
"""simple docstring"""




import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    UniSpeechConfig,
    UniSpeechForCTC,
    UniSpeechForPreTraining,
    WavaVecaFeatureExtractor,
    WavaVecaPhonemeCTCTokenizer,
    WavaVecaProcessor,
    logging,
)


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

_A			:       Tuple   		=       {
    """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""",
    """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""": """ctc_proj""",
    """mask_emb""": """masked_spec_embed""",
}
_A			:       List[str]   		=       [
    """ctc_proj""",
    """quantizer.weight_proj""",
    """quantizer.codevectors""",
    """project_q""",
    """project_hid""",
]


def   __magic_name__	(      __snake_case				:       str     , __snake_case				:       List[str]     , __snake_case				:       List[Any]     , __snake_case				:       int     , __snake_case				:       Any     , __snake_case				:       List[str]  ) ->				Dict:
       for attribute in key.split("."  ):
              if is_finetuned:
                     if attribute in ["quantizer", "project_q", "project_hid"]:
                            # those layers are only relevant for pretraining and should be dropped
                            return

                     if attribute == "ctc_proj":
                            # we should rename `ctc_proj` to `lm_head` for fine-tuned phoneme models
                            lowercase    :				str       		=							"lm_head"

              lowercase    :				int       		=							getattr(__snake_case     , __snake_case  )

       if weight_type is not None:
              lowercase    :				int       		=							getattr(__snake_case     , __snake_case  ).shape
       else:
              lowercase    :				Union[str, Any]       		=							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":
              lowercase    :				int       		=							value
       elif weight_type == "weight_g":
              lowercase    :				str       		=							value
       elif weight_type == "weight_v":
              lowercase    :				int       		=							value
       elif weight_type == "bias":
              lowercase    :				Optional[Any]       		=							value
       else:
              lowercase    :				Union[str, Any]       		=							value

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


def   __magic_name__	(      __snake_case				:       List[Any]     , __snake_case				:       str     , __snake_case				:       int  ) ->				str:
       lowercase    :				Optional[Any]       		=							[]
       lowercase    :				int       		=							fairseq_model.state_dict()

       lowercase    :				Any       		=							hf_model.unispeech.feature_extractor

       for name, value in fairseq_dict.items():
              lowercase    :				Optional[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"     , )
                     lowercase    :				int       		=							True
              else:
                     for key, mapped_key in MAPPING.items():
                            lowercase    :				Optional[int]       		=							"unispeech." + 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]:
                                   lowercase    :				List[str]       		=							True
                                   if "*" in mapped_key:
                                          lowercase    :				Dict       		=							name.split(__snake_case  )[0].split("."  )[-2]
                                          lowercase    :				int       		=							mapped_key.replace("*"     , __snake_case  )
                                   if "weight_g" in name:
                                          lowercase    :				Union[str, Any]       		=							"weight_g"
                                   elif "weight_v" in name:
                                          lowercase    :				Any       		=							"weight_v"
                                   elif "bias" in name:
                                          lowercase    :				Optional[int]       		=							"bias"
                                   elif "weight" in name:
                                          # TODO: don't match quantizer.weight_proj
                                          lowercase    :				List[str]       		=							"weight"
                                   else:
                                          lowercase    :				Any       		=							None
                                   set_recursively(__snake_case     , __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   __magic_name__	(      __snake_case				:       int     , __snake_case				:       str     , __snake_case				:       Optional[Any]     , __snake_case				:       Optional[int]     , __snake_case				:       Any  ) ->				Dict:
       lowercase    :				int       		=							full_name.split("conv_layers."  )[-1]
       lowercase    :				Tuple       		=							name.split("."  )
       lowercase    :				Optional[int]       		=							int(items[0]  )
       lowercase    :				Any       		=							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."""
                     )
                     lowercase    :				Any       		=							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."""
                     )
                     lowercase    :				Tuple       		=							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."
                     )
                     lowercase    :				str       		=							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."""
                     )
                     lowercase    :				int       		=							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   __magic_name__	(      __snake_case				:       Any     , __snake_case				:       Any     , __snake_case				:       Union[str, Any]=None     , __snake_case				:       Dict=None     , __snake_case				:       str=True  ) ->				int:
       if config_path is not None:
              lowercase    :				Optional[int]       		=							UniSpeechConfig.from_pretrained(__snake_case  )
       else:
              lowercase    :				str       		=							UniSpeechConfig()

       if is_finetuned:
              if dict_path:
                     lowercase    :				Tuple       		=							Dictionary.load_from_json(__snake_case  )

