






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 		SCREAMING_SNAKE_CASE							(					__lowerCAmelCase					,    __lowerCAmelCase					,    __lowerCAmelCase					,    __lowerCAmelCase			) ->	Tuple:
     if isinstance(__lowerCAmelCase					,    __lowerCAmelCase			):
          UpperCamelCase__			:   Any       				=  np.full((len(__lowerCAmelCase			), sequence_length, 2)					,    __lowerCAmelCase			)
     else:
          UpperCamelCase__			:   Tuple       				=  np.full((len(__lowerCAmelCase			), sequence_length)					,    __lowerCAmelCase			)

     for i, tensor in enumerate(__lowerCAmelCase			):
          if padding_side == "right":
               if isinstance(__lowerCAmelCase					,    __lowerCAmelCase			):
                    UpperCamelCase__			:   Optional[int]       				=  tensor[:sequence_length]
               else:
                    UpperCamelCase__			:   List[str]       				=  tensor[:sequence_length]
          else:
               if isinstance(__lowerCAmelCase					,    __lowerCAmelCase			):
                    UpperCamelCase__			:   List[Any]       				=  tensor[:sequence_length]
               else:
                    UpperCamelCase__			:   str       				=  tensor[:sequence_length]

     return out_tensor.tolist()







def 		SCREAMING_SNAKE_CASE							(					__lowerCAmelCase			) ->	Optional[Any]:
     UpperCamelCase__			:   List[Any]       				=  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__			:   int       				=  unicodedata.category(__lowerCAmelCase			)
     if cat.startswith("P"			):
          return True
     return False





@dataclass
class 			__a  (    A__       ):
 _lowerCAmelCase       :       PreTrainedTokenizerBase
 _lowerCAmelCase       :       Union[bool, str, PaddingStrategy]        = True
 _lowerCAmelCase       :       Optional[int]        = None
 _lowerCAmelCase       :       Optional[int]        = None
 _lowerCAmelCase       :       int        = -1_0_0
 _lowerCAmelCase       :       str        = "pt"







 def 					__lowercase					(		self					:   Optional[Any]							,   SCREAMING_SNAKE_CASE					:   List[str]						):


      '''simple docstring'''






      import torch

      UpperCamelCase__			:   List[str]       				=  "label" if "label" in features[0].keys() else "labels"
      UpperCamelCase__			:   Union[str, Any]       				=  [feature[label_name] for feature in features] if label_name in features[0].keys() else None
      UpperCamelCase__			:   Tuple       				=  self.tokenizer.pad(
          SCREAMING_SNAKE_CASE							,   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__			:   Dict       				=  torch.tensor(batch["entity_ids"]						).shape[1]
      UpperCamelCase__			:   Optional[int]       				=  self.tokenizer.padding_side
      if padding_side == "right":
           UpperCamelCase__			:   Optional[Any]       				=  [
               list(SCREAMING_SNAKE_CASE						) + [self.label_pad_token_id] * (sequence_length - len(SCREAMING_SNAKE_CASE						)) for label in labels
           ]
      else:
           UpperCamelCase__			:   List[Any]       				=  [
               [self.label_pad_token_id] * (sequence_length - len(SCREAMING_SNAKE_CASE						)) + list(SCREAMING_SNAKE_CASE						) for label in labels
           ]

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

      return batch







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


lowerCamelCase	:							Optional[int]				=logging.get_logger(__name__)

lowerCamelCase	:							Dict				={
    '''facebook/s2t-wav2vec2-large-en-de''': (
        '''https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/config.json'''
    ),
    # See all Speech2Text models at https://huggingface.co/models?filter=speech2text2
}





class 			__a  (    A__       ):
 _lowerCAmelCase       :       Tuple        = '''speech_to_text_2'''
 _lowerCAmelCase       :       Dict        = ['''past_key_values''']
 _lowerCAmelCase       :       Any        = {'''num_attention_heads''': '''decoder_attention_heads''', '''hidden_size''': '''d_model'''}







 def __init__(		self					:   List[str]							,   SCREAMING_SNAKE_CASE					:   Union[str, Any]=1_00_00							,   SCREAMING_SNAKE_CASE					:   List[Any]=6							,   SCREAMING_SNAKE_CASE					:   List[Any]=20_48							,   SCREAMING_SNAKE_CASE					:   Dict=4							,   SCREAMING_SNAKE_CASE					:   List[Any]=0.0							,   SCREAMING_SNAKE_CASE					:   Optional[int]=True							,   SCREAMING_SNAKE_CASE					:   List[Any]="relu"							,   SCREAMING_SNAKE_CASE					:   Tuple=2_56							,   SCREAMING_SNAKE_CASE					:   List[str]=0.1							,   SCREAMING_SNAKE_CASE					:   Tuple=0.0							,   SCREAMING_SNAKE_CASE					:   Any=0.0							,   SCREAMING_SNAKE_CASE					:   int=0.0_2							,   SCREAMING_SNAKE_CASE					:   Dict=2							,   SCREAMING_SNAKE_CASE					:   List[str]=True							,   SCREAMING_SNAKE_CASE					:   Any=1							,   SCREAMING_SNAKE_CASE					:   List[Any]=0							,   SCREAMING_SNAKE_CASE					:   Any=2							,   SCREAMING_SNAKE_CASE					:   str=10_24							,   **SCREAMING_SNAKE_CASE					:   int							,   ):


      '''simple docstring'''






      UpperCamelCase__			:   int       				=  vocab_size
      UpperCamelCase__			:   Optional[Any]       				=  d_model
      UpperCamelCase__			:   Optional[Any]       				=  decoder_ffn_dim
      UpperCamelCase__			:   str       				=  decoder_layers
      UpperCamelCase__			:   Any       				=  decoder_attention_heads
      UpperCamelCase__			:   List[str]       				=  dropout
      UpperCamelCase__			:   int       				=  attention_dropout
      UpperCamelCase__			:   Optional[int]       				=  activation_dropout
      UpperCamelCase__			:   Union[str, Any]       				=  activation_function
      UpperCamelCase__			:   Tuple       				=  init_std
      UpperCamelCase__			:   Optional[int]       				=  decoder_layerdrop
      UpperCamelCase__			:   Dict       				=  use_cache
      UpperCamelCase__			:   str       				=  decoder_layers
      UpperCamelCase__			:   Union[str, Any]       				=  scale_embedding  # scale factor will be sqrt(d_model) if True
      UpperCamelCase__			:   Optional[Any]       				=  max_target_positions

      super().__init__(
          pad_token_id=SCREAMING_SNAKE_CASE							,   bos_token_id=SCREAMING_SNAKE_CASE							,   eos_token_id=SCREAMING_SNAKE_CASE							,   decoder_start_token_id=SCREAMING_SNAKE_CASE							,   **SCREAMING_SNAKE_CASE							,   )