

from __future__ import annotations

import copy
import inspect
import json
import math
import os
import tempfile
import unittest
from importlib import import_module

import numpy as np

from transformers import ViTMAEConfig
from transformers.file_utils import cached_property, is_tf_available, is_vision_available
from transformers.testing_utils import require_tf, require_vision, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
      import tensorflow as tf

      from transformers import TFViTMAEForPreTraining, TFViTMAEModel


if is_vision_available():
      from PIL import Image

      from transformers import ViTImageProcessor




class      UpperCamelCase :

    def __init__(			self :      Optional[int]							,    snake_case__ :      int							,    snake_case__ :      Union[str, Any]=1_3							,    snake_case__ :      Tuple=3_0							,    snake_case__ :      Dict=2							,    snake_case__ :      Tuple=3							,    snake_case__ :      Any=True							,    snake_case__ :      Union[str, Any]=True							,    snake_case__ :      str=3_2							,    snake_case__ :      List[str]=2							,    snake_case__ :      Any=4							,    snake_case__ :      Union[str, Any]=3_7							,    snake_case__ :      Optional[Any]="gelu"							,    snake_case__ :      Optional[int]=0.1							,    snake_case__ :      str=0.1							,    snake_case__ :      Tuple=1_0							,    snake_case__ :      Tuple=0.02							,    snake_case__ :      List[str]=3							,    snake_case__ :      Tuple=0.6							,    snake_case__ :      List[str]=None							,    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			parent
     SCREAMING_SNAKE_CASE								=			batch_size
     SCREAMING_SNAKE_CASE								=			image_size
     SCREAMING_SNAKE_CASE								=			patch_size
     SCREAMING_SNAKE_CASE								=			num_channels
     SCREAMING_SNAKE_CASE								=			is_training
     SCREAMING_SNAKE_CASE								=			use_labels
     SCREAMING_SNAKE_CASE								=			hidden_size
     SCREAMING_SNAKE_CASE								=			num_hidden_layers
     SCREAMING_SNAKE_CASE								=			num_attention_heads
     SCREAMING_SNAKE_CASE								=			intermediate_size
     SCREAMING_SNAKE_CASE								=			hidden_act
     SCREAMING_SNAKE_CASE								=			hidden_dropout_prob
     SCREAMING_SNAKE_CASE								=			attention_probs_dropout_prob
     SCREAMING_SNAKE_CASE								=			type_sequence_label_size
     SCREAMING_SNAKE_CASE								=			initializer_range
     SCREAMING_SNAKE_CASE								=			mask_ratio
     SCREAMING_SNAKE_CASE								=			scope

     # in ViTMAE, the expected sequence length = (num_patches + 1) * (1 - config.mask_ratio), rounded above
     # (we add 1 for the [CLS] token)
     SCREAMING_SNAKE_CASE								=			(image_size // patch_size) ** 2
     SCREAMING_SNAKE_CASE								=			int(math.ceil((1 - mask_ratio) * (num_patches + 1)    )    )

    def UpperCamelCase			(			self :      List[str]    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]    )

     SCREAMING_SNAKE_CASE								=			None
     if self.use_labels:
      SCREAMING_SNAKE_CASE								=			ids_tensor([self.batch_size]							,    self.type_sequence_label_size    )

     SCREAMING_SNAKE_CASE								=			self.get_config()

     return config, pixel_values, labels

    def UpperCamelCase			(			self :      Any    ):

     """simple docstring"""






     return ViTMAEConfig(
         image_size=self.image_size							,    patch_size=self.patch_size							,    num_channels=self.num_channels							,    hidden_size=self.hidden_size							,    num_hidden_layers=self.num_hidden_layers							,    num_attention_heads=self.num_attention_heads							,    intermediate_size=self.intermediate_size							,    decoder_hidden_size=self.hidden_size							,    decoder_num_hidden_layers=self.num_hidden_layers							,    decoder_num_attention_heads=self.num_attention_heads							,    decoder_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							,    is_decoder=snake_case__							,    initializer_range=self.initializer_range							,    mask_ratio=self.mask_ratio							,    )

