





import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DiffusionPipeline,
    EulerDiscreteScheduler,
    StableDiffusionXLPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()



class 				_UpperCamelCase       (						lowerCAmelCase_	,    lowerCAmelCase_	,    unittest.TestCase  ):
       _UpperCamelCase					:       Any       = StableDiffusionXLPipeline
       _UpperCamelCase					:       Tuple       = TEXT_TO_IMAGE_PARAMS
       _UpperCamelCase					:       List[Any]       = TEXT_TO_IMAGE_BATCH_PARAMS
       _UpperCamelCase					:       Dict       = TEXT_TO_IMAGE_IMAGE_PARAMS
       _UpperCamelCase					:       Optional[int]       = TEXT_TO_IMAGE_IMAGE_PARAMS

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




              """simple docstring"""


              torch.manual_seed(0		)
              UpperCamelCase_ 							=	UNetaDConditionModel(
                  block_out_channels=(32, 64)       , layers_per_block=2       , sample_size=32       , in_channels=4       , out_channels=4       , down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")       , up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")       , attention_head_dim=(2, 4)       , use_linear_projection=_SCREAMING_SNAKE_CASE       , addition_embed_type="text_time"       , addition_time_embed_dim=8       , transformer_layers_per_block=(1, 2)       , projection_class_embeddings_input_dim=80       , cross_attention_dim=64       , )
              UpperCamelCase_ 							=	EulerDiscreteScheduler(
                  beta_start=0.0_00_85       , beta_end=0.0_12       , steps_offset=1       , beta_schedule="scaled_linear"       , timestep_spacing="leading"       , )
              torch.manual_seed(0		)
              UpperCamelCase_ 							=	AutoencoderKL(
                  block_out_channels=[32, 64]       , in_channels=3       , out_channels=3       , down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]       , up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]       , latent_channels=4       , sample_size=128       , )
              torch.manual_seed(0		)
              UpperCamelCase_ 							=	CLIPTextConfig(
                  bos_token_id=0       , eos_token_id=2       , hidden_size=32       , intermediate_size=37       , layer_norm_eps=1e-05       , num_attention_heads=4       , num_hidden_layers=5       , pad_token_id=1       , vocab_size=1000       , hidden_act="gelu"       , projection_dim=32       , )
              UpperCamelCase_ 							=	CLIPTextModel(_SCREAMING_SNAKE_CASE		)
              UpperCamelCase_ 							=	CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"       , local_files_only=_SCREAMING_SNAKE_CASE		)

              UpperCamelCase_ 							=	CLIPTextModelWithProjection(_SCREAMING_SNAKE_CASE		)
              UpperCamelCase_ 							=	CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"       , local_files_only=_SCREAMING_SNAKE_CASE		)

              UpperCamelCase_ 							=	{
                  "unet": unet,
                  "scheduler": scheduler,
                  "vae": vae,
                  "text_encoder": text_encoder,
                  "tokenizer": tokenizer,
                  "text_encoder_2": text_encoder_a,
                  "tokenizer_2": tokenizer_a,
                  # "safety_checker": None,
                  # "feature_extractor": None,
              }
              return components

       def lowercase			(		self:			Any       , _SCREAMING_SNAKE_CASE:			Any       , _SCREAMING_SNAKE_CASE:			int=0		)							->			List[Any]:




              """simple docstring"""


              if str(_SCREAMING_SNAKE_CASE		).startswith("mps"		):
                     UpperCamelCase_ 							=	torch.manual_seed(_SCREAMING_SNAKE_CASE		)
              else:
                     UpperCamelCase_ 							=	torch.Generator(device=_SCREAMING_SNAKE_CASE		).manual_seed(_SCREAMING_SNAKE_CASE		)
              UpperCamelCase_ 							=	{
                  "prompt": "A painting of a squirrel eating a burger",
                  "generator": generator,
                  "num_inference_steps": 2,
                  "guidance_scale": 5.0,
                  "output_type": "numpy",
              }
              return inputs

       def lowercase			(		self:			List[Any]		)							->			int:




              """simple docstring"""


