


"""simple docstring"""







from queue import Queue
from typing import TYPE_CHECKING, Optional


if TYPE_CHECKING:
     from ..models.auto import AutoTokenizer


class       SCREAMING_SNAKE_CASE__				:
       """simple docstring"""


       def        lowercase__							(						self		,     snake_case__						):






         """simple docstring"""





         raise NotImplementedError()







       def        lowercase__							(						self						):






         """simple docstring"""





         raise NotImplementedError()


class       SCREAMING_SNAKE_CASE__				(		__UpperCAmelCase				):
       """simple docstring"""


       def __init__(						self		,     snake_case__		,     snake_case__ = False		,     **snake_case__						):






         """simple docstring"""





         lowerCAmelCase     :     List[str]								=							tokenizer
         lowerCAmelCase     :     Union[str, Any]								=							skip_prompt
         lowerCAmelCase     :     Dict								=							decode_kwargs

         # variables used in the streaming process
         lowerCAmelCase     :     Optional[int]								=							[]
         lowerCAmelCase     :     Optional[Any]								=							0
         lowerCAmelCase     :     Dict								=							True

       def        lowercase__							(						self		,     snake_case__						):






         """simple docstring"""





         if len(value.shape						) > 1 and value.shape[0] > 1:
           raise ValueError("TextStreamer only supports batch size 1"						)
         elif len(value.shape						) > 1:
           lowerCAmelCase     :     Any								=							value[0]

         if self.skip_prompt and self.next_tokens_are_prompt:
           lowerCAmelCase     :     str								=							False
           return

         # Add the new token to the cache and decodes the entire thing.
         self.token_cache.extend(value.tolist()						)
         lowerCAmelCase     :     Any								=							self.tokenizer.decode(self.token_cache		,     **self.decode_kwargs						)

         # After the symbol for a new line, we flush the cache.
         if text.endswith("\n"						):
           lowerCAmelCase     :     Union[str, Any]								=							text[self.print_len :]
           lowerCAmelCase     :     Union[str, Any]								=							[]
           lowerCAmelCase     :     Union[str, Any]								=							0
         # If the last token is a CJK character, we print the characters.
         elif len(snake_case__						) > 0 and self._is_chinese_char(ord(text[-1]						)						):
           lowerCAmelCase     :     Dict								=							text[self.print_len :]
           self.print_len += len(snake_case__						)
         # Otherwise, prints until the last space char (simple heuristic to avoid printing incomplete words,
         # which may change with the subsequent token -- there are probably smarter ways to do this!)
         else:
           lowerCAmelCase     :     List[Any]								=							text[self.print_len : text.rfind(" "						) + 1]
           self.print_len += len(snake_case__						)

         self.on_finalized_text(snake_case__						)

       def        lowercase__							(						self						):






         """simple docstring"""





         if len(self.token_cache						) > 0:
           lowerCAmelCase     :     Dict								=							self.tokenizer.decode(self.token_cache		,     **self.decode_kwargs						)
           lowerCAmelCase     :     str								=							text[self.print_len :]
           lowerCAmelCase     :     List[Any]								=							[]
           lowerCAmelCase     :     Any								=							0
         else:
           lowerCAmelCase     :     Optional[int]								=							''

         lowerCAmelCase     :     Dict								=							True
         self.on_finalized_text(snake_case__		,     stream_end=snake_case__						)

       def        lowercase__							(						self		,     snake_case__		,     snake_case__ = False						):






         """simple docstring"""





         print(snake_case__		,     flush=snake_case__		,     end="" if not stream_end else None						)







       def        lowercase__							(						self		,     snake_case__						):






         """simple docstring"""





         if (
             (cp >= 0x4_e00 and cp <= 0x9_fff)
             or (cp >= 0x3_400 and cp <= 0x4_dbf)  #
             or (cp >= 0x20_000 and cp <= 0x2a_6df)  #
             or (cp >= 0x2a_700 and cp <= 0x2b_73f)  #
             or (cp >= 0x2b_740 and cp <= 0x2b_81f)  #
             or (cp >= 0x2b_820 and cp <= 0x2c_eaf)  #
             or (cp >= 0xf_900 and cp <= 0xf_aff)
             or (cp >= 0x2f_800 and cp <= 0x2f_a1f)  #
         ):  #
           return True

         return False


class       SCREAMING_SNAKE_CASE__				(		__UpperCAmelCase				):
       """simple docstring"""


       def __init__(						self		,     snake_case__		,     snake_case__ = False		,     snake_case__ = None		,     **snake_case__						):






