



import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

# Register SEW's fairseq modules
from sew_asapp import tasks  # noqa: F401

from transformers import (
    SEWDConfig,
    SEWDForCTC,
    SEWDModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
UpperCAmelCase							=       logging.get_logger(__name__)

UpperCAmelCase							=       {
    """post_extract_proj""": """feature_projection""",
    """encoder.pos_conv.0""": """encoder.pos_conv_embed.conv""",
    """attention.self.query_proj""": """encoder.encoder.layer.*.attention.self.query_proj""",
    """attention.self.key_proj""": """encoder.encoder.layer.*.attention.self.key_proj""",
    """attention.self.value_proj""": """encoder.encoder.layer.*.attention.self.value_proj""",
    """attention.output.dense""": """encoder.encoder.layer.*.attention.output.dense""",
    """attention.output.LayerNorm""": """encoder.encoder.layer.*.attention.output.LayerNorm""",
    """intermediate.dense""": """encoder.encoder.layer.*.intermediate.dense""",
    """output.dense""": """encoder.encoder.layer.*.output.dense""",
    """output.LayerNorm""": """encoder.encoder.layer.*.output.LayerNorm""",
    """encoder.encoder.rel_embeddings""": """encoder.encoder.rel_embeddings""",
    """encoder.encoder.LayerNorm""": """encoder.encoder.LayerNorm""",
    """encoder.upsample.0""": """encoder.upsample.projection""",
    """encoder.layer_norm""": """encoder.layer_norm""",
    """w2v_model.layer_norm""": """layer_norm""",
    """w2v_encoder.proj""": """lm_head""",
    """mask_emb""": """masked_spec_embed""",
}

def       A_   (						__a  :					int			,		__a  :					int			,		__a  :					Any			,		__a  :					List[str]			,		__a  :					List[Any]		):

				"""simple docstring"""
				for attribute in key.split("""."""		):
								a__					   =		getattr(__a			,		__a		)

				if weight_type is not None:
								a__					   =		getattr(__a			,		__a		).shape
				else:
								a__					   =		hf_pointer.shape

				assert hf_shape == value.shape, (
				    F'''Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be'''
				    F''' {value.shape} for {full_name}'''
				)

				if weight_type == "weight":
								a__					   =		value
				elif weight_type == "weight_g":
								a__					   =		value
				elif weight_type == "weight_v":
								a__					   =		value
				elif weight_type == "bias":
								a__					   =		value
				else:
								a__					   =		value

				logger.info(F'''{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.'''		)

def       A_   (						__a  :					Tuple			,		__a  :					Optional[Any]			,		__a  :					Any		):

				"""simple docstring"""
				a__					   =		[]
				a__					   =		fairseq_model.state_dict()

				a__					   =		hf_model.sew_d.feature_extractor if is_finetuned else hf_model.feature_extractor

				for name, value in fairseq_dict.items():
								a__					   =		False
								if "conv_layers" in name:
												load_conv_layer(
												    __a			,		__a			,		__a			,		__a			,		hf_model.config.feat_extract_norm == """group"""			,		)
												a__					   =		True
								else:
												for key, mapped_key in MAPPING.items():
																a__					   =		"""sew_d.""" + mapped_key if (is_finetuned and mapped_key != """lm_head""") else mapped_key

																if key in name or key.split("""w2v_model."""		)[-1] == name.split("""."""		)[0]:
																				a__					   =		True
																				if "*" in mapped_key:
																								a__					   =		name.split(__a		)[0].split("""."""		)[-2]
																								if not layer_index.isnumeric():
																												continue
																								a__					   =		mapped_key.replace("""*"""			,		__a		)
																				if "weight_g" in name:
																								a__					   =		"""weight_g"""
																				elif "weight_v" in name:
																								a__					   =		"""weight_v"""
																				elif "weight" in name:
																								a__					   =		"""weight"""
																				elif "bias" in name:
																								a__					   =		"""bias"""
																				else:
																								a__					   =		None
																				set_recursively(__a			,		__a			,		__a			,		__a			,		__a		)
																continue
								if not is_used:
												unused_weights.append(__a		)

				logger.warning(F'''Unused weights: {unused_weights}'''		)

def       A_   (						__a  :					List[str]			,		__a  :					int			,		__a  :					List[str]			,		__a  :					Optional[int]			,		__a  :					List[Any]		):

