"""simple docstring"""





import argparse
import json
import os

import fairseq
import torch
from torch import nn

from transformers import (
    SpeechaTextaConfig,
    SpeechaTextaForCausalLM,
    SpeechaTextaTokenizer,
    SpeechEncoderDecoderConfig,
    SpeechEncoderDecoderModel,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaModel,
    logging,
)


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

__A  =				{
    '''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''',
    '''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''',
    '''mask_emb''': '''masked_spec_embed''',
}
__A  =				[
    '''lm_head''',
    '''quantizer.weight_proj''',
    '''quantizer.codevectors''',
    '''project_q''',
    '''project_hid''',
]


def    lowercase_   (   _lowerCamelCase:	Tuple						,   _lowerCamelCase:	Any						,   _lowerCamelCase:	Optional[Any]						,   _lowerCamelCase:	Dict						,   _lowerCamelCase:	List[str]      ) ->      Union[str, Any]:



							'''simple docstring'''

							for attribute in key.split("."      ):
														__lowerCamelCase					:		Tuple		   =     getattr(_lowerCamelCase						,   _lowerCamelCase      )

							if weight_type is not None:
														__lowerCamelCase					:		Union[str, Any]		   =     getattr(_lowerCamelCase						,   _lowerCamelCase      ).shape
							else:
														__lowerCamelCase					:		List[Any]		   =     hf_pointer.shape

							assert hf_shape == value.shape, (
							    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":
														__lowerCamelCase					:		List[str]		   =     value
							elif weight_type == "weight_g":
														__lowerCamelCase					:		int		   =     value
							elif weight_type == "weight_v":
														__lowerCamelCase					:		List[str]		   =     value
							elif weight_type == "bias":
														__lowerCamelCase					:		Tuple		   =     value
							else:
														__lowerCamelCase					:		List[str]		   =     value

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


def    lowercase_   (   _lowerCamelCase:	str						,   _lowerCamelCase:	Optional[int]      ) ->      Any:



							'''simple docstring'''

							__lowerCamelCase					:		Optional[Any]		   =     []
							__lowerCamelCase					:		int		   =     fairseq_model.state_dict()

							__lowerCamelCase					:		List[Any]		   =     hf_model.feature_extractor

							# if encoder has different dim to decoder -> use proj_weight
							__lowerCamelCase					:		List[Any]		   =     None

							for name, value in fairseq_dict.items():
														__lowerCamelCase					:		str		   =     False
														if "conv_layers" in name:
																					load_conv_layer(
																					    _lowerCamelCase						,   _lowerCamelCase						,   _lowerCamelCase						,   _lowerCamelCase						,   hf_model.config.feat_extract_norm == "group"						,   )
																					__lowerCamelCase					:		Any		   =     True
														elif name.split("."      )[0] == "proj":
																					__lowerCamelCase					:		List[Any]		   =     fairseq_model.proj
																					__lowerCamelCase					:		Any		   =     True
														else:
																					for key, mapped_key in MAPPING.items():
																												if key in name or key.split("w2v_model."      )[-1] == name.split("."      )[0]:
																																			__lowerCamelCase					:		Optional[int]		   =     True
																																			if "*" in mapped_key:
																																										__lowerCamelCase					:		List[str]		   =     name.split(_lowerCamelCase      )[0].split("."      )[-2]
																																										__lowerCamelCase					:		Tuple		   =     mapped_key.replace("*"						,   _lowerCamelCase      )
																																			if "weight_g" in name:
																																										__lowerCamelCase					:		Union[str, Any]		   =     "weight_g"
																																			elif "weight_v" in name:
																																										__lowerCamelCase					:		Dict		   =     "weight_v"
																																			elif "bias" in name:
																																										__lowerCamelCase					:		List[Any]		   =     "bias"
																																			elif "weight" in name:
																																										__lowerCamelCase					:		int		   =     "weight"
																																			else:
																																										__lowerCamelCase					:		Union[str, Any]		   =     None
																																			set_recursively(_lowerCamelCase						,   _lowerCamelCase						,   _lowerCamelCase						,   _lowerCamelCase						,   _lowerCamelCase      )
																												continue
														if not is_used:
																					unused_weights.append(_lowerCamelCase      )

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

							return proj_weight


def    lowercase_   (   _lowerCamelCase:	List[Any]						,   _lowerCamelCase:	Optional[int]						,   _lowerCamelCase:	Union[str, Any]						,   _lowerCamelCase:	Any						,   _lowerCamelCase:	List[Any]      ) ->      List[Any]:



							'''simple docstring'''

