
"""simple docstring"""





import math
from typing import Any, Callable, List, Optional, Tuple, Union

import numpy as np
import torch

from ...models import TaFilmDecoder
from ...schedulers import DDPMScheduler
from ...utils import is_onnx_available, logging, randn_tensor


if is_onnx_available():
				from ..onnx_utils import OnnxRuntimeModel

from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline
from .continous_encoder import SpectrogramContEncoder
from .notes_encoder import SpectrogramNotesEncoder


_a														=		logging.get_logger(__name__)  # pylint: disable=invalid-name

_a														=		256






class     _UpperCAmelCase(    __lowerCamelCase	):
		lowercase__						=      ["melgan"]



		def __init__(					self				,		__a				,		__a				,		__a				,		__a				,		__a				,		)       ->       None:






							'''simple docstring'''
							super().__init__()

							# From MELGAN
							_UpperCamelCase							 =  math.log(1e-5)  # Matches MelGAN training.
							_UpperCamelCase							 =  4.0  # Largest value for most examples
							_UpperCamelCase							 =  1_28

							self.register_modules(
							    notes_encoder=__a				,		continuous_encoder=__a				,		decoder=__a				,		scheduler=__a				,		melgan=__a				,		)



		def 						UpperCAmelCase							(					self				,		__a				,		__a=(-1.0, 1.0)				,		__a=False)       ->       Any:






							'''simple docstring'''
							_UpperCamelCase							 =  output_range
							if clip:
												_UpperCamelCase							 =  torch.clip(__a				,		self.min_value				,		self.max_value)
							# Scale to [0, 1].
							_UpperCamelCase							 =  (features - self.min_value) / (self.max_value - self.min_value)
							# Scale to [min_out, max_out].
							return zero_one * (max_out - min_out) + min_out



		def 						UpperCAmelCase							(					self				,		__a				,		__a=(-1.0, 1.0)				,		__a=False)       ->       Optional[int]:






							'''simple docstring'''
							_UpperCamelCase							 =  input_range
							_UpperCamelCase							 =  torch.clip(__a				,		__a				,		__a) if clip else outputs
							# Scale to [0, 1].
							_UpperCamelCase							 =  (outputs - min_out) / (max_out - min_out)
							# Scale to [self.min_value, self.max_value].
							return zero_one * (self.max_value - self.min_value) + self.min_value



		def 						UpperCAmelCase							(					self				,		__a				,		__a				,		__a)       ->       List[str]:






							'''simple docstring'''
							_UpperCamelCase							 =  input_tokens > 0
							_UpperCamelCase							 =  self.notes_encoder(
							    encoder_input_tokens=__a				,		encoder_inputs_mask=__a)

							_UpperCamelCase							 =  self.continuous_encoder(
							    encoder_inputs=__a				,		encoder_inputs_mask=__a)

							return [(tokens_encoded, tokens_mask), (continuous_encoded, continuous_mask)]



		def 						UpperCAmelCase							(					self				,		__a				,		__a				,		__a)       ->       str:






							'''simple docstring'''
							_UpperCamelCase							 =  noise_time
							if not torch.is_tensor(__a):
												_UpperCamelCase							 =  torch.tensor([timesteps]				,		dtype=torch.long				,		device=input_tokens.device)
							elif torch.is_tensor(__a) and len(timesteps.shape) == 0:
												_UpperCamelCase							 =  timesteps[None].to(input_tokens.device)

							# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
							_UpperCamelCase							 =  timesteps * torch.ones(input_tokens.shape[0]				,		dtype=timesteps.dtype				,		device=timesteps.device)

							_UpperCamelCase							 =  self.decoder(
							    encodings_and_masks=__a				,		decoder_input_tokens=__a				,		decoder_noise_time=__a)
							return logits




		@torch.no_grad()
		def __call__(					self				,		__a				,		__a = None				,		__a = 1_00				,		__a = True				,		__a = "numpy"				,		__a = None				,		__a = 1				,		)       ->       Union[AudioPipelineOutput, Tuple]:






							'''simple docstring'''
							if (callback_steps is None) or (
							    callback_steps is not None and (not isinstance(__a				,		__a) or callback_steps <= 0)
							):
												raise ValueError(
												    F'''`callback_steps` has to be a positive integer but is {callback_steps} of type'''
												    F''' {type(__a)}.''')

