




import argparse
from pathlib import Path

import fairseq
import torch
from fairseq.models.xmod import XMODModel as FairseqXmodModel
from packaging import version

from transformers import XmodConfig, XmodForMaskedLM, XmodForSequenceClassification
from transformers.utils import logging


if version.parse(fairseq.__version__) < version.parse("0.12.2"):
 raise Exception("requires fairseq >= 0.12.2")
if version.parse(fairseq.__version__) > version.parse("2"):
 raise Exception("requires fairseq < v2")

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

a_				=     '''Hello, World!'''
a_				=     '''en_XX'''




def 					__SCREAMING_SNAKE_CASE   (  lowercase_					,     lowercase_					,     lowercase_      )       ->	Optional[Any]:

    """simple docstring"""


    __UpperCamelCase  		=						Path('''data_bin'''      )
    __UpperCamelCase  		=						FairseqXmodModel.from_pretrained(
        model_name_or_path=str(Path(_A      ).parent      )					,     checkpoint_file=Path(_A      ).name					,     _name='''xmod_base'''					,     arch='''xmod_base'''					,     task='''multilingual_masked_lm'''					,     data_name_or_path=str(_A      )					,     bpe='''sentencepiece'''					,     sentencepiece_model=str(Path(_A      ).parent / '''sentencepiece.bpe.model'''      )					,     src_dict=str(data_dir / '''dict.txt'''      )					,     )
    xmod.eval()  # disable dropout
    print(_A      )

    __UpperCamelCase  		=						xmod.model.encoder.sentence_encoder
    __UpperCamelCase  		=						XmodConfig(
        vocab_size=xmod_sent_encoder.embed_tokens.num_embeddings					,     hidden_size=xmod.cfg.model.encoder_embed_dim					,     num_hidden_layers=xmod.cfg.model.encoder_layers					,     num_attention_heads=xmod.cfg.model.encoder_attention_heads					,     intermediate_size=xmod.cfg.model.encoder_ffn_embed_dim					,     max_position_embeddings=5_14					,     type_vocab_size=1					,     layer_norm_eps=1E-5					,     pre_norm=xmod.cfg.model.encoder_normalize_before					,     adapter_reduction_factor=getattr(xmod.cfg.model					,     '''bottleneck'''					,     2      )					,     adapter_layer_norm=xmod.cfg.model.adapter_layer_norm					,     adapter_reuse_layer_norm=xmod.cfg.model.adapter_reuse_layer_norm					,     ln_before_adapter=xmod.cfg.model.ln_before_adapter					,     languages=xmod.cfg.model.languages					,     )
    if classification_head:
        __UpperCamelCase  		=						xmod.model.classification_heads['''mnli'''].out_proj.weight.shape[0]

    print('''Our X-MOD config:'''					,     _A      )

    __UpperCamelCase  		=						XmodForSequenceClassification(_A      ) if classification_head else XmodForMaskedLM(_A      )
    model.eval()

    # Now let's copy all the weights.
    # Embeddings
    __UpperCamelCase  		=						xmod_sent_encoder.embed_tokens.weight
    __UpperCamelCase  		=						xmod_sent_encoder.embed_positions.weight
    __UpperCamelCase  		=						torch.zeros_like(
        model.roberta.embeddings.token_type_embeddings.weight      )  # just zero them out b/c xmod doesn't use them.

    __UpperCamelCase  		=						xmod_sent_encoder.layernorm_embedding.weight
    __UpperCamelCase  		=						xmod_sent_encoder.layernorm_embedding.bias

    for i in range(config.num_hidden_layers      ):
        # Encoder: start of layer
        __UpperCamelCase  		=						model.roberta.encoder.layer[i]
        __UpperCamelCase  		=						xmod_sent_encoder.layers[i]

        # self attention
        __UpperCamelCase  		=						layer.attention.self
        if not (
            xmod_layer.self_attn.k_proj.weight.data.shape
            == xmod_layer.self_attn.q_proj.weight.data.shape
            == xmod_layer.self_attn.v_proj.weight.data.shape
            == torch.Size((config.hidden_size, config.hidden_size)      )
        ):
            raise AssertionError('''Dimensions of self-attention weights do not match.'''      )

