

import argparse

import fairseq
import torch

from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, 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""",
    """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""",
}
lowerCAmelCase__	   = [
    """lm_head""",
    """quantizer.weight_proj""",
    """quantizer.codevectors""",
    """project_q""",
    """project_hid""",
    """label_embeddings_concat""",
    """speaker_proj""",
    """layer_norm_for_extract""",
]



def 					lowerCAmelCase__							(	SCREAMING_SNAKE_CASE_:							List[Any]	,       SCREAMING_SNAKE_CASE_:							Union[str, Any]	,       SCREAMING_SNAKE_CASE_:							Any	,       SCREAMING_SNAKE_CASE_:							int	,       SCREAMING_SNAKE_CASE_:							Dict      )			->      int:




				'''simple docstring'''





				for attribute in key.split("."      ):
								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

				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__        =			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 					lowerCAmelCase__							(	SCREAMING_SNAKE_CASE_:							Optional[Any]	,       SCREAMING_SNAKE_CASE_:							Union[str, Any]      )			->      int:




				'''simple docstring'''





				A__        =			[]
				A__        =			fairseq_model.state_dict()

				A__        =			hf_model.unispeech_sat.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_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__        =			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_      )
																continue
								if not is_used:
												unused_weights.append(SCREAMING_SNAKE_CASE_      )

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



def 					lowerCAmelCase__							(	SCREAMING_SNAKE_CASE_:							Any	,       SCREAMING_SNAKE_CASE_:							List[Any]	,       SCREAMING_SNAKE_CASE_:							str	,       SCREAMING_SNAKE_CASE_:							Tuple	,       SCREAMING_SNAKE_CASE_:							str      )			->      List[Any]:




				'''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:
												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__        =			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__        =			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__        =			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__        =			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 					lowerCAmelCase__							(	SCREAMING_SNAKE_CASE_:							Union[str, Any]	,       SCREAMING_SNAKE_CASE_:							Any	,       SCREAMING_SNAKE_CASE_:							str=None	,       SCREAMING_SNAKE_CASE_:							List[str]=None	,       SCREAMING_SNAKE_CASE_:							Any=True      )			->      List[Any]:




				'''simple docstring'''





				if config_path is not None:
								A__        =			UniSpeechSatConfig.from_pretrained(SCREAMING_SNAKE_CASE_      )
				else:
								A__        =			UniSpeechSatConfig()

				A__        =			""

				if is_finetuned:
								A__        =			UniSpeechSatForCTC(SCREAMING_SNAKE_CASE_      )
				else:
								A__        =			UniSpeechSatForPreTraining(SCREAMING_SNAKE_CASE_      )

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

				recursively_load_weights(SCREAMING_SNAKE_CASE_	,       SCREAMING_SNAKE_CASE_      )

				hf_wavavec.save_pretrained(SCREAMING_SNAKE_CASE_      )


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("""--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"""
	)
	lowerCAmelCase__	   = 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
	)




from ..utils import is_flax_available, is_torch_available


if is_torch_available():
	from .autoencoder_kl import AutoencoderKL
	from .controlnet import ControlNetModel
	from .dual_transformer_ad import DualTransformeraDModel
	from .modeling_utils import ModelMixin
	from .prior_transformer import PriorTransformer
	from .ta_film_transformer import TaFilmDecoder
	from .transformer_ad import TransformeraDModel
	from .unet_ad import UNetaDModel
	from .unet_ad import UNetaDModel
	from .unet_ad_condition import UNetaDConditionModel
	from .unet_ad_condition import UNetaDConditionModel
	from .vq_model import VQModel

if is_flax_available():
	from .controlnet_flax import FlaxControlNetModel
	from .unet_ad_condition_flax import FlaxUNetaDConditionModel
	from .vae_flax import FlaxAutoencoderKL

