

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      lowerCamelCase_ :




 def __init__(			self  ,  _UpperCAmelCase  ,  _UpperCAmelCase=99  ,  _UpperCAmelCase=13  ,  _UpperCAmelCase=16  ,  _UpperCAmelCase=7  ,  _UpperCAmelCase=True  ,  _UpperCAmelCase=True  ,  _UpperCAmelCase=True  ,  _UpperCAmelCase=False  ,  _UpperCAmelCase=True  ,  _UpperCAmelCase=2  ,  _UpperCAmelCase=32  ,  _UpperCAmelCase=4  ,  _UpperCAmelCase=4  ,  _UpperCAmelCase=30  ,  _UpperCAmelCase=0  ,  _UpperCAmelCase=1  ,  _UpperCAmelCase=2  ,  _UpperCAmelCase=None  ,  ):
   __UpperCAmelCase	:  str		=     parent
   __UpperCAmelCase	:  Union[str, Any]		=     batch_size
   __UpperCAmelCase	:  int		=     decoder_seq_length
   # For common tests
   __UpperCAmelCase	:  Tuple		=     self.decoder_seq_length
   __UpperCAmelCase	:  Any		=     is_training
   __UpperCAmelCase	:  int		=     use_attention_mask
   __UpperCAmelCase	:  Optional[Any]		=     use_labels

   __UpperCAmelCase	:  Optional[int]		=     vocab_size
   __UpperCAmelCase	:  Optional[Any]		=     d_model
   __UpperCAmelCase	:  Optional[int]		=     d_model
   __UpperCAmelCase	:  Dict		=     decoder_layers
   __UpperCAmelCase	:  Tuple		=     decoder_layers
   __UpperCAmelCase	:  Union[str, Any]		=     decoder_ffn_dim
   __UpperCAmelCase	:  Optional[Any]		=     decoder_attention_heads
   __UpperCAmelCase	:  Optional[Any]		=     decoder_attention_heads
   __UpperCAmelCase	:  Tuple		=     eos_token_id
   __UpperCAmelCase	:  Tuple		=     bos_token_id
   __UpperCAmelCase	:  Tuple		=     pad_token_id
   __UpperCAmelCase	:  str		=     decoder_start_token_id
   __UpperCAmelCase	:  Optional[Any]		=     use_cache
   __UpperCAmelCase	:  Optional[Any]		=     max_position_embeddings

   __UpperCAmelCase	:  Union[str, Any]		=     None
   __UpperCAmelCase	:  Dict		=     decoder_seq_length
   __UpperCAmelCase	:  Dict		=     2
   __UpperCAmelCase	:  List[Any]		=     1




 def     a       (			self							):
   __UpperCAmelCase	:  Dict		=     ids_tensor([self.batch_size, self.decoder_seq_length]  ,  self.vocab_size							)

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

   __UpperCAmelCase	:  List[Any]		=     None
   if self.use_labels:
     __UpperCAmelCase	:  Optional[Any]		=     ids_tensor([self.batch_size, self.decoder_seq_length]  ,  self.vocab_size							)

   __UpperCAmelCase	:  Optional[int]		=     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     a       (			self  ,  _UpperCAmelCase  ,  _UpperCAmelCase  ,  _UpperCAmelCase  ,  _UpperCAmelCase  ,  ):
   __UpperCAmelCase	:  str		=     True
   __UpperCAmelCase	:  str		=     SpeechaTextaDecoder(config=__SCREAMING_SNAKE_CASE							).to(__SCREAMING_SNAKE_CASE							).eval()
   __UpperCAmelCase	:  Optional[int]		=     input_ids[:2]

   input_ids[input_ids == 0] += 1
   # first forward pass
   __UpperCAmelCase	:  List[Any]		=     model(__SCREAMING_SNAKE_CASE  ,  use_cache=__SCREAMING_SNAKE_CASE							)
   __UpperCAmelCase	:  List[Any]		=     model(__SCREAMING_SNAKE_CASE							)
   __UpperCAmelCase	:  List[Any]		=     model(__SCREAMING_SNAKE_CASE  ,  use_cache=__SCREAMING_SNAKE_CASE							)

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

   __UpperCAmelCase	:  List[Any]		=     outputs['past_key_values']

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

   # append to next input_ids and
   __UpperCAmelCase	:  List[str]		=     torch.cat([input_ids, next_tokens]  ,  dim=-1							)

   print(__SCREAMING_SNAKE_CASE							)

   __UpperCAmelCase	:  Tuple		=     model(__SCREAMING_SNAKE_CASE							)['last_hidden_state']
   __UpperCAmelCase	:  Any		=     model(__SCREAMING_SNAKE_CASE  ,  past_key_values=__SCREAMING_SNAKE_CASE							)['last_hidden_state']

