

import unittest

from transformers import MraConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_torch_available():
     import torch

     from transformers import (
         MraForMaskedLM,
         MraForMultipleChoice,
         MraForQuestionAnswering,
         MraForSequenceClassification,
         MraForTokenClassification,
         MraModel,
     )
     from transformers.models.mra.modeling_mra import MRA_PRETRAINED_MODEL_ARCHIVE_LIST


class  A						:







    """simple docstring"""





    def __init__(      self :       Optional[Any],lowercase_ :       Optional[int],lowercase_ :       Optional[int]=2,lowercase_ :       Union[str, Any]=8,lowercase_ :       int=True,lowercase_ :       Union[str, Any]=True,lowercase_ :       str=True,lowercase_ :       str=True,lowercase_ :       int=9_9,lowercase_ :       int=1_6,lowercase_ :       Union[str, Any]=5,lowercase_ :       List[Any]=2,lowercase_ :       Optional[Any]=3_6,lowercase_ :       List[Any]="gelu",lowercase_ :       str=0.0,lowercase_ :       List[str]=0.0,lowercase_ :       str=5_1_2,lowercase_ :       Optional[int]=1_6,lowercase_ :       int=2,lowercase_ :       Optional[int]=0.02,lowercase_ :       Union[str, Any]=3,lowercase_ :       Tuple=4,lowercase_ :       str=None,)->    str:





        '''simple docstring'''







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


    def 						snake_case__     (      self :       List[Any]		)->    Any:





        '''simple docstring'''







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

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

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

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

        A__          =   self.get_config()

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


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





        '''simple docstring'''







        return MraConfig(
            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=lowercase_,initializer_range=self.initializer_range,)


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





        '''simple docstring'''







        A__          =   self.get_config()
        A__          =   3_0_0
        return config


    def 						snake_case__     (      self :       List[Any]		)->    Tuple:





        '''simple docstring'''







        (
            (
            A__
        )      ,						(
            A__
        )      ,						(
            A__
        )      ,						(
            A__
        )      ,						(
            A__
        )      ,						(
            A__
        )      ,						(
            A__
        )      ,						
        )          =   self.prepare_config_and_inputs()

        A__          =   True
        A__          =   floats_tensor([self.batch_size, self.seq_length, self.hidden_size]		)
        A__          =   ids_tensor([self.batch_size, self.seq_length],vocab_size=2		)

        return (
            config,
            input_ids,
            token_type_ids,
            input_mask,
            sequence_labels,
            token_labels,
            choice_labels,
            encoder_hidden_states,
            encoder_attention_mask,
        )


    def 						snake_case__     (      self :       int,lowercase_ :       Dict,lowercase_ :       Optional[int],lowercase_ :       Tuple,lowercase_ :       List[str],lowercase_ :       Dict,lowercase_ :       Tuple,lowercase_ :       Tuple		)->    str:





        '''simple docstring'''







        A__          =   MraModel(config=lowercase_		)
        model.to(lowercase_		)
        model.eval()
        A__          =   model(lowercase_,attention_mask=lowercase_,token_type_ids=lowercase_		)
        A__          =   model(lowercase_,token_type_ids=lowercase_		)
        A__          =   model(lowercase_		)
        self.parent.assertEqual(result.last_hidden_state.shape,(self.batch_size, self.seq_length, self.hidden_size)		)


    def 						snake_case__     (      self :       Dict,lowercase_ :       Tuple,lowercase_ :       Any,lowercase_ :       Tuple,lowercase_ :       Dict,lowercase_ :       Optional[Any],lowercase_ :       Optional[Any],lowercase_ :       Dict,lowercase_ :       str,lowercase_ :       Dict,)->    List[str]:





        '''simple docstring'''







