





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							:int							=	logging.get_logger(__name__)

__lowercase							:Union[str, Any]							=	"Hello world! cécé herlolip"







def    UpperCAmelCase				(  _lowerCamelCase   :	str      , _lowerCamelCase   :	str      , _lowerCamelCase   :	bool  ):





						'''simple docstring'''


						SCREAMING_SNAKE_CASE__,				SCREAMING_SNAKE_CASE__  :					Dict =       os.path.split(_lowerCamelCase  )
						SCREAMING_SNAKE_CASE__  :					Optional[int] =       DataaVecTextModel.from_pretrained(
						    _lowerCamelCase      , checkpoint_file=_lowerCamelCase  )
						dataavec.eval()  # disable dropout
						SCREAMING_SNAKE_CASE__  :					Any =       dataavec.models[0]
						SCREAMING_SNAKE_CASE__  :					str =       dataavec_model.encoder.sentence_encoder
						SCREAMING_SNAKE_CASE__  :					Optional[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:
												SCREAMING_SNAKE_CASE__  :					str =       dataavec.model.classification_heads["mnli"].out_proj.weight.shape[0]
						print("Our BERT config:"      , _lowerCamelCase  )

						SCREAMING_SNAKE_CASE__  :					Any =       DataaVecTextForSequenceClassification(_lowerCamelCase  ) if classification_head else DataaVecTextForMaskedLM(_lowerCamelCase  )
						model.eval()

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

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

												# self attention
												SCREAMING_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)  )}"""
												)

												SCREAMING_SNAKE_CASE__  :					List[Any] =       dataavec_layer.self_attn.q_proj.weight
												SCREAMING_SNAKE_CASE__  :					List[Any] =       dataavec_layer.self_attn.q_proj.bias
												SCREAMING_SNAKE_CASE__  :					Tuple =       dataavec_layer.self_attn.k_proj.weight
												SCREAMING_SNAKE_CASE__  :					Tuple =       dataavec_layer.self_attn.k_proj.bias
												SCREAMING_SNAKE_CASE__  :					Tuple =       dataavec_layer.self_attn.v_proj.weight
												SCREAMING_SNAKE_CASE__  :					Tuple =       dataavec_layer.self_attn.v_proj.bias

												# self-attention output
												SCREAMING_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}"""
												SCREAMING_SNAKE_CASE__  :					Tuple =       dataavec_layer.self_attn.out_proj.weight
												SCREAMING_SNAKE_CASE__  :					List[Any] =       dataavec_layer.self_attn.out_proj.bias
												SCREAMING_SNAKE_CASE__  :					int =       dataavec_layer.self_attn_layer_norm.weight
												SCREAMING_SNAKE_CASE__  :					Tuple =       dataavec_layer.self_attn_layer_norm.bias

												# intermediate
												SCREAMING_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}"""
												SCREAMING_SNAKE_CASE__  :					str =       dataavec_layer.fca.weight
												SCREAMING_SNAKE_CASE__  :					Any =       dataavec_layer.fca.bias

												# output
												SCREAMING_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}"""
												SCREAMING_SNAKE_CASE__  :					Dict =       dataavec_layer.fca.weight
												SCREAMING_SNAKE_CASE__  :					Dict =       dataavec_layer.fca.bias
												SCREAMING_SNAKE_CASE__  :					str =       dataavec_layer.final_layer_norm.weight
												SCREAMING_SNAKE_CASE__  :					str =       dataavec_layer.final_layer_norm.bias
												# end of layer

						if classification_head:
												SCREAMING_SNAKE_CASE__  :					Dict =       dataavec.model.classification_heads["mnli"].dense.weight
												SCREAMING_SNAKE_CASE__  :					Optional[int] =       dataavec.model.classification_heads["mnli"].dense.bias
												SCREAMING_SNAKE_CASE__  :					List[Any] =       dataavec.model.classification_heads["mnli"].out_proj.weight
												SCREAMING_SNAKE_CASE__  :					List[str] =       dataavec.model.classification_heads["mnli"].out_proj.bias
						else:
												# LM Head
												SCREAMING_SNAKE_CASE__  :					str =       dataavec_model.encoder.lm_head.dense.weight
												SCREAMING_SNAKE_CASE__  :					Tuple =       dataavec_model.encoder.lm_head.dense.bias
												SCREAMING_SNAKE_CASE__  :					str =       dataavec_model.encoder.lm_head.layer_norm.weight
												SCREAMING_SNAKE_CASE__  :					Tuple =       dataavec_model.encoder.lm_head.layer_norm.bias
												SCREAMING_SNAKE_CASE__  :					Optional[int] =       dataavec_model.encoder.lm_head.weight
												SCREAMING_SNAKE_CASE__  :					Optional[int] =       dataavec_model.encoder.lm_head.bias

