






'''simple docstring'''







import numpy as np
import pandas as pd
from sklearn.preprocessing import Normalizer
from sklearn.svm import SVR
from statsmodels.tsa.statespace.sarimax import SARIMAX





def 		_UpperCAmelCase (							__A							:   list						,		__A							:   list						,		__A							:   list						,		__A							:   list						,		__A							:   list      ):
				a_		:  List[Any]											=      np.array([[1, item, train_mtch[i]] for i, item in enumerate(__A      )]      )
				a_		:  int											=      np.array(__A      )
				a_		:  str											=      np.dot(np.dot(np.linalg.inv(np.dot(x.transpose()						,		__A      )      )						,		x.transpose()      )						,		__A      )
				return abs(beta[0] + test_dt[0] * beta[1] + test_mtch[0] + beta[2]      )





def 		_UpperCAmelCase (							__A							:   list						,		__A							:   list						,		__A							:   list      ):
				a_		:  str											=      (1, 2, 1)
				a_		:  int											=      (1, 1, 0, 7)
				a_		:  Tuple											=      SARIMAX(
				    __A						,		exog=__A						,		order=__A						,		seasonal_order=__A      )
				a_		:  List[Any]											=      model.fit(disp=__A						,		maxiter=6_00						,		method='''nm'''      )
				a_		:  Union[str, Any]											=      model_fit.predict(1						,		len(__A      )						,		exog=[test_match]      )
				return result[0]





def 		_UpperCAmelCase (							__A							:   list						,		__A							:   list						,		__A							:   list      ):
				a_		:  str											=      SVR(kernel='''rbf'''						,		C=1						,		gamma=0.1						,		epsilon=0.1      )
				regressor.fit(__A						,		__A      )
				a_		:  Union[str, Any]											=      regressor.predict(__A      )
				return y_pred[0]





def 		_UpperCAmelCase (							__A							:   list      ):
				train_user.sort()
				a_		:  Optional[Any]											=      np.percentile(__A						,		25      )
				a_		:  Optional[Any]											=      np.percentile(__A						,		75      )
				a_		:  int											=      qa - qa
				a_		:  Optional[int]											=      qa - (iqr * 0.1)
				return low_lim





def 		_UpperCAmelCase (							__A							:   list						,		__A							:   float      ):
				a_		:  Optional[int]											=      0
				a_		:  List[str]											=      0
				for i in list_vote:
								if i > actual_result:
												a_		:  Optional[int]											=      not_safe + 1
								else:
												if abs(abs(__A      ) - abs(__A      )      ) <= 0.1:
																safe += 1
												else:
																not_safe += 1
				return safe > not_safe


if __name__ == "__main__":
		# data_input_df = pd.read_csv("ex_data.csv", header=None)
		__lowerCAmelCase       		=      [[18_231, 0.0, 1], [22_621, 1.0, 2], [15_675, 0.0, 3], [23_583, 1.0, 4]]
		__lowerCAmelCase       		=      pd.DataFrame(
		    data_input, columns=['total_user', 'total_even', 'days']
		)
		__lowerCAmelCase       		=      Normalizer().fit_transform(data_input_df.values)
		# split data
		__lowerCAmelCase       		=      normalize_df[:, 2].tolist()
		__lowerCAmelCase       		=      normalize_df[:, 0].tolist()
		__lowerCAmelCase       		=      normalize_df[:, 1].tolist()

		# for svr (input variable = total date and total match)
		__lowerCAmelCase       		=      normalize_df[:, [1, 2]].tolist()
		__lowerCAmelCase       		=      x[: len(x) - 1]
		__lowerCAmelCase       		=      x[len(x) - 1 :]

		# for linear regression & sarimax
		__lowerCAmelCase       		=      total_date[: len(total_date) - 1]
		__lowerCAmelCase       		=      total_user[: len(total_user) - 1]
		__lowerCAmelCase       		=      total_match[: len(total_match) - 1]

		__lowerCAmelCase       		=      total_date[len(total_date) - 1 :]
		__lowerCAmelCase       		=      total_user[len(total_user) - 1 :]
		__lowerCAmelCase       		=      total_match[len(total_match) - 1 :]

		# voting system with forecasting
		__lowerCAmelCase       		=      [
		    linear_regression_prediction(
		        trn_date, trn_user, trn_match, tst_date, tst_match
		    ),
		    sarimax_predictor(trn_user, trn_match, tst_match),
		    support_vector_regressor(x_train, x_test, trn_user),
		]

		# check the safety of today's data
		__lowerCAmelCase       		=      '' if data_safety_checker(res_vote, tst_user) else 'not '
		print('Today\'s data is {not_str}safe.')












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





def 		_UpperCAmelCase   (							__A							:   str						,		__A							:   str      ):
				if "xprophetnet" in prophetnet_checkpoint_path:
								a_		:  Tuple											=      XLMProphetNetForConditionalGenerationOld.from_pretrained(__A      )
								a_				,			a_		:  Optional[Any]											=      XLMProphetNetForConditionalGeneration.from_pretrained(
								    __A						,		output_loading_info=__A      )
				else:
								a_		:  List[Any]											=      ProphetNetForConditionalGenerationOld.from_pretrained(__A      )
								a_				,			a_		:  Any											=      ProphetNetForConditionalGeneration.from_pretrained(
								    __A						,		output_loading_info=__A      )

				a_		:  str											=      ['''key_proj''', '''value_proj''', '''query_proj''']

				a_		:  Tuple											=      {
				    '''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_		:  List[str]											=      key.split('''.'''      )

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

								a_		:  str											=      False
								for attribute in attributes:
												if attribute in mapping:
																a_		:  Dict											=      mapping[attribute]
																if not hasattr(__A						,		__A      ) and len(__A      ) > 0:
																				a_		:  List[str]											=      attribute
												elif hasattr(__A						,		__A      ):
																a_		:  Union[str, Any]											=      attribute

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

																elif attribute == "key_proj":
																				a_		:  List[Any]											=      nn.Parameter(old_model.in_proj_weight[embed_dim : 2 * embed_dim, :]      )
																				a_		:  Optional[int]											=      nn.Parameter(old_model.in_proj_bias[embed_dim : 2 * embed_dim]      )
																elif attribute == "value_proj":
																				a_		:  Tuple											=      nn.Parameter(old_model.in_proj_weight[2 * embed_dim :, :]      )
																				a_		:  Any											=      nn.Parameter(old_model.in_proj_bias[2 * embed_dim :]      )
																a_		:  Dict											=      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_		:  Union[str, Any]											=      nn.Parameter(old_model.embed_positions.weight[:5_12, :]      )
																a_		:  Optional[Any]											=      True
																break

												if attribute.isdigit():
																a_		:  Union[str, Any]											=      model[int(__A      )]
																a_		:  str											=      old_model[int(__A      )]
												else:
																a_		:  Tuple											=      getattr(__A						,		__A      )

																if old_attribute == "":
																				a_		:  List[str]											=      old_model
																else:
																				if not hasattr(__A						,		__A      ):
																								raise ValueError(f'{old_model} does not have {old_attribute}'      )
																				a_		:  Optional[Any]											=      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       		=      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       		=      parser.parse_args()
		convert_prophetnet_checkpoint_to_pytorch(args.prophetnet_checkpoint_path, args.pytorch_dump_folder_path)




