


'''simple docstring'''




import inspect
import unittest
import warnings

from transformers import DeiTConfig
from transformers.models.auto import get_values
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 (
     MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
     MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
     MODEL_MAPPING,
     DeiTForImageClassification,
     DeiTForImageClassificationWithTeacher,
     DeiTForMaskedImageModeling,
     DeiTModel,
 )
 from transformers.models.deit.modeling_deit import DEIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
 from PIL import Image

 from transformers import DeiTImageProcessor







class 					a			:
 '''simple docstring'''




 def __init__(    self		,      lowerCamelCase_		,      lowerCamelCase_=1_3		,      lowerCamelCase_=3_0		,      lowerCamelCase_=2		,      lowerCamelCase_=3		,      lowerCamelCase_=True		,      lowerCamelCase_=True		,      lowerCamelCase_=3_2		,      lowerCamelCase_=5		,      lowerCamelCase_=4		,      lowerCamelCase_=3_7		,      lowerCamelCase_="gelu"		,      lowerCamelCase_=0.1		,      lowerCamelCase_=0.1		,      lowerCamelCase_=1_0		,      lowerCamelCase_=0.02		,      lowerCamelCase_=3		,      lowerCamelCase_=None		,      lowerCamelCase_=2		,      )      ->    Tuple:
    _a					:  Any						=  parent
    _a					:  Any						=  batch_size
    _a					:  List[str]						=  image_size
    _a					:  Union[str, Any]						=  patch_size
    _a					:  str						=  num_channels
    _a					:  Dict						=  is_training
    _a					:  Tuple						=  use_labels
    _a					:  Union[str, Any]						=  hidden_size
    _a					:  int						=  num_hidden_layers
    _a					:  Tuple						=  num_attention_heads
    _a					:  Tuple						=  intermediate_size
    _a					:  Any						=  hidden_act
    _a					:  Tuple						=  hidden_dropout_prob
    _a					:  Optional[int]						=  attention_probs_dropout_prob
    _a					:  Tuple						=  type_sequence_label_size
    _a					:  List[Any]						=  initializer_range
    _a					:  Tuple						=  scope
    _a					:  Union[str, Any]						=  encoder_stride

    # in DeiT, the seq length equals the number of patches + 2 (we add 2 for the [CLS] and distilation tokens)
    _a					:  Optional[Any]						=  (image_size // patch_size) ** 2
    _a					:  List[str]						=  num_patches + 2




 def   __UpperCamelCase   (    self      )      ->    Tuple:
    _a					:  Union[str, Any]						=  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]      )

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

    _a					:  str						=  self.get_config()

    return config, pixel_values, labels




 def   __UpperCamelCase   (    self      )      ->    int:
    return DeiTConfig(
        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		,      encoder_stride=self.encoder_stride		,      )




 def   __UpperCamelCase   (    self		,      lowerCamelCase_		,      lowerCamelCase_		,      lowerCamelCase_      )      ->    str:
    _a					:  Any						=  DeiTModel(config=lowerCamelCase_      )
    model.to(lowerCamelCase_      )
    model.eval()
    _a					:  List[str]						=  model(lowerCamelCase_      )
    self.parent.assertEqual(result.last_hidden_state.shape		,      (self.batch_size, self.seq_length, self.hidden_size)      )




 def   __UpperCamelCase   (    self		,      lowerCamelCase_		,      lowerCamelCase_		,      lowerCamelCase_      )      ->    str:
    _a					:  List[str]						=  DeiTForMaskedImageModeling(config=lowerCamelCase_      )
    model.to(lowerCamelCase_      )
    model.eval()
    _a					:  Tuple						=  model(lowerCamelCase_      )
    self.parent.assertEqual(
        result.reconstruction.shape		,      (self.batch_size, self.num_channels, self.image_size, self.image_size)      )

    # test greyscale images
    _a					:  Optional[Any]						=  1
    _a					:  int						=  DeiTForMaskedImageModeling(lowerCamelCase_      )
    model.to(lowerCamelCase_      )
    model.eval()

    _a					:  List[Any]						=  floats_tensor([self.batch_size, 1, self.image_size, self.image_size]      )
    _a					:  Optional[Any]						=  model(lowerCamelCase_      )
    self.parent.assertEqual(result.reconstruction.shape		,      (self.batch_size, 1, self.image_size, self.image_size)      )




 def   __UpperCamelCase   (    self		,      lowerCamelCase_		,      lowerCamelCase_		,      lowerCamelCase_      )      ->    Dict:
    _a					:  int						=  self.type_sequence_label_size
    _a					:  List[str]						=  DeiTForImageClassification(lowerCamelCase_      )
    model.to(lowerCamelCase_      )
    model.eval()
    _a					:  Any						=  model(lowerCamelCase_		,      labels=lowerCamelCase_      )
    self.parent.assertEqual(result.logits.shape		,      (self.batch_size, self.type_sequence_label_size)      )

