



import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaControlnetImgaImgPipeline,
    KandinskyVaaPriorEmbaEmbPipeline,
    UNetaDConditionModel,
    VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()






class SCREAMING_SNAKE_CASE__     (		A__ ,						unittest.TestCase    ):




    '''simple docstring'''






    __lowerCamelCase			: int					=      KandinskyVaaControlnetImgaImgPipeline
    __lowerCamelCase			: Optional[int]					=      ["image_embeds", "negative_image_embeds", "image", "hint"]
    __lowerCamelCase			: Tuple					=      ["image_embeds", "negative_image_embeds", "image", "hint"]
    __lowerCamelCase			: List[str]					=      [
        "generator",
        "height",
        "width",
        "strength",
        "guidance_scale",
        "num_inference_steps",
        "return_dict",
        "guidance_scale",
        "num_images_per_prompt",
        "output_type",
        "return_dict",
    ]
    __lowerCamelCase			: Union[str, Any]					=      False






    @property
    def 			_lowerCAmelCase					(       self		):
        return 32






    @property
    def 			_lowerCAmelCase					(       self		):
        return 32






    @property
    def 			_lowerCAmelCase					(       self		):
        return self.time_input_dim






    @property
    def 			_lowerCAmelCase					(       self		):
        return self.time_input_dim * 4






    @property
    def 			_lowerCAmelCase					(       self		):
        return 100






    @property
    def 			_lowerCAmelCase					(       self		):
        torch.manual_seed(0		)

        A :       List[str]          =   {
            "in_channels": 8,
            # Out channels is double in channels because predicts mean and variance
            "out_channels": 8,
            "addition_embed_type": "image_hint",
            "down_block_types": ("ResnetDownsampleBlock2D", "SimpleCrossAttnDownBlock2D"),
            "up_block_types": ("SimpleCrossAttnUpBlock2D", "ResnetUpsampleBlock2D"),
            "mid_block_type": "UNetMidBlock2DSimpleCrossAttn",
            "block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
            "layers_per_block": 1,
            "encoder_hid_dim": self.text_embedder_hidden_size,
            "encoder_hid_dim_type": "image_proj",
            "cross_attention_dim": self.cross_attention_dim,
            "attention_head_dim": 4,
            "resnet_time_scale_shift": "scale_shift",
            "class_embed_type": None,
        }

        A :       Optional[int]          =   UNetaDConditionModel(**_A		)
        return model






    @property
    def 			_lowerCAmelCase					(       self		):
        return {
            "block_out_channels": [32, 32, 64, 64],
            "down_block_types": [
                "DownEncoderBlock2D",
                "DownEncoderBlock2D",
                "DownEncoderBlock2D",
                "AttnDownEncoderBlock2D",
            ],
            "in_channels": 3,
            "latent_channels": 4,
            "layers_per_block": 1,
            "norm_num_groups": 8,
            "norm_type": "spatial",
            "num_vq_embeddings": 12,
            "out_channels": 3,
            "up_block_types": ["AttnUpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D"],
            "vq_embed_dim": 4,
        }






    @property
    def 			_lowerCAmelCase					(       self		):
        torch.manual_seed(0		)
        A :       Optional[Any]          =   VQModel(**self.dummy_movq_kwargs		)
        return model






    def 			_lowerCAmelCase					(       self		):
        A :       Tuple          =   self.dummy_unet
        A :       Dict          =   self.dummy_movq

        A :       List[str]          =   {
            "num_train_timesteps": 1000,
            "beta_schedule": "linear",
            "beta_start": 0.0_0085,
            "beta_end": 0.012,
            "clip_sample": False,
            "set_alpha_to_one": False,
            "steps_offset": 0,
            "prediction_type": "epsilon",
            "thresholding": False,
        }

        A :       Optional[Any]          =   DDIMScheduler(**_A		)

