

'''simple docstring'''


import argparse

import fairseq
import torch

from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging


logging.set_verbosity_info()
lowerCAmelCase__						:       Optional[Any]										=						logging.get_logger(__name__)

lowerCAmelCase__						:       List[str]										=						{
    """post_extract_proj""": """feature_projection.projection""",
    """encoder.pos_conv.0""": """encoder.pos_conv_embed.conv""",
    """self_attn.k_proj""": """encoder.layers.*.attention.k_proj""",
    """self_attn.v_proj""": """encoder.layers.*.attention.v_proj""",
    """self_attn.q_proj""": """encoder.layers.*.attention.q_proj""",
    """self_attn.out_proj""": """encoder.layers.*.attention.out_proj""",
    """self_attn_layer_norm""": """encoder.layers.*.layer_norm""",
    """fc1""": """encoder.layers.*.feed_forward.intermediate_dense""",
    """fc2""": """encoder.layers.*.feed_forward.output_dense""",
    """final_layer_norm""": """encoder.layers.*.final_layer_norm""",
    """encoder.layer_norm""": """encoder.layer_norm""",
    """encoder.layer_norm_for_extract""": """layer_norm_for_extract""",
    """w2v_model.layer_norm""": """feature_projection.layer_norm""",
    """quantizer.weight_proj""": """quantizer.weight_proj""",
    """quantizer.vars""": """quantizer.codevectors""",
    """project_q""": """project_q""",
    """final_proj""": """project_hid""",
    """w2v_encoder.proj""": """lm_head""",
    """label_embs_concat""": """label_embeddings_concat""",
    """mask_emb""": """masked_spec_embed""",
    """spk_proj""": """speaker_proj""",
}
lowerCAmelCase__						:       Optional[int]										=						[
    """lm_head""",
    """quantizer.weight_proj""",
    """quantizer.codevectors""",
    """project_q""",
    """project_hid""",
    """label_embeddings_concat""",
    """speaker_proj""",
    """layer_norm_for_extract""",
]




def 	_a       (				__lowerCAmelCase		:      Optional[int]   ,							__lowerCAmelCase		:      Any   ,							__lowerCAmelCase		:      Tuple   ,							__lowerCAmelCase		:      Union[str, Any]   ,							__lowerCAmelCase		:      List[Any]     ):

		"""simple docstring"""

		for attribute in key.split('''.'''     ):
				snake_case__						:       List[str]       						=  getattr(__lowerCAmelCase   ,							__lowerCAmelCase     )

		if weight_type is not None:
				snake_case__						:       Any       						=  getattr(__lowerCAmelCase   ,							__lowerCAmelCase     ).shape
		else:
				snake_case__						:       Optional[Any]       						=  hf_pointer.shape

		if hf_shape != value.shape:
				raise ValueError(
				    F"""Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be"""
				    F""" {value.shape} for {full_name}"""     )

		if weight_type == "weight":
				snake_case__						:       str       						=  value
		elif weight_type == "weight_g":
				snake_case__						:       Union[str, Any]       						=  value
		elif weight_type == "weight_v":
				snake_case__						:       Tuple       						=  value
		elif weight_type == "bias":
				snake_case__						:       int       						=  value
		else:
				snake_case__						:       List[Any]       						=  value

		logger.info(F"""{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}."""     )




def 	_a       (				__lowerCAmelCase		:      Optional[int]   ,							__lowerCAmelCase		:      Union[str, Any]     ):

		"""simple docstring"""

		snake_case__						:       Any       						=  []
		snake_case__						:       int       						=  fairseq_model.state_dict()

		snake_case__						:       Tuple       						=  hf_model.unispeech_sat.feature_extractor

		for name, value in fairseq_dict.items():
				snake_case__						:       Dict       						=  False
				if "conv_layers" in name:
						load_conv_layer(
						    __lowerCAmelCase   ,							__lowerCAmelCase   ,							__lowerCAmelCase   ,							__lowerCAmelCase   ,							hf_model.config.feat_extract_norm == '''group'''   ,							)
						snake_case__						:       int       						=  True
				else:
						for key, mapped_key in MAPPING.items():
								snake_case__						:       Union[str, Any]       						=  '''unispeech_sat.''' + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
								if key in name or key.split('''w2v_model.'''     )[-1] == name.split('''.'''     )[0]:
										if "layer_norm_for_extract" in name and (".".join(name.split('''.'''     )[:-1]     ) != key):
												# special case since naming is very similar
												continue
										snake_case__						:       int       						=  True
										if "*" in mapped_key:
												snake_case__						:       Any       						=  name.split(__lowerCAmelCase     )[0].split('''.'''     )[-2]
												snake_case__						:       List[Any]       						=  mapped_key.replace('''*'''   ,							__lowerCAmelCase     )
										if "weight_g" in name:
												snake_case__						:       Optional[Any]       						=  '''weight_g'''
										elif "weight_v" in name:
												snake_case__						:       int       						=  '''weight_v'''
										elif "bias" in name:
												snake_case__						:       Any       						=  '''bias'''
										elif "weight" in name:
												# TODO: don't match quantizer.weight_proj
												snake_case__						:       List[str]       						=  '''weight'''
										else:
												snake_case__						:       Tuple       						=  None
										set_recursively(__lowerCAmelCase   ,							__lowerCAmelCase   ,							__lowerCAmelCase   ,							__lowerCAmelCase   ,							__lowerCAmelCase     )
								continue
				if not is_used:
						unused_weights.append(__lowerCAmelCase     )

