



'''simple docstring'''





from typing import Dict

import numpy as np

from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline, PipelineException


if is_tf_available():
  import tensorflow as tf

  from ..tf_utils import stable_softmax


if is_torch_available():
  import torch


lowercase_									=    logging.get_logger(__name__)






@add_end_docstrings(
    A  ,  R'\n        top_k (`int`, defaults to 5):\n            The number of predictions to return.\n        targets (`str` or `List[str]`, *optional*):\n            When passed, the model will limit the scores to the passed targets instead of looking up in the whole\n            vocab. If the provided targets are not in the model vocab, they will be tokenized and the first resulting\n            token will be used (with a warning, and that might be slower).\n\n    '  ,  )
class __A     (    A		):



 '''simple docstring'''







 def    a__    (self					,  A			)       ->	np.ndarray:


      """simple docstring"""



      if self.framework == "tf":
           _a           =    tf.where(input_ids == self.tokenizer.mask_token_id			).numpy()
      elif self.framework == "pt":
           _a           =    torch.nonzero(input_ids == self.tokenizer.mask_token_id					,  as_tuple=A			)
      else:
           raise ValueError('''Unsupported framework'''			)
      return masked_index



 def    a__    (self					,  A			)       ->	np.ndarray:


      """simple docstring"""



      _a           =    self.get_masked_index(A			)
      _a           =    np.prod(masked_index.shape			)
      if numel < 1:
           raise PipelineException(
               '''fill-mask'''					,  self.model.base_model_prefix					,  f'''No mask_token ({self.tokenizer.mask_token}) found on the input'''					,  )



 def    a__    (self					,  A			)       ->	Tuple:


      """simple docstring"""



      if isinstance(A					,  A			):
           for model_input in model_inputs:
                self._ensure_exactly_one_mask_token(model_input['''input_ids'''][0]			)
      else:
           for input_ids in model_inputs["input_ids"]:
                self._ensure_exactly_one_mask_token(A			)



 def    a__    (self					,  A					,  A=None					,  **A			)       ->	Dict[str, GenericTensor]:


      """simple docstring"""



      if return_tensors is None:
           _a           =    self.framework
      _a           =    self.tokenizer(A					,  return_tensors=A			)
      self.ensure_exactly_one_mask_token(A			)
      return model_inputs



 def    a__    (self					,  A			)       ->	List[Any]:


      """simple docstring"""



      _a           =    self.model(**A			)
      _a           =    model_inputs['''input_ids''']
      return model_outputs



 def    a__    (self					,  A					,  A=5					,  A=None			)       ->	Union[str, Any]:


      """simple docstring"""



      if target_ids is not None and target_ids.shape[0] < top_k:
           _a           =    target_ids.shape[0]
      _a           =    model_outputs['''input_ids'''][0]
      _a           =    model_outputs['''logits''']

      if self.framework == "tf":
           _a           =    tf.where(input_ids == self.tokenizer.mask_token_id			).numpy()[:, 0]

           _a           =    outputs.numpy()

           _a           =    outputs[0, masked_index, :]
           _a           =    stable_softmax(A					,  axis=-1			)
           if target_ids is not None:
                _a           =    tf.gather_nd(tf.squeeze(A					,  0			)					,  target_ids.reshape(-1					,  1			)			)
                _a           =    tf.expand_dims(A					,  0			)

           _a           =    tf.math.top_k(A					,  k=A			)
           _a    ,       _a           =    topk.values.numpy(), topk.indices.numpy()
      else:
           _a           =    torch.nonzero(input_ids == self.tokenizer.mask_token_id					,  as_tuple=A			).squeeze(-1			)
           # Fill mask pipeline supports only one ${mask_token} per sample

           _a           =    outputs[0, masked_index, :]
           _a           =    logits.softmax(dim=-1			)
           if target_ids is not None:
                _a           =    probs[..., target_ids]

           _a    ,       _a           =    probs.topk(A			)

      _a           =    []
      _a           =    values.shape[0] == 1
      for i, (_values, _predictions) in enumerate(zip(values.tolist()					,  predictions.tolist()			)			):
           _a           =    []
           for v, p in zip(_values					,  _predictions			):
                # Copy is important since we're going to modify this array in place
                _a           =    input_ids.numpy().copy()
                if target_ids is not None:
                     _a           =    target_ids[p].tolist()

                _a           =    p
                # Filter padding out:
                _a           =    tokens[np.where(tokens != self.tokenizer.pad_token_id			)]
                # Originally we skip special tokens to give readable output.
                # For multi masks though, the other [MASK] would be removed otherwise
                # making the output look odd, so we add them back
                _a           =    self.tokenizer.decode(A					,  skip_special_tokens=A			)
                _a           =    {'''score''': v, '''token''': p, '''token_str''': self.tokenizer.decode([p]			), '''sequence''': sequence}
                row.append(A			)
           result.append(A			)
      if single_mask:
           return result[0]
      return result



 def    a__    (self					,  A					,  A=None			)       ->	List[str]:


      """simple docstring"""



      if isinstance(A					,  A			):
           _a           =    [targets]
      try:
           _a           =    self.tokenizer.get_vocab()
      except Exception:
           _a           =    {}
      _a           =    []
      for target in targets:
           _a           =    vocab.get(A					,  A			)
           if id_ is None:
                _a           =    self.tokenizer(
                    A					,  add_special_tokens=A					,  return_attention_mask=A					,  return_token_type_ids=A					,  max_length=1					,  truncation=A					,  )['''input_ids''']
                if len(A			) == 0:
                     logger.warning(
                         f'''The specified target token `{target}` does not exist in the model vocabulary. '''
                         '''We cannot replace it with anything meaningful, ignoring it'''			)
                     continue
                _a           =    input_ids[0]
                # XXX: If users encounter this pass
                # it becomes pretty slow, so let's make sure
                # The warning enables them to fix the input to
                # get faster performance.
                logger.warning(
                    f'''The specified target token `{target}` does not exist in the model vocabulary. '''
                    f'''Replacing with `{self.tokenizer.convert_ids_to_tokens(id_			)}`.'''			)
           target_ids.append(id_			)
      _a           =    list(set(A			)			)
      if len(A			) == 0:
           raise ValueError('''At least one target must be provided when passed.'''			)
      _a           =    np.array(A			)
      return target_ids



 def    a__    (self					,  A=None					,  A=None			)       ->	Dict:


      """simple docstring"""



      _a           =    {}

      if targets is not None:
           _a           =    self.get_target_ids(A					,  A			)
           _a           =    target_ids

      if top_k is not None:
           _a           =    top_k

      if self.tokenizer.mask_token_id is None:
           raise PipelineException(
               '''fill-mask'''					,  self.model.base_model_prefix					,  '''The tokenizer does not define a `mask_token`.'''			)
      return {}, {}, postprocess_params



 def __call__(self					,  A					,  *A					,  **A			)       ->	Optional[Any]:


      """simple docstring"""



      _a           =    super().__call__(A					,  **A			)
      if isinstance(A					,  A			) and len(A			) == 1:
           return outputs[0]
      return outputs








'''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			)			)



