


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()
_A			=						logging.get_logger(__name__)

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

def 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : str      ,					SCREAMING_SNAKE_CASE__      : str      ,					SCREAMING_SNAKE_CASE__      : Dict      ,					SCREAMING_SNAKE_CASE__      : Optional[int]      ,					SCREAMING_SNAKE_CASE__      : Any      ,					SCREAMING_SNAKE_CASE__      : Union[str, Any]    ):
     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(SCREAMING_SNAKE_CASE__      ,					SCREAMING_SNAKE_CASE__    )

     if weight_type is not None:
          __UpperCamelCase   							=getattr(SCREAMING_SNAKE_CASE__      ,					SCREAMING_SNAKE_CASE__    ).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 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : Tuple      ,					SCREAMING_SNAKE_CASE__      : Optional[Any]      ,					SCREAMING_SNAKE_CASE__      : int    ):
     __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(
                   SCREAMING_SNAKE_CASE__      ,					SCREAMING_SNAKE_CASE__      ,					SCREAMING_SNAKE_CASE__      ,					SCREAMING_SNAKE_CASE__      ,					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(SCREAMING_SNAKE_CASE__    )[0].split('.'    )[-2]
                              __UpperCamelCase   							=mapped_key.replace('*'      ,					SCREAMING_SNAKE_CASE__    )
                         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(SCREAMING_SNAKE_CASE__      ,					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 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : Union[str, Any]      ,					SCREAMING_SNAKE_CASE__      : Optional[int]      ,					SCREAMING_SNAKE_CASE__      : str      ,					SCREAMING_SNAKE_CASE__      : Tuple      ,					SCREAMING_SNAKE_CASE__      : List[Any]    ):
     __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(SCREAMING_SNAKE_CASE__    )

@torch.no_grad()
def 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : Optional[Any]      ,					SCREAMING_SNAKE_CASE__      : Union[str, Any]      ,					SCREAMING_SNAKE_CASE__      : List[str]=None      ,					SCREAMING_SNAKE_CASE__      : Optional[Any]=None      ,					SCREAMING_SNAKE_CASE__      : Dict=True    ):
     if config_path is not None:
          __UpperCamelCase   							=UniSpeechConfig.from_pretrained(SCREAMING_SNAKE_CASE__    )
     else:
          __UpperCamelCase   							=UniSpeechConfig()

     if is_finetuned:
          if dict_path:
               __UpperCamelCase   							=Dictionary.load_from_json(SCREAMING_SNAKE_CASE__    )

               # 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(SCREAMING_SNAKE_CASE__      ,					'vocab.json'    )
               if not os.path.isdir(SCREAMING_SNAKE_CASE__    ):
                    logger.error('--pytorch_dump_folder_path ({}) should be a directory'.format(SCREAMING_SNAKE_CASE__    )    )
                    return
               os.makedirs(SCREAMING_SNAKE_CASE__      ,					exist_ok=SCREAMING_SNAKE_CASE__    )
               __UpperCamelCase   							=target_dict.indices

               # fairseq has the <pad> and <s> switched
               __UpperCamelCase   							=42
               __UpperCamelCase   							=43
               with open(SCREAMING_SNAKE_CASE__      ,					'w'      ,					encoding='utf-8'    ) as vocab_handle:
                    json.dump(SCREAMING_SNAKE_CASE__      ,					SCREAMING_SNAKE_CASE__    )
               __UpperCamelCase   							=WavaVecaPhonemeCTCTokenizer(
                   SCREAMING_SNAKE_CASE__      ,					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=SCREAMING_SNAKE_CASE__      ,					)
               __UpperCamelCase   							=True if config.feat_extract_norm == 'layer' else False
               __UpperCamelCase   							=WavaVecaFeatureExtractor(
                   feature_size=1      ,					sampling_rate=1_60_00      ,					padding_value=0      ,					do_normalize=SCREAMING_SNAKE_CASE__      ,					return_attention_mask=SCREAMING_SNAKE_CASE__      ,					)
               __UpperCamelCase   							=WavaVecaProcessor(feature_extractor=SCREAMING_SNAKE_CASE__      ,					tokenizer=SCREAMING_SNAKE_CASE__    )
               processor.save_pretrained(SCREAMING_SNAKE_CASE__    )

          __UpperCamelCase   							=UniSpeechForCTC(SCREAMING_SNAKE_CASE__    )
     else:
          __UpperCamelCase   							=UniSpeechForPreTraining(SCREAMING_SNAKE_CASE__    )

     if is_finetuned:
          __UpperCamelCase							,				__UpperCamelCase							,				__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							,				__UpperCamelCase							,				__UpperCamelCase   							=fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]    )

