





import argparse

import fairseq
import torch

from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging


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

__A									=				{
    "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									=				[
    "lm_head",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
    "label_embeddings_concat",
    "speaker_proj",
    "layer_norm_for_extract",
]





def      lowerCAmelCase_ (       __a	,    __a	,    __a	,    __a	,    __a		)     ->		Optional[Any]:







  """simple docstring"""





  for attribute in key.split("."		):
    lowerCamelCase__:      str			  =getattr(__a	,    __a		)

  if weight_type is not None:
    lowerCamelCase__:      Tuple			  =getattr(__a	,    __a		).shape
  else:
    lowerCamelCase__:      List[str]			  =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__:      List[Any]			  =value
  elif weight_type == "weight_g":
    lowerCamelCase__:      Tuple			  =value
  elif weight_type == "weight_v":
    lowerCamelCase__:      List[str]			  =value
  elif weight_type == "bias":
    lowerCamelCase__:      List[str]			  =value
  else:
    lowerCamelCase__:      str			  =value

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





def      lowerCAmelCase_ (       __a	,    __a		)     ->		Any:







  """simple docstring"""





  lowerCamelCase__:      Tuple			  =[]
  lowerCamelCase__:      List[str]			  =fairseq_model.state_dict()

  lowerCamelCase__:      Any			  =hf_model.unispeech_sat.feature_extractor

  for name, value in fairseq_dict.items():
    lowerCamelCase__:      Tuple			  =False
    if "conv_layers" in name:
      load_conv_layer(
          __a	,    __a	,    __a	,    __a	,    hf_model.config.feat_extract_norm == "group"	,    )
      lowerCamelCase__:      Optional[int]			  =True
    else:
      for key, mapped_key in MAPPING.items():
        lowerCamelCase__:      List[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[str]			  =name.split(__a		)[0].split("."		)[-2]
            lowerCamelCase__:      Tuple			  =mapped_key.replace("*"	,    __a		)
          if "weight_g" in name:
            lowerCamelCase__:      str			  ="weight_g"
          elif "weight_v" in name:
            lowerCamelCase__:      Union[str, Any]			  ="weight_v"
          elif "bias" in name:
            lowerCamelCase__:      Dict			  ="bias"
          elif "weight" in name:
            # TODO: don't match quantizer.weight_proj
            lowerCamelCase__:      Dict			  ="weight"
          else:
            lowerCamelCase__:      Any			  =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	,    __a	,    __a	,    __a	,    __a		)     ->		Any:







  """simple docstring"""





  lowerCamelCase__:      Any			  =full_name.split("conv_layers."		)[-1]
  lowerCamelCase__:      Tuple			  =name.split("."		)
  lowerCamelCase__:      Any			  =int(items[0]		)
  lowerCamelCase__:      Optional[int]			  =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__:      List[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__:      Any			  =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[int]			  =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__:      str			  =value
      logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""		)
  else:
    unused_weights.append(__a		)





@torch.no_grad()
def      lowerCAmelCase_ (       __a	,    __a	,    __a=None	,    __a=None	,    __a=True		)     ->		Optional[Any]:







  """simple docstring"""





  if config_path is not None:
    lowerCamelCase__:      Dict			  =UniSpeechSatConfig.from_pretrained(__a		)
  else:
    lowerCamelCase__:      Union[str, Any]			  =UniSpeechSatConfig()

  lowerCamelCase__:      Any			  =""

  if is_finetuned:
    lowerCamelCase__:      Optional[int]			  =UniSpeechSatForCTC(__a		)
  else:
    lowerCamelCase__:      Optional[int]			  =UniSpeechSatForPreTraining(__a		)

  lowerCamelCase__  ,					lowerCamelCase__  ,					lowerCamelCase__:      Optional[int]			  =fairseq.checkpoint_utils.load_model_ensemble_and_task(
      [checkpoint_path]	,    arg_overrides={"data": "/".join(dict_path.split("/"		)[:-1]		)}		)
  lowerCamelCase__:      Optional[Any]			  =model[0].eval()

  recursively_load_weights(__a	,    __a		)

  hf_wavavec.save_pretrained(__a		)


if __name__ == "__main__":
     __A									=				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									=				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
     )





import argparse
import collections

import torch
from flax import traverse_util
from tax import checkpoints

from transformers import TaConfig, TaEncoderModel, TaForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()
def 				__lowerCamelCase						( UpperCAmelCase_						:		Union[str, Any] ,  UpperCAmelCase_						:		int ,  UpperCAmelCase_						:		Optional[Any] ,  UpperCAmelCase_						:		Union[str, Any]="attention"						):




      """simple docstring"""





      a						:Optional[int] 							=       params[F'''{prefix}/layers_{i}/{layer_name}/key/kernel''']
      a						:Optional[Any] 							=       params[F'''{prefix}/layers_{i}/{layer_name}/out/kernel''']
      a						:int 							=       params[F'''{prefix}/layers_{i}/{layer_name}/query/kernel''']
      a						:Optional[Any] 							=       params[F'''{prefix}/layers_{i}/{layer_name}/value/kernel''']
      return k, o, q, v
def 				__lowerCamelCase						( UpperCAmelCase_						:		Optional[int] ,  UpperCAmelCase_						:		str ,  UpperCAmelCase_						:		List[str] ,  UpperCAmelCase_						:		int=False						):




