




from math import ceil
from typing import List, Optional, Union

import numpy as np

from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import BatchFeature, SequenceFeatureExtractor
from ...utils import TensorType, logging


_snake_case										=       logging.get_logger(__name__)







class 						lowercase							(   UpperCamelCase__		):
     _a							 =						["audio_values", "audio_mask"]

     def __init__(  self		,     _a=2048		,     _a=1		,     _a=[16, 16]		,     _a=128		,     _a=4_4100		,     _a=86		,     _a=2048		,     _a=0.0		,     **_a		,     )						->							List[Any]:
           super().__init__(
               feature_size=_a		,     sampling_rate=_a		,     padding_value=_a		,     **_a		,     )

           _A							:     Any			=						spectrogram_length
           _A							:     Dict			=						num_channels
           _A							:     Optional[Any]			=						patch_size
           _A							:     str			=						feature_size // self.patch_size[1]
           _A							:     List[Any]			=						n_fft
           _A							:     Optional[Any]			=						sampling_rate // hop_length_to_sampling_rate
           _A							:     List[str]			=						sampling_rate
           _A							:     Union[str, Any]			=						padding_value
           _A							:     List[str]			=						mel_filter_bank(
               num_frequency_bins=1 + n_fft // 2		,     num_mel_filters=_a		,     min_frequency=0.0		,     max_frequency=22050.0		,     sampling_rate=_a		,     norm="""slaney"""		,     mel_scale="""slaney"""		,     ).T

     def 					a__	(  self		,     _a				)						->							np.ndarray:
           _A							:     Optional[int]			=						spectrogram(
               _a		,     window_function(self.n_fft		,     """hann"""				)		,     frame_length=self.n_fft		,     hop_length=self.hop_length		,     power=2.0		,     mel_filters=self.mel_filters.T		,     log_mel="""dB"""		,     db_range=80.0		,     )
           _A							:     Tuple			=						log_spec[:, :-1]
           _A							:     int			=						log_spec - 20.0
           _A							:     Tuple			=						np.clip(log_spec / 40.0		,     -2.0		,     0.0				) + 1.0
           return log_spec



     def __call__(  self		,     _a		,     _a = None		,     _a = True		,     _a = None		,     _a = False		,     _a = False		,     **_a		,     )						->							BatchFeature:

           if sampling_rate is not None:
                 if sampling_rate != self.sampling_rate:
                       raise ValueError(
                           """This feature extractor is set to support sampling rate"""
                           F''' of {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled'''
                           F''' with {self.sampling_rate} and not {sampling_rate}.'''				)
           else:
                 logger.warning(
                     """It is strongly recommended to pass the `sampling_rate` argument to this function. """
                     """Failing to do so can result in silent errors that might be hard to debug."""				)

           _A							:     int			=						isinstance(_a		,     np.ndarray				) and len(raw_speech.shape				) > 1
           if is_batched_numpy and len(raw_speech.shape				) > 2:
                 raise ValueError(F'''Only mono-channel audio is supported for input to {self}'''				)
           _A							:     Optional[Any]			=						is_batched_numpy or (
               isinstance(_a		,     (list, tuple)				) and (isinstance(raw_speech[0]		,     (np.ndarray, tuple, list)				))
           )
           if is_batched:
                 _A							:     Union[str, Any]			=						[np.asarray([speech]		,     dtype=np.floataa				).T for speech in raw_speech]
           elif not is_batched and not isinstance(_a		,     np.ndarray				):
                 _A							:     str			=						np.asarray(_a		,     dtype=np.floataa				)
           elif isinstance(_a		,     np.ndarray				) and raw_speech.dtype is np.dtype(np.floataa				):
                 _A							:     Dict			=						raw_speech.astype(np.floataa				)
           # always return batch
           if not is_batched:
                 _A							:     Dict			=						[np.asarray([raw_speech]				).T]

           # Convert audio signals to log mel spectrograms, truncate by time axis
           _A							:     Union[str, Any]			=						[
               self._np_extract_fbank_features(waveform.squeeze()				).T[: self.spectrogram_length] for waveform in raw_speech
           ]
           if isinstance(audio_features[0]		,     _a				):
                 _A							:     Optional[int]			=						[np.asarray(_a		,     dtype=np.floataa				) for feature in audio_features]

           # Create audio attention mask
           _A							:     Any			=						max(
               [ceil(feature.shape[0] / self.patch_size[0]				) * self.freq_len for feature in audio_features]				)  # The maximum number of audio patches in a batch
           if return_attention_mask:
                 _A							:     str			=						[
                     (ceil(feature.shape[0] / self.patch_size[0]				) * self.freq_len) * [1]
                     + (max_patch_len - ceil(feature.shape[0] / self.patch_size[0]				) * self.freq_len) * [0]
                     for feature in audio_features
                 ]
                 _A							:     List[str]			=						np.array(_a				).astype(np.floataa				)

