
"""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


__A					=			logging.get_logger(__name__)







class 						lowerCamelCase__   (   __magic_name__							):





			'''simple docstring'''
			lowerCamelCase       =						['''input_values''', '''attention_mask''']



			def __init__(				self	,	__UpperCAmelCase=1	,	__UpperCAmelCase=1_60_00	,	__UpperCAmelCase=1_28	,	__UpperCAmelCase=10_24	,	__UpperCAmelCase=0.0	,	__UpperCAmelCase=True	,	__UpperCAmelCase=-4.2_6_7_7_3_9_3	,	__UpperCAmelCase=4.5_6_8_9_9_7_4	,	__UpperCAmelCase=False	,	**__UpperCAmelCase	,	)					->	str:
				super().__init__(feature_size=__UpperCAmelCase	,	sampling_rate=__UpperCAmelCase	,	padding_value=__UpperCAmelCase	,	**__UpperCAmelCase    )
				_lowerCAmelCase								=num_mel_bins
				_lowerCAmelCase								=max_length
				_lowerCAmelCase								=do_normalize
				_lowerCAmelCase								=mean
				_lowerCAmelCase								=std
				_lowerCAmelCase								=return_attention_mask



			def    _lowerCAmelCase						(				self	,	__UpperCAmelCase	,	__UpperCAmelCase	,	)					->	np.ndarray:
				_lowerCAmelCase								=torch.from_numpy(__UpperCAmelCase    ).unsqueeze(0    )
				_lowerCAmelCase								=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	,	)

				_lowerCAmelCase								=fbank.shape[0]
				_lowerCAmelCase								=max_length - n_frames

				# pad or truncate, depending on difference
				if difference > 0:
					_lowerCAmelCase								=torch.nn.ZeroPadad((0, 0, 0, difference)    )
					_lowerCAmelCase								=pad_module(__UpperCAmelCase    )
				elif difference < 0:
					_lowerCAmelCase								=fbank[0:max_length, :]

				_lowerCAmelCase								=fbank.numpy()

				return fbank



			def    _lowerCAmelCase						(				self	,	__UpperCAmelCase    )					->	np.ndarray:
				return (input_values - (self.mean)) / (self.std * 2)



			def __call__(				self	,	__UpperCAmelCase	,	__UpperCAmelCase = None	,	__UpperCAmelCase = None	,	**__UpperCAmelCase	,	)					->	BatchFeature:

				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."""    )

				_lowerCAmelCase								=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}'''    )
				_lowerCAmelCase								=is_batched_numpy or (
				    isinstance(__UpperCAmelCase	,	(list, tuple)    ) and (isinstance(raw_speech[0]	,	(np.ndarray, tuple, list)    ))
				)

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

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

				# extract fbank features and pad/truncate to max_length
				_lowerCAmelCase								=[self._extract_fbank_features(__UpperCAmelCase	,	max_length=self.max_length    ) for waveform in raw_speech]

				# convert into BatchFeature
				_lowerCAmelCase								=BatchFeature({"""input_values""": features}    )

				# make sure list is in array format
				_lowerCAmelCase								=padded_inputs.get("""input_values"""    )
				if isinstance(input_values[0]	,	__UpperCAmelCase    ):
					_lowerCAmelCase								=[np.asarray(__UpperCAmelCase	,	dtype=np.floataa    ) for feature in input_values]

				# normalization
				if self.do_normalize:
					_lowerCAmelCase								=[self.normalize(__UpperCAmelCase    ) for feature in input_values]

				if return_tensors is not None:
					_lowerCAmelCase								=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()
__A					=			logging.get_logger(__name__)

__A					=			'Hello world! cécé herlolip'



def        _lowerCamelCase(__UpperCamelCase       ,					__UpperCamelCase       ,					__UpperCamelCase			)							->					List[Any]:
	_lowerCAmelCase		,				_lowerCAmelCase								=os.path.split(__UpperCamelCase			)
	_lowerCAmelCase								=DataaVecTextModel.from_pretrained(
	    __UpperCamelCase       ,					checkpoint_file=__UpperCamelCase			)
	dataavec.eval()  # disable dropout
	_lowerCAmelCase								=dataavec.models[0]
	_lowerCAmelCase								=dataavec_model.encoder.sentence_encoder
	_lowerCAmelCase								=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:
		_lowerCAmelCase								=dataavec.model.classification_heads["""mnli"""].out_proj.weight.shape[0]
	print("""Our BERT config:"""       ,					__UpperCamelCase			)