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

						SCREAMING_SNAKE_CASE__  :					Tuple =       model(_lowerCamelCase  )[0]
						if classification_head:
												SCREAMING_SNAKE_CASE__  :					Tuple =       dataavec.model.classification_heads["mnli"](dataavec.extract_features(_lowerCamelCase  )  )
						else:
												SCREAMING_SNAKE_CASE__  :					int =       dataavec_model(_lowerCamelCase  )[0]
						print(our_output.shape      , their_output.shape  )
						SCREAMING_SNAKE_CASE__  :					int =       torch.max(torch.abs(our_output - their_output  )  ).item()
						print(f"""max_absolute_diff = {max_absolute_diff}"""  )  # ~ 1e-7
						SCREAMING_SNAKE_CASE__  :					Optional[int] =       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"  )

						pathlib.Path(_lowerCamelCase  ).mkdir(parents=_lowerCamelCase      , exist_ok=_lowerCamelCase  )
						print(f"""Saving model to {pytorch_dump_folder_path}"""  )
						model.save_pretrained(_lowerCamelCase  )


if __name__ == "__main__":
				__lowercase							:Optional[int]							=	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							:Optional[int]							=	parser.parse_args()
				convert_dataavec_checkpoint_to_pytorch(
				    args.checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
				)






from typing import List, Optional, Union

import numpy as np

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


__lowercase							:Optional[int]							=	logging.get_logger(__name__)



class  _a  (					lowercase__				):






							"""simple docstring"""

							snake_case_							=    ["input_values", "attention_mask"]

							def __init__(   self					:  List[str]				,						a					:  Dict=1				,						a					:  Tuple=1_60_00				,						a					:  Union[str, Any]=0.0				,						a					:  Dict=False				,						a					:  List[Any]=True				,						**a					:  Any				,						)						->Optional[int]:
													super().__init__(feature_size=a				,						sampling_rate=a				,						padding_value=a				,						**a     )
													SCREAMING_SNAKE_CASE__  :					int =       return_attention_mask
													SCREAMING_SNAKE_CASE__  :					str =       do_normalize

							@staticmethod
							def   A_	(   a					:  List[np.ndarray]				,						a					:  List[np.ndarray]				,						a					:  float = 0.0     )						->List[np.ndarray]:
													if attention_mask is not None:
																			SCREAMING_SNAKE_CASE__  :					int =       np.array(a				,						np.intaa     )
																			SCREAMING_SNAKE_CASE__  :					List[str] =       []

																			for vector, length in zip(a				,						attention_mask.sum(-1     )     ):
																									SCREAMING_SNAKE_CASE__  :					List[str] =       (vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1E-7     )
																									if length < normed_slice.shape[0]:
																															SCREAMING_SNAKE_CASE__  :					Tuple =       padding_value

																									normed_input_values.append(a     )
													else:
																			SCREAMING_SNAKE_CASE__  :					List[Any] =       [(x - x.mean()) / np.sqrt(x.var() + 1E-7     ) for x in input_values]

													return normed_input_values






							def __call__(   self					:  Union[str, Any]				,						a					:  Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]				,						a					:  Union[bool, str, PaddingStrategy] = False				,						a					:  Optional[int] = None				,						a					:  bool = False				,						a					:  Optional[int] = None				,						a					:  Optional[bool] = None				,						a					:  Optional[Union[str, TensorType]] = None				,						a					:  Optional[int] = None				,						**a					:  Optional[Any]				,						)						->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."     )

													SCREAMING_SNAKE_CASE__  :					List[Any] =       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}"""     )
													SCREAMING_SNAKE_CASE__  :					List[Any] =       is_batched_numpy or (
													    isinstance(a				,						(list, tuple)     ) and (isinstance(raw_speech[0]				,						(np.ndarray, tuple, list)     ))
													)

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

													# convert into correct format for padding
													SCREAMING_SNAKE_CASE__  :					Any =       BatchFeature({"input_values": raw_speech}     )

													SCREAMING_SNAKE_CASE__  :					Tuple =       self.pad(
													    a				,						padding=a				,						max_length=a				,						truncation=a				,						pad_to_multiple_of=a				,						return_attention_mask=a				,						)

													# convert input values to correct format
													SCREAMING_SNAKE_CASE__  :					Any =       padded_inputs["input_values"]
													if not isinstance(input_values[0]				,						np.ndarray     ):
																			SCREAMING_SNAKE_CASE__  :					Optional[Any] =       [np.asarray(a				,						dtype=np.floataa     ) for array in input_values]
													elif (
													    not isinstance(a				,						np.ndarray     )
													    and isinstance(input_values[0]				,						np.ndarray     )
													    and input_values[0].dtype is np.dtype(np.floataa     )
													):
																			SCREAMING_SNAKE_CASE__  :					List[Any] =       [array.astype(np.floataa     ) for array in input_values]
													elif isinstance(a				,						np.ndarray     ) and input_values.dtype is np.dtype(np.floataa     ):
																			SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       input_values.astype(np.floataa     )

													# convert attention_mask to correct format
													SCREAMING_SNAKE_CASE__  :					Dict =       padded_inputs.get("attention_mask"     )
													if attention_mask is not None:
																			SCREAMING_SNAKE_CASE__  :					Optional[int] =       [np.asarray(a				,						dtype=np.intaa     ) for array in attention_mask]

													# zero-mean and unit-variance normalization
													if self.do_normalize:
																			SCREAMING_SNAKE_CASE__  :					int =       (
																			    attention_mask
																			    if self._get_padding_strategies(a				,						max_length=a     ) is not PaddingStrategy.DO_NOT_PAD
																			    else None
																			)
																			SCREAMING_SNAKE_CASE__  :					Optional[int] =       self.zero_mean_unit_var_norm(
																			    padded_inputs["input_values"]				,						attention_mask=a				,						padding_value=self.padding_value     )

													if return_tensors is not None:
																			SCREAMING_SNAKE_CASE__  :					Optional[int] =       padded_inputs.convert_to_tensors(a     )

													return padded_inputs