




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


lowerCamelCase    :		str			   =       logging.get_logger(__name__)
logging.set_verbosity_info()

def       _SCREAMING_SNAKE_CASE      (							lowercase     :	str     ,							lowercase     :	str					):


      '''simple docstring'''



      if "xprophetnet" in prophetnet_checkpoint_path:
            lowerCamelCase_							=   XLMProphetNetForConditionalGenerationOld.from_pretrained(lowercase					)
            lowerCamelCase_					,    lowerCamelCase_							=   XLMProphetNetForConditionalGeneration.from_pretrained(
                lowercase     ,							output_loading_info=lowercase					)
      else:
            lowerCamelCase_							=   ProphetNetForConditionalGenerationOld.from_pretrained(lowercase					)
            lowerCamelCase_					,    lowerCamelCase_							=   ProphetNetForConditionalGeneration.from_pretrained(
                lowercase     ,							output_loading_info=lowercase					)

      lowerCamelCase_							=   ['key_proj', 'value_proj', 'query_proj']

      lowerCamelCase_							=   {
          '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"]:
            lowerCamelCase_							=   key.split('.'					)

            if attributes[0] == "lm_head":
                  lowerCamelCase_							=   prophet
                  lowerCamelCase_							=   prophet_old
            else:
                  lowerCamelCase_							=   prophet.prophetnet
                  lowerCamelCase_							=   prophet_old.model

            lowerCamelCase_							=   False
            for attribute in attributes:
                  if attribute in mapping:
                        lowerCamelCase_							=   mapping[attribute]
                        if not hasattr(lowercase     ,							lowercase					) and len(lowercase					) > 0:
                              lowerCamelCase_							=   attribute
                  elif hasattr(lowercase     ,							lowercase					):
                        lowerCamelCase_							=   attribute

                  if attribute == "weight":
                        assert old_model.weight.shape == model.weight.shape, "Shapes have to match!"
                        lowerCamelCase_							=   old_model.weight
                        logger.info(f"""{attribute} is initialized."""					)
                        lowerCamelCase_							=   True
                        break
                  elif attribute == "bias":
                        assert old_model.bias.shape == model.bias.shape, "Shapes have to match!"
                        lowerCamelCase_							=   old_model.bias
                        logger.info(f"""{attribute} is initialized"""					)
                        lowerCamelCase_							=   True
                        break
                  elif attribute in special_keys and hasattr(lowercase     ,							'in_proj_weight'					):
                        lowerCamelCase_							=   old_model.in_proj_weight.shape[0] // 3
                        lowerCamelCase_							=   getattr(lowercase     ,							lowercase					)
                        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":
                              lowerCamelCase_							=   nn.Parameter(old_model.in_proj_weight[:embed_dim, :]					)
                              lowerCamelCase_							=   nn.Parameter(old_model.in_proj_bias[:embed_dim]					)

                        elif attribute == "key_proj":
                              lowerCamelCase_							=   nn.Parameter(old_model.in_proj_weight[embed_dim : 2 * embed_dim, :]					)
                              lowerCamelCase_							=   nn.Parameter(old_model.in_proj_bias[embed_dim : 2 * embed_dim]					)
                        elif attribute == "value_proj":
                              lowerCamelCase_							=   nn.Parameter(old_model.in_proj_weight[2 * embed_dim :, :]					)
                              lowerCamelCase_							=   nn.Parameter(old_model.in_proj_bias[2 * embed_dim :]					)
                        lowerCamelCase_							=   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."
                        lowerCamelCase_							=   nn.Parameter(old_model.embed_positions.weight[:5_12, :]					)
                        lowerCamelCase_							=   True
                        break

                  if attribute.isdigit():
                        lowerCamelCase_							=   model[int(lowercase					)]
                        lowerCamelCase_							=   old_model[int(lowercase					)]
                  else:
                        lowerCamelCase_							=   getattr(lowercase     ,							lowercase					)

