


from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCamelCase		   =			logging.get_logger(__name__)

lowerCamelCase		   =			{
    'facebook/s2t-wav2vec2-large-en-de': (
        'https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/config.json'
    ),
    # See all Speech2Text models at https://huggingface.co/models?filter=speech2text2
}




class A    (  UpperCamelCase_	):
      UpperCamelCase__				:    Optional[int] ='speech_to_text_2'
      UpperCamelCase__				:    int =['past_key_values']
      UpperCamelCase__				:    Tuple ={'num_attention_heads': 'decoder_attention_heads', 'hidden_size': 'd_model'}






      def __init__(   self		:	Any     ,  lowercase_		:	str=1_0000     ,  lowercase_		:	Dict=6     ,  lowercase_		:	List[str]=2048     ,  lowercase_		:	Any=4     ,  lowercase_		:	List[Any]=0.0     ,  lowercase_		:	List[str]=True     ,  lowercase_		:	int="relu"     ,  lowercase_		:	Tuple=256     ,  lowercase_		:	Union[str, Any]=0.1     ,  lowercase_		:	List[str]=0.0     ,  lowercase_		:	Optional[int]=0.0     ,  lowercase_		:	List[str]=0.02     ,  lowercase_		:	Any=2     ,  lowercase_		:	Any=True     ,  lowercase_		:	int=1     ,  lowercase_		:	List[str]=0     ,  lowercase_		:	List[str]=2     ,  lowercase_		:	Union[str, Any]=1024     ,  **lowercase_		:	int     ,  )   ->		int:
             """simple docstring"""




             _lowerCamelCase			:						Optional[Any]            =vocab_size
             _lowerCamelCase			:						Tuple            =d_model
             _lowerCamelCase			:						Optional[int]            =decoder_ffn_dim
             _lowerCamelCase			:						Optional[int]            =decoder_layers
             _lowerCamelCase			:						List[Any]            =decoder_attention_heads
             _lowerCamelCase			:						str            =dropout
             _lowerCamelCase			:						Dict            =attention_dropout
             _lowerCamelCase			:						Dict            =activation_dropout
             _lowerCamelCase			:						int            =activation_function
             _lowerCamelCase			:						int            =init_std
             _lowerCamelCase			:						Dict            =decoder_layerdrop
             _lowerCamelCase			:						List[str]            =use_cache
             _lowerCamelCase			:						List[str]            =decoder_layers
             _lowerCamelCase			:						str            =scale_embedding  # scale factor will be sqrt(d_model) if True
             _lowerCamelCase			:						Dict            =max_target_positions

             super().__init__(
                 pad_token_id=lowercase_     ,  bos_token_id=lowercase_     ,  eos_token_id=lowercase_     ,  decoder_start_token_id=lowercase_     ,  **lowercase_     ,  )






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 			lowerCamelCase			(   self		:	Optional[int]     ,  lowercase_		:	int					)   ->		Optional[int]:
             """simple docstring"""




             _lowerCamelCase			:						List[str]            =3
             _lowerCamelCase			:						Dict            =250

             _lowerCamelCase			:						Tuple            =ids_tensor((batch_size, length)     ,  lowercase_					)
             _lowerCamelCase			:						str            =torch.ones((batch_size, length)     ,  device=lowercase_     ,  dtype=torch.float					) / length
             return input_ids, scores






      def 			lowerCamelCase			(   self		:	Optional[int]					)   ->		Optional[Any]:
             """simple docstring"""




             _lowerCamelCase      ,	_lowerCamelCase			:						Optional[Any]            =self._get_tensors(5					)

             _lowerCamelCase			:						Dict            =StoppingCriteriaList(
                 [
                     MaxLengthCriteria(max_length=10					),
                     MaxTimeCriteria(max_time=0.1					),
                 ]					)

             self.assertFalse(criteria(lowercase_     ,  lowercase_					)					)

             _lowerCamelCase      ,	_lowerCamelCase			:						List[str]            =self._get_tensors(9					)
             self.assertFalse(criteria(lowercase_     ,  lowercase_					)					)

             _lowerCamelCase      ,	_lowerCamelCase			:						Optional[int]            =self._get_tensors(10					)
             self.assertTrue(criteria(lowercase_     ,  lowercase_					)					)






      def 			lowerCamelCase			(   self		:	Optional[int]					)   ->		Tuple:
             """simple docstring"""




             _lowerCamelCase			:						Any            =MaxLengthCriteria(max_length=10					)

             _lowerCamelCase      ,	_lowerCamelCase			:						List[str]            =self._get_tensors(5					)
             self.assertFalse(criteria(lowercase_     ,  lowercase_					)					)

             _lowerCamelCase      ,	_lowerCamelCase			:						Optional[int]            =self._get_tensors(9					)
             self.assertFalse(criteria(lowercase_     ,  lowercase_					)					)

             _lowerCamelCase      ,	_lowerCamelCase			:						Optional[int]            =self._get_tensors(10					)
             self.assertTrue(criteria(lowercase_     ,  lowercase_					)					)






      def 			lowerCamelCase			(   self		:	List[str]					)   ->		Tuple:
             """simple docstring"""




             _lowerCamelCase			:						Union[str, Any]            =MaxNewTokensCriteria(start_length=5     ,  max_new_tokens=5					)

             _lowerCamelCase      ,	_lowerCamelCase			:						str            =self._get_tensors(5					)
             self.assertFalse(criteria(lowercase_     ,  lowercase_					)					)

             _lowerCamelCase      ,	_lowerCamelCase			:						Union[str, Any]            =self._get_tensors(9					)
             self.assertFalse(criteria(lowercase_     ,  lowercase_					)					)

             _lowerCamelCase      ,	_lowerCamelCase			:						Dict            =self._get_tensors(10					)
             self.assertTrue(criteria(lowercase_     ,  lowercase_					)					)

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






      def 			lowerCamelCase			(   self		:	Optional[Any]					)   ->		str:
             """simple docstring"""




             _lowerCamelCase      ,	_lowerCamelCase			:						Optional[Any]            =self._get_tensors(5					)

             _lowerCamelCase			:						Tuple            =MaxTimeCriteria(max_time=0.1					)
             self.assertFalse(criteria(lowercase_     ,  lowercase_					)					)

             _lowerCamelCase			:						int            =MaxTimeCriteria(max_time=0.1     ,  initial_timestamp=time.time() - 0.2					)
             self.assertTrue(criteria(lowercase_     ,  lowercase_					)					)






      def 			lowerCamelCase			(   self		:	Optional[int]					)   ->		Optional[int]:
             """simple docstring"""




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

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

             _lowerCamelCase			:						Optional[Any]            =validate_stopping_criteria(StoppingCriteriaList()     ,  11					)

             self.assertEqual(len(lowercase_					)     ,  1					)