		logger.warning(F"""Unused weights: {unused_weights}"""     )




def 	_a       (				__lowerCAmelCase		:      Tuple   ,							__lowerCAmelCase		:      Dict   ,							__lowerCAmelCase		:      List[Any]   ,							__lowerCAmelCase		:      List[str]   ,							__lowerCAmelCase		:      int     ):

		"""simple docstring"""

		snake_case__						:       List[Any]       						=  full_name.split('''conv_layers.'''     )[-1]
		snake_case__						:       Optional[int]       						=  name.split('''.'''     )
		snake_case__						:       Any       						=  int(items[0]     )
		snake_case__						:       List[Any]       						=  int(items[1]     )

		if type_id == 0:
				if "bias" in name:
						if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
								raise ValueError(
								    F"""{full_name} has size {value.shape}, but"""
								    F""" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."""     )
						snake_case__						:       Union[str, Any]       						=  value
						logger.info(F"""Feat extract conv layer {layer_id} was initialized from {full_name}."""     )
				elif "weight" in name:
						if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
								raise ValueError(
								    F"""{full_name} has size {value.shape}, but"""
								    F""" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."""     )
						snake_case__						:       Dict       						=  value
						logger.info(F"""Feat extract conv layer {layer_id} was initialized from {full_name}."""     )
		elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
				if "bias" in name:
						if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
								raise ValueError(
								    F"""{full_name} has size {value.shape}, but"""
								    F""" {feature_extractor[layer_id].layer_norm.bias.data.shape} was found."""     )
						snake_case__						:       Optional[Any]       						=  value
						logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""     )
				elif "weight" in name:
						if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
								raise ValueError(
								    F"""{full_name} has size {value.shape}, but"""
								    F""" {feature_extractor[layer_id].layer_norm.weight.data.shape} was found."""     )
						snake_case__						:       Optional[int]       						=  value
						logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""     )
		else:
				unused_weights.append(__lowerCAmelCase     )




@torch.no_grad()
def 	_a       (				__lowerCAmelCase		:      Union[str, Any]   ,							__lowerCAmelCase		:      Dict   ,							__lowerCAmelCase		:      List[Any]=None   ,							__lowerCAmelCase		:      List[str]=None   ,							__lowerCAmelCase		:      Any=True     ):

		"""simple docstring"""

		if config_path is not None:
				snake_case__						:       Any       						=  UniSpeechSatConfig.from_pretrained(__lowerCAmelCase     )
		else:
				snake_case__						:       List[str]       						=  UniSpeechSatConfig()

		snake_case__						:       Optional[int]       						=  ''''''

		if is_finetuned:
				snake_case__						:       Tuple       						=  UniSpeechSatForCTC(__lowerCAmelCase     )
		else:
				snake_case__						:       Tuple       						=  UniSpeechSatForPreTraining(__lowerCAmelCase     )

		snake_case__						,       snake_case__						,       snake_case__						:       Any       						=  fairseq.checkpoint_utils.load_model_ensemble_and_task(
		    [checkpoint_path]   ,							arg_overrides={'''data''': '''/'''.join(dict_path.split('''/'''     )[:-1]     )}     )
		snake_case__						:       Optional[int]       						=  model[0].eval()

		recursively_load_weights(__lowerCAmelCase   ,							__lowerCAmelCase     )

		hf_wavavec.save_pretrained(__lowerCAmelCase     )


if __name__ == "__main__":
					lowerCAmelCase__						:       Union[str, Any]										=						argparse.ArgumentParser()
					parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
					parser.add_argument("""--checkpoint_path""", default=None, type=str, help="""Path to fairseq checkpoint""")
					parser.add_argument("""--dict_path""", default=None, type=str, help="""Path to dict of fine-tuned model""")
					parser.add_argument("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
					parser.add_argument(
					    """--not_finetuned""", action="""store_true""", help="""Whether the model to convert is a fine-tuned model or not"""
					)
					lowerCAmelCase__						:       Any										=						parser.parse_args()
					convert_unispeech_sat_checkpoint(
					    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
					)






'''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 	_a       (				__lowerCAmelCase		:      list   ,							__lowerCAmelCase		:      list   ,							__lowerCAmelCase		:      list   ,							__lowerCAmelCase		:      list   ,							__lowerCAmelCase		:      list     ):

