



'''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 						__snake_case(				_lowerCAmelCase     ,   _lowerCAmelCase		)		-> Any:
      if "xprophetnet" in prophetnet_checkpoint_path:
            snake_case__  :    Any    						=      XLMProphetNetForConditionalGenerationOld.from_pretrained(_lowerCAmelCase		)
            snake_case__						,				snake_case__  :    Union[str, Any]    						=      XLMProphetNetForConditionalGeneration.from_pretrained(
                _lowerCAmelCase     ,   output_loading_info=_lowerCAmelCase		)
      else:
            snake_case__  :    str    						=      ProphetNetForConditionalGenerationOld.from_pretrained(_lowerCAmelCase		)
            snake_case__						,				snake_case__  :    Tuple    						=      ProphetNetForConditionalGeneration.from_pretrained(
                _lowerCAmelCase     ,   output_loading_info=_lowerCAmelCase		)

      snake_case__  :    List[Any]    						=      ["""key_proj""", """value_proj""", """query_proj"""]

      snake_case__  :    Optional[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"]:
            snake_case__  :    Optional[Any]    						=      key.split("""."""		)

            if attributes[0] == "lm_head":
                  snake_case__  :    List[str]    						=      prophet
                  snake_case__  :    Optional[int]    						=      prophet_old
            else:
                  snake_case__  :    int    						=      prophet.prophetnet
                  snake_case__  :    Tuple    						=      prophet_old.model

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

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

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

                  if attribute.isdigit():
                        snake_case__  :    Optional[int]    						=      model[int(_lowerCAmelCase		)]
                        snake_case__  :    Dict    						=      old_model[int(_lowerCAmelCase		)]
                  else:
                        snake_case__  :    Optional[int]    						=      getattr(_lowerCAmelCase     ,   _lowerCAmelCase		)

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

            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(_lowerCAmelCase		)


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)

'''simple docstring'''




import unittest

import numpy as np

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

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
						import torch

if is_vision_available():
						from PIL import Image

						from transformers import DPTImageProcessor






class 				a__(					unittest.TestCase	):


			'''simple docstring'''






			def __init__(   self       ,    __lowerCAmelCase       ,    __lowerCAmelCase=7       ,    __lowerCAmelCase=3       ,    __lowerCAmelCase=18       ,    __lowerCAmelCase=30       ,    __lowerCAmelCase=400       ,    __lowerCAmelCase=True       ,    __lowerCAmelCase=None       ,    __lowerCAmelCase=True       ,    __lowerCAmelCase=[0.5, 0.5, 0.5]       ,    __lowerCAmelCase=[0.5, 0.5, 0.5]       ,    ):




					"""simple docstring"""


					lowerCAmelCase   					=  size if 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_normalize
					lowerCAmelCase   					=  image_mean
					lowerCAmelCase   					=  image_std







			def 							a_       (   self):




					"""simple docstring"""


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







@require_torch
@require_vision
class 				a__(					lowerCAmelCase__ ,						unittest.TestCase	):


			'''simple docstring'''






			UpperCAmelCase_			:     Tuple      =       DPTImageProcessor if is_vision_available() else None



			def 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  DPTImageProcessingTester(self)



			@property
			def 							a_       (   self):




					"""simple docstring"""


					return self.image_processor_tester.prepare_image_processor_dict()



			def 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  self.image_processing_class(**self.image_processor_dict)
					self.assertTrue(hasattr(__lowerCAmelCase       ,    """image_mean"""))
					self.assertTrue(hasattr(__lowerCAmelCase       ,    """image_std"""))
					self.assertTrue(hasattr(__lowerCAmelCase       ,    """do_normalize"""))
					self.assertTrue(hasattr(__lowerCAmelCase       ,    """do_resize"""))
					self.assertTrue(hasattr(__lowerCAmelCase       ,    """size"""))



			def 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  self.image_processing_class.from_dict(self.image_processor_dict)
					self.assertEqual(image_processor.size       ,    {"""height""": 18, """width""": 18})

					lowerCAmelCase   					=  self.image_processing_class.from_dict(self.image_processor_dict       ,    size=42)
					self.assertEqual(image_processor.size       ,    {"""height""": 42, """width""": 42})



			def 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  self.image_processing_class(**self.image_processor_dict)
					# create random PIL images
					lowerCAmelCase   					=  prepare_image_inputs(self.image_processor_tester       ,    equal_resolution=__lowerCAmelCase)
					for image in image_inputs:
							self.assertIsInstance(__lowerCAmelCase       ,    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.size["""height"""],
					        self.image_processor_tester.size["""width"""],
					    )       ,    )

					# Test batched
					lowerCAmelCase   					=  image_processing(__lowerCAmelCase       ,    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.size["""height"""],
					        self.image_processor_tester.size["""width"""],
					    )       ,    )



			def 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  self.image_processing_class(**self.image_processor_dict)
					# create random numpy tensors
					lowerCAmelCase   					=  prepare_image_inputs(self.image_processor_tester       ,    equal_resolution=__lowerCAmelCase       ,    numpify=__lowerCAmelCase)
					for image in image_inputs:
							self.assertIsInstance(__lowerCAmelCase       ,    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.size["""height"""],
					        self.image_processor_tester.size["""width"""],
					    )       ,    )

					# Test batched
					lowerCAmelCase   					=  image_processing(__lowerCAmelCase       ,    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.size["""height"""],
					        self.image_processor_tester.size["""width"""],
					    )       ,    )







			def 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  self.image_processing_class(**self.image_processor_dict)
					# create random PyTorch tensors
					lowerCAmelCase   					=  prepare_image_inputs(self.image_processor_tester       ,    equal_resolution=__lowerCAmelCase       ,    torchify=__lowerCAmelCase)
					for image in image_inputs:
							self.assertIsInstance(__lowerCAmelCase       ,    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.size["""height"""],
					        self.image_processor_tester.size["""width"""],
					    )       ,    )

					# Test batched
					lowerCAmelCase   					=  image_processing(__lowerCAmelCase       ,    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.size["""height"""],
					        self.image_processor_tester.size["""width"""],
					    )       ,    )




