

from typing import TYPE_CHECKING

from ...utils import _LazyModule


__lowercase     : int       =						{'''tokenization_wav2vec2_phoneme''': ['''Wav2Vec2PhonemeCTCTokenizer''']}


if TYPE_CHECKING:
     from .tokenization_wavaveca_phoneme import WavaVecaPhonemeCTCTokenizer
else:
     import sys

     __lowercase     : List[Any]       =						_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)








'''simple docstring'''





import unittest

from parameterized import parameterized

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

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


if is_torch_available():
  import torch

  from transformers import OpenLlamaForCausalLM, OpenLlamaForSequenceClassification, OpenLlamaModel






class __A     :



 '''simple docstring'''







 def __init__(self					,  A					,  A=13					,  A=7					,  A=True					,  A=True					,  A=False					,  A=True					,  A=99					,  A=32					,  A=5					,  A=4					,  A=37					,  A="gelu"					,  A=0.1					,  A=0.1					,  A=512					,  A=16					,  A=2					,  A=0.02					,  A=3					,  A=4					,  A=None					,  )       ->	str:


      """simple docstring"""



      _a           =    parent
      _a           =    batch_size
      _a           =    seq_length
      _a           =    is_training
      _a           =    use_input_mask
      _a           =    use_token_type_ids
      _a           =    use_labels
      _a           =    vocab_size
      _a           =    hidden_size
      _a           =    num_hidden_layers
      _a           =    num_attention_heads
      _a           =    intermediate_size
      _a           =    hidden_act
      _a           =    hidden_dropout_prob
      _a           =    attention_probs_dropout_prob
      _a           =    max_position_embeddings
      _a           =    type_vocab_size
      _a           =    type_sequence_label_size
      _a           =    initializer_range
      _a           =    num_labels
      _a           =    num_choices
      _a           =    scope



 def    a__    (self			)       ->	List[str]:


      """simple docstring"""



      _a           =    ids_tensor([self.batch_size, self.seq_length]					,  self.vocab_size			)

      _a           =    None
      if self.use_input_mask:
           _a           =    random_attention_mask([self.batch_size, self.seq_length]			)

      _a           =    None
      if self.use_token_type_ids:
           _a           =    ids_tensor([self.batch_size, self.seq_length]					,  self.type_vocab_size			)

      _a           =    None
      _a           =    None
      _a           =    None
      if self.use_labels:
           _a           =    ids_tensor([self.batch_size]					,  self.type_sequence_label_size			)
           _a           =    ids_tensor([self.batch_size, self.seq_length]					,  self.num_labels			)
           _a           =    ids_tensor([self.batch_size]					,  self.num_choices			)

      _a           =    self.get_config()

      return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels



 def    a__    (self			)       ->	Optional[int]:


      """simple docstring"""



      return OpenLlamaConfig(
          vocab_size=self.vocab_size					,  hidden_size=self.hidden_size					,  num_hidden_layers=self.num_hidden_layers					,  num_attention_heads=self.num_attention_heads					,  intermediate_size=self.intermediate_size					,  hidden_act=self.hidden_act					,  hidden_dropout_prob=self.hidden_dropout_prob					,  attention_probs_dropout_prob=self.attention_probs_dropout_prob					,  max_position_embeddings=self.max_position_embeddings					,  type_vocab_size=self.type_vocab_size					,  is_decoder=A					,  initializer_range=self.initializer_range					,  use_stable_embedding=A					,  )



 def    a__    (self					,  A					,  A					,  A					,  A					,  A					,  A					,  A			)       ->	Any:


      """simple docstring"""



      _a           =    OpenLlamaModel(config=A			)
      model.to(A			)
      model.eval()
      _a           =    model(A					,  attention_mask=A			)
      _a           =    model(A			)
      self.parent.assertEqual(result.last_hidden_state.shape					,  (self.batch_size, self.seq_length, self.hidden_size)			)



 def    a__    (self					,  A					,  A					,  A					,  A					,  A					,  A					,  A					,  A					,  A					,  )       ->	Any:


      """simple docstring"""



