
import inspect
import unittest

from datasets import load_dataset
from packaging import version

from transformers import BeitConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_multi_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, _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 (
       MODEL_MAPPING,
       BeitForImageClassification,
       BeitForMaskedImageModeling,
       BeitForSemanticSegmentation,
       BeitModel,
   )
   from transformers.models.beit.modeling_beit import BEIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
   import PIL
   from PIL import Image

   from transformers import BeitImageProcessor






class UpperCAmelCase   :





   '''simple docstring'''


   def __init__(					self     :							str			,			lowerCAmelCase_     :							int			,			lowerCAmelCase_     :							Tuple=1_0_0			,			lowerCAmelCase_     :							Tuple=1_3			,			lowerCAmelCase_     :							Tuple=3_0			,			lowerCAmelCase_     :							Tuple=2			,			lowerCAmelCase_     :							List[str]=3			,			lowerCAmelCase_     :							Tuple=True			,			lowerCAmelCase_     :							Optional[int]=True			,			lowerCAmelCase_     :							List[Any]=3_2			,			lowerCAmelCase_     :							Tuple=4			,			lowerCAmelCase_     :							int=4			,			lowerCAmelCase_     :							Union[str, Any]=3_7			,			lowerCAmelCase_     :							Tuple="gelu"			,			lowerCAmelCase_     :							List[str]=0.1			,			lowerCAmelCase_     :							Optional[int]=0.1			,			lowerCAmelCase_     :							Tuple=1_0			,			lowerCAmelCase_     :							Dict=0.02			,			lowerCAmelCase_     :							List[str]=3			,			lowerCAmelCase_     :							Dict=None			,			lowerCAmelCase_     :							int=[0, 1, 2, 3]			,			):

      """simple docstring"""
      _A:  str					=       parent
      _A:  str					=       1_0_0
      _A:  Optional[int]					=       batch_size
      _A:  Optional[int]					=       image_size
      _A:  Tuple					=       patch_size
      _A:  Union[str, Any]					=       num_channels
      _A:  int					=       is_training
      _A:  Union[str, Any]					=       use_labels
      _A:  List[str]					=       hidden_size
      _A:  Optional[Any]					=       num_hidden_layers
      _A:  Any					=       num_attention_heads
      _A:  Dict					=       intermediate_size
      _A:  Union[str, Any]					=       hidden_act
      _A:  List[str]					=       hidden_dropout_prob
      _A:  Tuple					=       attention_probs_dropout_prob
      _A:  Union[str, Any]					=       type_sequence_label_size
      _A:  int					=       initializer_range
      _A:  Optional[int]					=       scope
      _A:  Optional[Any]					=       out_indices
      _A:  Optional[Any]					=       num_labels

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


   def  __magic_name__				(					self     :							Optional[int]     ):

      """simple docstring"""
      _A:  Optional[int]					=       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]     )

      _A:  Optional[Any]					=       None
      _A:  int					=       None
      if self.use_labels:
         _A:  Union[str, Any]					=       ids_tensor([self.batch_size]			,			self.type_sequence_label_size     )
         _A:  Optional[int]					=       ids_tensor([self.batch_size, self.image_size, self.image_size]			,			self.num_labels     )

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

      return config, pixel_values, labels, pixel_labels


   def  __magic_name__				(					self     :							Optional[Any]     ):

      """simple docstring"""
      return BeitConfig(
          vocab_size=self.vocab_size			,			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=lowerCAmelCase_			,			initializer_range=self.initializer_range			,			out_indices=self.out_indices			,			)


   def  __magic_name__				(					self     :							Optional[Any]			,			lowerCAmelCase_     :							List[str]			,			lowerCAmelCase_     :							Tuple			,			lowerCAmelCase_     :							Optional[Any]			,			lowerCAmelCase_     :							Dict     ):

      """simple docstring"""
      _A:  Optional[Any]					=       BeitModel(config=lowerCAmelCase_     )
      model.to(lowerCAmelCase_     )
      model.eval()
      _A:  Dict					=       model(lowerCAmelCase_     )
      self.parent.assertEqual(result.last_hidden_state.shape			,			(self.batch_size, self.seq_length, self.hidden_size)     )


   def  __magic_name__				(					self     :							Optional[Any]			,			lowerCAmelCase_     :							Dict			,			lowerCAmelCase_     :							Tuple			,			lowerCAmelCase_     :							Union[str, Any]			,			lowerCAmelCase_     :							str     ):