                        if old_attribute == "":
                              lowerCamelCase_							=   old_model
                        else:
                              if not hasattr(lowercase     ,							lowercase					):
                                    raise ValueError(f"""{old_model} does not have {old_attribute}"""					)
                              lowerCamelCase_							=   getattr(lowercase     ,							lowercase					)

            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(lowercase					)


if __name__ == "__main__":
 lowerCamelCase    :		List[str]			   =       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."
 )
 lowerCamelCase    :		int			   =       parser.parse_args()
 convert_prophetnet_checkpoint_to_pytorch(args.prophetnet_checkpoint_path, args.pytorch_dump_folder_path)









import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin


if is_torch_available():
 import torch

if is_vision_available():
 from PIL import Image

 from transformers import ChineseCLIPImageProcessor







class   A(						unittest.TestCase       ):



 '''simple docstring'''


 def __init__(    self :  str						,							A_ :  int						,							A_ :  Any=7						,							A_ :  Tuple=3						,							A_ :  Union[str, Any]=18						,							A_ :  Tuple=30						,							A_ :  Union[str, Any]=400						,							A_ :  Optional[int]=True						,							A_ :  List[Any]=None						,							A_ :  Dict=True						,							A_ :  Union[str, Any]=None						,							A_ :  Optional[int]=True						,							A_ :  str=[0.48145466, 0.4578275, 0.40821073]						,							A_ :  Tuple=[0.26862954, 0.26130258, 0.27577711]						,							A_ :  Any=True						,							)       ->     str:

       """simple docstring"""






       lowerCamelCase_							=   size if size is not None else {'height': 224, 'width': 224}
       lowerCamelCase_							=   crop_size if crop_size is not None else {'height': 18, 'width': 18}
       lowerCamelCase_							=   parent
       lowerCamelCase_							=   batch_size
       lowerCamelCase_							=   num_channels
       lowerCamelCase_							=   image_size
       lowerCamelCase_							=   min_resolution
       lowerCamelCase_							=   max_resolution
       lowerCamelCase_							=   do_resize
       lowerCamelCase_							=   size
       lowerCamelCase_							=   do_center_crop
       lowerCamelCase_							=   crop_size
       lowerCamelCase_							=   do_normalize
       lowerCamelCase_							=   image_mean
       lowerCamelCase_							=   image_std
       lowerCamelCase_							=   do_convert_rgb

 def     a__			(    self :  Optional[int]  )       ->     Tuple:

       """simple docstring"""






       return {
           "do_resize": self.do_resize,
           "size": self.size,
           "do_center_crop": self.do_center_crop,
           "crop_size": self.crop_size,
           "do_normalize": self.do_normalize,
           "image_mean": self.image_mean,
           "image_std": self.image_std,
           "do_convert_rgb": self.do_convert_rgb,
       }





 def     a__			(    self :  Any						,							A_ :  Any=False						,							A_ :  Dict=False						,							A_ :  str=False  )       ->     Union[str, Any]:

       """simple docstring"""






       assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

       if equal_resolution:
             lowerCamelCase_							=   []
             for i in range(self.batch_size  ):
                   image_inputs.append(
                       np.random.randint(
                           255						,							size=(self.num_channels, self.max_resolution, self.max_resolution)						,							dtype=np.uinta  )  )
       else:
             lowerCamelCase_							=   []
             for i in range(self.batch_size  ):
                   lowerCamelCase_					,    lowerCamelCase_							=   np.random.choice(np.arange(self.min_resolution						,							self.max_resolution  )						,							2  )
                   image_inputs.append(np.random.randint(255						,							size=(self.num_channels, width, height)						,							dtype=np.uinta  )  )

       if not numpify and not torchify:
             # PIL expects the channel dimension as last dimension
             lowerCamelCase_							=   [Image.fromarray(np.moveaxis(A_						,							0						,							-1  )  ) for x in image_inputs]

       if torchify:
             lowerCamelCase_							=   [torch.from_numpy(A_  ) for x in image_inputs]

       return image_inputs







@require_torch
@require_vision
class   A(						UpperCamelCase							,				unittest.TestCase       ):