   # select random slice
   __UpperCAmelCase	:  int		=     ids_tensor((1,)  ,  output_from_past.shape[-1]							).item()
   __UpperCAmelCase	:  List[Any]		=     output_from_no_past[:, next_input_ids.shape[-1] - 1, random_slice_idx].detach()
   __UpperCAmelCase	:  str		=     output_from_past[:, 0, random_slice_idx].detach()

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




 def     a       (			self							):
   __UpperCAmelCase	:  Any		=     self.prepare_config_and_inputs()
   (
       (
       __UpperCAmelCase 
   ),			(
       __UpperCAmelCase 
   ),			(
       __UpperCAmelCase 
   ),			(
       __UpperCAmelCase 
   ),			 
   )	:  Optional[int]		=     config_and_inputs

   __UpperCAmelCase	:  str		=     {
       'input_ids': input_ids,
       'attention_mask': attention_mask,
   }
   return config, inputs_dict



@require_torch
class      lowerCamelCase_ (			__lowerCAmelCase	,				__lowerCAmelCase	,				__lowerCAmelCase	,				unittest.TestCase):
 _lowerCAmelCase									=	(SpeechaTextaDecoder, SpeechaTextaForCausalLM) if is_torch_available() else ()
 _lowerCAmelCase									=	(SpeechaTextaForCausalLM,) if is_torch_available() else ()
 _lowerCAmelCase									=	{"""text-generation""": SpeechaTextaForCausalLM} if is_torch_available() else {}
 _lowerCAmelCase									=	True
 _lowerCAmelCase									=	False




 def     a       (			self  ,  ):
   __UpperCAmelCase	:  List[Any]		=     SpeechaTextaStandaloneDecoderModelTester(self  ,  is_training=__SCREAMING_SNAKE_CASE							)
   __UpperCAmelCase	:  Dict		=     ConfigTester(self  ,  config_class=__SCREAMING_SNAKE_CASE							)




 def     a       (			self							):
   pass




 def     a       (			self							):
   pass




 def     a       (			self							):
   pass




 def     a       (			self							):
   self.config_tester.run_common_tests()




 def     a       (			self							):
   __UpperCAmelCase	:  Optional[Any]		=     self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_decoder_model_past(*__SCREAMING_SNAKE_CASE							)




 def     a       (			self							):
   return












"""simple docstring"""


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


a__       :  List[Any]       		=					logging.get_logger(__name__)

a__       :  int       		=					{
    """microsoft/mpnet-base""": """https://huggingface.co/microsoft/mpnet-base/resolve/main/config.json""",
}



class   lowercase_					(  __lowerCAmelCase				):
       snake_case__					:				int									=  """mpnet"""

       def __init__(      self ,       __SCREAMING_SNAKE_CASE=3_0527 ,       __SCREAMING_SNAKE_CASE=768 ,       __SCREAMING_SNAKE_CASE=12 ,       __SCREAMING_SNAKE_CASE=12 ,       __SCREAMING_SNAKE_CASE=3072 ,       __SCREAMING_SNAKE_CASE="gelu" ,       __SCREAMING_SNAKE_CASE=0.1 ,       __SCREAMING_SNAKE_CASE=0.1 ,       __SCREAMING_SNAKE_CASE=512 ,       __SCREAMING_SNAKE_CASE=0.02 ,       __SCREAMING_SNAKE_CASE=1e-12 ,       __SCREAMING_SNAKE_CASE=32 ,       __SCREAMING_SNAKE_CASE=1 ,       __SCREAMING_SNAKE_CASE=0 ,       __SCREAMING_SNAKE_CASE=2 ,       **__SCREAMING_SNAKE_CASE ,       ):






        '''simple docstring'''

        super().__init__(pad_token_id=__SCREAMING_SNAKE_CASE ,       bos_token_id=__SCREAMING_SNAKE_CASE ,       eos_token_id=__SCREAMING_SNAKE_CASE ,       **__SCREAMING_SNAKE_CASE  )

        snake_case__     				=					vocab_size
        snake_case__     				=					hidden_size
        snake_case__     				=					num_hidden_layers
        snake_case__     				=					num_attention_heads
        snake_case__     				=					hidden_act
        snake_case__     				=					intermediate_size
        snake_case__     				=					hidden_dropout_prob
        snake_case__     				=					attention_probs_dropout_prob
        snake_case__     				=					max_position_embeddings
        snake_case__     				=					initializer_range
        snake_case__     				=					layer_norm_eps
        snake_case__     				=					relative_attention_num_buckets

