


'''simple docstring'''




import unittest

import numpy as np
from datasets import load_dataset

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
 import torch

if is_vision_available():
 from PIL import Image

 from transformers import BeitImageProcessor







class 					a			(				unittest.TestCase  ):
 '''simple docstring'''




 def __init__(    self		,      lowerCamelCase_		,      lowerCamelCase_=7		,      lowerCamelCase_=3		,      lowerCamelCase_=1_8		,      lowerCamelCase_=3_0		,      lowerCamelCase_=4_0_0		,      lowerCamelCase_=True		,      lowerCamelCase_=None		,      lowerCamelCase_=True		,      lowerCamelCase_=None		,      lowerCamelCase_=True		,      lowerCamelCase_=[0.5, 0.5, 0.5]		,      lowerCamelCase_=[0.5, 0.5, 0.5]		,      lowerCamelCase_=False		,      )      ->    Tuple:
    _a					:  Optional[Any]						=  size if size is not None else {'height': 2_0, 'width': 2_0}
    _a					:  Tuple						=  crop_size if crop_size is not None else {'height': 1_8, 'width': 1_8}
    _a					:  Dict						=  parent
    _a					:  Union[str, Any]						=  batch_size
    _a					:  Optional[int]						=  num_channels
    _a					:  Dict						=  image_size
    _a					:  str						=  min_resolution
    _a					:  List[str]						=  max_resolution
    _a					:  Any						=  do_resize
    _a					:  str						=  size
    _a					:  List[str]						=  do_center_crop
    _a					:  Optional[Any]						=  crop_size
    _a					:  Optional[Any]						=  do_normalize
    _a					:  List[str]						=  image_mean
    _a					:  str						=  image_std
    _a					:  str						=  do_reduce_labels




 def   __UpperCamelCase   (    self      )      ->    Dict:
    return {
        "do_resize": self.do_resize,
        "size": self.size,
        "do_center_crop": self.do_center_crop,
        "crop_size": self.crop_size,
        "do_normalize": self.do_normalize,
        "image_mean": self.image_mean,
        "image_std": self.image_std,
        "do_reduce_labels": self.do_reduce_labels,
    }





def    UpperCAmelCase_  (       ):
   '''simple docstring'''





   _a					:  Any						=  load_dataset('hf-internal-testing/fixtures_ade20k'		,	split='test'      )

   _a					:  Optional[Any]						=  Image.open(dataset[0]['file']      )
   _a					:  str						=  Image.open(dataset[1]['file']      )

   return image, map


def    UpperCAmelCase_  (       ):
   '''simple docstring'''





   _a					:  Any						=  load_dataset('hf-internal-testing/fixtures_ade20k'		,	split='test'      )

   _a					:  List[str]						=  Image.open(ds[0]['file']      )
   _a					:  List[Any]						=  Image.open(ds[1]['file']      )
   _a					:  int						=  Image.open(ds[2]['file']      )
   _a					:  Union[str, Any]						=  Image.open(ds[3]['file']      )

   return [imagea, imagea], [mapa, mapa]







@require_torch
@require_vision
class 					a			(				snake_case__			,							unittest.TestCase  ):
 '''simple docstring'''




 __lowerCAmelCase						: int							  =		BeitImageProcessor if is_vision_available() else None




 def   __UpperCamelCase   (    self      )      ->    Dict:
    _a					:  Union[str, Any]						=  BeitImageProcessingTester(self      )




 @property
 def   __UpperCamelCase   (    self      )      ->    int:
    return self.image_processor_tester.prepare_image_processor_dict()




 def   __UpperCamelCase   (    self      )      ->    Dict:
    _a					:  Optional[int]						=  self.image_processing_class(**self.image_processor_dict      )
    self.assertTrue(hasattr(lowerCamelCase_		,      'do_resize'      )      )
    self.assertTrue(hasattr(lowerCamelCase_		,      'size'      )      )
    self.assertTrue(hasattr(lowerCamelCase_		,      'do_center_crop'      )      )
    self.assertTrue(hasattr(lowerCamelCase_		,      'center_crop'      )      )
    self.assertTrue(hasattr(lowerCamelCase_		,      'do_normalize'      )      )
    self.assertTrue(hasattr(lowerCamelCase_		,      'image_mean'      )      )
    self.assertTrue(hasattr(lowerCamelCase_		,      'image_std'      )      )




 def   __UpperCamelCase   (    self      )      ->    List[Any]:
    _a					:  Union[str, Any]						=  self.image_processing_class.from_dict(self.image_processor_dict      )
    self.assertEqual(image_processor.size		,      {'height': 2_0, 'width': 2_0}      )
    self.assertEqual(image_processor.crop_size		,      {'height': 1_8, 'width': 1_8}      )
    self.assertEqual(image_processor.do_reduce_labels		,      lowerCamelCase_      )