        A__          =   True
        A__          =   MraModel(lowercase_		)
        model.to(lowercase_		)
        model.eval()
        A__          =   model(
            lowercase_,attention_mask=lowercase_,token_type_ids=lowercase_,encoder_hidden_states=lowercase_,encoder_attention_mask=lowercase_,)
        A__          =   model(
            lowercase_,attention_mask=lowercase_,token_type_ids=lowercase_,encoder_hidden_states=lowercase_,)
        A__          =   model(lowercase_,attention_mask=lowercase_,token_type_ids=lowercase_		)
        self.parent.assertEqual(result.last_hidden_state.shape,(self.batch_size, self.seq_length, self.hidden_size)		)


    def 						snake_case__     (      self :       List[Any],lowercase_ :       List[str],lowercase_ :       Any,lowercase_ :       int,lowercase_ :       Optional[Any],lowercase_ :       List[str],lowercase_ :       Dict,lowercase_ :       str		)->    Optional[int]:





        '''simple docstring'''







        A__          =   MraForMaskedLM(config=lowercase_		)
        model.to(lowercase_		)
        model.eval()
        A__          =   model(lowercase_,attention_mask=lowercase_,token_type_ids=lowercase_,labels=lowercase_		)
        self.parent.assertEqual(result.logits.shape,(self.batch_size, self.seq_length, self.vocab_size)		)


    def 						snake_case__     (      self :       int,lowercase_ :       int,lowercase_ :       Optional[Any],lowercase_ :       Tuple,lowercase_ :       Dict,lowercase_ :       Union[str, Any],lowercase_ :       Dict,lowercase_ :       Optional[Any]		)->    Dict:





        '''simple docstring'''







        A__          =   MraForQuestionAnswering(config=lowercase_		)
        model.to(lowercase_		)
        model.eval()
        A__          =   model(
            lowercase_,attention_mask=lowercase_,token_type_ids=lowercase_,start_positions=lowercase_,end_positions=lowercase_,)
        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 						snake_case__     (      self :       Optional[Any],lowercase_ :       str,lowercase_ :       Union[str, Any],lowercase_ :       Union[str, Any],lowercase_ :       List[Any],lowercase_ :       Optional[Any],lowercase_ :       str,lowercase_ :       Any		)->    Optional[int]:





        '''simple docstring'''







        A__          =   self.num_labels
        A__          =   MraForSequenceClassification(lowercase_		)
        model.to(lowercase_		)
        model.eval()
        A__          =   model(lowercase_,attention_mask=lowercase_,token_type_ids=lowercase_,labels=lowercase_		)
        self.parent.assertEqual(result.logits.shape,(self.batch_size, self.num_labels)		)


    def 						snake_case__     (      self :       List[Any],lowercase_ :       List[str],lowercase_ :       Optional[Any],lowercase_ :       Union[str, Any],lowercase_ :       List[Any],lowercase_ :       Any,lowercase_ :       Optional[int],lowercase_ :       int		)->    int:





        '''simple docstring'''







        A__          =   self.num_labels
        A__          =   MraForTokenClassification(config=lowercase_		)
        model.to(lowercase_		)
        model.eval()
        A__          =   model(lowercase_,attention_mask=lowercase_,token_type_ids=lowercase_,labels=lowercase_		)
        self.parent.assertEqual(result.logits.shape,(self.batch_size, self.seq_length, self.num_labels)		)


    def 						snake_case__     (      self :       Dict,lowercase_ :       List[Any],lowercase_ :       int,lowercase_ :       Tuple,lowercase_ :       Dict,lowercase_ :       Tuple,lowercase_ :       str,lowercase_ :       List[str]		)->    List[str]:





        '''simple docstring'''







        A__          =   self.num_choices
        A__          =   MraForMultipleChoice(config=lowercase_		)
        model.to(lowercase_		)
        model.eval()
        A__          =   input_ids.unsqueeze(1		).expand(-1,self.num_choices,-1		).contiguous()
        A__          =   token_type_ids.unsqueeze(1		).expand(-1,self.num_choices,-1		).contiguous()
        A__          =   input_mask.unsqueeze(1		).expand(-1,self.num_choices,-1		).contiguous()
        A__          =   model(
            lowercase_,attention_mask=lowercase_,token_type_ids=lowercase_,labels=lowercase_,)
        self.parent.assertEqual(result.logits.shape,(self.batch_size, self.num_choices)		)


    def 						snake_case__     (      self :       Dict		)->    int:





        '''simple docstring'''







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




@require_torch
class  A						(							_UpperCAmelCase    ,							unittest.TestCase ):







    """simple docstring"""





    lowerCamelCase			=							(
        (
            MraModel,
            MraForMaskedLM,
            MraForMultipleChoice,
            MraForQuestionAnswering,
            MraForSequenceClassification,
            MraForTokenClassification,
        )
        if is_torch_available()
        else ()
    )
    lowerCamelCase			=							False
    lowerCamelCase			=							False
    lowerCamelCase			=							False
    lowerCamelCase			=							False

    lowerCamelCase			=							()


    def 						snake_case__     (      self :       Any		)->    List[Any]:





        '''simple docstring'''







        A__          =   MraModelTester(self		)
        A__          =   ConfigTester(self,config_class=lowercase_,hidden_size=3_7		)


    def 						snake_case__     (      self :       Dict		)->    List[Any]:





        '''simple docstring'''







        self.config_tester.run_common_tests()


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





        '''simple docstring'''







        A__          =   self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*lowercase_		)


    def 						snake_case__     (      self :       int		)->    int:





        '''simple docstring'''







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


    def 						snake_case__     (      self :       Optional[int]		)->    str:





        '''simple docstring'''







        A__          =   self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_masked_lm(*lowercase_		)


    def 						snake_case__     (      self :       Optional[Any]		)->    Any:





        '''simple docstring'''







        A__          =   self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_multiple_choice(*lowercase_		)


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





        '''simple docstring'''







        A__          =   self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_question_answering(*lowercase_		)


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





        '''simple docstring'''







        A__          =   self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_sequence_classification(*lowercase_		)


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





        '''simple docstring'''







        A__          =   self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_token_classification(*lowercase_		)


    @slow
    def 						snake_case__     (      self :       Any		)->    List[Any]:





        '''simple docstring'''







        for model_name in MRA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            A__          =   MraModel.from_pretrained(lowercase_		)
            self.assertIsNotNone(lowercase_		)


    @unittest.skip(reason='MRA does not output attentions'		)
    def 						snake_case__     (      self :       Tuple		)->    str:





        '''simple docstring'''







        return




@require_torch
class  A						(							unittest.TestCase ):







    """simple docstring"""





    @slow
    def 						snake_case__     (      self :       int		)->    str:





        '''simple docstring'''







        A__          =   MraModel.from_pretrained('uw-madison/mra-base-512-4'		)
        A__          =   torch.arange(2_5_6		).unsqueeze(0		)

        with torch.no_grad():
            A__          =   model(lowercase_		)[0]

        A__          =   torch.Size((1, 2_5_6, 7_6_8)		)
        self.assertEqual(output.shape,lowercase_		)

        A__          =   torch.tensor(
            [[[-0.0_140, 0.0_830, -0.0_381], [0.1_546, 0.1_402, 0.0_220], [0.1_162, 0.0_851, 0.0_165]]]		)

        self.assertTrue(torch.allclose(output[:, :3, :3],lowercase_,atol=1E-4		)		)


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





        '''simple docstring'''







        A__          =   MraForMaskedLM.from_pretrained('uw-madison/mra-base-512-4'		)
        A__          =   torch.arange(2_5_6		).unsqueeze(0		)

        with torch.no_grad():
            A__          =   model(lowercase_		)[0]

        A__          =   5_0_2_6_5

        A__          =   torch.Size((1, 2_5_6, vocab_size)		)
        self.assertEqual(output.shape,lowercase_		)

        A__          =   torch.tensor(
            [[[9.2_595, -3.6_038, 11.8_819], [9.3_869, -3.2_693, 11.0_956], [11.8_524, -3.4_938, 13.1_210]]]		)

        self.assertTrue(torch.allclose(output[:, :3, :3],lowercase_,atol=1E-4		)		)


