputs()
									(
									    __a						
									) :						Optional[Any]			       =  config_and_inputs
									__a :						Optional[int]			       =  {'''pixel_values''': pixel_values}
									return config, inputs_dict







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







					'''simple docstring'''




					__lowerCAmelCase  =  (
					    (
					        ViTModel,
					        ViTForImageClassification,
					        ViTForMaskedImageModeling,
					    )
					    if is_torch_available()
					    else ()
					)
					__lowerCAmelCase  =  (
					    {'''feature-extraction''': ViTModel, '''image-classification''': ViTForImageClassification}
					    if is_torch_available()
					    else {}
					)
					__lowerCAmelCase  =  True

					__lowerCAmelCase  =  False
					__lowerCAmelCase  =  False
					__lowerCAmelCase  =  False







					def  _lowerCamelCase		(						self     ):
									__a :						List[str]			       =  ViTModelTester(self     )
									__a :						Optional[int]			       =  ConfigTester(self	,			config_class=_UpperCAmelCase	,			has_text_modality=_UpperCAmelCase	,			hidden_size=37     )







					def  _lowerCamelCase		(						self     ):
									self.config_tester.run_common_tests()







					@unittest.skip(reason='''ViT does not use inputs_embeds'''     )
					def  _lowerCamelCase		(						self     ):
									pass







					def  _lowerCamelCase		(						self     ):
									__a :						int			       =  self.model_tester.prepare_config_and_inputs_for_common()

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







					def  _lowerCamelCase		(						self     ):
									__a :						Tuple			       =  self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
													__a :						List[Any]			       =  model_class(_UpperCAmelCase     )
													__a :						Dict			       =  inspect.signature(model.forward     )
													# signature.parameters is an OrderedDict => so arg_names order is deterministic
													__a :						Optional[Any]			       =  [*signature.parameters.keys()]

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







					def  _lowerCamelCase		(						self     ):
									__a :						Optional[int]			       =  self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*_UpperCAmelCase     )







					def  _lowerCamelCase		(						self     ):
									__a :						List[Any]			       =  self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_masked_image_modeling(*_UpperCAmelCase     )







					def  _lowerCamelCase		(						self     ):
									__a :						List[str]			       =  self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_image_classification(*_UpperCAmelCase     )







					@slow
					def  _lowerCamelCase		(						self     ):
									for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
													__a :						Union[str, Any]			       =  ViTModel.from_pretrained(_UpperCAmelCase     )
													self.assertIsNotNone(_UpperCAmelCase     )







def     __A      (			)		->		Union[str, Any]:
				__a :						Optional[int]			       =  Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''')
				return image






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







					'''simple docstring'''




					@cached_property
					def  _lowerCamelCase		(						self     ):
									return ViTImageProcessor.from_pretrained('''google/vit-base-patch16-224'''     ) if is_vision_available() else None







					@slow
					def  _lowerCamelCase		(						self     ):
									__a :						str			       =  ViTForImageClassification.from_pretrained('''google/vit-base-patch16-224'''     ).to(_UpperCAmelCase     )

									__a :						str			       =  self.default_image_processor
									__a :						int			       =  prepare_img()
									__a :						Tuple			       =  image_processor(images=_UpperCAmelCase	,			return_tensors='''pt'''     ).to(_UpperCAmelCase     )

									# forward pass
									with torch.no_grad():
													__a :						Union[str, Any]			       =  model(**_UpperCAmelCase     )

									# verify the logits
									__a :						Dict			       =  torch.Size((1, 1000)     )
									self.assertEqual(outputs.logits.shape	,			_UpperCAmelCase     )

									__a :						List[Any]			       =  torch.tensor([-0.2_7_4_4, 0.8_2_1_5, -0.0_8_3_6]     ).to(_UpperCAmelCase     )

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







					@slow
					def  _lowerCamelCase		(						self     ):
									# ViT models have an `interpolate_pos_encoding` argument in their forward method,
									# allowing to interpolate the pre-trained position embeddings in order to use
									# the model on higher resolutions. The DINO model by Facebook AI leverages this
									# to visualize self-attention on higher resolution images.
									__a :						Tuple			       =  ViTModel.from_pretrained('''facebook/dino-vits8'''     ).to(_UpperCAmelCase     )

