
"""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      :			List[str]					=      logging.get_logger(__name__)

_a      :			int					=      {
    """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      :			List[str]					=      [
    """lm_head""",
    """quantizer.weight_proj""",
    """quantizer.codevectors""",
    """project_q""",
    """project_hid""",
]
def 				a__   (   a   :				Dict       ,     a   :				int       ,     a   :				List[Any]       ,     a   :				Tuple       ,     a   :				str		):

  """simple docstring"""







  for attribute in key.split("."		):
    _snake_case     :						int						=   getattr(_lowercase       ,     _lowercase		)

  if weight_type is not None:
    _snake_case     :						Dict						=   getattr(_lowercase       ,     _lowercase		).shape
  else:
    _snake_case     :						int						=   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":
    _snake_case     :						str						=   value
  elif weight_type == "weight_g":
    _snake_case     :						Dict						=   value
  elif weight_type == "weight_v":
    _snake_case     :						int						=   value
  elif weight_type == "bias":
    _snake_case     :						List[Any]						=   value
  else:
    _snake_case     :						int						=   value

  logger.info(f'{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.'		)
def 				a__   (   a   :				int       ,     a   :				Dict		):

  """simple docstring"""







  _snake_case     :						Optional[int]						=   []
  _snake_case     :						Optional[int]						=   fairseq_model.state_dict()

  _snake_case     :						Any						=   hf_model.feature_extractor

  # if encoder has different dim to decoder -> use proj_weight
  _snake_case     :						Any						=   None

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

  logger.warning(f'Unused weights: {unused_weights}'		)

  return proj_weight
def 				a__   (   a   :				Dict       ,     a   :				int       ,     a   :				List[Any]       ,     a   :				int       ,     a   :				Optional[int]		):

  """simple docstring"""







  _snake_case     :						int						=   full_name.split("conv_layers."		)[-1]
  _snake_case     :						Optional[int]						=   name.split("."		)
  _snake_case     :						int						=   int(items[0]		)
  _snake_case     :						Tuple						=   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.'
      )
      _snake_case     :						Optional[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.'
      )
      _snake_case     :						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."
      )
      _snake_case     :						Any						=   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.'
      )
      _snake_case     :						Tuple						=   value
      logger.info(f'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'		)
  else:
    unused_weights.append(_lowercase		)
def 				a__   (   a   :				Optional[Any]		):

  """simple docstring"""







  _snake_case			,					_snake_case     :						Dict						=   emb.weight.shape
  _snake_case     :						Any						=   nn.Linear(_lowercase       ,     _lowercase       ,     bias=_lowercase		)
  _snake_case     :						Tuple						=   emb.weight.data
  return lin_layer
def 				a__   (   a   :				List[str]		):

  """simple docstring"""







  with open(_lowercase       ,     "r"       ,     encoding="utf-8"		) as f:
    _snake_case     :						Dict						=   f.readlines()
    _snake_case     :						Any						=   [line.split(" "		)[0] for line in lines]

  _snake_case     :						List[str]						=   len(_lowercase		)

  _snake_case     :						Optional[Any]						=   {
      "<s>": 0,
      "<pad>": 1,
      "</s>": 2,
      "<unk>": 3,
  }

  vocab_dict.update(dict(zip(_lowercase       ,     range(4       ,     num_words + 4		)		)		)		)
  return vocab_dict
@torch.no_grad()
def 				a__   (   a   :				Dict       ,     a   :				List[Any]       ,     a   :				List[Any]       ,     a   :				Union[str, Any]       ,     a   :				Any       ,     a   :				List[Any]       ,     a   :				Optional[Any]       ,     ):

  """simple docstring"""







  _snake_case     :						Union[str, Any]						=   WavaVecaConfig.from_pretrained(_lowercase		)
  _snake_case     :						Tuple						=   SpeechaTextaConfig.from_pretrained(
      _lowercase       ,     vocab_size=_lowercase       ,     decoder_layers=_lowercase       ,     do_stable_layer_norm=_lowercase		)

  _snake_case     :						Optional[Any]						=   WavaVecaFeatureExtractor(
      feature_size=1       ,     sampling_rate=16_000       ,     padding_value=0       ,     do_normalize=_lowercase       ,     return_attention_mask=_lowercase       ,     )

  _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     :						List[str]						=   model[0].eval()

  # set weights for wav2vec2 encoder
  _snake_case     :						List[str]						=   WavaVecaModel(_lowercase		)
  _snake_case     :						int						=   recursively_load_weights_wavaveca(model.encoder       ,     _lowercase		)

  _snake_case     :						Optional[int]						=   SpeechaTextaForCausalLM(_lowercase		)
  _snake_case			,					_snake_case     :						Tuple						=   hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict()       ,     strict=_lowercase		)

  # set output linear layer
  unexpected_keys.remove("embed_out"		)
  _snake_case     :						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}'		)

  _snake_case     :						List[str]						=   SpeechEncoderDecoderModel(encoder=_lowercase       ,     decoder=_lowercase		)
  _snake_case     :						Optional[int]						=   False

  # add projection layer
  _snake_case     :						List[Any]						=   nn.Parameter(projection_layer.weight		)
  _snake_case     :						Union[str, Any]						=   nn.Parameter(projection_layer.bias		)

  _snake_case     :						List[Any]						=   create_vocab_dict(_lowercase		)

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

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

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

  _snake_case     :						Tuple						=   SpeechEncoderDecoderConfig.from_dict(_lowercase		)

  hf_wavavec.save_pretrained(_lowercase		)
  feature_extractor.save_pretrained(_lowercase		)


if __name__ == "__main__":
    _a      :			int					=      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=10_224, type=int, help="""Vocab size of decoder""")
    parser.add_argument("""--num_decoder_layers""", default=7, type=int, help="""Number of decoder layers""")

    _a      :			int					=      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"""
from __future__ import annotations






class      _UpperCAmelCase				:
    def __init__(    self						,    snake_case_						,    snake_case_     ):
      _snake_case			,					_snake_case     :						Dict						=   text, pattern
      _snake_case			,					_snake_case     :						int						=   len(snake_case_     ), len(snake_case_     )
    def 					lowerCamelCase__	(    self						,    snake_case_     ):

      for i in range(self.patLen - 1						,    -1						,    -1     ):
        if char == self.pattern[i]:
          return i
      return -1
    def 					lowerCamelCase__	(    self						,    snake_case_     ):

      for i in range(self.patLen - 1						,    -1						,    -1     ):
        if self.pattern[i] != self.text[current_pos + i]:
          return current_pos + i
      return -1


    def 					lowerCamelCase__	(    self     ):
      # searches pattern in text and returns index positions
      _snake_case     :						List[str]						=   []
      for i in range(self.textLen - self.patLen + 1     ):
        _snake_case     :						Union[str, Any]						=   self.mismatch_in_text(snake_case_     )
        if mismatch_index == -1:
          positions.append(snake_case_     )
        else:
          _snake_case     :						Tuple						=   self.match_in_pattern(self.text[mismatch_index]     )
          _snake_case     :						Tuple						=   (
              mismatch_index - match_index
          )  # shifting index lgtm [py/multiple-definition]
      return positions


_a      :			List[Any]					=							"""ABAABA"""
_a      :			str					=							"""AB"""
_a      :			List[Any]					=							BoyerMooreSearch(text, pattern)
_a      :			Any					=							bms.bad_character_heuristic()

if len(positions) == 0:
    print("""No match found""")
else:
    print("""Pattern found in following positions: """)
    print(positions)

