
import argparse

import torch

# Step 1. clone https://github.com/microsoft/unilm
# Step 2. git checkout to https://github.com/microsoft/unilm/commit/b94ec76c36f02fb2b0bf0dcb0b8554a2185173cd
# Step 3. cd unilm
# Step 4. ln -s $(realpath wavlm/modules.py) ./  # create simlink
# import classes
from unilm.wavlm.WavLM import WavLM as WavLMOrig
from unilm.wavlm.WavLM import WavLMConfig as WavLMConfigOrig

from transformers import WavLMConfig, WavLMModel, logging


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

__lowerCamelCase							=       {
    """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.grep_linear""": """encoder.layers.*.attention.gru_rel_pos_linear""",
    """self_attn.relative_attention_bias""": """encoder.layers.*.attention.rel_attn_embed""",
    """self_attn.grep_a""": """encoder.layers.*.attention.gru_rel_pos_const""",
    """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""",
}
__lowerCamelCase							=       [
    """ctc_proj""",
    """quantizer.weight_proj""",
    """quantizer.codevectors""",
    """project_q""",
    """project_hid""",
]


def 			UpperCamelCase					(    __lowerCamelCase     : List[Any]				,					__lowerCamelCase     : Any				,					__lowerCamelCase     : List[str]				,					__lowerCamelCase     : Tuple				,					__lowerCamelCase     : int    ):
       for attribute in key.split("."    ):
              snake_case       :   int							=      getattr(__lowerCamelCase				,					__lowerCamelCase    )

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

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


def 			UpperCamelCase					(    __lowerCamelCase     : int				,					__lowerCamelCase     : Any    ):
       snake_case       :   List[Any]							=      []
       snake_case       :   List[Any]							=      fairseq_model.state_dict()

       snake_case       :   str							=      hf_model.feature_extractor

       for name, value in fairseq_dict.items():
              snake_case       :   Tuple							=      False
              if "conv_layers" in name:
                     load_conv_layer(
                         __lowerCamelCase				,					__lowerCamelCase				,					__lowerCamelCase				,					__lowerCamelCase				,					hf_model.config.feat_extract_norm == "group"				,					)
                     snake_case       :   Optional[Any]							=      True
              else:
                     for key, mapped_key in MAPPING.items():
                            if key in name or key.split("w2v_model."    )[-1] == name.split("."    )[0]:
                                   snake_case       :   Dict							=      True
                                   if "*" in mapped_key:
                                          snake_case       :   Union[str, Any]							=      name.split(__lowerCamelCase    )[0].split("."    )[-2]
                                          snake_case       :   Dict							=      mapped_key.replace("*"				,					__lowerCamelCase    )
                                   if "weight_g" in name:
                                          snake_case       :   int							=      "weight_g"
                                   elif "weight_v" in name:
                                          snake_case       :   Dict							=      "weight_v"
                                   elif "bias" in name and "relative_attention_bias" not in name:
                                          snake_case       :   Dict							=      "bias"
                                   elif "weight" in name:
                                          # TODO: don't match quantizer.weight_proj
                                          snake_case       :   List[Any]							=      "weight"
                                   else:
                                          snake_case       :   List[str]							=      None

                                   set_recursively(__lowerCamelCase				,					__lowerCamelCase				,					__lowerCamelCase				,					__lowerCamelCase				,					__lowerCamelCase    )
                            continue
              if not is_used:
                     unused_weights.append(__lowerCamelCase    )

       logger.warning(f"""Unused weights: {unused_weights}"""    )


def 			UpperCamelCase					(    __lowerCamelCase     : Any				,					__lowerCamelCase     : Tuple				,					__lowerCamelCase     : Optional[int]				,					__lowerCamelCase     : Any				,					__lowerCamelCase     : Any    ):
       snake_case       :   Dict							=      full_name.split("conv_layers."    )[-1]
       snake_case       :   List[Any]							=      name.split("."    )
       snake_case       :   Tuple							=      int(items[0]    )
       snake_case       :   List[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."""
                     )
                     snake_case       :   Optional[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."""
                     )
                     snake_case       :   List[str]							=      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."
                     )
                     snake_case       :   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."""
                     )
                     snake_case       :   List[str]							=      value
                     logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""    )
       else:
              unused_weights.append(__lowerCamelCase    )


@torch.no_grad()
def 			UpperCamelCase					(    __lowerCamelCase     : Dict				,					__lowerCamelCase     : List[str]				,					__lowerCamelCase     : Union[str, Any]=None    ):
       # load the pre-trained checkpoints
       snake_case       :   Optional[int]							=      torch.load(__lowerCamelCase    )
       snake_case       :   List[Any]							=      WavLMConfigOrig(checkpoint["cfg"]    )
       snake_case       :   Optional[int]							=      WavLMOrig(__lowerCamelCase    )
       model.load_state_dict(checkpoint["model"]    )
       model.eval()

       if config_path is not None:
              snake_case       :   str							=      WavLMConfig.from_pretrained(__lowerCamelCase    )
       else:
              snake_case       :   Optional[int]							=      WavLMConfig()

       snake_case       :   Any							=      WavLMModel(__lowerCamelCase    )

       recursively_load_weights(__lowerCamelCase				,					__lowerCamelCase    )

       hf_wavlm.save_pretrained(__lowerCamelCase    )


if __name__ == "__main__":
   __lowerCamelCase							=       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("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
   __lowerCamelCase							=       parser.parse_args()
   convert_wavlm_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)