    # test greyscale images
    _a					:  Any						=  1
    _a					:  List[str]						=  DeiTForImageClassification(lowerCamelCase_      )
    model.to(lowerCamelCase_      )
    model.eval()

    _a					:  List[str]						=  floats_tensor([self.batch_size, 1, self.image_size, self.image_size]      )
    _a					:  int						=  model(lowerCamelCase_		,      labels=lowerCamelCase_      )
    self.parent.assertEqual(result.logits.shape		,      (self.batch_size, self.type_sequence_label_size)      )




 def   __UpperCamelCase   (    self      )      ->    Optional[int]:
    _a					:  Optional[int]						=  self.prepare_config_and_inputs()
    (
        (
        _a
    )		,						(
        _a
    )		,						(
        _a
    )		,						
    )					:  List[str]						=  config_and_inputs
    _a					:  int						=  {'pixel_values': pixel_values}
    return config, inputs_dict







@require_torch
class 					a			(				snake_case__			,							snake_case__			,							unittest.TestCase  ):
 '''simple docstring'''




 __lowerCAmelCase						: Optional[int]							  =		(
     (
         DeiTModel,
         DeiTForImageClassification,
         DeiTForImageClassificationWithTeacher,
         DeiTForMaskedImageModeling,
     )
     if is_torch_available()
     else ()
 )
 __lowerCAmelCase						: Union[str, Any]							  =		(
     {
         """feature-extraction""": DeiTModel,
         """image-classification""": (DeiTForImageClassification, DeiTForImageClassificationWithTeacher),
     }
     if is_torch_available()
     else {}
 )

 __lowerCAmelCase						: int							  =		False
 __lowerCAmelCase						: str							  =		False
 __lowerCAmelCase						: Tuple							  =		False




 def   __UpperCamelCase   (    self      )      ->    Optional[int]:
    _a					:  Tuple						=  DeiTModelTester(self      )
    _a					:  List[Any]						=  ConfigTester(self		,      config_class=lowerCamelCase_		,      has_text_modality=lowerCamelCase_		,      hidden_size=3_7      )




 def   __UpperCamelCase   (    self      )      ->    Dict:
    self.config_tester.run_common_tests()




 @unittest.skip(reason='DeiT does not use inputs_embeds'      )
 def   __UpperCamelCase   (    self      )      ->    Dict:
    pass




 def   __UpperCamelCase   (    self      )      ->    int:
    _a		,						_a					:  Any						=  self.model_tester.prepare_config_and_inputs_for_common()

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




 def   __UpperCamelCase   (    self      )      ->    List[Any]:
    _a		,						_a					:  Any						=  self.model_tester.prepare_config_and_inputs_for_common()

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

       _a					:  Any						=  ['pixel_values']
       self.assertListEqual(arg_names[:1]		,      lowerCamelCase_      )




 def   __UpperCamelCase   (    self      )      ->    Any:
    _a					:  List[Any]						=  self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_model(*lowerCamelCase_      )




 def   __UpperCamelCase   (    self      )      ->    Optional[Any]:
    _a					:  Any						=  self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_masked_image_modeling(*lowerCamelCase_      )




 def   __UpperCamelCase   (    self      )      ->    Any:
    _a					:  Union[str, Any]						=  self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_image_classification(*lowerCamelCase_      )




 def   __UpperCamelCase   (    self		,      lowerCamelCase_		,      lowerCamelCase_		,      lowerCamelCase_=False      )      ->    Dict:
    _a					:  Optional[Any]						=  super()._prepare_for_class(lowerCamelCase_		,      lowerCamelCase_		,      return_labels=lowerCamelCase_      )

    if return_labels:
       if model_class.__name__ == "DeiTForImageClassificationWithTeacher":
          del inputs_dict["labels"]

    return inputs_dict




 def   __UpperCamelCase   (    self      )      ->    Tuple:
    if not self.model_tester.is_training:
       return

