



'''simple docstring'''



from typing import List, Optional, Union

import numpy as np
import torch
import torchaudio.compliance.kaldi as ta_kaldi

from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import TensorType, logging


_lowercase       :						str						=logging.get_logger(__name__)






class 			_SCREAMING_SNAKE_CASE				(lowercase__      ):
			A__								=  ['input_values', 'attention_mask']



			def __init__(     self		:						str    ,			__UpperCamelCase		:						int=1    ,			__UpperCamelCase		:						Tuple=16000    ,			__UpperCamelCase		:						str=128    ,			__UpperCamelCase		:						Tuple=1024    ,			__UpperCamelCase		:						Union[str, Any]=0.0    ,			__UpperCamelCase		:						Optional[int]=True    ,			__UpperCamelCase		:						Optional[int]=-4.2677393    ,			__UpperCamelCase		:						int=4.5689974    ,			__UpperCamelCase		:						str=False    ,			**__UpperCamelCase		:						Any    ,			)      ->      List[str]:
							"""simple docstring"""





							super().__init__(feature_size=__UpperCamelCase    ,			sampling_rate=__UpperCamelCase    ,			padding_value=__UpperCamelCase    ,			**__UpperCamelCase			)
							snake_case__      :   Optional[int]      =    num_mel_bins
							snake_case__      :   Dict      =    max_length
							snake_case__      :   List[str]      =    do_normalize
							snake_case__      :   List[Any]      =    mean
							snake_case__      :   Tuple      =    std
							snake_case__      :   Optional[int]      =    return_attention_mask



			def 				lowerCAmelCase (     self		:						int    ,			__UpperCamelCase		:						np.ndarray    ,			__UpperCamelCase		:						int    ,			)      ->      np.ndarray:
							"""simple docstring"""





							snake_case__      :   Optional[Any]      =    torch.from_numpy(__UpperCamelCase			).unsqueeze(0			)
							snake_case__      :   Dict      =    ta_kaldi.fbank(
							    __UpperCamelCase    ,			htk_compat=__UpperCamelCase    ,			sample_frequency=self.sampling_rate    ,			use_energy=__UpperCamelCase    ,			window_type='''hanning'''    ,			num_mel_bins=self.num_mel_bins    ,			dither=0.0    ,			frame_shift=10    ,			)

							snake_case__      :   List[str]      =    fbank.shape[0]
							snake_case__      :   int      =    max_length - n_frames

							# pad or truncate, depending on difference
							if difference > 0:
											snake_case__      :   List[Any]      =    torch.nn.ZeroPadad((0, 0, 0, difference)			)
											snake_case__      :   Any      =    pad_module(__UpperCamelCase			)
							elif difference < 0:
											snake_case__      :   Dict      =    fbank[0:max_length, :]

							snake_case__      :   List[str]      =    fbank.numpy()

							return fbank



			def 				lowerCAmelCase (     self		:						int    ,			__UpperCamelCase		:						np.ndarray			)      ->      np.ndarray:
							"""simple docstring"""





							return (input_values - (self.mean)) / (self.std * 2)







			def __call__(     self		:						str    ,			__UpperCamelCase		:						Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]    ,			__UpperCamelCase		:						Optional[int] = None    ,			__UpperCamelCase		:						Optional[Union[str, TensorType]] = None    ,			**__UpperCamelCase		:						Tuple    ,			)      ->      BatchFeature:
							"""simple docstring"""





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

							snake_case__      :   Optional[Any]      =    isinstance(__UpperCamelCase    ,			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}'''			)
							snake_case__      :   List[Any]      =    is_batched_numpy or (
							    isinstance(__UpperCamelCase    ,			(list, tuple)			) and (isinstance(raw_speech[0]    ,			(np.ndarray, tuple, list)			))
							)

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

							# always return batch
							if not is_batched:
											snake_case__      :   int      =    [raw_speech]

							# extract fbank features and pad/truncate to max_length
							snake_case__      :   str      =    [self._extract_fbank_features(__UpperCamelCase    ,			max_length=self.max_length			) for waveform in raw_speech]

							# convert into BatchFeature
							snake_case__      :   int      =    BatchFeature({'''input_values''': features}			)

							# make sure list is in array format
							snake_case__      :   Dict      =    padded_inputs.get('''input_values'''			)
							if isinstance(input_values[0]    ,			__UpperCamelCase			):
											snake_case__      :   Optional[int]      =    [np.asarray(__UpperCamelCase    ,			dtype=np.floataa			) for feature in input_values]

