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(     UpperCamelCase_				, UpperCamelCase_				, UpperCamelCase_				, UpperCamelCase_				, UpperCamelCase_				, UpperCamelCase_    )						->     Dict:







 '''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
    UpperCamelCase          =	"""lm_head"""

  UpperCamelCase          =	getattr(lowercase_				, lowercase_    )

 if weight_type is not None:
  UpperCamelCase          =	getattr(lowercase_				, lowercase_    ).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        lowercase(     UpperCamelCase_				, UpperCamelCase_				, UpperCamelCase_    )						->     List[Any]:







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

 UpperCamelCase          =	hf_model.unispeech.feature_extractor

 for name, value in fairseq_dict.items():
  UpperCamelCase          =	False
  if "conv_layers" in name:
   load_conv_layer(
       lowercase_				, lowercase_				, lowercase_				, lowercase_				, hf_model.config.feat_extract_norm == """group"""				, )
   UpperCamelCase          =	True
  else:
   for key, mapped_key in MAPPING.items():
    UpperCamelCase          =	"""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]:
     UpperCamelCase          =	True
     if "*" in mapped_key:
      UpperCamelCase          =	name.split(lowercase_    )[0].split("""."""    )[-2]
      UpperCamelCase          =	mapped_key.replace("""*"""				, lowercase_    )
     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:
      # TODO: don't match quantizer.weight_proj
      UpperCamelCase          =	"""weight"""
     else:
      UpperCamelCase          =	None
     set_recursively(lowercase_				, lowercase_				, lowercase_				, lowercase_				, lowercase_				, lowercase_    )
    continue
  if not is_used:
   unused_weights.append(lowercase_    )

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





def        lowercase(     UpperCamelCase_				, UpperCamelCase_				, UpperCamelCase_				, UpperCamelCase_				, UpperCamelCase_    )						->     List[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(lowercase_    )





@torch.no_grad()
def        lowercase(     UpperCamelCase_				, UpperCamelCase_				, UpperCamelCase_=None				, UpperCamelCase_=None				, UpperCamelCase_=True    )						->     List[Any]:







 '''simple docstring'''
 if config_path is not None:
  UpperCamelCase          =	UniSpeechConfig.from_pretrained(lowercase_    )
 else:
  UpperCamelCase          =	UniSpeechConfig()

 if is_finetuned:
  if dict_path:
   UpperCamelCase          =	Dictionary.load_from_json(lowercase_    )

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

   # fairseq has the <pad> and <s> switched
   UpperCamelCase          =	42
   UpperCamelCase          =	43
   with open(lowercase_				, """w"""				, encoding="""utf-8"""    ) as vocab_handle:
    json.dump(lowercase_				, lowercase_    )
   UpperCamelCase          =	WavaVecaPhonemeCTCTokenizer(
       lowercase_				, 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=lowercase_				, )
   UpperCamelCase          =	True if config.feat_extract_norm == """layer""" else False
   UpperCamelCase          =	WavaVecaFeatureExtractor(
       feature_size=1				, sampling_rate=16000				, padding_value=0				, do_normalize=lowercase_				, return_attention_mask=lowercase_				, )
   UpperCamelCase          =	WavaVecaProcessor(feature_extractor=lowercase_				, tokenizer=lowercase_    )
   processor.save_pretrained(lowercase_    )

  UpperCamelCase          =	UniSpeechForCTC(lowercase_    )
 else:
  UpperCamelCase          =	UniSpeechForPreTraining(lowercase_    )

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

 UpperCamelCase          =	model[0].eval()

 recursively_load_weights(lowercase_				, lowercase_				, lowercase_    )

 hf_unispeech.save_pretrained(lowercase_    )


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
     )



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


A_       : Optional[int]         =			{'configuration_ibert': ['IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'IBertConfig', 'IBertOnnxConfig']}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A_       : str         =			[
	    'IBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'IBertForMaskedLM',
	    'IBertForMultipleChoice',
	    'IBertForQuestionAnswering',
	    'IBertForSequenceClassification',
	    'IBertForTokenClassification',
	    'IBertModel',
	    'IBertPreTrainedModel',
	]

if TYPE_CHECKING:
	from .configuration_ibert import IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, IBertConfig, IBertOnnxConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_ibert import (
		    IBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    IBertForMaskedLM,
		    IBertForMultipleChoice,
		    IBertForQuestionAnswering,
		    IBertForSequenceClassification,
		    IBertForTokenClassification,
		    IBertModel,
		    IBertPreTrainedModel,
		)

else:
	import sys

	A_       : Union[str, Any]         =			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



