




from dataclasses import dataclass
from typing import Optional

import numpy as np
import torch
import torch.nn as nn

from ..utils import BaseOutput, is_torch_version, randn_tensor
from .attention_processor import SpatialNorm
from .unet_ad_blocks import UNetMidBlockaD, get_down_block, get_up_block





@dataclass
class 			__lowerCamelCase	(   UpperCamelCase__   ):




    """simple docstring"""

    snake_case__ =				42






class 			__lowerCamelCase	(   nn.Module   ):




    """simple docstring"""

    def __init__(	self					:					List[Any]       ,     SCREAMING_SNAKE_CASE__					:					Any=3       ,     SCREAMING_SNAKE_CASE__					:					int=3       ,     SCREAMING_SNAKE_CASE__					:					List[str]=("DownEncoderBlock2D",)       ,     SCREAMING_SNAKE_CASE__					:					Tuple=(64,)       ,     SCREAMING_SNAKE_CASE__					:					str=2       ,     SCREAMING_SNAKE_CASE__					:					List[Any]=32       ,     SCREAMING_SNAKE_CASE__					:					List[str]="silu"       ,     SCREAMING_SNAKE_CASE__					:					Optional[int]=True       ,     )				->	str:
         super().__init__()
         lowerCAmelCase__          =			layers_per_block

         lowerCAmelCase__          =			torch.nn.Convad(
             SCREAMING_SNAKE_CASE__       ,     block_out_channels[0]       ,     kernel_size=3       ,     stride=1       ,     padding=1       ,     )

         lowerCAmelCase__          =			None
         lowerCAmelCase__          =			nn.ModuleList([]       )

         # down
         lowerCAmelCase__          =			block_out_channels[0]
         for i, down_block_type in enumerate(SCREAMING_SNAKE_CASE__       ):
              lowerCAmelCase__          =			output_channel
              lowerCAmelCase__          =			block_out_channels[i]
              lowerCAmelCase__          =			i == len(SCREAMING_SNAKE_CASE__       ) - 1

              lowerCAmelCase__          =			get_down_block(
                  SCREAMING_SNAKE_CASE__       ,     num_layers=self.layers_per_block       ,     in_channels=SCREAMING_SNAKE_CASE__       ,     out_channels=SCREAMING_SNAKE_CASE__       ,     add_downsample=not is_final_block       ,     resnet_eps=1e-6       ,     downsample_padding=0       ,     resnet_act_fn=SCREAMING_SNAKE_CASE__       ,     resnet_groups=SCREAMING_SNAKE_CASE__       ,     attention_head_dim=SCREAMING_SNAKE_CASE__       ,     temb_channels=SCREAMING_SNAKE_CASE__       ,     )
              self.down_blocks.append(SCREAMING_SNAKE_CASE__       )

         # mid
         lowerCAmelCase__          =			UNetMidBlockaD(
             in_channels=block_out_channels[-1]       ,     resnet_eps=1e-6       ,     resnet_act_fn=SCREAMING_SNAKE_CASE__       ,     output_scale_factor=1       ,     resnet_time_scale_shift="default"       ,     attention_head_dim=block_out_channels[-1]       ,     resnet_groups=SCREAMING_SNAKE_CASE__       ,     temb_channels=SCREAMING_SNAKE_CASE__       ,     )

         # out
         lowerCAmelCase__          =			nn.GroupNorm(num_channels=block_out_channels[-1]       ,     num_groups=SCREAMING_SNAKE_CASE__       ,     eps=1e-6       )
         lowerCAmelCase__          =			nn.SiLU()

         lowerCAmelCase__          =			2 * out_channels if double_z else out_channels
         lowerCAmelCase__          =			nn.Convad(block_out_channels[-1]       ,     SCREAMING_SNAKE_CASE__       ,     3       ,     padding=1       )

         lowerCAmelCase__          =			False


    def        a				(	self					:					Tuple       ,     SCREAMING_SNAKE_CASE__					:					Dict       )				->	int:
         lowerCAmelCase__          =			x
         lowerCAmelCase__          =			self.conv_in(SCREAMING_SNAKE_CASE__       )

         if self.training and self.gradient_checkpointing:

