






import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin







class     UpperCAmelCase_		(      lowercase				):



      """simple docstring"""
      UpperCamelCase_       :      Optional[Any]     ='AutoFeatureExtractor'
      UpperCamelCase_       :      Tuple     ='Speech2Text2Tokenizer'
      def __init__(       self					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_				)       ->   Any:
          super().__init__(SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_				)
          UpperCamelCase :Union[str, Any]										=     self.feature_extractor
          UpperCamelCase :int										=     False
      def __call__(       self					,       *SCREAMING_SNAKE_CASE_					,       **SCREAMING_SNAKE_CASE_				)       ->   Any:
          # For backward compatibility
          if self._in_target_context_manager:
              return self.current_processor(*SCREAMING_SNAKE_CASE_					,       **SCREAMING_SNAKE_CASE_				)

          if "raw_speech" in kwargs:
              warnings.warn('''Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead.'''				)
              UpperCamelCase :Tuple										=     kwargs.pop('''raw_speech'''				)
          else:
              UpperCamelCase :Any										=     kwargs.pop('''audio'''					,       SCREAMING_SNAKE_CASE_				)
          UpperCamelCase :Dict										=     kwargs.pop('''sampling_rate'''					,       SCREAMING_SNAKE_CASE_				)
          UpperCamelCase :List[Any]										=     kwargs.pop('''text'''					,       SCREAMING_SNAKE_CASE_				)
          if len(SCREAMING_SNAKE_CASE_				) > 0:
              UpperCamelCase :Optional[int]										=     args[0]
              UpperCamelCase :Optional[int]										=     args[1:]

          if audio is None and text is None:
              raise ValueError('''You need to specify either an `audio` or `text` input to process.'''				)

          if audio is not None:
              UpperCamelCase :int										=     self.feature_extractor(SCREAMING_SNAKE_CASE_					,       *SCREAMING_SNAKE_CASE_					,       sampling_rate=SCREAMING_SNAKE_CASE_					,       **SCREAMING_SNAKE_CASE_				)
          if text is not None:
              UpperCamelCase :List[Any]										=     self.tokenizer(SCREAMING_SNAKE_CASE_					,       **SCREAMING_SNAKE_CASE_				)

          if text is None:
              return inputs
          elif audio is None:
              return encodings
          else:
              UpperCamelCase :List[str]										=     encodings['''input_ids''']
              return inputs
      def 					UpperCAmelCase		(       self					,       *SCREAMING_SNAKE_CASE_					,       **SCREAMING_SNAKE_CASE_				)       ->   Optional[Any]:
          return self.tokenizer.batch_decode(*SCREAMING_SNAKE_CASE_					,       **SCREAMING_SNAKE_CASE_				)
      def 					UpperCAmelCase		(       self					,       *SCREAMING_SNAKE_CASE_					,       **SCREAMING_SNAKE_CASE_				)       ->   Tuple:
          return self.tokenizer.decode(*SCREAMING_SNAKE_CASE_					,       **SCREAMING_SNAKE_CASE_				)




      @contextmanager
      def 					UpperCAmelCase		(       self				)       ->   str:
          warnings.warn(
              '''`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your '''
              '''labels by using the argument `text` of the regular `__call__` method (either in the same call as '''
              '''your audio inputs, or in a separate call.'''				)
          UpperCamelCase :List[str]										=     True
          UpperCamelCase :List[Any]										=     self.tokenizer
          yield
          UpperCamelCase :Union[str, Any]										=     self.feature_extractor
          UpperCamelCase :List[Any]										=     False













from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


__snake_case							      =						{
    """configuration_open_llama""": ["""OPEN_LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """OpenLlamaConfig"""],
}

try:
 if not is_sentencepiece_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __snake_case							      =						["""LlamaTokenizer"""]

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __snake_case							      =						["""LlamaTokenizerFast"""]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __snake_case							      =						[
     """OpenLlamaForCausalLM""",
     """OpenLlamaModel""",
     """OpenLlamaPreTrainedModel""",
     """OpenLlamaForSequenceClassification""",
 ]


if TYPE_CHECKING:
 from .configuration_open_llama import OPEN_LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP, OpenLlamaConfig

 try:
  if not is_sentencepiece_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from transformers import LlamaTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from transformers import LlamaTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_open_llama import (
      OpenLlamaForCausalLM,
      OpenLlamaForSequenceClassification,
      OpenLlamaModel,
      OpenLlamaPreTrainedModel,
  )


else:
 import sys

 __snake_case							      =						_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)