        __UpperCamelCase  		=						xmod_layer.self_attn.q_proj.weight
        __UpperCamelCase  		=						xmod_layer.self_attn.q_proj.bias
        __UpperCamelCase  		=						xmod_layer.self_attn.k_proj.weight
        __UpperCamelCase  		=						xmod_layer.self_attn.k_proj.bias
        __UpperCamelCase  		=						xmod_layer.self_attn.v_proj.weight
        __UpperCamelCase  		=						xmod_layer.self_attn.v_proj.bias

        # self-attention output
        __UpperCamelCase  		=						layer.attention.output
        if self_output.dense.weight.shape != xmod_layer.self_attn.out_proj.weight.shape:
            raise AssertionError('''Dimensions of self-attention output weights do not match.'''      )
        __UpperCamelCase  		=						xmod_layer.self_attn.out_proj.weight
        __UpperCamelCase  		=						xmod_layer.self_attn.out_proj.bias
        __UpperCamelCase  		=						xmod_layer.self_attn_layer_norm.weight
        __UpperCamelCase  		=						xmod_layer.self_attn_layer_norm.bias

        # intermediate
        __UpperCamelCase  		=						layer.intermediate
        if intermediate.dense.weight.shape != xmod_layer.fca.weight.shape:
            raise AssertionError('''Dimensions of intermediate weights do not match.'''      )
        __UpperCamelCase  		=						xmod_layer.fca.weight
        __UpperCamelCase  		=						xmod_layer.fca.bias

        # output
        __UpperCamelCase  		=						layer.output
        if bert_output.dense.weight.shape != xmod_layer.fca.weight.shape:
            raise AssertionError('''Dimensions of feed-forward weights do not match.'''      )
        __UpperCamelCase  		=						xmod_layer.fca.weight
        __UpperCamelCase  		=						xmod_layer.fca.bias
        __UpperCamelCase  		=						xmod_layer.final_layer_norm.weight
        __UpperCamelCase  		=						xmod_layer.final_layer_norm.bias
        if bert_output.adapter_layer_norm is not None:
            __UpperCamelCase  		=						xmod_layer.adapter_layer_norm.weight
            __UpperCamelCase  		=						xmod_layer.adapter_layer_norm.bias

        if sorted(bert_output.adapter_modules.keys()      ) != sorted(xmod_layer.adapter_modules.keys()      ):
            raise AssertionError('''Lists of language adapters do not match.'''      )
        for lang_code, adapter in xmod_layer.adapter_modules.items():
            __UpperCamelCase  		=						bert_output.adapter_modules[lang_code]
            __UpperCamelCase  		=						xmod_layer.adapter_modules[lang_code]
            __UpperCamelCase  		=						from_adapter.fca.weight
            __UpperCamelCase  		=						from_adapter.fca.bias
            __UpperCamelCase  		=						from_adapter.fca.weight
            __UpperCamelCase  		=						from_adapter.fca.bias

        # end of layer

    if xmod_sent_encoder.layer_norm is not None:
        __UpperCamelCase  		=						xmod_sent_encoder.layer_norm.weight
        __UpperCamelCase  		=						xmod_sent_encoder.layer_norm.bias

    if classification_head:
        __UpperCamelCase  		=						xmod.model.classification_heads['''mnli'''].dense.weight
        __UpperCamelCase  		=						xmod.model.classification_heads['''mnli'''].dense.bias
        __UpperCamelCase  		=						xmod.model.classification_heads['''mnli'''].out_proj.weight
        __UpperCamelCase  		=						xmod.model.classification_heads['''mnli'''].out_proj.bias
    else:
        # LM Head
        __UpperCamelCase  		=						xmod.model.encoder.lm_head.dense.weight
        __UpperCamelCase  		=						xmod.model.encoder.lm_head.dense.bias
        __UpperCamelCase  		=						xmod.model.encoder.lm_head.layer_norm.weight
        __UpperCamelCase  		=						xmod.model.encoder.lm_head.layer_norm.bias
        __UpperCamelCase  		=						xmod.model.encoder.lm_head.weight
        __UpperCamelCase  		=						xmod.model.encoder.lm_head.bias