              def create_custom_forward(SCREAMING_SNAKE_CASE__					:					Optional[int]       ):
                   def custom_forward(*SCREAMING_SNAKE_CASE__					:					Dict       ):
                        return module(*SCREAMING_SNAKE_CASE__       )

                   return custom_forward

              # down
              if is_torch_version(">="       ,     "1.11.0"       ):
                   for down_block in self.down_blocks:
                        lowerCAmelCase__          =			torch.utils.checkpoint.checkpoint(
                            create_custom_forward(SCREAMING_SNAKE_CASE__       )       ,     SCREAMING_SNAKE_CASE__       ,     use_reentrant=SCREAMING_SNAKE_CASE__       )
                   # middle
                   lowerCAmelCase__          =			torch.utils.checkpoint.checkpoint(
                       create_custom_forward(self.mid_block       )       ,     SCREAMING_SNAKE_CASE__       ,     use_reentrant=SCREAMING_SNAKE_CASE__       )
              else:
                   for down_block in self.down_blocks:
                        lowerCAmelCase__          =			torch.utils.checkpoint.checkpoint(create_custom_forward(SCREAMING_SNAKE_CASE__       )       ,     SCREAMING_SNAKE_CASE__       )
                   # middle
                   lowerCAmelCase__          =			torch.utils.checkpoint.checkpoint(create_custom_forward(self.mid_block       )       ,     SCREAMING_SNAKE_CASE__       )

         else:
              # down
              for down_block in self.down_blocks:
                   lowerCAmelCase__          =			down_block(SCREAMING_SNAKE_CASE__       )

              # middle
              lowerCAmelCase__          =			self.mid_block(SCREAMING_SNAKE_CASE__       )

         # post-process
         lowerCAmelCase__          =			self.conv_norm_out(SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			self.conv_act(SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			self.conv_out(SCREAMING_SNAKE_CASE__       )

         return sample






class 			__lowerCamelCase	(   nn.Module   ):




    """simple docstring"""

    def __init__(	self					:					Any       ,     SCREAMING_SNAKE_CASE__					:					str=3       ,     SCREAMING_SNAKE_CASE__					:					Dict=3       ,     SCREAMING_SNAKE_CASE__					:					int=("UpDecoderBlock2D",)       ,     SCREAMING_SNAKE_CASE__					:					Tuple=(64,)       ,     SCREAMING_SNAKE_CASE__					:					Tuple=2       ,     SCREAMING_SNAKE_CASE__					:					Union[str, Any]=32       ,     SCREAMING_SNAKE_CASE__					:					List[str]="silu"       ,     SCREAMING_SNAKE_CASE__					:					List[str]="group"       ,     )				->	Union[str, Any]:
         super().__init__()
         lowerCAmelCase__          =			layers_per_block

         lowerCAmelCase__          =			nn.Convad(
             SCREAMING_SNAKE_CASE__       ,     block_out_channels[-1]       ,     kernel_size=3       ,     stride=1       ,     padding=1       ,     )

         lowerCAmelCase__          =			None
         lowerCAmelCase__          =			nn.ModuleList([]       )

         lowerCAmelCase__          =			in_channels if norm_type == "spatial" else None

         # mid
         lowerCAmelCase__          =			UNetMidBlockaD(
             in_channels=block_out_channels[-1]       ,     resnet_eps=1e-6       ,     resnet_act_fn=SCREAMING_SNAKE_CASE__       ,     output_scale_factor=1       ,     resnet_time_scale_shift="default" if norm_type == "group" else norm_type       ,     attention_head_dim=block_out_channels[-1]       ,     resnet_groups=SCREAMING_SNAKE_CASE__       ,     temb_channels=SCREAMING_SNAKE_CASE__       ,     )

         # up
         lowerCAmelCase__          =			list(reversed(SCREAMING_SNAKE_CASE__       )       )
         lowerCAmelCase__          =			reversed_block_out_channels[0]
         for i, up_block_type in enumerate(SCREAMING_SNAKE_CASE__       ):
              lowerCAmelCase__          =			output_channel
              lowerCAmelCase__          =			reversed_block_out_channels[i]