      """simple docstring"""
      _A:  str					=       BeitForMaskedImageModeling(config=lowerCAmelCase_     )
      model.to(lowerCAmelCase_     )
      model.eval()
      _A:  Dict					=       model(lowerCAmelCase_     )
      self.parent.assertEqual(result.logits.shape			,			(self.batch_size, self.seq_length - 1, self.vocab_size)     )


   def  __magic_name__				(					self     :							Optional[int]			,			lowerCAmelCase_     :							str			,			lowerCAmelCase_     :							Tuple			,			lowerCAmelCase_     :							List[str]			,			lowerCAmelCase_     :							str     ):

      """simple docstring"""
      _A:  Tuple					=       self.type_sequence_label_size
      _A:  str					=       BeitForImageClassification(lowerCAmelCase_     )
      model.to(lowerCAmelCase_     )
      model.eval()
      _A:  List[Any]					=       model(lowerCAmelCase_			,			labels=lowerCAmelCase_     )
      self.parent.assertEqual(result.logits.shape			,			(self.batch_size, self.type_sequence_label_size)     )

      # test greyscale images
      _A:  Optional[Any]					=       1
      _A:  List[str]					=       BeitForImageClassification(lowerCAmelCase_     )
      model.to(lowerCAmelCase_     )
      model.eval()

      _A:  Dict					=       floats_tensor([self.batch_size, 1, self.image_size, self.image_size]     )
      _A:  Optional[int]					=       model(lowerCAmelCase_			,			labels=lowerCAmelCase_     )
      self.parent.assertEqual(result.logits.shape			,			(self.batch_size, self.type_sequence_label_size)     )


   def  __magic_name__				(					self     :							str			,			lowerCAmelCase_     :							Union[str, Any]			,			lowerCAmelCase_     :							Any			,			lowerCAmelCase_     :							int			,			lowerCAmelCase_     :							int     ):

      """simple docstring"""
      _A:  List[str]					=       self.num_labels
      _A:  str					=       BeitForSemanticSegmentation(lowerCAmelCase_     )
      model.to(lowerCAmelCase_     )
      model.eval()
      _A:  Tuple					=       model(lowerCAmelCase_     )
      self.parent.assertEqual(
          result.logits.shape			,			(self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2)     )
      _A:  Tuple					=       model(lowerCAmelCase_			,			labels=lowerCAmelCase_     )
      self.parent.assertEqual(
          result.logits.shape			,			(self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2)     )


   def  __magic_name__				(					self     :							Tuple     ):

      """simple docstring"""
      _A:  str					=       self.prepare_config_and_inputs()
      _A			,  _A			,  _A			,  _A:  Optional[Any]					=       config_and_inputs
      _A:  Any					=       {'''pixel_values''': pixel_values}
      return config, inputs_dict






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





   '''simple docstring'''
   __UpperCamelCase		:    int 				=    (
       (BeitModel, BeitForImageClassification, BeitForMaskedImageModeling, BeitForSemanticSegmentation)
       if is_torch_available()
       else ()
   )
   __UpperCamelCase		:    List[str] 				=    (
       {
           '''feature-extraction''': BeitModel,
           '''image-classification''': BeitForImageClassification,
           '''image-segmentation''': BeitForSemanticSegmentation,
       }
       if is_torch_available()
       else {}
   )

   __UpperCamelCase		:    str 				=    False
   __UpperCamelCase		:    List[Any] 				=    False
   __UpperCamelCase		:    List[str] 				=    False


   def  __magic_name__				(					self     :							Union[str, Any]     ):

      """simple docstring"""
      _A:  Dict					=       BeitModelTester(self     )
      _A:  Tuple					=       ConfigTester(self			,			config_class=lowerCAmelCase_			,			has_text_modality=lowerCAmelCase_			,			hidden_size=3_7     )


   def  __magic_name__				(					self     :							List[Any]     ):

      """simple docstring"""
      self.config_tester.run_common_tests()