    @slow
    def 						snake_case__     (      self :       List[str]		)->    Union[str, Any]:





        '''simple docstring'''







        A__          =   MraForMaskedLM.from_pretrained('uw-madison/mra-base-4096-8-d3'		)
        A__          =   torch.arange(4_0_9_6		).unsqueeze(0		)

        with torch.no_grad():
            A__          =   model(lowercase_		)[0]

        A__          =   5_0_2_6_5

        A__          =   torch.Size((1, 4_0_9_6, vocab_size)		)
        self.assertEqual(output.shape,lowercase_		)

        A__          =   torch.tensor(
            [[[5.4_789, -2.3_564, 7.5_064], [7.9_067, -1.3_369, 9.9_668], [9.0_712, -1.8_106, 7.0_380]]]		)

        self.assertTrue(torch.allclose(output[:, :3, :3],lowercase_,atol=1E-4		)		)




import gc
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    StableDiffusionAttendAndExcitePipeline,
    UNetaDConditionModel,
)
from diffusers.utils import load_numpy, skip_mps, slow
from diffusers.utils.testing_utils import 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 PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


lowercase_       		=  False


@skip_mps
class  A						(							_UpperCAmelCase    ,							_UpperCAmelCase    ,							_UpperCAmelCase    ,							unittest.TestCase ):







    """simple docstring"""





    lowerCamelCase			=							StableDiffusionAttendAndExcitePipeline
    lowerCamelCase			=							False
    lowerCamelCase			=							TEXT_TO_IMAGE_PARAMS
    lowerCamelCase			=							TEXT_TO_IMAGE_BATCH_PARAMS.union({'token_indices'} )
    lowerCamelCase			=							TEXT_TO_IMAGE_IMAGE_PARAMS
    lowerCamelCase			=							TEXT_TO_IMAGE_IMAGE_PARAMS


    @classmethod
    def 						snake_case__     (      cls :       Any		)->    Optional[Any]:





        '''simple docstring'''







        super().setUpClass()
        torch.use_deterministic_algorithms(lowercase_		)


    @classmethod
    def 						snake_case__     (      cls :       Optional[Any]		)->    Dict:





        '''simple docstring'''







        super().tearDownClass()
        torch.use_deterministic_algorithms(lowercase_		)


    def 						snake_case__     (      self :       List[str]		)->    int:





        '''simple docstring'''







        torch.manual_seed(0		)
        A__          =   UNetaDConditionModel(
            block_out_channels=(3_2, 6_4),layers_per_block=1,sample_size=3_2,in_channels=4,out_channels=4,down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D'),up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D'),cross_attention_dim=3_2,attention_head_dim=(2, 4),use_linear_projection=lowercase_,)
        A__          =   DDIMScheduler(
            beta_start=0.00_085,beta_end=0.012,beta_schedule='scaled_linear',clip_sample=lowercase_,set_alpha_to_one=lowercase_,)
        torch.manual_seed(0		)
        A__          =   AutoencoderKL(
            block_out_channels=[3_2, 6_4],in_channels=3,out_channels=3,down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D'],up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D'],latent_channels=4,sample_size=1_2_8,)
        torch.manual_seed(0		)
        A__          =   CLIPTextConfig(
            bos_token_id=0,eos_token_id=2,hidden_size=3_2,intermediate_size=3_7,layer_norm_eps=1E-05,num_attention_heads=4,num_hidden_layers=5,pad_token_id=1,vocab_size=1_0_0_0,hidden_act='gelu',projection_dim=5_1_2,)
        A__          =   CLIPTextModel(lowercase_		)
        A__          =   CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'		)

        A__          =   {
            'unet': unet,
            'scheduler': scheduler,
            'vae': vae,
            'text_encoder': text_encoder,
            'tokenizer': tokenizer,
            'safety_checker': None,
            'feature_extractor': None,
        }

        return components


    def 						snake_case__     (      self :       Tuple,lowercase_ :       str,lowercase_ :       List[Any]=0		)->    int:





        '''simple docstring'''







        if str(lowercase_		).startswith('mps'		):
            A__          =   torch.manual_seed(lowercase_		)
        else:
            A__          =   torch.Generator(device=lowercase_		).manual_seed(lowercase_		)
        A__          =   A__          =   {
            'prompt': 'a cat and a frog',
            'token_indices': [2, 5],
            'generator': generator,
            'num_inference_steps': 1,
            'guidance_scale': 6.0,
            'output_type': 'numpy',
            'max_iter_to_alter': 2,
            'thresholds': {0: 0.7},
        }
        return inputs


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





        '''simple docstring'''







        A__          =   'cpu'

        A__          =   self.get_dummy_components()
        A__          =   self.pipeline_class(**lowercase_		)
        pipe.to(lowercase_		)
        pipe.set_progress_bar_config(disable=lowercase_		)

        A__          =   self.get_dummy_inputs(lowercase_		)
        A__          =   pipe(**lowercase_		).images
        A__          =   image[0, -3:, -3:, -1]

        self.assertEqual(image.shape,(1, 6_4, 6_4, 3)		)
        A__          =   np.array(
            [0.63_905_364, 0.62_897_307, 0.48_599_017, 0.5_133_624, 0.5_550_048, 0.45_769_516, 0.50_326_973, 0.5_023_139, 0.45_384_496]		)
        A__          =   np.abs(image_slice.flatten() - expected_slice		).max()
        self.assertLessEqual(lowercase_,1E-3		)


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





        '''simple docstring'''







        super().test_cpu_offload_forward_pass(expected_max_diff=5E-4		)


    def 						snake_case__     (      self :       str		)->    int:





        '''simple docstring'''







        self._test_inference_batch_consistent(batch_sizes=[1, 2]		)


    def 						snake_case__     (      self :       str		)->    Optional[int]:





        '''simple docstring'''







        self._test_inference_batch_single_identical(batch_size=2,expected_max_diff=7E-4		)


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





        '''simple docstring'''







        super().test_dict_tuple_outputs_equivalent(expected_max_difference=3E-3		)


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





        '''simple docstring'''







        super().test_pt_np_pil_outputs_equivalent(expected_max_diff=5E-4		)


    def 						snake_case__     (      self :       Dict		)->    Any:





        '''simple docstring'''







        super().test_save_load_local(expected_max_difference=5E-4		)


    def 						snake_case__     (      self :       Dict		)->    List[str]:





        '''simple docstring'''







        super().test_save_load_optional_components(expected_max_difference=4E-4		)




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







    """simple docstring"""





    @classmethod
    def 						snake_case__     (      cls :       Any		)->    Optional[int]:





        '''simple docstring'''







        super().setUpClass()
        torch.use_deterministic_algorithms(lowercase_		)


    @classmethod
    def 						snake_case__     (      cls :       int		)->    List[Any]:





        '''simple docstring'''







        super().tearDownClass()
        torch.use_deterministic_algorithms(lowercase_		)


    def 						snake_case__     (      self :       List[Any]		)->    Any:





        '''simple docstring'''







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


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





        '''simple docstring'''







        A__          =   torch.manual_seed(5_1		)

        A__          =   StableDiffusionAttendAndExcitePipeline.from_pretrained(
            'CompVis/stable-diffusion-v1-4',safety_checker=lowercase_,torch_dtype=torch.floataa		)
        pipe.to('cuda'		)

        A__          =   'a painting of an elephant with glasses'
        A__          =   [5, 7]

        A__          =   pipe(
            prompt=lowercase_,token_indices=lowercase_,guidance_scale=7.5,generator=lowercase_,num_inference_steps=5,max_iter_to_alter=5,output_type='numpy',).images[0]

        A__          =   load_numpy(
            'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/attend-and-excite/elephant_glasses.npy'		)
        assert np.abs((expected_image - image).max()		) < 5E-1