              lowerCAmelCase__          =			i == len(SCREAMING_SNAKE_CASE__       ) - 1

              lowerCAmelCase__          =			get_up_block(
                  SCREAMING_SNAKE_CASE__       ,     num_layers=self.layers_per_block + 1       ,     in_channels=SCREAMING_SNAKE_CASE__       ,     out_channels=SCREAMING_SNAKE_CASE__       ,     prev_output_channel=SCREAMING_SNAKE_CASE__       ,     add_upsample=not is_final_block       ,     resnet_eps=1e-6       ,     resnet_act_fn=SCREAMING_SNAKE_CASE__       ,     resnet_groups=SCREAMING_SNAKE_CASE__       ,     attention_head_dim=SCREAMING_SNAKE_CASE__       ,     temb_channels=SCREAMING_SNAKE_CASE__       ,     resnet_time_scale_shift=SCREAMING_SNAKE_CASE__       ,     )
              self.up_blocks.append(SCREAMING_SNAKE_CASE__       )
              lowerCAmelCase__          =			output_channel

         # out
         if norm_type == "spatial":
              lowerCAmelCase__          =			SpatialNorm(block_out_channels[0]       ,     SCREAMING_SNAKE_CASE__       )
         else:
              lowerCAmelCase__          =			nn.GroupNorm(num_channels=block_out_channels[0]       ,     num_groups=SCREAMING_SNAKE_CASE__       ,     eps=1e-6       )
         lowerCAmelCase__          =			nn.SiLU()
         lowerCAmelCase__          =			nn.Convad(block_out_channels[0]       ,     SCREAMING_SNAKE_CASE__       ,     3       ,     padding=1       )

         lowerCAmelCase__          =			False


    def        a				(	self					:					Any       ,     SCREAMING_SNAKE_CASE__					:					Union[str, Any]       ,     SCREAMING_SNAKE_CASE__					:					Dict=None       )				->	Dict:
         lowerCAmelCase__          =			z
         lowerCAmelCase__          =			self.conv_in(SCREAMING_SNAKE_CASE__       )

         lowerCAmelCase__          =			next(iter(self.up_blocks.parameters()       )       ).dtype
         if self.training and self.gradient_checkpointing:

              def create_custom_forward(SCREAMING_SNAKE_CASE__					:					Union[str, Any]       ):
                   def custom_forward(*SCREAMING_SNAKE_CASE__					:					List[str]       ):
                        return module(*SCREAMING_SNAKE_CASE__       )

                   return custom_forward

              if is_torch_version(">="       ,     "1.11.0"       ):
                   # middle
                   lowerCAmelCase__          =			torch.utils.checkpoint.checkpoint(
                       create_custom_forward(self.mid_block       )       ,     SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       ,     use_reentrant=SCREAMING_SNAKE_CASE__       )
                   lowerCAmelCase__          =			sample.to(SCREAMING_SNAKE_CASE__       )

                   # up
                   for up_block in self.up_blocks:
                        lowerCAmelCase__          =			torch.utils.checkpoint.checkpoint(
                            create_custom_forward(SCREAMING_SNAKE_CASE__       )       ,     SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       ,     use_reentrant=SCREAMING_SNAKE_CASE__       )
              else:
                   # middle
                   lowerCAmelCase__          =			torch.utils.checkpoint.checkpoint(
                       create_custom_forward(self.mid_block       )       ,     SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       )
                   lowerCAmelCase__          =			sample.to(SCREAMING_SNAKE_CASE__       )

                   # up
                   for up_block in self.up_blocks:
                        lowerCAmelCase__          =			torch.utils.checkpoint.checkpoint(create_custom_forward(SCREAMING_SNAKE_CASE__       )       ,     SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       )
         else:
              # middle
              lowerCAmelCase__          =			self.mid_block(SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       )
              lowerCAmelCase__          =			sample.to(SCREAMING_SNAKE_CASE__       )

              # up
              for up_block in self.up_blocks:
                   lowerCAmelCase__          =			up_block(SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       )

        # post-process
         if latent_embeds is None:
              lowerCAmelCase__          =			self.conv_norm_out(SCREAMING_SNAKE_CASE__       )
         else:
              lowerCAmelCase__          =			self.conv_norm_out(SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			self.conv_act(SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			self.conv_out(SCREAMING_SNAKE_CASE__       )

         return sample






class 			__lowerCamelCase	(   nn.Module   ):




