





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





def     _UpperCamelCase     (a__   :str   ,			a__   :str       ):






							"""simple docstring"""





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

							UpperCamelCase__				     =			["""key_proj""", """value_proj""", """query_proj"""]

							UpperCamelCase__				     =			{
							    """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"]:
														UpperCamelCase__				     =			key.split("""."""       )

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

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

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

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

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

																												if old_attribute == "":
																																			UpperCamelCase__				     =			old_model
																												else:
																																			if not hasattr(a__   ,			a__       ):
																																										raise ValueError(f"""{old_model} does not have {old_attribute}"""       )
																																			UpperCamelCase__				     =			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__											= 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__											= parser.parse_args()
					convert_prophetnet_checkpoint_to_pytorch(args.prophetnet_checkpoint_path, args.pytorch_dump_folder_path)











import unittest

from transformers import AutoConfig, AutoTokenizer, BertConfig, TensorType, is_flax_available
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, require_flax, slow


if is_flax_available():
					import jax

					from transformers.models.auto.modeling_flax_auto import FlaxAutoModel
					from transformers.models.bert.modeling_flax_bert import FlaxBertModel
					from transformers.models.roberta.modeling_flax_roberta import FlaxRobertaModel

@require_flax
class        __SCREAMING_SNAKE_CASE					(     unittest.TestCase       ):






							@slow
							def 				_lowerCamelCase		(						self  ):
														for model_name in ["bert-base-cased", "bert-large-uncased"]:
																					with self.subTest(__lowerCAmelCase  ):
																												UpperCamelCase__				     =			AutoConfig.from_pretrained(__lowerCAmelCase  )
																												self.assertIsNotNone(__lowerCAmelCase  )
																												self.assertIsInstance(__lowerCAmelCase  , __lowerCAmelCase  )

																												UpperCamelCase__				     =			FlaxAutoModel.from_pretrained(__lowerCAmelCase  )
																												self.assertIsNotNone(__lowerCAmelCase  )
																												self.assertIsInstance(__lowerCAmelCase  , __lowerCAmelCase  )






							@slow
							def 				_lowerCamelCase		(						self  ):
														for model_name in ["roberta-base", "roberta-large"]:
																					with self.subTest(__lowerCAmelCase  ):
																												UpperCamelCase__				     =			AutoConfig.from_pretrained(__lowerCAmelCase  )
																												self.assertIsNotNone(__lowerCAmelCase  )
																												self.assertIsInstance(__lowerCAmelCase  , __lowerCAmelCase  )

																												UpperCamelCase__				     =			FlaxAutoModel.from_pretrained(__lowerCAmelCase  )
																												self.assertIsNotNone(__lowerCAmelCase  )
																												self.assertIsInstance(__lowerCAmelCase  , __lowerCAmelCase  )






							@slow
							def 				_lowerCamelCase		(						self  ):
														for model_name in ["bert-base-cased", "bert-large-uncased"]:
																					UpperCamelCase__				     =			AutoTokenizer.from_pretrained(__lowerCAmelCase  )
																					UpperCamelCase__				     =			FlaxBertModel.from_pretrained(__lowerCAmelCase  )
																					UpperCamelCase__				     =			tokenizer("""Do you support jax jitted function?"""  , return_tensors=TensorType.JAX  )

																					@jax.jit
																					def eval(**__lowerCAmelCase  ):
																												return model(**__lowerCAmelCase  )

																					eval(**__lowerCAmelCase  ).block_until_ready()






							@slow
							def 				_lowerCamelCase		(						self  ):
														for model_name in ["roberta-base", "roberta-large"]:
																					UpperCamelCase__				     =			AutoTokenizer.from_pretrained(__lowerCAmelCase  )
																					UpperCamelCase__				     =			FlaxRobertaModel.from_pretrained(__lowerCAmelCase  )
																					UpperCamelCase__				     =			tokenizer("""Do you support jax jitted function?"""  , return_tensors=TensorType.JAX  )

																					@jax.jit
																					def eval(**__lowerCAmelCase  ):
																												return model(**__lowerCAmelCase  )

																					eval(**__lowerCAmelCase  ).block_until_ready()






							def 				_lowerCamelCase		(						self  ):
														with self.assertRaisesRegex(
														    __lowerCAmelCase  , """bert-base is not a local folder and is not a valid model identifier"""  ):
																					UpperCamelCase__				     =			FlaxAutoModel.from_pretrained("""bert-base"""  )






							def 				_lowerCamelCase		(						self  ):
														with self.assertRaisesRegex(
														    __lowerCAmelCase  , r"""aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)"""  ):
																					UpperCamelCase__				     =			FlaxAutoModel.from_pretrained(__lowerCAmelCase  , revision="""aaaaaa"""  )






							def 				_lowerCamelCase		(						self  ):
														with self.assertRaisesRegex(
														    __lowerCAmelCase  , """hf-internal-testing/config-no-model does not appear to have a file named flax_model.msgpack"""  , ):
																					UpperCamelCase__				     =			FlaxAutoModel.from_pretrained("""hf-internal-testing/config-no-model"""  )







							def 				_lowerCamelCase		(						self  ):
														with self.assertRaisesRegex(__lowerCAmelCase  , """Use `from_pt=True` to load this model"""  ):
																					UpperCamelCase__				     =			FlaxAutoModel.from_pretrained("""hf-internal-testing/tiny-bert-pt-only"""  )