      _a           =    True
      _a           =    OpenLlamaModel(A			)
      model.to(A			)
      model.eval()
      _a           =    model(
          A					,  attention_mask=A					,  encoder_hidden_states=A					,  encoder_attention_mask=A					,  )
      _a           =    model(
          A					,  attention_mask=A					,  encoder_hidden_states=A					,  )
      _a           =    model(A					,  attention_mask=A			)
      self.parent.assertEqual(result.last_hidden_state.shape					,  (self.batch_size, self.seq_length, self.hidden_size)			)



 def    a__    (self					,  A					,  A					,  A					,  A					,  A					,  A					,  A					,  A					,  A					,  )       ->	Tuple:


      """simple docstring"""



      _a           =    OpenLlamaForCausalLM(config=A			)
      model.to(A			)
      model.eval()
      _a           =    model(A					,  attention_mask=A					,  labels=A			)
      self.parent.assertEqual(result.logits.shape					,  (self.batch_size, self.seq_length, self.vocab_size)			)



 def    a__    (self					,  A					,  A					,  A					,  A					,  A					,  A					,  A					,  A					,  A					,  )       ->	Dict:


      """simple docstring"""



      _a           =    True
      _a           =    True
      _a           =    OpenLlamaForCausalLM(config=A			)
      model.to(A			)
      model.eval()

      # first forward pass
      _a           =    model(
          A					,  attention_mask=A					,  encoder_hidden_states=A					,  encoder_attention_mask=A					,  use_cache=A					,  )
      _a           =    outputs.past_key_values

      # create hypothetical multiple next token and extent to next_input_ids
      _a           =    ids_tensor((self.batch_size, 3)					,  config.vocab_size			)
      _a           =    ids_tensor((self.batch_size, 3)					,  vocab_size=2			)

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

      _a           =    model(
          A					,  attention_mask=A					,  encoder_hidden_states=A					,  encoder_attention_mask=A					,  output_hidden_states=A					,  )['''hidden_states'''][0]
      _a           =    model(
          A					,  attention_mask=A					,  encoder_hidden_states=A					,  encoder_attention_mask=A					,  past_key_values=A					,  output_hidden_states=A					,  )['''hidden_states'''][0]

      # select random slice
      _a           =    ids_tensor((1,)					,  output_from_past.shape[-1]			).item()
      _a           =    output_from_no_past[:, -3:, random_slice_idx].detach()
      _a           =    output_from_past[:, :, random_slice_idx].detach()

      self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]			)

      # test that outputs are equal for slice
      self.parent.assertTrue(torch.allclose(A					,  A					,  atol=1E-3			)			)



 def    a__    (self			)       ->	Optional[Any]:


      """simple docstring"""



      _a           =    self.prepare_config_and_inputs()
      (
          (
          _a
      )    ,       (
          _a
      )    ,       (
          _a
      )    ,       (
          _a
      )    ,       (
          _a
      )    ,       (
          _a
      )    ,       (
          _a
      )    ,       
      )           =    config_and_inputs
      _a           =    {'''input_ids''': input_ids, '''attention_mask''': input_mask}
      return config, inputs_dict






@require_torch
class __A     (    A  ,  A  ,  A  ,  unittest.TestCase		):



 '''simple docstring'''







 __lowerCamelCase       :			Optional[int]		=			(
     (OpenLlamaModel, OpenLlamaForCausalLM, OpenLlamaForSequenceClassification) if is_torch_available() else ()
 )
 __lowerCamelCase       :			Any		=			(OpenLlamaForCausalLM,) if is_torch_available() else ()
 __lowerCamelCase       :			List[Any]		=			(
     {
         'feature-extraction': OpenLlamaModel,
         'text-classification': OpenLlamaForSequenceClassification,
         'text-generation': OpenLlamaForCausalLM,
         'zero-shot': OpenLlamaForSequenceClassification,
     }
     if is_torch_available()
     else {}
 )
 __lowerCamelCase       :			List[str]		=			False
 __lowerCamelCase       :			List[str]		=			False



 def    a__    (self			)       ->	Tuple:


      """simple docstring"""



      _a           =    OpenLlamaModelTester(self			)
      _a           =    ConfigTester(self					,  config_class=A					,  hidden_size=37			)



 def    a__    (self			)       ->	List[str]:


      """simple docstring"""



      self.config_tester.run_common_tests()



 def    a__    (self			)       ->	Union[str, Any]:


      """simple docstring"""



      _a           =    self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*A			)



 def    a__    (self			)       ->	str:


      """simple docstring"""



      _a           =    self.model_tester.prepare_config_and_inputs()
      for type in ["absolute", "relative_key", "relative_key_query"]:
           _a           =    type
           self.model_tester.create_and_check_model(*A			)



 def    a__    (self			)       ->	Any:


      """simple docstring"""



      _a    ,       _a           =    self.model_tester.prepare_config_and_inputs_for_common()
      _a           =    3
      _a           =    input_dict['''input_ids''']
      _a           =    input_ids.ne(1			).to(A			)
      _a           =    ids_tensor([self.model_tester.batch_size]					,  self.model_tester.type_sequence_label_size			)
      _a           =    OpenLlamaForSequenceClassification(A			)
      model.to(A			)
      model.eval()
      _a           =    model(A					,  attention_mask=A					,  labels=A			)
      self.assertEqual(result.logits.shape					,  (self.model_tester.batch_size, self.model_tester.num_labels)			)



 def    a__    (self			)       ->	Dict:


      """simple docstring"""



      _a    ,       _a           =    self.model_tester.prepare_config_and_inputs_for_common()
      _a           =    3
      _a           =    '''single_label_classification'''
      _a           =    input_dict['''input_ids''']
      _a           =    input_ids.ne(1			).to(A			)
      _a           =    ids_tensor([self.model_tester.batch_size]					,  self.model_tester.type_sequence_label_size			)
      _a           =    OpenLlamaForSequenceClassification(A			)
      model.to(A			)
      model.eval()
      _a           =    model(A					,  attention_mask=A					,  labels=A			)
      self.assertEqual(result.logits.shape					,  (self.model_tester.batch_size, self.model_tester.num_labels)			)



 def    a__    (self			)       ->	Optional[Any]:


      """simple docstring"""



      _a    ,       _a           =    self.model_tester.prepare_config_and_inputs_for_common()
      _a           =    3
      _a           =    '''multi_label_classification'''
      _a           =    input_dict['''input_ids''']
      _a           =    input_ids.ne(1			).to(A			)
      _a           =    ids_tensor(
          [self.model_tester.batch_size, config.num_labels]					,  self.model_tester.type_sequence_label_size			).to(torch.float			)
      _a           =    OpenLlamaForSequenceClassification(A			)
      model.to(A			)
      model.eval()
      _a           =    model(A					,  attention_mask=A					,  labels=A			)
      self.assertEqual(result.logits.shape					,  (self.model_tester.batch_size, self.model_tester.num_labels)			)



 @unittest.skip('''Open-Llama buffers include complex numbers, which breaks this test'''			)
 def    a__    (self			)       ->	Optional[Any]:


      """simple docstring"""



      pass



 @parameterized.expand([('''linear''',), ('''dynamic''',)]			)
 def    a__    (self					,  A			)       ->	Optional[int]:


      """simple docstring"""



      _a    ,       _a           =    self.model_tester.prepare_config_and_inputs_for_common()
      _a           =    ids_tensor([1, 10]					,  config.vocab_size			)
      _a           =    ids_tensor([1, int(config.max_position_embeddings * 1.5			)]					,  config.vocab_size			)

      set_seed(42			)  # Fixed seed at init time so the two models get the same random weights
      _a           =    OpenLlamaModel(A			)
      original_model.to(A			)
      original_model.eval()
      _a           =    original_model(A			).last_hidden_state
      _a           =    original_model(A			).last_hidden_state

      set_seed(42			)  # Fixed seed at init time so the two models get the same random weights
      _a           =    {'''type''': scaling_type, '''factor''': 10.0}
      _a           =    OpenLlamaModel(A			)
      scaled_model.to(A			)
      scaled_model.eval()
      _a           =    scaled_model(A			).last_hidden_state
      _a           =    scaled_model(A			).last_hidden_state

      # Dynamic scaling does not change the RoPE embeddings until it receives an input longer than the original
      # maximum sequence length, so the outputs for the short input should match.
      if scaling_type == "dynamic":
           self.assertTrue(torch.allclose(A					,  A					,  atol=1E-5			)			)
      else:
           self.assertFalse(torch.allclose(A					,  A					,  atol=1E-5			)			)

      # The output should be different for long inputs
      self.assertFalse(torch.allclose(A					,  A					,  atol=1E-5			)			)