    """simple docstring"""

    def __init__(	self					:					Tuple       ,     SCREAMING_SNAKE_CASE__					:					int       ,     SCREAMING_SNAKE_CASE__					:					Any       ,     SCREAMING_SNAKE_CASE__					:					int       ,     SCREAMING_SNAKE_CASE__					:					Optional[Any]=None       ,     SCREAMING_SNAKE_CASE__					:					Optional[int]="random"       ,     SCREAMING_SNAKE_CASE__					:					Optional[Any]=False       ,     SCREAMING_SNAKE_CASE__					:					Any=True       )				->	List[Any]:
         super().__init__()
         lowerCAmelCase__          =			n_e
         lowerCAmelCase__          =			vq_embed_dim
         lowerCAmelCase__          =			beta
         lowerCAmelCase__          =			legacy

         lowerCAmelCase__          =			nn.Embedding(self.n_e       ,     self.vq_embed_dim       )
         self.embedding.weight.data.uniform_(-1.0 / self.n_e       ,     1.0 / self.n_e       )

         lowerCAmelCase__          =			remap
         if self.remap is not None:
              self.register_buffer("used"       ,     torch.tensor(np.load(self.remap       )       )       )
              lowerCAmelCase__          =			self.used.shape[0]
              lowerCAmelCase__          =			unknown_index  # "random" or "extra" or integer
              if self.unknown_index == "extra":
                   lowerCAmelCase__          =			self.re_embed
                   lowerCAmelCase__          =			self.re_embed + 1
              print(
                  f'Remapping {self.n_e} indices to {self.re_embed} indices. '
                  f'Using {self.unknown_index} for unknown indices.'       )
         else:
              lowerCAmelCase__          =			n_e

         lowerCAmelCase__          =			sane_index_shape


    def        a				(	self					:					Tuple       ,     SCREAMING_SNAKE_CASE__					:					Union[str, Any]       )				->	Optional[int]:
         lowerCAmelCase__          =			inds.shape
         assert len(SCREAMING_SNAKE_CASE__       ) > 1
         lowerCAmelCase__          =			inds.reshape(ishape[0]       ,     -1       )
         lowerCAmelCase__          =			self.used.to(SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			(inds[:, :, None] == used[None, None, ...]).long()
         lowerCAmelCase__          =			match.argmax(-1       )
         lowerCAmelCase__          =			match.sum(2       ) < 1
         if self.unknown_index == "random":
              lowerCAmelCase__          =			torch.randint(0       ,     self.re_embed       ,     size=new[unknown].shape       ).to(device=new.device       )
         else:
              lowerCAmelCase__          =			self.unknown_index
         return new.reshape(SCREAMING_SNAKE_CASE__       )


    def        a				(	self					:					Optional[Any]       ,     SCREAMING_SNAKE_CASE__					:					Dict       )				->	Optional[int]:
         lowerCAmelCase__          =			inds.shape
         assert len(SCREAMING_SNAKE_CASE__       ) > 1
         lowerCAmelCase__          =			inds.reshape(ishape[0]       ,     -1       )
         lowerCAmelCase__          =			self.used.to(SCREAMING_SNAKE_CASE__       )
         if self.re_embed > self.used.shape[0]:  # extra token
              lowerCAmelCase__          =			0  # simply set to zero
         lowerCAmelCase__          =			torch.gather(used[None, :][inds.shape[0] * [0], :]       ,     1       ,     SCREAMING_SNAKE_CASE__       )
         return back.reshape(SCREAMING_SNAKE_CASE__       )


    def        a				(	self					:					List[Any]       ,     SCREAMING_SNAKE_CASE__					:					Tuple       )				->	Tuple:
         # reshape z -> (batch, height, width, channel) and flatten
         lowerCAmelCase__          =			z.permute(0       ,     2       ,     3       ,     1       ).contiguous()
         lowerCAmelCase__          =			z.view(-1       ,     self.vq_embed_dim       )

         # distances from z to embeddings e_j (z - e)^2 = z^2 + e^2 - 2 e * z
         lowerCAmelCase__          =			torch.argmin(torch.cdist(SCREAMING_SNAKE_CASE__       ,     self.embedding.weight       )       ,     dim=1       )

         lowerCAmelCase__          =			self.embedding(SCREAMING_SNAKE_CASE__       ).view(z.shape       )
         lowerCAmelCase__          =			None
         lowerCAmelCase__          =			None