              UpperCamelCase_ 							=	"cpu"  # ensure determinism for the device-dependent torch.Generator
              UpperCamelCase_ 							=	self.get_dummy_components()
              UpperCamelCase_ 							=	StableDiffusionXLPipeline(**_SCREAMING_SNAKE_CASE		)
              UpperCamelCase_ 							=	sd_pipe.to(_SCREAMING_SNAKE_CASE		)
              sd_pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE		)

              UpperCamelCase_ 							=	self.get_dummy_inputs(_SCREAMING_SNAKE_CASE		)
              UpperCamelCase_ 							=	sd_pipe(**_SCREAMING_SNAKE_CASE		).images
              UpperCamelCase_ 							=	image[0, -3:, -3:, -1]

              assert image.shape == (1, 64, 64, 3)
              UpperCamelCase_ 							=	np.array([0.58_73, 0.61_28, 0.47_97, 0.51_22, 0.56_74, 0.46_39, 0.52_27, 0.51_49, 0.47_47]		)

              assert np.abs(image_slice.flatten() - expected_slice		).max() < 1e-2

       def lowercase			(		self:			Dict		)							->			Optional[int]:




              """simple docstring"""


              UpperCamelCase_ 							=	self.get_dummy_components()
              UpperCamelCase_ 							=	StableDiffusionXLPipeline(**_SCREAMING_SNAKE_CASE		)
              UpperCamelCase_ 							=	sd_pipe.to(_SCREAMING_SNAKE_CASE		)
              UpperCamelCase_ 							=	sd_pipe.to(_SCREAMING_SNAKE_CASE		)
              sd_pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE		)

              # forward without prompt embeds
              UpperCamelCase_ 							=	self.get_dummy_inputs(_SCREAMING_SNAKE_CASE		)
              UpperCamelCase_ 							=	3 * ["this is a negative prompt"]
              UpperCamelCase_ 							=	negative_prompt
              UpperCamelCase_ 							=	3 * [inputs["prompt"]]

              UpperCamelCase_ 							=	sd_pipe(**_SCREAMING_SNAKE_CASE		)
              UpperCamelCase_ 							=	output.images[0, -3:, -3:, -1]

              # forward with prompt embeds
              UpperCamelCase_ 							=	self.get_dummy_inputs(_SCREAMING_SNAKE_CASE		)
              UpperCamelCase_ 							=	3 * ["this is a negative prompt"]
              UpperCamelCase_ 							=	3 * [inputs.pop("prompt"		)]

              (
                  (
                  UpperCamelCase_
              )							,			(
                  UpperCamelCase_
              )							,			(
                  UpperCamelCase_
              )							,			(
                  UpperCamelCase_
              )							,			
              ) 							=	sd_pipe.encode_prompt(_SCREAMING_SNAKE_CASE       , negative_prompt=_SCREAMING_SNAKE_CASE		)

              UpperCamelCase_ 							=	sd_pipe(
                  **_SCREAMING_SNAKE_CASE       , prompt_embeds=_SCREAMING_SNAKE_CASE       , negative_prompt_embeds=_SCREAMING_SNAKE_CASE       , pooled_prompt_embeds=_SCREAMING_SNAKE_CASE       , negative_pooled_prompt_embeds=_SCREAMING_SNAKE_CASE       , )
              UpperCamelCase_ 							=	output.images[0, -3:, -3:, -1]

              # make sure that it's equal
              assert np.abs(image_slice_a.flatten() - image_slice_a.flatten()		).max() < 1e-4

       def lowercase			(		self:			Optional[Any]		)							->			List[str]:




              """simple docstring"""


              super().test_attention_slicing_forward_pass(expected_max_diff=3e-3		)



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




              """simple docstring"""


              super().test_inference_batch_single_identical(expected_max_diff=3e-3		)



@slow
@require_torch_gpu
class 				_UpperCamelCase       (						unittest.TestCase  ):

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




              """simple docstring"""


              super().tearDown()
              gc.collect()
              torch.cuda.empty_cache()

       def lowercase			(		self:			str       , _SCREAMING_SNAKE_CASE:			List[Any]       , _SCREAMING_SNAKE_CASE:			Union[str, Any]="cpu"       , _SCREAMING_SNAKE_CASE:			int=torch.floataa       , _SCREAMING_SNAKE_CASE:			Any=0		)							->			str:




              """simple docstring"""


              UpperCamelCase_ 							=	torch.Generator(device=_SCREAMING_SNAKE_CASE		).manual_seed(_SCREAMING_SNAKE_CASE		)
              UpperCamelCase_ 							=	np.random.RandomState(_SCREAMING_SNAKE_CASE		).standard_normal((1, 4, 64, 64)		)
              UpperCamelCase_ 							=	torch.from_numpy(_SCREAMING_SNAKE_CASE		).to(device=_SCREAMING_SNAKE_CASE       , dtype=_SCREAMING_SNAKE_CASE		)
              UpperCamelCase_ 							=	{
                  "prompt": "a photograph of an astronaut riding a horse",
                  "latents": latents,
                  "generator": generator,
                  "num_inference_steps": 3,
                  "guidance_scale": 7.5,
                  "output_type": "numpy",
              }
              return inputs



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




              """simple docstring"""


              UpperCamelCase_ 							=	DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2-base"		)
              pipe.to(_SCREAMING_SNAKE_CASE		)
              pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE		)

              UpperCamelCase_ 							=	self.get_inputs(_SCREAMING_SNAKE_CASE		)
              UpperCamelCase_ 							=	pipe(**_SCREAMING_SNAKE_CASE		).images
              UpperCamelCase_ 							=	image[0, -3:, -3:, -1].flatten()

              assert image.shape == (1, 512, 512, 3)
              UpperCamelCase_ 							=	np.array([0.4_94_93, 0.4_78_96, 0.4_07_98, 0.5_42_14, 0.5_32_12, 0.4_82_02, 0.4_76_56, 0.4_63_29, 0.4_85_06]		)
              assert np.abs(image_slice - expected_slice		).max() < 7e-3







from __future__ import annotations

import unittest

from transformers import AlbertConfig, is_tf_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_tf, slow

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


if is_tf_available():
  import tensorflow as tf

  from transformers import TF_MODEL_FOR_PRETRAINING_MAPPING
  from transformers.models.albert.modeling_tf_albert import (
      TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFAlbertForMaskedLM,
      TFAlbertForMultipleChoice,
      TFAlbertForPreTraining,
      TFAlbertForQuestionAnswering,
      TFAlbertForSequenceClassification,
      TFAlbertForTokenClassification,
      TFAlbertModel,
  )
class       _UpperCamelCase   :
      def __init__(      self :Optional[int]			,   lowerCamelCase :Tuple			,   lowerCamelCase :Union[str, Any]=13			,   lowerCamelCase :List[str]=7			,   lowerCamelCase :List[Any]=True			,   lowerCamelCase :Any=True			,   lowerCamelCase :Any=True			,   lowerCamelCase :Optional[Any]=True			,   lowerCamelCase :Optional[int]=99			,   lowerCamelCase :Tuple=16			,   lowerCamelCase :Optional[Any]=32			,   lowerCamelCase :Optional[Any]=2			,   lowerCamelCase :List[Any]=4			,   lowerCamelCase :Dict=37			,   lowerCamelCase :Optional[Any]="gelu"			,   lowerCamelCase :Dict=0.1			,   lowerCamelCase :Optional[Any]=0.1			,   lowerCamelCase :Tuple=512			,   lowerCamelCase :Optional[int]=16			,   lowerCamelCase :Any=2			,   lowerCamelCase :Tuple=0.02			,   lowerCamelCase :Union[str, Any]=3			,   lowerCamelCase :str=4			,   lowerCamelCase :Dict=None			,   )  ->      List[Any]:
       UpperCAmelCase__							=    parent
       UpperCAmelCase__							=    13
       UpperCAmelCase__							=    7
       UpperCAmelCase__							=    True
       UpperCAmelCase__							=    True
       UpperCAmelCase__							=    True
       UpperCAmelCase__							=    True
       UpperCAmelCase__							=    99
       UpperCAmelCase__							=    16
       UpperCAmelCase__							=    32
       UpperCAmelCase__							=    2
       UpperCAmelCase__							=    4
       UpperCAmelCase__							=    37
       UpperCAmelCase__							=    "gelu"
       UpperCAmelCase__							=    0.1
       UpperCAmelCase__							=    0.1
       UpperCAmelCase__							=    512
       UpperCAmelCase__							=    16
       UpperCAmelCase__							=    2
       UpperCAmelCase__							=    0.02
       UpperCAmelCase__							=    3
       UpperCAmelCase__							=    4
       UpperCAmelCase__							=    None
      def 						UpperCAmelCase_    (      self :Optional[int]   )  ->      Dict:
       UpperCAmelCase__							=    ids_tensor([self.batch_size, self.seq_length]			,   self.vocab_size   )

