

import argparse

import fairseq
import torch

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

def 							__SCREAMING_SNAKE_CASE							(			lowerCAmelCase:					Optional[Any]					, lowerCAmelCase:					Optional[int]					, lowerCAmelCase:					Any					, lowerCAmelCase:					str					, lowerCAmelCase:					Dict	)				->    Union[str, Any]:
				for attribute in key.split("."	):
								_UpperCAmelCase    :						List[str]							=      getattr(lowerCAmelCase					, lowerCAmelCase	)

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

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

def 							__SCREAMING_SNAKE_CASE							(			lowerCAmelCase:					Tuple					, lowerCAmelCase:					int	)				->    int:
				_UpperCAmelCase    :						Optional[Any]							=      []
				_UpperCAmelCase    :						Optional[int]							=      fairseq_model.state_dict()

				_UpperCAmelCase    :						int							=      hf_model.unispeech_sat.feature_extractor

				for name, value in fairseq_dict.items():
								_UpperCAmelCase    :						Dict							=      False
								if "conv_layers" in name:
												load_conv_layer(
												    lowerCAmelCase					, lowerCAmelCase					, lowerCAmelCase					, lowerCAmelCase					, hf_model.config.feat_extract_norm == "group"					, )
												_UpperCAmelCase    :						List[str]							=      True
								else:
												for key, mapped_key in MAPPING.items():
																_UpperCAmelCase    :						List[str]							=      "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
																				_UpperCAmelCase    :						Dict							=      True
																				if "*" in mapped_key:
																								_UpperCAmelCase    :						Optional[int]							=      name.split(lowerCAmelCase	)[0].split("."	)[-2]
																								_UpperCAmelCase    :						Optional[int]							=      mapped_key.replace("*"					, lowerCAmelCase	)
																				if "weight_g" in name:
																								_UpperCAmelCase    :						Dict							=      "weight_g"
																				elif "weight_v" in name:
																								_UpperCAmelCase    :						str							=      "weight_v"
																				elif "bias" in name:
																								_UpperCAmelCase    :						Optional[int]							=      "bias"
																				elif "weight" in name:
																								# TODO: don't match quantizer.weight_proj
																								_UpperCAmelCase    :						Any							=      "weight"
																				else:
																								_UpperCAmelCase    :						Union[str, Any]							=      None
																				set_recursively(lowerCAmelCase					, lowerCAmelCase					, lowerCAmelCase					, lowerCAmelCase					, lowerCAmelCase	)
																continue
								if not is_used:
												unused_weights.append(lowerCAmelCase	)

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

def 							__SCREAMING_SNAKE_CASE							(			lowerCAmelCase:					Optional[int]					, lowerCAmelCase:					Tuple					, lowerCAmelCase:					List[str]					, lowerCAmelCase:					int					, lowerCAmelCase:					Tuple	)				->    Union[str, Any]:
				_UpperCAmelCase    :						int							=      full_name.split("conv_layers."	)[-1]
				_UpperCAmelCase    :						List[str]							=      name.split("."	)
				_UpperCAmelCase    :						Union[str, Any]							=      int(items[0]	)
				_UpperCAmelCase    :						Tuple							=      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.'	)
												_UpperCAmelCase    :						str							=      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.'	)
												_UpperCAmelCase    :						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:
												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.'	)
												_UpperCAmelCase    :						Union[str, Any]							=      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.'	)
												_UpperCAmelCase    :						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(lowerCAmelCase	)

@torch.no_grad()
def 							__SCREAMING_SNAKE_CASE							(			lowerCAmelCase:					Optional[Any]					, lowerCAmelCase:					Any					, lowerCAmelCase:					Optional[Any]=None					, lowerCAmelCase:					Tuple=None					, lowerCAmelCase:					Optional[Any]=True	)				->    Optional[Any]:
				if config_path is not None:
								_UpperCAmelCase    :						List[str]							=      UniSpeechSatConfig.from_pretrained(lowerCAmelCase	)
				else:
								_UpperCAmelCase    :						List[Any]							=      UniSpeechSatConfig()

				_UpperCAmelCase    :						Any							=      ""

				if is_finetuned:
								_UpperCAmelCase    :						Tuple							=      UniSpeechSatForCTC(lowerCAmelCase	)
				else:
								_UpperCAmelCase    :						int							=      UniSpeechSatForPreTraining(lowerCAmelCase	)

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

				recursively_load_weights(lowerCAmelCase					, lowerCAmelCase	)

				hf_wavavec.save_pretrained(lowerCAmelCase	)


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_sat_checkpoint(
					    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
					)





from __future__ import annotations

class 			a					:


						def __init__(    self  ,   A_		):







										'''simple docstring'''


										_UpperCAmelCase    :						Union[str, Any]							=      order

										# a_{0} ... a_{k}
										_UpperCAmelCase    :						Tuple							=      [1.0] + [0.0] * order
										# b_{0} ... b_{k}
										_UpperCAmelCase    :						int							=      [1.0] + [0.0] * order

										# x[n-1] ... x[n-k]
										_UpperCAmelCase    :						Optional[Any]							=      [0.0] * self.order
										# y[n-1] ... y[n-k]
										_UpperCAmelCase    :						Dict							=      [0.0] * self.order


						def     _UpperCAmelCase			(    self  ,   A_  ,   A_		):







										'''simple docstring'''


										if len(A_		) < self.order:
														_UpperCAmelCase    :						List[str]							=      [1.0, *a_coeffs]

										if len(A_		) != self.order + 1:
														_UpperCAmelCase    :						List[Any]							=      (
														    f'Expected a_coeffs to have {self.order + 1} elements '
														    f'for {self.order}-order filter, got {len(A_		)}'
														)
														raise ValueError(A_		)

										if len(A_		) != self.order + 1:
														_UpperCAmelCase    :						int							=      (
														    f'Expected b_coeffs to have {self.order + 1} elements '
														    f'for {self.order}-order filter, got {len(A_		)}'
														)
														raise ValueError(A_		)

										_UpperCAmelCase    :						Optional[Any]							=      a_coeffs
										_UpperCAmelCase    :						Union[str, Any]							=      b_coeffs





						def     _UpperCAmelCase			(    self  ,   A_		):







										'''simple docstring'''


										_UpperCAmelCase    :						Tuple							=      0.0

										# Start at index 1 and do index 0 at the end.
										for i in range(1  ,   self.order + 1		):
														result += (
														    self.b_coeffs[i] * self.input_history[i - 1]
														    - self.a_coeffs[i] * self.output_history[i - 1]
														)

										_UpperCAmelCase    :						Dict							=      (result + self.b_coeffs[0] * sample) / self.a_coeffs[0]

										_UpperCAmelCase    :						Optional[Any]							=      self.input_history[:-1]
										_UpperCAmelCase    :						Optional[int]							=      self.output_history[:-1]

										_UpperCAmelCase    :						Optional[Any]							=      sample
										_UpperCAmelCase    :						str							=      result

										return result