     __UpperCamelCase   							=model[0].eval()

     recursively_load_weights(SCREAMING_SNAKE_CASE__      ,					SCREAMING_SNAKE_CASE__      ,					SCREAMING_SNAKE_CASE__    )

     hf_unispeech.save_pretrained(SCREAMING_SNAKE_CASE__    )


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









import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin






@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->UnCLIP
class    UpperCAmelCase__	( A_		):



    """simple docstring"""






    UpperCAmelCase__						:		torch.FloatTensor
    UpperCAmelCase__						:		Optional[torch.FloatTensor]              =  None

def 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : Optional[Any]      ,					SCREAMING_SNAKE_CASE__      : int=0.999      ,					SCREAMING_SNAKE_CASE__      : str="cosine"      ,					):
     if alpha_transform_type == "cosine":

          def alpha_bar_fn(SCREAMING_SNAKE_CASE__      : Any    ):
               return math.cos((t + 0.008) / 1.008 * math.pi / 2    ) ** 2

     elif alpha_transform_type == "exp":

          def alpha_bar_fn(SCREAMING_SNAKE_CASE__      : Optional[Any]    ):
               return math.exp(t * -12.0    )

     else:
          raise ValueError(F'Unsupported alpha_tranform_type: {alpha_transform_type}'    )

     __UpperCamelCase   							=[]
     for i in range(SCREAMING_SNAKE_CASE__    ):
          __UpperCamelCase   							=i / num_diffusion_timesteps
          __UpperCamelCase   							=(i + 1) / num_diffusion_timesteps
          betas.append(min(1 - alpha_bar_fn(SCREAMING_SNAKE_CASE__    ) / alpha_bar_fn(SCREAMING_SNAKE_CASE__    )      ,					SCREAMING_SNAKE_CASE__    )    )
     return torch.tensor(SCREAMING_SNAKE_CASE__      ,					dtype=torch.floataa    )






class    UpperCAmelCase__	( A_       ,  A_		):



    """simple docstring"""






    @register_to_config
    def __init__(      self ,       A_ = 1000 ,       A_ = "fixed_small_log" ,       A_ = True ,       A_ = 1.0 ,       A_ = "epsilon" ,       A_ = "squaredcos_cap_v2" ,       )  ->	Tuple:
         if beta_schedule != "squaredcos_cap_v2":
              raise ValueError('UnCLIPScheduler only supports `beta_schedule`: \'squaredcos_cap_v2\''   )

         __UpperCamelCase   							=betas_for_alpha_bar(A_   )

         __UpperCamelCase   							=1.0 - self.betas
         __UpperCamelCase   							=torch.cumprod(self.alphas ,       dim=0   )
         __UpperCamelCase   							=torch.tensor(1.0   )

         # standard deviation of the initial noise distribution
         __UpperCamelCase   							=1.0

         # setable values
         __UpperCamelCase   							=None
         __UpperCamelCase   							=torch.from_numpy(np.arange(0 ,       A_   )[::-1].copy()   )

         __UpperCamelCase   							=variance_type






    def _a (      self ,       A_ ,       A_ = None   )  ->	torch.FloatTensor:
         return sample






    def _a (      self ,       A_ ,       A_ = None   )  ->	Tuple:
         __UpperCamelCase   							=num_inference_steps
         __UpperCamelCase   							=(self.config.num_train_timesteps - 1) / (self.num_inference_steps - 1)
         __UpperCamelCase   							=(np.arange(0 ,       A_   ) * step_ratio).round()[::-1].copy().astype(np.intaa   )
         __UpperCamelCase   							=torch.from_numpy(A_   ).to(A_   )






    def _a (      self ,       A_ ,       A_=None ,       A_=None ,       A_=None   )  ->	List[Any]:
         if prev_timestep is None:
              __UpperCamelCase   							=t - 1

         __UpperCamelCase   							=self.alphas_cumprod[t]
         __UpperCamelCase   							=self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
         __UpperCamelCase   							=1 - alpha_prod_t
         __UpperCamelCase   							=1 - alpha_prod_t_prev

         if prev_timestep == t - 1:
              __UpperCamelCase   							=self.betas[t]
         else:
              __UpperCamelCase   							=1 - alpha_prod_t / alpha_prod_t_prev

         # For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)
         # and sample from it to get previous sample
         # x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample
         __UpperCamelCase   							=beta_prod_t_prev / beta_prod_t * beta

         if variance_type is None:
              __UpperCamelCase   							=self.config.variance_type

         # hacks - were probably added for training stability
         if variance_type == "fixed_small_log":
              __UpperCamelCase   							=torch.log(torch.clamp(A_ ,       min=1E-20   )   )
              __UpperCamelCase   							=torch.exp(0.5 * variance   )
         elif variance_type == "learned_range":
              # NOTE difference with DDPM scheduler
              __UpperCamelCase   							=variance.log()
              __UpperCamelCase   							=beta.log()

