





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__ :			List[str]       	=			logging.get_logger(__name__)

UpperCamelCase__ :			str       	=			{
    """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					,   A					,   A					,   A					,   A ):
	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  A_(      A					,   A					,   A ):
	UpperCAmelCase_		    =  []
	UpperCAmelCase_		    =  fairseq_model.state_dict()

	UpperCAmelCase_		    =  hf_model.sew_d.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_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]:
					UpperCAmelCase_		    =  True
					if "*" in mapped_key:
						UpperCAmelCase_		    =  name.split(A )[0].split(""".""" )[-2]
						if not layer_index.isnumeric():
							continue
						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  A_(      A					,   A					,   A					,   A					,   A ):
	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  A_(      A					,   A ):
	UpperCAmelCase_		    =  SEWDConfig()
	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
	# DeBERTa-specific parameters:
	UpperCAmelCase_		    =  fs_config.max_position_embeddings
	UpperCAmelCase_		    =  fs_config.position_buckets
	UpperCAmelCase_		    =  fs_config.share_att_key
	UpperCAmelCase_		    =  fs_config.relative_attention
	UpperCAmelCase_		    =  fs_config.position_biased_input
	UpperCAmelCase_		    =  tuple(fs_config.pos_att_type.split("""|""" ) )
	UpperCAmelCase_		    =  fs_config.norm_rel_ebd

	# 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_(      A					,   A					,   A=None					,   A=None					,   A=True ):

	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_		    =  SEWDConfig.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=16000					,   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_		    =  SEWDForCTC(A )
	else:
		UpperCAmelCase_		    =  SEWDModel(A )
		feature_extractor.save_pretrained(A )

	recursively_load_weights(A					,   A					,   A )

	hf_model.save_pretrained(A )


if __name__ == "__main__":
				UpperCamelCase__ :			str       	=			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__ :			List[Any]       	=			parser.parse_args()
				convert_sew_checkpoint(
				    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
				)









from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
    is_vision_available,
)


UpperCamelCase__ :			Union[str, Any]       	=			{
    """configuration_mobilevitv2""": [
        """MOBILEVITV2_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """MobileViTV2Config""",
        """MobileViTV2OnnxConfig""",
    ],
}


try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				UpperCamelCase__ :			int       	=			[
				    """MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST""",
				    """MobileViTV2ForImageClassification""",
				    """MobileViTV2ForSemanticSegmentation""",
				    """MobileViTV2Model""",
				    """MobileViTV2PreTrainedModel""",
				]

if TYPE_CHECKING:
				from .configuration_mobilevitva import (
				    MOBILEVITV2_PRETRAINED_CONFIG_ARCHIVE_MAP,
				    MobileViTVaConfig,
				    MobileViTVaOnnxConfig,
				)

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_mobilevitva import (
								    MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST,
								    MobileViTVaForImageClassification,
								    MobileViTVaForSemanticSegmentation,
								    MobileViTVaModel,
								    MobileViTVaPreTrainedModel,
								)

else:
				import sys

				UpperCamelCase__ :			List[Any]       	=			_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