							__lowerCamelCase					:		Optional[int]		   =     full_name.split("conv_layers."      )[-1]
							__lowerCamelCase					:		Dict		   =     name.split("."      )
							__lowerCamelCase					:		Optional[Any]		   =     int(items[0]      )
							__lowerCamelCase					:		Optional[int]		   =     int(items[1]      )

							if type_id == 0:
														if "bias" in name:
																					assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
																					    F"""{full_name} has size {value.shape}, but"""
																					    F""" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."""
																					)
																					__lowerCamelCase					:		Dict		   =     value
																					logger.info(F"""Feat extract conv layer {layer_id} was initialized from {full_name}."""      )
														elif "weight" in name:
																					assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
																					    F"""{full_name} has size {value.shape}, but"""
																					    F""" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."""
																					)
																					__lowerCamelCase					:		Optional[int]		   =     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:
																					assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
																					    F"""{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was"""
																					    " found."
																					)
																					__lowerCamelCase					:		Dict		   =     value
																					logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""      )
														elif "weight" in name:
																					assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
																					    F"""{full_name} has size {value.shape}, but"""
																					    F""" {feature_extractor[layer_id].layer_norm.weight.data.shape} was found."""
																					)
																					__lowerCamelCase					:		int		   =     value
																					logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""      )
							else:
														unused_weights.append(_lowerCamelCase      )


def    lowercase_   (   _lowerCamelCase:	Optional[Any]      ) ->      Optional[int]:



							'''simple docstring'''

							__lowerCamelCase					:		str		   =     emb.weight.shape
							__lowerCamelCase					:		Dict		   =     nn.Linear(_lowerCamelCase						,   _lowerCamelCase						,   bias=_lowerCamelCase      )
							__lowerCamelCase					:		List[str]		   =     emb.weight.data
							return lin_layer


def    lowercase_   (   _lowerCamelCase:	str      ) ->      Tuple:



							'''simple docstring'''

							with open(_lowerCamelCase						,   "r"						,   encoding="utf-8"      ) as f:
														__lowerCamelCase					:		Any		   =     f.readlines()
														__lowerCamelCase					:		Union[str, Any]		   =     [line.split(" "      )[0] for line in lines]

							__lowerCamelCase					:		int		   =     len(_lowerCamelCase      )

							__lowerCamelCase					:		Any		   =     {
							    "<s>": 0,
							    "<pad>": 1,
							    "</s>": 2,
							    "<unk>": 3,
							}

							vocab_dict.update(dict(zip(_lowerCamelCase						,   range(4						,   num_words + 4      )      )      )      )
							return vocab_dict


@torch.no_grad()
def    lowercase_   (   _lowerCamelCase:	Dict						,   _lowerCamelCase:	Dict						,   _lowerCamelCase:	Dict						,   _lowerCamelCase:	Dict						,   _lowerCamelCase:	List[str]						,   _lowerCamelCase:	List[Any]						,   _lowerCamelCase:	Optional[Any]						,   ) ->      Tuple:



							'''simple docstring'''

							__lowerCamelCase					:		Dict		   =     WavaVecaConfig.from_pretrained(_lowerCamelCase      )
							__lowerCamelCase					:		Optional[int]		   =     SpeechaTextaConfig.from_pretrained(
							    _lowerCamelCase						,   vocab_size=_lowerCamelCase						,   decoder_layers=_lowerCamelCase						,   do_stable_layer_norm=_lowerCamelCase      )

							__lowerCamelCase					:		Any		   =     WavaVecaFeatureExtractor(
							    feature_size=1						,   sampling_rate=16000						,   padding_value=0						,   do_normalize=_lowerCamelCase						,   return_attention_mask=_lowerCamelCase						,   )

							__lowerCamelCase					:		Tuple		   =     fairseq.checkpoint_utils.load_model_ensemble_and_task(
							    [checkpoint_path]						,   arg_overrides={"data": "/".join(dict_path.split("/"      )[:-1]      )}      )
							__lowerCamelCase					:		Any		   =     model[0].eval()

							# set weights for wav2vec2 encoder
							__lowerCamelCase					:		Tuple		   =     WavaVecaModel(_lowerCamelCase      )
							__lowerCamelCase					:		Any		   =     recursively_load_weights_wavaveca(model.encoder						,   _lowerCamelCase      )

							__lowerCamelCase					:		Optional[int]		   =     SpeechaTextaForCausalLM(_lowerCamelCase      )
							__lowerCamelCase					:		Union[str, Any]		   =     hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict()						,   strict=_lowerCamelCase      )

							# set output linear layer
							unexpected_keys.remove("embed_out"      )
							__lowerCamelCase					:		Optional[int]		   =     nn.Parameter(model.decoder.embed_out.detach()      )

							# layer norm is init to identity matrix so leaving it is fine
							logger.warning(F"""The following keys are missing when loading the decoder weights: {missing_keys}"""      )
							logger.warning(F"""The following keys are unexpected when loading the decoder weights: {unexpected_keys}"""      )