							_UpperCamelCase							 =  np.zeros([1, TARGET_FEATURE_LENGTH, self.n_dims]				,		dtype=np.floataa)
							_UpperCamelCase							 =  np.zeros([1, 0, self.n_dims]				,		np.floataa)
							_UpperCamelCase							 =  torch.ones((1, TARGET_FEATURE_LENGTH)				,		dtype=__a				,		device=self.device)

							for i, encoder_input_tokens in enumerate(__a):
												if i == 0:
																	_UpperCamelCase							 =  torch.from_numpy(pred_mel[:1].copy()).to(
																	    device=self.device				,		dtype=self.decoder.dtype)
																	# The first chunk has no previous context.
																	_UpperCamelCase							 =  torch.zeros((1, TARGET_FEATURE_LENGTH)				,		dtype=__a				,		device=self.device)
												else:
																	# The full song pipeline does not feed in a context feature, so the mask
																	# will be all 0s after the feature converter. Because we know we're
																	# feeding in a full context chunk from the previous prediction, set it
																	# to all 1s.
																	_UpperCamelCase							 =  ones

												_UpperCamelCase							 =  self.scale_features(
												    __a				,		output_range=[-1.0, 1.0]				,		clip=__a)

												_UpperCamelCase							 =  self.encode(
												    input_tokens=torch.IntTensor([encoder_input_tokens]).to(device=self.device)				,		continuous_inputs=__a				,		continuous_mask=__a				,		)

												# Sample encoder_continuous_inputs shaped gaussian noise to begin loop
												_UpperCamelCase							 =  randn_tensor(
												    shape=encoder_continuous_inputs.shape				,		generator=__a				,		device=self.device				,		dtype=self.decoder.dtype				,		)

												# set step values
												self.scheduler.set_timesteps(__a)

												# Denoising diffusion loop
												for j, t in enumerate(self.progress_bar(self.scheduler.timesteps)):
																	_UpperCamelCase							 =  self.decode(
																	    encodings_and_masks=__a				,		input_tokens=__a				,		noise_time=t / self.scheduler.config.num_train_timesteps				,		)

																	# Compute previous output: x_t -> x_t-1
																	_UpperCamelCase							 =  self.scheduler.step(__a				,		__a				,		__a				,		generator=__a).prev_sample

												_UpperCamelCase							 =  self.scale_to_features(__a				,		input_range=[-1.0, 1.0])
												_UpperCamelCase							 =  mel[:1]
												_UpperCamelCase							 =  mel.cpu().float().numpy()

												_UpperCamelCase							 =  np.concatenate([full_pred_mel, pred_mel[:1]]				,		axis=1)

												# call the callback, if provided
												if callback is not None and i % callback_steps == 0:
																	callback(__a				,		__a)

												logger.info('''Generated segment'''				,		__a)

							if output_type == "numpy" and not is_onnx_available():
												raise ValueError(
												    '''Cannot return output in \'np\' format if ONNX is not available. Make sure to have ONNX installed or set \'output_type\' to \'mel\'.''')
							elif output_type == "numpy" and self.melgan is None:
												raise ValueError(
												    '''Cannot return output in \'np\' format if melgan component is not defined. Make sure to define `self.melgan` or set \'output_type\' to \'mel\'.''')

							if output_type == "numpy":
												_UpperCamelCase							 =  self.melgan(input_features=full_pred_mel.astype(np.floataa))
							else:
												_UpperCamelCase							 =  full_pred_mel

							if not return_dict:
												return (output,)

							return AudioPipelineOutput(audios=__a)













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()
UpperCAmelCase_     							= logging.get_logger(__name__)

UpperCAmelCase_     							= {
    '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',
}
UpperCAmelCase_     							= [
    'ctc_proj',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
]




def 		lowerCAmelCase_    (  __UpperCAmelCase:			Tuple		,   __UpperCAmelCase:			int		,   __UpperCAmelCase:			Optional[Any]		,   __UpperCAmelCase:			Optional[int]		,   __UpperCAmelCase:			str		,   __UpperCAmelCase:			Any      )      ->	List[str]:
    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__							:				Tuple          =      '''lm_head'''