    _a					:  List[str]						=  self.image_processing_class.from_dict(
        self.image_processor_dict		,      size=4_2		,      crop_size=8_4		,      reduce_labels=lowerCamelCase_      )
    self.assertEqual(image_processor.size		,      {'height': 4_2, 'width': 4_2}      )
    self.assertEqual(image_processor.crop_size		,      {'height': 8_4, 'width': 8_4}      )
    self.assertEqual(image_processor.do_reduce_labels		,      lowerCamelCase_      )




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




 def   __UpperCamelCase   (    self      )      ->    List[str]:
    # Initialize image_processing
    _a					:  Any						=  self.image_processing_class(**self.image_processor_dict      )
    # create random PIL images
    _a					:  Optional[Any]						=  prepare_image_inputs(self.image_processor_tester		,      equal_resolution=lowerCamelCase_      )
    for image in image_inputs:
       self.assertIsInstance(lowerCamelCase_		,      Image.Image      )

    # Test not batched input
    _a					:  Tuple						=  image_processing(image_inputs[0]		,      return_tensors='pt'      ).pixel_values
    self.assertEqual(
        encoded_images.shape		,      (
            1,
            self.image_processor_tester.num_channels,
            self.image_processor_tester.crop_size['height'],
            self.image_processor_tester.crop_size['width'],
        )		,      )

    # Test batched
    _a					:  int						=  image_processing(lowerCamelCase_		,      return_tensors='pt'      ).pixel_values
    self.assertEqual(
        encoded_images.shape		,      (
            self.image_processor_tester.batch_size,
            self.image_processor_tester.num_channels,
            self.image_processor_tester.crop_size['height'],
            self.image_processor_tester.crop_size['width'],
        )		,      )




 def   __UpperCamelCase   (    self      )      ->    Any:
    # Initialize image_processing
    _a					:  Optional[int]						=  self.image_processing_class(**self.image_processor_dict      )
    # create random numpy tensors
    _a					:  Dict						=  prepare_image_inputs(self.image_processor_tester		,      equal_resolution=lowerCamelCase_		,      numpify=lowerCamelCase_      )
    for image in image_inputs:
       self.assertIsInstance(lowerCamelCase_		,      np.ndarray      )

    # Test not batched input
    _a					:  Any						=  image_processing(image_inputs[0]		,      return_tensors='pt'      ).pixel_values
    self.assertEqual(
        encoded_images.shape		,      (
            1,
            self.image_processor_tester.num_channels,
            self.image_processor_tester.crop_size['height'],
            self.image_processor_tester.crop_size['width'],
        )		,      )

    # Test batched
    _a					:  Any						=  image_processing(lowerCamelCase_		,      return_tensors='pt'      ).pixel_values
    self.assertEqual(
        encoded_images.shape		,      (
            self.image_processor_tester.batch_size,
            self.image_processor_tester.num_channels,
            self.image_processor_tester.crop_size['height'],
            self.image_processor_tester.crop_size['width'],
        )		,      )




 def   __UpperCamelCase   (    self      )      ->    str:
    # Initialize image_processing
    _a					:  Any						=  self.image_processing_class(**self.image_processor_dict      )
    # create random PyTorch tensors
    _a					:  Union[str, Any]						=  prepare_image_inputs(self.image_processor_tester		,      equal_resolution=lowerCamelCase_		,      torchify=lowerCamelCase_      )
    for image in image_inputs:
       self.assertIsInstance(lowerCamelCase_		,      torch.Tensor      )

    # Test not batched input
    _a					:  Union[str, Any]						=  image_processing(image_inputs[0]		,      return_tensors='pt'      ).pixel_values
    self.assertEqual(
        encoded_images.shape		,      (
            1,
            self.image_processor_tester.num_channels,
            self.image_processor_tester.crop_size['height'],
            self.image_processor_tester.crop_size['width'],
        )		,      )

    # Test batched
    _a					:  Dict						=  image_processing(lowerCamelCase_		,      return_tensors='pt'      ).pixel_values
    self.assertEqual(
        encoded_images.shape		,      (
            self.image_processor_tester.batch_size,
            self.image_processor_tester.num_channels,
            self.image_processor_tester.crop_size['height'],
            self.image_processor_tester.crop_size['width'],
        )		,      )




 def   __UpperCamelCase   (    self      )      ->    List[Any]:
    # Initialize image_processing
    _a					:  Any						=  self.image_processing_class(**self.image_processor_dict      )
    # create random PyTorch tensors
    _a					:  List[str]						=  prepare_image_inputs(self.image_processor_tester		,      equal_resolution=lowerCamelCase_		,      torchify=lowerCamelCase_      )
    _a					:  Any						=  []
    for image in image_inputs:
       self.assertIsInstance(lowerCamelCase_		,      torch.Tensor      )
       maps.append(torch.zeros(image.shape[-2:]      ).long()      )