        A :       Optional[Any]          =   {
            "unet": unet,
            "scheduler": scheduler,
            "movq": movq,
        }

        return components






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__,		lowerCamelCase__=0		):
        A :       str          =   floats_tensor((1, self.text_embedder_hidden_size),		rng=random.Random(_A		)		).to(_A		)
        A :       str          =   floats_tensor((1, self.text_embedder_hidden_size),		rng=random.Random(seed + 1		)		).to(
            _A		)
        # create init_image
        A :       Union[str, Any]          =   floats_tensor((1, 3, 64, 64),		rng=random.Random(_A		)		).to(_A		)
        A :       Tuple          =   image.cpu().permute(0,		2,		3,		1		)[0]
        A :       Any          =   Image.fromarray(np.uinta(_A		)		).convert("""RGB"""		).resize((256, 256)		)
        # create hint
        A :       str          =   floats_tensor((1, 3, 64, 64),		rng=random.Random(_A		)		).to(_A		)

        if str(_A		).startswith("""mps"""		):
            A :       Optional[int]          =   torch.manual_seed(_A		)
        else:
            A :       str          =   torch.Generator(device=_A		).manual_seed(_A		)
        A :       Tuple          =   {
            "image": init_image,
            "image_embeds": image_embeds,
            "negative_image_embeds": negative_image_embeds,
            "hint": hint,
            "generator": generator,
            "height": 64,
            "width": 64,
            "num_inference_steps": 10,
            "guidance_scale": 7.0,
            "strength": 0.2,
            "output_type": "np",
        }
        return inputs






    def 			_lowerCAmelCase					(       self		):
        A :       Dict          =   "cpu"

        A :       Dict          =   self.get_dummy_components()

        A :       List[Any]          =   self.pipeline_class(**_A		)
        A :       Optional[int]          =   pipe.to(_A		)

        pipe.set_progress_bar_config(disable=_A		)

        A :       Optional[int]          =   pipe(**self.get_dummy_inputs(_A		)		)
        A :       Tuple          =   output.images

        A :       int          =   pipe(
            **self.get_dummy_inputs(_A		),		return_dict=_A,		)[0]

        A :       Tuple          =   image[0, -3:, -3:, -1]
        A :       str          =   image_from_tuple[0, -3:, -3:, -1]

        assert image.shape == (1, 64, 64, 3)

        A :       Dict          =   np.array(
            [0.5498_5034, 0.5550_9365, 0.5256_1504, 0.557_0494, 0.559_3818, 0.526_3979, 0.5028_5643, 0.506_9846, 0.5119_6736]		)
        assert (
            np.abs(image_slice.flatten() - expected_slice		).max() < 1e-2
        ), f''' expected_slice {expected_slice}, but got {image_slice.flatten()}'''
        assert (
            np.abs(image_from_tuple_slice.flatten() - expected_slice		).max() < 1e-2
        ), f''' expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}'''






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




    '''simple docstring'''






    def 			_lowerCAmelCase					(       self		):
        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()






    def 			_lowerCAmelCase					(       self		):
        A :       Dict          =   load_numpy(
            """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
            """/kandinskyv22/kandinskyv22_controlnet_img2img_robotcat_fp16.npy"""		)

        A :       Any          =   load_image(
            """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main""" """/kandinsky/cat.png"""		)
        A :       int          =   init_image.resize((512, 512)		)

        A :       List[Any]          =   load_image(
            """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
            """/kandinskyv22/hint_image_cat.png"""		)
        A :       Tuple          =   torch.from_numpy(np.array(_A		)		).float() / 255.0
        A :       str          =   hint.permute(2,		0,		1		).unsqueeze(0		)

        A :       int          =   "A robot, 4k photo"

        A :       List[Any]          =   KandinskyVaaPriorEmbaEmbPipeline.from_pretrained(
            """kandinsky-community/kandinsky-2-2-prior""",		torch_dtype=torch.floataa		)
        pipe_prior.to(_A		)

        A :       Union[str, Any]          =   KandinskyVaaControlnetImgaImgPipeline.from_pretrained(
            """kandinsky-community/kandinsky-2-2-controlnet-depth""",		torch_dtype=torch.floataa		)
        A :       Union[str, Any]          =   pipeline.to(_A		)

        pipeline.set_progress_bar_config(disable=_A		)