        UpperCamelCase__							:				Optional[int]          =      getattr(__UpperCAmelCase		,   __UpperCAmelCase      )

    if weight_type is not None:
        UpperCamelCase__							:				List[str]          =      getattr(__UpperCAmelCase		,   __UpperCAmelCase      ).shape
    else:
        UpperCamelCase__							:				Optional[Any]          =      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__							:				List[Any]          =      value
    elif weight_type == "weight_g":
        UpperCamelCase__							:				List[str]          =      value
    elif weight_type == "weight_v":
        UpperCamelCase__							:				str          =      value
    elif weight_type == "bias":
        UpperCamelCase__							:				Tuple          =      value
    else:
        UpperCamelCase__							:				int          =      value

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




def 		lowerCAmelCase_    (  __UpperCAmelCase:			Optional[Any]		,   __UpperCAmelCase:			Dict		,   __UpperCAmelCase:			str      )      ->	List[Any]:
    UpperCamelCase__							:				Optional[Any]          =      []
    UpperCamelCase__							:				str          =      fairseq_model.state_dict()

    UpperCamelCase__							:				Optional[int]          =      hf_model.unispeech.feature_extractor

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

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




def 		lowerCAmelCase_    (  __UpperCAmelCase:			Tuple		,   __UpperCAmelCase:			Any		,   __UpperCAmelCase:			Optional[int]		,   __UpperCAmelCase:			List[Any]		,   __UpperCAmelCase:			Tuple      )      ->	Optional[int]:
    UpperCamelCase__							:				List[str]          =      full_name.split('''conv_layers.'''      )[-1]
    UpperCamelCase__							:				List[str]          =      name.split('''.'''      )
    UpperCamelCase__							:				str          =      int(items[0]      )
    UpperCamelCase__							:				Union[str, Any]          =      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__							:				int          =      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__							:				Tuple          =      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__							:				int          =      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__							:				List[str]          =      value
            logger.info(f"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."      )
    else:
        unused_weights.append(__UpperCAmelCase      )




@torch.no_grad()
def 		lowerCAmelCase_    (  __UpperCAmelCase:			Tuple		,   __UpperCAmelCase:			Optional[Any]		,   __UpperCAmelCase:			Dict=None		,   __UpperCAmelCase:			Optional[Any]=None		,   __UpperCAmelCase:			Optional[int]=True      )      ->	Union[str, Any]:
    if config_path is not None:
        UpperCamelCase__							:				str          =      UniSpeechConfig.from_pretrained(__UpperCAmelCase      )
    else:
        UpperCamelCase__							:				List[Any]          =      UniSpeechConfig()

    if is_finetuned:
        if dict_path:
            UpperCamelCase__							:				str          =      Dictionary.load_from_json(__UpperCAmelCase      )

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

            # fairseq has the <pad> and <s> switched
            UpperCamelCase__							:				Optional[Any]          =      42
            UpperCamelCase__							:				List[str]          =      43
            with open(__UpperCAmelCase		,   '''w'''		,   encoding='''utf-8'''      ) as vocab_handle:
                json.dump(__UpperCAmelCase		,   __UpperCAmelCase      )
            UpperCamelCase__							:				Dict          =      WavaVecaPhonemeCTCTokenizer(
                __UpperCAmelCase		,   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=__UpperCAmelCase		,   )
            UpperCamelCase__							:				List[Any]          =      True if config.feat_extract_norm == '''layer''' else False
            UpperCamelCase__							:				str          =      WavaVecaFeatureExtractor(
                feature_size=1		,   sampling_rate=1_6000		,   padding_value=0		,   do_normalize=__UpperCAmelCase		,   return_attention_mask=__UpperCAmelCase		,   )
            UpperCamelCase__							:				str          =      WavaVecaProcessor(feature_extractor=__UpperCAmelCase		,   tokenizer=__UpperCAmelCase      )
            processor.save_pretrained(__UpperCAmelCase      )

        UpperCamelCase__							:				Optional[int]          =      UniSpeechForCTC(__UpperCAmelCase      )
    else:
        UpperCamelCase__							:				Any          =      UniSpeechForPreTraining(__UpperCAmelCase      )

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

    UpperCamelCase__							:				Tuple          =      model[0].eval()

    recursively_load_weights(__UpperCAmelCase		,   __UpperCAmelCase		,   __UpperCAmelCase      )

    hf_unispeech.save_pretrained(__UpperCAmelCase      )


if __name__ == "__main__":
  UpperCAmelCase_     							= 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'
  )
  UpperCAmelCase_     							= parser.parse_args()
  convert_unispeech_checkpoint(
      args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
  )

