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					:



 '''simple docstring'''

 def __init__(     self    :						Union[str, Any]    ,     lowerCamelCase    :						Optional[Any]    ,     lowerCamelCase    :						str=9_9    ,     lowerCamelCase    :						List[Any]=1_3    ,     lowerCamelCase    :						Any=1_6    ,     lowerCamelCase    :						Optional[Any]=7    ,     lowerCamelCase    :						int=True    ,     lowerCamelCase    :						Dict=True    ,     lowerCamelCase    :						str=True    ,     lowerCamelCase    :						List[str]=False    ,     lowerCamelCase    :						str=True    ,     lowerCamelCase    :						Dict=2    ,     lowerCamelCase    :						int=3_2    ,     lowerCamelCase    :						int=4    ,     lowerCamelCase    :						Dict=4    ,     lowerCamelCase    :						Optional[Any]=3_0    ,     lowerCamelCase    :						int=0    ,     lowerCamelCase    :						str=1    ,     lowerCamelCase    :						Dict=2    ,     lowerCamelCase    :						Dict=None    ,     )	->							List[Any]:
        _UpperCamelCase       :    Dict					  =					parent
        _UpperCamelCase       :    str					  =					batch_size
        _UpperCamelCase       :    str					  =					decoder_seq_length
        # For common tests
        _UpperCamelCase       :    Union[str, Any]					  =					self.decoder_seq_length
        _UpperCamelCase       :    List[Any]					  =					is_training
        _UpperCamelCase       :    List[str]					  =					use_attention_mask
        _UpperCamelCase       :    Any					  =					use_labels

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

        _UpperCamelCase       :    Tuple					  =					None
        _UpperCamelCase       :    List[Any]					  =					decoder_seq_length
        _UpperCamelCase       :    List[str]					  =					2
        _UpperCamelCase       :    List[str]					  =					1

 def    __snake_case   (     self    :						Tuple					)	->							Optional[int]:
        _UpperCamelCase       :    Union[str, Any]					  =					ids_tensor([self.batch_size, self.decoder_seq_length]    ,     self.vocab_size					)

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

        _UpperCamelCase       :    Union[str, Any]					  =					None
        if self.use_labels:
               _UpperCamelCase       :    int					  =					ids_tensor([self.batch_size, self.decoder_seq_length]    ,     self.vocab_size					)

        _UpperCamelCase       :    List[Any]					  =					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    __snake_case   (     self    :						Optional[int]    ,     lowerCamelCase    :						Optional[int]    ,     lowerCamelCase    :						List[str]    ,     lowerCamelCase    :						Optional[int]    ,     lowerCamelCase    :						Tuple    ,     )	->							Optional[Any]:
        _UpperCamelCase       :    Any					  =					True
        _UpperCamelCase       :    Optional[int]					  =					SpeechaTextaDecoder(config=lowerCamelCase					).to(lowerCamelCase					).eval()
        _UpperCamelCase       :    Dict					  =					input_ids[:2]

        input_ids[input_ids == 0] += 1
        # first forward pass
        _UpperCamelCase       :    List[str]					  =					model(lowerCamelCase    ,     use_cache=lowerCamelCase					)
        _UpperCamelCase       :    Any					  =					model(lowerCamelCase					)
        _UpperCamelCase       :    Dict					  =					model(lowerCamelCase    ,     use_cache=lowerCamelCase					)

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

        _UpperCamelCase       :    Optional[int]					  =					outputs["past_key_values"]

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

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

        print(lowerCamelCase					)

        _UpperCamelCase       :    List[Any]					  =					model(lowerCamelCase					)["last_hidden_state"]
        _UpperCamelCase       :    List[Any]					  =					model(lowerCamelCase    ,     past_key_values=lowerCamelCase					)["last_hidden_state"]

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

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

 def    __snake_case   (     self    :						Dict					)	->							Tuple:
        _UpperCamelCase       :    Any					  =					self.prepare_config_and_inputs()
        (
            _UpperCamelCase       
        )       :    List[str]					  =					config_and_inputs