		"""simple docstring"""

		snake_case__						:       Union[str, Any]       						=  np.array([[1, item, train_mtch[i]] for i, item in enumerate(__lowerCAmelCase     )]     )
		snake_case__						:       Dict       						=  np.array(__lowerCAmelCase     )
		snake_case__						:       List[str]       						=  np.dot(np.dot(np.linalg.inv(np.dot(x.transpose()   ,							__lowerCAmelCase     )     )   ,							x.transpose()     )   ,							__lowerCAmelCase     )
		return abs(beta[0] + test_dt[0] * beta[1] + test_mtch[0] + beta[2]     )




def 	_a       (				__lowerCAmelCase		:      list   ,							__lowerCAmelCase		:      list   ,							__lowerCAmelCase		:      list     ):

		"""simple docstring"""

		snake_case__						:       List[str]       						=  (1, 2, 1)
		snake_case__						:       Union[str, Any]       						=  (1, 1, 0, 7)
		snake_case__						:       List[str]       						=  SARIMAX(
		    __lowerCAmelCase   ,							exog=__lowerCAmelCase   ,							order=__lowerCAmelCase   ,							seasonal_order=__lowerCAmelCase     )
		snake_case__						:       str       						=  model.fit(disp=__lowerCAmelCase   ,							maxiter=6_00   ,							method='''nm'''     )
		snake_case__						:       List[str]       						=  model_fit.predict(1   ,							len(__lowerCAmelCase     )   ,							exog=[test_match]     )
		return result[0]




def 	_a       (				__lowerCAmelCase		:      list   ,							__lowerCAmelCase		:      list   ,							__lowerCAmelCase		:      list     ):

		"""simple docstring"""

		snake_case__						:       List[Any]       						=  SVR(kernel='''rbf'''   ,							C=1   ,							gamma=0.1   ,							epsilon=0.1     )
		regressor.fit(__lowerCAmelCase   ,							__lowerCAmelCase     )
		snake_case__						:       Optional[Any]       						=  regressor.predict(__lowerCAmelCase     )
		return y_pred[0]




def 	_a       (				__lowerCAmelCase		:      list     ):

		"""simple docstring"""

		train_user.sort()
		snake_case__						:       int       						=  np.percentile(__lowerCAmelCase   ,							25     )
		snake_case__						:       List[str]       						=  np.percentile(__lowerCAmelCase   ,							75     )
		snake_case__						:       Dict       						=  qa - qa
		snake_case__						:       Optional[int]       						=  qa - (iqr * 0.1)
		return low_lim




def 	_a       (				__lowerCAmelCase		:      list   ,							__lowerCAmelCase		:      float     ):

		"""simple docstring"""

		snake_case__						:       int       						=  0
		snake_case__						:       List[Any]       						=  0
		for i in list_vote:
				if i > actual_result:
						snake_case__						:       Optional[Any]       						=  not_safe + 1
				else:
						if abs(abs(__lowerCAmelCase     ) - abs(__lowerCAmelCase     )     ) <= 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__						:       Optional[int]										=						[[18231, 0.0, 1], [22621, 1.0, 2], [15675, 0.0, 3], [23583, 1.0, 4]]
					lowerCAmelCase__						:       List[Any]										=						pd.DataFrame(
					    data_input, columns=["""total_user""", """total_even""", """days"""]
					)
					lowerCAmelCase__						:       Union[str, Any]										=						Normalizer().fit_transform(data_input_df.values)
					# split data
					lowerCAmelCase__						:       Union[str, Any]										=						normalize_df[:, 2].tolist()
					lowerCAmelCase__						:       List[str]										=						normalize_df[:, 0].tolist()
					lowerCAmelCase__						:       List[str]										=						normalize_df[:, 1].tolist()

					# for svr (input variable = total date and total match)
					lowerCAmelCase__						:       Dict										=						normalize_df[:, [1, 2]].tolist()
					lowerCAmelCase__						:       Union[str, Any]										=						x[: len(x) - 1]
					lowerCAmelCase__						:       List[str]										=						x[len(x) - 1 :]

					# for linear regression & sarimax
					lowerCAmelCase__						:       Union[str, Any]										=						total_date[: len(total_date) - 1]
					lowerCAmelCase__						:       Union[str, Any]										=						total_user[: len(total_user) - 1]
					lowerCAmelCase__						:       List[Any]										=						total_match[: len(total_match) - 1]

					lowerCAmelCase__						:       Optional[int]										=						total_date[len(total_date) - 1 :]
					lowerCAmelCase__						:       Union[str, Any]										=						total_user[len(total_user) - 1 :]
					lowerCAmelCase__						:       List[Any]										=						total_match[len(total_match) - 1 :]

					# voting system with forecasting
					lowerCAmelCase__						:       Optional[Any]										=						[
					    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__						:       Optional[int]										=						"""""" if data_safety_checker(res_vote, tst_user) else """not """
					print("""Today's data is {not_str}safe.""")



