




import time
import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch, torch_device

from ..test_modeling_common import ids_tensor


if is_torch_available():
							import torch

							from transformers.generation import (
							    MaxLengthCriteria,
							    MaxNewTokensCriteria,
							    MaxTimeCriteria,
							    StoppingCriteriaList,
							    validate_stopping_criteria,
							)




@require_torch
class 		__A      (			unittest.TestCase			):

						def     A__						(		self   :Dict    , __snake_case   :Dict ):
										'''simple docstring'''

										__magic_name__	:	Any								=3
										__magic_name__	:	int								=2_50

										__magic_name__	:	Optional[int]								=ids_tensor((batch_size, length)    , __snake_case )
										__magic_name__	:	str								=torch.ones((batch_size, length)    , device=__snake_case    , dtype=torch.float ) / length
										return input_ids, scores

						def     A__						(		self   :str ):
										'''simple docstring'''

										__magic_name__				, __magic_name__	:	Any								=self._get_tensors(5 )

										__magic_name__	:	List[str]								=StoppingCriteriaList(
										    [
										        MaxLengthCriteria(max_length=10 ),
										        MaxTimeCriteria(max_time=0.1 ),
										    ] )

										self.assertFalse(criteria(__snake_case    , __snake_case ) )

										__magic_name__				, __magic_name__	:	Dict								=self._get_tensors(9 )
										self.assertFalse(criteria(__snake_case    , __snake_case ) )

										__magic_name__				, __magic_name__	:	Dict								=self._get_tensors(10 )
										self.assertTrue(criteria(__snake_case    , __snake_case ) )

						def     A__						(		self   :Dict ):
										'''simple docstring'''

										__magic_name__	:	Optional[int]								=MaxLengthCriteria(max_length=10 )

										__magic_name__				, __magic_name__	:	Optional[int]								=self._get_tensors(5 )
										self.assertFalse(criteria(__snake_case    , __snake_case ) )

										__magic_name__				, __magic_name__	:	Tuple								=self._get_tensors(9 )
										self.assertFalse(criteria(__snake_case    , __snake_case ) )

										__magic_name__				, __magic_name__	:	str								=self._get_tensors(10 )
										self.assertTrue(criteria(__snake_case    , __snake_case ) )

						def     A__						(		self   :Union[str, Any] ):
										'''simple docstring'''

										__magic_name__	:	List[str]								=MaxNewTokensCriteria(start_length=5    , max_new_tokens=5 )

										__magic_name__				, __magic_name__	:	Optional[int]								=self._get_tensors(5 )
										self.assertFalse(criteria(__snake_case    , __snake_case ) )

										__magic_name__				, __magic_name__	:	Optional[int]								=self._get_tensors(9 )
										self.assertFalse(criteria(__snake_case    , __snake_case ) )

										__magic_name__				, __magic_name__	:	List[str]								=self._get_tensors(10 )
										self.assertTrue(criteria(__snake_case    , __snake_case ) )

										__magic_name__	:	List[Any]								=StoppingCriteriaList([criteria] )
										self.assertEqual(criteria_list.max_length    , 10 )

						def     A__						(		self   :Optional[int] ):
										'''simple docstring'''

										__magic_name__				, __magic_name__	:	int								=self._get_tensors(5 )

										__magic_name__	:	List[str]								=MaxTimeCriteria(max_time=0.1 )
										self.assertFalse(criteria(__snake_case    , __snake_case ) )

										__magic_name__	:	Tuple								=MaxTimeCriteria(max_time=0.1    , initial_timestamp=time.time() - 0.2 )
										self.assertTrue(criteria(__snake_case    , __snake_case ) )


						def     A__						(		self   :Union[str, Any] ):
										'''simple docstring'''

										validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(10 )] )    , 10 )

										with self.assertWarns(__snake_case ):
														validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(10 )] )    , 11 )

										__magic_name__	:	Union[str, Any]								=validate_stopping_criteria(StoppingCriteriaList()    , 11 )

										self.assertEqual(len(__snake_case )    , 1 )










import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin




class 		__A      (			UpperCamelCase__			):
						UpperCamelCase     						=					"""Speech2TextFeatureExtractor"""
						UpperCamelCase     						=					"""Speech2TextTokenizer"""

						def __init__(		self   :Any    , __snake_case   :int    , __snake_case   :Any ):
										'''simple docstring'''

										super().__init__(__snake_case    , __snake_case )
										__magic_name__	:	Optional[int]								=self.feature_extractor
										__magic_name__	:	Any								=False

						def __call__(		self   :Tuple    , *__snake_case   :List[str]    , **__snake_case   :List[str] ):
										'''simple docstring'''

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

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

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

						def     A__						(		self   :List[Any]    , *__snake_case   :str    , **__snake_case   :List[str] ):
										'''simple docstring'''

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

						def     A__						(		self   :int    , *__snake_case   :int    , **__snake_case   :Optional[int] ):
										'''simple docstring'''

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


						@contextmanager
						def     A__						(		self   :int ):
										'''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.""" )
										__magic_name__	:	int								=True
										__magic_name__	:	List[Any]								=self.tokenizer
										yield
										__magic_name__	:	List[Any]								=self.feature_extractor
										__magic_name__	:	str								=False