           # convert into correct format for padding
           _A							:     Tuple			=						max_patch_len // self.freq_len * self.patch_size[0]  # The maximum audio size in a batch
           _A							:     int			=						np.ones([len(_a				), 1, max_time_len, self.feature_size]				).astype(np.floataa				)
           _A							:     Optional[int]			=						padded_audio_features * self.padding_value
           for i in range(len(_a				)				):
                 _A							:     Union[str, Any]			=						audio_features[i]
                 _A							:     Optional[Any]			=						feature

           # return as BatchFeature
           if return_attention_mask:
                 _A							:     Optional[Any]			=						{"""audio_values""": padded_audio_features, """audio_mask""": audio_mask}
           else:
                 _A							:     Any			=						{"""audio_values""": padded_audio_features}

           _A							:     int			=						BatchFeature(data=_a		,     tensor_type=_a				)
           return encoded_inputs







import argparse

import torch

from transformers import BlenderbotConfig, BlenderbotForConditionalGeneration
from transformers.utils import logging


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

_snake_case										=       [
    ["attention", "attn"],
    ["encoder_attention", "encoder_attn"],
    ["q_lin", "q_proj"],
    ["k_lin", "k_proj"],
    ["v_lin", "v_proj"],
    ["out_lin", "out_proj"],
    ["norm_embeddings", "layernorm_embedding"],
    ["position_embeddings", "embed_positions"],
    ["embeddings", "embed_tokens"],
    ["ffn.lin", "fc"],
]







def 				lowerCAmelCase_	(     snake_case_     ):
      if k == "embeddings.weight":
            return "shared.weight"

      for parlai_name, hf_name in PATTERNS:
            _A							:     str			=						k.replace(snake_case_,snake_case_     )

      if k.startswith("""encoder"""     ):
            _A							:     Optional[Any]			=						k.replace(""".attn""",""".self_attn"""     )
            _A							:     Dict			=						k.replace("""norm1""","""self_attn_layer_norm"""     )
            _A							:     Optional[Any]			=						k.replace("""norm2""","""final_layer_norm"""     )
      elif k.startswith("""decoder"""     ):
            _A							:     str			=						k.replace("""norm1""","""self_attn_layer_norm"""     )
            _A							:     Any			=						k.replace("""norm2""","""encoder_attn_layer_norm"""     )
            _A							:     Optional[int]			=						k.replace("""norm3""","""final_layer_norm"""     )
      return k







def 				lowerCAmelCase_	(     snake_case_     ):
      _A							:     List[Any]			=						[
          """model.encoder.layernorm_embedding.weight""",
          """model.encoder.layernorm_embedding.bias""",
          """model.decoder.layernorm_embedding.weight""",
          """model.decoder.layernorm_embedding.bias""",
      ]
      for k in keys:
            _A							:     str			=						sd.pop(snake_case_     )
            _A							:     Optional[int]			=						k.replace("""layernorm_embedding""","""layer_norm"""     )
            assert new_k not in sd
            _A							:     Optional[int]			=						v


_snake_case										=       ["START"]







@torch.no_grad()
def 				lowerCAmelCase_	(     snake_case_,snake_case_,snake_case_     ):
      _A							:     Tuple			=						torch.load(snake_case_,map_location="""cpu"""     )
      _A							:     List[Any]			=						model["""model"""]
      _A							:     Optional[Any]			=						BlenderbotConfig.from_json_file(snake_case_     )
      _A							:     List[str]			=						BlenderbotForConditionalGeneration(snake_case_     )
      _A							:     Tuple			=						m.model.state_dict().keys()
      _A							:     Any			=						[]
      _A							:     Dict			=						{}
      for k, v in sd.items():
            if k in IGNORE_KEYS:
                  continue

            _A							:     Optional[int]			=						rename_state_dict_key(snake_case_     )
            if new_k not in valid_keys:
                  failures.append([k, new_k]     )
            else:
                  _A							:     Dict			=						v
      if cfg.normalize_before:  # Blenderbot-3B checkpoints. Rename layernorm_embedding -> layer_norm
            rename_layernorm_keys(snake_case_     )
      m.model.load_state_dict(snake_case_,strict=snake_case_     )
      m.half()
      m.save_pretrained(snake_case_     )


if __name__ == "__main__":
  _snake_case										=       argparse.ArgumentParser()
  # Required parameters
  parser.add_argument("--src_path", type=str, help="like blenderbot-model.bin")
  parser.add_argument("--save_dir", default="hf_blenderbot", type=str, help="Where to save converted model.")
  parser.add_argument(
      "--hf_config_json", default="blenderbot-3b-config.json", type=str, help="Path to config to use"
  )
  _snake_case										=       parser.parse_args()
  convert_parlai_checkpoint(args.src_path, args.save_dir, args.hf_config_json)