   @unittest.skip(reason='''BEiT does not use inputs_embeds'''     )
   def  __magic_name__				(					self     :							Any     ):

      """simple docstring"""
      pass


   @require_torch_multi_gpu
   @unittest.skip(reason='''BEiT has some layers using `add_module` which doesn\'t work well with `nn.DataParallel`'''     )
   def  __magic_name__				(					self     :							Union[str, Any]     ):

      """simple docstring"""
      pass


   def  __magic_name__				(					self     :							str     ):

      """simple docstring"""
      _A			,  _A:  str					=       self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
         _A:  Optional[int]					=       model_class(lowerCAmelCase_     )
         self.assertIsInstance(model.get_input_embeddings()			,			(nn.Module)     )
         _A:  Union[str, Any]					=       model.get_output_embeddings()
         self.assertTrue(x is None or isinstance(lowerCAmelCase_			,			nn.Linear     )     )


   def  __magic_name__				(					self     :							List[Any]     ):

      """simple docstring"""
      _A			,  _A:  Union[str, Any]					=       self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
         _A:  Optional[int]					=       model_class(lowerCAmelCase_     )
         _A:  Tuple					=       inspect.signature(model.forward     )
         # signature.parameters is an OrderedDict => so arg_names order is deterministic
         _A:  int					=       [*signature.parameters.keys()]

         _A:  Optional[int]					=       ['''pixel_values''']
         self.assertListEqual(arg_names[:1]			,			lowerCAmelCase_     )


   def  __magic_name__				(					self     :							Dict     ):

      """simple docstring"""
      _A:  Tuple					=       self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*lowerCAmelCase_     )


   def  __magic_name__				(					self     :							Optional[Any]     ):

      """simple docstring"""
      _A:  List[Any]					=       self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_masked_lm(*lowerCAmelCase_     )


   def  __magic_name__				(					self     :							List[str]     ):

      """simple docstring"""
      _A:  str					=       self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_image_classification(*lowerCAmelCase_     )


   def  __magic_name__				(					self     :							List[str]     ):

      """simple docstring"""
      _A:  int					=       self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_semantic_segmentation(*lowerCAmelCase_     )


   def  __magic_name__				(					self     :							List[str]     ):

      """simple docstring"""
      if not self.model_tester.is_training:
         return

      _A			,  _A:  Tuple					=       self.model_tester.prepare_config_and_inputs_for_common()
      _A:  str					=       True

      for model_class in self.all_model_classes:
         # we don't test BeitForMaskedImageModeling
         if model_class in [*get_values(lowerCAmelCase_     ), BeitForMaskedImageModeling]:
            continue

         _A:  str					=       model_class(lowerCAmelCase_     )
         model.to(lowerCAmelCase_     )
         model.train()
         _A:  Tuple					=       self._prepare_for_class(lowerCAmelCase_			,			lowerCAmelCase_			,			return_labels=lowerCAmelCase_     )
         _A:  str					=       model(**lowerCAmelCase_     ).loss
         loss.backward()


   def  __magic_name__				(					self     :							str     ):

      """simple docstring"""
      _A			,  _A:  Tuple					=       self.model_tester.prepare_config_and_inputs_for_common()
      if not self.model_tester.is_training:
         return

      _A:  List[Any]					=       False
      _A:  int					=       True

      for model_class in self.all_model_classes:
         # we don't test BeitForMaskedImageModeling
         if (
             model_class in [*get_values(lowerCAmelCase_     ), BeitForMaskedImageModeling]
             or not model_class.supports_gradient_checkpointing
         ):
            continue

         _A:  str					=       model_class(lowerCAmelCase_     )
         model.gradient_checkpointing_enable()
         model.to(lowerCAmelCase_     )
         model.train()
         _A:  Optional[int]					=       self._prepare_for_class(lowerCAmelCase_			,			lowerCAmelCase_			,			return_labels=lowerCAmelCase_     )
         _A:  Union[str, Any]					=       model(**lowerCAmelCase_     ).loss
         loss.backward()


   def  __magic_name__				(					self     :							Dict     ):

      """simple docstring"""
      _A			,  _A:  Optional[Any]					=       self.model_tester.prepare_config_and_inputs_for_common()