    def UpperCamelCase			(			self :      Any							,    snake_case__ :      List[Any]							,    snake_case__ :      int							,    snake_case__ :      List[Any]    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			TFViTMAEModel(config=snake_case__    )
     SCREAMING_SNAKE_CASE								=			model(snake_case__							,    training=snake_case__    )
     self.parent.assertEqual(result.last_hidden_state.shape							,    (self.batch_size, self.seq_length, self.hidden_size)    )

    def UpperCamelCase			(			self :      str							,    snake_case__ :      Any							,    snake_case__ :      int							,    snake_case__ :      Optional[int]    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			TFViTMAEForPreTraining(snake_case__    )
     SCREAMING_SNAKE_CASE								=			model(snake_case__							,    training=snake_case__    )
     # expected sequence length = num_patches
     SCREAMING_SNAKE_CASE								=			(self.image_size // self.patch_size) ** 2
     SCREAMING_SNAKE_CASE								=			self.patch_size**2 * self.num_channels
     self.parent.assertEqual(result.logits.shape							,    (self.batch_size, num_patches, expected_num_channels)    )

     # test greyscale images
     SCREAMING_SNAKE_CASE								=			1
     SCREAMING_SNAKE_CASE								=			TFViTMAEForPreTraining(snake_case__    )

     SCREAMING_SNAKE_CASE								=			floats_tensor([self.batch_size, 1, self.image_size, self.image_size]    )
     SCREAMING_SNAKE_CASE								=			model(snake_case__							,    training=snake_case__    )
     SCREAMING_SNAKE_CASE								=			self.patch_size**2
     self.parent.assertEqual(result.logits.shape							,    (self.batch_size, num_patches, expected_num_channels)    )




    def UpperCamelCase			(			self :      Optional[Any]    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			self.prepare_config_and_inputs()
     ((SCREAMING_SNAKE_CASE)						,	(SCREAMING_SNAKE_CASE)						,	(SCREAMING_SNAKE_CASE))								=			config_and_inputs
     SCREAMING_SNAKE_CASE								=			{'pixel_values': pixel_values}
     return config, inputs_dict




@require_tf
class      UpperCamelCase (     SCREAMING_SNAKE_CASE	,			SCREAMING_SNAKE_CASE	,			unittest.TestCase   ):
    __UpperCamelCase         =(TFViTMAEModel, TFViTMAEForPreTraining) if is_tf_available() else ()
    __UpperCamelCase         ={"feature-extraction": TFViTMAEModel} if is_tf_available() else {}

    __UpperCamelCase         =False
    __UpperCamelCase         =False
    __UpperCamelCase         =False
    __UpperCamelCase         =False

    def UpperCamelCase			(			self :      Dict    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			TFViTMAEModelTester(self    )
     SCREAMING_SNAKE_CASE								=			ConfigTester(self							,    config_class=snake_case__							,    has_text_modality=snake_case__							,    hidden_size=3_7    )

    def UpperCamelCase			(			self :      Any    ):

     """simple docstring"""






     self.config_tester.run_common_tests()

    @unittest.skip(reason='ViTMAE does not use inputs_embeds'    )
    def UpperCamelCase			(			self :      List[Any]    ):

     """simple docstring"""






     pass

    def UpperCamelCase			(			self :      List[str]    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE						,	SCREAMING_SNAKE_CASE								=			self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
      SCREAMING_SNAKE_CASE								=			model_class(snake_case__    )
      self.assertIsInstance(model.get_input_embeddings()							,    (tf.keras.layers.Layer)    )
      SCREAMING_SNAKE_CASE								=			model.get_output_embeddings()
      self.assertTrue(x is None or isinstance(snake_case__							,    tf.keras.layers.Layer    )    )

    def UpperCamelCase			(			self :      Tuple    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE						,	SCREAMING_SNAKE_CASE								=			self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
      SCREAMING_SNAKE_CASE								=			model_class(snake_case__    )
      SCREAMING_SNAKE_CASE								=			inspect.signature(model.call    )
      # signature.parameters is an OrderedDict => so arg_names order is deterministic
      SCREAMING_SNAKE_CASE								=			[*signature.parameters.keys()]

