

import argparse

import fairseq
import torch

from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging


logging.set_verbosity_info()
__snake_case							:	Any											=       logging.get_logger(__name__)

__snake_case							:	int											=       {
    "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",
    "encoder.layer_norm_for_extract": "layer_norm_for_extract",
    "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",
    "label_embs_concat": "label_embeddings_concat",
    "mask_emb": "masked_spec_embed",
    "spk_proj": "speaker_proj",
}
__snake_case							:	List[Any]											=       [
    "lm_head",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
    "label_embeddings_concat",
    "speaker_proj",
    "layer_norm_for_extract",
]


def  _UpperCAmelCase							(		a__   ,     a__   ,     a__   ,     a__   ,     a__):

			'''simple docstring'''



			for attribute in key.split("""."""):
						a_		:		Any      				=      getattr(A__   ,     A__)

			if weight_type is not None:
						a_		:		List[str]      				=      getattr(A__   ,     A__).shape
			else:
						a_		:		str      				=      hf_pointer.shape

			if hf_shape != value.shape:
						raise ValueError(
						    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_		:		List[str]      				=      value
			elif weight_type == "weight_g":
						a_		:		str      				=      value
			elif weight_type == "weight_v":
						a_		:		Tuple      				=      value
			elif weight_type == "bias":
						a_		:		Optional[int]      				=      value
			else:
						a_		:		Union[str, Any]      				=      value

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


def  _UpperCAmelCase							(		a__   ,     a__):

			'''simple docstring'''



			a_		:		Dict      				=      []
			a_		:		int      				=      fairseq_model.state_dict()

			a_		:		Any      				=      hf_model.unispeech_sat.feature_extractor

			for name, value in fairseq_dict.items():
						a_		:		List[Any]      				=      False
						if "conv_layers" in name:
									load_conv_layer(
									    A__   ,     A__   ,     A__   ,     A__   ,     hf_model.config.feat_extract_norm == """group"""   ,     )
									a_		:		Any      				=      True
						else:
									for key, mapped_key in MAPPING.items():
												a_		:		int      				=      """unispeech_sat.""" + 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]:
															if "layer_norm_for_extract" in name and (".".join(name.split(""".""")[:-1]) != key):
																		# special case since naming is very similar
																		continue
															a_		:		str      				=      True
															if "*" in mapped_key:
																		a_		:		Any      				=      name.split(A__)[0].split(""".""")[-2]
																		a_		:		Union[str, Any]      				=      mapped_key.replace("""*"""   ,     A__)
															if "weight_g" in name:
																		a_		:		Union[str, Any]      				=      """weight_g"""
															elif "weight_v" in name:
																		a_		:		Optional[Any]      				=      """weight_v"""
															elif "bias" in name:
																		a_		:		Any      				=      """bias"""
															elif "weight" in name:
																		# TODO: don't match quantizer.weight_proj
																		a_		:		int      				=      """weight"""
															else:
																		a_		:		int      				=      None
															set_recursively(A__   ,     A__   ,     A__   ,     A__   ,     A__)
												continue
						if not is_used:
									unused_weights.append(A__)

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


def  _UpperCAmelCase							(		a__   ,     a__   ,     a__   ,     a__   ,     a__):

			'''simple docstring'''



			a_		:		Optional[int]      				=      full_name.split("""conv_layers.""")[-1]
			a_		:		Optional[Any]      				=      name.split(""".""")
			a_		:		List[str]      				=      int(items[0])
			a_		:		int      				=      int(items[1])

			if type_id == 0:
						if "bias" in name:
									if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
												raise ValueError(
												    f'''{full_name} has size {value.shape}, but'''
												    f''' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.''')
									a_		:		int      				=      value
									logger.info(f'''Feat extract conv layer {layer_id} was initialized from {full_name}.''')
						elif "weight" in name:
									if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
												raise ValueError(
												    f'''{full_name} has size {value.shape}, but'''
												    f''' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.''')
									a_		:		Any      				=      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:
									if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
												raise ValueError(
												    f'''{full_name} has size {value.shape}, but'''
												    f''' {feature_extractor[layer_id].layer_norm.bias.data.shape} was found.''')
									a_		:		List[str]      				=      value
									logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.''')
						elif "weight" in name:
									if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
												raise ValueError(
												    f'''{full_name} has size {value.shape}, but'''
												    f''' {feature_extractor[layer_id].layer_norm.weight.data.shape} was found.''')
									a_		:		Union[str, Any]      				=      value
									logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.''')
			else:
						unused_weights.append(A__)


@torch.no_grad()
def  _UpperCAmelCase							(		a__   ,     a__   ,     a__=None   ,     a__=None   ,     a__=True):

			'''simple docstring'''



			if config_path is not None:
						a_		:		Union[str, Any]      				=      UniSpeechSatConfig.from_pretrained(A__)
			else:
						a_		:		Any      				=      UniSpeechSatConfig()

			a_		:		List[Any]      				=      """"""

			if is_finetuned:
						a_		:		Tuple      				=      UniSpeechSatForCTC(A__)
			else:
						a_		:		str      				=      UniSpeechSatForPreTraining(A__)

			a_				, a_				, a_		:		Optional[int]      				=      fairseq.checkpoint_utils.load_model_ensemble_and_task(
			    [checkpoint_path]   ,     arg_overrides={"""data""": """/""".join(dict_path.split("""/""")[:-1])})
			a_		:		int      				=      model[0].eval()

			recursively_load_weights(A__   ,     A__)

			hf_wavavec.save_pretrained(A__)


if __name__ == "__main__":
	__snake_case							:	Tuple											=       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"""
	)
	__snake_case							:	Any											=       parser.parse_args()
	convert_unispeech_sat_checkpoint(
	    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
	)







'''simple docstring'''


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_lowerCamelCase	:       Tuple      	=	{"configuration_ibert": ["IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "IBertConfig", "IBertOnnxConfig"]}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _lowerCamelCase	:       Dict      	=	[
       "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

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