      _A:  int					=       _config_zero_init(lowerCAmelCase_     )
      for model_class in self.all_model_classes:
         _A:  Any					=       model_class(config=lowerCAmelCase_     )
         for name, param in model.named_parameters():
            # we skip lambda parameters as these require special initial values
            # determined by config.layer_scale_init_value
            if "lambda" in name:
               continue
            if 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"""			,			)


   @slow
   def  __magic_name__				(					self     :							List[Any]     ):

      """simple docstring"""
      for model_name in BEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         _A:  Optional[Any]					=       BeitModel.from_pretrained(lowerCAmelCase_     )
         self.assertIsNotNone(lowerCAmelCase_     )






def   lowerCamelCase__							(					)      ->   Optional[int]:
   _A:  Dict					=       Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''      )
   return image






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





   '''simple docstring'''


   @cached_property
   def  __magic_name__				(					self     :							Optional[Any]     ):

      """simple docstring"""
      return BeitImageProcessor.from_pretrained('''microsoft/beit-base-patch16-224'''     ) if is_vision_available() else None


   @slow
   def  __magic_name__				(					self     :							Union[str, Any]     ):

      """simple docstring"""
      _A:  int					=       BeitForMaskedImageModeling.from_pretrained('''microsoft/beit-base-patch16-224-pt22k'''     ).to(lowerCAmelCase_     )

      _A:  Optional[Any]					=       self.default_image_processor
      _A:  Optional[Any]					=       prepare_img()
      _A:  str					=       image_processor(images=lowerCAmelCase_			,			return_tensors='''pt'''     ).pixel_values.to(lowerCAmelCase_     )

      # prepare bool_masked_pos
      _A:  List[str]					=       torch.ones((1, 1_9_6)			,			dtype=torch.bool     ).to(lowerCAmelCase_     )

      # forward pass
      with torch.no_grad():
         _A:  Union[str, Any]					=       model(pixel_values=lowerCAmelCase_			,			bool_masked_pos=lowerCAmelCase_     )
      _A:  Optional[Any]					=       outputs.logits

      # verify the logits
      _A:  Any					=       torch.Size((1, 1_9_6, 8_1_9_2)     )
      self.assertEqual(logits.shape			,			lowerCAmelCase_     )

      _A:  str					=       torch.tensor(
          [[-3.2437, 0.5072, -13.9174], [-3.2456, 0.4948, -13.9401], [-3.2033, 0.5121, -13.8550]]     ).to(lowerCAmelCase_     )

      self.assertTrue(torch.allclose(logits[bool_masked_pos][:3, :3]			,			lowerCAmelCase_			,			atol=1e-2     )     )


   @slow
   def  __magic_name__				(					self     :							Optional[int]     ):

      """simple docstring"""
      _A:  List[str]					=       BeitForImageClassification.from_pretrained('''microsoft/beit-base-patch16-224'''     ).to(lowerCAmelCase_     )

      _A:  Dict					=       self.default_image_processor
      _A:  Optional[Any]					=       prepare_img()
      _A:  Optional[Any]					=       image_processor(images=lowerCAmelCase_			,			return_tensors='''pt'''     ).to(lowerCAmelCase_     )

      # forward pass
      with torch.no_grad():
         _A:  List[Any]					=       model(**lowerCAmelCase_     )
      _A:  Dict					=       outputs.logits

      # verify the logits
      _A:  List[str]					=       torch.Size((1, 1_0_0_0)     )
      self.assertEqual(logits.shape			,			lowerCAmelCase_     )

      _A:  Optional[int]					=       torch.tensor([-1.2385, -1.0987, -1.0108]     ).to(lowerCAmelCase_     )

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

      _A:  Optional[Any]					=       2_8_1
      self.assertEqual(logits.argmax(-1     ).item()			,			lowerCAmelCase_     )


   @slow
   def  __magic_name__				(					self     :							List[str]     ):

      """simple docstring"""
      _A:  Any					=       BeitForImageClassification.from_pretrained('''microsoft/beit-large-patch16-224-pt22k-ft22k'''     ).to(
          lowerCAmelCase_     )

      _A:  List[Any]					=       self.default_image_processor
      _A:  Tuple					=       prepare_img()
      _A:  Optional[Any]					=       image_processor(images=lowerCAmelCase_			,			return_tensors='''pt'''     ).to(lowerCAmelCase_     )