      SCREAMING_SNAKE_CASE								=			['pixel_values']
      self.assertListEqual(arg_names[:1]							,    snake_case__    )

    def UpperCamelCase			(			self :      str    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*snake_case__    )

    def UpperCamelCase			(			self :      Optional[Any]    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_pretraining(*snake_case__    )

    def UpperCamelCase			(			self :      Union[str, Any]    ):

     """simple docstring"""






     np.random.seed(2    )

     SCREAMING_SNAKE_CASE						,	SCREAMING_SNAKE_CASE								=			self.model_tester.prepare_config_and_inputs_for_common()

     SCREAMING_SNAKE_CASE								=			int((config.image_size // config.patch_size) ** 2    )
     SCREAMING_SNAKE_CASE								=			np.random.uniform(size=(self.model_tester.batch_size, num_patches)    )

     for model_class in self.all_model_classes:
      SCREAMING_SNAKE_CASE								=			model_class(snake_case__    )
      SCREAMING_SNAKE_CASE								=			self._prepare_for_class(snake_case__							,    snake_case__    )

      SCREAMING_SNAKE_CASE								=			model(snake_case__							,    noise=snake_case__    )

      SCREAMING_SNAKE_CASE								=			copy.deepcopy(self._prepare_for_class(snake_case__							,    snake_case__    )    )
      SCREAMING_SNAKE_CASE								=			model(**snake_case__							,    noise=snake_case__    )
      SCREAMING_SNAKE_CASE								=			outputs_dict[0].numpy()
      SCREAMING_SNAKE_CASE								=			outputs_keywords[0].numpy()

      self.assertLess(np.sum(np.abs(output_dict - output_keywords    )    )							,    1E-6    )

    def UpperCamelCase			(			self :      Dict    ):

     """simple docstring"""






     np.random.seed(2    )

     SCREAMING_SNAKE_CASE						,	SCREAMING_SNAKE_CASE								=			self.model_tester.prepare_config_and_inputs_for_common()

     SCREAMING_SNAKE_CASE								=			int((config.image_size // config.patch_size) ** 2    )
     SCREAMING_SNAKE_CASE								=			np.random.uniform(size=(self.model_tester.batch_size, num_patches)    )

     def prepare_numpy_arrays(snake_case__ :      Tuple    ):
      SCREAMING_SNAKE_CASE								=			{}
      for k, v in inputs_dict.items():
       if tf.is_tensor(snake_case__    ):
        SCREAMING_SNAKE_CASE								=			v.numpy()
       else:
        SCREAMING_SNAKE_CASE								=			np.array(snake_case__    )

      return inputs_np_dict

     for model_class in self.all_model_classes:
      SCREAMING_SNAKE_CASE								=			model_class(snake_case__    )

      SCREAMING_SNAKE_CASE								=			self._prepare_for_class(snake_case__							,    snake_case__    )
      SCREAMING_SNAKE_CASE								=			prepare_numpy_arrays(snake_case__    )

      SCREAMING_SNAKE_CASE								=			model(snake_case__							,    noise=snake_case__    )
      SCREAMING_SNAKE_CASE								=			model(**snake_case__							,    noise=snake_case__    )
      self.assert_outputs_same(snake_case__							,    snake_case__    )

    def UpperCamelCase			(			self :      List[str]							,    snake_case__ :      List[Any]							,    snake_case__ :      Tuple							,    snake_case__ :      List[Any]    ):

     """simple docstring"""






     np.random.seed(2    )

     SCREAMING_SNAKE_CASE								=			int((tf_model.config.image_size // tf_model.config.patch_size) ** 2    )
     SCREAMING_SNAKE_CASE								=			np.random.uniform(size=(self.model_tester.batch_size, num_patches)    )
     SCREAMING_SNAKE_CASE								=			tf.constant(snake_case__    )

     # Add `noise` argument.
     # PT inputs will be prepared in `super().check_pt_tf_models()` with this added `noise` argument
     SCREAMING_SNAKE_CASE								=			tf_noise

     super().check_pt_tf_models(snake_case__							,    snake_case__							,    snake_case__    )

    def UpperCamelCase			(			self :      Tuple    ):

     """simple docstring"""






     np.random.seed(2    )

     SCREAMING_SNAKE_CASE						,	SCREAMING_SNAKE_CASE								=			self.model_tester.prepare_config_and_inputs_for_common()

