


'''simple docstring'''







import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    UniSpeechConfig,
    UniSpeechForCTC,
    UniSpeechForPreTraining,
    WavaVecaFeatureExtractor,
    WavaVecaPhonemeCTCTokenizer,
    WavaVecaProcessor,
    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': 'ctc_proj',
    'mask_emb': 'masked_spec_embed',
}
SCREAMING_SNAKE_CASE_      =			[
    'ctc_proj',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
]




def        __lowercase						(      __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE		)				->					List[Any]:
     """simple docstring"""




     for attribute in key.split("""."""		):
          if is_finetuned:
               if attribute in ["quantizer", "project_q", "project_hid"]:
                    # those layers are only relevant for pretraining and should be dropped
                    return

               if attribute == "ctc_proj":
                    # we should rename `ctc_proj` to `lm_head` for fine-tuned phoneme models
                    __a       =   """lm_head"""

          __a       =   getattr(__SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE		)

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

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




def        __lowercase						(      __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE		)				->					Union[str, Any]:
     """simple docstring"""




     __a       =   []
     __a       =   fairseq_model.state_dict()

     __a       =   hf_model.unispeech.feature_extractor

     for name, value in fairseq_dict.items():
          __a       =   False
          if "conv_layers" in name:
               load_conv_layer(
                   __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE				,  hf_model.config.feat_extract_norm == """group"""				,  )
               __a       =   True
          else:
               for key, mapped_key in MAPPING.items():
                    __a       =   """unispeech.""" + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
                    if key in name or key.split("""w2v_model."""		)[-1] == name.split("""."""		)[0]:
                         __a       =   True
                         if "*" in mapped_key:
                              __a       =   name.split(__SCREAMING_SNAKE_CASE		)[0].split("""."""		)[-2]
                              __a       =   mapped_key.replace("""*"""				,  __SCREAMING_SNAKE_CASE		)
                         if "weight_g" in name:
                              __a       =   """weight_g"""
                         elif "weight_v" in name:
                              __a       =   """weight_v"""
                         elif "bias" in name:
                              __a       =   """bias"""
                         elif "weight" in name:
                              # TODO: don't match quantizer.weight_proj
                              __a       =   """weight"""
                         else:
                              __a       =   None
                         set_recursively(__SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE		)
                    continue
          if not is_used:
               unused_weights.append(__SCREAMING_SNAKE_CASE		)

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




def        __lowercase						(      __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE		)				->					str:
     """simple docstring"""




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




@torch.no_grad()
def        __lowercase						(      __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE=None				,  __SCREAMING_SNAKE_CASE=None				,  __SCREAMING_SNAKE_CASE=True		)				->					Dict:
     """simple docstring"""




     if config_path is not None:
          __a       =   UniSpeechConfig.from_pretrained(__SCREAMING_SNAKE_CASE		)
     else:
          __a       =   UniSpeechConfig()

     if is_finetuned:
          if dict_path:
               __a       =   Dictionary.load_from_json(__SCREAMING_SNAKE_CASE		)

               # important change bos & pad token id since CTC symbol is <pad> and
               # not <s> as in fairseq
               __a       =   target_dict.pad_index
               __a       =   target_dict.bos_index
               __a       =   target_dict.eos_index
               __a       =   len(target_dict.symbols		)
               __a       =   os.path.join(__SCREAMING_SNAKE_CASE				,  """vocab.json"""		)
               if not os.path.isdir(__SCREAMING_SNAKE_CASE		):
                    logger.error("""--pytorch_dump_folder_path ({}) should be a directory""".format(__SCREAMING_SNAKE_CASE		)		)
                    return
               os.makedirs(__SCREAMING_SNAKE_CASE				,  exist_ok=__SCREAMING_SNAKE_CASE		)
               __a       =   target_dict.indices

