


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


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






def 				_lowercase     (		SCREAMING_SNAKE_CASE_ :		str							,	SCREAMING_SNAKE_CASE_ :		Optional[int]						):





			"""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__":
			__snake_case      = 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."
			)
			__snake_case      = parser.parse_args()
			convert_prophetnet_checkpoint_to_pytorch(args.prophetnet_checkpoint_path, args.pytorch_dump_folder_path)






'''simple docstring'''
import enum
import shutil
import sys


lowercase__				, lowercase__											=shutil.get_terminal_size()

lowercase__											={'UP': 'A', 'DOWN': 'B', 'RIGHT': 'C', 'LEFT': 'D'}







class   a_ (      enum.Enum					):
			lowerCamelCase__	:			int       =    0
			lowerCamelCase__	:			Union[str, Any]       =    1







def      UpperCamelCase_				(      A__  ,      A__=""						):
	sys.stdout.write(str(A__						) + end						)
	sys.stdout.flush()






def      UpperCamelCase_				(      A__  ,      A__  ,      A__=""						):
	forceWrite(F'''\u001b[{color}m{content}\u001b[0m'''  ,      A__						)






def      UpperCamelCase_				(      ):
	forceWrite("""\r"""						)






def      UpperCamelCase_				(      A__  ,      A__						):
	forceWrite(F'''\033[{num_lines}{CURSOR_TO_CHAR[direction.upper()]}'''						)






def      UpperCamelCase_				(      ):
	forceWrite(""" """ * TERMINAL_WIDTH						)
	reset_cursor()






def      UpperCamelCase_				(      ):
	reset_cursor()
	forceWrite("""-""" * TERMINAL_WIDTH						)