     SCREAMING_SNAKE_CASE								=			{
         module_member
         for model_class in self.all_model_classes
         for module in (import_module(model_class.__module__    ),)
         for module_member_name in dir(snake_case__    )
         if module_member_name.endswith('MainLayer'    )
         # This condition is required, since `modeling_tf_clip.py` has 3 classes whose names end with `MainLayer`.
         and module_member_name[: -len('MainLayer'    )] == model_class.__name__[: -len('Model'    )]
         for module_member in (getattr(snake_case__							,    snake_case__    ),)
         if isinstance(snake_case__							,    snake_case__    )
         and tf.keras.layers.Layer in module_member.__bases__
         and getattr(snake_case__							,    '_keras_serializable'							,    snake_case__    )
     }

     SCREAMING_SNAKE_CASE								=			int((config.image_size // config.patch_size) ** 2    )
     SCREAMING_SNAKE_CASE								=			np.random.uniform(size=(self.model_tester.batch_size, num_patches)    )
     SCREAMING_SNAKE_CASE								=			tf.convert_to_tensor(snake_case__    )
     inputs_dict.update({'noise': noise}    )

     for main_layer_class in tf_main_layer_classes:
      SCREAMING_SNAKE_CASE								=			main_layer_class(snake_case__    )

      SCREAMING_SNAKE_CASE								=			{
          name: tf.keras.Input(tensor.shape[1:]							,    dtype=tensor.dtype    ) for name, tensor in inputs_dict.items()
      }

      SCREAMING_SNAKE_CASE								=			tf.keras.Model(snake_case__							,    outputs=main_layer(snake_case__    )    )
      SCREAMING_SNAKE_CASE								=			model(snake_case__    )

      with tempfile.TemporaryDirectory() as tmpdirname:
       SCREAMING_SNAKE_CASE								=			os.path.join(snake_case__							,    'keras_model.h5'    )
       model.save(snake_case__    )
       SCREAMING_SNAKE_CASE								=			tf.keras.models.load_model(
           snake_case__							,    custom_objects={main_layer_class.__name__: main_layer_class}    )
       assert isinstance(snake_case__							,    tf.keras.Model    )
       SCREAMING_SNAKE_CASE								=			model(snake_case__    )
       self.assert_outputs_same(snake_case__							,    snake_case__    )

    @slow
    def UpperCamelCase			(			self :      Any    ):

     """simple docstring"""






     np.random.seed(2    )

     SCREAMING_SNAKE_CASE						,	SCREAMING_SNAKE_CASE								=			self.model_tester.prepare_config_and_inputs_for_common()

     SCREAMING_SNAKE_CASE								=			int((config.image_size // config.patch_size) ** 2    )
     SCREAMING_SNAKE_CASE								=			np.random.uniform(size=(self.model_tester.batch_size, num_patches)    )

     for model_class in self.all_model_classes:
      SCREAMING_SNAKE_CASE								=			model_class(snake_case__    )
      SCREAMING_SNAKE_CASE								=			self._prepare_for_class(snake_case__							,    snake_case__    )
      SCREAMING_SNAKE_CASE								=			model(snake_case__							,    noise=snake_case__    )

      if model_class.__name__ == "TFViTMAEModel":
       SCREAMING_SNAKE_CASE								=			outputs.last_hidden_state.numpy()
       SCREAMING_SNAKE_CASE								=			0
      else:
       SCREAMING_SNAKE_CASE								=			outputs.logits.numpy()
       SCREAMING_SNAKE_CASE								=			0

      with tempfile.TemporaryDirectory() as tmpdirname:
       model.save_pretrained(snake_case__							,    saved_model=snake_case__    )
       SCREAMING_SNAKE_CASE								=			model_class.from_pretrained(snake_case__    )
       SCREAMING_SNAKE_CASE								=			model(snake_case__							,    noise=snake_case__    )

       if model_class.__name__ == "TFViTMAEModel":
        SCREAMING_SNAKE_CASE								=			after_outputs['last_hidden_state'].numpy()
        SCREAMING_SNAKE_CASE								=			0
       else:
        SCREAMING_SNAKE_CASE								=			after_outputs['logits'].numpy()
        SCREAMING_SNAKE_CASE								=			0