         """simple docstring"""





         super().__init__(snake_case__		,     snake_case__		,     **snake_case__						)
         lowerCAmelCase     :     Tuple								=							Queue()
         lowerCAmelCase     :     List[str]								=							None
         lowerCAmelCase     :     Union[str, Any]								=							timeout

       def        lowercase__							(						self		,     snake_case__		,     snake_case__ = False						):






         """simple docstring"""





         self.text_queue.put(snake_case__		,     timeout=self.timeout						)
         if stream_end:
           self.text_queue.put(self.stop_signal		,     timeout=self.timeout						)

       def __iter__(						self						):






         """simple docstring"""





         return self







       def        lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase     :     Union[str, Any]								=							self.text_queue.get(timeout=self.timeout						)
         if value == self.stop_signal:
           raise StopIteration()
         else:
           return value












from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxSeqaSeqConfigWithPast
from ...utils import logging


if TYPE_CHECKING:
    from ...feature_extraction_utils import FeatureExtractionMixin
    from ...tokenization_utils_base import PreTrainedTokenizerBase
    from ...utils import TensorType

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

lowercase      :       Any									=  {
    """openai/whisper-base""": """https://huggingface.co/openai/whisper-base/resolve/main/config.json""",
}

# fmt: off
lowercase      :       List[str]									=  [
    1, 2, 7, 8, 9, 1_0, 1_4, 2_5,
    2_6, 2_7, 2_8, 2_9, 3_1, 5_8, 5_9, 6_0, 6_1, 6_2,
    6_3, 9_0, 9_1, 9_2, 9_3, 3_5_7, 3_6_6, 4_3_8, 5_3_2, 6_8_5,
    7_0_5, 7_9_6, 9_3_0, 1_0_5_8, 1_2_2_0, 1_2_6_7, 1_2_7_9, 1_3_0_3, 1_3_4_3, 1_3_7_7,
    1_3_9_1, 1_6_3_5, 1_7_8_2, 1_8_7_5, 2_1_6_2, 2_3_6_1, 2_4_8_8, 3_4_6_7, 4_0_0_8, 4_2_1_1,
    4_6_0_0, 4_8_0_8, 5_2_9_9, 5_8_5_5, 6_3_2_9, 7_2_0_3, 9_6_0_9, 9_9_5_9, 1_0_5_6_3, 1_0_7_8_6,
    1_1_4_2_0, 1_1_7_0_9, 1_1_9_0_7, 1_3_1_6_3, 1_3_6_9_7, 1_3_7_0_0, 1_4_8_0_8, 1_5_3_0_6, 1_6_4_1_0, 1_6_7_9_1,
    1_7_9_9_2, 1_9_2_0_3, 1_9_5_1_0, 2_0_7_2_4, 2_2_3_0_5, 2_2_9_3_5, 2_7_0_0_7, 3_0_1_0_9, 3_0_4_2_0, 3_3_4_0_9,
    3_4_9_4_9, 4_0_2_8_3, 4_0_4_9_3, 4_0_5_4_9, 4_7_2_8_2, 4_9_1_4_6, 5_0_2_5_7, 5_0_3_5_9, 5_0_3_6_0, 5_0_3_6_1
]
lowercase      :       List[Any]									=  [
    1, 2, 7, 8, 9, 1_0, 1_4, 2_5,
    2_6, 2_7, 2_8, 2_9, 3_1, 5_8, 5_9, 6_0, 6_1, 6_2,
    6_3, 9_0, 9_1, 9_2, 9_3, 3_5_9, 5_0_3, 5_2_2, 5_4_2, 8_7_3,
    8_9_3, 9_0_2, 9_1_8, 9_2_2, 9_3_1, 1_3_5_0, 1_8_5_3, 1_9_8_2, 2_4_6_0, 2_6_2_7,
    3_2_4_6, 3_2_5_3, 3_2_6_8, 3_5_3_6, 3_8_4_6, 3_9_6_1, 4_1_8_3, 4_6_6_7, 6_5_8_5, 6_6_4_7,
    7_2_7_3, 9_0_6_1, 9_3_8_3, 1_0_4_2_8, 1_0_9_2_9, 1_1_9_3_8, 1_2_0_3_3, 1_2_3_3_1, 1_2_5_6_2, 1_3_7_9_3,
    1_4_1_5_7, 1_4_6_3_5, 1_5_2_6_5, 1_5_6_1_8, 1_6_5_5_3, 1_6_6_0_4, 1_8_3_6_2, 1_8_9_5_6, 2_0_0_7_5, 2_1_6_7_5,
    2_2_5_2_0, 2_6_1_3_0, 2_6_1_6_1, 2_6_4_3_5, 2_8_2_7_9, 2_9_4_6_4, 3_1_6_5_0, 3_2_3_0_2, 3_2_4_7_0, 3_6_8_6_5,
    4_2_8_6_3, 4_7_4_2_5, 4_9_8_7_0, 5_0_2_5_4, 5_0_2_5_8, 5_0_3_6_0, 5_0_3_6_1, 5_0_3_6_2
]





class  A__    (							__UpperCAmelCase	):