    # Test not batched input
    _a					:  Optional[int]						=  image_processing(image_inputs[0]		,      maps[0]		,      return_tensors='pt'      )
    self.assertEqual(
        encoding['pixel_values'].shape		,      (
            1,
            self.image_processor_tester.num_channels,
            self.image_processor_tester.crop_size['height'],
            self.image_processor_tester.crop_size['width'],
        )		,      )
    self.assertEqual(
        encoding['labels'].shape		,      (
            1,
            self.image_processor_tester.crop_size['height'],
            self.image_processor_tester.crop_size['width'],
        )		,      )
    self.assertEqual(encoding['labels'].dtype		,      torch.long      )
    self.assertTrue(encoding['labels'].min().item() >= 0      )
    self.assertTrue(encoding['labels'].max().item() <= 2_5_5      )

    # Test batched
    _a					:  List[Any]						=  image_processing(lowerCamelCase_		,      lowerCamelCase_		,      return_tensors='pt'      )
    self.assertEqual(
        encoding['pixel_values'].shape		,      (
            self.image_processor_tester.batch_size,
            self.image_processor_tester.num_channels,
            self.image_processor_tester.crop_size['height'],
            self.image_processor_tester.crop_size['width'],
        )		,      )
    self.assertEqual(
        encoding['labels'].shape		,      (
            self.image_processor_tester.batch_size,
            self.image_processor_tester.crop_size['height'],
            self.image_processor_tester.crop_size['width'],
        )		,      )
    self.assertEqual(encoding['labels'].dtype		,      torch.long      )
    self.assertTrue(encoding['labels'].min().item() >= 0      )
    self.assertTrue(encoding['labels'].max().item() <= 2_5_5      )

    # Test not batched input (PIL images)
    _a		,						_a					:  Dict						=  prepare_semantic_single_inputs()

    _a					:  List[str]						=  image_processing(lowerCamelCase_		,      lowerCamelCase_		,      return_tensors='pt'      )
    self.assertEqual(
        encoding['pixel_values'].shape		,      (
            1,
            self.image_processor_tester.num_channels,
            self.image_processor_tester.crop_size['height'],
            self.image_processor_tester.crop_size['width'],
        )		,      )
    self.assertEqual(
        encoding['labels'].shape		,      (
            1,
            self.image_processor_tester.crop_size['height'],
            self.image_processor_tester.crop_size['width'],
        )		,      )
    self.assertEqual(encoding['labels'].dtype		,      torch.long      )
    self.assertTrue(encoding['labels'].min().item() >= 0      )
    self.assertTrue(encoding['labels'].max().item() <= 2_5_5      )

    # Test batched input (PIL images)
    _a		,						_a					:  Optional[Any]						=  prepare_semantic_batch_inputs()

    _a					:  Any						=  image_processing(lowerCamelCase_		,      lowerCamelCase_		,      return_tensors='pt'      )
    self.assertEqual(
        encoding['pixel_values'].shape		,      (
            2,
            self.image_processor_tester.num_channels,
            self.image_processor_tester.crop_size['height'],
            self.image_processor_tester.crop_size['width'],
        )		,      )
    self.assertEqual(
        encoding['labels'].shape		,      (
            2,
            self.image_processor_tester.crop_size['height'],
            self.image_processor_tester.crop_size['width'],
        )		,      )
    self.assertEqual(encoding['labels'].dtype		,      torch.long      )
    self.assertTrue(encoding['labels'].min().item() >= 0      )
    self.assertTrue(encoding['labels'].max().item() <= 2_5_5      )




 def   __UpperCamelCase   (    self      )      ->    Optional[int]:
    # Initialize image_processing
    _a					:  int						=  self.image_processing_class(**self.image_processor_dict      )

    # ADE20k has 150 classes, and the background is included, so labels should be between 0 and 150
    _a		,						_a					:  Optional[int]						=  prepare_semantic_single_inputs()
    _a					:  Tuple						=  image_processing(lowerCamelCase_		,      lowerCamelCase_		,      return_tensors='pt'      )
    self.assertTrue(encoding['labels'].min().item() >= 0      )
    self.assertTrue(encoding['labels'].max().item() <= 1_5_0      )

    _a					:  Optional[int]						=  True
    _a					:  str						=  image_processing(lowerCamelCase_		,      lowerCamelCase_		,      return_tensors='pt'      )
    self.assertTrue(encoding['labels'].min().item() >= 0      )
    self.assertTrue(encoding['labels'].max().item() <= 2_5_5      )