							# normalization
							if self.do_normalize:
											snake_case__      :   int      =    [self.normalize(__UpperCamelCase			) for feature in input_values]

							if return_tensors is not None:
											snake_case__      :   Dict      =    padded_inputs.convert_to_tensors(__UpperCamelCase			)

							return padded_inputs









'''simple docstring'''



import argparse
import os
import pathlib

import fairseq
import torch
from fairseq.modules import TransformerSentenceEncoderLayer
from packaging import version

from transformers import (
    DataaVecTextConfig,
    DataaVecTextForMaskedLM,
    DataaVecTextForSequenceClassification,
    DataaVecTextModel,
)
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertSelfAttention,
    BertSelfOutput,
)

# IMPORTANT: In order for this script to run, please make sure to download the dictionary: `dict.txt` from wget https://dl.fbaipublicfiles.com/fairseq/models/roberta.large.tar.gz
# File copied from https://github.com/pytorch/fairseq/blob/main/examples/data2vec/models/data2vec_text.py
from transformers.utils import logging


if version.parse(fairseq.__version__) < version.parse("0.9.0"):
				raise Exception("requires fairseq >= 0.9.0")


logging.set_verbosity_info()
_lowercase       :						Tuple						=logging.get_logger(__name__)

_lowercase       :						Optional[int]						="Hello world! cécé herlolip"


def 							__UpperCAmelCase				(				UpperCamelCase__				:str   ,				UpperCamelCase__				:str   ,				UpperCamelCase__				:bool			)		->						int:
				snake_case__  ,      snake_case__      :   int      =    os.path.split(UpperCamelCase__			)
				snake_case__      :   List[Any]      =    DataaVecTextModel.from_pretrained(
				    UpperCamelCase__   ,				checkpoint_file=UpperCamelCase__			)
				dataavec.eval()  # disable dropout
				snake_case__      :   Dict      =    dataavec.models[0]
				snake_case__      :   int      =    dataavec_model.encoder.sentence_encoder
				snake_case__      :   List[Any]      =    DataaVecTextConfig(
				    vocab_size=dataavec_sent_encoder.embed_tokens.num_embeddings   ,				hidden_size=dataavec_model.args.encoder_embed_dim   ,				num_hidden_layers=dataavec_model.args.encoder_layers   ,				num_attention_heads=dataavec_model.args.encoder_attention_heads   ,				intermediate_size=dataavec_model.args.encoder_ffn_embed_dim   ,				max_position_embeddings=514   ,				type_vocab_size=1   ,				layer_norm_eps=1e-5   ,				)
				if classification_head:
								snake_case__      :   int      =    dataavec.model.classification_heads['''mnli'''].out_proj.weight.shape[0]
				print('''Our BERT config:'''   ,				UpperCamelCase__			)

				snake_case__      :   Optional[int]      =    DataaVecTextForSequenceClassification(UpperCamelCase__			) if classification_head else DataaVecTextForMaskedLM(UpperCamelCase__			)
				model.eval()

				# Now let's copy all the weights.
				# Embeddings
				snake_case__      :   Optional[Any]      =    dataavec_sent_encoder.embed_tokens.weight
				snake_case__      :   Union[str, Any]      =    dataavec_sent_encoder.embed_positions.weight
				snake_case__      :   Any      =    torch.zeros_like(
				    model.dataavec_text.embeddings.token_type_embeddings.weight			)  # just zero them out b/c data2vec doesn't use them.
				snake_case__      :   Tuple      =    dataavec_sent_encoder.layernorm_embedding.weight
				snake_case__      :   Dict      =    dataavec_sent_encoder.layernorm_embedding.bias

				for i in range(config.num_hidden_layers			):
								# Encoder: start of layer
								snake_case__      :   BertLayer      =    model.dataavec_text.encoder.layer[i]
								snake_case__      :   TransformerSentenceEncoderLayer      =    dataavec_sent_encoder.layers[i]

								# self attention
								snake_case__      :   BertSelfAttention      =    layer.attention.self
								assert dataavec_layer.self_attn.k_proj.weight.data.shape == torch.Size(
								    (config.hidden_size, config.hidden_size)			), (
								    "Shape for data2vec_layer.self_attn.k_proj.weight.data should be"
								    F''' {torch.Size((config.hidden_size, config.hidden_size)			)}'''
								)
								assert dataavec_layer.self_attn.q_proj.weight.data.shape == torch.Size(
								    (config.hidden_size, config.hidden_size)			), (
								    "Shape for data2vec_layer.self_attn.q_proj.weight.data should be"
								    F''' {torch.Size((config.hidden_size, config.hidden_size)			)}'''
								)
								assert dataavec_layer.self_attn.v_proj.weight.data.shape == torch.Size(
								    (config.hidden_size, config.hidden_size)			), (
								    "Shape for data2vec_layer.self_attn.v_proj.weight.data should be"
								    F''' {torch.Size((config.hidden_size, config.hidden_size)			)}'''
								)