									__a :						Union[str, Any]			       =  ViTImageProcessor.from_pretrained('''facebook/dino-vits8'''	,			size=480     )
									__a :						Any			       =  prepare_img()
									__a :						Tuple			       =  image_processor(images=_UpperCAmelCase	,			return_tensors='''pt'''     )
									__a :						int			       =  inputs.pixel_values.to(_UpperCAmelCase     )

									# forward pass
									with torch.no_grad():
													__a :						Union[str, Any]			       =  model(_UpperCAmelCase	,			interpolate_pos_encoding=_UpperCAmelCase     )

									# verify the logits
									__a :						Any			       =  torch.Size((1, 3601, 384)     )
									self.assertEqual(outputs.last_hidden_state.shape	,			_UpperCAmelCase     )

									__a :						str			       =  torch.tensor(
									    [[4.2_3_4_0, 4.3_9_0_6, -6.6_6_9_2], [4.5_4_6_3, 1.8_9_2_8, -6.7_2_5_7], [4.4_4_2_9, 0.8_4_9_6, -5.8_5_8_5]]     ).to(_UpperCAmelCase     )

									self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :3, :3]	,			_UpperCAmelCase	,			atol=1e-4     )     )







					@slow
					@require_accelerate
					@require_torch_gpu
					def  _lowerCamelCase		(						self     ):
									__a :						Tuple			       =  ViTModel.from_pretrained('''facebook/dino-vits8'''	,			torch_dtype=torch.floataa	,			device_map='''auto'''     )
									__a :						Union[str, Any]			       =  self.default_image_processor

									__a :						List[Any]			       =  prepare_img()
									__a :						Any			       =  image_processor(images=_UpperCAmelCase	,			return_tensors='''pt'''     )
									__a :						Dict			       =  inputs.pixel_values.to(_UpperCAmelCase     )

									# forward pass to make sure inference works in fp16
									with torch.no_grad():
													__a :						int			       =  model(_UpperCAmelCase     )






"""simple docstring"""






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


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

def     __A      (			a_						:str   ,       a_						:str)		->		List[Any]:
				if "xprophetnet" in prophetnet_checkpoint_path:
								__a :						List[str]			       =  XLMProphetNetForConditionalGenerationOld.from_pretrained(a_)
								__a       ,						__a :						int			       =  XLMProphetNetForConditionalGeneration.from_pretrained(
								    a_   ,       output_loading_info=a_)
				else:
								__a :						Optional[int]			       =  ProphetNetForConditionalGenerationOld.from_pretrained(a_)
								__a       ,						__a :						Optional[Any]			       =  ProphetNetForConditionalGeneration.from_pretrained(
								    a_   ,       output_loading_info=a_)

				__a :						Tuple			       =  ['''key_proj''', '''value_proj''', '''query_proj''']

				__a :						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"]:
								__a :						List[str]			       =  key.split('''.''')

								if attributes[0] == "lm_head":
												__a :						Dict			       =  prophet
												__a :						Optional[Any]			       =  prophet_old
								else:
												__a :						Union[str, Any]			       =  prophet.prophetnet
												__a :						int			       =  prophet_old.model

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

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

																elif attribute == "key_proj":
																				__a :						Union[str, Any]			       =  nn.Parameter(old_model.in_proj_weight[embed_dim : 2 * embed_dim, :])
																				__a :						int			       =  nn.Parameter(old_model.in_proj_bias[embed_dim : 2 * embed_dim])
																elif attribute == "value_proj":
																				__a :						Dict			       =  nn.Parameter(old_model.in_proj_weight[2 * embed_dim :, :])
																				__a :						List[Any]			       =  nn.Parameter(old_model.in_proj_bias[2 * embed_dim :])
																__a :						int			       =  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."
																__a :						Optional[Any]			       =  nn.Parameter(old_model.embed_positions.weight[:5_12, :])
																__a :						List[Any]			       =  True
																break

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

																if old_attribute == "":
																				__a :						str			       =  old_model
																else:
																				if not hasattr(a_   ,       a_):
																								raise ValueError(F"""{old_model} does not have {old_attribute}""")
																				__a :						Union[str, Any]			       =  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__":
		A 			=  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.'''
		)
		A 			=  parser.parse_args()
		convert_prophetnet_checkpoint_to_pytorch(args.prophetnet_checkpoint_path, args.pytorch_dump_folder_path)