






import argparse

import fairseq
import torch

from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging


logging.set_verbosity_info()
_a     : List[Any]   	=      logging.get_logger(__name__)

_a     : Any   	=      {
    'post_extract_proj': 'feature_projection.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.layer_norm': 'encoder.layer_norm',
    'encoder.layer_norm_for_extract': 'layer_norm_for_extract',
    'w2v_model.layer_norm': 'feature_projection.layer_norm',
    'quantizer.weight_proj': 'quantizer.weight_proj',
    'quantizer.vars': 'quantizer.codevectors',
    'project_q': 'project_q',
    'final_proj': 'project_hid',
    'w2v_encoder.proj': 'lm_head',
    'label_embs_concat': 'label_embeddings_concat',
    'mask_emb': 'masked_spec_embed',
    'spk_proj': 'speaker_proj',
}
_a     : Tuple   	=      [
    'lm_head',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
    'label_embeddings_concat',
    'speaker_proj',
    'layer_norm_for_extract',
]


def 					snake_case__				(				UpperCAmelCase				:	List[Any]     ,      UpperCAmelCase				:	Optional[Any]     ,      UpperCAmelCase				:	Dict     ,      UpperCAmelCase				:	int     ,      UpperCAmelCase				:	str							):
    for attribute in key.split("."							):
        lowerCAmelCase__ :List[Any]   				=					getattr(__snake_case     ,      __snake_case							)

    if weight_type is not None:
        lowerCAmelCase__ :Optional[int]   				=					getattr(__snake_case     ,      __snake_case							).shape
    else:
        lowerCAmelCase__ :Any   				=					hf_pointer.shape

    if hf_shape != value.shape:
        raise ValueError(
            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":
        lowerCAmelCase__ :str   				=					value
    elif weight_type == "weight_g":
        lowerCAmelCase__ :Dict   				=					value
    elif weight_type == "weight_v":
        lowerCAmelCase__ :Optional[int]   				=					value
    elif weight_type == "bias":
        lowerCAmelCase__ :List[Any]   				=					value
    else:
        lowerCAmelCase__ :Optional[Any]   				=					value

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


def 					snake_case__				(				UpperCAmelCase				:	Optional[Any]     ,      UpperCAmelCase				:	Union[str, Any]							):
    lowerCAmelCase__ :Union[str, Any]   				=					[]
    lowerCAmelCase__ :Optional[int]   				=					fairseq_model.state_dict()

    lowerCAmelCase__ :List[str]   				=					hf_model.unispeech_sat.feature_extractor

    for name, value in fairseq_dict.items():
        lowerCAmelCase__ :Any   				=					False
        if "conv_layers" in name:
            load_conv_layer(
                __snake_case     ,      __snake_case     ,      __snake_case     ,      __snake_case     ,      hf_model.config.feat_extract_norm == "group"     ,      )
            lowerCAmelCase__ :Dict   				=					True
        else:
            for key, mapped_key in MAPPING.items():
                lowerCAmelCase__ :Union[str, Any]   				=					"unispeech_sat." + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
                if key in name or key.split("w2v_model."							)[-1] == name.split("."							)[0]:
                    if "layer_norm_for_extract" in name and (".".join(name.split("."							)[:-1]							) != key):
                        # special case since naming is very similar
                        continue
                    lowerCAmelCase__ :str   				=					True
                    if "*" in mapped_key:
                        lowerCAmelCase__ :List[Any]   				=					name.split(__snake_case							)[0].split("."							)[-2]
                        lowerCAmelCase__ :Optional[int]   				=					mapped_key.replace("*"     ,      __snake_case							)
                    if "weight_g" in name:
                        lowerCAmelCase__ :Dict   				=					"weight_g"
                    elif "weight_v" in name:
                        lowerCAmelCase__ :Dict   				=					"weight_v"
                    elif "bias" in name:
                        lowerCAmelCase__ :str   				=					"bias"
                    elif "weight" in name:
                        # TODO: don't match quantizer.weight_proj
                        lowerCAmelCase__ :Dict   				=					"weight"
                    else:
                        lowerCAmelCase__ :Optional[int]   				=					None
                    set_recursively(__snake_case     ,      __snake_case     ,      __snake_case     ,      __snake_case     ,      __snake_case							)
                continue
        if not is_used:
            unused_weights.append(__snake_case							)

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


def 					snake_case__				(				UpperCAmelCase				:	List[str]     ,      UpperCAmelCase				:	Tuple     ,      UpperCAmelCase				:	Optional[Any]     ,      UpperCAmelCase				:	str     ,      UpperCAmelCase				:	str							):
    lowerCAmelCase__ :List[Any]   				=					full_name.split("conv_layers."							)[-1]
    lowerCAmelCase__ :List[str]   				=					name.split("."							)
    lowerCAmelCase__ :Optional[int]   				=					int(items[0]							)
    lowerCAmelCase__ :Union[str, Any]   				=					int(items[1]							)

    if type_id == 0:
        if "bias" in name:
            if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
                raise ValueError(
                    F'''{full_name} has size {value.shape}, but'''
                    F''' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'''							)
            lowerCAmelCase__ :Any   				=					value
            logger.info(F'''Feat extract conv layer {layer_id} was initialized from {full_name}.'''							)
        elif "weight" in name:
            if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
                raise ValueError(
                    F'''{full_name} has size {value.shape}, but'''
                    F''' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'''							)
            lowerCAmelCase__ :int   				=					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:
            if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
                raise ValueError(
                    F'''{full_name} has size {value.shape}, but'''
                    F''' {feature_extractor[layer_id].layer_norm.bias.data.shape} was found.'''							)
            lowerCAmelCase__ :Optional[Any]   				=					value
            logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''							)
        elif "weight" in name:
            if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
                raise ValueError(
                    F'''{full_name} has size {value.shape}, but'''
                    F''' {feature_extractor[layer_id].layer_norm.weight.data.shape} was found.'''							)
            lowerCAmelCase__ :List[Any]   				=					value
            logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''							)
    else:
        unused_weights.append(__snake_case							)