    # Let's check that we get the same results.
    __UpperCamelCase  		=						xmod.encode(_A      ).unsqueeze(0      )  # batch of size 1
    model.roberta.set_default_language(_A      )

    __UpperCamelCase  		=						model(_A      )[0]
    if classification_head:
        __UpperCamelCase  		=						xmod.model.classification_heads['''mnli'''](xmod.extract_features(_A      )      )
    else:
        __UpperCamelCase  		=						xmod.model(_A					,     lang_id=[SAMPLE_LANGUAGE]      )[0]
    print(our_output.shape					,     their_output.shape      )
    __UpperCamelCase  		=						torch.max(torch.abs(our_output - their_output      )      ).item()
    print(F"max_absolute_diff = {max_absolute_diff}"      )  # ~ 1e-7
    __UpperCamelCase  		=						torch.allclose(_A					,     _A					,     atol=1E-3      )
    print('''Do both models output the same tensors?'''					,     '''🔥''' if success else '''💩'''      )
    if not success:
        raise Exception('''Something went wRoNg'''      )

    Path(_A      ).mkdir(parents=_A					,     exist_ok=_A      )
    print(F"Saving model to {pytorch_dump_folder_path}"      )
    model.save_pretrained(_A      )


if __name__ == "__main__":
 a_				=     argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     "--xmod_checkpoint_path", default=None, type=str, required=True, help="Path the official PyTorch dump."
 )
 parser.add_argument(
     "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
 )
 parser.add_argument(
     "--classification_head", action="store_true", help="Whether to convert a final classification head."
 )
 a_				=     parser.parse_args()
 convert_xmod_checkpoint_to_pytorch(
     args.xmod_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
 )












import copy
from typing import Any, Dict, List, Optional, Union

import numpy as np
import torch

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


a_   		=     logging.get_logger(__name__)

class  _lowerCamelCase						(			_SCREAMING_SNAKE_CASE   ):

      """simple docstring"""

      lowerCAmelCase__			: Dict    =					["input_features", "is_longer"]







      def __init__(     self					: Dict ,       snake_case					: int=64 ,       snake_case					: Dict=48000 ,       snake_case					: Tuple=480 ,       snake_case					: Optional[Any]=10 ,       snake_case					: Union[str, Any]=1024 ,       snake_case					: Union[str, Any]=0.0 ,       snake_case					: List[str]=False ,       snake_case					: float = 0 ,       snake_case					: float = 14000 ,       snake_case					: int = None ,       snake_case					: str = "fusion" ,       snake_case					: str = "repeatpad" ,       **snake_case					: int ,       ):
          super().__init__(
              feature_size=snake_case ,       sampling_rate=snake_case ,       padding_value=snake_case ,       return_attention_mask=snake_case ,       **snake_case ,       )
          __UpperCamelCase  		=						top_db
          __UpperCamelCase  		=						truncation
          __UpperCamelCase  		=						padding
          __UpperCamelCase  		=						fft_window_size
          __UpperCamelCase  		=						(fft_window_size >> 1) + 1
          __UpperCamelCase  		=						hop_length
          __UpperCamelCase  		=						max_length_s
          __UpperCamelCase  		=						max_length_s * sampling_rate
          __UpperCamelCase  		=						sampling_rate
          __UpperCamelCase  		=						frequency_min
          __UpperCamelCase  		=						frequency_max
          __UpperCamelCase  		=						mel_filter_bank(
              num_frequency_bins=self.nb_frequency_bins ,       num_mel_filters=snake_case ,       min_frequency=snake_case ,       max_frequency=snake_case ,       sampling_rate=snake_case ,       norm=snake_case ,       mel_scale='''htk''' ,       )
          __UpperCamelCase  		=						mel_filter_bank(
              num_frequency_bins=self.nb_frequency_bins ,       num_mel_filters=snake_case ,       min_frequency=snake_case ,       max_frequency=snake_case ,       sampling_rate=snake_case ,       norm='''slaney''' ,       mel_scale='''slaney''' ,       )