        A :       Dict          =   torch.Generator(device="""cpu"""		).manual_seed(0		)

        A :       int          =   pipe_prior(
            _A,		image=_A,		strength=0.85,		generator=_A,		negative_prompt="""""",		).to_tuple()

        A :       str          =   pipeline(
            image=_A,		image_embeds=_A,		negative_image_embeds=_A,		hint=_A,		generator=_A,		num_inference_steps=100,		height=512,		width=512,		strength=0.5,		output_type="""np""",		)

        A :       Tuple          =   output.images[0]

        assert image.shape == (512, 512, 3)

        assert_mean_pixel_difference(_A,		_A		)



import unittest

from transformers import SqueezeBertConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device

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 (
		    SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    SqueezeBertForMaskedLM,
		    SqueezeBertForMultipleChoice,
		    SqueezeBertForQuestionAnswering,
		    SqueezeBertForSequenceClassification,
		    SqueezeBertForTokenClassification,
		    SqueezeBertModel,
		)






class 		a__						(					A__					):

					def __init__(    self      :       Tuple,_A      :       Optional[int],_A      :       Any=13,_A      :       List[str]=7,_A      :       int=True,_A      :       Dict=True,_A      :       Dict=False,_A      :       List[Any]=True,_A      :       Any=99,_A      :       Optional[int]=32,_A      :       Any=5,_A      :       List[Any]=4,_A      :       Dict=64,_A      :       Optional[Any]="gelu",_A      :       Tuple=0.1,_A      :       Any=0.1,_A      :       List[Any]=512,_A      :       Dict=16,_A      :       Optional[Any]=2,_A      :       Union[str, Any]=0.02,_A      :       List[str]=3,_A      :       Optional[Any]=4,_A      :       Union[str, Any]=None,_A      :       Tuple=2,_A      :       List[str]=2,_A      :       str=2,_A      :       Dict=2,_A      :       Optional[Any]=4,_A      :       Union[str, Any]=1,):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    parent
							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    batch_size
							SCREAMING_SNAKE_CASE_  :		Dict				      =    seq_length
							SCREAMING_SNAKE_CASE_  :		Dict				      =    is_training
							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    use_input_mask
							SCREAMING_SNAKE_CASE_  :		int				      =    use_token_type_ids
							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    use_labels
							SCREAMING_SNAKE_CASE_  :		Tuple				      =    vocab_size
							SCREAMING_SNAKE_CASE_  :		Any				      =    hidden_size
							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    num_hidden_layers
							SCREAMING_SNAKE_CASE_  :		Tuple				      =    num_attention_heads
							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    intermediate_size
							SCREAMING_SNAKE_CASE_  :		List[str]				      =    hidden_act
							SCREAMING_SNAKE_CASE_  :		List[str]				      =    hidden_dropout_prob
							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    attention_probs_dropout_prob
							SCREAMING_SNAKE_CASE_  :		str				      =    max_position_embeddings
							SCREAMING_SNAKE_CASE_  :		str				      =    type_vocab_size
							SCREAMING_SNAKE_CASE_  :		List[str]				      =    type_sequence_label_size
							SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    initializer_range
							SCREAMING_SNAKE_CASE_  :		Tuple				      =    num_labels
							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    num_choices
							SCREAMING_SNAKE_CASE_  :		Dict				      =    scope
							SCREAMING_SNAKE_CASE_  :		int				      =    q_groups
							SCREAMING_SNAKE_CASE_  :		Tuple				      =    k_groups
							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    v_groups
							SCREAMING_SNAKE_CASE_  :		Tuple				      =    post_attention_groups
							SCREAMING_SNAKE_CASE_  :		int				      =    intermediate_groups
							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    output_groups

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







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    ids_tensor([self.batch_size, self.seq_length],self.vocab_size    )

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

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

							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    self.get_config()

