


import unittest

from transformers import AutoTokenizer, NystromformerConfig, 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, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
				import torch

				from transformers import (
				    NystromformerForMaskedLM,
				    NystromformerForMultipleChoice,
				    NystromformerForQuestionAnswering,
				    NystromformerForSequenceClassification,
				    NystromformerForTokenClassification,
				    NystromformerModel,
				)
				from transformers.models.nystromformer.modeling_nystromformer import NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST






class    _UpperCamelCase     :

				"""simple docstring"""







				def __init__(    self					, a__					, a__=13					, a__=7					, a__=True					, a__=True					, a__=True					, a__=True					, a__=99					, a__=32					, a__=5					, a__=4					, a__=37					, a__="gelu"					, a__=0.1					, a__=0.1					, a__=512					, a__=16					, a__=2					, a__=0.02					, a__=3					, a__=4					, a__=None					, )						->					str:
					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   _UpperCAmelCase       (    self )						->					Optional[int]:
					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   _UpperCAmelCase       (    self )						->					str:
					return NystromformerConfig(
					    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=_lowerCamelCase					, initializer_range=self.initializer_range					, )

				def   _UpperCAmelCase       (    self					, a__					, a__					, a__					, a__					, a__					, a__					, a__ )						->					Any:
					A             =					NystromformerModel(config=_lowerCamelCase )
					model.to(_lowerCamelCase )
					model.eval()
					A             =					model(_lowerCamelCase					, attention_mask=_lowerCamelCase					, token_type_ids=_lowerCamelCase )
					A             =					model(_lowerCamelCase					, token_type_ids=_lowerCamelCase )
					A             =					model(_lowerCamelCase )
					self.parent.assertEqual(result.last_hidden_state.shape					, (self.batch_size, self.seq_length, self.hidden_size) )

				def   _UpperCAmelCase       (    self					, a__					, a__					, a__					, a__					, a__					, a__					, a__ )						->					List[Any]:
					A             =					NystromformerForMaskedLM(config=_lowerCamelCase )
					model.to(_lowerCamelCase )
					model.eval()
					A             =					model(_lowerCamelCase					, attention_mask=_lowerCamelCase					, token_type_ids=_lowerCamelCase					, labels=_lowerCamelCase )
					self.parent.assertEqual(result.logits.shape					, (self.batch_size, self.seq_length, self.vocab_size) )

				def   _UpperCAmelCase       (    self					, a__					, a__					, a__					, a__					, a__					, a__					, a__ )						->					Union[str, Any]:
					A             =					NystromformerForQuestionAnswering(config=_lowerCamelCase )
					model.to(_lowerCamelCase )
					model.eval()
					A             =					model(
					    _lowerCamelCase					, attention_mask=_lowerCamelCase					, token_type_ids=_lowerCamelCase					, start_positions=_lowerCamelCase					, end_positions=_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					, a__					, a__					, a__					, a__					, a__					, a__					, a__ )						->					Optional[int]:
					A             =					self.num_labels
					A             =					NystromformerForSequenceClassification(_lowerCamelCase )
					model.to(_lowerCamelCase )
					model.eval()
					A             =					model(_lowerCamelCase					, attention_mask=_lowerCamelCase					, token_type_ids=_lowerCamelCase					, labels=_lowerCamelCase )
					self.parent.assertEqual(result.logits.shape					, (self.batch_size, self.num_labels) )

				def   _UpperCAmelCase       (    self					, a__					, a__					, a__					, a__					, a__					, a__					, a__ )						->					Tuple:
					A             =					self.num_labels
					A             =					NystromformerForTokenClassification(config=_lowerCamelCase )
					model.to(_lowerCamelCase )
					model.eval()
					A             =					model(_lowerCamelCase					, attention_mask=_lowerCamelCase					, token_type_ids=_lowerCamelCase					, labels=_lowerCamelCase )
					self.parent.assertEqual(result.logits.shape					, (self.batch_size, self.seq_length, self.num_labels) )

				def   _UpperCAmelCase       (    self					, a__					, a__					, a__					, a__					, a__					, a__					, a__ )						->					Optional[int]:
					A             =					self.num_choices
					A             =					NystromformerForMultipleChoice(config=_lowerCamelCase )
					model.to(_lowerCamelCase )
					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(
					    _lowerCamelCase					, attention_mask=_lowerCamelCase					, token_type_ids=_lowerCamelCase					, labels=_lowerCamelCase					, )
					self.parent.assertEqual(result.logits.shape					, (self.batch_size, self.num_choices) )

				def   _UpperCAmelCase       (    self )						->					Optional[int]:
					A             =					self.prepare_config_and_inputs()
					(
					    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    _UpperCamelCase     (       lowerCAmelCase__     ,		lowerCAmelCase__     ,		unittest.TestCase					):