      def       snake_case	(     self					: Union[str, Any]				):
          __UpperCamelCase  		=						copy.deepcopy(self.__dict__				)
          __UpperCamelCase  		=						self.__class__.__name__
          if "mel_filters" in output:
              del output["mel_filters"]
          if "mel_filters_slaney" in output:
              del output["mel_filters_slaney"]
          return output







      def       snake_case	(     self					: Tuple ,       snake_case					: np.array ,       snake_case					: Optional[np.array] = None				):
          __UpperCamelCase  		=						spectrogram(
              snake_case ,       window_function(self.fft_window_size ,       '''hann'''				) ,       frame_length=self.fft_window_size ,       hop_length=self.hop_length ,       power=2.0 ,       mel_filters=snake_case ,       log_mel='''dB''' ,       )
          return log_mel_spectrogram.T







      def       snake_case	(     self					: Optional[int] ,       snake_case					: Any ,       snake_case					: List[Any] ,       snake_case					: int				):
          __UpperCamelCase  		=						np.array_split(list(range(0 ,       total_frames - chunk_frames + 1				)				) ,       3				)
          if len(ranges[1]				) == 0:
              # if the audio is too short, we just use the first chunk
              __UpperCamelCase  		=						[0]
          if len(ranges[2]				) == 0:
              # if the audio is too short, we just use the first chunk
              __UpperCamelCase  		=						[0]
          # randomly choose index for each part
          __UpperCamelCase  		=						np.random.choice(ranges[0]				)
          __UpperCamelCase  		=						np.random.choice(ranges[1]				)
          __UpperCamelCase  		=						np.random.choice(ranges[2]				)

          __UpperCamelCase  		=						mel[idx_front : idx_front + chunk_frames, :]
          __UpperCamelCase  		=						mel[idx_middle : idx_middle + chunk_frames, :]
          __UpperCamelCase  		=						mel[idx_back : idx_back + chunk_frames, :]

          __UpperCamelCase  		=						torch.tensor(mel[None, None, :]				)
          __UpperCamelCase  		=						torch.nn.functional.interpolate(
              snake_case ,       size=[chunk_frames, 64] ,       mode='''bilinear''' ,       align_corners=snake_case				)
          __UpperCamelCase  		=						mel_shrink[0][0].numpy()
          __UpperCamelCase  		=						np.stack([mel_shrink, mel_chunk_front, mel_chunk_middle, mel_chunk_back] ,       axis=0				)
          return mel_fusion







      def       snake_case	(     self					: Optional[Any] ,       snake_case					: np.array ,       snake_case					: Optional[int] ,       snake_case					: Tuple ,       snake_case					: str				):
          if waveform.shape[0] > max_length:
              if truncation == "rand_trunc":
                  __UpperCamelCase  		=						True
                  # random crop to max_length (for compatibility) -> this should be handled by self.pad
                  __UpperCamelCase  		=						len(snake_case				) - max_length
                  __UpperCamelCase  		=						np.random.randint(0 ,       overflow + 1				)
                  __UpperCamelCase  		=						waveform[idx : idx + max_length]
                  __UpperCamelCase  		=						self._np_extract_fbank_features(snake_case ,       self.mel_filters_slaney				)[None, :]
              elif truncation == "fusion":
                  __UpperCamelCase  		=						self._np_extract_fbank_features(snake_case ,       self.mel_filters				)
                  __UpperCamelCase  		=						max_length // self.hop_length + 1  # the +1 related to how the spectrogram is computed
                  __UpperCamelCase  		=						mel.shape[0]
                  if chunk_frames == total_frames:
                      # there is a corner case where the audio length is larger than max_length but smaller than max_length+hop_length.
                      # In this case, we just use the whole audio.
                      __UpperCamelCase  		=						np.stack([mel, mel, mel, mel] ,       axis=0				)
                      __UpperCamelCase  		=						False
                  else:
                      __UpperCamelCase  		=						self._random_mel_fusion(snake_case ,       snake_case ,       snake_case				)
                      __UpperCamelCase  		=						True
              else:
                  raise NotImplementedError(F"data_truncating {truncation} not implemented"				)

