





import argparse
import json
import os

import fairseq
import torch
from torch import nn

from transformers import (
    SpeechaTextaConfig,
    SpeechaTextaForCausalLM,
    SpeechaTextaTokenizer,
    SpeechEncoderDecoderConfig,
    SpeechEncoderDecoderModel,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaModel,
    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_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""",
}
_lowerCAmelCase 				=     [
    """lm_head""",
    """quantizer.weight_proj""",
    """quantizer.codevectors""",
    """project_q""",
    """project_hid""",
]



def     _lowerCAmelCase		(  _lowerCAmelCase  ,_lowerCAmelCase  ,_lowerCAmelCase  ,_lowerCAmelCase  ,_lowerCAmelCase     ):
   '''simple docstring'''


   for attribute in key.split("""."""     ):
      A_     :				Optional[int]			      =	getattr(_lowerCAmelCase  ,_lowerCAmelCase     )

   if weight_type is not None:
      A_     :				List[str]			      =	getattr(_lowerCAmelCase  ,_lowerCAmelCase     ).shape
   else:
      A_     :				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":
      A_     :				Tuple			      =	value
   elif weight_type == "weight_g":
      A_     :				int			      =	value
   elif weight_type == "weight_v":
      A_     :				Optional[int]			      =	value
   elif weight_type == "bias":
      A_     :				List[Any]			      =	value
   else:
      A_     :				Any			      =	value

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



def     _lowerCAmelCase		(  _lowerCAmelCase  ,_lowerCAmelCase     ):
   '''simple docstring'''


   A_     :				Optional[int]			      =	[]
   A_     :				Union[str, Any]			      =	fairseq_model.state_dict()

   A_     :				Union[str, Any]			      =	hf_model.feature_extractor

   # if encoder has different dim to decoder -> use proj_weight
   A_     :				Optional[int]			      =	None

   for name, value in fairseq_dict.items():
      A_     :				Any			      =	False
      if "conv_layers" in name:
         load_conv_layer(
             _lowerCAmelCase  ,_lowerCAmelCase  ,_lowerCAmelCase  ,_lowerCAmelCase  ,hf_model.config.feat_extract_norm == """group"""  ,)
         A_     :				Union[str, Any]			      =	True
      elif name.split("""."""     )[0] == "proj":
         A_     :				str			      =	fairseq_model.proj
         A_     :				Any			      =	True
      else:
         for key, mapped_key in MAPPING.items():
            if key in name or key.split("""w2v_model."""     )[-1] == name.split("""."""     )[0]:
               A_     :				int			      =	True
               if "*" in mapped_key:
                  A_     :				Optional[Any]			      =	name.split(_lowerCAmelCase     )[0].split("""."""     )[-2]
                  A_     :				str			      =	mapped_key.replace("""*"""  ,_lowerCAmelCase     )
               if "weight_g" in name:
                  A_     :				Union[str, Any]			      =	"""weight_g"""
               elif "weight_v" in name:
                  A_     :				Any			      =	"""weight_v"""
               elif "bias" in name:
                  A_     :				List[Any]			      =	"""bias"""
               elif "weight" in name:
                  A_     :				int			      =	"""weight"""
               else:
                  A_     :				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}"""     )

   return proj_weight



def     _lowerCAmelCase		(  _lowerCAmelCase  ,_lowerCAmelCase  ,_lowerCAmelCase  ,_lowerCAmelCase  ,_lowerCAmelCase     ):
   '''simple docstring'''


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



def     _lowerCAmelCase		(  _lowerCAmelCase     ):
   '''simple docstring'''


   A_      ,				A_     :				Tuple			      =	emb.weight.shape
   A_     :				str			      =	nn.Linear(_lowerCAmelCase  ,_lowerCAmelCase  ,bias=_lowerCAmelCase     )
   A_     :				Tuple			      =	emb.weight.data
   return lin_layer



def     _lowerCAmelCase		(  _lowerCAmelCase     ):
   '''simple docstring'''


   with open(_lowerCAmelCase  ,"""r"""  ,encoding="""utf-8"""     ) as f:
      A_     :				List[str]			      =	f.readlines()
      A_     :				Dict			      =	[line.split(""" """     )[0] for line in lines]

   A_     :				List[str]			      =	len(_lowerCAmelCase     )

   A_     :				List[str]			      =	{
       """<s>""": 0,
       """<pad>""": 1,
       """</s>""": 2,
       """<unk>""": 3,
   }

   vocab_dict.update(dict(zip(_lowerCAmelCase  ,range(4  ,num_words + 4     )     )     )     )
   return vocab_dict



@torch.no_grad()
def     _lowerCAmelCase		(  _lowerCAmelCase  ,_lowerCAmelCase  ,_lowerCAmelCase  ,_lowerCAmelCase  ,_lowerCAmelCase  ,_lowerCAmelCase  ,_lowerCAmelCase  ,):
   '''simple docstring'''


