




'''simple docstring'''




import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

# Register SEW's fairseq modules
from sew_asapp import tasks  # noqa: F401

from transformers import (
    SEWConfig,
    SEWForCTC,
    SEWModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
a_      :		str							      =       logging.get_logger(__name__)

a_      :		List[Any]							      =       {
    """post_extract_proj""": """feature_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.upsample.0""": """encoder.upsample.projection""",
    """encoder.layer_norm""": """encoder.layer_norm""",
    """w2v_model.layer_norm""": """layer_norm""",
    """w2v_encoder.proj""": """lm_head""",
    """mask_emb""": """masked_spec_embed""",
}





def      a_							(      __snake_case							:			Dict   ,       __snake_case							:			List[str]   ,       __snake_case							:			Union[str, Any]   ,       __snake_case							:			Optional[Any]   ,       __snake_case							:			Union[str, Any]   )							->						List[Any]:



       """simple docstring"""
       for attribute in key.split('''.'''   ):
              lowerCamelCase_							=getattr(__snake_case   ,       __snake_case   )

       if weight_type is not None:
              lowerCamelCase_							=getattr(__snake_case   ,       __snake_case   ).shape
       else:
              lowerCamelCase_							=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":
              lowerCamelCase_							=value
       elif weight_type == "weight_g":
              lowerCamelCase_							=value
       elif weight_type == "weight_v":
              lowerCamelCase_							=value
       elif weight_type == "bias":
              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_							(      __snake_case							:			int   ,       __snake_case							:			Optional[int]   ,       __snake_case							:			Optional[int]   )							->						Union[str, Any]:



       """simple docstring"""
       lowerCamelCase_							=[]
       lowerCamelCase_							=fairseq_model.state_dict()

       lowerCamelCase_							=hf_model.sew.feature_extractor if is_finetuned else hf_model.feature_extractor

       for name, value in fairseq_dict.items():
              lowerCamelCase_							=False
              if "conv_layers" in name:
                     load_conv_layer(
                         __snake_case   ,       __snake_case   ,       __snake_case   ,       __snake_case   ,       hf_model.config.feat_extract_norm == '''group'''   ,       )
                     lowerCamelCase_							=True
              else:
                     for key, mapped_key in MAPPING.items():
                            lowerCamelCase_							='''sew.''' + mapped_key if (is_finetuned and mapped_key != '''lm_head''') 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(__snake_case   )[0].split('''.'''   )[-2]
                                          lowerCamelCase_							=mapped_key.replace('''*'''   ,       __snake_case   )
                                   if "weight_g" in name:
                                          lowerCamelCase_							='''weight_g'''
                                   elif "weight_v" in name:
                                          lowerCamelCase_							='''weight_v'''
                                   elif "weight" in name:
                                          lowerCamelCase_							='''weight'''
                                   elif "bias" in name:
                                          lowerCamelCase_							='''bias'''
                                   else:
                                          lowerCamelCase_							=None
                                   set_recursively(__snake_case   ,       __snake_case   ,       __snake_case   ,       __snake_case   ,       __snake_case   )
                            continue
              if not is_used:
                     unused_weights.append(__snake_case   )

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





def      a_							(      __snake_case							:			Tuple   ,       __snake_case							:			Any   ,       __snake_case							:			Optional[Any]   ,       __snake_case							:			List[Any]   ,       __snake_case							:			Dict   )							->						str:



       """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:
                     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.'''
                     )
                     lowerCamelCase_							=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.'''
                     )
                     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:
                     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."
                     )
                     lowerCamelCase_							=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.'''
                     )
                     lowerCamelCase_							=value
                     logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''   )
       else:
              unused_weights.append(__snake_case   )





def      a_							(      __snake_case							:			List[str]   ,       __snake_case							:			List[Any]   )							->						Tuple:



       """simple docstring"""
       lowerCamelCase_							=SEWConfig()
       if is_finetuned:
              lowerCamelCase_							=model.wav_encoder.wav_model.cfg
       else:
              lowerCamelCase_							=model.cfg

       lowerCamelCase_							=fs_config.conv_bias
       lowerCamelCase_							=eval(fs_config.conv_feature_layers   )
       lowerCamelCase_							=[x[0] for x in conv_layers]
       lowerCamelCase_							=[x[1] for x in conv_layers]
       lowerCamelCase_							=[x[2] for x in conv_layers]
       lowerCamelCase_							='''gelu'''
       lowerCamelCase_							='''layer''' if fs_config.extractor_mode == '''layer_norm''' else '''group'''
       lowerCamelCase_							=0.0
       lowerCamelCase_							=fs_config.activation_fn.name
       lowerCamelCase_							=fs_config.encoder_embed_dim
       lowerCamelCase_							=0.0_2
       lowerCamelCase_							=fs_config.encoder_ffn_embed_dim
       lowerCamelCase_							=1e-5
       lowerCamelCase_							=fs_config.encoder_layerdrop
       lowerCamelCase_							=fs_config.encoder_attention_heads
       lowerCamelCase_							=fs_config.conv_pos_groups
       lowerCamelCase_							=fs_config.conv_pos
       lowerCamelCase_							=len(__snake_case   )
       lowerCamelCase_							=fs_config.encoder_layers
       lowerCamelCase_							=fs_config.squeeze_factor

       # take care of any params that are overridden by the Wav2VecCtc model
       if is_finetuned:
              lowerCamelCase_							=model.cfg
              lowerCamelCase_							=fs_config.final_dropout
              lowerCamelCase_							=fs_config.layerdrop
       lowerCamelCase_							=fs_config.activation_dropout
       lowerCamelCase_							=fs_config.mask_prob > 0 or fs_config.mask_channel_prob > 0
       lowerCamelCase_							=fs_config.attention_dropout
       lowerCamelCase_							=fs_config.dropout_input
       lowerCamelCase_							=fs_config.dropout
       lowerCamelCase_							=fs_config.mask_channel_length
       lowerCamelCase_							=fs_config.mask_channel_prob
       lowerCamelCase_							=fs_config.mask_length
       lowerCamelCase_							=fs_config.mask_prob

       lowerCamelCase_							='''Wav2Vec2FeatureExtractor'''
       lowerCamelCase_							='''Wav2Vec2CTCTokenizer'''

       return config





@torch.no_grad()
def      a_							(      __snake_case							:			Union[str, Any]   ,       __snake_case							:			Tuple   ,       __snake_case							:			str=None   ,       __snake_case							:			str=None   ,       __snake_case							:			Dict=True   )							->						List[str]:



       """simple docstring"""

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

       if config_path is not None:
              lowerCamelCase_							=SEWConfig.from_pretrained(__snake_case   )
       else:
              lowerCamelCase_							=convert_config(model[0]   ,       __snake_case   )
       lowerCamelCase_							=model[0].eval()

       lowerCamelCase_							=True if config.feat_extract_norm == '''layer''' else False
       lowerCamelCase_							=WavaVecaFeatureExtractor(
           feature_size=1   ,       sampling_rate=1_6000   ,       padding_value=0   ,       do_normalize=__snake_case   ,       return_attention_mask=__snake_case   ,       )

       if is_finetuned:
              if dict_path:
                     lowerCamelCase_							=Dictionary.load(__snake_case   )

                     # 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.pad_index
                     lowerCamelCase_							=target_dict.bos_index
                     lowerCamelCase_							=target_dict.eos_index
                     lowerCamelCase_							=len(target_dict.symbols   )
                     lowerCamelCase_							=os.path.join(__snake_case   ,       '''vocab.json'''   )
                     if not os.path.isdir(__snake_case   ):
                            logger.error('''--pytorch_dump_folder_path ({}) should be a directory'''.format(__snake_case   )   )
                            return
                     os.makedirs(__snake_case   ,       exist_ok=__snake_case   )
                     with open(__snake_case   ,       '''w'''   ,       encoding='''utf-8'''   ) as vocab_handle:
                            json.dump(target_dict.indices   ,       __snake_case   )
                     lowerCamelCase_							=WavaVecaCTCTokenizer(
                         __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=__snake_case   ,       )
                     lowerCamelCase_							=WavaVecaProcessor(feature_extractor=__snake_case   ,       tokenizer=__snake_case   )
                     processor.save_pretrained(__snake_case   )

