



"""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:   Optional[int]								=		logging.get_logger(__name__)

A:   Tuple								=		{
    '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:   Optional[int]								=		[
    'lm_head',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
]

def       _snake_case						(						UpperCamelCase						:		List[str]			,		UpperCamelCase						:		Any			,		UpperCamelCase						:		str			,		UpperCamelCase						:		Union[str, Any]			,		UpperCamelCase						:		Any						):
 for attribute in key.split("""."""						):
  UpperCAmelCase	:					Any	=	getattr(lowerCAmelCase_			,		lowerCAmelCase_						)

 if weight_type is not None:
  UpperCAmelCase	:					Tuple	=	getattr(lowerCAmelCase_			,		lowerCAmelCase_						).shape
 else:
  UpperCAmelCase	:					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":
  UpperCAmelCase	:					List[Any]	=	value
 elif weight_type == "weight_g":
  UpperCAmelCase	:					Union[str, Any]	=	value
 elif weight_type == "weight_v":
  UpperCAmelCase	:					str	=	value
 elif weight_type == "bias":
  UpperCAmelCase	:					List[Any]	=	value
 else:
  UpperCAmelCase	:					List[str]	=	value

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

def       _snake_case						(						UpperCamelCase						:		int			,		UpperCamelCase						:		List[str]						):
 UpperCAmelCase	:					List[Any]	=	[]
 UpperCAmelCase	:					Optional[int]	=	fairseq_model.state_dict()

 UpperCAmelCase	:					Optional[Any]	=	hf_model.feature_extractor

 # if encoder has different dim to decoder -> use proj_weight
 UpperCAmelCase	:					Union[str, Any]	=	None

 for name, value in fairseq_dict.items():
  UpperCAmelCase	:					int	=	False
  if "conv_layers" in name:
   load_conv_layer(
       lowerCAmelCase_			,		lowerCAmelCase_			,		lowerCAmelCase_			,		lowerCAmelCase_			,		hf_model.config.feat_extract_norm == """group"""			,		)
   UpperCAmelCase	:					int	=	True
  elif name.split("""."""						)[0] == "proj":
   UpperCAmelCase	:					Tuple	=	fairseq_model.proj
   UpperCAmelCase	:					List[str]	=	True
  else:
   for key, mapped_key in MAPPING.items():
    if key in name or key.split("""w2v_model."""						)[-1] == name.split("""."""						)[0]:
     UpperCAmelCase	:					Optional[int]	=	True
     if "*" in mapped_key:
      UpperCAmelCase	:					List[str]	=	name.split(lowerCAmelCase_						)[0].split("""."""						)[-2]
      UpperCAmelCase	:					List[str]	=	mapped_key.replace("""*"""			,		lowerCAmelCase_						)
     if "weight_g" in name:
      UpperCAmelCase	:					Optional[int]	=	"""weight_g"""
     elif "weight_v" in name:
      UpperCAmelCase	:					Optional[int]	=	"""weight_v"""
     elif "bias" in name:
      UpperCAmelCase	:					List[Any]	=	"""bias"""
     elif "weight" in name:
      UpperCAmelCase	:					Tuple	=	"""weight"""
     else:
      UpperCAmelCase	:					List[str]	=	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       _snake_case						(						UpperCamelCase						:		List[Any]			,		UpperCamelCase						:		int			,		UpperCamelCase						:		str			,		UpperCamelCase						:		Union[str, Any]			,		UpperCamelCase						:		List[Any]						):
 UpperCAmelCase	:					Tuple	=	full_name.split("""conv_layers."""						)[-1]
 UpperCAmelCase	:					List[str]	=	name.split("""."""						)
 UpperCAmelCase	:					str	=	int(items[0]						)
 UpperCAmelCase	:					List[Any]	=	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."
   )
   UpperCAmelCase	:					Union[str, Any]	=	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."
   )
   UpperCAmelCase	:					List[str]	=	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."
   )
   UpperCAmelCase	:					List[str]	=	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."
   )
   UpperCAmelCase	:					List[str]	=	value
   logger.info(F"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."						)
 else:
  unused_weights.append(lowerCAmelCase_						)

def       _snake_case						(						UpperCamelCase						:		List[str]						):
 UpperCAmelCase	,		UpperCAmelCase	:					Tuple	=	emb.weight.shape
 UpperCAmelCase	:					int	=	nn.Linear(lowerCAmelCase_			,		lowerCAmelCase_			,		bias=lowerCAmelCase_						)
 UpperCAmelCase	:					Tuple	=	emb.weight.data
 return lin_layer

def       _snake_case						(						UpperCamelCase						:		Optional[Any]						):
 with open(lowerCAmelCase_			,		"""r"""			,		encoding="""utf-8"""						) as f:
  UpperCAmelCase	:					Optional[Any]	=	f.readlines()
  UpperCAmelCase	:					str	=	[line.split(""" """						)[0] for line in lines]

 UpperCAmelCase	:					List[Any]	=	len(lowerCAmelCase_						)