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

					def 				__UpperCamelCase   (    self      :       str    ):







							"""simple docstring"""






							return SqueezeBertConfig(
							    embedding_size=self.hidden_size,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,attention_probs_dropout_prob=self.hidden_dropout_prob,attention_dropout=self.attention_probs_dropout_prob,max_position_embeddings=self.max_position_embeddings,initializer_range=self.initializer_range,q_groups=self.q_groups,k_groups=self.k_groups,v_groups=self.v_groups,post_attention_groups=self.post_attention_groups,intermediate_groups=self.intermediate_groups,output_groups=self.output_groups,)

					def 				__UpperCamelCase   (    self      :       Tuple,_A      :       Union[str, Any],_A      :       Union[str, Any],_A      :       int,_A      :       Optional[int],_A      :       Union[str, Any],_A      :       Any    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    SqueezeBertModel(config=_A    )
							model.to(_A    )
							model.eval()
							SCREAMING_SNAKE_CASE_  :		Any				      =    model(_A,_A    )
							SCREAMING_SNAKE_CASE_  :		List[str]				      =    model(_A    )
							self.parent.assertEqual(result.last_hidden_state.shape,(self.batch_size, self.seq_length, self.hidden_size)    )

					def 				__UpperCamelCase   (    self      :       Dict,_A      :       Any,_A      :       Tuple,_A      :       str,_A      :       Any,_A      :       Union[str, Any],_A      :       Any    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    SqueezeBertForMaskedLM(config=_A    )
							model.to(_A    )
							model.eval()
							SCREAMING_SNAKE_CASE_  :		List[str]				      =    model(_A,attention_mask=_A,labels=_A    )
							self.parent.assertEqual(result.logits.shape,(self.batch_size, self.seq_length, self.vocab_size)    )

					def 				__UpperCamelCase   (    self      :       Optional[int],_A      :       Union[str, Any],_A      :       Union[str, Any],_A      :       Any,_A      :       Tuple,_A      :       int,_A      :       List[Any]    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    SqueezeBertForQuestionAnswering(config=_A    )
							model.to(_A    )
							model.eval()
							SCREAMING_SNAKE_CASE_  :		Dict				      =    model(
							    _A,attention_mask=_A,start_positions=_A,end_positions=_A    )
							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      :       List[Any],_A      :       List[str],_A      :       Tuple,_A      :       List[Any],_A      :       List[str],_A      :       List[str],_A      :       List[str]    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		int				      =    self.num_labels
							SCREAMING_SNAKE_CASE_  :		List[str]				      =    SqueezeBertForSequenceClassification(_A    )
							model.to(_A    )
							model.eval()
							SCREAMING_SNAKE_CASE_  :		Dict				      =    model(_A,attention_mask=_A,labels=_A    )
							self.parent.assertEqual(result.logits.shape,(self.batch_size, self.num_labels)    )

					def 				__UpperCamelCase   (    self      :       str,_A      :       Optional[int],_A      :       str,_A      :       List[Any],_A      :       List[str],_A      :       str,_A      :       Dict    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    self.num_labels
							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    SqueezeBertForTokenClassification(config=_A    )
							model.to(_A    )
							model.eval()

							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    model(_A,attention_mask=_A,labels=_A    )
							self.parent.assertEqual(result.logits.shape,(self.batch_size, self.seq_length, self.num_labels)    )

					def 				__UpperCamelCase   (    self      :       List[Any],_A      :       Tuple,_A      :       str,_A      :       Optional[Any],_A      :       int,_A      :       str,_A      :       List[Any]    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    self.num_choices
							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    SqueezeBertForMultipleChoice(config=_A    )
							model.to(_A    )
							model.eval()
							SCREAMING_SNAKE_CASE_  :		Dict				      =    input_ids.unsqueeze(1    ).expand(-1,self.num_choices,-1    ).contiguous()
							SCREAMING_SNAKE_CASE_  :		str				      =    input_mask.unsqueeze(1    ).expand(-1,self.num_choices,-1    ).contiguous()
							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    model(
							    _A,attention_mask=_A,labels=_A,)
							self.parent.assertEqual(result.logits.shape,(self.batch_size, self.num_choices)    )