      """simple docstring"""





      if split_mlp_wi:
            a						:int 							=       params[F'''{prefix}/layers_{i}/mlp/wi_0/kernel''']
            a						:Optional[Any] 							=       params[F'''{prefix}/layers_{i}/mlp/wi_1/kernel''']
            a						:Dict 							=       (wi_a, wi_a)
      else:
            a						:Optional[Any] 							=       params[F'''{prefix}/layers_{i}/mlp/wi/kernel''']

      a						:Dict 							=       params[F'''{prefix}/layers_{i}/mlp/wo/kernel''']
      return wi, wo
def 				__lowerCamelCase						( UpperCAmelCase_						:		str ,  UpperCAmelCase_						:		int ,  UpperCAmelCase_						:		List[str] ,  UpperCAmelCase_						:		Optional[int]						):




      """simple docstring"""





      return params[F'''{prefix}/layers_{i}/{layer_name}/scale''']
def 				__lowerCamelCase						( UpperCAmelCase_						:		dict ,  *, UpperCAmelCase_						:		int ,  UpperCAmelCase_						:		bool						):




      """simple docstring"""





      a						:str 							=       traverse_util.flatten_dict(variables['''target''']						)
      a						:Any 							=       {'''/'''.join(UpperCAmelCase_						): v for k, v in old.items()}

      # v1.1 models have a gated GeLU with wi_0 and wi_1 instead of wi
      a						:Any 							=       '''encoder/layers_0/mlp/wi_0/kernel''' in old
      print('''Split MLP:''' ,  UpperCAmelCase_						)

      a						:Optional[Any] 							=       collections.OrderedDict()

      # Shared embeddings.
      a						:Union[str, Any] 							=       old['''token_embedder/embedding''']

      # Encoder.
      for i in range(UpperCAmelCase_						):
            # Block i, layer 0 (Self Attention).
            a						:Optional[Any] 							=       tax_layer_norm_lookup(UpperCAmelCase_ ,  UpperCAmelCase_ ,  '''encoder''' ,  '''pre_attention_layer_norm'''						)
            a							,				a							,				a							,				a						:Optional[int] 							=       tax_attention_lookup(UpperCAmelCase_ ,  UpperCAmelCase_ ,  '''encoder''' ,  '''attention'''						)
            a						:List[Any] 							=       layer_norm
            a						:str 							=       k.T
            a						:Dict 							=       o.T
            a						:int 							=       q.T
            a						:Optional[Any] 							=       v.T

            # Block i, layer 1 (MLP).
            a						:Tuple 							=       tax_layer_norm_lookup(UpperCAmelCase_ ,  UpperCAmelCase_ ,  '''encoder''' ,  '''pre_mlp_layer_norm'''						)
            a							,				a						:List[Any] 							=       tax_mlp_lookup(UpperCAmelCase_ ,  UpperCAmelCase_ ,  '''encoder''' ,  UpperCAmelCase_						)
            a						:Any 							=       layer_norm
            if split_mlp_wi:
                  a						:Any 							=       wi[0].T
                  a						:Tuple 							=       wi[1].T
            else:
                  a						:List[str] 							=       wi.T
            a						:List[Any] 							=       wo.T

      a						:Union[str, Any] 							=       old[
          '''encoder/relpos_bias/rel_embedding'''
      ].T
      a						:Optional[Any] 							=       old['''encoder/encoder_norm/scale''']

      if not is_encoder_only:
            # Decoder.
            for i in range(UpperCAmelCase_						):
                  # Block i, layer 0 (Self Attention).
                  a						:List[str] 							=       tax_layer_norm_lookup(UpperCAmelCase_ ,  UpperCAmelCase_ ,  '''decoder''' ,  '''pre_self_attention_layer_norm'''						)
                  a							,				a							,				a							,				a						:List[Any] 							=       tax_attention_lookup(UpperCAmelCase_ ,  UpperCAmelCase_ ,  '''decoder''' ,  '''self_attention'''						)
                  a						:List[Any] 							=       layer_norm
                  a						:Tuple 							=       k.T
                  a						:int 							=       o.T
                  a						:Any 							=       q.T
                  a						:Optional[int] 							=       v.T

                  # Block i, layer 1 (Cross Attention).
                  a						:str 							=       tax_layer_norm_lookup(UpperCAmelCase_ ,  UpperCAmelCase_ ,  '''decoder''' ,  '''pre_cross_attention_layer_norm'''						)
                  a							,				a							,				a							,				a						:Any 							=       tax_attention_lookup(UpperCAmelCase_ ,  UpperCAmelCase_ ,  '''decoder''' ,  '''encoder_decoder_attention'''						)
                  a						:str 							=       layer_norm
                  a						:Optional[Any] 							=       k.T
                  a						:Any 							=       o.T
                  a						:Dict 							=       q.T
                  a						:Optional[Any] 							=       v.T