'''simple docstring'''




import unittest

from parameterized import parameterized

from transformers import LlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, slow, 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 LlamaForCausalLM, LlamaForSequenceClassification, LlamaModel, LlamaTokenizer







class 					a			:
 '''simple docstring'''




 def __init__(    self		,      lowerCamelCase_		,      lowerCamelCase_=1_3		,      lowerCamelCase_=7		,      lowerCamelCase_=True		,      lowerCamelCase_=True		,      lowerCamelCase_=False		,      lowerCamelCase_=True		,      lowerCamelCase_=9_9		,      lowerCamelCase_=3_2		,      lowerCamelCase_=5		,      lowerCamelCase_=4		,      lowerCamelCase_=3_7		,      lowerCamelCase_="gelu"		,      lowerCamelCase_=0.1		,      lowerCamelCase_=0.1		,      lowerCamelCase_=5_1_2		,      lowerCamelCase_=1_6		,      lowerCamelCase_=2		,      lowerCamelCase_=0.02		,      lowerCamelCase_=3		,      lowerCamelCase_=4		,      lowerCamelCase_=None		,      )      ->    str:
    _a					:  int						=  parent
    _a					:  str						=  batch_size
    _a					:  Any						=  seq_length
    _a					:  Tuple						=  is_training
    _a					:  int						=  use_input_mask
    _a					:  Any						=  use_token_type_ids
    _a					:  List[Any]						=  use_labels
    _a					:  Optional[int]						=  vocab_size
    _a					:  str						=  hidden_size
    _a					:  Any						=  num_hidden_layers
    _a					:  List[Any]						=  num_attention_heads
    _a					:  Tuple						=  intermediate_size
    _a					:  Optional[int]						=  hidden_act
    _a					:  str						=  hidden_dropout_prob
    _a					:  Tuple						=  attention_probs_dropout_prob
    _a					:  List[Any]						=  max_position_embeddings
    _a					:  Union[str, Any]						=  type_vocab_size
    _a					:  Dict						=  type_sequence_label_size
    _a					:  List[Any]						=  initializer_range
    _a					:  Optional[Any]						=  num_labels
    _a					:  Dict						=  num_choices
    _a					:  Dict						=  scope




 def   __UpperCamelCase   (    self      )      ->    Dict:
    _a					:  Any						=  ids_tensor([self.batch_size, self.seq_length]		,      self.vocab_size      )

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

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

    _a					:  Union[str, Any]						=  None
    _a					:  Any						=  None
    _a					:  Optional[Any]						=  None
    if self.use_labels:
       _a					:  Dict						=  ids_tensor([self.batch_size]		,      self.type_sequence_label_size      )
       _a					:  str						=  ids_tensor([self.batch_size, self.seq_length]		,      self.num_labels      )
       _a					:  List[Any]						=  ids_tensor([self.batch_size]		,      self.num_choices      )

    _a					:  List[str]						=  self.get_config()

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




 def   __UpperCamelCase   (    self      )      ->    List[Any]:
    return LlamaConfig(
        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=lowerCamelCase_		,      initializer_range=self.initializer_range		,      )




 def   __UpperCamelCase   (    self		,      lowerCamelCase_		,      lowerCamelCase_		,      lowerCamelCase_		,      lowerCamelCase_		,      lowerCamelCase_		,      lowerCamelCase_		,      lowerCamelCase_      )      ->    int:
    _a					:  List[Any]						=  LlamaModel(config=lowerCamelCase_      )
    model.to(lowerCamelCase_      )
    model.eval()
    _a					:  Any						=  model(lowerCamelCase_		,      attention_mask=lowerCamelCase_      )
    _a					:  List[Any]						=  model(lowerCamelCase_      )
    self.parent.assertEqual(result.last_hidden_state.shape		,      (self.batch_size, self.seq_length, self.hidden_size)      )




 def   __UpperCamelCase   (    self		,      lowerCamelCase_		,      lowerCamelCase_		,      lowerCamelCase_		,      lowerCamelCase_		,      lowerCamelCase_		,      lowerCamelCase_		,      lowerCamelCase_		,      lowerCamelCase_		,      lowerCamelCase_		,      )      ->    Any:
    _a					:  Dict						=  True
    _a					:  Tuple						=  LlamaModel(lowerCamelCase_      )
    model.to(lowerCamelCase_      )
    model.eval()
    _a					:  List[Any]						=  model(
        lowerCamelCase_		,      attention_mask=lowerCamelCase_		,      encoder_hidden_states=lowerCamelCase_		,      encoder_attention_mask=lowerCamelCase_		,      )
    _a					:  Optional[Any]						=  model(
        lowerCamelCase_		,      attention_mask=lowerCamelCase_		,      encoder_hidden_states=lowerCamelCase_		,      )
    _a					:  str						=  model(lowerCamelCase_		,      attention_mask=lowerCamelCase_      )
    self.parent.assertEqual(result.last_hidden_state.shape		,      (self.batch_size, self.seq_length, self.hidden_size)      )




