


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

_lowercase      :      Tuple            =					{
    """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 				lowerCamelCase__				(	A       :      Optional[int]				,       A       :      List[Any]				,       A       :      Any				,       A       :      Dict				,       A       :      Optional[int]					):




						'''simple docstring'''






						for attribute in key.split('''.'''					):
												UpperCAmelCase					    =     getattr(A				,       A					)

						if weight_type is not None:
												UpperCAmelCase					    =     getattr(A				,       A					).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 				lowerCamelCase__				(	A       :      Tuple				,       A       :      int				,       A       :      Any					):




						'''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(
																		    A				,       A				,       A				,       A				,       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(A					)[0].split('''.'''					)[-2]
																																				UpperCAmelCase					    =     mapped_key.replace('''*'''				,       A					)
																														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(A				,       A				,       A				,       A				,       A					)
																								continue
												if not is_used:
																		unused_weights.append(A					)

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


def 				lowerCamelCase__				(	A       :      Dict				,       A       :      int				,       A       :      Optional[Any]				,       A       :      Optional[Any]				,       A       :      List[Any]					):




						'''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(A					)


def 				lowerCamelCase__				(	A       :      List[Any]				,       A       :      Optional[int]					):




						'''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(A					)
						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 				lowerCamelCase__				(	A       :      Optional[int]				,       A       :      List[Any]				,       A       :      int=None				,       A       :      Optional[Any]=None				,       A       :      List[str]=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(A					)
						else:
												UpperCAmelCase					    =     convert_config(model[0]				,       A					)
						UpperCAmelCase					    =     model[0].eval()

						UpperCAmelCase					    =     True if config.feat_extract_norm == '''layer''' else False
						UpperCAmelCase					    =     WavaVecaFeatureExtractor(
						    feature_size=1				,       sampling_rate=1_60_00				,       padding_value=0				,       do_normalize=A				,       return_attention_mask=A				,       )

						if is_finetuned:
												if dict_path:
																		UpperCAmelCase					    =     Dictionary.load(A					)

																		# 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(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					)
																		UpperCAmelCase					    =     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				,       )
																		UpperCAmelCase					    =     WavaVecaProcessor(feature_extractor=A				,       tokenizer=A					)
																		processor.save_pretrained(A					)

												UpperCAmelCase					    =     SEWForCTC(A					)
						else:
												UpperCAmelCase					    =     SEWModel(A					)
												feature_extractor.save_pretrained(A					)

						recursively_load_weights(A				,       A				,       A					)

						hf_model.save_pretrained(A					)


if __name__ == "__main__":
	_lowercase      :      Dict            =					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"""
	)
	_lowercase      :      Tuple            =					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'''



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_lowercase      :      List[str]            =					{
    """configuration_time_series_transformer""": [
        """TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """TimeSeriesTransformerConfig""",
    ],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowercase      :      Dict            =					[
	    """TIME_SERIES_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """TimeSeriesTransformerForPrediction""",
	    """TimeSeriesTransformerModel""",
	    """TimeSeriesTransformerPreTrainedModel""",
	]


if TYPE_CHECKING:
	from .configuration_time_series_transformer import (
	    TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    TimeSeriesTransformerConfig,
	)

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_time_series_transformer import (
		    TIME_SERIES_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TimeSeriesTransformerForPrediction,
		    TimeSeriesTransformerModel,
		    TimeSeriesTransformerPreTrainedModel,
		)

else:
	import sys

	_lowercase      :      Tuple            =					_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)





