



'''simple docstring'''






import json
import re
from typing import TYPE_CHECKING, List, Optional, Tuple, Union

import numpy as np

from ...utils import is_tf_available, is_torch_available, logging


if TYPE_CHECKING:
     if is_torch_available():
          import torch
     if is_tf_available():
          import tensorflow as tf

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_codegen import CodeGenTokenizer


lowerCAmelCase:      str		 =		logging.get_logger(__name__)

lowerCAmelCase:      Dict		 =		{'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

lowerCAmelCase:      Tuple		 =		{
    'vocab_file': {
        'Salesforce/codegen-350M-mono': 'https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/vocab.json',
    },
    'merges_file': {
        'Salesforce/codegen-350M-mono': 'https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/merges.txt',
    },
    'tokenizer_file': {
        'Salesforce/codegen-350M-mono': (
            'https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/tokenizer.json'
        ),
    },
}

lowerCAmelCase:      Optional[Any]		 =		{
    'Salesforce/codegen-350M-mono': 2_0_4_8,
}

class 							a__(      lowerCamelCase__			):
    lowercase__				=					VOCAB_FILES_NAMES
    lowercase__				=					PRETRAINED_VOCAB_FILES_MAP
    lowercase__				=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    lowercase__				=					["""input_ids""", """attention_mask"""]
    lowercase__				=					CodeGenTokenizer







    def __init__(   self				:						Dict		,    __snake_case				:						Union[str, Any]=None		,    __snake_case				:						List[str]=None		,    __snake_case				:						List[str]=None		,    __snake_case				:						List[Any]="<|endoftext|>"		,    __snake_case				:						List[str]="<|endoftext|>"		,    __snake_case				:						Dict="<|endoftext|>"		,    __snake_case				:						Union[str, Any]=False		,    **__snake_case				:						str		,    ):
      super().__init__(
          __snake_case		,    __snake_case		,    tokenizer_file=__snake_case		,    unk_token=__snake_case		,    bos_token=__snake_case		,    eos_token=__snake_case		,    add_prefix_space=__snake_case		,    **__snake_case		,    )

      if kwargs.pop('add_bos_token'		,    __snake_case			):
        a		:      List[Any]       =  kwargs.pop('name_or_path'		,    ''			)
        raise ValueError(
            'Currenty GPT2\'s fast tokenizer does NOT support adding a BOS token.'
            'Instead you should use GPT2\'s slow tokenizer class `CodeGenTokenizer` as follows: \n'
            F"""`CodeGenTokenizer.from_pretrained('{model_id}')`\nor\n"""
            F"""`AutoTokenizer.from_pretrained('{model_id}', use_fast=False)`\n"""
            'This issue will be fixed soon, see: https://github.com/huggingface/tokenizers/pull/1005.'
            ' so that the fast tokenizer works correctly.'			)

      a		:      Any       =  json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()			)
      if pre_tok_state.get('add_prefix_space'		,    __snake_case			) != add_prefix_space:
        a		:      Union[str, Any]       =  getattr(__snake_case		,    pre_tok_state.pop('type'			)			)
        a		:      str       =  add_prefix_space
        a		:      Union[str, Any]       =  pre_tok_class(**__snake_case			)

      a		:      Optional[int]       =  add_prefix_space







    def     lowercase_ (   self				:						Optional[Any]		,    *__snake_case				:						List[Any]		,    **__snake_case				:						int			):
      a		:      Any       =  kwargs.get('is_split_into_words'		,    __snake_case			)
      assert self.add_prefix_space or not is_split_into_words, (
          F"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
          "to use it with pretokenized inputs."
      )

      return super()._batch_encode_plus(*__snake_case		,    **__snake_case			)







    def     lowercase_ (   self				:						int		,    *__snake_case				:						Dict		,    **__snake_case				:						int			):
      a		:      List[str]       =  kwargs.get('is_split_into_words'		,    __snake_case			)

      assert self.add_prefix_space or not is_split_into_words, (
          F"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
          "to use it with pretokenized inputs."
      )

      return super()._encode_plus(*__snake_case		,    **__snake_case			)







    def     lowercase_ (   self				:						Union[str, Any]		,    __snake_case				:						str		,    __snake_case				:						Optional[str] = None			):
      a		:      Tuple       =  self._tokenizer.model.save(__snake_case		,    name=__snake_case			)
      return tuple(__snake_case			)







    def     lowercase_ (   self				:						int		,    __snake_case				:						Union[int, List[int], "np.ndarray", "torch.Tensor", "tf.Tensor"]		,    __snake_case				:						bool = False		,    __snake_case				:						bool = None		,    __snake_case				:						Optional[List[str]] = None		,    **__snake_case				:						Dict		,    ):
      a		:      Optional[Any]       =  super().decode(
          token_ids=__snake_case		,    skip_special_tokens=__snake_case		,    clean_up_tokenization_spaces=__snake_case		,    **__snake_case		,    )

      if truncate_before_pattern is not None and len(__snake_case			) > 0:
        a		:      Dict       =  self.truncate(__snake_case		,    __snake_case			)