'''simple docstring'''
import os

import numpy
import onnx
def 				lowercase__   (   __lowercase						:  Optional[int]				,  __lowercase						:  Union[str, Any]     ) ->      Dict:



       """simple docstring"""






       __UpperCamelCase       =  a.name
       __UpperCamelCase       =  b.name

       __UpperCamelCase       =  ''
       __UpperCamelCase       =  ''

       __UpperCamelCase       =  a == b

       __UpperCamelCase       =  name_a
       __UpperCamelCase       =  name_b

       return res
def 				lowercase__   (   __lowercase						:  int				,  __lowercase						:  int				,  __lowercase						:  List[Any]     ) ->      Optional[int]:



       """simple docstring"""






       for i, input_name in enumerate(node_proto.input     ):
              if input_name == name:
                     node_proto.input.insert(__lowercase				,  __lowercase     )
                     node_proto.input.pop(i + 1     )

       if node_proto.op_type == "If":
              _graph_replace_input_with(node_proto.attribute[0].g				,  __lowercase				,  __lowercase     )
              _graph_replace_input_with(node_proto.attribute[1].g				,  __lowercase				,  __lowercase     )
       if node_proto.op_type == "Loop":
              _graph_replace_input_with(node_proto.attribute[0].g				,  __lowercase				,  __lowercase     )
def 				lowercase__   (   __lowercase						:  int				,  __lowercase						:  List[Any]				,  __lowercase						:  Dict     ) ->      int:



       """simple docstring"""






       for n in graph_proto.node:
              _node_replace_input_with(__lowercase				,  __lowercase				,  __lowercase     )
def 				lowercase__   (   __lowercase						:  List[str]				,  __lowercase						:  Union[str, Any]				,  __lowercase						:  str     ) ->      Union[str, Any]:



       """simple docstring"""






       __UpperCamelCase       =  list(model.graph.initializer     )
       __UpperCamelCase       =  list(model_without_ext.graph.initializer     )
       for i, ref_i in ind_to_replace:
              assert inits_with_data[i].name == inits[i].name
              assert inits_with_data[ref_i].name == inits[ref_i].name
              assert i > ref_i

              __UpperCamelCase       =  inits[i].name
              __UpperCamelCase       =  inits[ref_i].name

              model_without_ext.graph.initializer.remove(inits[i]     )

              # for n in model.graph.node:
              _graph_replace_input_with(model_without_ext.graph				,  __lowercase				,  __lowercase     )


def 				lowercase__   (   __lowercase						:  Dict     ) ->      Union[str, Any]:



       """simple docstring"""






       __UpperCamelCase       =  os.path.dirname(__lowercase     )
       __UpperCamelCase       =  os.path.basename(__lowercase     )

       __UpperCamelCase       =  onnx.load(os.path.join(__lowercase				,  __lowercase     )     )

       __UpperCamelCase       =  list(model.graph.initializer     )

       __UpperCamelCase       =  set()
       __UpperCamelCase       =  {}
       __UpperCamelCase       =  []

       __UpperCamelCase       =  0

       for i in range(len(__lowercase     )     ):
              if i in dup_set:
                     continue

              for j in range(i + 1				,  len(__lowercase     )     ):
                     if j in dup_set:
                            continue
                     if _is_equal_tensor_proto(inits[i]				,  inits[j]     ):
                            dup_set.add(__lowercase     )
                            dup_set.add(__lowercase     )

                            __UpperCamelCase       =  inits[j].data_type
                            __UpperCamelCase       =  numpy.prod(inits[j].dims     )
                            if dtype == 1:
                                   mem_size *= 4
                            elif dtype == 6:
                                   mem_size *= 4
                            elif dtype == 7 or dtype == 11:
                                   mem_size *= 8
                            else:
                                   print('unexpected data type: '				,  __lowercase     )
                            total_reduced_size += mem_size

                            __UpperCamelCase       =  inits[i].name
                            __UpperCamelCase       =  inits[j].name

                            if name_i in dup_map:
                                   dup_map[name_i].append(__lowercase     )
                            else:
                                   __UpperCamelCase       =  [name_j]
                            ind_to_replace.append((j, i)     )

       print('total reduced size: '				,  total_reduced_size / 1024 / 1024 / 1024				,  'GB'     )

       __UpperCamelCase       =  sorted(__lowercase     )
       _remove_dup_initializers_from_model(__lowercase				,  __lowercase				,  __lowercase     )

       __UpperCamelCase       =  'optimized_' + model_file_name
       __UpperCamelCase       =  os.path.join(__lowercase				,  __lowercase     )
       onnx.save(__lowercase				,  __lowercase     )

       return new_model