       SCREAMING_SNAKE_CASE								=			np.amax(np.abs(out_a - out_a    )    )
       self.assertLessEqual(snake_case__							,    1E-5    )

    def UpperCamelCase			(			self :      Optional[int]    ):

     """simple docstring"""






     np.random.seed(2    )

     SCREAMING_SNAKE_CASE						,	SCREAMING_SNAKE_CASE								=			self.model_tester.prepare_config_and_inputs_for_common()

     SCREAMING_SNAKE_CASE								=			int((config.image_size // config.patch_size) ** 2    )
     SCREAMING_SNAKE_CASE								=			np.random.uniform(size=(self.model_tester.batch_size, num_patches)    )

     for model_class in self.all_model_classes:
      SCREAMING_SNAKE_CASE								=			model_class(snake_case__    )
      SCREAMING_SNAKE_CASE								=			self._prepare_for_class(snake_case__							,    snake_case__    )

      SCREAMING_SNAKE_CASE								=			model(snake_case__							,    noise=snake_case__    )
      SCREAMING_SNAKE_CASE								=			model.get_config()
      # make sure that returned config is jsonifiable, which is required by keras
      json.dumps(snake_case__    )
      SCREAMING_SNAKE_CASE								=			model_class.from_config(model.get_config()    )
      # make sure it also accepts a normal config
      SCREAMING_SNAKE_CASE								=			model_class.from_config(model.config    )
      SCREAMING_SNAKE_CASE								=			new_model(snake_case__    )  # Build model
      new_model.set_weights(model.get_weights()    )
      SCREAMING_SNAKE_CASE								=			new_model(snake_case__							,    noise=snake_case__    )

      self.assert_outputs_same(snake_case__							,    snake_case__    )

    @unittest.skip(
        reason='ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load\n    to get deterministic results.'    )
    def UpperCamelCase			(			self :      Optional[Any]    ):

     """simple docstring"""






     pass

    @unittest.skip(reason='ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load'    )
    def UpperCamelCase			(			self :      List[str]    ):

     """simple docstring"""






     pass




    @slow
    def UpperCamelCase			(			self :      List[Any]    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			TFViTMAEModel.from_pretrained('google/vit-base-patch16-224'    )
     self.assertIsNotNone(snake_case__    )


def        __lowerCAmelCase    (       )		->							List[str]:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE								=			Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'	)
 return image




@require_tf
@require_vision
class      UpperCamelCase (     unittest.TestCase   ):

    @cached_property
    def UpperCamelCase			(			self :      str    ):

     """simple docstring"""






     return ViTImageProcessor.from_pretrained('facebook/vit-mae-base'    ) if is_vision_available() else None




    @slow
    def UpperCamelCase			(			self :      Tuple    ):

     """simple docstring"""






     np.random.seed(2    )

     SCREAMING_SNAKE_CASE								=			TFViTMAEForPreTraining.from_pretrained('facebook/vit-mae-base'    )

     SCREAMING_SNAKE_CASE								=			self.default_image_processor
     SCREAMING_SNAKE_CASE								=			prepare_img()
     SCREAMING_SNAKE_CASE								=			image_processor(images=snake_case__							,    return_tensors='tf'    )

     # prepare a noise vector that will be also used for testing the TF model
     # (this way we can ensure that the PT and TF models operate on the same inputs)
     SCREAMING_SNAKE_CASE								=			ViTMAEConfig()
     SCREAMING_SNAKE_CASE								=			int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2    )
     SCREAMING_SNAKE_CASE								=			np.random.uniform(size=(1, num_patches)    )

     # forward pass
     SCREAMING_SNAKE_CASE								=			model(**snake_case__							,    noise=snake_case__    )

     # verify the logits
     SCREAMING_SNAKE_CASE								=			tf.convert_to_tensor([1, 1_9_6, 7_6_8]    )
     self.assertEqual(outputs.logits.shape							,    snake_case__    )

     SCREAMING_SNAKE_CASE								=			tf.convert_to_tensor(
         [[-0.0_548, -1.7_023, -0.9_325], [0.3_721, -0.5_670, -0.2_233], [0.8_235, -1.3_878, -0.3_524]]    )

     tf.debugging.assert_near(outputs.logits[0, :3, :3]							,    snake_case__							,    atol=1E-4    )




