





"""simple docstring"""


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


__magic_name__       :					int						=			logging.get_logger(__name__)






class 							lowercase__			(   __SCREAMING_SNAKE_CASE	):

			"""simple docstring"""
			__lowerCAmelCase     :				Any										=  ["""input_features""", """is_longer"""]







			def __init__(      self						,   _A=6_4						,   _A=4_8_0_0_0						,   _A=4_8_0						,   _A=1_0						,   _A=1_0_2_4						,   _A=0.0						,   _A=False						,   _A = 0						,   _A = 1_4_0_0_0						,   _A = None						,   _A = "fusion"						,   _A = "repeatpad"						,   **_A						,   ):






									'''simple docstring'''







									super().__init__(
									    feature_size=_A						,   sampling_rate=_A						,   padding_value=_A						,   return_attention_mask=_A						,   **_A						,   )
									UpperCamelCase				:		Optional[int]				   =			top_db
									UpperCamelCase				:		Tuple				   =			truncation
									UpperCamelCase				:		str				   =			padding
									UpperCamelCase				:		str				   =			fft_window_size
									UpperCamelCase				:		int				   =			(fft_window_size >> 1) + 1
									UpperCamelCase				:		Optional[int]				   =			hop_length
									UpperCamelCase				:		Tuple				   =			max_length_s
									UpperCamelCase				:		Optional[Any]				   =			max_length_s * sampling_rate
									UpperCamelCase				:		List[str]				   =			sampling_rate
									UpperCamelCase				:		List[Any]				   =			frequency_min
									UpperCamelCase				:		Union[str, Any]				   =			frequency_max
									UpperCamelCase				:		Optional[int]				   =			mel_filter_bank(
									    num_frequency_bins=self.nb_frequency_bins						,   num_mel_filters=_A						,   min_frequency=_A						,   max_frequency=_A						,   sampling_rate=_A						,   norm=_A						,   mel_scale="""htk"""						,   )
									UpperCamelCase				:		str				   =			mel_filter_bank(
									    num_frequency_bins=self.nb_frequency_bins						,   num_mel_filters=_A						,   min_frequency=_A						,   max_frequency=_A						,   sampling_rate=_A						,   norm="""slaney"""						,   mel_scale="""slaney"""						,   )







			def   _a      (      self    ):






									'''simple docstring'''







									UpperCamelCase				:		Tuple				   =			copy.deepcopy(self.__dict__    )
									UpperCamelCase				:		Dict				   =			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   _a      (      self						,   _A						,   _A = None    ):






									'''simple docstring'''







									UpperCamelCase				:		Dict				   =			spectrogram(
									    _A						,   window_function(self.fft_window_size						,   """hann"""    )						,   frame_length=self.fft_window_size						,   hop_length=self.hop_length						,   power=2.0						,   mel_filters=_A						,   log_mel="""dB"""						,   )
									return log_mel_spectrogram.T







			def   _a      (      self						,   _A						,   _A						,   _A    ):






									'''simple docstring'''







									UpperCamelCase				:		int				   =			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				:		int				   =			[0]
									if len(ranges[2]    ) == 0:
															# if the audio is too short, we just use the first chunk
															UpperCamelCase				:		Union[str, Any]				   =			[0]
									# randomly choose index for each part
									UpperCamelCase				:		Optional[int]				   =			np.random.choice(ranges[0]    )
									UpperCamelCase				:		List[Any]				   =			np.random.choice(ranges[1]    )
									UpperCamelCase				:		int				   =			np.random.choice(ranges[2]    )

									UpperCamelCase				:		str				   =			mel[idx_front : idx_front + chunk_frames, :]
									UpperCamelCase				:		List[Any]				   =			mel[idx_middle : idx_middle + chunk_frames, :]
									UpperCamelCase				:		int				   =			mel[idx_back : idx_back + chunk_frames, :]

									UpperCamelCase				:		int				   =			torch.tensor(mel[None, None, :]    )
									UpperCamelCase				:		Union[str, Any]				   =			torch.nn.functional.interpolate(
									    _A						,   size=[chunk_frames, 6_4]						,   mode="""bilinear"""						,   align_corners=_A    )
									UpperCamelCase				:		List[Any]				   =			mel_shrink[0][0].numpy()
									UpperCamelCase				:		Optional[int]				   =			np.stack([mel_shrink, mel_chunk_front, mel_chunk_middle, mel_chunk_back]						,   axis=0    )
									return mel_fusion







			def   _a      (      self						,   _A						,   _A						,   _A						,   _A    ):






									'''simple docstring'''







									if waveform.shape[0] > max_length:
															if truncation == "rand_trunc":
																					UpperCamelCase				:		Any				   =			True
																					# random crop to max_length (for compatibility) -> this should be handled by self.pad
																					UpperCamelCase				:		Union[str, Any]				   =			len(_A    ) - max_length
																					UpperCamelCase				:		Optional[int]				   =			np.random.randint(0						,   overflow + 1    )
																					UpperCamelCase				:		Optional[int]				   =			waveform[idx : idx + max_length]
																					UpperCamelCase				:		Union[str, Any]				   =			self._np_extract_fbank_features(_A						,   self.mel_filters_slaney    )[None, :]
															elif truncation == "fusion":
																					UpperCamelCase				:		List[Any]				   =			self._np_extract_fbank_features(_A						,   self.mel_filters    )
																					UpperCamelCase				:		Union[str, Any]				   =			max_length // self.hop_length + 1  # the +1 related to how the spectrogram is computed
																					UpperCamelCase				:		Any				   =			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				:		Optional[Any]				   =			np.stack([mel, mel, mel, mel]						,   axis=0    )
																											UpperCamelCase				:		Optional[Any]				   =			False
																					else:
																											UpperCamelCase				:		Optional[int]				   =			self._random_mel_fusion(_A						,   _A						,   _A    )
																											UpperCamelCase				:		Any				   =			True
															else:
																					raise NotImplementedError(f"""data_truncating {truncation} not implemented"""    )