 def   __UpperCamelCase   (    self		,      lowerCamelCase_		,      lowerCamelCase_		,      lowerCamelCase_		,      lowerCamelCase_		,      lowerCamelCase_		,      lowerCamelCase_		,      lowerCamelCase_		,      lowerCamelCase_		,      lowerCamelCase_		,      )      ->    Dict:
    _a					:  Dict						=  LlamaForCausalLM(config=lowerCamelCase_      )
    model.to(lowerCamelCase_      )
    model.eval()
    _a					:  int						=  model(lowerCamelCase_		,      attention_mask=lowerCamelCase_		,      labels=lowerCamelCase_      )
    self.parent.assertEqual(result.logits.shape		,      (self.batch_size, self.seq_length, self.vocab_size)      )




 def   __UpperCamelCase   (    self		,      lowerCamelCase_		,      lowerCamelCase_		,      lowerCamelCase_		,      lowerCamelCase_		,      lowerCamelCase_		,      lowerCamelCase_		,      lowerCamelCase_		,      lowerCamelCase_		,      lowerCamelCase_		,      )      ->    str:
    _a					:  Optional[Any]						=  True
    _a					:  Tuple						=  True
    _a					:  Optional[int]						=  LlamaForCausalLM(config=lowerCamelCase_      )
    model.to(lowerCamelCase_      )
    model.eval()

    # first forward pass
    _a					:  str						=  model(
        lowerCamelCase_		,      attention_mask=lowerCamelCase_		,      encoder_hidden_states=lowerCamelCase_		,      encoder_attention_mask=lowerCamelCase_		,      use_cache=lowerCamelCase_		,      )
    _a					:  Union[str, Any]						=  outputs.past_key_values

    # create hypothetical multiple next token and extent to next_input_ids
    _a					:  Optional[Any]						=  ids_tensor((self.batch_size, 3)		,      config.vocab_size      )
    _a					:  str						=  ids_tensor((self.batch_size, 3)		,      vocab_size=2      )

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

    _a					:  Any						=  model(
        lowerCamelCase_		,      attention_mask=lowerCamelCase_		,      encoder_hidden_states=lowerCamelCase_		,      encoder_attention_mask=lowerCamelCase_		,      output_hidden_states=lowerCamelCase_		,      )['hidden_states'][0]
    _a					:  List[Any]						=  model(
        lowerCamelCase_		,      attention_mask=lowerCamelCase_		,      encoder_hidden_states=lowerCamelCase_		,      encoder_attention_mask=lowerCamelCase_		,      past_key_values=lowerCamelCase_		,      output_hidden_states=lowerCamelCase_		,      )['hidden_states'][0]

    # select random slice
    _a					:  List[Any]						=  ids_tensor((1,)		,      output_from_past.shape[-1]      ).item()
    _a					:  int						=  output_from_no_past[:, -3:, random_slice_idx].detach()
    _a					:  Optional[Any]						=  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(lowerCamelCase_		,      lowerCamelCase_		,      atol=1e-3      )      )




 def   __UpperCamelCase   (    self      )      ->    Tuple:
    _a					:  Optional[int]						=  self.prepare_config_and_inputs()
    (
        (
        _a
    )		,						(
        _a
    )		,						(
        _a
    )		,						(
        _a
    )		,						(
        _a
    )		,						(
        _a
    )		,						(
        _a
    )		,						
    )					:  Dict						=  config_and_inputs
    _a					:  Tuple						=  {'input_ids': input_ids, 'attention_mask': input_mask}
    return config, inputs_dict







@require_torch
class 					a			(				snake_case__			,							snake_case__			,							snake_case__			,							unittest.TestCase  ):
 '''simple docstring'''




