"""simple docstring"""



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()
_UpperCamelCase								=		logging.get_logger(__name__)

_UpperCamelCase								=		"Hello, World!"
_UpperCamelCase								=		"en_XX"

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

							A__       :     Any								=			xmod.model.encoder.sentence_encoder
							A__       :     Optional[int]								=			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__       :     Optional[int]								=			xmod.model.classification_heads["""mnli"""].out_proj.weight.shape[0]

							print("""Our X-MOD config:"""			,  lowerCAmelCase							)

							A__       :     List[str]								=			XmodForSequenceClassification(lowerCAmelCase							) if classification_head else XmodForMaskedLM(lowerCAmelCase							)
							model.eval()

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

							A__       :     str								=			xmod_sent_encoder.layernorm_embedding.weight
							A__       :     Union[str, Any]								=			xmod_sent_encoder.layernorm_embedding.bias

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

														# self attention
														A__       :     Optional[int]								=			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__       :     int								=			xmod_layer.self_attn.q_proj.weight
														A__       :     str								=			xmod_layer.self_attn.q_proj.bias
														A__       :     Optional[Any]								=			xmod_layer.self_attn.k_proj.weight
														A__       :     List[str]								=			xmod_layer.self_attn.k_proj.bias
														A__       :     Any								=			xmod_layer.self_attn.v_proj.weight
														A__       :     Tuple								=			xmod_layer.self_attn.v_proj.bias

														# self-attention output
														A__       :     List[Any]								=			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__       :     List[Any]								=			xmod_layer.self_attn.out_proj.weight
														A__       :     Union[str, Any]								=			xmod_layer.self_attn.out_proj.bias
														A__       :     Union[str, Any]								=			xmod_layer.self_attn_layer_norm.weight
														A__       :     Tuple								=			xmod_layer.self_attn_layer_norm.bias

														# intermediate
														A__       :     Optional[Any]								=			layer.intermediate
														if intermediate.dense.weight.shape != xmod_layer.fca.weight.shape:
																					raise AssertionError("""Dimensions of intermediate weights do not match."""							)
														A__       :     List[Any]								=			xmod_layer.fca.weight
														A__       :     Optional[int]								=			xmod_layer.fca.bias

														# output
														A__       :     List[Any]								=			layer.output
														if bert_output.dense.weight.shape != xmod_layer.fca.weight.shape:
																					raise AssertionError("""Dimensions of feed-forward weights do not match."""							)
														A__       :     Dict								=			xmod_layer.fca.weight
														A__       :     Tuple								=			xmod_layer.fca.bias
														A__       :     List[Any]								=			xmod_layer.final_layer_norm.weight
														A__       :     Union[str, Any]								=			xmod_layer.final_layer_norm.bias
														if bert_output.adapter_layer_norm is not None:
																					A__       :     Optional[Any]								=			xmod_layer.adapter_layer_norm.weight
																					A__       :     Dict								=			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__       :     Union[str, Any]								=			bert_output.adapter_modules[lang_code]
																					A__       :     int								=			xmod_layer.adapter_modules[lang_code]
																					A__       :     List[Any]								=			from_adapter.fca.weight
																					A__       :     Optional[Any]								=			from_adapter.fca.bias
																					A__       :     int								=			from_adapter.fca.weight
																					A__       :     List[Any]								=			from_adapter.fca.bias

														# end of layer

							if xmod_sent_encoder.layer_norm is not None:
														A__       :     Dict								=			xmod_sent_encoder.layer_norm.weight
														A__       :     List[str]								=			xmod_sent_encoder.layer_norm.bias

							if classification_head:
														A__       :     Optional[Any]								=			xmod.model.classification_heads["""mnli"""].dense.weight
														A__       :     Any								=			xmod.model.classification_heads["""mnli"""].dense.bias
														A__       :     Optional[int]								=			xmod.model.classification_heads["""mnli"""].out_proj.weight
														A__       :     Optional[int]								=			xmod.model.classification_heads["""mnli"""].out_proj.bias
							else:
														# LM Head
														A__       :     Optional[int]								=			xmod.model.encoder.lm_head.dense.weight
														A__       :     int								=			xmod.model.encoder.lm_head.dense.bias
														A__       :     Optional[int]								=			xmod.model.encoder.lm_head.layer_norm.weight
														A__       :     int								=			xmod.model.encoder.lm_head.layer_norm.bias
														A__       :     str								=			xmod.model.encoder.lm_head.weight
														A__       :     int								=			xmod.model.encoder.lm_head.bias

							# Let's check that we get the same results.
							A__       :     Optional[int]								=			xmod.encode(lowerCAmelCase							).unsqueeze(0							)  # batch of size 1
							model.roberta.set_default_language(lowerCAmelCase							)

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

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


if __name__ == "__main__":
				_UpperCamelCase								=		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."
				)
				_UpperCamelCase								=		parser.parse_args()
				convert_xmod_checkpoint_to_pytorch(
				    args.xmod_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
				)






"""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


_UpperCamelCase								=		logging.get_logger(__name__)
class 						__UpperCAmelCase    (__A     ):