              __UpperCamelCase   							=(predicted_variance + 1) / 2
              __UpperCamelCase   							=frac * max_log + (1 - frac) * min_log

         return variance






    def _a (      self ,       A_ ,       A_ ,       A_ ,       A_ = None ,       A_=None ,       A_ = True ,       )  ->	Union[UnCLIPSchedulerOutput, Tuple]:
         __UpperCamelCase   							=timestep

         if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type == "learned_range":
              __UpperCamelCase							,				__UpperCamelCase   							=torch.split(A_ ,       sample.shape[1] ,       dim=1   )
         else:
              __UpperCamelCase   							=None

         # 1. compute alphas, betas
         if prev_timestep is None:
              __UpperCamelCase   							=t - 1

         __UpperCamelCase   							=self.alphas_cumprod[t]
         __UpperCamelCase   							=self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
         __UpperCamelCase   							=1 - alpha_prod_t
         __UpperCamelCase   							=1 - alpha_prod_t_prev

         if prev_timestep == t - 1:
              __UpperCamelCase   							=self.betas[t]
              __UpperCamelCase   							=self.alphas[t]
         else:
              __UpperCamelCase   							=1 - alpha_prod_t / alpha_prod_t_prev
              __UpperCamelCase   							=1 - beta

         # 2. compute predicted original sample from predicted noise also called
         # "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
         if self.config.prediction_type == "epsilon":
              __UpperCamelCase   							=(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
         elif self.config.prediction_type == "sample":
              __UpperCamelCase   							=model_output
         else:
              raise ValueError(
                  f'prediction_type given as {self.config.prediction_type} must be one of `epsilon` or `sample`'
                  ' for the UnCLIPScheduler.'   )

         # 3. Clip "predicted x_0"
         if self.config.clip_sample:
              __UpperCamelCase   							=torch.clamp(
                  A_ ,       -self.config.clip_sample_range ,       self.config.clip_sample_range   )

         # 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
         # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
         __UpperCamelCase   							=(alpha_prod_t_prev ** 0.5 * beta) / beta_prod_t
         __UpperCamelCase   							=alpha ** 0.5 * beta_prod_t_prev / beta_prod_t

         # 5. Compute predicted previous sample µ_t
         # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
         __UpperCamelCase   							=pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

         # 6. Add noise
         __UpperCamelCase   							=0
         if t > 0:
              __UpperCamelCase   							=randn_tensor(
                  model_output.shape ,       dtype=model_output.dtype ,       generator=A_ ,       device=model_output.device   )

              __UpperCamelCase   							=self._get_variance(
                  A_ ,       predicted_variance=A_ ,       prev_timestep=A_ ,       )

              if self.variance_type == "fixed_small_log":
                   __UpperCamelCase   							=variance
              elif self.variance_type == "learned_range":
                   __UpperCamelCase   							=(0.5 * variance).exp()
              else:
                   raise ValueError(
                       f'variance_type given as {self.variance_type} must be one of `fixed_small_log` or `learned_range`'
                       ' for the UnCLIPScheduler.'   )

              __UpperCamelCase   							=variance * variance_noise

         __UpperCamelCase   							=pred_prev_sample + variance

         if not return_dict:
              return (pred_prev_sample,)

         return UnCLIPSchedulerOutput(prev_sample=A_ ,       pred_original_sample=A_   )






    def _a (      self ,       A_ ,       A_ ,       A_ ,       )  ->	torch.FloatTensor:
         # Make sure alphas_cumprod and timestep have same device and dtype as original_samples
         __UpperCamelCase   							=self.alphas_cumprod.to(device=original_samples.device ,       dtype=original_samples.dtype   )
         __UpperCamelCase   							=timesteps.to(original_samples.device   )

         __UpperCamelCase   							=alphas_cumprod[timesteps] ** 0.5
         __UpperCamelCase   							=sqrt_alpha_prod.flatten()
         while len(sqrt_alpha_prod.shape   ) < len(original_samples.shape   ):
              __UpperCamelCase   							=sqrt_alpha_prod.unsqueeze(-1   )

         __UpperCamelCase   							=(1 - alphas_cumprod[timesteps]) ** 0.5
         __UpperCamelCase   							=sqrt_one_minus_alpha_prod.flatten()
         while len(sqrt_one_minus_alpha_prod.shape   ) < len(original_samples.shape   ):
              __UpperCamelCase   							=sqrt_one_minus_alpha_prod.unsqueeze(-1   )

         __UpperCamelCase   							=sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
         return noisy_samples