         # compute loss for embedding
         if not self.legacy:
              lowerCAmelCase__          =			self.beta * torch.mean((z_q.detach() - z) ** 2       ) + torch.mean((z_q - z.detach()) ** 2       )
         else:
              lowerCAmelCase__          =			torch.mean((z_q.detach() - z) ** 2       ) + self.beta * torch.mean((z_q - z.detach()) ** 2       )

         # preserve gradients
         lowerCAmelCase__          =			z + (z_q - z).detach()

         # reshape back to match original input shape
         lowerCAmelCase__          =			z_q.permute(0       ,     3       ,     1       ,     2       ).contiguous()

         if self.remap is not None:
              lowerCAmelCase__          =			min_encoding_indices.reshape(z.shape[0]       ,     -1       )  # add batch axis
              lowerCAmelCase__          =			self.remap_to_used(SCREAMING_SNAKE_CASE__       )
              lowerCAmelCase__          =			min_encoding_indices.reshape(-1       ,     1       )  # flatten

         if self.sane_index_shape:
              lowerCAmelCase__          =			min_encoding_indices.reshape(z_q.shape[0]       ,     z_q.shape[2]       ,     z_q.shape[3]       )

         return z_q, loss, (perplexity, min_encodings, min_encoding_indices)


    def        a				(	self					:					str       ,     SCREAMING_SNAKE_CASE__					:					int       ,     SCREAMING_SNAKE_CASE__					:					str       )				->	Any:
         # shape specifying (batch, height, width, channel)
         if self.remap is not None:
              lowerCAmelCase__          =			indices.reshape(shape[0]       ,     -1       )  # add batch axis
              lowerCAmelCase__          =			self.unmap_to_all(SCREAMING_SNAKE_CASE__       )
              lowerCAmelCase__          =			indices.reshape(-1       )  # flatten again

         # get quantized latent vectors
         lowerCAmelCase__          =			self.embedding(SCREAMING_SNAKE_CASE__       )

         if shape is not None:
              lowerCAmelCase__          =			z_q.view(SCREAMING_SNAKE_CASE__       )
              # reshape back to match original input shape
              lowerCAmelCase__          =			z_q.permute(0       ,     3       ,     1       ,     2       ).contiguous()

         return z_q






class 			__lowerCamelCase	(   UpperCamelCase__   ):




    """simple docstring"""

    def __init__(	self					:					Optional[Any]       ,     SCREAMING_SNAKE_CASE__					:					Tuple       ,     SCREAMING_SNAKE_CASE__					:					Any=False       )				->	Optional[int]:
         lowerCAmelCase__          =			parameters
         lowerCAmelCase__     ,		lowerCAmelCase__          =			torch.chunk(SCREAMING_SNAKE_CASE__       ,     2       ,     dim=1       )
         lowerCAmelCase__          =			torch.clamp(self.logvar       ,     -30.0       ,     20.0       )
         lowerCAmelCase__          =			deterministic
         lowerCAmelCase__          =			torch.exp(0.5 * self.logvar       )
         lowerCAmelCase__          =			torch.exp(self.logvar       )
         if self.deterministic:
              lowerCAmelCase__          =			lowerCAmelCase__          =			torch.zeros_like(
                  self.mean       ,     device=self.parameters.device       ,     dtype=self.parameters.dtype       )


    def        a				(	self					:					str       ,     SCREAMING_SNAKE_CASE__					:					Optional[torch.Generator] = None       )				->	torch.FloatTensor:
         # make sure sample is on the same device as the parameters and has same dtype
         lowerCAmelCase__          =			randn_tensor(
             self.mean.shape       ,     generator=SCREAMING_SNAKE_CASE__       ,     device=self.parameters.device       ,     dtype=self.parameters.dtype       )
         lowerCAmelCase__          =			self.mean + self.std * sample
         return x


    def        a				(	self					:					List[str]       ,     SCREAMING_SNAKE_CASE__					:					List[Any]=None       )				->	Union[str, Any]:
         if self.deterministic:
              return torch.Tensor([0.0]       )
         else:
              if other is None:
                   return 0.5 * torch.sum(torch.pow(self.mean       ,     2       ) + self.var - 1.0 - self.logvar       ,     dim=[1, 2, 3]       )
              else:
                   return 0.5 * torch.sum(
                       torch.pow(self.mean - other.mean       ,     2       ) / other.var
                       + self.var / other.var
                       - 1.0
                       - self.logvar
                       + other.logvar       ,     dim=[1, 2, 3]       ,     )