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

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

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

       UpperCAmelCase__							=    AlbertConfig(
           vocab_size=self.vocab_size			,   hidden_size=self.hidden_size			,   embedding_size=self.embedding_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			,   initializer_range=self.initializer_range			,   )

       return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
      def 						UpperCAmelCase_    (      self :Optional[int]			,   lowerCamelCase :str			,   lowerCamelCase :Tuple			,   lowerCamelCase :Tuple			,   lowerCamelCase :List[Any]			,   lowerCamelCase :Union[str, Any]			,   lowerCamelCase :int			,   lowerCamelCase :str   )  ->      Optional[Any]:
       UpperCAmelCase__							=    TFAlbertModel(config=lowerCamelCase   )
       # inputs = {'input_ids': input_ids,
       #           'attention_mask': input_mask,
       #           'token_type_ids': token_type_ids}
       # sequence_output, pooled_output = model(**inputs)
       UpperCAmelCase__							=    {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
       UpperCAmelCase__							=    model(lowerCamelCase   )

       UpperCAmelCase__							=    [input_ids, input_mask]
       UpperCAmelCase__							=    model(lowerCamelCase   )

       UpperCAmelCase__							=    model(lowerCamelCase   )

       self.parent.assertEqual(result.last_hidden_state.shape			,   (self.batch_size, self.seq_length, self.hidden_size)   )
       self.parent.assertEqual(result.pooler_output.shape			,   (self.batch_size, self.hidden_size)   )
      def 						UpperCAmelCase_    (      self :Optional[Any]			,   lowerCamelCase :Any			,   lowerCamelCase :List[Any]			,   lowerCamelCase :Any			,   lowerCamelCase :List[str]			,   lowerCamelCase :Optional[int]			,   lowerCamelCase :List[Any]			,   lowerCamelCase :Tuple   )  ->      Optional[int]:
       UpperCAmelCase__							=    self.num_labels
       UpperCAmelCase__							=    TFAlbertForPreTraining(config=lowerCamelCase   )
       UpperCAmelCase__							=    {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
       UpperCAmelCase__							=    model(lowerCamelCase   )
       self.parent.assertEqual(result.prediction_logits.shape			,   (self.batch_size, self.seq_length, self.vocab_size)   )
       self.parent.assertEqual(result.sop_logits.shape			,   (self.batch_size, self.num_labels)   )
      def 						UpperCAmelCase_    (      self :int			,   lowerCamelCase :Dict			,   lowerCamelCase :List[Any]			,   lowerCamelCase :List[str]			,   lowerCamelCase :Union[str, Any]			,   lowerCamelCase :Optional[int]			,   lowerCamelCase :Tuple			,   lowerCamelCase :Any   )  ->      Optional[Any]:
       UpperCAmelCase__							=    TFAlbertForMaskedLM(config=lowerCamelCase   )
       UpperCAmelCase__							=    {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
       UpperCAmelCase__							=    model(lowerCamelCase   )
       self.parent.assertEqual(result.logits.shape			,   (self.batch_size, self.seq_length, self.vocab_size)   )
      def 						UpperCAmelCase_    (      self :Tuple			,   lowerCamelCase :str			,   lowerCamelCase :Dict			,   lowerCamelCase :Dict			,   lowerCamelCase :Optional[Any]			,   lowerCamelCase :List[Any]			,   lowerCamelCase :Union[str, Any]			,   lowerCamelCase :Optional[Any]   )  ->      str:
       UpperCAmelCase__							=    self.num_labels
       UpperCAmelCase__							=    TFAlbertForSequenceClassification(config=lowerCamelCase   )
