




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


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



def __lowerCAmelCase (     SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_  ):






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

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

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

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

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

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

												elif attribute == "key_proj":
															lowercase__					  =				nn.Parameter(old_model.in_proj_weight[embed_dim : 2 * embed_dim, :]  )
															lowercase__					  =				nn.Parameter(old_model.in_proj_bias[embed_dim : 2 * embed_dim]  )
												elif attribute == "value_proj":
															lowercase__					  =				nn.Parameter(old_model.in_proj_weight[2 * embed_dim :, :]  )
															lowercase__					  =				nn.Parameter(old_model.in_proj_bias[2 * embed_dim :]  )
												lowercase__					  =				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."
												lowercase__					  =				nn.Parameter(old_model.embed_positions.weight[:512, :]  )
												lowercase__					  =				True
												break

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

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

						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(SCREAMING_SNAKE_CASE_  )


if __name__ == "__main__":
							lowercase_								=     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."""
							)
							lowercase_								=     parser.parse_args()
							convert_prophetnet_checkpoint_to_pytorch(args.prophetnet_checkpoint_path, args.pytorch_dump_folder_path)









import re
from pathlib import Path
from unittest import TestCase

import pytest
@pytest.mark.integration
class 				_snake_case			(					lowercase__):




				def 							A__      (						self					:						Optional[Any],					__lowercase					:						str						):

							with open(__lowercase,					encoding="utf-8"						) as input_file:
										lowercase__					  =				re.compile(R"(?!.*\b(?:encoding|rb|w|wb|w+|wb+|ab|ab+)\b)(?<=\s)(open)\((.*)\)"						)
										lowercase__					  =				input_file.read()
										lowercase__					  =				regexp.search(__lowercase						)

							return match




				def 							A__      (						self					:						str,					__lowercase					:						str						):

							with open(__lowercase,					encoding="utf-8"						) as input_file:
										lowercase__					  =				re.compile(R"#[^\r\n]*print\(|\"[^\r\n]*print\(|\"\"\".*?print\(.*?\"\"\"|(print\()",					re.DOTALL						)
										lowercase__					  =				input_file.read()
										# use `re.finditer` to handle the case where the ignored groups would be matched first by `re.search`
										lowercase__					  =				regexp.finditer(__lowercase						)

							lowercase__					  =				[match for match in matches if match is not None and match.group(1						) is not None]
							return matches[0] if matches else None




				def 							A__      (						self					:						Union[str, Any]						):
							lowercase__					  =				Path("./datasets"						)
							lowercase__					  =				list(dataset_paths.absolute().glob("**/*.py"						)						)

							for dataset in dataset_files:
										if self._no_encoding_on_file_open(str(__lowercase						)						):
													raise AssertionError(F'''open(...) must use utf-8 encoding in {dataset}'''						)




				def 							A__      (						self					:						Union[str, Any]						):
							lowercase__					  =				Path("./datasets"						)
							lowercase__					  =				list(dataset_paths.absolute().glob("**/*.py"						)						)

							for dataset in dataset_files:
										if self._no_print_statements(str(__lowercase						)						):
													raise AssertionError(F'''print statement found in {dataset}. Use datasets.logger/logging instead.'''						)