	_lowerCAmelCase								=DataaVecTextForSequenceClassification(__UpperCamelCase			) if classification_head else DataaVecTextForMaskedLM(__UpperCamelCase			)
	model.eval()

	# Now let's copy all the weights.
	# Embeddings
	_lowerCAmelCase								=dataavec_sent_encoder.embed_tokens.weight
	_lowerCAmelCase								=dataavec_sent_encoder.embed_positions.weight
	_lowerCAmelCase								=torch.zeros_like(
	    model.dataavec_text.embeddings.token_type_embeddings.weight			)  # just zero them out b/c data2vec doesn't use them.
	_lowerCAmelCase								=dataavec_sent_encoder.layernorm_embedding.weight
	_lowerCAmelCase								=dataavec_sent_encoder.layernorm_embedding.bias

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

		# self attention
		_lowerCAmelCase								=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)			)}'''
		)

		_lowerCAmelCase								=dataavec_layer.self_attn.q_proj.weight
		_lowerCAmelCase								=dataavec_layer.self_attn.q_proj.bias
		_lowerCAmelCase								=dataavec_layer.self_attn.k_proj.weight
		_lowerCAmelCase								=dataavec_layer.self_attn.k_proj.bias
		_lowerCAmelCase								=dataavec_layer.self_attn.v_proj.weight
		_lowerCAmelCase								=dataavec_layer.self_attn.v_proj.bias

		# self-attention output
		_lowerCAmelCase								=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}'''
		_lowerCAmelCase								=dataavec_layer.self_attn.out_proj.weight
		_lowerCAmelCase								=dataavec_layer.self_attn.out_proj.bias
		_lowerCAmelCase								=dataavec_layer.self_attn_layer_norm.weight
		_lowerCAmelCase								=dataavec_layer.self_attn_layer_norm.bias

		# intermediate
		_lowerCAmelCase								=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}'''
		_lowerCAmelCase								=dataavec_layer.fca.weight
		_lowerCAmelCase								=dataavec_layer.fca.bias

		# output
		_lowerCAmelCase								=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}'''
		_lowerCAmelCase								=dataavec_layer.fca.weight
		_lowerCAmelCase								=dataavec_layer.fca.bias
		_lowerCAmelCase								=dataavec_layer.final_layer_norm.weight
		_lowerCAmelCase								=dataavec_layer.final_layer_norm.bias
		# end of layer

	if classification_head:
		_lowerCAmelCase								=dataavec.model.classification_heads["""mnli"""].dense.weight
		_lowerCAmelCase								=dataavec.model.classification_heads["""mnli"""].dense.bias
		_lowerCAmelCase								=dataavec.model.classification_heads["""mnli"""].out_proj.weight
		_lowerCAmelCase								=dataavec.model.classification_heads["""mnli"""].out_proj.bias
	else:
		# LM Head
		_lowerCAmelCase								=dataavec_model.encoder.lm_head.dense.weight
		_lowerCAmelCase								=dataavec_model.encoder.lm_head.dense.bias
		_lowerCAmelCase								=dataavec_model.encoder.lm_head.layer_norm.weight
		_lowerCAmelCase								=dataavec_model.encoder.lm_head.layer_norm.bias
		_lowerCAmelCase								=dataavec_model.encoder.lm_head.weight
		_lowerCAmelCase								=dataavec_model.encoder.lm_head.bias

	# Let's check that we get the same results.
	_lowerCAmelCase								=dataavec.encode(__UpperCamelCase			).unsqueeze(0			)  # batch of size 1

	_lowerCAmelCase								=model(__UpperCamelCase			)[0]
	if classification_head:
		_lowerCAmelCase								=dataavec.model.classification_heads["""mnli"""](dataavec.extract_features(__UpperCamelCase			)			)
	else:
		_lowerCAmelCase								=dataavec_model(__UpperCamelCase			)[0]
	print(our_output.shape       ,					their_output.shape			)
	_lowerCAmelCase								=torch.max(torch.abs(our_output - their_output			)			).item()
	print(F'''max_absolute_diff = {max_absolute_diff}'''			)  # ~ 1e-7
	_lowerCAmelCase								=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__":
			__A					=			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.'
			)
			__A					=			parser.parse_args()
			convert_dataavec_checkpoint_to_pytorch(
			    args.checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
			)