    _a		,						_a					:  int						=  self.model_tester.prepare_config_and_inputs_for_common()
    _a					:  int						=  True

    for model_class in self.all_model_classes:
       # DeiTForImageClassificationWithTeacher supports inference-only
       if (
           model_class in get_values(lowerCamelCase_      )
           or model_class.__name__ == "DeiTForImageClassificationWithTeacher"
       ):
          continue
       _a					:  Optional[int]						=  model_class(lowerCamelCase_      )
       model.to(lowerCamelCase_      )
       model.train()
       _a					:  int						=  self._prepare_for_class(lowerCamelCase_		,      lowerCamelCase_		,      return_labels=lowerCamelCase_      )
       _a					:  Union[str, Any]						=  model(**lowerCamelCase_      ).loss
       loss.backward()




 def   __UpperCamelCase   (    self      )      ->    Dict:
    _a		,						_a					:  Dict						=  self.model_tester.prepare_config_and_inputs_for_common()
    if not self.model_tester.is_training:
       return

    _a					:  Tuple						=  False
    _a					:  List[str]						=  True

    for model_class in self.all_model_classes:
       if model_class in get_values(lowerCamelCase_      ) or not model_class.supports_gradient_checkpointing:
          continue
       # DeiTForImageClassificationWithTeacher supports inference-only
       if model_class.__name__ == "DeiTForImageClassificationWithTeacher":
          continue
       _a					:  Optional[int]						=  model_class(lowerCamelCase_      )
       model.gradient_checkpointing_enable()
       model.to(lowerCamelCase_      )
       model.train()
       _a					:  List[Any]						=  self._prepare_for_class(lowerCamelCase_		,      lowerCamelCase_		,      return_labels=lowerCamelCase_      )
       _a					:  Union[str, Any]						=  model(**lowerCamelCase_      ).loss
       loss.backward()




 def   __UpperCamelCase   (    self      )      ->    List[Any]:
    _a		,						_a					:  List[str]						=  self.model_tester.prepare_config_and_inputs_for_common()

    _a					:  Dict						=  [
        {'title': 'multi_label_classification', 'num_labels': 2, 'dtype': torch.float},
        {'title': 'single_label_classification', 'num_labels': 1, 'dtype': torch.long},
        {'title': 'regression', 'num_labels': 1, 'dtype': torch.float},
    ]

    for model_class in self.all_model_classes:
       if (
           model_class
           not in [
               *get_values(lowerCamelCase_      ),
               *get_values(lowerCamelCase_      ),
           ]
           or model_class.__name__ == "DeiTForImageClassificationWithTeacher"
       ):
          continue

       for problem_type in problem_types:
          with self.subTest(msg=F'''Testing {model_class} with {problem_type["title"]}'''      ):
             _a					:  List[str]						=  problem_type['title']
             _a					:  Optional[int]						=  problem_type['num_labels']

             _a					:  Dict						=  model_class(lowerCamelCase_      )
             model.to(lowerCamelCase_      )
             model.train()

             _a					:  str						=  self._prepare_for_class(lowerCamelCase_		,      lowerCamelCase_		,      return_labels=lowerCamelCase_      )

             if problem_type["num_labels"] > 1:
                _a					:  Optional[Any]						=  inputs['labels'].unsqueeze(1      ).repeat(1		,      problem_type['num_labels']      )

             _a					:  Dict						=  inputs['labels'].to(problem_type['dtype']      )

             # This tests that we do not trigger the warning form PyTorch "Using a target size that is different
             # to the input size. This will likely lead to incorrect results due to broadcasting. Please ensure
             # they have the same size." which is a symptom something in wrong for the regression problem.
             # See https://github.com/huggingface/transformers/issues/11780
             with warnings.catch_warnings(record=lowerCamelCase_      ) as warning_list:
                _a					:  str						=  model(**lowerCamelCase_      ).loss
             for w in warning_list:
                if "Using a target size that is different to the input size" in str(w.message      ):
                   raise ValueError(
                       F'''Something is going wrong in the regression problem: intercepted {w.message}'''      )

             loss.backward()




 @slow
 def   __UpperCamelCase   (    self      )      ->    List[Any]:
    for model_name in DEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
       _a					:  List[Any]						=  DeiTModel.from_pretrained(lowerCamelCase_      )
       self.assertIsNotNone(lowerCamelCase_      )





def    UpperCAmelCase_  (       ):
   '''simple docstring'''





   _a					:  str						=  Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'      )
   return image







@require_torch
@require_vision
class 					a			(				unittest.TestCase  ):
 '''simple docstring'''




 @cached_property
 def   __UpperCamelCase   (    self      )      ->    Optional[int]:
    return (
        DeiTImageProcessor.from_pretrained('facebook/deit-base-distilled-patch16-224'      )
        if is_vision_available()
        else None
    )