              lowerCamelCase_							=SEWForCTC(__snake_case   )
       else:
              lowerCamelCase_							=SEWModel(__snake_case   )
              feature_extractor.save_pretrained(__snake_case   )

       recursively_load_weights(__snake_case   ,       __snake_case   ,       __snake_case   )

       hf_model.save_pretrained(__snake_case   )


if __name__ == "__main__":
     a_      :		List[Any]							      =       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(
         """--is_finetuned""", action="""store_true""", help="""Whether the model to convert is a fine-tuned model or not"""
     )
     a_      :		List[Any]							      =       parser.parse_args()
     convert_sew_checkpoint(
         args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
     )








from __future__ import annotations

import math
import random
from collections.abc import Collection
from typing import overload






class     UpperCAmelCase__       :



    """simple docstring"""






    def __init__(			self					,   A_ = None    )  ->       None:
         if components is None:
              __UpperCamelCase    							=[]
         __UpperCamelCase    							=list(A_    )






    def __len__(			self    )  ->       int:
         return len(self.__components    )






    def __str__(			self    )  ->       str:
         return "(" + ",".join(map(A_					,   self.__components    )    ) + ")"






    def __add__(			self					,   A_    )  ->       Vector:
         __UpperCamelCase    							=len(self    )
         if size == len(A_    ):
              __UpperCamelCase    							=[self.__components[i] + other.component(A_    ) for i in range(A_    )]
              return Vector(A_    )
         else:
              raise Exception('must have the same size'    )






    def __sub__(			self					,   A_    )  ->       Vector:
         __UpperCamelCase    							=len(self    )
         if size == len(A_    ):
              __UpperCamelCase    							=[self.__components[i] - other.component(A_    ) for i in range(A_    )]
              return Vector(A_    )
         else:  # error case
              raise Exception('must have the same size'    )






    @overload
    def __mul__(			self					,   A_    )  ->       Vector:
         ...






    @overload
    def __mul__(			self					,   A_    )  ->       float:
         ...






    def __mul__(			self					,   A_    )  ->       float | Vector:
         if isinstance(A_					,   (float, int)    ):
              __UpperCamelCase    							=[c * other for c in self.__components]
              return Vector(A_    )
         elif isinstance(A_					,   A_    ) and len(self    ) == len(A_    ):
              __UpperCamelCase    							=len(self    )
              __UpperCamelCase    							=[self.__components[i] * other.component(A_    ) for i in range(A_    )]
              return sum(A_    )
         else:  # error case
              raise Exception('invalid operand!'    )






    def _a					(			self    )  ->       Vector:
         return Vector(self.__components    )






    def _a					(			self					,   A_    )  ->       float:
         if isinstance(A_					,   A_    ) and -len(self.__components    ) <= i < len(self.__components    ):
              return self.__components[i]
         else:
              raise Exception('index out of range'    )






    def _a					(			self					,   A_					,   A_    )  ->       None:
         assert -len(self.__components    ) <= pos < len(self.__components    )
         __UpperCamelCase    							=value






    def _a					(			self    )  ->       float:
         if len(self.__components    ) == 0:
              raise Exception('Vector is empty'    )
         __UpperCamelCase    							=[c**2 for c in self.__components]
         return math.sqrt(sum(A_    )    )






    def _a					(			self					,   A_					,   A_ = False    )  ->       float:
         __UpperCamelCase    							=self * other
         __UpperCamelCase    							=self.euclidean_length() * other.euclidean_length()
         if deg:
              return math.degrees(math.acos(num / den    )    )
         else:
              return math.acos(num / den    )

