
"""simple docstring"""


import argparse

import fairseq
import torch
from torch import nn

from transformers import (
    MBartaaTokenizer,
    MBartConfig,
    MBartForCausalLM,
    SpeechEncoderDecoderConfig,
    SpeechEncoderDecoderModel,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaModel,
    logging,
)


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

SCREAMING_SNAKE_CASE			  =  {
    "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": "lm_head",
    "mask_emb": "masked_spec_embed",
}
SCREAMING_SNAKE_CASE			  =  [
    "lm_head",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
]



def 						lowerCamelCase__      (				UpperCAmelCase_		,   UpperCAmelCase_		,   UpperCAmelCase_		,   UpperCAmelCase_		,   UpperCAmelCase_					)->				Optional[int]:







       """simple docstring"""


       for attribute in key.split("."					):
              UpperCamelCase       			=		getattr(_UpperCamelCase		,   _UpperCamelCase					)

       if weight_type is not None:
              UpperCamelCase       			=		getattr(_UpperCamelCase		,   _UpperCamelCase					).shape
       else:
              UpperCamelCase       			=		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":
              UpperCamelCase       			=		value
       elif weight_type == "weight_g":
              UpperCamelCase       			=		value
       elif weight_type == "weight_v":
              UpperCamelCase       			=		value
       elif weight_type == "bias":
              UpperCamelCase       			=		value
       else:
              UpperCamelCase       			=		value

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



def 						lowerCamelCase__      (				UpperCAmelCase_		,   UpperCAmelCase_					)->				Tuple:







       """simple docstring"""
       UpperCamelCase       			=		[]
       UpperCamelCase       			=		fairseq_model.state_dict()

       UpperCamelCase       			=		hf_model.feature_extractor
       UpperCamelCase       			=		hf_model.adapter

       for name, value in fairseq_dict.items():
              UpperCamelCase       			=		False
              if "conv_layers" in name:
                     load_conv_layer(
                         _UpperCamelCase		,   _UpperCamelCase		,   _UpperCamelCase		,   _UpperCamelCase		,   hf_model.config.feat_extract_norm == "group"		,   )
                     UpperCamelCase       			=		True
              elif any(x in name for x in ["adaptor", "w2v_encoder.proj.", "w2v_proj_ln."]					):
                     load_adapter(_UpperCamelCase		,   _UpperCamelCase		,   _UpperCamelCase		,   _UpperCamelCase					)
                     UpperCamelCase       			=		True
              else:
                     for key, mapped_key in MAPPING.items():
                            if key in name or key.split("w2v_model."					)[-1] == name.split("."					)[0]:
                                   UpperCamelCase       			=		True
                                   if "*" in mapped_key:
                                          UpperCamelCase       			=		name.split(_UpperCamelCase					)[0].split("."					)[-2]
                                          UpperCamelCase       			=		mapped_key.replace("*"		,   _UpperCamelCase					)
                                   if "weight_g" in name:
                                          UpperCamelCase       			=		"weight_g"
                                   elif "weight_v" in name:
                                          UpperCamelCase       			=		"weight_v"
                                   elif "bias" in name:
                                          UpperCamelCase       			=		"bias"
                                   elif "weight" in name:
                                          UpperCamelCase       			=		"weight"
                                   else:
                                          UpperCamelCase       			=		None
                                   set_recursively(_UpperCamelCase		,   _UpperCamelCase		,   _UpperCamelCase		,   _UpperCamelCase		,   _UpperCamelCase					)
                            continue
              if not is_used:
                     unused_weights.append(_UpperCamelCase					)

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



def 						lowerCamelCase__      (				UpperCAmelCase_		,   UpperCAmelCase_		,   UpperCAmelCase_		,   UpperCAmelCase_		,   UpperCAmelCase_					)->				Optional[Any]:







       """simple docstring"""
       UpperCamelCase       			=		full_name.split("conv_layers."					)[-1]
       UpperCamelCase       			=		name.split("."					)
       UpperCamelCase       			=		int(items[0]					)
       UpperCamelCase       			=		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."
                     )
                     UpperCamelCase       			=		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."
                     )
                     UpperCamelCase       			=		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."
                     )
                     UpperCamelCase       			=		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."
                     )
                     UpperCamelCase       			=		value
                     logger.info(F"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."					)
       else:
              unused_weights.append(_UpperCamelCase					)



def 						lowerCamelCase__      (				UpperCAmelCase_		,   UpperCAmelCase_		,   UpperCAmelCase_		,   UpperCAmelCase_					)->				int:







       """simple docstring"""
       UpperCamelCase       			=		full_name.split("adaptor."					)[-1]
       UpperCamelCase       			=		name.split("."					)

       if items[1].isdigit():
              UpperCamelCase       			=		int(items[1]					)
       else:
              UpperCamelCase       			=		None

       if "adaptor" not in full_name:
              if "proj_ln" in full_name:
                     # has to be layer norm
                     if "bias" in name:
                            assert (
                                value.shape == adapter.proj_layer_norm.bias.data.shape
                            ), F"{full_name} has size {value.shape}, but {adapter.proj_layer_norm.bias.data.shape} was found."
                            UpperCamelCase       			=		value
                            logger.info(F"Adapter proj layer norm bias was initialized from {full_name}."					)
                     if "weight" in name:
                            assert (
                                value.shape == adapter.proj_layer_norm.weight.data.shape
                            ), F"{full_name} has size {value.shape}, but {adapter.proj_layer_norm.weight.data.shape} was found."
                            UpperCamelCase       			=		value
              else:
                     # has to be projection layer
                     if "bias" in name:
                            assert (
                                value.shape == adapter.proj.bias.data.shape
                            ), F"{full_name} has size {value.shape}, but {adapter.proj.bias.data.shape} was found."
                            UpperCamelCase       			=		value
                            logger.info(F"Adapter proj layer bias was initialized from {full_name}."					)
                     if "weight" in name:
                            assert (
                                value.shape == adapter.proj.weight.data.shape
                            ), F"{full_name} has size {value.shape}, but {adapter.proj.weight.data.shape} was found."
                            UpperCamelCase       			=		value
                            logger.info(F"Adapter proj layer weight was initialized from {full_name}."					)
       elif isinstance(_UpperCamelCase		,   _UpperCamelCase					):
              if "bias" in name:
                     assert (
                         value.shape == adapter.layers[layer_id].conv.bias.data.shape
                     ), F"{full_name} has size {value.shape}, but {adapter.layers[layer_id].conv.bias.data.shape} was found."
                     UpperCamelCase       			=		value
                     logger.info(F"Adapter layer {layer_id} bias was initialized from {full_name}."					)
              elif "weight" in name:
                     assert (
                         value.shape == adapter.layers[layer_id].conv.weight.data.shape
                     ), F"{full_name} has size {value.shape}, but {adapter.layers[layer_id].conv.weight.data.shape} was found."
                     UpperCamelCase       			=		value
                     logger.info(F"Adapter layer {layer_id} bias was initialized from {full_name}."					)
       else:
              unused_weights.append(_UpperCamelCase					)



def 						lowerCamelCase__      (				UpperCAmelCase_					)->				str:







       """simple docstring"""
       UpperCamelCase			,							UpperCamelCase       			=		emb.weight.shape
       UpperCamelCase       			=		nn.Linear(_UpperCamelCase		,   _UpperCamelCase		,   bias=_UpperCamelCase					)
       UpperCamelCase       			=		emb.weight.data
       return lin_layer



@torch.no_grad()
def 						lowerCamelCase__      (				UpperCAmelCase_		,   UpperCAmelCase_		,   UpperCAmelCase_		,   UpperCAmelCase_		,   UpperCAmelCase_		,   UpperCAmelCase_		,   UpperCAmelCase_		,   UpperCAmelCase_		,   UpperCAmelCase_		,   UpperCAmelCase_		,   UpperCAmelCase_		,   )->				int:







       """simple docstring"""
       UpperCamelCase       			=		WavaVecaConfig.from_pretrained(
           _UpperCamelCase		,   add_adapter=_UpperCamelCase		,   adapter_stride=_UpperCamelCase		,   adapter_kernel_size=_UpperCamelCase		,   use_auth_token=_UpperCamelCase		,   output_hidden_size=_UpperCamelCase		,   )
       UpperCamelCase       			=		MBartConfig.from_pretrained(_UpperCamelCase					)

       # load model
       UpperCamelCase			,							UpperCamelCase			,							UpperCamelCase       			=		fairseq.checkpoint_utils.load_model_ensemble_and_task(
           [checkpoint_path]		,   arg_overrides={
               "config_yaml": config_yaml_path,
               "data": "/".join(dict_path.split("/"					)[:-1]					),
               "w2v_path": checkpoint_path,
               "load_pretrained_decoder_from": None,
           }		,   )
       UpperCamelCase       			=		model[0].eval()

       # load feature extractor
       UpperCamelCase       			=		WavaVecaFeatureExtractor.from_pretrained(_UpperCamelCase		,   use_auth_token=_UpperCamelCase					)