    def        a				(	self					:					Any       ,     SCREAMING_SNAKE_CASE__					:					Union[str, Any]       ,     SCREAMING_SNAKE_CASE__					:					List[str]=[1, 2, 3]       )				->	Tuple:
         if self.deterministic:
              return torch.Tensor([0.0]       )
         lowerCAmelCase__          =			np.log(2.0 * np.pi       )
         return 0.5 * torch.sum(logtwopi + self.logvar + torch.pow(sample - self.mean       ,     2       ) / self.var       ,     dim=SCREAMING_SNAKE_CASE__       )


    def        a				(	self					:					Union[str, Any]       )				->	Dict:
         return self.mean






import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    WavaVecaConfig,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaForCTC,
    WavaVecaForPreTraining,
    WavaVecaProcessor,
    logging,
)
from transformers.models.wavaveca.modeling_wavaveca import WavaVecaForSequenceClassification


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',
    'adapter_layer': 'encoder.layers.*.adapter_layer',
    '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',
    'mask_emb': 'masked_spec_embed',
    'pooling_layer.linear': 'projector',
    'pooling_layer.projection': 'classifier',
}
UpperCamelCase								=					[
    'lm_head',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
    'projector',
    'classifier',
]



def 							_A       (						lowerCAmelCase_					: Optional[int]      ):
     """simple docstring"""

     lowerCAmelCase__          =			{}
     with open(lowerCAmelCase_       ,    "r"      ) as file:
          for line_number, line in enumerate(lowerCAmelCase_      ):
               lowerCAmelCase__          =			line.strip()
               if line:
                    lowerCAmelCase__          =			line.split()
                    lowerCAmelCase__          =			line_number
                    lowerCAmelCase__          =			words[0]
                    lowerCAmelCase__          =			value
     return result



def 							_A       (						lowerCAmelCase_					: Any       ,    lowerCAmelCase_					: int       ,    lowerCAmelCase_					: int       ,    lowerCAmelCase_					: Union[str, Any]       ,    lowerCAmelCase_					: int      ):
     """simple docstring"""

     for attribute in key.split("."      ):
          lowerCAmelCase__          =			getattr(lowerCAmelCase_       ,    lowerCAmelCase_      )

     lowerCAmelCase__          =			None
     for param_key in PARAM_MAPPING.keys():
          if full_name.endswith(lowerCAmelCase_      ):
               lowerCAmelCase__          =			PARAM_MAPPING[full_name.split("."      )[-1]]
               lowerCAmelCase__          =			"param"

     if weight_type is not None and weight_type != "param":
          lowerCAmelCase__          =			getattr(lowerCAmelCase_       ,    lowerCAmelCase_      ).shape
     elif weight_type is not None and weight_type == "param":
          lowerCAmelCase__          =			hf_pointer
          for attribute in hf_param_name.split("."      ):
               lowerCAmelCase__          =			getattr(lowerCAmelCase_       ,    lowerCAmelCase_      )
          lowerCAmelCase__          =			shape_pointer.shape

          # let's reduce dimension
          lowerCAmelCase__          =			value[0]
     else:
          lowerCAmelCase__          =			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":
          lowerCAmelCase__          =			value
     elif weight_type == "weight_g":
          lowerCAmelCase__          =			value
     elif weight_type == "weight_v":
          lowerCAmelCase__          =			value
     elif weight_type == "bias":
          lowerCAmelCase__          =			value
     elif weight_type == "param":
          for attribute in hf_param_name.split("."      ):
               lowerCAmelCase__          =			getattr(lowerCAmelCase_       ,    lowerCAmelCase_      )
          lowerCAmelCase__          =			value
     else:
          lowerCAmelCase__          =			value