   A_     :				Tuple			      =	WavaVecaConfig.from_pretrained(_lowerCAmelCase     )
   A_     :				Any			      =	SpeechaTextaConfig.from_pretrained(
       _lowerCAmelCase  ,vocab_size=_lowerCAmelCase  ,decoder_layers=_lowerCAmelCase  ,do_stable_layer_norm=_lowerCAmelCase     )

   A_     :				List[Any]			      =	WavaVecaFeatureExtractor(
       feature_size=1  ,sampling_rate=1_6_0_0_0  ,padding_value=0  ,do_normalize=_lowerCAmelCase  ,return_attention_mask=_lowerCAmelCase  ,)

   A_      ,				A_      ,				A_     :				Union[str, Any]			      =	fairseq.checkpoint_utils.load_model_ensemble_and_task(
       [checkpoint_path]  ,arg_overrides={"""data""": """/""".join(dict_path.split("""/"""     )[:-1]     )}     )
   A_     :				Any			      =	model[0].eval()

   # set weights for wav2vec2 encoder
   A_     :				str			      =	WavaVecaModel(_lowerCAmelCase     )
   A_     :				Dict			      =	recursively_load_weights_wavaveca(model.encoder  ,_lowerCAmelCase     )

   A_     :				Any			      =	SpeechaTextaForCausalLM(_lowerCAmelCase     )
   A_      ,				A_     :				Optional[Any]			      =	hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict()  ,strict=_lowerCAmelCase     )

   # set output linear layer
   unexpected_keys.remove("""embed_out"""     )
   A_     :				Any			      =	nn.Parameter(model.decoder.embed_out.detach()     )

   # layer norm is init to identity matrix so leaving it is fine
   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}"""     )

   A_     :				Union[str, Any]			      =	SpeechEncoderDecoderModel(encoder=_lowerCAmelCase  ,decoder=_lowerCAmelCase     )
   A_     :				List[str]			      =	False

   # add projection layer
   A_     :				Tuple			      =	nn.Parameter(projection_layer.weight     )
   A_     :				Any			      =	nn.Parameter(projection_layer.bias     )

   A_     :				int			      =	create_vocab_dict(_lowerCAmelCase     )

   with open(os.path.join(_lowerCAmelCase  ,"""vocab.json"""     )  ,"""w"""     ) as fp:
      json.dump(_lowerCAmelCase  ,_lowerCAmelCase     )

   A_     :				Any			      =	SpeechaTextaTokenizer(os.path.join(_lowerCAmelCase  ,"""vocab.json"""     )     )
   tokenizer.save_pretrained(_lowerCAmelCase     )

   A_     :				List[str]			      =	hf_wavavec.config.to_dict()
   A_     :				Tuple			      =	tokenizer.pad_token_id
   A_     :				Optional[int]			      =	tokenizer.bos_token_id
   A_     :				Optional[int]			      =	tokenizer.eos_token_id
   A_     :				List[Any]			      =	"""speech_to_text_2"""
   A_     :				Dict			      =	"""wav2vec2"""

   A_     :				Union[str, Any]			      =	SpeechEncoderDecoderConfig.from_dict(_lowerCAmelCase     )

   hf_wavavec.save_pretrained(_lowerCAmelCase     )
   feature_extractor.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("""--dict_path""", default=None, type=str, help="""Path to dict of fine-tuned model""")
      parser.add_argument(
          """--encoder_config_path""",
          default="""facebook/wav2vec2-large-lv60""",
          type=str,
          help="""Path to hf encoder wav2vec2 checkpoint config""",
      )
      parser.add_argument(
          """--decoder_config_path""",
          default="""facebook/s2t-small-mustc-en-fr-st""",
          type=str,
          help="""Path to hf decoder s2t checkpoint config""",
      )
      parser.add_argument("""--vocab_size""", default=10_224, type=int, help="""Vocab size of decoder""")
      parser.add_argument("""--num_decoder_layers""", default=7, type=int, help="""Number of decoder layers""")

      _lowerCAmelCase 				=     parser.parse_args()
      convert_wavaveca_checkpoint(
          args.checkpoint_path,
          args.pytorch_dump_folder_path,
          args.dict_path,
          encoder_config_path=args.encoder_config_path,
          decoder_config_path=args.decoder_config_path,
          vocab_size=args.vocab_size,
          num_decoder_layers=args.num_decoder_layers,
      )













def     _lowerCAmelCase		(  _lowerCAmelCase     ):
   '''simple docstring'''


   A_     :				Union[str, Any]			      =	[0] * len(_lowerCAmelCase     )
   A_     :				Optional[int]			      =	[]
   A_     :				str			      =	[]
   A_     :				Dict			      =	0

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

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

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

   if cnt != len(_lowerCAmelCase     ):
      print("""Cycle exists"""     )
   else:
      print(_lowerCAmelCase     )


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







topological_sort(graph)