				"""simple docstring"""
				a__					   =		full_name.split("""conv_layers."""		)[-1]
				a__					   =		name.split("""."""		)
				a__					   =		int(items[0]		)
				a__					   =		int(items[1]		)

				if type_id == 0:
								if "bias" in name:
												assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
												    F'''{full_name} has size {value.shape}, but'''
												    F''' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'''
												)
												a__					   =		value
												logger.info(F'''Feat extract conv layer {layer_id} was initialized from {full_name}.'''		)
								elif "weight" in name:
												assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
												    F'''{full_name} has size {value.shape}, but'''
												    F''' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'''
												)
												a__					   =		value
												logger.info(F'''Feat extract conv layer {layer_id} was initialized from {full_name}.'''		)
				elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
								if "bias" in name:
												assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
												    F'''{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was'''
												    " found."
												)
												a__					   =		value
												logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''		)
								elif "weight" in name:
												assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
												    F'''{full_name} has size {value.shape}, but'''
												    F''' {feature_extractor[layer_id].layer_norm.weight.data.shape} was found.'''
												)
												a__					   =		value
												logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''		)
				else:
								unused_weights.append(__a		)

def       A_   (						__a  :					List[str]			,		__a  :					Union[str, Any]		):

				"""simple docstring"""
				a__					   =		SEWDConfig()
				if is_finetuned:
								a__					   =		model.wav_encoder.wav_model.cfg
				else:
								a__					   =		model.cfg

				a__					   =		fs_config.conv_bias
				a__					   =		eval(fs_config.conv_feature_layers		)
				a__					   =		[x[0] for x in conv_layers]
				a__					   =		[x[1] for x in conv_layers]
				a__					   =		[x[2] for x in conv_layers]
				a__					   =		"""gelu"""
				a__					   =		"""layer""" if fs_config.extractor_mode == """layer_norm""" else """group"""
				a__					   =		0.0
				a__					   =		fs_config.activation_fn.name
				a__					   =		fs_config.encoder_embed_dim
				a__					   =		0.0_2
				a__					   =		fs_config.encoder_ffn_embed_dim
				a__					   =		1e-5
				a__					   =		fs_config.encoder_layerdrop
				a__					   =		fs_config.encoder_attention_heads
				a__					   =		fs_config.conv_pos_groups
				a__					   =		fs_config.conv_pos
				a__					   =		len(__a		)
				a__					   =		fs_config.encoder_layers
				a__					   =		fs_config.squeeze_factor
				# DeBERTa-specific parameters:
				a__					   =		fs_config.max_position_embeddings
				a__					   =		fs_config.position_buckets
				a__					   =		fs_config.share_att_key
				a__					   =		fs_config.relative_attention
				a__					   =		fs_config.position_biased_input
				a__					   =		tuple(fs_config.pos_att_type.split("""|"""		)		)
				a__					   =		fs_config.norm_rel_ebd

				# take care of any params that are overridden by the Wav2VecCtc model
				if is_finetuned:
								a__					   =		model.cfg
								a__					   =		fs_config.final_dropout
								a__					   =		fs_config.layerdrop
				a__					   =		fs_config.activation_dropout
				a__					   =		fs_config.mask_prob > 0 or fs_config.mask_channel_prob > 0
				a__					   =		fs_config.attention_dropout
				a__					   =		fs_config.dropout_input
				a__					   =		fs_config.dropout
				a__					   =		fs_config.mask_channel_length
				a__					   =		fs_config.mask_channel_prob
				a__					   =		fs_config.mask_length
				a__					   =		fs_config.mask_prob

				a__					   =		"""Wav2Vec2FeatureExtractor"""
				a__					   =		"""Wav2Vec2CTCTokenizer"""

				return config

@torch.no_grad()
def       A_   (						__a  :					int			,		__a  :					Dict			,		__a  :					str=None			,		__a  :					Any=None			,		__a  :					List[str]=True		):

