

import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin

class   __UpperCamelCase      (   lowercase  ):
	SCREAMING_SNAKE_CASE__      			=       'AutoFeatureExtractor'
	SCREAMING_SNAKE_CASE__      			=       'Speech2Text2Tokenizer'




	def __init__(						self		:  List[Any]						,		lowerCAmelCase		:  str						,		lowerCAmelCase		:  List[Any]							):


		'''simple docstring'''







		super().__init__(lowerCAmelCase						,		lowerCAmelCase							)
		UpperCAmelCase_    			=     self.feature_extractor
		UpperCAmelCase_    			=     False




	def __call__(						self		:  Tuple						,		*lowerCAmelCase		:  str						,		**lowerCAmelCase		:  Tuple							):


		'''simple docstring'''







		if self._in_target_context_manager:
			return self.current_processor(*lowerCAmelCase						,		**lowerCAmelCase							)

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

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




	def  __A   (						self		:  Tuple						,		*lowerCAmelCase		:  Tuple						,		**lowerCAmelCase		:  Optional[int]							):


		'''simple docstring'''







		return self.tokenizer.batch_decode(*lowerCAmelCase						,		**lowerCAmelCase							)




	def  __A   (						self		:  Union[str, Any]						,		*lowerCAmelCase		:  List[str]						,		**lowerCAmelCase		:  Tuple							):


		'''simple docstring'''







		return self.tokenizer.decode(*lowerCAmelCase						,		**lowerCAmelCase							)




	@contextmanager
	def  __A   (						self		:  Any							):


		'''simple docstring'''







		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_    			=     True
		UpperCAmelCase_    			=     self.tokenizer
		yield
		UpperCAmelCase_    			=     self.feature_extractor
		UpperCAmelCase_    			=     False


import unittest

from datasets import load_dataset

from transformers import load_tool

from .test_tools_common import ToolTesterMixin

class   __UpperCamelCase      (   unittest.TestCase       ,       lowercase  ):




	def  __A   (						self		:  str							):


		'''simple docstring'''







		UpperCAmelCase_    			=     load_tool("document-question-answering"							)
		self.tool.setup()
		UpperCAmelCase_    			=     load_tool("document-question-answering"						,		remote=lowerCAmelCase							)




	def  __A   (						self		:  Optional[Any]							):


		'''simple docstring'''







		UpperCAmelCase_    			=     load_dataset("hf-internal-testing/example-documents"						,		split="test"							)
		UpperCAmelCase_    			=     dataset[0]["image"]

		UpperCAmelCase_    			=     self.tool(lowerCAmelCase						,		"When is the coffee break?"							)
		self.assertEqual(lowerCAmelCase						,		"11-14 to 11:39 a.m."							)




	def  __A   (						self		:  Any							):


		'''simple docstring'''







		UpperCAmelCase_    			=     load_dataset("hf-internal-testing/example-documents"						,		split="test"							)
		UpperCAmelCase_    			=     dataset[0]["image"]

		UpperCAmelCase_    			=     self.remote_tool(lowerCAmelCase						,		"When is the coffee break?"							)
		self.assertEqual(lowerCAmelCase						,		"11-14 to 11:39 a.m."							)




	def  __A   (						self		:  int							):


		'''simple docstring'''







		UpperCAmelCase_    			=     load_dataset("hf-internal-testing/example-documents"						,		split="test"							)
		UpperCAmelCase_    			=     dataset[0]["image"]

		self.tool(document=lowerCAmelCase						,		question="When is the coffee break?"							)




	def  __A   (						self		:  Any							):


		'''simple docstring'''







		UpperCAmelCase_    			=     load_dataset("hf-internal-testing/example-documents"						,		split="test"							)
		UpperCAmelCase_    			=     dataset[0]["image"]

		UpperCAmelCase_    			=     self.remote_tool(document=lowerCAmelCase						,		question="When is the coffee break?"							)
		self.assertEqual(lowerCAmelCase						,		"11-14 to 11:39 a.m."							)