      return decoded_text







    def     lowercase_ (   self				:						Union[str, Any]		,    __snake_case				:						Optional[Any]		,    __snake_case				:						Dict			):
      def find_re(__snake_case				:						Any		,    __snake_case				:						List[str]		,    __snake_case				:						int			):
        a		:      Optional[int]       =  pattern.search(__snake_case		,    __snake_case			)
        return m.start() if m else -1

      a		:      Optional[Any]       =  [re.compile(__snake_case		,    re.MULTILINE			) for pattern in truncate_before_pattern]

      a		:      Any       =  list(re.finditer('^print'		,    __snake_case		,    re.MULTILINE			)			)

      if len(__snake_case			) > 1:
        a		:      Union[str, Any]       =  completion[: prints[1].start()]

      a		:      Any       =  list(re.finditer('^def'		,    __snake_case		,    re.MULTILINE			)			)

      if len(__snake_case			) > 1:
        a		:      Union[str, Any]       =  completion[: defs[1].start()]

      a		:      Dict       =  0

      a		:      Optional[Any]       =  [
          pos for pos in [find_re(__snake_case		,    __snake_case		,    __snake_case			) for terminal in terminals] if pos != -1
      ]

      if len(__snake_case			) > 0:
        return completion[: min(__snake_case			)]
      else:
        return completion




'''simple docstring'''






import argparse

import fairseq
import torch

from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging


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

lowerCAmelCase:      Union[str, Any]		 =		{
    '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:      int		 =		[
    'lm_head',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
    'label_embeddings_concat',
    'speaker_proj',
    'layer_norm_for_extract',
]
def    lowerCamelCase__ (		_A				,    _A				,    _A				,    _A				,    _A       ):
  for attribute in key.split('.'       ):
    a		:      Tuple       =  getattr(_A				,    _A       )

  if weight_type is not None:
    a		:      Union[str, Any]       =  getattr(_A				,    _A       ).shape
  else:
    a		:      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":
    a		:      str       =  value
  elif weight_type == "weight_g":
    a		:      Optional[int]       =  value
  elif weight_type == "weight_v":
    a		:      List[Any]       =  value
  elif weight_type == "bias":
    a		:      Any       =  value
  else:
    a		:      Optional[Any]       =  value

  logger.info(f"""{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}."""       )
def    lowerCamelCase__ (		_A				,    _A       ):
  a		:      Optional[int]       =  []
  a		:      Tuple       =  fairseq_model.state_dict()

  a		:      List[Any]       =  hf_model.unispeech_sat.feature_extractor

  for name, value in fairseq_dict.items():
    a		:      Optional[Any]       =  False
    if "conv_layers" in name:
      load_conv_layer(
          _A				,    _A				,    _A				,    _A				,    hf_model.config.feat_extract_norm == 'group'				,    )
      a		:      int       =  True
    else:
      for key, mapped_key in MAPPING.items():
        a		:      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
          a		:      str       =  True
          if "*" in mapped_key:
            a		:      Union[str, Any]       =  name.split(_A       )[0].split('.'       )[-2]
            a		:      List[str]       =  mapped_key.replace('*'				,    _A       )
          if "weight_g" in name:
            a		:      Optional[int]       =  'weight_g'
          elif "weight_v" in name:
            a		:      Any       =  'weight_v'
          elif "bias" in name:
            a		:      str       =  'bias'
          elif "weight" in name:
            # TODO: don't match quantizer.weight_proj
            a		:      List[Any]       =  'weight'
          else:
            a		:      Any       =  None
          set_recursively(_A				,    _A				,    _A				,    _A				,    _A       )
        continue
    if not is_used:
      unused_weights.append(_A       )

  logger.warning(f"""Unused weights: {unused_weights}"""       )
def    lowerCamelCase__ (		_A				,    _A				,    _A				,    _A				,    _A       ):
  a		:      List[str]       =  full_name.split('conv_layers.'       )[-1]
  a		:      Any       =  name.split('.'       )
  a		:      Optional[Any]       =  int(items[0]       )
  a		:      Union[str, 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."""       )
      a		:      Optional[int]       =  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."""       )
      a		:      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:
      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."""       )
      a		:      Union[str, 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."""       )
      a		:      Union[str, Any]       =  value
      logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""       )
  else:
    unused_weights.append(_A       )
@torch.no_grad()
def    lowerCamelCase__ (		_A				,    _A				,    _A=None				,    _A=None				,    _A=True       ):
  if config_path is not None:
    a		:      str       =  UniSpeechSatConfig.from_pretrained(_A       )
  else:
    a		:      Dict       =  UniSpeechSatConfig()

  a		:      Any       =  ''

  if is_finetuned:
    a		:      Tuple       =  UniSpeechSatForCTC(_A       )
  else:
    a		:      List[str]       =  UniSpeechSatForPreTraining(_A       )

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

  recursively_load_weights(_A				,    _A       )

  hf_wavavec.save_pretrained(_A       )


if __name__ == "__main__":
     lowerCAmelCase:      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('--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:      Optional[int]		 =		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
     )