      # forward pass
      with torch.no_grad():
         _A:  Union[str, Any]					=       model(**lowerCAmelCase_     )
      _A:  List[str]					=       outputs.logits

      # verify the logits
      _A:  Optional[int]					=       torch.Size((1, 2_1_8_4_1)     )
      self.assertEqual(logits.shape			,			lowerCAmelCase_     )

      _A:  Optional[int]					=       torch.tensor([1.6881, -0.2787, 0.5901]     ).to(lowerCAmelCase_     )

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

      _A:  str					=       2_3_9_6
      self.assertEqual(logits.argmax(-1     ).item()			,			lowerCAmelCase_     )


   @slow
   def  __magic_name__				(					self     :							Any     ):

      """simple docstring"""
      _A:  Any					=       BeitForSemanticSegmentation.from_pretrained('''microsoft/beit-base-finetuned-ade-640-640'''     )
      _A:  Tuple					=       model.to(lowerCAmelCase_     )

      _A:  List[str]					=       BeitImageProcessor(do_resize=lowerCAmelCase_			,			size=6_4_0			,			do_center_crop=lowerCAmelCase_     )

      _A:  Any					=       load_dataset('''hf-internal-testing/fixtures_ade20k'''			,			split='''test'''     )
      _A:  str					=       Image.open(ds[0]['''file''']     )
      _A:  Optional[int]					=       image_processor(images=lowerCAmelCase_			,			return_tensors='''pt'''     ).to(lowerCAmelCase_     )

      # forward pass
      with torch.no_grad():
         _A:  Tuple					=       model(**lowerCAmelCase_     )
      _A:  Optional[Any]					=       outputs.logits

      # verify the logits
      _A:  Any					=       torch.Size((1, 1_5_0, 1_6_0, 1_6_0)     )
      self.assertEqual(logits.shape			,			lowerCAmelCase_     )

      _A:  Union[str, Any]					=       version.parse(PIL.__version__     ) < version.parse('''9.0.0'''     )

      if is_pillow_less_than_a:
         _A:  List[Any]					=       torch.tensor(
             [
                 [[-4.9225, -2.3954, -3.0522], [-2.8822, -1.0046, -1.7561], [-2.9549, -1.3228, -2.1347]],
                 [[-5.8168, -3.4129, -4.0778], [-3.8651, -2.2214, -3.0277], [-3.8356, -2.4643, -3.3535]],
                 [[-0.0078, 3.9952, 4.0754], [2.9856, 4.6944, 5.0035], [3.2413, 4.7813, 4.9969]],
             ]			,			device=lowerCAmelCase_			,			)
      else:
         _A:  Tuple					=       torch.tensor(
             [
                 [[-4.8960, -2.3688, -3.0355], [-2.8478, -0.9836, -1.7418], [-2.9449, -1.3332, -2.1456]],
                 [[-5.8081, -3.4124, -4.1006], [-3.8561, -2.2081, -3.0323], [-3.8365, -2.4601, -3.3669]],
                 [[-0.0309, 3.9868, 4.0540], [2.9640, 4.6877, 4.9976], [3.2081, 4.7690, 4.9942]],
             ]			,			device=lowerCAmelCase_			,			)

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


   @slow
   def  __magic_name__				(					self     :							Optional[Any]     ):

      """simple docstring"""
      _A:  int					=       BeitForSemanticSegmentation.from_pretrained('''microsoft/beit-base-finetuned-ade-640-640'''     )
      _A:  str					=       model.to(lowerCAmelCase_     )

      _A:  List[str]					=       BeitImageProcessor(do_resize=lowerCAmelCase_			,			size=6_4_0			,			do_center_crop=lowerCAmelCase_     )

      _A:  List[str]					=       load_dataset('''hf-internal-testing/fixtures_ade20k'''			,			split='''test'''     )
      _A:  Tuple					=       Image.open(ds[0]['''file''']     )
      _A:  List[Any]					=       image_processor(images=lowerCAmelCase_			,			return_tensors='''pt'''     ).to(lowerCAmelCase_     )

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

      _A:  int					=       outputs.logits.detach().cpu()

