
"""simple docstring"""




import math
from typing import Optional

import numpy as np

from ...configuration_utils import PretrainedConfig
from ...utils import logging


A__      :	List[str]          =    logging.get_logger(__name__)

A__      :	int          =    {
    """facebook/encodec_24khz""": """https://huggingface.co/facebook/encodec_24khz/resolve/main/config.json""",
    """facebook/encodec_48khz""": """https://huggingface.co/facebook/encodec_48khz/resolve/main/config.json""",
}







class   _lowercase						(       lowerCAmelCase_				):


     '''simple docstring'''





     _A								=							'encodec'



     def __init__(      self ,			__UpperCamelCase=[1.5, 3.0, 6.0, 12.0, 24.0] ,			__UpperCamelCase=2_40_00 ,			__UpperCamelCase=1 ,			__UpperCamelCase=False ,			__UpperCamelCase=None ,			__UpperCamelCase=None ,			__UpperCamelCase=1_28 ,			__UpperCamelCase=32 ,			__UpperCamelCase=1 ,			__UpperCamelCase=[8, 5, 4, 2] ,			__UpperCamelCase="weight_norm" ,			__UpperCamelCase=7 ,			__UpperCamelCase=7 ,			__UpperCamelCase=3 ,			__UpperCamelCase=2 ,			__UpperCamelCase=True ,			__UpperCamelCase="reflect" ,			__UpperCamelCase=2 ,			__UpperCamelCase=2 ,			__UpperCamelCase=1.0 ,			__UpperCamelCase=10_24 ,			__UpperCamelCase=None ,			__UpperCamelCase=True ,			**__UpperCamelCase ,			)->					str:
          UpperCAmelCase__							: Any 							=						target_bandwidths
          UpperCAmelCase__							: Tuple 							=						sampling_rate
          UpperCAmelCase__							: Union[str, Any] 							=						audio_channels
          UpperCAmelCase__							: List[Any] 							=						normalize
          UpperCAmelCase__							: Optional[Any] 							=						chunk_length_s
          UpperCAmelCase__							: int 							=						overlap
          UpperCAmelCase__							: Union[str, Any] 							=						hidden_size
          UpperCAmelCase__							: Dict 							=						num_filters
          UpperCAmelCase__							: Any 							=						num_residual_layers
          UpperCAmelCase__							: Tuple 							=						upsampling_ratios
          UpperCAmelCase__							: Dict 							=						norm_type
          UpperCAmelCase__							: Optional[int] 							=						kernel_size
          UpperCAmelCase__							: Optional[int] 							=						last_kernel_size
          UpperCAmelCase__							: str 							=						residual_kernel_size
          UpperCAmelCase__							: Union[str, Any] 							=						dilation_growth_rate
          UpperCAmelCase__							: str 							=						use_causal_conv
          UpperCAmelCase__							: Optional[Any] 							=						pad_mode
          UpperCAmelCase__							: Any 							=						compress
          UpperCAmelCase__							: Any 							=						num_lstm_layers
          UpperCAmelCase__							: Any 							=						trim_right_ratio
          UpperCAmelCase__							: List[str] 							=						codebook_size
          UpperCAmelCase__							: Optional[int] 							=						codebook_dim if codebook_dim is not None else hidden_size
          UpperCAmelCase__							: Optional[Any] 							=						use_conv_shortcut

          if self.norm_type not in ["weight_norm", "time_group_norm"]:
               raise ValueError(
                   F"self.norm_type must be one of `\"weight_norm\"`, `\"time_group_norm\"`), got {self.norm_type}"  )

          super().__init__(**__UpperCamelCase  )



     @property
     def  lowerCAmelCase__       (      self  )->					Optional[int]:
          if self.chunk_length_s is None:
               return None
          else:
               return int(self.chunk_length_s * self.sampling_rate  )



     @property
     def  lowerCAmelCase__       (      self  )->					Optional[int]:
          if self.chunk_length_s is None or self.overlap is None:
               return None
          else:
               return max(1 ,			int((1.0 - self.overlap) * self.chunk_length  )  )



     @property
     def  lowerCAmelCase__       (      self  )->					int:
          UpperCAmelCase__							: Union[str, Any] 							=						np.prod(self.upsampling_ratios  )
          return math.ceil(self.sampling_rate / hop_length  )



     @property
     def  lowerCAmelCase__       (      self  )->					int:
          return int(10_00 * self.target_bandwidths[-1] // (self.frame_rate * 10)  )





