






"""simple docstring"""


import unittest

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

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
       import torch

       from transformers.models.speech_to_text_a.modeling_speech_to_text_a import (
           SpeechaTextaDecoder,
           SpeechaTextaForCausalLM,
       )


@require_torch
class    snake_case						:


   '''simple docstring'''

   def __init__(      self					:	Dict,			_lowerCamelCase					:	str,			_lowerCamelCase					:	Dict=99,			_lowerCamelCase					:	Optional[int]=13,			_lowerCamelCase					:	List[Any]=16,			_lowerCamelCase					:	int=7,			_lowerCamelCase					:	str=True,			_lowerCamelCase					:	Optional[int]=True,			_lowerCamelCase					:	Optional[int]=True,			_lowerCamelCase					:	Tuple=False,			_lowerCamelCase					:	Any=True,			_lowerCamelCase					:	Dict=2,			_lowerCamelCase					:	List[Any]=32,			_lowerCamelCase					:	List[str]=4,			_lowerCamelCase					:	Dict=4,			_lowerCamelCase					:	List[str]=30,			_lowerCamelCase					:	Optional[Any]=0,			_lowerCamelCase					:	Any=1,			_lowerCamelCase					:	Optional[int]=2,			_lowerCamelCase					:	Optional[Any]=None,			):

     '''simple docstring'''
     __A		     =					parent
     __A		     =					batch_size
     __A		     =					decoder_seq_length
     # For common tests
     __A		     =					self.decoder_seq_length
     __A		     =					is_training
     __A		     =					use_attention_mask
     __A		     =					use_labels

     __A		     =					vocab_size
     __A		     =					d_model
     __A		     =					d_model
     __A		     =					decoder_layers
     __A		     =					decoder_layers
     __A		     =					decoder_ffn_dim
     __A		     =					decoder_attention_heads
     __A		     =					decoder_attention_heads
     __A		     =					eos_token_id
     __A		     =					bos_token_id
     __A		     =					pad_token_id
     __A		     =					decoder_start_token_id
     __A		     =					use_cache
     __A		     =					max_position_embeddings

     __A		     =					None
     __A		     =					decoder_seq_length
     __A		     =					2
     __A		     =					1


   def 							_SCREAMING_SNAKE_CASE							(      self					:	Any    ):

     '''simple docstring'''
     __A		     =					ids_tensor([self.batch_size, self.decoder_seq_length],			self.vocab_size    )

     __A		     =					None
     if self.use_attention_mask:
       __A		     =					ids_tensor([self.batch_size, self.decoder_seq_length],			vocab_size=2    )

     __A		     =					None
     if self.use_labels:
       __A		     =					ids_tensor([self.batch_size, self.decoder_seq_length],			self.vocab_size    )

     __A		     =					SpeechaTextaConfig(
         vocab_size=self.vocab_size,			d_model=self.d_model,			decoder_layers=self.decoder_layers,			decoder_ffn_dim=self.decoder_ffn_dim,			decoder_attention_heads=self.decoder_attention_heads,			eos_token_id=self.eos_token_id,			bos_token_id=self.bos_token_id,			use_cache=self.use_cache,			pad_token_id=self.pad_token_id,			decoder_start_token_id=self.decoder_start_token_id,			max_position_embeddings=self.max_position_embeddings,			)

     return (
         config,
         input_ids,
         attention_mask,
         lm_labels,
     )


   def 							_SCREAMING_SNAKE_CASE							(      self					:	Dict,			_lowerCamelCase					:	str,			_lowerCamelCase					:	Dict,			_lowerCamelCase					:	Optional[Any],			_lowerCamelCase					:	Union[str, Any],			):

     '''simple docstring'''
     __A		     =					True
     __A		     =					SpeechaTextaDecoder(config=_lowerCamelCase    ).to(_lowerCamelCase    ).eval()
     __A		     =					input_ids[:2]

     input_ids[input_ids == 0] += 1
     # first forward pass
     __A		     =					model(_lowerCamelCase,			use_cache=_lowerCamelCase    )
     __A		     =					model(_lowerCamelCase    )
     __A		     =					model(_lowerCamelCase,			use_cache=_lowerCamelCase    )

     self.parent.assertTrue(len(_lowerCamelCase    ) == len(_lowerCamelCase    )    )
     self.parent.assertTrue(len(_lowerCamelCase    ) == len(_lowerCamelCase    ) + 1    )