			'''simple docstring'''
			_UpperCamelCase  :						Optional[int]					     =				['input_features', 'is_longer']





			def __init__(			self   ,      snake_case_=64   ,      snake_case_=48_000   ,      snake_case_=480   ,      snake_case_=10   ,      snake_case_=1_024   ,      snake_case_=0.0   ,      snake_case_=False   ,      snake_case_ = 0   ,      snake_case_ = 14_000   ,      snake_case_ = None   ,      snake_case_ = "fusion"   ,      snake_case_ = "repeatpad"   ,      **snake_case_   ,      ):







										'''simple docstring'''


										super().__init__(
										    feature_size=snake_case_   ,      sampling_rate=snake_case_   ,      padding_value=snake_case_   ,      return_attention_mask=snake_case_   ,      **snake_case_   ,      )
										A__       :     List[str]								=			top_db
										A__       :     Optional[int]								=			truncation
										A__       :     str								=			padding
										A__       :     str								=			fft_window_size
										A__       :     Any								=			(fft_window_size >> 1) + 1
										A__       :     Optional[Any]								=			hop_length
										A__       :     Any								=			max_length_s
										A__       :     int								=			max_length_s * sampling_rate
										A__       :     Any								=			sampling_rate
										A__       :     List[str]								=			frequency_min
										A__       :     int								=			frequency_max
										A__       :     Tuple								=			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"""   ,      )
										A__       :     List[Any]								=			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 		lowerCamelCase					(			self							):







										'''simple docstring'''


										A__       :     List[Any]								=			copy.deepcopy(self.__dict__							)
										A__       :     int								=			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 		lowerCamelCase					(			self   ,      snake_case_   ,      snake_case_ = None							):







										'''simple docstring'''


										A__       :     Any								=			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 		lowerCamelCase					(			self   ,      snake_case_   ,      snake_case_   ,      snake_case_							):







										'''simple docstring'''


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

										A__       :     Any								=			mel[idx_front : idx_front + chunk_frames, :]
										A__       :     Optional[int]								=			mel[idx_middle : idx_middle + chunk_frames, :]
										A__       :     Optional[int]								=			mel[idx_back : idx_back + chunk_frames, :]

										A__       :     Dict								=			torch.tensor(mel[None, None, :]							)
										A__       :     Dict								=			torch.nn.functional.interpolate(
										    snake_case_   ,      size=[chunk_frames, 64]   ,      mode="""bilinear"""   ,      align_corners=snake_case_							)
										A__       :     List[Any]								=			mel_shrink[0][0].numpy()
										A__       :     Dict								=			np.stack([mel_shrink, mel_chunk_front, mel_chunk_middle, mel_chunk_back]   ,      axis=0							)
										return mel_fusion





			def 		lowerCamelCase					(			self   ,      snake_case_   ,      snake_case_   ,      snake_case_   ,      snake_case_							):







										'''simple docstring'''


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

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

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

										return input_mel, longer





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







										'''simple docstring'''


										A__       :     Optional[Any]								=			truncation if truncation is not None else self.truncation
										A__       :     Union[str, Any]								=			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."""							)

										A__       :     List[Any]								=			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}'''							)
										A__       :     int								=			is_batched_numpy or (
										    isinstance(snake_case_   ,      (list, tuple)							) and (isinstance(raw_speech[0]   ,      (np.ndarray, tuple, list)							))
										)

										if is_batched:
																	A__       :     Any								=			[np.asarray(snake_case_   ,      dtype=np.floataa							) for speech in raw_speech]
										elif not is_batched and not isinstance(snake_case_   ,      np.ndarray							):
																	A__       :     Any								=			np.asarray(snake_case_   ,      dtype=np.floataa							)
										elif isinstance(snake_case_   ,      np.ndarray							) and raw_speech.dtype is np.dtype(np.floataa							):
																	A__       :     Optional[int]								=			raw_speech.astype(np.floataa							)

										# always return batch
										if not is_batched:
																	A__       :     Union[str, Any]								=			[np.asarray(snake_case_							)]

										# convert to mel spectrogram, truncate and pad if needed.
										A__       :     Optional[Any]								=			[
										    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
										]

										A__       :     List[str]								=			[]
										A__       :     Optional[Any]								=			[]
										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
																	A__       :     Optional[Any]								=			np.random.randint(0   ,      len(snake_case_							)							)
																	A__       :     str								=			True

										if isinstance(input_mel[0]   ,      snake_case_							):
																	A__       :     Optional[Any]								=			[np.asarray(snake_case_   ,      dtype=np.floataa							) for feature in input_mel]

										# is_longer is a list of bool
										A__       :     Union[str, Any]								=			[[longer] for longer in is_longer]

										A__       :     str								=			{"""input_features""": input_mel, """is_longer""": is_longer}
										A__       :     Optional[int]								=			BatchFeature(snake_case_							)

										if return_tensors is not None:
																	A__       :     Dict								=			input_features.convert_to_tensors(snake_case_							)

										return input_features