      _A:  Optional[int]					=       image_processor.post_process_semantic_segmentation(outputs=lowerCAmelCase_			,			target_sizes=[(5_0_0, 3_0_0)]     )
      _A:  List[Any]					=       torch.Size((5_0_0, 3_0_0)     )
      self.assertEqual(segmentation[0].shape			,			lowerCAmelCase_     )

      _A:  Any					=       image_processor.post_process_semantic_segmentation(outputs=lowerCAmelCase_     )
      _A:  int					=       torch.Size((1_6_0, 1_6_0)     )
      self.assertEqual(segmentation[0].shape			,			lowerCAmelCase_     )





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


UpperCAmelCase__		:     List[Any]     			=      logging.get_logger(__name__)
logging.set_verbosity_info()






def   lowerCamelCase__							(					a		,		a      )      ->   Dict:
   if "xprophetnet" in prophetnet_checkpoint_path:
      _A:  List[Any]					=       XLMProphetNetForConditionalGenerationOld.from_pretrained(a      )
      _A			,  _A:  Union[str, Any]					=       XLMProphetNetForConditionalGeneration.from_pretrained(
          a		,		output_loading_info=a      )
   else:
      _A:  Dict					=       ProphetNetForConditionalGenerationOld.from_pretrained(a      )
      _A			,  _A:  Tuple					=       ProphetNetForConditionalGeneration.from_pretrained(
          a		,		output_loading_info=a      )

   _A:  Optional[int]					=       ['''key_proj''', '''value_proj''', '''query_proj''']

   _A:  List[Any]					=       {
       '''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:  Optional[int]					=       prophet
         _A:  Tuple					=       prophet_old
      else:
         _A:  Tuple					=       prophet.prophetnet
         _A:  Any					=       prophet_old.model

      _A:  int					=       False
      for attribute in attributes:
         if attribute in mapping:
            _A:  Optional[int]					=       mapping[attribute]
            if not hasattr(a		,		a      ) and len(a      ) > 0:
               _A:  int					=       attribute
         elif hasattr(a		,		a      ):
            _A:  Tuple					=       attribute

         if attribute == "weight":
            assert old_model.weight.shape == model.weight.shape, "Shapes have to match!"
            _A:  Union[str, Any]					=       old_model.weight
            logger.info(f"""{attribute} is initialized."""      )
            _A:  Any					=       True
            break
         elif attribute == "bias":
            assert old_model.bias.shape == model.bias.shape, "Shapes have to match!"
            _A:  str					=       old_model.bias
            logger.info(f"""{attribute} is initialized"""      )
            _A:  Dict					=       True
            break
         elif attribute in special_keys and hasattr(a		,		'''in_proj_weight'''      ):
            _A:  Optional[int]					=       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:  List[str]					=       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:  int					=       nn.Parameter(old_model.in_proj_weight[embed_dim : 2 * embed_dim, :]      )
               _A:  Optional[int]					=       nn.Parameter(old_model.in_proj_bias[embed_dim : 2 * embed_dim]      )
            elif attribute == "value_proj":
               _A:  List[Any]					=       nn.Parameter(old_model.in_proj_weight[2 * embed_dim :, :]      )
               _A:  int					=       nn.Parameter(old_model.in_proj_bias[2 * embed_dim :]      )
            _A:  Tuple					=       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:  Union[str, Any]					=       nn.Parameter(old_model.embed_positions.weight[:5_12, :]      )
            _A:  List[Any]					=       True
            break

         if attribute.isdigit():
            _A:  Tuple					=       model[int(a      )]
            _A:  int					=       old_model[int(a      )]
         else:
            _A:  Union[str, Any]					=       getattr(a		,		a      )

            if old_attribute == "":
               _A:  Union[str, Any]					=       old_model
            else:
               if not hasattr(a		,		a      ):
                  raise ValueError(f"""{old_model} does not have {old_attribute}"""      )
               _A:  List[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__":
   UpperCAmelCase__		:     Dict     			=      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.'
   )
   UpperCAmelCase__		:     Tuple     			=      parser.parse_args()
   convert_prophetnet_checkpoint_to_pytorch(args.prophetnet_checkpoint_path, args.pytorch_dump_folder_path)