     __A		     =					outputs['''past_key_values''']

     # create hypothetical next token and extent to next_input_ids
     __A		     =					ids_tensor((2, 1),			config.vocab_size - 1    ) + 1

     # append to next input_ids and
     __A		     =					torch.cat([input_ids, next_tokens],			dim=-1    )

     print(_lowerCamelCase    )

     __A		     =					model(_lowerCamelCase    )['''last_hidden_state''']
     __A		     =					model(_lowerCamelCase,			past_key_values=_lowerCamelCase    )['''last_hidden_state''']

     # select random slice
     __A		     =					ids_tensor((1,),			output_from_past.shape[-1]    ).item()
     __A		     =					output_from_no_past[:, next_input_ids.shape[-1] - 1, random_slice_idx].detach()
     __A		     =					output_from_past[:, 0, random_slice_idx].detach()

     # test that outputs are equal for slice
     assert torch.allclose(_lowerCamelCase,			_lowerCamelCase,			atol=1e-3    )






   def 							_SCREAMING_SNAKE_CASE							(      self					:	Dict    ):

     '''simple docstring'''
     __A		     =					self.prepare_config_and_inputs()
     (
         (
         __A
     )     ,       (
         __A
     )     ,       (
         __A
     )     ,       (
         __A
     )     ,       
     )		     =					config_and_inputs

     __A		     =					{
         '''input_ids''': input_ids,
         '''attention_mask''': attention_mask,
     }
     return config, inputs_dict





@require_torch
class    snake_case						(				_lowerCAmelCase  ,      _lowerCAmelCase  ,      _lowerCAmelCase  ,      unittest.TestCase		):


   '''simple docstring'''

   A_		:      List[str]							=					(SpeechaTextaDecoder, SpeechaTextaForCausalLM) if is_torch_available() else ()
   A_		:      Dict							=					(SpeechaTextaForCausalLM,) if is_torch_available() else ()
   A_		:      Optional[Any]							=					{"text-generation": SpeechaTextaForCausalLM} if is_torch_available() else {}
   A_		:      Any							=					True
   A_		:      str							=					False


   def 							_SCREAMING_SNAKE_CASE							(      self					:	str,			):

     '''simple docstring'''
     __A		     =					SpeechaTextaStandaloneDecoderModelTester(self,			is_training=_lowerCamelCase    )
     __A		     =					ConfigTester(self,			config_class=_lowerCamelCase    )


   def 							_SCREAMING_SNAKE_CASE							(      self					:	str    ):

     '''simple docstring'''
     pass


   def 							_SCREAMING_SNAKE_CASE							(      self					:	Optional[int]    ):

     '''simple docstring'''
     pass


   def 							_SCREAMING_SNAKE_CASE							(      self					:	Tuple    ):

     '''simple docstring'''
     pass


   def 							_SCREAMING_SNAKE_CASE							(      self					:	Optional[Any]    ):

     '''simple docstring'''
     self.config_tester.run_common_tests()


   def 							_SCREAMING_SNAKE_CASE							(      self					:	Optional[int]    ):

     '''simple docstring'''
     __A		     =					self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_decoder_model_past(*_lowerCamelCase    )






   def 							_SCREAMING_SNAKE_CASE							(      self					:	Any    ):

     '''simple docstring'''
     return












"""simple docstring"""


import warnings

from ...utils import logging
from .image_processing_vilt import ViltImageProcessor


lowercase_								=	logging.get_logger(__name__)


class    snake_case						(				_lowerCAmelCase		):


   '''simple docstring'''

   def __init__(      self					:	Optional[int],			*_lowerCamelCase					:	int,			**_lowerCamelCase					:	str    ):

     '''simple docstring'''
     warnings.warn(
         '''The class ViltFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'''
         ''' use ViltImageProcessor instead.''',			_lowerCamelCase,			)
     super().__init__(*_lowerCamelCase,			**_lowerCamelCase    )