def 						_UpperCAmelCase				(			SCREAMING_SNAKE_CASE__			:					int				):
     assert isinstance(SCREAMING_SNAKE_CASE__			,     SCREAMING_SNAKE_CASE__				)
     return Vector([0] * dimension				)

def 						_UpperCAmelCase				(			SCREAMING_SNAKE_CASE__			:					int			,     SCREAMING_SNAKE_CASE__			:					int				):
     assert isinstance(SCREAMING_SNAKE_CASE__			,     SCREAMING_SNAKE_CASE__				) and (isinstance(SCREAMING_SNAKE_CASE__			,     SCREAMING_SNAKE_CASE__				))
     __UpperCamelCase    							=[0] * dimension
     __UpperCamelCase    							=1
     return Vector(SCREAMING_SNAKE_CASE__				)

def 						_UpperCAmelCase				(			SCREAMING_SNAKE_CASE__			:					float			,     SCREAMING_SNAKE_CASE__			:					Vector			,     SCREAMING_SNAKE_CASE__			:					Vector				):
     assert (
         isinstance(SCREAMING_SNAKE_CASE__			,     SCREAMING_SNAKE_CASE__				)
         and isinstance(SCREAMING_SNAKE_CASE__			,     SCREAMING_SNAKE_CASE__				)
         and (isinstance(SCREAMING_SNAKE_CASE__			,     (int, float)				))
     )
     return x * scalar + y

def 						_UpperCAmelCase				(			SCREAMING_SNAKE_CASE__			:					int			,     SCREAMING_SNAKE_CASE__			:					int			,     SCREAMING_SNAKE_CASE__			:					int				):
     random.seed(SCREAMING_SNAKE_CASE__				)
     __UpperCamelCase    							=[random.randint(SCREAMING_SNAKE_CASE__			,     SCREAMING_SNAKE_CASE__				) for _ in range(SCREAMING_SNAKE_CASE__				)]
     return Vector(SCREAMING_SNAKE_CASE__				)






class     UpperCAmelCase__       :



    """simple docstring"""






    def __init__(			self					,   A_					,   A_					,   A_    )  ->       None:
         __UpperCamelCase    							=matrix
         __UpperCamelCase    							=w
         __UpperCamelCase    							=h






    def __str__(			self    )  ->       str:
         __UpperCamelCase    							=''
         for i in range(self.__height    ):
              ans += "|"
              for j in range(self.__width    ):
                   if j < self.__width - 1:
                        ans += str(self.__matrix[i][j]    ) + ","
                   else:
                        ans += str(self.__matrix[i][j]    ) + "|\n"
         return ans






    def __add__(			self					,   A_    )  ->       Matrix:
         if self.__width == other.width() and self.__height == other.height():
              __UpperCamelCase    							=[]
              for i in range(self.__height    ):
                   __UpperCamelCase    							=[
                       self.__matrix[i][j] + other.component(A_					,   A_    )
                       for j in range(self.__width    )
                   ]
                   matrix.append(A_    )
              return Matrix(A_					,   self.__width					,   self.__height    )
         else:
              raise Exception('matrix must have the same dimension!'    )






    def __sub__(			self					,   A_    )  ->       Matrix:
         if self.__width == other.width() and self.__height == other.height():
              __UpperCamelCase    							=[]
              for i in range(self.__height    ):
                   __UpperCamelCase    							=[
                       self.__matrix[i][j] - other.component(A_					,   A_    )
                       for j in range(self.__width    )
                   ]
                   matrix.append(A_    )
              return Matrix(A_					,   self.__width					,   self.__height    )
         else:
              raise Exception('matrices must have the same dimension!'    )






    @overload
    def __mul__(			self					,   A_    )  ->       Matrix:
         ...