       # set weights for wav2vec2 encoder
       UpperCamelCase       			=		WavaVecaModel(_UpperCamelCase					)

       recursively_load_weights_wavaveca(model.encoder		,   _UpperCamelCase					)

       # load decoder weights
       UpperCamelCase       			=		MBartForCausalLM(_UpperCamelCase					)
       UpperCamelCase			,							UpperCamelCase       			=		hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict()		,   strict=_UpperCamelCase					)
       logger.warning(F"The following keys are missing when loading the decoder weights: {missing_keys}"					)
       logger.warning(F"The following keys are unexpected when loading the decoder weights: {unexpected_keys}"					)

       UpperCamelCase       			=		SpeechEncoderDecoderModel(encoder=_UpperCamelCase		,   decoder=_UpperCamelCase					)
       UpperCamelCase       			=		False

       UpperCamelCase       			=		MBartaaTokenizer(_UpperCamelCase					)
       tokenizer.save_pretrained(_UpperCamelCase					)

       UpperCamelCase       			=		hf_wavavec.config.to_dict()
       UpperCamelCase       			=		tokenizer.pad_token_id
       UpperCamelCase       			=		tokenizer.bos_token_id
       UpperCamelCase       			=		tokenizer.eos_token_id
       UpperCamelCase       			=		"mbart50"
       UpperCamelCase       			=		"wav2vec2"

       UpperCamelCase       			=		tokenizer.eos_token_id
       UpperCamelCase       			=		25_00_04
       UpperCamelCase       			=		tokenizer.eos_token_id

       UpperCamelCase       			=		SpeechEncoderDecoderConfig.from_dict(_UpperCamelCase					)

       hf_wavavec.save_pretrained(_UpperCamelCase					)
       feature_extractor.save_pretrained(_UpperCamelCase					)


if __name__ == "__main__":
      SCREAMING_SNAKE_CASE			  =  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_yaml_path""", default=None, type=str, help="""Path to yaml file of fine-tuned model""")
      parser.add_argument(
          """--encoder_config_path""",
          default="""facebook/wav2vec2-xls-r-1b""",
          type=str,
          help="""Path to hf encoder wav2vec2 checkpoint config""",
      )
      parser.add_argument(
          """--decoder_config_path""",
          default="""facebook/mbart-large-50-one-to-many-mmt""",
          type=str,
          help="""Path to hf decoder checkpoint config""",
      )
      parser.add_argument("""--add_adapter""", default=True, type=bool, help="""whethere to add model adapter layers""")
      parser.add_argument("""--adapter_stride""", default=2, type=int, help="""stride of adapter layers""")
      parser.add_argument("""--adapter_kernel_size""", default=3, type=int, help="""kernel size of adapter layers""")
      parser.add_argument("""--encoder_output_dim""", default=1_024, type=int, help="""encoder output dim""")
      parser.add_argument("""--start_token_id""", default=250_004, type=int, help="""`decoder_start_token_id` of model config""")

      SCREAMING_SNAKE_CASE			  =  parser.parse_args()
      convert_wavaveca_checkpoint(
          args.checkpoint_path,
          args.pytorch_dump_folder_path,
          args.dict_path,
          args.config_yaml_path,
          encoder_config_path=args.encoder_config_path,
          decoder_config_path=args.decoder_config_path,
          add_adapter=args.add_adapter,
          adapter_kernel_size=args.adapter_kernel_size,
          adapter_stride=args.adapter_stride,
          decoder_start_token_id=args.start_token_id,
          encoder_output_dim=args.encoder_output_dim,
      )








from graphs.minimum_spanning_tree_kruskal import kruskal


def 							__lowerCAmelCase     (							)		->				Optional[Any]:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE		    =      9
 SCREAMING_SNAKE_CASE		    =      [
     [0, 1, 4],
     [0, 7, 8],
     [1, 2, 8],
     [7, 8, 7],
     [7, 6, 1],
     [2, 8, 2],
     [8, 6, 6],
     [2, 3, 7],
     [2, 5, 4],
     [6, 5, 2],
     [3, 5, 14],
     [3, 4, 9],
     [5, 4, 10],
     [1, 7, 11],
 ]

 SCREAMING_SNAKE_CASE		    =      kruskal(_UpperCamelCase		,				_UpperCamelCase						)

 SCREAMING_SNAKE_CASE		    =      [
     [7, 6, 1],
     [2, 8, 2],
     [6, 5, 2],
     [0, 1, 4],
     [2, 5, 4],
     [2, 3, 7],
     [0, 7, 8],
     [3, 4, 9],
 ]

 assert sorted(_UpperCamelCase						) == sorted(_UpperCamelCase						)

