





"""simple docstring"""





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 (
    SEWConfig,
    SEWForCTC,
    SEWModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


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

lowerCAmelCase__     			=			{
    '''post_extract_proj''': '''feature_projection''',
    '''encoder.pos_conv.0''': '''encoder.pos_conv_embed.conv''',
    '''self_attn.k_proj''': '''encoder.layers.*.attention.k_proj''',
    '''self_attn.v_proj''': '''encoder.layers.*.attention.v_proj''',
    '''self_attn.q_proj''': '''encoder.layers.*.attention.q_proj''',
    '''self_attn.out_proj''': '''encoder.layers.*.attention.out_proj''',
    '''self_attn_layer_norm''': '''encoder.layers.*.layer_norm''',
    '''fc1''': '''encoder.layers.*.feed_forward.intermediate_dense''',
    '''fc2''': '''encoder.layers.*.feed_forward.output_dense''',
    '''final_layer_norm''': '''encoder.layers.*.final_layer_norm''',
    '''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__					(					_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE    ):




		"""simple docstring"""


		for attribute in key.split("."    ):
				UpperCamelCase   =	getattr(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE    )

		if weight_type is not None:
				UpperCamelCase   =	getattr(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE    ).shape
		else:
				UpperCamelCase   =	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":
				UpperCamelCase   =	value
		elif weight_type == "weight_g":
				UpperCamelCase   =	value
		elif weight_type == "weight_v":
				UpperCamelCase   =	value
		elif weight_type == "bias":
				UpperCamelCase   =	value
		else:
				UpperCamelCase   =	value

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


def       a__					(					_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE    ):




		"""simple docstring"""


		UpperCamelCase   =	[]
		UpperCamelCase   =	fairseq_model.state_dict()

		UpperCamelCase   =	hf_model.sew.feature_extractor if is_finetuned else hf_model.feature_extractor

		for name, value in fairseq_dict.items():
				UpperCamelCase   =	False
				if "conv_layers" in name:
						load_conv_layer(
						    _SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	hf_model.config.feat_extract_norm == "group"  ,	)
						UpperCamelCase   =	True
				else:
						for key, mapped_key in MAPPING.items():
								UpperCamelCase   =	"sew." + 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]:
										UpperCamelCase   =	True
										if "*" in mapped_key:
												UpperCamelCase   =	name.split(_SCREAMING_SNAKE_CASE    )[0].split("."    )[-2]
												UpperCamelCase   =	mapped_key.replace("*"  ,	_SCREAMING_SNAKE_CASE    )
										if "weight_g" in name:
												UpperCamelCase   =	"weight_g"
										elif "weight_v" in name:
												UpperCamelCase   =	"weight_v"
										elif "weight" in name:
												UpperCamelCase   =	"weight"
										elif "bias" in name:
												UpperCamelCase   =	"bias"
										else:
												UpperCamelCase   =	None
										set_recursively(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE    )
								continue
				if not is_used:
						unused_weights.append(_SCREAMING_SNAKE_CASE    )

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


def       a__					(					_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE    ):




		"""simple docstring"""


		UpperCamelCase   =	full_name.split("conv_layers."    )[-1]
		UpperCamelCase   =	name.split("."    )
		UpperCamelCase   =	int(items[0]    )
		UpperCamelCase   =	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."
						)
						UpperCamelCase   =	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."
						)
						UpperCamelCase   =	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."
						)
						UpperCamelCase   =	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."
						)
						UpperCamelCase   =	value
						logger.info(F"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."    )
		else:
				unused_weights.append(_SCREAMING_SNAKE_CASE    )


def       a__					(					_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE    ):




		"""simple docstring"""


		UpperCamelCase   =	SEWConfig()
		if is_finetuned:
				UpperCamelCase   =	model.wav_encoder.wav_model.cfg
		else:
				UpperCamelCase   =	model.cfg

		UpperCamelCase   =	fs_config.conv_bias
		UpperCamelCase   =	eval(fs_config.conv_feature_layers    )
		UpperCamelCase   =	[x[0] for x in conv_layers]
		UpperCamelCase   =	[x[1] for x in conv_layers]
		UpperCamelCase   =	[x[2] for x in conv_layers]
		UpperCamelCase   =	"gelu"
		UpperCamelCase   =	"layer" if fs_config.extractor_mode == "layer_norm" else "group"
		UpperCamelCase   =	0.0
		UpperCamelCase   =	fs_config.activation_fn.name
		UpperCamelCase   =	fs_config.encoder_embed_dim
		UpperCamelCase   =	0.02
		UpperCamelCase   =	fs_config.encoder_ffn_embed_dim
		UpperCamelCase   =	1e-5
		UpperCamelCase   =	fs_config.encoder_layerdrop
		UpperCamelCase   =	fs_config.encoder_attention_heads
		UpperCamelCase   =	fs_config.conv_pos_groups
		UpperCamelCase   =	fs_config.conv_pos
		UpperCamelCase   =	len(_SCREAMING_SNAKE_CASE    )
		UpperCamelCase   =	fs_config.encoder_layers
		UpperCamelCase   =	fs_config.squeeze_factor

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

		UpperCamelCase   =	"Wav2Vec2FeatureExtractor"
		UpperCamelCase   =	"Wav2Vec2CTCTokenizer"

		return config


@torch.no_grad()
def       a__					(					_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE=None  ,	_SCREAMING_SNAKE_CASE=None  ,	_SCREAMING_SNAKE_CASE=True    ):




		"""simple docstring"""


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

		if config_path is not None:
				UpperCamelCase   =	SEWConfig.from_pretrained(_SCREAMING_SNAKE_CASE    )
		else:
				UpperCamelCase   =	convert_config(model[0]  ,	_SCREAMING_SNAKE_CASE    )
		UpperCamelCase   =	model[0].eval()

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

		if is_finetuned:
				if dict_path:
						UpperCamelCase   =	Dictionary.load(_SCREAMING_SNAKE_CASE    )

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

				UpperCamelCase   =	SEWForCTC(_SCREAMING_SNAKE_CASE    )
		else:
				UpperCamelCase   =	SEWModel(_SCREAMING_SNAKE_CASE    )
				feature_extractor.save_pretrained(_SCREAMING_SNAKE_CASE    )

		recursively_load_weights(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE    )

		hf_model.save_pretrained(_SCREAMING_SNAKE_CASE    )


if __name__ == "__main__":
						lowerCAmelCase__     			=			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'''
						)
						lowerCAmelCase__     			=			parser.parse_args()
						convert_sew_checkpoint(
						    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
						)













"""simple docstring"""





import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
						from transformers import AutoModelForSeqaSeqLM, AutoTokenizer





@require_torch
@require_sentencepiece
@require_tokenizers
class        _lowerCamelCase      (       unittest.TestCase ):


						@slow
						def     snake_case_   (self		) ->   List[str]:
								UpperCamelCase   =	AutoModelForSeqaSeqLM.from_pretrained("google/mt5-small"   ,							return_dict=__a		).to(__a		)
								UpperCamelCase   =	AutoTokenizer.from_pretrained("google/mt5-small"		)

								UpperCamelCase   =	tokenizer("Hello there"   ,							return_tensors="pt"		).input_ids
								UpperCamelCase   =	tokenizer("Hi I am"   ,							return_tensors="pt"		).input_ids

								UpperCamelCase   =	model(input_ids.to(__a		)   ,							labels=labels.to(__a		)		).loss
								UpperCamelCase   =	-(labels.shape[-1] * loss.item())

								UpperCamelCase   =	-84.9127
								self.assertTrue(abs(mtf_score - EXPECTED_SCORE		) < 1e-4		)