    @overload
    def __mul__(			self					,   A_    )  ->       Vector:
         ...






    def __mul__(			self					,   A_    )  ->       Vector | Matrix:
         if isinstance(A_					,   A_    ):  # matrix-vector
              if len(A_    ) == self.__width:
                   __UpperCamelCase    							=zero_vector(self.__height    )
                   for i in range(self.__height    ):
                        __UpperCamelCase    							=[
                            self.__matrix[i][j] * other.component(A_    )
                            for j in range(self.__width    )
                        ]
                        ans.change_component(A_					,   sum(A_    )    )
                   return ans
              else:
                   raise Exception(
                       'vector must have the same size as the '
                       'number of columns of the matrix!'    )
         elif isinstance(A_					,   (int, float)    ):  # matrix-scalar
              __UpperCamelCase    							=[
                  [self.__matrix[i][j] * other for j in range(self.__width    )]
                  for i in range(self.__height    )
              ]
              return Matrix(A_					,   self.__width					,   self.__height    )
         return None






    def _a					(			self    )  ->       int:
         return self.__height






    def _a					(			self    )  ->       int:
         return self.__width






    def _a					(			self					,   A_					,   A_    )  ->       float:
         if 0 <= x < self.__height and 0 <= y < self.__width:
              return self.__matrix[x][y]
         else:
              raise Exception('change_component: indices out of bounds'    )






    def _a					(			self					,   A_					,   A_					,   A_    )  ->       None:
         if 0 <= x < self.__height and 0 <= y < self.__width:
              __UpperCamelCase    							=value
         else:
              raise Exception('change_component: indices out of bounds'    )






    def _a					(			self					,   A_					,   A_    )  ->       float:
         if self.__height != self.__width:
              raise Exception('Matrix is not square'    )
         __UpperCamelCase    							=self.__matrix[:x] + self.__matrix[x + 1 :]
         for i in range(len(A_    )    ):
              __UpperCamelCase    							=minor[i][:y] + minor[i][y + 1 :]
         return Matrix(A_					,   self.__width - 1					,   self.__height - 1    ).determinant()






    def _a					(			self					,   A_					,   A_    )  ->       float:
         if self.__height != self.__width:
              raise Exception('Matrix is not square'    )
         if 0 <= x < self.__height and 0 <= y < self.__width:
              return (-1) ** (x + y) * self.minor(A_					,   A_    )
         else:
              raise Exception('Indices out of bounds'    )






    def _a					(			self    )  ->       float:
         if self.__height != self.__width:
              raise Exception('Matrix is not square'    )
         if self.__height < 1:
              raise Exception('Matrix has no element'    )
         elif self.__height == 1:
              return self.__matrix[0][0]
         elif self.__height == 2:
              return (
                  self.__matrix[0][0] * self.__matrix[1][1]
                  - self.__matrix[0][1] * self.__matrix[1][0]
              )
         else:
              __UpperCamelCase    							=[
                  self.__matrix[0][y] * self.cofactor(0					,   A_    ) for y in range(self.__width    )
              ]
              return sum(A_    )

def 						_UpperCAmelCase				(			SCREAMING_SNAKE_CASE__			:					int				):
     __UpperCamelCase    							=[[0] * n for _ in range(SCREAMING_SNAKE_CASE__				)]
     return Matrix(SCREAMING_SNAKE_CASE__			,     SCREAMING_SNAKE_CASE__			,     SCREAMING_SNAKE_CASE__				)





def 						_UpperCAmelCase				(			SCREAMING_SNAKE_CASE__			:					int			,     SCREAMING_SNAKE_CASE__			:					int			,     SCREAMING_SNAKE_CASE__			:					int			,     SCREAMING_SNAKE_CASE__			:					int				):
     random.seed(SCREAMING_SNAKE_CASE__				)
     __UpperCamelCase    							=[
         [random.randint(SCREAMING_SNAKE_CASE__			,     SCREAMING_SNAKE_CASE__				) for _ in range(SCREAMING_SNAKE_CASE__				)] for _ in range(SCREAMING_SNAKE_CASE__				)
     ]
     return Matrix(SCREAMING_SNAKE_CASE__			,     SCREAMING_SNAKE_CASE__			,     SCREAMING_SNAKE_CASE__				)