								snake_case__      :   List[Any]      =    dataavec_layer.self_attn.q_proj.weight
								snake_case__      :   List[str]      =    dataavec_layer.self_attn.q_proj.bias
								snake_case__      :   str      =    dataavec_layer.self_attn.k_proj.weight
								snake_case__      :   List[Any]      =    dataavec_layer.self_attn.k_proj.bias
								snake_case__      :   Optional[Any]      =    dataavec_layer.self_attn.v_proj.weight
								snake_case__      :   int      =    dataavec_layer.self_attn.v_proj.bias

								# self-attention output
								snake_case__      :   BertSelfOutput      =    layer.attention.output
								assert (
								    self_output.dense.weight.shape == dataavec_layer.self_attn.out_proj.weight.shape
								), F'''Shape for self_output.dense.weight should be {dataavec_layer.self_attn.out_proj.weight.shape}'''
								snake_case__      :   Optional[int]      =    dataavec_layer.self_attn.out_proj.weight
								snake_case__      :   str      =    dataavec_layer.self_attn.out_proj.bias
								snake_case__      :   int      =    dataavec_layer.self_attn_layer_norm.weight
								snake_case__      :   List[Any]      =    dataavec_layer.self_attn_layer_norm.bias

								# intermediate
								snake_case__      :   BertIntermediate      =    layer.intermediate
								assert (
								    intermediate.dense.weight.shape == dataavec_layer.fca.weight.shape
								), F'''Shape for intermediate.dense.weight should be {dataavec_layer.fca.weight.shape}'''
								snake_case__      :   List[str]      =    dataavec_layer.fca.weight
								snake_case__      :   str      =    dataavec_layer.fca.bias

								# output
								snake_case__      :   BertOutput      =    layer.output
								assert (
								    bert_output.dense.weight.shape == dataavec_layer.fca.weight.shape
								), F'''Shape for bert_output.dense.weight should be {dataavec_layer.fca.weight.shape}'''
								snake_case__      :   List[str]      =    dataavec_layer.fca.weight
								snake_case__      :   List[Any]      =    dataavec_layer.fca.bias
								snake_case__      :   str      =    dataavec_layer.final_layer_norm.weight
								snake_case__      :   List[Any]      =    dataavec_layer.final_layer_norm.bias
								# end of layer

				if classification_head:
								snake_case__      :   Any      =    dataavec.model.classification_heads['''mnli'''].dense.weight
								snake_case__      :   List[Any]      =    dataavec.model.classification_heads['''mnli'''].dense.bias
								snake_case__      :   List[Any]      =    dataavec.model.classification_heads['''mnli'''].out_proj.weight
								snake_case__      :   List[Any]      =    dataavec.model.classification_heads['''mnli'''].out_proj.bias
				else:
								# LM Head
								snake_case__      :   str      =    dataavec_model.encoder.lm_head.dense.weight
								snake_case__      :   str      =    dataavec_model.encoder.lm_head.dense.bias
								snake_case__      :   Union[str, Any]      =    dataavec_model.encoder.lm_head.layer_norm.weight
								snake_case__      :   Tuple      =    dataavec_model.encoder.lm_head.layer_norm.bias
								snake_case__      :   Tuple      =    dataavec_model.encoder.lm_head.weight
								snake_case__      :   List[str]      =    dataavec_model.encoder.lm_head.bias

				# Let's check that we get the same results.
				snake_case__      :   torch.Tensor      =    dataavec.encode(UpperCamelCase__			).unsqueeze(0			)  # batch of size 1

				snake_case__      :   str      =    model(UpperCamelCase__			)[0]
				if classification_head:
								snake_case__      :   List[str]      =    dataavec.model.classification_heads['''mnli'''](dataavec.extract_features(UpperCamelCase__			)			)
				else:
								snake_case__      :   Tuple      =    dataavec_model(UpperCamelCase__			)[0]
				print(our_output.shape   ,				their_output.shape			)
				snake_case__      :   Dict      =    torch.max(torch.abs(our_output - their_output			)			).item()
				print(F'''max_absolute_diff = {max_absolute_diff}'''			)  # ~ 1e-7
				snake_case__      :   Tuple      =    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'''			)

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


if __name__ == "__main__":
				_lowercase       :						Any						=argparse.ArgumentParser()
				# Required parameters
				parser.add_argument(
				    "--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."
				)
				_lowercase       :						Tuple						=parser.parse_args()
				convert_dataavec_checkpoint_to_pytorch(
				    args.checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
				)