   """simple docstring"""



   __A   :				int         =  '''whisper'''
   __A   :				List[Any]         =  ['''past_key_values''']
   __A   :				Optional[int]         =  {'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}

   def __init__(					self					,  lowercase=5_1865					,  lowercase=80					,  lowercase=6					,  lowercase=4					,  lowercase=6					,  lowercase=4					,  lowercase=1536					,  lowercase=1536					,  lowercase=0.0					,  lowercase=0.0					,  lowercase=5_0257					,  lowercase=True					,  lowercase=True					,  lowercase="gelu"					,  lowercase=256					,  lowercase=0.0					,  lowercase=0.0					,  lowercase=0.0					,  lowercase=0.02					,  lowercase=False					,  lowercase=1500					,  lowercase=448					,  lowercase=5_0256					,  lowercase=5_0256					,  lowercase=5_0256					,  lowercase=None					,  lowercase=[220, 5_0256]					,  lowercase=False					,  lowercase=256					,  lowercase=False					,  lowercase=0.05					,  lowercase=10					,  lowercase=2					,  lowercase=0.0					,  lowercase=10					,  lowercase=0					,  lowercase=7					,  **lowercase					,  )			->						str:







          '''simple docstring'''

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

          # Audio Classification-specific parameters. Feel free to ignore for other classes.
          a__						:	Optional[int] =				classifier_proj_size
          a__						:	List[Any] =				use_weighted_layer_sum

          # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
          a__						:	List[Any] =				apply_spec_augment
          a__						:	int =				mask_time_prob
          a__						:	int =				mask_time_length
          a__						:	List[Any] =				mask_time_min_masks
          a__						:	str =				mask_feature_prob
          a__						:	Optional[int] =				mask_feature_length
          a__						:	Union[str, Any] =				mask_feature_min_masks

          a__						:	Tuple =				median_filter_width

          super().__init__(
              pad_token_id=lowercase					,  bos_token_id=lowercase					,  eos_token_id=lowercase					,  is_encoder_decoder=lowercase					,  decoder_start_token_id=lowercase					,  suppress_tokens=lowercase					,  begin_suppress_tokens=lowercase					,  **lowercase					,  )





class  A__    (							__UpperCAmelCase	):




   """simple docstring"""



   @property
   def 							__lowercase		(					self)			->						Mapping[str, Mapping[int, str]]:







          '''simple docstring'''

          a__						:	List[str] =				OrderedDict(
              [
                  ('input_features', {0: 'batch', 1: 'feature_size', 2: 'encoder_sequence'}),
              ])
          if self.use_past:
                 a__						:	Optional[Any] =				{0: 'batch'}
          else:
                 a__						:	Optional[Any] =				{0: 'batch', 1: 'decoder_sequence'}

          if self.use_past:
                 self.fill_with_past_key_values_(lowercase					,  direction='inputs')

          return common_inputs

   def 							__lowercase		(					self					,  lowercase					,  lowercase = -1					,  lowercase = -1					,  lowercase = False					,  lowercase = None					,  lowercase = 2_2050					,  lowercase = 5.0					,  lowercase = 220					,  )			->						Mapping[str, Any]:







          '''simple docstring'''

          a__						:	Union[str, Any] =				OrderedDict()
          a__						:	int =				OnnxConfig.generate_dummy_inputs(
              self					,  preprocessor=preprocessor.feature_extractor					,  batch_size=lowercase					,  framework=lowercase					,  sampling_rate=lowercase					,  time_duration=lowercase					,  frequency=lowercase					,  )
          a__						:	List[Any] =				encoder_inputs['input_features'].shape[2]
          a__						:	Optional[int] =				encoder_sequence_length // 2 if self.use_past else seq_length

          a__						:	Any =				super().generate_dummy_inputs(
              preprocessor.tokenizer					,  lowercase					,  lowercase					,  lowercase					,  lowercase)

          a__						:	List[str] =				encoder_inputs.pop('input_features')
          a__						:	Optional[int] =				decoder_inputs.pop('decoder_input_ids')

          if "past_key_values" in decoder_inputs:
                 a__						:	List[str] =				decoder_inputs.pop('past_key_values')

          return dummy_inputs

   @property
   def 							__lowercase		(					self)			->						float:







          '''simple docstring'''

          return 1e-3