 '''simple docstring'''


 UpperCamelCase						       =						ChineseCLIPImageProcessor if is_vision_available() else None

 def     a__			(    self :  int  )       ->     Any:

       """simple docstring"""






       lowerCamelCase_							=   ChineseCLIPImageProcessingTester(self						,							do_center_crop=A_  )

 @property
 def     a__			(    self :  str  )       ->     List[Any]:

       """simple docstring"""






       return self.image_processor_tester.prepare_image_processor_dict()

 def     a__			(    self :  int  )       ->     Optional[Any]:

       """simple docstring"""






       lowerCamelCase_							=   self.image_processing_class(**self.image_processor_dict  )
       self.assertTrue(hasattr(A_						,							'do_resize'  )  )
       self.assertTrue(hasattr(A_						,							'size'  )  )
       self.assertTrue(hasattr(A_						,							'do_center_crop'  )  )
       self.assertTrue(hasattr(A_						,							'center_crop'  )  )
       self.assertTrue(hasattr(A_						,							'do_normalize'  )  )
       self.assertTrue(hasattr(A_						,							'image_mean'  )  )
       self.assertTrue(hasattr(A_						,							'image_std'  )  )
       self.assertTrue(hasattr(A_						,							'do_convert_rgb'  )  )

 def     a__			(    self :  Any  )       ->     Any:

       """simple docstring"""






       lowerCamelCase_							=   self.image_processing_class.from_dict(self.image_processor_dict  )
       self.assertEqual(image_processor.size						,							{'height': 224, 'width': 224}  )
       self.assertEqual(image_processor.crop_size						,							{'height': 18, 'width': 18}  )

       lowerCamelCase_							=   self.image_processing_class.from_dict(self.image_processor_dict						,							size=42						,							crop_size=84  )
       self.assertEqual(image_processor.size						,							{'shortest_edge': 42}  )
       self.assertEqual(image_processor.crop_size						,							{'height': 84, 'width': 84}  )

 def     a__			(    self :  List[Any]  )       ->     List[Any]:

       """simple docstring"""






       pass

 def     a__			(    self :  str  )       ->     str:

       """simple docstring"""






       lowerCamelCase_							=   self.image_processing_class(**self.image_processor_dict  )
       # create random PIL images
       lowerCamelCase_							=   self.image_processor_tester.prepare_inputs(equal_resolution=A_  )
       for image in image_inputs:
             self.assertIsInstance(A_						,							Image.Image  )

       # Test not batched input
       lowerCamelCase_							=   image_processing(image_inputs[0]						,							return_tensors='pt'  ).pixel_values
       self.assertEqual(
           encoded_images.shape						,							(
               1,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size['height'],
               self.image_processor_tester.crop_size['width'],
           )						,							)

       # Test batched
       lowerCamelCase_							=   image_processing(A_						,							return_tensors='pt'  ).pixel_values
       self.assertEqual(
           encoded_images.shape						,							(
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size['height'],
               self.image_processor_tester.crop_size['width'],
           )						,							)

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

       """simple docstring"""






       lowerCamelCase_							=   self.image_processing_class(**self.image_processor_dict  )
       # create random numpy tensors
       lowerCamelCase_							=   self.image_processor_tester.prepare_inputs(equal_resolution=A_						,							numpify=A_  )
       for image in image_inputs:
             self.assertIsInstance(A_						,							np.ndarray  )

       # Test not batched input
       lowerCamelCase_							=   image_processing(image_inputs[0]						,							return_tensors='pt'  ).pixel_values
       self.assertEqual(
           encoded_images.shape						,							(
               1,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size['height'],
               self.image_processor_tester.crop_size['width'],
           )						,							)