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



def 							_A       (						lowerCAmelCase_					: Optional[int]       ,    lowerCAmelCase_					: Tuple       ,    lowerCAmelCase_					: Optional[int]       ,    lowerCAmelCase_					: int       ,    lowerCAmelCase_					: Optional[Any]      ):
     """simple docstring"""

     lowerCAmelCase__          =			None
     for param_key in PARAM_MAPPING.keys():
          if full_name.endswith(lowerCAmelCase_      ):
               lowerCAmelCase__          =			PARAM_MAPPING[full_name.split("."      )[-1]]
               lowerCAmelCase__          =			"param"

     if weight_type is not None and weight_type != "param":
          lowerCAmelCase__          =			".".join([key, weight_type]      )
     elif weight_type is not None and weight_type == "param":
          lowerCAmelCase__          =			".".join([key, hf_param_name]      )
     else:
          lowerCAmelCase__          =			key

     lowerCAmelCase__          =			value if "lm_head" in full_key else value[0]


UpperCamelCase								=					{
    'W_a': 'linear_1.weight',
    'W_b': 'linear_2.weight',
    'b_a': 'linear_1.bias',
    'b_b': 'linear_2.bias',
    'ln_W': 'norm.weight',
    'ln_b': 'norm.bias',
}



def 							_A       (						lowerCAmelCase_					: int       ,    lowerCAmelCase_					: Tuple       ,    lowerCAmelCase_					: str=None       ,    lowerCAmelCase_					: Any=None      ):
     """simple docstring"""

     lowerCAmelCase__          =			False
     for key, mapped_key in MAPPING.items():
          lowerCAmelCase__          =			"wav2vec2." + 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]:
               lowerCAmelCase__          =			True
               if "*" in mapped_key:
                    lowerCAmelCase__          =			name.split(lowerCAmelCase_      )[0].split("."      )[-2]
                    lowerCAmelCase__          =			mapped_key.replace("*"       ,    lowerCAmelCase_      )
               if "weight_g" in name:
                    lowerCAmelCase__          =			"weight_g"
               elif "weight_v" in name:
                    lowerCAmelCase__          =			"weight_v"
               elif "bias" in name:
                    lowerCAmelCase__          =			"bias"
               elif "weight" in name:
                    # TODO: don't match quantizer.weight_proj
                    lowerCAmelCase__          =			"weight"
               else:
                    lowerCAmelCase__          =			None
               if hf_dict is not None:
                    rename_dict(lowerCAmelCase_       ,    lowerCAmelCase_       ,    lowerCAmelCase_       ,    lowerCAmelCase_       ,    lowerCAmelCase_      )
               else:
                    set_recursively(lowerCAmelCase_       ,    lowerCAmelCase_       ,    lowerCAmelCase_       ,    lowerCAmelCase_       ,    lowerCAmelCase_      )
               return is_used
     return is_used



def 							_A       (						lowerCAmelCase_					: Optional[Any]       ,    lowerCAmelCase_					: Tuple       ,    lowerCAmelCase_					: Dict      ):
     """simple docstring"""

     lowerCAmelCase__          =			[]
     lowerCAmelCase__          =			fairseq_model.state_dict()

     lowerCAmelCase__          =			hf_model.wavaveca.feature_extractor

     for name, value in fairseq_dict.items():
          lowerCAmelCase__          =			False
          if "conv_layers" in name:
               load_conv_layer(
                   lowerCAmelCase_       ,    lowerCAmelCase_       ,    lowerCAmelCase_       ,    lowerCAmelCase_       ,    hf_model.config.feat_extract_norm == "group"       ,    )
               lowerCAmelCase__          =			True
          else:
               lowerCAmelCase__          =			load_wavaveca_layer(lowerCAmelCase_       ,    lowerCAmelCase_       ,    lowerCAmelCase_      )
          if not is_used:
               unused_weights.append(lowerCAmelCase_      )

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



def 							_A       (						lowerCAmelCase_					: str       ,    lowerCAmelCase_					: Dict       ,    lowerCAmelCase_					: List[Any]       ,    lowerCAmelCase_					: Any       ,    lowerCAmelCase_					: Optional[int]      ):
     """simple docstring"""