				"""simple docstring"""

				if is_finetuned:
								a__   ,      a__   ,      a__					   =		fairseq.checkpoint_utils.load_model_ensemble_and_task(
								    [checkpoint_path]			,		arg_overrides={"""data""": """/""".join(dict_path.split("""/"""		)[:-1]		)}		)
				else:
								a__   ,      a__   ,      a__					   =		fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]		)

				if config_path is not None:
								a__					   =		SEWDConfig.from_pretrained(__a		)
				else:
								a__					   =		convert_config(model[0]			,		__a		)
				a__					   =		model[0].eval()

				a__					   =		True if config.feat_extract_norm == """layer""" else False
				a__					   =		WavaVecaFeatureExtractor(
				    feature_size=1			,		sampling_rate=16_000			,		padding_value=0			,		do_normalize=__a			,		return_attention_mask=__a			,		)

				if is_finetuned:
								if dict_path:
												a__					   =		Dictionary.load(__a		)

												# important change bos & pad token id since CTC symbol is <pad> and
												# not <s> as in fairseq
												a__					   =		target_dict.pad_index
												a__					   =		target_dict.bos_index
												a__					   =		target_dict.pad_index
												a__					   =		target_dict.bos_index
												a__					   =		target_dict.eos_index
												a__					   =		len(target_dict.symbols		)
												a__					   =		os.path.join(__a			,		"""vocab.json"""		)
												if not os.path.isdir(__a		):
																logger.error("""--pytorch_dump_folder_path ({}) should be a directory""".format(__a		)		)
																return
												os.makedirs(__a			,		exist_ok=__a		)
												with open(__a			,		"""w"""			,		encoding="""utf-8"""		) as vocab_handle:
																json.dump(target_dict.indices			,		__a		)
												a__					   =		WavaVecaCTCTokenizer(
												    __a			,		unk_token=target_dict.unk_word			,		pad_token=target_dict.pad_word			,		bos_token=target_dict.bos_word			,		eos_token=target_dict.eos_word			,		word_delimiter_token="""|"""			,		do_lower_case=__a			,		)
												a__					   =		WavaVecaProcessor(feature_extractor=__a			,		tokenizer=__a		)
												processor.save_pretrained(__a		)

								a__					   =		SEWDForCTC(__a		)
				else:
								a__					   =		SEWDModel(__a		)
								feature_extractor.save_pretrained(__a		)

				recursively_load_weights(__a			,		__a			,		__a		)

				hf_model.save_pretrained(__a		)


if __name__ == "__main__":
	UpperCAmelCase							=       argparse.ArgumentParser()
	parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
	parser.add_argument("""--checkpoint_path""", default=None, type=str, help="""Path to fairseq checkpoint""")
	parser.add_argument("""--dict_path""", default=None, type=str, help="""Path to dict of fine-tuned model""")
	parser.add_argument("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
	parser.add_argument(
	    """--is_finetuned""", action="""store_true""", help="""Whether the model to convert is a fine-tuned model or not"""
	)
	UpperCAmelCase							=       parser.parse_args()
	convert_sew_checkpoint(
	    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
	)






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()
UpperCAmelCase							=       logging.get_logger(__name__)

UpperCAmelCase							=       """Hello world! cécé herlolip"""

def       A_   (						__a  :					str			,		__a  :					str			,		__a  :					bool		):

				"""simple docstring"""
				a__   ,      a__					   =		os.path.split(__a		)
				a__					   =		DataaVecTextModel.from_pretrained(
				    __a			,		checkpoint_file=__a		)
				dataavec.eval()  # disable dropout
				a__					   =		dataavec.models[0]
				a__					   =		dataavec_model.encoder.sentence_encoder
				a__					   =		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:
								a__					   =		dataavec.model.classification_heads["""mnli"""].out_proj.weight.shape[0]
				print("""Our BERT config:"""			,		__a		)

				a__					   =		DataaVecTextForSequenceClassification(__a		) if classification_head else DataaVecTextForMaskedLM(__a		)
				model.eval()

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

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

								# self attention
								a__					   =		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)		)}'''
								)

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

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

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

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

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

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

				a__					   =		model(__a		)[0]
				if classification_head:
								a__					   =		dataavec.model.classification_heads["""mnli"""](dataavec.extract_features(__a		)		)
				else:
								a__					   =		dataavec_model(__a		)[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(__a			,		__a			,		atol=1e-3		)
				print("""Do both models output the same tensors?"""			,		"""🔥""" if success else """💩"""		)
				if not success:
								raise Exception("""Something went wRoNg"""		)

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


if __name__ == "__main__":
	UpperCAmelCase							=       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."""
	)
	UpperCAmelCase							=       parser.parse_args()
	convert_dataavec_checkpoint_to_pytorch(
	    args.checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
	)