"""simple docstring"""




import unicodedata
from dataclasses import dataclass
from typing import Optional, Union

import numpy as np

from transformers.data.data_collator import DataCollatorMixin
from transformers.file_utils import PaddingStrategy
from transformers.tokenization_utils_base import PreTrainedTokenizerBase



def 							a__ (				lowerCAmelCase	:       str       ,    lowerCAmelCase	:       Optional[Any]       ,    lowerCAmelCase	:       Dict       ,    lowerCAmelCase	:       List[Any]		):






     '''simple docstring'''
     if isinstance(lowerCAmelCase       ,    lowerCAmelCase		):
          UpperCAmelCase__							: Optional[int] 							=						np.full((len(lowerCAmelCase		), sequence_length, 2)       ,    lowerCAmelCase		)
     else:
          UpperCAmelCase__							: Optional[Any] 							=						np.full((len(lowerCAmelCase		), sequence_length)       ,    lowerCAmelCase		)

     for i, tensor in enumerate(lowerCAmelCase		):
          if padding_side == "right":
               if isinstance(lowerCAmelCase       ,    lowerCAmelCase		):
                    UpperCAmelCase__							: Dict 							=						tensor[:sequence_length]
               else:
                    UpperCAmelCase__							: Tuple 							=						tensor[:sequence_length]
          else:
               if isinstance(lowerCAmelCase       ,    lowerCAmelCase		):
                    UpperCAmelCase__							: Optional[Any] 							=						tensor[:sequence_length]
               else:
                    UpperCAmelCase__							: int 							=						tensor[:sequence_length]

     return out_tensor.tolist()



def 							a__ (				lowerCAmelCase	:       Optional[int]		):






     '''simple docstring'''
     UpperCAmelCase__							: Tuple 							=						ord(lowerCAmelCase		)
     if (cp >= 33 and cp <= 47) or (cp >= 58 and cp <= 64) or (cp >= 91 and cp <= 96) or (cp >= 123 and cp <= 126):
          return True
     UpperCAmelCase__							: Optional[Any] 							=						unicodedata.category(lowerCAmelCase		)
     if cat.startswith("P"		):
          return True
     return False







@dataclass
class   _lowercase						(       lowerCAmelCase_				):


     '''simple docstring'''





     _A								=							42
     _A								=							True
     _A								=							None
     _A								=							None
     _A								=							-100
     _A								=							"pt"



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase  )->					List[str]:
          import torch

          UpperCAmelCase__							: Optional[Any] 							=						"label" if "label" in features[0].keys() else "labels"
          UpperCAmelCase__							: Dict 							=						[feature[label_name] for feature in features] if label_name in features[0].keys() else None
          UpperCAmelCase__							: str 							=						self.tokenizer.pad(
              __UpperCamelCase ,			padding=self.padding ,			max_length=self.max_length ,			pad_to_multiple_of=self.pad_to_multiple_of ,			return_tensors="pt" if labels is None else None ,			)

          if labels is None:
               return batch

          UpperCAmelCase__							: Optional[Any] 							=						torch.tensor(batch["entity_ids"]  ).shape[1]
          UpperCAmelCase__							: int 							=						self.tokenizer.padding_side
          if padding_side == "right":
               UpperCAmelCase__							: int 							=						[
                   list(__UpperCamelCase  ) + [self.label_pad_token_id] * (sequence_length - len(__UpperCamelCase  )) for label in labels
               ]
          else:
               UpperCAmelCase__							: List[Any] 							=						[
                   [self.label_pad_token_id] * (sequence_length - len(__UpperCamelCase  )) + list(__UpperCamelCase  ) for label in labels
               ]

          UpperCAmelCase__							: Optional[Any] 							=						[feature["ner_tags"] for feature in features]
          UpperCAmelCase__							: int 							=						padding_tensor(__UpperCamelCase ,			-1 ,			__UpperCamelCase ,			__UpperCamelCase  )
          UpperCAmelCase__							: List[Any] 							=						[feature["original_entity_spans"] for feature in features]
          UpperCAmelCase__							: int 							=						padding_tensor(__UpperCamelCase ,			(-1, -1) ,			__UpperCamelCase ,			__UpperCamelCase  )
          UpperCAmelCase__							: Optional[int] 							=						{k: torch.tensor(__UpperCamelCase ,			dtype=torch.intaa  ) for k, v in batch.items()}

          return batch