 __lowerCAmelCase						: Optional[int]							  =		(LlamaModel, LlamaForCausalLM, LlamaForSequenceClassification) if is_torch_available() else ()
 __lowerCAmelCase						: int							  =		(LlamaForCausalLM,) if is_torch_available() else ()
 __lowerCAmelCase						: int							  =		(
     {
         """feature-extraction""": LlamaModel,
         """text-classification""": LlamaForSequenceClassification,
         """text-generation""": LlamaForCausalLM,
         """zero-shot""": LlamaForSequenceClassification,
     }
     if is_torch_available()
     else {}
 )
 __lowerCAmelCase						: str							  =		False
 __lowerCAmelCase						: List[Any]							  =		False




 def   __UpperCamelCase   (    self      )      ->    str:
    _a					:  Optional[int]						=  LlamaModelTester(self      )
    _a					:  Tuple						=  ConfigTester(self		,      config_class=lowerCamelCase_		,      hidden_size=3_7      )




 def   __UpperCamelCase   (    self      )      ->    Any:
    self.config_tester.run_common_tests()




 def   __UpperCamelCase   (    self      )      ->    Union[str, Any]:
    _a					:  Dict						=  self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_model(*lowerCamelCase_      )




 def   __UpperCamelCase   (    self      )      ->    Optional[Any]:
    _a					:  Tuple						=  self.model_tester.prepare_config_and_inputs()
    for type in ["absolute", "relative_key", "relative_key_query"]:
       _a					:  List[str]						=  type
       self.model_tester.create_and_check_model(*lowerCamelCase_      )




 def   __UpperCamelCase   (    self      )      ->    List[Any]:
    _a		,						_a					:  int						=  self.model_tester.prepare_config_and_inputs_for_common()
    _a					:  Optional[Any]						=  3
    _a					:  Union[str, Any]						=  input_dict['input_ids']
    _a					:  List[Any]						=  input_ids.ne(1      ).to(lowerCamelCase_      )
    _a					:  int						=  ids_tensor([self.model_tester.batch_size]		,      self.model_tester.type_sequence_label_size      )
    _a					:  Any						=  LlamaForSequenceClassification(lowerCamelCase_      )
    model.to(lowerCamelCase_      )
    model.eval()
    _a					:  List[Any]						=  model(lowerCamelCase_		,      attention_mask=lowerCamelCase_		,      labels=lowerCamelCase_      )
    self.assertEqual(result.logits.shape		,      (self.model_tester.batch_size, self.model_tester.num_labels)      )




 def   __UpperCamelCase   (    self      )      ->    List[str]:
    _a		,						_a					:  Tuple						=  self.model_tester.prepare_config_and_inputs_for_common()
    _a					:  List[str]						=  3
    _a					:  List[str]						=  'single_label_classification'
    _a					:  Union[str, Any]						=  input_dict['input_ids']
    _a					:  str						=  input_ids.ne(1      ).to(lowerCamelCase_      )
    _a					:  Any						=  ids_tensor([self.model_tester.batch_size]		,      self.model_tester.type_sequence_label_size      )
    _a					:  str						=  LlamaForSequenceClassification(lowerCamelCase_      )
    model.to(lowerCamelCase_      )
    model.eval()
    _a					:  List[Any]						=  model(lowerCamelCase_		,      attention_mask=lowerCamelCase_		,      labels=lowerCamelCase_      )
    self.assertEqual(result.logits.shape		,      (self.model_tester.batch_size, self.model_tester.num_labels)      )




 def   __UpperCamelCase   (    self      )      ->    Tuple:
    _a		,						_a					:  Union[str, Any]						=  self.model_tester.prepare_config_and_inputs_for_common()
    _a					:  Union[str, Any]						=  3
    _a					:  str						=  'multi_label_classification'
    _a					:  Union[str, Any]						=  input_dict['input_ids']
    _a					:  Union[str, Any]						=  input_ids.ne(1      ).to(lowerCamelCase_      )
    _a					:  str						=  ids_tensor(
        [self.model_tester.batch_size, config.num_labels]		,      self.model_tester.type_sequence_label_size      ).to(torch.float      )
    _a					:  str						=  LlamaForSequenceClassification(lowerCamelCase_      )
    model.to(lowerCamelCase_      )
    model.eval()
    _a					:  int						=  model(lowerCamelCase_		,      attention_mask=lowerCamelCase_		,      labels=lowerCamelCase_      )
    self.assertEqual(result.logits.shape		,      (self.model_tester.batch_size, self.model_tester.num_labels)      )




 @unittest.skip('LLaMA buffers include complex numbers, which breaks this test'      )
 def   __UpperCamelCase   (    self      )      ->    Optional[int]:
    pass




