


"""simple docstring"""


from typing import TYPE_CHECKING

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


A	:  Dict            =     {
    "configuration_trocr": ["TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP", "TrOCRConfig"],
    "processing_trocr": ["TrOCRProcessor"],
}


try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A	:  List[Any]            =     [
	    "TROCR_PRETRAINED_MODEL_ARCHIVE_LIST",
	    "TrOCRForCausalLM",
	    "TrOCRPreTrainedModel",
	]


if TYPE_CHECKING:
	from .configuration_trocr import TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP, TrOCRConfig
	from .processing_trocr import TrOCRProcessor

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_trocr import TROCR_PRETRAINED_MODEL_ARCHIVE_LIST, TrOCRForCausalLM, TrOCRPreTrainedModel

else:
	import sys

	A	:  str            =     _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)








"""simple docstring"""


import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin
class     _UpperCamelCase    (  lowerCAmelCase__						):
							'''simple docstring'''







							__UpperCAmelCase		:       List[str]							 ="""Speech2TextFeatureExtractor"""
							__UpperCAmelCase		:       Union[str, Any]							 ="""Speech2TextTokenizer"""

							def __init__(	self       ,  __a       ,  __a      ):
										super().__init__(__a       ,  __a      )
										__lowerCAmelCase							       =     self.feature_extractor
										__lowerCAmelCase							       =     False

							def __call__(	self       ,  *__a       ,  **__a      ):
										# For backward compatibility
										if self._in_target_context_manager:
													return self.current_processor(*__a       ,  **__a      )

										if "raw_speech" in kwargs:
													warnings.warn("Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead."      )
													__lowerCAmelCase							       =     kwargs.pop("raw_speech"      )
										else:
													__lowerCAmelCase							       =     kwargs.pop("audio"       ,  __a      )
										__lowerCAmelCase							       =     kwargs.pop("sampling_rate"       ,  __a      )
										__lowerCAmelCase							       =     kwargs.pop("text"       ,  __a      )
										if len(__a      ) > 0:
													__lowerCAmelCase							       =     args[0]
													__lowerCAmelCase							       =     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:
													__lowerCAmelCase							       =     self.feature_extractor(__a       ,  *__a       ,  sampling_rate=__a       ,  **__a      )
										if text is not None:
													__lowerCAmelCase							       =     self.tokenizer(__a       ,  **__a      )

										if text is None:
													return inputs
										elif audio is None:
													return encodings
										else:
													__lowerCAmelCase							       =     encodings["input_ids"]
													return inputs

							def      snake_case							(	self       ,  *__a       ,  **__a      ):
										return self.tokenizer.batch_decode(*__a       ,  **__a      )

							def      snake_case							(	self       ,  *__a       ,  **__a      ):
										return self.tokenizer.decode(*__a       ,  **__a      )



							@contextmanager
							def      snake_case							(	self      ):
										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."      )
										__lowerCAmelCase							       =     True
										__lowerCAmelCase							       =     self.tokenizer
										yield
										__lowerCAmelCase							       =     self.feature_extractor
										__lowerCAmelCase							       =     False