        _UpperCamelCase       :    List[Any]					  =					{
            "input_ids": input_ids,
            "attention_mask": attention_mask,
        }
        return config, inputs_dict







@require_torch
class    __lowerCAmelCase					(    snake_case_,		snake_case_,		snake_case_,		unittest.TestCase  ):



 '''simple docstring'''
 _lowerCamelCase  :		Optional[int]			      =	(SpeechaTextaDecoder, SpeechaTextaForCausalLM) if is_torch_available() else ()
 _lowerCamelCase  :		str			      =	(SpeechaTextaForCausalLM,) if is_torch_available() else ()
 _lowerCamelCase  :		Tuple			      =	{"""text-generation""": SpeechaTextaForCausalLM} if is_torch_available() else {}
 _lowerCamelCase  :		Dict			      =	True
 _lowerCamelCase  :		int			      =	False

 def    __snake_case   (     self    :						int    ,     )	->							Tuple:
        _UpperCamelCase       :    Optional[int]					  =					SpeechaTextaStandaloneDecoderModelTester(self    ,     is_training=lowerCamelCase					)
        _UpperCamelCase       :    Optional[int]					  =					ConfigTester(self    ,     config_class=lowerCamelCase					)

 def    __snake_case   (     self    :						List[str]					)	->							int:
        pass

 def    __snake_case   (     self    :						int					)	->							str:
        pass

 def    __snake_case   (     self    :						int					)	->							str:
        pass

 def    __snake_case   (     self    :						Any					)	->							str:
        self.config_tester.run_common_tests()

 def    __snake_case   (     self    :						List[Any]					)	->							Optional[int]:
        _UpperCamelCase       :    Any					  =					self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_decoder_model_past(*lowerCamelCase					)

 def    __snake_case   (     self    :						List[str]					)	->							Union[str, Any]:
        return




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCAmelCase     						=			{
    '''configuration_mpnet''': ['''MPNET_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MPNetConfig'''],
    '''tokenization_mpnet''': ['''MPNetTokenizer'''],
}

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 UpperCAmelCase     						=			['''MPNetTokenizerFast''']

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 UpperCAmelCase     						=			[
     '''MPNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''MPNetForMaskedLM''',
     '''MPNetForMultipleChoice''',
     '''MPNetForQuestionAnswering''',
     '''MPNetForSequenceClassification''',
     '''MPNetForTokenClassification''',
     '''MPNetLayer''',
     '''MPNetModel''',
     '''MPNetPreTrainedModel''',
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 UpperCAmelCase     						=			[
     '''TF_MPNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''TFMPNetEmbeddings''',
     '''TFMPNetForMaskedLM''',
     '''TFMPNetForMultipleChoice''',
     '''TFMPNetForQuestionAnswering''',
     '''TFMPNetForSequenceClassification''',
     '''TFMPNetForTokenClassification''',
     '''TFMPNetMainLayer''',
     '''TFMPNetModel''',
     '''TFMPNetPreTrainedModel''',
 ]


if TYPE_CHECKING:
 from .configuration_mpnet import MPNET_PRETRAINED_CONFIG_ARCHIVE_MAP, MPNetConfig
 from .tokenization_mpnet import MPNetTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_mpnet_fast import MPNetTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_mpnet import (
      MPNET_PRETRAINED_MODEL_ARCHIVE_LIST,
      MPNetForMaskedLM,
      MPNetForMultipleChoice,
      MPNetForQuestionAnswering,
      MPNetForSequenceClassification,
      MPNetForTokenClassification,
      MPNetLayer,
      MPNetModel,
      MPNetPreTrainedModel,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_mpnet import (
      TF_MPNET_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFMPNetEmbeddings,
      TFMPNetForMaskedLM,
      TFMPNetForMultipleChoice,
      TFMPNetForQuestionAnswering,
      TFMPNetForSequenceClassification,
      TFMPNetForTokenClassification,
      TFMPNetMainLayer,
      TFMPNetModel,
      TFMPNetPreTrainedModel,
  )

else:
 import sys

 UpperCAmelCase     						=			_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