               # fairseq has the <pad> and <s> switched
               __a       =   42
               __a       =   43
               with open(__SCREAMING_SNAKE_CASE				,  """w"""				,  encoding="""utf-8"""		) as vocab_handle:
                    json.dump(__SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE		)
               __a       =   WavaVecaPhonemeCTCTokenizer(
                   __SCREAMING_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=__SCREAMING_SNAKE_CASE				,  )
               __a       =   True if config.feat_extract_norm == """layer""" else False
               __a       =   WavaVecaFeatureExtractor(
                   feature_size=1				,  sampling_rate=1_6000				,  padding_value=0				,  do_normalize=__SCREAMING_SNAKE_CASE				,  return_attention_mask=__SCREAMING_SNAKE_CASE				,  )
               __a       =   WavaVecaProcessor(feature_extractor=__SCREAMING_SNAKE_CASE				,  tokenizer=__SCREAMING_SNAKE_CASE		)
               processor.save_pretrained(__SCREAMING_SNAKE_CASE		)

          __a       =   UniSpeechForCTC(__SCREAMING_SNAKE_CASE		)
     else:
          __a       =   UniSpeechForPreTraining(__SCREAMING_SNAKE_CASE		)

     if is_finetuned:
          __a ,						__a ,						__a       =   fairseq.checkpoint_utils.load_model_ensemble_and_task(
              [checkpoint_path]				,  arg_overrides={"""data""": """/""".join(dict_path.split("""/"""		)[:-1]		), """w2v_path""": checkpoint_path}		)
     else:
          __a ,						__a ,						__a       =   fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]		)

     __a       =   model[0].eval()

     recursively_load_weights(__SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE		)

     hf_unispeech.save_pretrained(__SCREAMING_SNAKE_CASE		)


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_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'
 )
 SCREAMING_SNAKE_CASE_      =			parser.parse_args()
 convert_unispeech_checkpoint(
     args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
 )










'''simple docstring'''







import argparse
from typing import Dict

import tensorflow as tf
import torch
from tqdm import tqdm

from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration


SCREAMING_SNAKE_CASE_      =			[
    # tf -> hf
    ('/', '.'),
    ('layer_', 'layers.'),
    ('kernel', 'weight'),
    ('beta', 'bias'),
    ('gamma', 'weight'),
    ('pegasus', 'model'),
]
SCREAMING_SNAKE_CASE_      =			[
    ('.output.dense', '.fc2'),
    ('intermediate.LayerNorm', 'final_layer_norm'),
    ('intermediate.dense', 'fc1'),
]

SCREAMING_SNAKE_CASE_      =			(
    INIT_COMMON
    + [
        ('attention.self.LayerNorm', 'self_attn_layer_norm'),
        ('attention.output.dense', 'self_attn.out_proj'),
        ('attention.self', 'self_attn'),
        ('attention.encdec.LayerNorm', 'encoder_attn_layer_norm'),
        ('attention.encdec_output.dense', 'encoder_attn.out_proj'),
        ('attention.encdec', 'encoder_attn'),
        ('key', 'k_proj'),
        ('value', 'v_proj'),
        ('query', 'q_proj'),
        ('decoder.LayerNorm', 'decoder.layernorm_embedding'),
    ]
    + END_COMMON
)

SCREAMING_SNAKE_CASE_      =			(
    INIT_COMMON
    + [
        ('embeddings.word_embeddings', 'shared.weight'),
        ('embeddings.position_embeddings', 'embed_positions.weight'),
        ('attention.self.LayerNorm', 'self_attn_layer_norm'),
        ('attention.output.dense', 'self_attn.output'),
        ('attention.self', 'self_attn.self'),
        ('encoder.LayerNorm', 'encoder.layernorm_embedding'),
    ]
    + END_COMMON
)

SCREAMING_SNAKE_CASE_      =			[
    'encdec/key/bias',
    'encdec/query/bias',
    'encdec/value/bias',
    'self/key/bias',
    'self/query/bias',
    'self/value/bias',
    'encdec_output/dense/bias',
    'attention/output/dense/bias',
]




def        __lowercase						(      __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE		)				->					int:
     """simple docstring"""




     for tf_name, hf_name in patterns:
          __a       =   k.replace(__SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE		)
     return k




def        __lowercase						(      __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE		)				->					BigBirdPegasusForConditionalGeneration:
     """simple docstring"""




     __a       =   BigBirdPegasusConfig(**__SCREAMING_SNAKE_CASE		)
     __a       =   BigBirdPegasusForConditionalGeneration(__SCREAMING_SNAKE_CASE		)
     __a       =   torch_model.state_dict()
     __a       =   {}