     lowerCAmelCase__          =			full_name.split("conv_layers."      )[-1]
     lowerCAmelCase__          =			name.split("."      )
     lowerCAmelCase__          =			int(items[0]      )
     lowerCAmelCase__          =			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.'      )
               lowerCAmelCase__          =			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.'      )
               lowerCAmelCase__          =			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.conv_layers[layer_id].layer_norm.bias.data.shape} was found.'      )
               lowerCAmelCase__          =			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.conv_layers[layer_id].layer_norm.weight.data.shape} was found.'      )
               lowerCAmelCase__          =			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 							_A       (						lowerCAmelCase_					: str       ,    lowerCAmelCase_					: Dict       ,    lowerCAmelCase_					: List[Any]=None       ,    lowerCAmelCase_					: Optional[Any]=None       ,    lowerCAmelCase_					: Optional[int]=True       ,    lowerCAmelCase_					: List[str]=False      ):
     """simple docstring"""

     if config_path is not None:
          lowerCAmelCase__          =			WavaVecaConfig.from_pretrained(lowerCAmelCase_      )
     else:
          lowerCAmelCase__          =			WavaVecaConfig()

     if is_seq_class:
          lowerCAmelCase__          =			read_txt_into_dict(lowerCAmelCase_      )
          lowerCAmelCase__          =			idalabel
          lowerCAmelCase__          =			WavaVecaForSequenceClassification(lowerCAmelCase_      )
          lowerCAmelCase__          =			WavaVecaFeatureExtractor(
              feature_size=1       ,    sampling_rate=1_6000       ,    padding_value=0       ,    do_normalize=lowerCAmelCase_       ,    return_attention_mask=lowerCAmelCase_       ,    )
          feature_extractor.save_pretrained(lowerCAmelCase_      )

     elif is_finetuned:
          if dict_path:
               lowerCAmelCase__          =			Dictionary.load(lowerCAmelCase_      )

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

               # fairseq has the <pad> and <s> switched
               lowerCAmelCase__          =			0
               lowerCAmelCase__          =			1
               with open(lowerCAmelCase_       ,    "w"       ,    encoding="utf-8"      ) as vocab_handle:
                    json.dump(lowerCAmelCase_       ,    lowerCAmelCase_      )
               lowerCAmelCase__          =			WavaVecaCTCTokenizer(
                   lowerCAmelCase_       ,    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=lowerCAmelCase_       ,    )
               lowerCAmelCase__          =			True if config.feat_extract_norm == "layer" else False
               lowerCAmelCase__          =			WavaVecaFeatureExtractor(
                   feature_size=1       ,    sampling_rate=1_6000       ,    padding_value=0       ,    do_normalize=lowerCAmelCase_       ,    return_attention_mask=lowerCAmelCase_       ,    )
               lowerCAmelCase__          =			WavaVecaProcessor(feature_extractor=lowerCAmelCase_       ,    tokenizer=lowerCAmelCase_      )
               processor.save_pretrained(lowerCAmelCase_      )

          lowerCAmelCase__          =			WavaVecaForCTC(lowerCAmelCase_      )
     else:
          lowerCAmelCase__          =			WavaVecaForPreTraining(lowerCAmelCase_      )

     if is_finetuned or is_seq_class:
          lowerCAmelCase__     ,		lowerCAmelCase__     ,		lowerCAmelCase__          =			fairseq.checkpoint_utils.load_model_ensemble_and_task(
              [checkpoint_path]       ,    arg_overrides={"data": "/".join(dict_path.split("/"      )[:-1]      )}      )
     else:
          lowerCAmelCase__          =			argparse.Namespace(task="audio_pretraining"      )
          lowerCAmelCase__          =			fairseq.tasks.setup_task(lowerCAmelCase_      )

          lowerCAmelCase__     ,		lowerCAmelCase__     ,		lowerCAmelCase__          =			fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]       ,    task=lowerCAmelCase_      )

     lowerCAmelCase__          =			model[0].eval()

     recursively_load_weights(lowerCAmelCase_       ,    lowerCAmelCase_       ,    not is_finetuned      )

     hf_wavavec.save_pretrained(lowerCAmelCase_      )


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'
     )
     parser.add_argument(
         '--is_seq_class',
         action='store_true',
         help='Whether the model to convert is a fine-tuned sequence classification model or not',
     )
     UpperCamelCase								=					parser.parse_args()

     UpperCamelCase								=					not args.not_finetuned and not args.is_seq_class
     convert_wavaveca_checkpoint(
         args.checkpoint_path,
         args.pytorch_dump_folder_path,
         args.config_path,
         args.dict_path,
         is_finetuned,
         args.is_seq_class,
     )
