





import gc
import random
import unittest

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

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

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()

class 	UpperCamelCase__				(   __lowerCAmelCase							,   __lowerCAmelCase							,   unittest.TestCase     ):



     '''simple docstring'''

     __snake_case					:			Any				=    StableDiffusionXLImgaImgPipeline
     __snake_case					:			List[str]				=    TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'''height''', '''width'''}
     __snake_case					:			List[str]				=    PipelineTesterMixin.required_optional_params - {'''latents'''}
     __snake_case					:			Union[str, Any]				=    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
     __snake_case					:			Optional[int]				=    IMAGE_TO_IMAGE_IMAGE_PARAMS
     __snake_case					:			Dict				=    IMAGE_TO_IMAGE_IMAGE_PARAMS






     def        SCREAMING_SNAKE_CASE__						(							self		:					Tuple )  ->	Any:




           '''simple docstring'''


           torch.manual_seed(0 )
           SCREAMING_SNAKE_CASE	=		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=lowerCAmelCase_		,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		,)
           SCREAMING_SNAKE_CASE	=		EulerDiscreteScheduler(
               beta_start=0.00085		,beta_end=0.012		,steps_offset=1		,beta_schedule="""scaled_linear"""		,timestep_spacing="""leading"""		,)
           torch.manual_seed(0 )
           SCREAMING_SNAKE_CASE	=		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 )
           SCREAMING_SNAKE_CASE	=		CLIPTextConfig(
               bos_token_id=0		,eos_token_id=2		,hidden_size=32		,intermediate_size=37		,layer_norm_eps=1e-0_5		,num_attention_heads=4		,num_hidden_layers=5		,pad_token_id=1		,vocab_size=1000		,hidden_act="""gelu"""		,projection_dim=32		,)
           SCREAMING_SNAKE_CASE	=		CLIPTextModel(lowerCAmelCase_ )
           SCREAMING_SNAKE_CASE	=		CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""		,local_files_only=lowerCAmelCase_ )

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

           SCREAMING_SNAKE_CASE	=		{
               """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        SCREAMING_SNAKE_CASE__						(							self		:					Dict		,lowerCamelCase__		:					List[Any]		,lowerCamelCase__		:					Union[str, Any]=0 )  ->	Optional[Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		floats_tensor((1, 3, 32, 32)		,rng=random.Random(lowerCAmelCase_ ) ).to(lowerCAmelCase_ )
           SCREAMING_SNAKE_CASE	=		image / 2 + 0.5
           if str(lowerCAmelCase_ ).startswith("""mps""" ):
                 SCREAMING_SNAKE_CASE	=		torch.manual_seed(lowerCAmelCase_ )
           else:
                 SCREAMING_SNAKE_CASE	=		torch.Generator(device=lowerCAmelCase_ ).manual_seed(lowerCAmelCase_ )
           SCREAMING_SNAKE_CASE	=		{
               """prompt""": """A painting of a squirrel eating a burger""",
               """image""": image,
               """generator""": generator,
               """num_inference_steps""": 2,
               """guidance_scale""": 5.0,
               """output_type""": """numpy""",
               """strength""": 0.75,
           }
           return inputs






     def        SCREAMING_SNAKE_CASE__						(							self		:					int )  ->	int:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		"""cpu"""  # ensure determinism for the device-dependent torch.Generator
           SCREAMING_SNAKE_CASE	=		self.get_dummy_components()
           SCREAMING_SNAKE_CASE	=		StableDiffusionXLImgaImgPipeline(**lowerCAmelCase_ )
           SCREAMING_SNAKE_CASE	=		sd_pipe.to(lowerCAmelCase_ )
           sd_pipe.set_progress_bar_config(disable=lowerCAmelCase_ )

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

           assert image.shape == (1, 32, 32, 3)

           SCREAMING_SNAKE_CASE	=		np.array([0.4656, 0.4840, 0.4439, 0.6698, 0.5574, 0.4524, 0.5799, 0.5943, 0.5165] )

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






     def        SCREAMING_SNAKE_CASE__						(							self		:					Dict )  ->	str:




           '''simple docstring'''