@torch.no_grad()
def 					snake_case__				(				UpperCAmelCase				:	Optional[Any]     ,      UpperCAmelCase				:	Dict     ,      UpperCAmelCase				:	List[Any]=None     ,      UpperCAmelCase				:	Optional[int]=None     ,      UpperCAmelCase				:	Optional[int]=True							):
    if config_path is not None:
        lowerCAmelCase__ :Optional[int]   				=					UniSpeechSatConfig.from_pretrained(__snake_case							)
    else:
        lowerCAmelCase__ :Any   				=					UniSpeechSatConfig()

    lowerCAmelCase__ :List[Any]   				=					""

    if is_finetuned:
        lowerCAmelCase__ :Any   				=					UniSpeechSatForCTC(__snake_case							)
    else:
        lowerCAmelCase__ :Optional[int]   				=					UniSpeechSatForPreTraining(__snake_case							)

    lowerCAmelCase__ :Union[str, Any]   				=					fairseq.checkpoint_utils.load_model_ensemble_and_task(
        [checkpoint_path]     ,      arg_overrides={"data": "/".join(dict_path.split("/"							)[:-1]							)}							)
    lowerCAmelCase__ :List[str]   				=					model[0].eval()

    recursively_load_weights(__snake_case     ,      __snake_case							)

    hf_wavavec.save_pretrained(__snake_case							)


if __name__ == "__main__":
 _a     : Optional[int]   	=      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(
     """--not_finetuned""", action="""store_true""", help="""Whether the model to convert is a fine-tuned model or not"""
 )
 _a     : Any   	=      parser.parse_args()
 convert_unispeech_sat_checkpoint(
     args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
 )








"""simple docstring"""






import argparse

import torch

from transformers import BertForMaskedLM


if __name__ == "__main__":
	__snake_case				:  Optional[Any] =       argparse.ArgumentParser(
	    description=(
	        'Extraction some layers of the full BertForMaskedLM or RObertaForMaskedLM for Transfer Learned'
	        ' Distillation'
	    )
	)
	parser.add_argument('--model_type', default='bert', choices=['bert'])
	parser.add_argument('--model_name', default='bert-base-uncased', type=str)
	parser.add_argument('--dump_checkpoint', default='serialization_dir/tf_bert-base-uncased_0247911.pth', type=str)
	parser.add_argument('--vocab_transform', action='store_true')
	__snake_case				:  List[Any] =       parser.parse_args()

	if args.model_type == "bert":
		__snake_case				:  int =       BertForMaskedLM.from_pretrained(args.model_name)
		__snake_case				:  Tuple =       'bert'
	else:
		raise ValueError('args.model_type should be "bert".')

	__snake_case				:  List[Any] =       model.state_dict()
	__snake_case				:  Any =       {}

	for w in ["word_embeddings", "position_embeddings"]:
		__snake_case				:  List[str] =       state_dict[F"""{prefix}.embeddings.{w}.weight"""]
	for w in ["weight", "bias"]:
		__snake_case				:  Optional[int] =       state_dict[F"""{prefix}.embeddings.LayerNorm.{w}"""]

	__snake_case				:  List[str] =       0
	for teacher_idx in [0, 2, 4, 7, 9, 11]:
		for w in ["weight", "bias"]:
			__snake_case				:  str =       state_dict[
			    F"""{prefix}.encoder.layer.{teacher_idx}.attention.self.query.{w}"""
			]
			__snake_case				:  Tuple =       state_dict[
			    F"""{prefix}.encoder.layer.{teacher_idx}.attention.self.key.{w}"""
			]
			__snake_case				:  str =       state_dict[
			    F"""{prefix}.encoder.layer.{teacher_idx}.attention.self.value.{w}"""
			]

			__snake_case				:  Any =       state_dict[
			    F"""{prefix}.encoder.layer.{teacher_idx}.attention.output.dense.{w}"""
			]
			__snake_case				:  Union[str, Any] =       state_dict[
			    F"""{prefix}.encoder.layer.{teacher_idx}.attention.output.LayerNorm.{w}"""
			]

			__snake_case				:  Union[str, Any] =       state_dict[
			    F"""{prefix}.encoder.layer.{teacher_idx}.intermediate.dense.{w}"""
			]
			__snake_case				:  Any =       state_dict[
			    F"""{prefix}.encoder.layer.{teacher_idx}.output.dense.{w}"""
			]
			__snake_case				:  Optional[int] =       state_dict[
			    F"""{prefix}.encoder.layer.{teacher_idx}.output.LayerNorm.{w}"""
			]
		std_idx += 1

	__snake_case				:  Union[str, Any] =       state_dict['cls.predictions.decoder.weight']
	__snake_case				:  Union[str, Any] =       state_dict['cls.predictions.bias']
	if args.vocab_transform:
		for w in ["weight", "bias"]:
			__snake_case				:  Union[str, Any] =       state_dict[F"""cls.predictions.transform.dense.{w}"""]
			__snake_case				:  List[str] =       state_dict[F"""cls.predictions.transform.LayerNorm.{w}"""]

	print(F"""N layers selected for distillation: {std_idx}""")
	print(F"""Number of params transferred for distillation: {len(compressed_sd.keys())}""")

	print(F"""Save transferred checkpoint to {args.dump_checkpoint}.""")
	torch.save(compressed_sd, args.dump_checkpoint)