                     # important change bos & pad token id since CTC symbol is <pad> and
                     # not <s> as in fairseq
                     lowercase    :				Optional[Any]       		=							target_dict.pad_index
                     lowercase    :				Union[str, Any]       		=							target_dict.bos_index
                     lowercase    :				List[str]       		=							target_dict.eos_index
                     lowercase    :				int       		=							len(target_dict.symbols  )
                     lowercase    :				int       		=							os.path.join(__snake_case     , "vocab.json"  )
                     if not os.path.isdir(__snake_case  ):
                            logger.error("--pytorch_dump_folder_path ({}) should be a directory".format(__snake_case  )  )
                            return
                     os.makedirs(__snake_case     , exist_ok=__snake_case  )
                     lowercase    :				Dict       		=							target_dict.indices

                     # fairseq has the <pad> and <s> switched
                     lowercase    :				Union[str, Any]       		=							42
                     lowercase    :				Optional[Any]       		=							43
                     with open(__snake_case     , "w"     , encoding="utf-8"  ) as vocab_handle:
                            json.dump(__snake_case     , __snake_case  )
                     lowercase    :				int       		=							WavaVecaPhonemeCTCTokenizer(
                         __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=__snake_case     , )
                     lowercase    :				Dict       		=							True if config.feat_extract_norm == "layer" else False
                     lowercase    :				Optional[int]       		=							WavaVecaFeatureExtractor(
                         feature_size=1     , sampling_rate=1_6000     , padding_value=0     , do_normalize=__snake_case     , return_attention_mask=__snake_case     , )
                     lowercase    :				int       		=							WavaVecaProcessor(feature_extractor=__snake_case     , tokenizer=__snake_case  )
                     processor.save_pretrained(__snake_case  )

              lowercase    :				str       		=							UniSpeechForCTC(__snake_case  )
       else:
              lowercase    :				Dict       		=							UniSpeechForPreTraining(__snake_case  )

       if is_finetuned:
              lowercase		,    lowercase		,    lowercase    :				Union[str, Any]       		=							fairseq.checkpoint_utils.load_model_ensemble_and_task(
                  [checkpoint_path]     , arg_overrides={"data": "/".join(dict_path.split("/"  )[:-1]  ), "w2v_path": checkpoint_path}  )
       else:
              lowercase		,    lowercase		,    lowercase    :				Union[str, Any]       		=							fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]  )

       lowercase    :				Tuple       		=							model[0].eval()

       recursively_load_weights(__snake_case     , __snake_case     , __snake_case  )

       hf_unispeech.save_pretrained(__snake_case  )


if __name__ == "__main__":
   _A			:       Optional[Any]   		=       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			:       Dict   		=       parser.parse_args()
   convert_unispeech_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 RemBertConfig, RemBertModel, load_tf_weights_in_rembert
from transformers.utils import logging


logging.set_verbosity_info()


def   __magic_name__	(      __snake_case				:       Optional[Any]     , __snake_case				:       Dict     , __snake_case				:       Any  ) ->				Any:
       # Initialise PyTorch model
       lowercase    :				Union[str, Any]       		=							RemBertConfig.from_json_file(__snake_case  )
       print("Building PyTorch model from configuration: {}".format(str(__snake_case  )  )  )
       lowercase    :				str       		=							RemBertModel(__snake_case  )

       # Load weights from tf checkpoint
       load_tf_weights_in_rembert(__snake_case     , __snake_case     , __snake_case  )

       # Save pytorch-model
       print("Save PyTorch model to {}".format(__snake_case  )  )
       torch.save(model.state_dict()     , __snake_case  )


if __name__ == "__main__":
   _A			:       str   		=       argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       """--tf_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
   )
   parser.add_argument(
       """--rembert_config_file""",
       default=None,
       type=str,
       required=True,
       help=(
           """The config json file corresponding to the pre-trained RemBERT model. \n"""
           """This specifies the model architecture."""
       ),
   )
   parser.add_argument(
       """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
   )
   _A			:       List[str]   		=       parser.parse_args()
   convert_rembert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.rembert_config_file, args.pytorch_dump_path)