					def 				__UpperCamelCase   (    self      :       Optional[int]    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    self.prepare_config_and_inputs()
							((SCREAMING_SNAKE_CASE_)      ,    (SCREAMING_SNAKE_CASE_)      ,    (SCREAMING_SNAKE_CASE_)      ,    (SCREAMING_SNAKE_CASE_)      ,    (SCREAMING_SNAKE_CASE_)      ,    (SCREAMING_SNAKE_CASE_))  :		Dict				      =    config_and_inputs
							SCREAMING_SNAKE_CASE_  :		Dict				      =    {"input_ids": input_ids, "attention_mask": input_mask}
							return config, inputs_dict







@require_torch
class 		a__						(					A__    ,		A__    ,		unittest.TestCase					):
					A	       =							(
					    (
					        SqueezeBertModel,
					        SqueezeBertForMaskedLM,
					        SqueezeBertForMultipleChoice,
					        SqueezeBertForQuestionAnswering,
					        SqueezeBertForSequenceClassification,
					        SqueezeBertForTokenClassification,
					    )
					    if is_torch_available()
					    else None
					)
					A	       =							(
					    {
					        'feature-extraction': SqueezeBertModel,
					        'fill-mask': SqueezeBertForMaskedLM,
					        'question-answering': SqueezeBertForQuestionAnswering,
					        'text-classification': SqueezeBertForSequenceClassification,
					        'token-classification': SqueezeBertForTokenClassification,
					        'zero-shot': SqueezeBertForSequenceClassification,
					    }
					    if is_torch_available()
					    else {}
					)
					A	       =							False
					A	       =							True
					A	       =							False

					def 				__UpperCamelCase   (    self      :       int    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		int				      =    SqueezeBertModelTester(self    )
							SCREAMING_SNAKE_CASE_  :		List[str]				      =    ConfigTester(self,config_class=_A,dim=37    )

					def 				__UpperCamelCase   (    self      :       List[str]    ):







							"""simple docstring"""






							self.config_tester.run_common_tests()

					def 				__UpperCamelCase   (    self      :       Tuple    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Tuple				      =    self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_squeezebert_model(*_A    )

					def 				__UpperCamelCase   (    self      :       List[Any]    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		List[str]				      =    self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_squeezebert_for_masked_lm(*_A    )

					def 				__UpperCamelCase   (    self      :       int    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Tuple				      =    self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_squeezebert_for_question_answering(*_A    )

					def 				__UpperCamelCase   (    self      :       Dict    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_squeezebert_for_sequence_classification(*_A    )

					def 				__UpperCamelCase   (    self      :       Tuple    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		int				      =    self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_squeezebert_for_token_classification(*_A    )

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







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Tuple				      =    self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_squeezebert_for_multiple_choice(*_A    )






					@slow
					def 				__UpperCamelCase   (    self      :       Any    ):







							"""simple docstring"""






							for model_name in SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									SCREAMING_SNAKE_CASE_  :		Tuple				      =    SqueezeBertModel.from_pretrained(_A    )
									self.assertIsNotNone(_A    )







@require_sentencepiece
@require_tokenizers
@require_torch
class 		a__						(					unittest.TestCase					):

					@slow
					def 				__UpperCamelCase   (    self      :       int    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		str				      =    SqueezeBertForSequenceClassification.from_pretrained("squeezebert/squeezebert-mnli"    )

							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    torch.tensor([[1, 2_9414, 232, 328, 740, 1140, 1_2695, 69, 13, 1588, 2]]    )
							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    model(_A    )[0]
							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    torch.Size((1, 3)    )
							self.assertEqual(output.shape,_A    )
							SCREAMING_SNAKE_CASE_  :		int				      =    torch.tensor([[0.6401, -0.0349, -0.6041]]    )
							self.assertTrue(torch.allclose(_A,_A,atol=1E-4    )    )