       UpperCAmelCase__							=    {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
       UpperCAmelCase__							=    model(lowerCamelCase   )
       self.parent.assertEqual(result.logits.shape			,   (self.batch_size, self.num_labels)   )
      def 						UpperCAmelCase_    (      self :Dict			,   lowerCamelCase :Optional[Any]			,   lowerCamelCase :str			,   lowerCamelCase :Dict			,   lowerCamelCase :List[Any]			,   lowerCamelCase :List[str]			,   lowerCamelCase :int			,   lowerCamelCase :Optional[Any]   )  ->      Dict:
       UpperCAmelCase__							=    TFAlbertForQuestionAnswering(config=lowerCamelCase   )
       UpperCAmelCase__							=    {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
       UpperCAmelCase__							=    model(lowerCamelCase   )
       self.parent.assertEqual(result.start_logits.shape			,   (self.batch_size, self.seq_length)   )
       self.parent.assertEqual(result.end_logits.shape			,   (self.batch_size, self.seq_length)   )
      def 						UpperCAmelCase_    (      self :Optional[int]			,   lowerCamelCase :Optional[Any]			,   lowerCamelCase :Dict			,   lowerCamelCase :Union[str, Any]			,   lowerCamelCase :Union[str, Any]			,   lowerCamelCase :Tuple			,   lowerCamelCase :List[Any]			,   lowerCamelCase :Union[str, Any]   )  ->      int:
       UpperCAmelCase__							=    self.num_choices
       UpperCAmelCase__							=    TFAlbertForMultipleChoice(config=lowerCamelCase   )
       UpperCAmelCase__							=    tf.tile(tf.expand_dims(lowerCamelCase			,   1   )			,   (1, self.num_choices, 1)   )
       UpperCAmelCase__							=    tf.tile(tf.expand_dims(lowerCamelCase			,   1   )			,   (1, self.num_choices, 1)   )
       UpperCAmelCase__							=    tf.tile(tf.expand_dims(lowerCamelCase			,   1   )			,   (1, self.num_choices, 1)   )
       UpperCAmelCase__							=    {
           "input_ids": multiple_choice_inputs_ids,
           "attention_mask": multiple_choice_input_mask,
           "token_type_ids": multiple_choice_token_type_ids,
       }
       UpperCAmelCase__							=    model(lowerCamelCase   )
       self.parent.assertListEqual(list(result["logits"].shape   )			,   [self.batch_size, self.num_choices]   )
      def 						UpperCAmelCase_    (      self :int			,   lowerCamelCase :Union[str, Any]			,   lowerCamelCase :List[str]			,   lowerCamelCase :int			,   lowerCamelCase :int			,   lowerCamelCase :Union[str, Any]			,   lowerCamelCase :Dict			,   lowerCamelCase :Dict   )  ->      List[str]:
       UpperCAmelCase__							=    self.num_labels
       UpperCAmelCase__							=    TFAlbertForTokenClassification(config=lowerCamelCase   )
       UpperCAmelCase__							=    {
           "input_ids": input_ids,
           "attention_mask": input_mask,
           "token_type_ids": token_type_ids,
       }
       UpperCAmelCase__							=    model(lowerCamelCase   )
       self.parent.assertListEqual(list(result["logits"].shape   )			,   [self.batch_size, self.seq_length, self.num_labels]   )







      def 						UpperCAmelCase_    (      self :str   )  ->      Any:
       UpperCAmelCase__							=    self.prepare_config_and_inputs()
       (
           (
           UpperCAmelCase__
       )			,			(
           UpperCAmelCase__
       )			,			(
           UpperCAmelCase__
       )			,			(
           UpperCAmelCase__
       )			,			(
           UpperCAmelCase__
       )			,			(
           UpperCAmelCase__
       )			,			(
           UpperCAmelCase__
       )			,			
       )							=    config_and_inputs
       UpperCAmelCase__							=    {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
       return config, inputs_dict