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






     def        SCREAMING_SNAKE_CASE__						(							self		:					Tuple )  ->	Optional[int]:




           '''simple docstring'''


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






     def        SCREAMING_SNAKE_CASE__						(							self		:					Tuple )  ->	Optional[Any]:




           '''simple docstring'''


           pass






     def        SCREAMING_SNAKE_CASE__						(							self		:					Tuple )  ->	int:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		self.get_dummy_components()
           SCREAMING_SNAKE_CASE	=		StableDiffusionXLImgaImgPipeline(**lowerCAmelCase_ )
           SCREAMING_SNAKE_CASE	=		sd_pipe.to(lowerCAmelCase_ )
           SCREAMING_SNAKE_CASE	=		sd_pipe.to(lowerCAmelCase_ )
           sd_pipe.set_progress_bar_config(disable=lowerCAmelCase_ )

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

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

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

           (
               (
               SCREAMING_SNAKE_CASE						
           ),      (
               SCREAMING_SNAKE_CASE						
           ),      (
               SCREAMING_SNAKE_CASE						
           ),      (
               SCREAMING_SNAKE_CASE						
           ),      						
           )	=		sd_pipe.encode_prompt(lowerCAmelCase_		,negative_prompt=lowerCAmelCase_ )

           SCREAMING_SNAKE_CASE	=		sd_pipe(
               **lowerCAmelCase_		,prompt_embeds=lowerCAmelCase_		,negative_prompt_embeds=lowerCAmelCase_		,pooled_prompt_embeds=lowerCAmelCase_		,negative_pooled_prompt_embeds=lowerCAmelCase_		,)
           SCREAMING_SNAKE_CASE	=		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

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



     '''simple docstring'''

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




           '''simple docstring'''


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






     def        SCREAMING_SNAKE_CASE__						(							self		:					Dict		,lowerCamelCase__		:					Dict		,lowerCamelCase__		:					Optional[Any]="cpu"		,lowerCamelCase__		:					List[str]=torch.floataa		,lowerCamelCase__		:					int=0 )  ->	Tuple:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		torch.Generator(device=lowerCAmelCase_ ).manual_seed(lowerCAmelCase_ )
           SCREAMING_SNAKE_CASE	=		np.random.RandomState(lowerCAmelCase_ ).standard_normal((1, 4, 64, 64) )
           SCREAMING_SNAKE_CASE	=		torch.from_numpy(lowerCAmelCase_ ).to(device=lowerCAmelCase_		,dtype=lowerCAmelCase_ )
           SCREAMING_SNAKE_CASE	=		{
               """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        SCREAMING_SNAKE_CASE__						(							self		:					Tuple )  ->	int:




           '''simple docstring'''


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

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

           assert image.shape == (1, 512, 512, 3)
           SCREAMING_SNAKE_CASE	=		np.array([0.49493, 0.47896, 0.40798, 0.54214, 0.53212, 0.48202, 0.47656, 0.46329, 0.48506] )
           assert np.abs(image_slice - expected_slice ).max() < 7e-3









from __future__ import annotations

import inspect
import unittest

import numpy as np

from transformers import ResNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

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 TFResNetForImageClassification, TFResNetModel
      from transformers.models.resnet.modeling_tf_resnet import TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
      from PIL import Image

      from transformers import AutoImageProcessor

class 	UpperCamelCase__				:



     '''simple docstring'''

     def __init__(							self		:					str		,lowerCamelCase__		:					int		,lowerCamelCase__		:					List[str]=3		,lowerCamelCase__		:					Optional[Any]=32		,lowerCamelCase__		:					Any=3		,lowerCamelCase__		:					Any=10		,lowerCamelCase__		:					Any=[10, 20, 30, 40]		,lowerCamelCase__		:					Any=[1, 1, 2, 1]		,lowerCamelCase__		:					Union[str, Any]=True		,lowerCamelCase__		:					List[str]=True		,lowerCamelCase__		:					Union[str, Any]="relu"		,lowerCamelCase__		:					Dict=3		,lowerCamelCase__		:					Any=None		,)  ->	Union[str, Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		parent
           SCREAMING_SNAKE_CASE	=		batch_size
           SCREAMING_SNAKE_CASE	=		image_size
           SCREAMING_SNAKE_CASE	=		num_channels
           SCREAMING_SNAKE_CASE	=		embeddings_size
           SCREAMING_SNAKE_CASE	=		hidden_sizes
           SCREAMING_SNAKE_CASE	=		depths
           SCREAMING_SNAKE_CASE	=		is_training
           SCREAMING_SNAKE_CASE	=		use_labels
           SCREAMING_SNAKE_CASE	=		hidden_act
           SCREAMING_SNAKE_CASE	=		num_labels
           SCREAMING_SNAKE_CASE	=		scope
           SCREAMING_SNAKE_CASE	=		len(lowerCamelCase__ )