 UpperCAmelCase	:					Dict	=	{
     """<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       _snake_case						(						UpperCamelCase						:		Any			,		UpperCamelCase						:		Tuple			,		UpperCamelCase						:		Union[str, Any]			,		UpperCamelCase						:		str			,		UpperCamelCase						:		Optional[int]			,		UpperCamelCase						:		Optional[Any]			,		UpperCamelCase						:		Optional[int]			,		):
 UpperCAmelCase	:					Optional[int]	=	WavaVecaConfig.from_pretrained(lowerCAmelCase_						)
 UpperCAmelCase	:					Dict	=	SpeechaTextaConfig.from_pretrained(
     lowerCAmelCase_			,		vocab_size=lowerCAmelCase_			,		decoder_layers=lowerCAmelCase_			,		do_stable_layer_norm=lowerCAmelCase_						)

 UpperCAmelCase	:					Tuple	=	WavaVecaFeatureExtractor(
     feature_size=1			,		sampling_rate=16000			,		padding_value=0			,		do_normalize=lowerCAmelCase_			,		return_attention_mask=lowerCAmelCase_			,		)

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

 # set weights for wav2vec2 encoder
 UpperCAmelCase	:					Optional[Any]	=	WavaVecaModel(lowerCAmelCase_						)
 UpperCAmelCase	:					List[Any]	=	recursively_load_weights_wavaveca(model.encoder			,		lowerCAmelCase_						)

 UpperCAmelCase	:					Optional[int]	=	SpeechaTextaForCausalLM(lowerCAmelCase_						)
 UpperCAmelCase	,		UpperCAmelCase	:					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"""						)
 UpperCAmelCase	:					Union[str, Any]	=	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}"						)

 UpperCAmelCase	:					int	=	SpeechEncoderDecoderModel(encoder=lowerCAmelCase_			,		decoder=lowerCAmelCase_						)
 UpperCAmelCase	:					str	=	False

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

 UpperCAmelCase	:					Tuple	=	create_vocab_dict(lowerCAmelCase_						)

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

 UpperCAmelCase	:					str	=	SpeechaTextaTokenizer(os.path.join(lowerCAmelCase_			,		"""vocab.json"""						)						)
 tokenizer.save_pretrained(lowerCAmelCase_						)

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

 UpperCAmelCase	:					Any	=	SpeechEncoderDecoderConfig.from_dict(lowerCAmelCase_						)

 hf_wavavec.save_pretrained(lowerCAmelCase_						)
 feature_extractor.save_pretrained(lowerCAmelCase_						)


if __name__ == "__main__":
      A:   Tuple								=		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=1_0_2_2_4, type=int, help="Vocab size of decoder")
      parser.add_argument("--num_decoder_layers", default=7, type=int, help="Number of decoder layers")

      A:   Tuple								=		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,
      )







from __future__ import annotations

import string
from itertools import cycle, product
from pathlib import Path

_snake_case  :    str						    =  (
    string.ascii_letters + string.digits + string.punctuation + string.whitespace
)
_snake_case  :    list[int]						    =  [ord(letter) for letter in string.ascii_lowercase]
_snake_case  :    set[int]						    =  {ord(char) for char in VALID_CHARS}

_snake_case  :    list[str]						    =  ["the", "be", "to", "of", "and", "in", "that", "have"]

def   a_    ( lowerCAmelCase_      :			list[int],	lowerCAmelCase_      :			tuple[int, ...]	):
   __lowerCAmelCase								=	""
   __lowerCAmelCase								=	42
   __lowerCAmelCase								=	42
   __lowerCAmelCase								=	42

   for keychar, cipherchar in zip(cycle(lowerCAmelCase_	),	lowerCAmelCase_	):
      __lowerCAmelCase								=	cipherchar ^ keychar
      if decodedchar not in VALID_INTS:
         return None
      decoded += chr(lowerCAmelCase_	)

   return decoded

def   a_    ( lowerCAmelCase_      :			list[int]	):
   __lowerCAmelCase								=	[]
   for key in product(lowerCAmelCase_,	repeat=3	):
      __lowerCAmelCase								=	try_key(lowerCAmelCase_,	lowerCAmelCase_	)
      if encoded is not None:
         possibles.append(lowerCAmelCase_	)
   return possibles

def   a_    ( lowerCAmelCase_      :			list[str],	lowerCAmelCase_      :			str	):
   return [possible for possible in possibles if common_word in possible.lower()]

def   a_    ( lowerCAmelCase_      :			str = "p059_cipher.txt"	):
   __lowerCAmelCase								=	42
   __lowerCAmelCase								=	42
   __lowerCAmelCase								=	42
   __lowerCAmelCase								=	42
   __lowerCAmelCase								=	Path(lowerCAmelCase_	).parent.joinpath(lowerCAmelCase_	).read_text(encoding='utf-8'	)

   __lowerCAmelCase								=	[int(lowerCAmelCase_	) for number in data.strip().split(','	)]

   __lowerCAmelCase								=	filter_valid_chars(lowerCAmelCase_	)
   for common_word in COMMON_WORDS:
      __lowerCAmelCase								=	filter_common_word(lowerCAmelCase_,	lowerCAmelCase_	)
      if len(lowerCAmelCase_	) == 1:
         break

   __lowerCAmelCase								=	possibles[0]
   return sum(ord(lowerCAmelCase_	) for char in decoded_text	)


if __name__ == "__main__":
     print(F"""{solution() = }""")