       # Test batched
       lowerCamelCase_							=   image_processing(A_						,							return_tensors='pt'  ).pixel_values
       self.assertEqual(
           encoded_images.shape						,							(
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size['height'],
               self.image_processor_tester.crop_size['width'],
           )						,							)





 def     a__			(    self :  str  )       ->     Dict:

       """simple docstring"""






       lowerCamelCase_							=   self.image_processing_class(**self.image_processor_dict  )
       # create random PyTorch tensors
       lowerCamelCase_							=   self.image_processor_tester.prepare_inputs(equal_resolution=A_						,							torchify=A_  )
       for image in image_inputs:
             self.assertIsInstance(A_						,							torch.Tensor  )

       # Test not batched input
       lowerCamelCase_							=   image_processing(image_inputs[0]						,							return_tensors='pt'  ).pixel_values
       self.assertEqual(
           encoded_images.shape						,							(
               1,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size['height'],
               self.image_processor_tester.crop_size['width'],
           )						,							)

       # Test batched
       lowerCamelCase_							=   image_processing(A_						,							return_tensors='pt'  ).pixel_values
       self.assertEqual(
           encoded_images.shape						,							(
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size['height'],
               self.image_processor_tester.crop_size['width'],
           )						,							)







@require_torch
@require_vision
class   A(						UpperCamelCase							,				unittest.TestCase       ):



 '''simple docstring'''


 UpperCamelCase						       =						ChineseCLIPImageProcessor if is_vision_available() else None

 def     a__			(    self :  Dict  )       ->     int:

       """simple docstring"""






       lowerCamelCase_							=   ChineseCLIPImageProcessingTester(self						,							num_channels=4						,							do_center_crop=A_  )
       lowerCamelCase_							=   3

 @property
 def     a__			(    self :  Any  )       ->     int:

       """simple docstring"""






       return self.image_processor_tester.prepare_image_processor_dict()

 def     a__			(    self :  Dict  )       ->     Tuple:

       """simple docstring"""






       lowerCamelCase_							=   self.image_processing_class(**self.image_processor_dict  )
       self.assertTrue(hasattr(A_						,							'do_resize'  )  )
       self.assertTrue(hasattr(A_						,							'size'  )  )
       self.assertTrue(hasattr(A_						,							'do_center_crop'  )  )
       self.assertTrue(hasattr(A_						,							'center_crop'  )  )
       self.assertTrue(hasattr(A_						,							'do_normalize'  )  )
       self.assertTrue(hasattr(A_						,							'image_mean'  )  )
       self.assertTrue(hasattr(A_						,							'image_std'  )  )
       self.assertTrue(hasattr(A_						,							'do_convert_rgb'  )  )

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

       """simple docstring"""






       pass





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

       """simple docstring"""






       lowerCamelCase_							=   self.image_processing_class(**self.image_processor_dict  )
       # create random PIL images
       lowerCamelCase_							=   self.image_processor_tester.prepare_inputs(equal_resolution=A_  )
       for image in image_inputs:
             self.assertIsInstance(A_						,							Image.Image  )

       # Test not batched input
       lowerCamelCase_							=   image_processing(image_inputs[0]						,							return_tensors='pt'  ).pixel_values
       self.assertEqual(
           encoded_images.shape						,							(
               1,
               self.expected_encoded_image_num_channels,
               self.image_processor_tester.crop_size['height'],
               self.image_processor_tester.crop_size['width'],
           )						,							)

       # Test batched
       lowerCamelCase_							=   image_processing(A_						,							return_tensors='pt'  ).pixel_values
       self.assertEqual(
           encoded_images.shape						,							(
               self.image_processor_tester.batch_size,
               self.expected_encoded_image_num_channels,
               self.image_processor_tester.crop_size['height'],
               self.image_processor_tester.crop_size['width'],
           )						,							)



