
'''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


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






def      UpperCamelCase_				(      A__  ,      A__						):
	if "xprophetnet" in prophetnet_checkpoint_path:
		a_      				=						XLMProphetNetForConditionalGenerationOld.from_pretrained(A__						)
		a_				,  a_      				=						XLMProphetNetForConditionalGeneration.from_pretrained(
		    A__  ,      output_loading_info=A__						)
	else:
		a_      				=						ProphetNetForConditionalGenerationOld.from_pretrained(A__						)
		a_				,  a_      				=						ProphetNetForConditionalGeneration.from_pretrained(
		    A__  ,      output_loading_info=A__						)

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

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

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

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

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

				elif attribute == "key_proj":
					a_      				=						nn.Parameter(old_model.in_proj_weight[embed_dim : 2 * embed_dim, :]						)
					a_      				=						nn.Parameter(old_model.in_proj_bias[embed_dim : 2 * embed_dim]						)
				elif attribute == "value_proj":
					a_      				=						nn.Parameter(old_model.in_proj_weight[2 * embed_dim :, :]						)
					a_      				=						nn.Parameter(old_model.in_proj_bias[2 * embed_dim :]						)
				a_      				=						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] == 5_12, "We want 512 position_embeddings."
				a_      				=						nn.Parameter(old_model.embed_positions.weight[:5_12, :]						)
				a_      				=						True
				break

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

				if old_attribute == "":
					a_      				=						old_model
				else:
					if not hasattr(A__  ,      A__						):
						raise ValueError(F'''{old_model} does not have {old_attribute}'''						)
					a_      				=						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__":
		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)


'''simple docstring'''
import argparse
import os

import torch

from transformers import (
    XLNetConfig,
    XLNetForQuestionAnswering,
    XLNetForSequenceClassification,
    XLNetLMHeadModel,
    load_tf_weights_in_xlnet,
)
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


lowercase__											={
    'cola': 2,
    'mnli': 3,
    'mrpc': 2,
    'sst-2': 2,
    'sts-b': 1,
    'qqp': 2,
    'qnli': 2,
    'rte': 2,
    'wnli': 2,
}


logging.set_verbosity_info()






def      UpperCamelCase_				(      A__  ,      A__  ,      A__  ,      A__=None						):
	# Initialise PyTorch model
	a_      				=						XLNetConfig.from_json_file(A__						)

	a_      				=						finetuning_task.lower() if finetuning_task is not None else """"""
	if finetuning_task in GLUE_TASKS_NUM_LABELS:
		print(F'''Building PyTorch XLNetForSequenceClassification model from configuration: {config}'''						)
		a_      				=						finetuning_task
		a_      				=						GLUE_TASKS_NUM_LABELS[finetuning_task]
		a_      				=						XLNetForSequenceClassification(A__						)
	elif "squad" in finetuning_task:
		a_      				=						finetuning_task
		a_      				=						XLNetForQuestionAnswering(A__						)
	else:
		a_      				=						XLNetLMHeadModel(A__						)

	# Load weights from tf checkpoint
	load_tf_weights_in_xlnet(A__  ,      A__  ,      A__						)

	# Save pytorch-model
	a_      				=						os.path.join(A__  ,      A__						)
	a_      				=						os.path.join(A__  ,      A__						)
	print(F'''Save PyTorch model to {os.path.abspath(A__						)}'''						)
	torch.save(model.state_dict()  ,      A__						)
	print(F'''Save configuration file to {os.path.abspath(A__						)}'''						)
	with open(A__  ,      """w"""  ,      encoding="""utf-8"""						) as f:
		f.write(config.to_json_string()						)


if __name__ == "__main__":
		lowercase__											=argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '--tf_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
		)
		parser.add_argument(
		    '--xlnet_config_file',
		    default=None,
		    type=str,
		    required=True,
		    help=(
		        'The config json file corresponding to the pre-trained XLNet model. \n'
		        'This specifies the model architecture.'
		    ),
		)
		parser.add_argument(
		    '--pytorch_dump_folder_path',
		    default=None,
		    type=str,
		    required=True,
		    help='Path to the folder to store the PyTorch model or dataset/vocab.',
		)
		parser.add_argument(
		    '--finetuning_task',
		    default=None,
		    type=str,
		    help='Name of a task on which the XLNet TensorFlow model was fine-tuned',
		)
		lowercase__											=parser.parse_args()
		print(args)

		convert_xlnet_checkpoint_to_pytorch(
		    args.tf_checkpoint_path, args.xlnet_config_file, args.pytorch_dump_folder_path, args.finetuning_task
		)