 @parameterized.expand([('linear',), ('dynamic',)]      )
 def   __UpperCamelCase   (    self		,      lowerCamelCase_      )      ->    List[str]:
    _a		,						_a					:  Dict						=  self.model_tester.prepare_config_and_inputs_for_common()
    _a					:  Union[str, Any]						=  ids_tensor([1, 1_0]		,      config.vocab_size      )
    _a					:  Optional[Any]						=  ids_tensor([1, int(config.max_position_embeddings * 1.5      )]		,      config.vocab_size      )

    set_seed(4_2      )  # Fixed seed at init time so the two models get the same random weights
    _a					:  int						=  LlamaModel(lowerCamelCase_      )
    original_model.to(lowerCamelCase_      )
    original_model.eval()
    _a					:  str						=  original_model(lowerCamelCase_      ).last_hidden_state
    _a					:  Union[str, Any]						=  original_model(lowerCamelCase_      ).last_hidden_state

    set_seed(4_2      )  # Fixed seed at init time so the two models get the same random weights
    _a					:  Optional[Any]						=  {'type': scaling_type, 'factor': 10.0}
    _a					:  Any						=  LlamaModel(lowerCamelCase_      )
    scaled_model.to(lowerCamelCase_      )
    scaled_model.eval()
    _a					:  Optional[int]						=  scaled_model(lowerCamelCase_      ).last_hidden_state
    _a					:  int						=  scaled_model(lowerCamelCase_      ).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(lowerCamelCase_		,      lowerCamelCase_		,      atol=1e-5      )      )
    else:
       self.assertFalse(torch.allclose(lowerCamelCase_		,      lowerCamelCase_		,      atol=1e-5      )      )

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







@require_torch
class 					a			(				unittest.TestCase  ):
 '''simple docstring'''




 @unittest.skip('Logits are not exactly the same, once we fix the instabalities somehow, will update!'      )
 @slow
 def   __UpperCamelCase   (    self      )      ->    int:
    _a					:  Optional[Any]						=  [1, 3_0_6, 4_6_5_8, 2_7_8, 6_5_9_3, 3_1_0, 2_8_3_4, 3_3_8]
    _a					:  Optional[int]						=  LlamaForCausalLM.from_pretrained('meta-llama/Llama-2-7b-hf'		,      device_map='auto'      )
    _a					:  Tuple						=  model(torch.tensor([input_ids]      )      )
    # Expected mean on dim = -1
    _a					:  List[Any]						=  torch.tensor([[-6.6550, -4.1227, -4.9859, -3.2406, 0.8262, -3.0033, 1.2964, -3.3699]]      )
    torch.testing.assert_close(out.mean(-1      )		,      lowerCamelCase_		,      atol=1e-2		,      rtol=1e-2      )
    # slicing logits[0, 0, 0:30]
    # fmt: off
    _a					:  str						=  torch.tensor([-12.8281, -7.4453, -0.4639, -8.0625, -7.2500, -8.0000, -6.4883, -7.7695, -7.8438, -7.0312, -6.2188, -7.1328, -1.8496, 1.9961, -8.6250, -6.7227, -12.8281, -6.9492, -7.0742, -7.7852, -7.5820, -7.9062, -6.9375, -7.9805, -8.3438, -8.1562, -8.0469, -7.6250, -7.7422, -7.3398,]      )
    # fmt: on
    torch.testing.assert_close(out[0, 0, :3_0]		,      lowerCamelCase_		,      atol=1e-5		,      rtol=1e-5      )




 @unittest.skip('Logits are not exactly the same, once we fix the instabalities somehow, will update!'      )
 @slow
 def   __UpperCamelCase   (    self      )      ->    Optional[int]:
    _a					:  Any						=  [1, 3_0_6, 4_6_5_8, 2_7_8, 6_5_9_3, 3_1_0, 2_8_3_4, 3_3_8]
    _a					:  Any						=  LlamaForCausalLM.from_pretrained('meta-llama/Llama-2-13b-hf'		,      device_map='auto'      )
    _a					:  List[Any]						=  model(torch.tensor(lowerCamelCase_      )      )
    # Expected mean on dim = -1
    _a					:  Optional[int]						=  torch.tensor([[-2.0622, -1.2794, -1.1638, -0.9788, -1.4603, -1.0238, -1.7893, -1.4411]]      )
    torch.testing.assert_close(out.mean(-1      )		,      lowerCamelCase_		,      atol=1e-2		,      rtol=1e-2      )
    # slicing logits[0, 0, 0:30]
    # fmt: off
    _a					:  List[Any]						=  torch.tensor([-8.1406, -8.0547, 2.7461, -1.2344, -0.1448, -1.8262, -1.0020, -1.8154, -1.6895, -1.8516, -2.3574, -0.9277, 3.7598, 6.5742, -1.2998, -0.1177, -8.1406, -2.9688, -2.9199, -3.1699, -3.5254, -2.3555, -2.7988, -3.4141, -2.8262, -4.5195, -3.3379, -3.3164, -2.7832, -3.0273]      )
    # fmt: on
    torch.testing.assert_close(out[0, 0, :3_0]		,      lowerCamelCase_		,      atol=1e-5		,      rtol=1e-5      )