     def        SCREAMING_SNAKE_CASE__						(							self		:					Any )  ->	Optional[int]:




           '''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.num_labels )

           SCREAMING_SNAKE_CASE	=		self.get_config()

           return config, pixel_values, labels






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




           '''simple docstring'''


           return ResNetConfig(
               num_channels=self.num_channels		,embeddings_size=self.embeddings_size		,hidden_sizes=self.hidden_sizes		,depths=self.depths		,hidden_act=self.hidden_act		,num_labels=self.num_labels		,image_size=self.image_size		,)






     def        SCREAMING_SNAKE_CASE__						(							self		:					Dict		,lowerCamelCase__		:					Union[str, Any]		,lowerCamelCase__		:					Tuple		,lowerCamelCase__		:					List[Any] )  ->	Union[str, Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		TFResNetModel(config=lowerCamelCase__ )
           SCREAMING_SNAKE_CASE	=		model(lowerCamelCase__ )
           # expected last hidden states: B, C, H // 32, W // 32
           self.parent.assertEqual(
               result.last_hidden_state.shape		,(self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)		,)






     def        SCREAMING_SNAKE_CASE__						(							self		:					Optional[int]		,lowerCamelCase__		:					Optional[int]		,lowerCamelCase__		:					Union[str, Any]		,lowerCamelCase__		:					Dict )  ->	str:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		self.num_labels
           SCREAMING_SNAKE_CASE	=		TFResNetForImageClassification(lowerCamelCase__ )
           SCREAMING_SNAKE_CASE	=		model(lowerCamelCase__		,labels=lowerCamelCase__ )
           self.parent.assertEqual(result.logits.shape		,(self.batch_size, self.num_labels) )






     def        SCREAMING_SNAKE_CASE__						(							self		:					Optional[Any] )  ->	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__				(   lowerCAmelCase_							,   lowerCAmelCase_							,   unittest.TestCase     ):



     '''simple docstring'''

     __snake_case					:			Union[str, Any]				=    (TFResNetModel, TFResNetForImageClassification) if is_tf_available() else ()
     __snake_case					:			int				=    (
         {"feature-extraction": TFResNetModel, "image-classification": TFResNetForImageClassification}
         if is_tf_available()
         else {}
     )

     __snake_case					:			Optional[int]				=    False
     __snake_case					:			int				=    False
     __snake_case					:			Optional[Any]				=    False
     __snake_case					:			Union[str, Any]				=    False
     __snake_case					:			str				=    False






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




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		TFResNetModelTester(self )
           SCREAMING_SNAKE_CASE	=		ConfigTester(self		,config_class=lowerCamelCase__		,has_text_modality=lowerCamelCase__ )






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




           '''simple docstring'''


           self.create_and_test_config_common_properties()
           self.config_tester.create_and_test_config_to_json_string()
           self.config_tester.create_and_test_config_to_json_file()
           self.config_tester.create_and_test_config_from_and_save_pretrained()
           self.config_tester.create_and_test_config_with_num_labels()
           self.config_tester.check_config_can_be_init_without_params()
           self.config_tester.check_config_arguments_init()






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




           '''simple docstring'''


           return






     @unittest.skip(reason="""ResNet does not use inputs_embeds""" )
     def        SCREAMING_SNAKE_CASE__						(							self		:					Tuple )  ->	Any:




           '''simple docstring'''


           pass






     @unittest.skip(reason="""ResNet does not support input and output embeddings""" )
     def        SCREAMING_SNAKE_CASE__						(							self		:					Optional[int] )  ->	Optional[int]:




           '''simple docstring'''


           pass






     def        SCREAMING_SNAKE_CASE__						(							self		:					Optional[int] )  ->	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(lowerCamelCase__ )
                 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]		,lowerCamelCase__ )






     def        SCREAMING_SNAKE_CASE__						(							self		:					Optional[int] )  ->	Union[str, Any]:




           '''simple docstring'''


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






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




           '''simple docstring'''


           def check_hidden_states_output(lowerCamelCase__		:					str		,lowerCamelCase__		:					Dict		,lowerCamelCase__		:					Dict ):
                 SCREAMING_SNAKE_CASE	=		model_class(lowerCamelCase__ )
                 SCREAMING_SNAKE_CASE	=		model(**self._prepare_for_class(lowerCamelCase__		,lowerCamelCase__ ) )

                 SCREAMING_SNAKE_CASE	=		outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

                 SCREAMING_SNAKE_CASE	=		self.model_tester.num_stages
                 self.assertEqual(len(lowerCamelCase__ )		,expected_num_stages + 1 )

                 # ResNet's feature maps are of shape (batch_size, num_channels, height, width)
                 self.assertListEqual(
                     list(hidden_states[0].shape[-2:] )		,[self.model_tester.image_size // 4, self.model_tester.image_size // 4]		,)

           SCREAMING_SNAKE_CASE,      SCREAMING_SNAKE_CASE	=		self.model_tester.prepare_config_and_inputs_for_common()
           SCREAMING_SNAKE_CASE	=		["""basic""", """bottleneck"""]
           for model_class in self.all_model_classes:
                 for layer_type in layers_type:
                       SCREAMING_SNAKE_CASE	=		layer_type
                       SCREAMING_SNAKE_CASE	=		True
                       check_hidden_states_output(lowerCamelCase__		,lowerCamelCase__		,lowerCamelCase__ )

                       # check that output_hidden_states also work using config
                       del inputs_dict["output_hidden_states"]
                       SCREAMING_SNAKE_CASE	=		True

                       check_hidden_states_output(lowerCamelCase__		,lowerCamelCase__		,lowerCamelCase__ )






     def        SCREAMING_SNAKE_CASE__						(							self		:					Any )  ->	Tuple:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_image_classification(*lowerCamelCase__ )






     @slow
     def        SCREAMING_SNAKE_CASE__						(							self		:					Optional[int] )  ->	List[Any]:




           '''simple docstring'''


           for model_name in TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                 SCREAMING_SNAKE_CASE	=		TFResNetModel.from_pretrained(lowerCamelCase__ )
                 self.assertIsNotNone(lowerCamelCase__ )



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


      '''simple docstring'''


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



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



     '''simple docstring'''

     @cached_property
     def        SCREAMING_SNAKE_CASE__						(							self		:					Tuple )  ->	Dict:




           '''simple docstring'''


           return (
               AutoImageProcessor.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0] )
               if is_vision_available()
               else None
           )






     @slow
     def        SCREAMING_SNAKE_CASE__						(							self		:					List[str] )  ->	Optional[int]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		TFResNetForImageClassification.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0] )

           SCREAMING_SNAKE_CASE	=		self.default_image_processor
           SCREAMING_SNAKE_CASE	=		prepare_img()
           SCREAMING_SNAKE_CASE	=		image_processor(images=lowerCamelCase__		,return_tensors="""tf""" )

           # forward pass
           SCREAMING_SNAKE_CASE	=		model(**lowerCamelCase__ )

           # verify the logits
           SCREAMING_SNAKE_CASE	=		tf.TensorShape((1, 1000) )
           self.assertEqual(outputs.logits.shape		,lowerCamelCase__ )

           SCREAMING_SNAKE_CASE	=		tf.constant([-11.1069, -9.7877, -8.3777] )

           self.assertTrue(np.allclose(outputs.logits[0, :3].numpy()		,lowerCamelCase__		,atol=1e-4 ) )