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      UpperCamelCase :

    def __init__(			self :      Dict							,    snake_case__ :      str							,    snake_case__ :      str=1_3							,    snake_case__ :      Tuple=7							,    snake_case__ :      Tuple=True							,    snake_case__ :      Tuple=True							,    snake_case__ :      List[str]=False							,    snake_case__ :      Any=True							,    snake_case__ :      Union[str, Any]=9_9							,    snake_case__ :      Dict=3_2							,    snake_case__ :      Optional[Any]=5							,    snake_case__ :      Optional[Any]=4							,    snake_case__ :      Union[str, Any]=3_7							,    snake_case__ :      Tuple="gelu"							,    snake_case__ :      Dict=0.1							,    snake_case__ :      Any=0.1							,    snake_case__ :      int=5_1_2							,    snake_case__ :      Dict=1_6							,    snake_case__ :      str=2							,    snake_case__ :      Any=0.02							,    snake_case__ :      List[str]=3							,    snake_case__ :      int=4							,    snake_case__ :      List[str]=None							,    ):

     """simple docstring"""






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

    def UpperCamelCase			(			self :      List[Any]    ):

     """simple docstring"""






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

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

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

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

     SCREAMING_SNAKE_CASE								=			self.get_config()

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

    def UpperCamelCase			(			self :      Dict    ):

     """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=snake_case__							,    initializer_range=self.initializer_range							,    use_stable_embedding=snake_case__							,    )

    def UpperCamelCase			(			self :      int							,    snake_case__ :      Tuple							,    snake_case__ :      str							,    snake_case__ :      Optional[Any]							,    snake_case__ :      Union[str, Any]							,    snake_case__ :      List[str]							,    snake_case__ :      Any							,    snake_case__ :      Optional[int]    ):

     """simple docstring"""






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

    def UpperCamelCase			(			self :      Optional[int]							,    snake_case__ :      Dict							,    snake_case__ :      List[Any]							,    snake_case__ :      str							,    snake_case__ :      str							,    snake_case__ :      int							,    snake_case__ :      Union[str, Any]							,    snake_case__ :      Optional[int]							,    snake_case__ :      List[Any]							,    snake_case__ :      str							,    ):

     """simple docstring"""






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

    def UpperCamelCase			(			self :      Dict							,    snake_case__ :      Union[str, Any]							,    snake_case__ :      int							,    snake_case__ :      int							,    snake_case__ :      List[str]							,    snake_case__ :      Optional[Any]							,    snake_case__ :      Optional[Any]							,    snake_case__ :      Optional[int]							,    snake_case__ :      Optional[int]							,    snake_case__ :      Tuple							,    ):

     """simple docstring"""






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

    def UpperCamelCase			(			self :      Optional[int]							,    snake_case__ :      Union[str, Any]							,    snake_case__ :      List[Any]							,    snake_case__ :      Any							,    snake_case__ :      int							,    snake_case__ :      Any							,    snake_case__ :      Union[str, Any]							,    snake_case__ :      int							,    snake_case__ :      str							,    snake_case__ :      Union[str, Any]							,    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			True
     SCREAMING_SNAKE_CASE								=			True
     SCREAMING_SNAKE_CASE								=			OpenLlamaForCausalLM(config=snake_case__    )
     model.to(snake_case__    )
     model.eval()

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

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

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

     SCREAMING_SNAKE_CASE								=			model(
         snake_case__							,    attention_mask=snake_case__							,    encoder_hidden_states=snake_case__							,    encoder_attention_mask=snake_case__							,    output_hidden_states=snake_case__							,    )['hidden_states'][0]
     SCREAMING_SNAKE_CASE								=			model(
         snake_case__							,    attention_mask=snake_case__							,    encoder_hidden_states=snake_case__							,    encoder_attention_mask=snake_case__							,    past_key_values=snake_case__							,    output_hidden_states=snake_case__							,    )['hidden_states'][0]

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




    def UpperCamelCase			(			self :      Any    ):

     """simple docstring"""