									else:
															UpperCamelCase				:		Union[str, Any]				   =			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				   =			int(max_length / len(_A    )    )
																											UpperCamelCase				:		Tuple				   =			np.stack(np.tile(_A						,   n_repeat + 1    )    )[:max_length]
																					if padding == "repeatpad":
																											UpperCamelCase				:		Any				   =			int(max_length / len(_A    )    )
																											UpperCamelCase				:		str				   =			np.stack(np.tile(_A						,   _A    )    )
																					UpperCamelCase				:		Optional[int]				   =			np.pad(_A						,   (0, max_length - waveform.shape[0])						,   mode="""constant"""						,   constant_values=0    )

															if truncation == "fusion":
																					UpperCamelCase				:		List[Any]				   =			self._np_extract_fbank_features(_A						,   self.mel_filters    )
																					UpperCamelCase				:		Any				   =			np.stack([input_mel, input_mel, input_mel, input_mel]						,   axis=0    )
															else:
																					UpperCamelCase				:		Union[str, Any]				   =			self._np_extract_fbank_features(_A						,   self.mel_filters_slaney    )[None, :]

									return input_mel, longer







			def __call__(      self						,   _A						,   _A = None						,   _A = None						,   _A = None						,   _A = None						,   _A = None						,   **_A						,   ):






									'''simple docstring'''







									UpperCamelCase				:		str				   =			truncation if truncation is not None else self.truncation
									UpperCamelCase				:		int				   =			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				:		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}"""    )
									UpperCamelCase				:		int				   =			is_batched_numpy or (
									    isinstance(_A						,   (list, tuple)    ) and (isinstance(raw_speech[0]						,   (np.ndarray, tuple, list)    ))
									)

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

									# always return batch
									if not is_batched:
															UpperCamelCase				:		Optional[int]				   =			[np.asarray(_A    )]

									# convert to mel spectrogram, truncate and pad if needed.
									UpperCamelCase				:		List[str]				   =			[
									    self._get_input_mel(_A						,   max_length if max_length else self.nb_max_samples						,   _A						,   _A    )
									    for waveform in raw_speech
									]

									UpperCamelCase				:		Union[str, Any]				   =			[]
									UpperCamelCase				:		Optional[int]				   =			[]
									for mel, longer in padded_inputs:
															input_mel.append(_A    )
															is_longer.append(_A    )

									if truncation == "fusion" and sum(_A    ) == 0:
															# if no audio is longer than 10s, then randomly select one audio to be longer
															UpperCamelCase				:		int				   =			np.random.randint(0						,   len(_A    )    )
															UpperCamelCase				:		List[str]				   =			True

									if isinstance(input_mel[0]						,   _A    ):
															UpperCamelCase				:		Dict				   =			[np.asarray(_A						,   dtype=np.floataa    ) for feature in input_mel]

									# is_longer is a list of bool
									UpperCamelCase				:		Optional[int]				   =			[[longer] for longer in is_longer]

									UpperCamelCase				:		Union[str, Any]				   =			{"""input_features""": input_mel, """is_longer""": is_longer}
									UpperCamelCase				:		List[Any]				   =			BatchFeature(_A    )

									if return_tensors is not None:
															UpperCamelCase				:		str				   =			input_features.convert_to_tensors(_A    )

									return input_features










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__      :     Any         =		logging.get_logger(__name__)

a__      :     Tuple         =		'Hello, World!'
a__      :     Any         =		'en_XX'

def      UpperCAmelCase_    (    _UpperCAmelCase							:str  ,						_UpperCAmelCase							:str  ,						_UpperCAmelCase							:bool				)	->				Optional[Any]:
     '''simple docstring'''


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

     A_          =       xmod.model.encoder.sentence_encoder
     A_          =       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=514  ,						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:
          A_          =       xmod.model.classification_heads['''mnli'''].out_proj.weight.shape[0]

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

     A_          =       XmodForSequenceClassification(_UpperCAmelCase				) if classification_head else XmodForMaskedLM(_UpperCAmelCase				)
     model.eval()

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

     A_          =       xmod_sent_encoder.layernorm_embedding.weight
     A_          =       xmod_sent_encoder.layernorm_embedding.bias

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

          # self attention
          A_          =       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.'''				)

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

          # self-attention output
          A_          =       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.'''				)
          A_          =       xmod_layer.self_attn.out_proj.weight
          A_          =       xmod_layer.self_attn.out_proj.bias
          A_          =       xmod_layer.self_attn_layer_norm.weight
          A_          =       xmod_layer.self_attn_layer_norm.bias

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

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

          # end of layer

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

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

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

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

     Path(_UpperCAmelCase				).mkdir(parents=_UpperCAmelCase  ,						exist_ok=_UpperCAmelCase				)
     print(f'Saving model to {pytorch_dump_folder_path}'				)
     model.save_pretrained(_UpperCAmelCase				)


if __name__ == "__main__":
      a__      :     List[str]         =		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__      :     Tuple         =		parser.parse_args()
      convert_xmod_checkpoint_to_pytorch(
          args.xmod_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
      )