 @unittest.skip('Logits are not exactly the same, once we fix the instabalities somehow, will update!'      )
 @slow
 def   __UpperCamelCase   (    self      )      ->    Any:
    _a					:  Optional[int]						=  [1, 3_0_6, 4_6_5_8, 2_7_8, 6_5_9_3, 3_1_0, 2_8_3_4, 3_3_8]
    _a					:  Union[str, Any]						=  LlamaForCausalLM.from_pretrained('meta-llama/Llama-2-13b-chat-hf'		,      device_map='auto'      )
    _a					:  str						=  model(torch.tensor(lowerCamelCase_      )      )
    # Expected mean on dim = -1
    _a					:  int						=  torch.tensor([[-0.8562, -1.8520, -0.7551, -0.4162, -1.5161, -1.2038, -2.4823, -2.3254]]      )
    torch.testing.assert_close(out.mean(-1      )		,      lowerCamelCase_		,      atol=1e-2		,      rtol=1e-2      )
    # slicing logits[0, 0, 0:30]
    # fmt: off
    _a					:  List[Any]						=  torch.tensor([-2.2227, 4.8828, 0.9023, -0.4578, -0.7871, -0.1033, -0.6221, -0.5786, -0.7803, -1.0674, -1.2920, -0.1570, 0.8008, 2.0723, -0.9497, 0.2771, -2.2227, -0.7612, -1.4346, -1.2061, -1.6426, -0.3000, -0.7139, -1.1934, -1.8691, -1.6973, -1.5947, -1.2705, -0.3523, -0.5513]      )
    # fmt: on
    torch.testing.assert_close(out.mean(-1      )		,      lowerCamelCase_		,      atol=1e-2		,      rtol=1e-2      )




 @unittest.skip(
     'Logits are not exactly the same, once we fix the instabalities somehow, will update! Also it is gonna be a `too_slow` test'      )
 @slow
 def   __UpperCamelCase   (    self      )      ->    Dict:
    _a					:  Dict						=  [1, 3_0_6, 4_6_5_8, 2_7_8, 6_5_9_3, 3_1_0, 2_8_3_4, 3_3_8]
    _a					:  str						=  LlamaForCausalLM.from_pretrained('meta-llama/Llama-2-70b-hf'		,      device_map='auto'      )
    _a					:  Dict						=  model(torch.tensor(lowerCamelCase_      )      )

    _a					:  str						=  torch.tensor(
        [[-4.2327, -3.3360, -4.6665, -4.7631, -1.8180, -3.4170, -1.4211, -3.1810]]		,      dtype=torch.floataa      )
    torch.testing.assert_close(out.mean(-1      )		,      lowerCamelCase_		,      atol=1e-2		,      rtol=1e-2      )
    # fmt: off
    _a					:  Any						=  torch.tensor([-9.4922, -3.9551, 1.7998, -5.6758, -5.1055, -5.8984, -4.8320, -6.8086, -6.5391, -5.6172, -5.5820, -5.5352, 1.7881, 3.6289, -6.5117, -3.4785, -9.5000, -6.0352, -6.8125, -6.0195, -6.6836, -5.4727, -6.2812, -6.0391, -7.3398, -7.4297, -7.4844, -6.5820, -5.8789, -5.5312]      )
    # fmt: on
    torch.testing.assert_close(out[0, 0, :3_0]		,      lowerCamelCase_		,      atol=1e-5		,      rtol=1e-5      )




 @unittest.skip('Model is curently gated'      )
 @slow
 def   __UpperCamelCase   (    self      )      ->    Optional[Any]:
    _a					:  List[Any]						=  'Simply put, the theory of relativity states that 1) the laws of physics are the same everywhere in the universe and 2) the passage of time and the length of objects can vary depending on the observer\'s frame of reference.\n\nThe first part of the theory, that the laws of physics are the same everywhere, is known as the "princi'
    _a					:  List[Any]						=  'Simply put, the theory of relativity states that '
    _a					:  List[Any]						=  LlamaTokenizer.from_pretrained('meta-llama/Llama-2-13b-chat-hf'      )
    _a					:  Any						=  tokenizer.encode(lowerCamelCase_		,      return_tensors='pt'      )
    _a					:  Union[str, Any]						=  LlamaForCausalLM.from_pretrained(
        'meta-llama/Llama-2-13b-chat-hf'		,      device_map='sequential'		,      use_safetensors=lowerCamelCase_      )

    # greedy generation outputs
    _a					:  List[Any]						=  model.generate(lowerCamelCase_		,      max_new_tokens=6_4		,      top_p=lowerCamelCase_		,      temperature=1		,      do_sample=lowerCamelCase_      )
    _a					:  Dict						=  tokenizer.decode(generated_ids[0]		,      skip_special_tokens=lowerCamelCase_      )
    self.assertEqual(lowerCamelCase_		,      lowerCamelCase_      )


