






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

__a:     List[Any]   					=					{
    '''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''',
}
__a:     Optional[int]   					=					[
    '''lm_head''',
    '''quantizer.weight_proj''',
    '''quantizer.codevectors''',
    '''project_q''',
    '''project_hid''',
]




def       _SCREAMING_SNAKE_CASE (							__snake_case   ,      __snake_case   ,      __snake_case   ,      __snake_case   ,      __snake_case )				->						List[Any]:
       for attribute in key.split(""".""" ):
              _UpperCAmelCase							=    getattr(__snake_case   ,      __snake_case )

       if weight_type is not None:
              _UpperCAmelCase							=    getattr(__snake_case   ,      __snake_case ).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       _SCREAMING_SNAKE_CASE (							__snake_case   ,      __snake_case )				->						Dict:
       _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(
                         __snake_case   ,      __snake_case   ,      __snake_case   ,      __snake_case   ,      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(__snake_case   ,      __snake_case   ,      __snake_case   ,      __snake_case )
                     _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(__snake_case )[0].split(""".""" )[-2]
                                          _UpperCAmelCase							=    mapped_key.replace("""*"""   ,      __snake_case )
                                   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(__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       _SCREAMING_SNAKE_CASE (							__snake_case   ,      __snake_case   ,      __snake_case   ,      __snake_case   ,      __snake_case )				->						Optional[int]:
       _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(__snake_case )




def       _SCREAMING_SNAKE_CASE (							__snake_case   ,      __snake_case   ,      __snake_case   ,      __snake_case )				->						Union[str, Any]:
       _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(__snake_case   ,      __snake_case ):
              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(__snake_case )




def       _SCREAMING_SNAKE_CASE (							__snake_case )				->						List[Any]:
       _UpperCAmelCase	,			_UpperCAmelCase							=    emb.weight.shape
       _UpperCAmelCase							=    nn.Linear(__snake_case   ,      __snake_case   ,      bias=__snake_case )
       _UpperCAmelCase							=    emb.weight.data
       return lin_layer




@torch.no_grad()
def       _SCREAMING_SNAKE_CASE (							__snake_case   ,      __snake_case   ,      __snake_case   ,      __snake_case   ,      __snake_case   ,      __snake_case   ,      __snake_case   ,      __snake_case   ,      __snake_case   ,      __snake_case   ,      __snake_case   ,      )				->						List[Any]:
       _UpperCAmelCase							=    WavaVecaConfig.from_pretrained(
           __snake_case   ,      add_adapter=__snake_case   ,      adapter_stride=__snake_case   ,      adapter_kernel_size=__snake_case   ,      use_auth_token=__snake_case   ,      output_hidden_size=__snake_case   ,      )
       _UpperCAmelCase							=    MBartConfig.from_pretrained(__snake_case )

       # 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(__snake_case   ,      use_auth_token=__snake_case )

       # set weights for wav2vec2 encoder
       _UpperCAmelCase							=    WavaVecaModel(__snake_case )

       recursively_load_weights_wavaveca(model.encoder   ,      __snake_case )

       # load decoder weights
       _UpperCAmelCase							=    MBartForCausalLM(__snake_case )
       _UpperCAmelCase	,			_UpperCAmelCase							=    hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict()   ,      strict=__snake_case )
       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=__snake_case   ,      decoder=__snake_case )
       _UpperCAmelCase							=    False

       _UpperCAmelCase							=    MBartaaTokenizer(__snake_case )
       tokenizer.save_pretrained(__snake_case )

       _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							=    2_5_0_0_0_4
       _UpperCAmelCase							=    tokenizer.eos_token_id

       _UpperCAmelCase							=    SpeechEncoderDecoderConfig.from_dict(__snake_case )

       hf_wavavec.save_pretrained(__snake_case )
       feature_extractor.save_pretrained(__snake_case )


if __name__ == "__main__":
  __a:     Dict   					=					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=1024, type=int, help='''encoder output dim''')
  parser.add_argument('''--start_token_id''', default=250004, type=int, help='''`decoder_start_token_id` of model config''')

  __a:     str   					=					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,
  )





'''simple docstring'''


def 						lowerCamelCase__   (				a							):
						__snake_case						 =   [0] * len(a							)
						__snake_case						 =   []
						__snake_case						 =   []
						__snake_case						 =   0

						for values in graph.values():
												for i in values:
																		indegree[i] += 1

						for i in range(len(a							)							):
												if indegree[i] == 0:
																		queue.append(a							)

						while queue:
												__snake_case						 =   queue.pop(0							)
												cnt += 1
												topo.append(a							)
												for x in graph[vertex]:
																		indegree[x] -= 1
																		if indegree[x] == 0:
																								queue.append(a							)

						if cnt != len(a							):
												print('Cycle exists'							)
						else:
												print(a							)


# Adjacency List of Graph
_lowercase  =			{0: [1, 2], 1: [3], 2: [3], 3: [4, 5], 4: [], 5: []}






topological_sort(graph)