          else:
              __UpperCamelCase  		=						False
              # only use repeat as a new possible value for padding. you repeat the audio before applying the usual max_length padding
              if waveform.shape[0] < max_length:
                  if padding == "repeat":
                      __UpperCamelCase  		=						int(max_length / len(snake_case				)				)
                      __UpperCamelCase  		=						np.stack(np.tile(snake_case ,       n_repeat + 1				)				)[:max_length]
                  if padding == "repeatpad":
                      __UpperCamelCase  		=						int(max_length / len(snake_case				)				)
                      __UpperCamelCase  		=						np.stack(np.tile(snake_case ,       snake_case				)				)
                  __UpperCamelCase  		=						np.pad(snake_case ,       (0, max_length - waveform.shape[0]) ,       mode='''constant''' ,       constant_values=0				)

              if truncation == "fusion":
                  __UpperCamelCase  		=						self._np_extract_fbank_features(snake_case ,       self.mel_filters				)
                  __UpperCamelCase  		=						np.stack([input_mel, input_mel, input_mel, input_mel] ,       axis=0				)
              else:
                  __UpperCamelCase  		=						self._np_extract_fbank_features(snake_case ,       self.mel_filters_slaney				)[None, :]

          return input_mel, longer







      def __call__(     self					: List[str] ,       snake_case					: Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]] ,       snake_case					: str = None ,       snake_case					: Optional[str] = None ,       snake_case					: Optional[int] = None ,       snake_case					: Optional[int] = None ,       snake_case					: Optional[Union[str, TensorType]] = None ,       **snake_case					: Any ,       ):
          __UpperCamelCase  		=						truncation if truncation is not None else self.truncation
          __UpperCamelCase  		=						padding if padding else self.padding

          if sampling_rate is not None:
              if sampling_rate != self.sampling_rate:
                  raise ValueError(
                      F"The model corresponding to this feature extractor: {self.__class__.__name__} was trained using a"
                      F" sampling rate of {self.sampling_rate}. Please make sure that the provided `raw_speech` input"
                      F" was sampled 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.'''				)

          __UpperCamelCase  		=						isinstance(snake_case ,       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}"				)
          __UpperCamelCase  		=						is_batched_numpy or (
              isinstance(snake_case ,       (list, tuple)				) and (isinstance(raw_speech[0] ,       (np.ndarray, tuple, list)				))
          )

          if is_batched:
              __UpperCamelCase  		=						[np.asarray(snake_case ,       dtype=np.floataa				) for speech in raw_speech]
          elif not is_batched and not isinstance(snake_case ,       np.ndarray				):
              __UpperCamelCase  		=						np.asarray(snake_case ,       dtype=np.floataa				)
          elif isinstance(snake_case ,       np.ndarray				) and raw_speech.dtype is np.dtype(np.floataa				):
              __UpperCamelCase  		=						raw_speech.astype(np.floataa				)

          # always return batch
          if not is_batched:
              __UpperCamelCase  		=						[np.asarray(snake_case				)]

          # convert to mel spectrogram, truncate and pad if needed.
          __UpperCamelCase  		=						[
              self._get_input_mel(snake_case ,       max_length if max_length else self.nb_max_samples ,       snake_case ,       snake_case				)
              for waveform in raw_speech
          ]

          __UpperCamelCase  		=						[]
          __UpperCamelCase  		=						[]
          for mel, longer in padded_inputs:
              input_mel.append(snake_case				)
              is_longer.append(snake_case				)

          if truncation == "fusion" and sum(snake_case				) == 0:
              # if no audio is longer than 10s, then randomly select one audio to be longer
              __UpperCamelCase  		=						np.random.randint(0 ,       len(snake_case				)				)
              __UpperCamelCase  		=						True

          if isinstance(input_mel[0] ,       snake_case				):
              __UpperCamelCase  		=						[np.asarray(snake_case ,       dtype=np.floataa				) for feature in input_mel]

          # is_longer is a list of bool
          __UpperCamelCase  		=						[[longer] for longer in is_longer]

          __UpperCamelCase  		=						{'''input_features''': input_mel, '''is_longer''': is_longer}
          __UpperCamelCase  		=						BatchFeature(snake_case				)

          if return_tensors is not None:
              __UpperCamelCase  		=						input_features.convert_to_tensors(snake_case				)

          return input_features