@require_tf
class       _UpperCamelCase   (					lowerCAmelCase						,							lowerCAmelCase						,							unittest.TestCase		):
      UpperCAmelCase_          =		(
          (
              TFAlbertModel,
              TFAlbertForPreTraining,
              TFAlbertForMaskedLM,
              TFAlbertForSequenceClassification,
              TFAlbertForQuestionAnswering,
              TFAlbertForTokenClassification,
              TFAlbertForMultipleChoice,
          )
          if is_tf_available()
          else ()
      )
      UpperCAmelCase_          =		(
          {
              """feature-extraction""": TFAlbertModel,
              """fill-mask""": TFAlbertForMaskedLM,
              """question-answering""": TFAlbertForQuestionAnswering,
              """text-classification""": TFAlbertForSequenceClassification,
              """token-classification""": TFAlbertForTokenClassification,
              """zero-shot""": TFAlbertForSequenceClassification,
          }
          if is_tf_available()
          else {}
      )
      UpperCAmelCase_          =		False
      UpperCAmelCase_          =		False
      def 						UpperCAmelCase_    (      self :str			,   lowerCamelCase :Any			,   lowerCamelCase :Optional[int]			,   lowerCamelCase :Optional[int]=False   )  ->      str:
       UpperCAmelCase__							=    super()._prepare_for_class(lowerCamelCase			,   lowerCamelCase			,   return_labels=lowerCamelCase   )

       if return_labels:
        if model_class in get_values(lowerCamelCase   ):
         UpperCAmelCase__							=    tf.zeros(self.model_tester.batch_size			,   dtype=tf.intaa   )

       return inputs_dict
      def 						UpperCAmelCase_    (      self :List[Any]   )  ->      List[Any]:
       UpperCAmelCase__							=    TFAlbertModelTester(self   )
       UpperCAmelCase__							=    ConfigTester(self			,   config_class=lowerCamelCase			,   hidden_size=37   )
      def 						UpperCAmelCase_    (      self :Dict   )  ->      Union[str, Any]:
       self.config_tester.run_common_tests()
      def 						UpperCAmelCase_    (      self :Optional[Any]   )  ->      Tuple:
       UpperCAmelCase__							=    self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_albert_model(*lowerCamelCase   )
      def 						UpperCAmelCase_    (      self :List[Any]   )  ->      Dict:
       UpperCAmelCase__							=    self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_albert_for_pretraining(*lowerCamelCase   )
      def 						UpperCAmelCase_    (      self :List[Any]   )  ->      str:
       UpperCAmelCase__							=    self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_albert_for_masked_lm(*lowerCamelCase   )
      def 						UpperCAmelCase_    (      self :Dict   )  ->      int:
       UpperCAmelCase__							=    self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_albert_for_multiple_choice(*lowerCamelCase   )
      def 						UpperCAmelCase_    (      self :Union[str, Any]   )  ->      Dict:
       UpperCAmelCase__							=    self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_albert_for_sequence_classification(*lowerCamelCase   )
      def 						UpperCAmelCase_    (      self :str   )  ->      int:
       UpperCAmelCase__							=    self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_albert_for_question_answering(*lowerCamelCase   )







      @slow
      def 						UpperCAmelCase_    (      self :int   )  ->      str:
       for model_name in TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
        UpperCAmelCase__							=    TFAlbertModel.from_pretrained(lowerCamelCase   )
        self.assertIsNotNone(lowerCamelCase   )



@require_tf
class       _UpperCamelCase   (					unittest.TestCase		):
      @slow
      def 						UpperCAmelCase_    (      self :List[str]   )  ->      List[str]:
       UpperCAmelCase__							=    TFAlbertForPreTraining.from_pretrained("albert-base-v2"   )
       UpperCAmelCase__							=    tf.constant([[0, 1, 2, 3, 4, 5]]   )
       UpperCAmelCase__							=    model(lowerCamelCase   )[0]

       UpperCAmelCase__							=    [1, 6, 3_0000]
       self.assertEqual(output.shape			,   lowerCamelCase   )

       UpperCAmelCase__							=    tf.constant(
           [
               [
                   [4.59_56_68, 0.74_46_27_54, -1.81_81_47],
                   [4.5_95_43_47, 0.7_45_41_84, -1.8_18_82_58],
                   [4.5_95_49_05, 0.7_44_82_35, -1.8_18_23_16],
               ]
           ]   )
       tf.debugging.assert_near(output[:, :3, :3]			,   lowerCamelCase			,   atol=1e-4   )





