






'''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


lowerCamelCase_				: Union[str, Any]    	=							logging.get_logger(__name__)
logging.set_verbosity_info()







def 	__magic_name__( _A						,      _A					):
						'''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__":
					lowerCamelCase_				: int    	=							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_				: Union[str, Any]    	=							parser.parse_args()
					convert_prophetnet_checkpoint_to_pytorch(args.prophetnet_checkpoint_path, args.pytorch_dump_folder_path)








'''simple docstring'''


import numpy as np

from transformers import Pipeline







def 	__magic_name__( _A					):
						'''simple docstring'''





						UpperCamelCase__         = np.max(_A						,      axis=-1						,      keepdims=_A					)
						UpperCamelCase__         = np.exp(outputs - maxes					)
						return shifted_exp / shifted_exp.sum(axis=-1						,      keepdims=_A					)







class    _SCREAMING_SNAKE_CASE							(		SCREAMING_SNAKE_CASE	):


			'''simple docstring'''





			def 				A      (   self							:						int ,      **lowercase							:						List[Any]			)							->    List[str]:

									'''simple docstring'''







									UpperCamelCase__         = {}
									if "second_text" in kwargs:
															UpperCamelCase__         = kwargs["""second_text"""]
									return preprocess_kwargs, {}, {}






			def 				A      (   self							:						Optional[int] ,      lowercase							:						Union[str, Any] ,      lowercase							:						Any=None			)							->    Optional[int]:

									'''simple docstring'''







									return self.tokenizer(lowercase ,      text_pair=lowercase ,      return_tensors=self.framework			)






			def 				A      (   self							:						Union[str, Any] ,      lowercase							:						Optional[int]			)							->    Any:

									'''simple docstring'''







									return self.model(**lowercase			)







			def 				A      (   self							:						List[str] ,      lowercase							:						Any			)							->    Optional[int]:

									'''simple docstring'''







									UpperCamelCase__         = model_outputs.logits[0].numpy()
									UpperCamelCase__         = softmax(lowercase			)

									UpperCamelCase__         = np.argmax(lowercase			)
									UpperCamelCase__         = self.model.config.idalabel[best_class]
									UpperCamelCase__         = probabilities[best_class].item()
									UpperCamelCase__         = logits.tolist()
									return {"label": label, "score": score, "logits": logits}