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




@require_torch
class      UpperCamelCase (     SCREAMING_SNAKE_CASE	,			SCREAMING_SNAKE_CASE	,			SCREAMING_SNAKE_CASE	,			unittest.TestCase   ):
    __UpperCamelCase         =(
        (OpenLlamaModel, OpenLlamaForCausalLM, OpenLlamaForSequenceClassification) if is_torch_available() else ()
    )
    __UpperCamelCase         =(OpenLlamaForCausalLM,) if is_torch_available() else ()
    __UpperCamelCase         =(
        {
            "feature-extraction": OpenLlamaModel,
            "text-classification": OpenLlamaForSequenceClassification,
            "text-generation": OpenLlamaForCausalLM,
            "zero-shot": OpenLlamaForSequenceClassification,
        }
        if is_torch_available()
        else {}
    )
    __UpperCamelCase         =False
    __UpperCamelCase         =False

    def UpperCamelCase			(			self :      int    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			OpenLlamaModelTester(self    )
     SCREAMING_SNAKE_CASE								=			ConfigTester(self							,    config_class=snake_case__							,    hidden_size=3_7    )

    def UpperCamelCase			(			self :      Optional[int]    ):

     """simple docstring"""






     self.config_tester.run_common_tests()

    def UpperCamelCase			(			self :      Any    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*snake_case__    )

    def UpperCamelCase			(			self :      List[str]    ):

     """simple docstring"""






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

    def UpperCamelCase			(			self :      Any    ):

     """simple docstring"""






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

    def UpperCamelCase			(			self :      int    ):

     """simple docstring"""






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

    def UpperCamelCase			(			self :      Union[str, Any]    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE						,	SCREAMING_SNAKE_CASE								=			self.model_tester.prepare_config_and_inputs_for_common()
     SCREAMING_SNAKE_CASE								=			3
     SCREAMING_SNAKE_CASE								=			'multi_label_classification'
     SCREAMING_SNAKE_CASE								=			input_dict['input_ids']
     SCREAMING_SNAKE_CASE								=			input_ids.ne(1    ).to(snake_case__    )
     SCREAMING_SNAKE_CASE								=			ids_tensor(
         [self.model_tester.batch_size, config.num_labels]							,    self.model_tester.type_sequence_label_size    ).to(torch.float    )
     SCREAMING_SNAKE_CASE								=			OpenLlamaForSequenceClassification(snake_case__    )
     model.to(snake_case__    )
     model.eval()
     SCREAMING_SNAKE_CASE								=			model(snake_case__							,    attention_mask=snake_case__							,    labels=snake_case__    )
     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 UpperCamelCase			(			self :      Optional[int]    ):

     """simple docstring"""






     pass




    @parameterized.expand([('linear',), ('dynamic',)]    )
    def UpperCamelCase			(			self :      str							,    snake_case__ :      Optional[Any]    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE						,	SCREAMING_SNAKE_CASE								=			self.model_tester.prepare_config_and_inputs_for_common()
     SCREAMING_SNAKE_CASE								=			ids_tensor([1, 1_0]							,    config.vocab_size    )
     SCREAMING_SNAKE_CASE								=			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
     SCREAMING_SNAKE_CASE								=			OpenLlamaModel(snake_case__    )
     original_model.to(snake_case__    )
     original_model.eval()
     SCREAMING_SNAKE_CASE								=			original_model(snake_case__    ).last_hidden_state
     SCREAMING_SNAKE_CASE								=			original_model(snake_case__    ).last_hidden_state

     set_seed(4_2    )  # Fixed seed at init time so the two models get the same random weights
     SCREAMING_SNAKE_CASE								=			{'type': scaling_type, 'factor': 10.0}
     SCREAMING_SNAKE_CASE								=			OpenLlamaModel(snake_case__    )
     scaled_model.to(snake_case__    )
     scaled_model.eval()
     SCREAMING_SNAKE_CASE								=			scaled_model(snake_case__    ).last_hidden_state
     SCREAMING_SNAKE_CASE								=			scaled_model(snake_case__    ).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(snake_case__							,    snake_case__							,    atol=1E-5    )    )
     else:
      self.assertFalse(torch.allclose(snake_case__							,    snake_case__							,    atol=1E-5    )    )

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