 @slow
 def   __UpperCamelCase   (    self      )      ->    Optional[Any]:
    _a					:  str						=  DeiTForImageClassificationWithTeacher.from_pretrained('facebook/deit-base-distilled-patch16-224'      ).to(
        lowerCamelCase_      )

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

    # forward pass
    with torch.no_grad():
       _a					:  Tuple						=  model(**lowerCamelCase_      )

    # verify the logits
    _a					:  List[Any]						=  torch.Size((1, 1_0_0_0)      )
    self.assertEqual(outputs.logits.shape		,      lowerCamelCase_      )

    _a					:  Union[str, Any]						=  torch.tensor([-1.0266, 0.1912, -1.2861]      ).to(lowerCamelCase_      )

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




 @slow
 @require_accelerate
 @require_torch_gpu
 def   __UpperCamelCase   (    self      )      ->    str:
    _a					:  List[str]						=  DeiTModel.from_pretrained(
        'facebook/deit-base-distilled-patch16-224'		,      torch_dtype=torch.floataa		,      device_map='auto'      )
    _a					:  Tuple						=  self.default_image_processor

    _a					:  Optional[int]						=  prepare_img()
    _a					:  List[Any]						=  image_processor(images=lowerCamelCase_		,      return_tensors='pt'      )
    _a					:  str						=  inputs.pixel_values.to(lowerCamelCase_      )

    # forward pass to make sure inference works in fp16
    with torch.no_grad():
       _a					:  Dict						=  model(lowerCamelCase_      )






'''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


UpperCAmelCase_      :		List[str]          =		logging.get_logger(__name__)
logging.set_verbosity_info()


def    UpperCAmelCase_  (       A		,	A      ):
   '''simple docstring'''





   if "xprophetnet" in prophetnet_checkpoint_path:
      _a					:  str						=  XLMProphetNetForConditionalGenerationOld.from_pretrained(A      )
      _a		,						_a					:  Any						=  XLMProphetNetForConditionalGeneration.from_pretrained(
          A		,	output_loading_info=A      )
   else:
      _a					:  Optional[Any]						=  ProphetNetForConditionalGenerationOld.from_pretrained(A      )
      _a		,						_a					:  Dict						=  ProphetNetForConditionalGeneration.from_pretrained(
          A		,	output_loading_info=A      )

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

   _a					:  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					:  Tuple						=  key.split('.'      )

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

      _a					:  Dict						=  False
      for attribute in attributes:
         if attribute in mapping:
            _a					:  List[str]						=  mapping[attribute]
            if not hasattr(A		,	A      ) and len(A      ) > 0:
               _a					:  Any						=  attribute
         elif hasattr(A		,	A      ):
            _a					:  Dict						=  attribute

         if attribute == "weight":
            assert old_model.weight.shape == model.weight.shape, "Shapes have to match!"
            _a					:  str						=  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					:  int						=  old_model.bias
            logger.info(f'''{attribute} is initialized'''      )
            _a					:  Optional[int]						=  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					:  List[str]						=  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					:  Optional[int]						=  nn.Parameter(old_model.in_proj_weight[:embed_dim, :]      )
               _a					:  Union[str, Any]						=  nn.Parameter(old_model.in_proj_bias[:embed_dim]      )

            elif attribute == "key_proj":
               _a					:  str						=  nn.Parameter(old_model.in_proj_weight[embed_dim : 2 * embed_dim, :]      )
               _a					:  List[Any]						=  nn.Parameter(old_model.in_proj_bias[embed_dim : 2 * embed_dim]      )
            elif attribute == "value_proj":
               _a					:  Optional[int]						=  nn.Parameter(old_model.in_proj_weight[2 * embed_dim :, :]      )
               _a					:  str						=  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_1_2, "We want 512 position_embeddings."
            _a					:  Tuple						=  nn.Parameter(old_model.embed_positions.weight[:5_1_2, :]      )
            _a					:  Any						=  True
            break

         if attribute.isdigit():
            _a					:  List[str]						=  model[int(A      )]
            _a					:  Union[str, Any]						=  old_model[int(A      )]
         else:
            _a					:  List[Any]						=  getattr(A		,	A      )

            if old_attribute == "":
               _a					:  List[str]						=  old_model
            else:
               if not hasattr(A		,	A      ):
                  raise ValueError(f'''{old_model} does not have {old_attribute}'''      )
               _a					:  Optional[int]						=  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_      :		Any          =		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_      :		List[str]          =		parser.parse_args()
 convert_prophetnet_checkpoint_to_pytorch(args.prophetnet_checkpoint_path, args.pytorch_dump_folder_path)