     # separating decoder weights
     __a       =   {k: tf_weights[k] for k in tf_weights if k.startswith("""pegasus/decoder"""		)}
     __a       =   {k: tf_weights[k] for k in tf_weights if not k.startswith("""pegasus/decoder"""		)}

     for k, v in tqdm(decoder_weights.items()				,  """tf -> hf conversion"""		):
          __a       =   [k.endswith(__SCREAMING_SNAKE_CASE		) for ending in KEYS_TO_IGNORE]
          if any(__SCREAMING_SNAKE_CASE		):
               continue
          __a       =   DECODER_PATTERNS
          __a       =   rename_state_dict_key(__SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE		)
          if new_k not in state_dict:
               raise ValueError(F'''could not find new key {new_k} in state dict. (converted from {k})'''		)
          if any(True if i in k else False for i in ["""dense""", """query""", """key""", """value"""]		):
               __a       =   v.T
          __a       =   torch.from_numpy(__SCREAMING_SNAKE_CASE		)
          assert v.shape == state_dict[new_k].shape, F'''{new_k}, {k}, {v.shape}, {state_dict[new_k].shape}'''

     for k, v in tqdm(remaining_weights.items()				,  """tf -> hf conversion"""		):
          __a       =   [k.endswith(__SCREAMING_SNAKE_CASE		) for ending in KEYS_TO_IGNORE]
          if any(__SCREAMING_SNAKE_CASE		):
               continue
          __a       =   REMAINING_PATTERNS
          __a       =   rename_state_dict_key(__SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE		)
          if new_k not in state_dict and k != "pegasus/embeddings/position_embeddings":
               raise ValueError(F'''could not find new key {new_k} in state dict. (converted from {k})'''		)
          if any(True if i in k else False for i in ["""dense""", """query""", """key""", """value"""]		):
               __a       =   v.T
          __a       =   torch.from_numpy(__SCREAMING_SNAKE_CASE		)
          if k != "pegasus/embeddings/position_embeddings":
               assert v.shape == state_dict[new_k].shape, F'''{new_k}, {k}, {v.shape}, {state_dict[new_k].shape}'''

     __a       =   mapping["""model.embed_positions.weight"""]
     __a       =   mapping.pop("""model.embed_positions.weight"""		)
     __a ,						__a       =   torch_model.load_state_dict(__SCREAMING_SNAKE_CASE				,  strict=__SCREAMING_SNAKE_CASE		)
     __a       =   [
         k
         for k in missing
         if k
         not in [
             """final_logits_bias""",
             """model.encoder.embed_tokens.weight""",
             """model.decoder.embed_tokens.weight""",
             """lm_head.weight""",
         ]
     ]
     assert unexpected_missing == [], F'''no matches found for the following torch keys {unexpected_missing}'''
     assert extra == [], F'''no matches found for the following tf keys {extra}'''
     return torch_model




def        __lowercase						(      __SCREAMING_SNAKE_CASE		)				->					Dict:
     """simple docstring"""




     __a       =   tf.train.list_variables(__SCREAMING_SNAKE_CASE		)
     __a       =   {}
     __a       =   ["""global_step"""]
     for name, shape in tqdm(__SCREAMING_SNAKE_CASE				,  desc="""converting tf checkpoint to dict"""		):
          __a       =   any(pat in name for pat in ignore_name		)
          if skip_key:
               continue
          __a       =   tf.train.load_variable(__SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE		)
          __a       =   array
     return tf_weights




def        __lowercase						(      __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE		)				->					Tuple:
     """simple docstring"""




     __a       =   get_tf_weights_as_numpy(__SCREAMING_SNAKE_CASE		)
     __a       =   convert_bigbird_pegasus(__SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE		)
     torch_model.save_pretrained(__SCREAMING_SNAKE_CASE		)


if __name__ == "__main__":
 SCREAMING_SNAKE_CASE_      =			argparse.ArgumentParser()
 parser.add_argument('--tf_ckpt_path', type=str, help='passed to tf.train.list_variables')
 parser.add_argument('--save_dir', default=None, type=str, help='Path to the output PyTorch model.')
 SCREAMING_SNAKE_CASE_      =			parser.parse_args()
 SCREAMING_SNAKE_CASE_      =			{}
 convert_bigbird_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir, config_update=config_update)