							__lowerCamelCase					:		Optional[int]		   =     SpeechEncoderDecoderModel(encoder=_lowerCamelCase						,   decoder=_lowerCamelCase      )
							__lowerCamelCase					:		int		   =     False

							# add projection layer
							__lowerCamelCase					:		Optional[int]		   =     nn.Parameter(projection_layer.weight      )
							__lowerCamelCase					:		Optional[Any]		   =     nn.Parameter(projection_layer.bias      )

							__lowerCamelCase					:		Optional[int]		   =     create_vocab_dict(_lowerCamelCase      )

							with open(os.path.join(_lowerCamelCase						,   "vocab.json"      )						,   "w"      ) as fp:
														json.dump(_lowerCamelCase						,   _lowerCamelCase      )

							__lowerCamelCase					:		Dict		   =     SpeechaTextaTokenizer(os.path.join(_lowerCamelCase						,   "vocab.json"      )      )
							tokenizer.save_pretrained(_lowerCamelCase      )

							__lowerCamelCase					:		int		   =     hf_wavavec.config.to_dict()
							__lowerCamelCase					:		Dict		   =     tokenizer.pad_token_id
							__lowerCamelCase					:		str		   =     tokenizer.bos_token_id
							__lowerCamelCase					:		Optional[int]		   =     tokenizer.eos_token_id
							__lowerCamelCase					:		Any		   =     "speech_to_text_2"
							__lowerCamelCase					:		Union[str, Any]		   =     "wav2vec2"

							__lowerCamelCase					:		str		   =     SpeechEncoderDecoderConfig.from_dict(_lowerCamelCase      )

							hf_wavavec.save_pretrained(_lowerCamelCase      )
							feature_extractor.save_pretrained(_lowerCamelCase      )


if __name__ == "__main__":
				__A  =				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(
				    '''--encoder_config_path''',
				    default='''facebook/wav2vec2-large-lv60''',
				    type=str,
				    help='''Path to hf encoder wav2vec2 checkpoint config''',
				)
				parser.add_argument(
				    '''--decoder_config_path''',
				    default='''facebook/s2t-small-mustc-en-fr-st''',
				    type=str,
				    help='''Path to hf decoder s2t checkpoint config''',
				)
				parser.add_argument('''--vocab_size''', default=10224, type=int, help='''Vocab size of decoder''')
				parser.add_argument('''--num_decoder_layers''', default=7, type=int, help='''Number of decoder layers''')

				__A  =				parser.parse_args()
				convert_wavaveca_checkpoint(
				    args.checkpoint_path,
				    args.pytorch_dump_folder_path,
				    args.dict_path,
				    encoder_config_path=args.encoder_config_path,
				    decoder_config_path=args.decoder_config_path,
				    vocab_size=args.vocab_size,
				    num_decoder_layers=args.num_decoder_layers,
				)
"""simple docstring"""





import math
from datetime import datetime, timedelta


def    lowercase_   (   _lowerCamelCase:	int      ) ->      datetime:



							'''simple docstring'''

							__lowerCamelCase					:		List[Any]		   =     year % 19
							__lowerCamelCase					:		List[str]		   =     year % 4
							__lowerCamelCase					:		Dict		   =     year % 7
							__lowerCamelCase					:		Optional[Any]		   =     math.floor(year / 100      )
							__lowerCamelCase					:		Tuple		   =     math.floor((13 + 8 * leap_day_inhibits) / 25      )
							__lowerCamelCase					:		List[str]		   =     leap_day_inhibits / 4
							__lowerCamelCase					:		Optional[Any]		   =     (
							    15 - lunar_orbit_correction + leap_day_inhibits - leap_day_reinstall_number
							) % 30
							__lowerCamelCase					:		Union[str, Any]		   =     (4 + leap_day_inhibits - leap_day_reinstall_number) % 7

							# days to be added to March 21
							__lowerCamelCase					:		Union[str, Any]		   =     (19 * metonic_cycle + secular_moon_shift) % 30

							# PHM -> Paschal Full Moon
							__lowerCamelCase					:		List[Any]		   =     (
							    2 * julian_leap_year
							    + 4 * non_leap_year
							    + 6 * days_to_add
							    + century_starting_point
							) % 7

							if days_to_add == 29 and days_from_phm_to_sunday == 6:
														return datetime(_lowerCamelCase						,   4						,   19      )
							elif days_to_add == 28 and days_from_phm_to_sunday == 6:
														return datetime(_lowerCamelCase						,   4						,   18      )
							else:
														return datetime(_lowerCamelCase						,   3						,   22      ) + timedelta(
														    days=int(days_to_add + days_from_phm_to_sunday      )      )


if __name__ == "__main__":
				for year in (1994, 2000, 2010, 2021, 2023):
								__A  =				'''will be''' if year > datetime.now().year else '''was'''
								print(F"""Easter in {year} {tense} {gauss_easter(year)}""")