                  # Block i, layer 2 (MLP).
                  a						:Optional[int] 							=       tax_layer_norm_lookup(UpperCAmelCase_ ,  UpperCAmelCase_ ,  '''decoder''' ,  '''pre_mlp_layer_norm'''						)
                  a							,				a						:List[Any] 							=       tax_mlp_lookup(UpperCAmelCase_ ,  UpperCAmelCase_ ,  '''decoder''' ,  UpperCAmelCase_						)
                  a						:Optional[int] 							=       layer_norm
                  if split_mlp_wi:
                        a						:int 							=       wi[0].T
                        a						:Tuple 							=       wi[1].T
                  else:
                        a						:str 							=       wi.T
                  a						:Dict 							=       wo.T

            a						:Any 							=       old['''decoder/decoder_norm/scale''']
            a						:Optional[Any] 							=       old[
                '''decoder/relpos_bias/rel_embedding'''
            ].T

            # LM Head (only in v1.1 checkpoints, in v1.0 embeddings are used instead)
            if "decoder/logits_dense/kernel" in old:
                  a						:Union[str, Any] 							=       old['''decoder/logits_dense/kernel'''].T

      return new
def 				__lowerCamelCase						( UpperCAmelCase_						:		Any ,  UpperCAmelCase_						:		bool						):




      """simple docstring"""





      a						:List[Any] 							=       collections.OrderedDict([(k, torch.from_numpy(v.copy()						)) for (k, v) in converted_params.items()]						)

      # Add what is missing.
      if "encoder.embed_tokens.weight" not in state_dict:
            a						:Optional[Any] 							=       state_dict['''shared.weight''']

      if not is_encoder_only:
            if "decoder.embed_tokens.weight" not in state_dict:
                  a						:Tuple 							=       state_dict['''shared.weight''']

            if "lm_head.weight" not in state_dict:  # For old 1.0 models.
                  print('''Using shared word embeddings as lm_head.'''						)
                  a						:Optional[Any] 							=       state_dict['''shared.weight''']

      return state_dict
def 				__lowerCamelCase						( UpperCAmelCase_						:		Any ,  UpperCAmelCase_						:		Union[str, Any] ,  UpperCAmelCase_						:		Optional[int] ,  UpperCAmelCase_						:		Optional[int]						):




      """simple docstring"""





      a						:Tuple 							=       checkpoints.load_tax_checkpoint(UpperCAmelCase_						)
      a						:Optional[int] 							=       convert_tax_to_pytorch(UpperCAmelCase_ ,  num_layers=config.num_layers ,  is_encoder_only=UpperCAmelCase_						)
      a						:Tuple 							=       make_state_dict(UpperCAmelCase_ ,  UpperCAmelCase_						)
      model.load_state_dict(UpperCAmelCase_ ,  strict=UpperCAmelCase_						)
def 				__lowerCamelCase						( UpperCAmelCase_						:		int ,  UpperCAmelCase_						:		Optional[Any] ,  UpperCAmelCase_						:		str ,  UpperCAmelCase_						:		bool = False						):




      """simple docstring"""





      a						:List[Any] 							=       TaConfig.from_json_file(UpperCAmelCase_						)
      print(F'''Building PyTorch model from configuration: {config}'''						)
      # Non-v1.1 checkpoints could also use T5Model, but this works for all.
      # The v1.0 checkpoints will simply have an LM head that is the word embeddings.
      if is_encoder_only:
            a						:Any 							=       TaEncoderModel(UpperCAmelCase_						)
      else:
            a						:List[str] 							=       TaForConditionalGeneration(UpperCAmelCase_						)

      # Load weights from tf checkpoint
      load_tax_weights_in_ta(UpperCAmelCase_ ,  UpperCAmelCase_ ,  UpperCAmelCase_ ,  UpperCAmelCase_						)

      # Save pytorch-model
      print(F'''Save PyTorch model to {pytorch_dump_path}'''						)
      model.save_pretrained(UpperCAmelCase_						)

      # Verify that we can load the checkpoint.
      model.from_pretrained(UpperCAmelCase_						)
      print('''Done'''						)


if __name__ == "__main__":
     snake_case				:    Any       				=     argparse.ArgumentParser(description='''Converts a native T5X checkpoint into a PyTorch checkpoint.''')
     # Required parameters
     parser.add_argument(
         '''--t5x_checkpoint_path''', default=None, type=str, required=True, help='''Path to the T5X checkpoint.'''
     )
     parser.add_argument(
         '''--config_file''',
         default=None,
         type=str,
         required=True,
         help='''The config json file corresponding to the pre-trained T5 model.\nThis specifies the model architecture.''',
     )
     parser.add_argument(
         '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
     )
     parser.add_argument(
         '''--is_encoder_only''', action='''store_true''', help='''Check if the model is encoder-decoder model''', default=False
     )
     snake_case				:    Optional[Any]       				=     parser.parse_args()
     convert_tax_checkpoint_to_pytorch(
         args.tax_checkpoint_path, args.config_file, args.pytorch_dump_path, args.is_encoder_only
     )





