
"""simple docstring"""




import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    HubertConfig,
    HubertForCTC,
    HubertModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
_A     :			Optional[int]  						=			logging.get_logger(__name__)

_A     :			List[str]  						=			{
    """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""",
    """w2v_encoder.proj""": """lm_head""",
    """mask_emb""": """masked_spec_embed""",
}


def     __magic_name__      ( __snake_case							:			Dict	,					__snake_case							:			Any	,					__snake_case							:			Any	,					__snake_case							:			Any	,					__snake_case							:			List[Any]    )					->							Optional[Any]:
       for attribute in key.split("."    ):
              lowercase       :							Tuple									=				getattr(__snake_case	,					__snake_case    )

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

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


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

       lowercase       :							Tuple									=				hf_model.hubert.feature_extractor if is_finetuned else hf_model.feature_extractor

       for name, value in fairseq_dict.items():
              lowercase       :							List[str]									=				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       :							Dict									=				True
              else:
                     for key, mapped_key in MAPPING.items():
                            lowercase       :							Any									=				"hubert." + 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] and not is_finetuned):
                                   lowercase       :							str									=				True
                                   if "*" in mapped_key:
                                          lowercase       :							Union[str, Any]									=				name.split(__snake_case    )[0].split("."    )[-2]
                                          lowercase       :							Optional[Any]									=				mapped_key.replace("*"	,					__snake_case    )
                                   if "weight_g" in name:
                                          lowercase       :							Dict									=				"weight_g"
                                   elif "weight_v" in name:
                                          lowercase       :							Union[str, Any]									=				"weight_v"
                                   elif "weight" in name:
                                          lowercase       :							Optional[Any]									=				"weight"
                                   elif "bias" in name:
                                          lowercase       :							Dict									=				"bias"
                                   else:
                                          lowercase       :							Tuple									=				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     __magic_name__      ( __snake_case							:			Union[str, Any]	,					__snake_case							:			Union[str, Any]	,					__snake_case							:			Optional[int]	,					__snake_case							:			List[Any]	,					__snake_case							:			Tuple    )					->							int:
       lowercase       :							Union[str, Any]									=				full_name.split("conv_layers."    )[-1]
       lowercase       :							List[str]									=				name.split("."    )
       lowercase       :							List[Any]									=				int(items[0]    )
       lowercase       :							int									=				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       :							int									=				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       :							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:
                     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       :							Dict									=				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       :							Union[str, 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     __magic_name__      ( __snake_case							:			List[str]	,					__snake_case							:			Tuple	,					__snake_case							:			Tuple=None	,					__snake_case							:			Any=None	,					__snake_case							:			Dict=True    )					->							int:
       if config_path is not None:
              lowercase       :							Optional[Any]									=				HubertConfig.from_pretrained(__snake_case    )
       else:
              lowercase       :							Dict									=				HubertConfig()

       if is_finetuned:
              if dict_path:
                     lowercase       :							Dict									=				Dictionary.load(__snake_case    )

                     # important change bos & pad token id since CTC symbol is <pad> and
                     # not <s> as in fairseq
                     lowercase       :							List[str]									=				target_dict.pad_index
                     lowercase       :							str									=				target_dict.bos_index
                     lowercase       :							Optional[int]									=				target_dict.eos_index
                     lowercase       :							List[str]									=				len(target_dict.symbols    )
                     lowercase       :							List[Any]									=				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    )
                     with open(__snake_case	,					"w"	,					encoding="utf-8"    ) as vocab_handle:
                            json.dump(target_dict.indices	,					__snake_case    )
                     lowercase       :							int									=				WavaVecaCTCTokenizer(
                         __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       :							Union[str, Any]									=				True if config.feat_extract_norm == "layer" else False
                     lowercase       :							Optional[Any]									=				WavaVecaFeatureExtractor(
                         feature_size=1	,					sampling_rate=1_6000	,					padding_value=0	,					do_normalize=__snake_case	,					return_attention_mask=__snake_case	,					)
                     lowercase       :							Any									=				WavaVecaProcessor(feature_extractor=__snake_case	,					tokenizer=__snake_case    )
                     processor.save_pretrained(__snake_case    )

              lowercase       :							Tuple									=				HubertForCTC(__snake_case    )
       else:
              lowercase       :							Union[str, Any]									=				HubertModel(__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]    )}    )
       else:
              lowercase						,       lowercase						,       lowercase       :							str									=				fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]    )

       lowercase       :							Optional[int]									=				model[0].eval()

       recursively_load_weights(__snake_case	,					__snake_case	,					__snake_case    )

       hf_wavavec.save_pretrained(__snake_case    )


if __name__ == "__main__":
   _A     :			Union[str, 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     :			Optional[int]  						=			parser.parse_args()
   convert_hubert_checkpoint(
       args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
   )




"""simple docstring"""




import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
   from transformers import AutoModelForSeqaSeqLM, AutoTokenizer

@require_torch
@require_sentencepiece
@require_tokenizers
class 						a__		( unittest.TestCase		):
   @slow
   def      __magic_name__     (     self							):
          lowercase       :							Tuple									=				AutoModelForSeqaSeqLM.from_pretrained("google/mt5-small"      ,       return_dict=_a							).to(_a							)
          lowercase       :							Union[str, Any]									=				AutoTokenizer.from_pretrained("google/mt5-small"							)

          lowercase       :							Optional[Any]									=				tokenizer("Hello there"      ,       return_tensors="pt"							).input_ids
          lowercase       :							Union[str, Any]									=				tokenizer("Hi I am"      ,       return_tensors="pt"							).input_ids

          lowercase       :							int									=				model(input_ids.to(_a							)      ,       labels=labels.to(_a							)							).loss
          lowercase       :							str									=				-(labels.shape[-1] * loss.item())

          lowercase       :							List[str]									=				-8_4.9_1_2_7
          self.assertTrue(abs(mtf_score - EXPECTED_SCORE							) < 1E-4							)