				"""simple docstring"""







				lowerCAmelCase					=			(
				    (
				        NystromformerModel,
				        NystromformerForMaskedLM,
				        NystromformerForMultipleChoice,
				        NystromformerForQuestionAnswering,
				        NystromformerForSequenceClassification,
				        NystromformerForTokenClassification,
				    )
				    if is_torch_available()
				    else ()
				)
				lowerCAmelCase					=			(
				    {
				        'feature-extraction': NystromformerModel,
				        'fill-mask': NystromformerForMaskedLM,
				        'question-answering': NystromformerForQuestionAnswering,
				        'text-classification': NystromformerForSequenceClassification,
				        'token-classification': NystromformerForTokenClassification,
				        'zero-shot': NystromformerForSequenceClassification,
				    }
				    if is_torch_available()
				    else {}
				)
				lowerCAmelCase					=			False
				lowerCAmelCase					=			False

				def   _UpperCAmelCase       (    self )						->					Optional[Any]:
					A             =					NystromformerModelTester(self )
					A             =					ConfigTester(self					, config_class=_lowerCamelCase					, hidden_size=37 )

				def   _UpperCAmelCase       (    self )						->					List[str]:
					self.config_tester.run_common_tests()

				def   _UpperCAmelCase       (    self )						->					Optional[Any]:
					A             =					self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_model(*_lowerCamelCase )

				def   _UpperCAmelCase       (    self )						->					Optional[Any]:
					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(*_lowerCamelCase )

				def   _UpperCAmelCase       (    self )						->					Optional[int]:
					A             =					self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_masked_lm(*_lowerCamelCase )

				def   _UpperCAmelCase       (    self )						->					Optional[Any]:
					A             =					self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_multiple_choice(*_lowerCamelCase )

				def   _UpperCAmelCase       (    self )						->					List[Any]:
					A             =					self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_question_answering(*_lowerCamelCase )

				def   _UpperCAmelCase       (    self )						->					str:
					A             =					self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_sequence_classification(*_lowerCamelCase )

				def   _UpperCAmelCase       (    self )						->					int:
					A             =					self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_token_classification(*_lowerCamelCase )

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

					for model_name in NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
						A             =					NystromformerModel.from_pretrained(_lowerCamelCase )
						self.assertIsNotNone(_lowerCamelCase )






@require_torch
class    _UpperCamelCase     (       unittest.TestCase					):

				"""simple docstring"""







				@slow
				def   _UpperCAmelCase       (    self )						->					int:
					A             =					NystromformerModel.from_pretrained("""uw-madison/nystromformer-512""" )
					A             =					torch.tensor([[0, 1, 2, 3, 4, 5]] )

					with torch.no_grad():
						A             =					model(_lowerCamelCase )[0]

					A             =					torch.Size((1, 6, 768) )
					self.assertEqual(output.shape					, _lowerCamelCase )

					A             =					torch.tensor(
					    [[[-0.45_32, -0.09_36, 0.51_37], [-0.26_76, 0.06_28, 0.61_86], [-0.36_29, -0.17_26, 0.47_16]]] )

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

				@slow
				def   _UpperCAmelCase       (    self )						->					int:
					A             =					"""the [MASK] of Belgium is Brussels"""

					A             =					AutoTokenizer.from_pretrained("""uw-madison/nystromformer-512""" )
					A             =					NystromformerForMaskedLM.from_pretrained("""uw-madison/nystromformer-512""" )

					A             =					tokenizer(_lowerCamelCase					, return_tensors="""pt""" )

					with torch.no_grad():
						A             =					model(encoding.input_ids ).logits

					A             =					token_logits[:, 2, :].argmax(-1 )[0]

					self.assertEqual(tokenizer.decode(_lowerCamelCase )					, """capital""" )






"""simple docstring"""
import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyInpaintPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
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       lowerCamelCase	(       lowerCAmelCase__					,					unittest.TestCase   ):


  '''simple docstring'''







  SCREAMING_SNAKE_CASE					      =  KandinskyInpaintPipeline
  SCREAMING_SNAKE_CASE					      =  ['prompt', 'image_embeds', 'negative_image_embeds', 'image', 'mask_image']
  SCREAMING_SNAKE_CASE					      =  [
      'prompt',
      'negative_prompt',
      'image_embeds',
      'negative_image_embeds',
      'image',
      'mask_image',
  ]
  SCREAMING_SNAKE_CASE					      =  [
      'generator',
      'height',
      'width',
      'latents',
      'guidance_scale',
      'negative_prompt',
      'num_inference_steps',
      'return_dict',
      'guidance_scale',
      'num_images_per_prompt',
      'output_type',
      'return_dict',
  ]
  SCREAMING_SNAKE_CASE					      =  False



  @property
  def  _a      (self	):


        """simple docstring"""

        return 32



  @property
  def  _a      (self	):


        """simple docstring"""

        return 32



  @property
  def  _a      (self	):


        """simple docstring"""

        return self.time_input_dim



  @property
  def  _a      (self	):


        """simple docstring"""

        return self.time_input_dim * 4



  @property
  def  _a      (self	):


        """simple docstring"""

        return 100



  @property
  def  _a      (self	):


        """simple docstring"""

        UpperCAmelCase__			:							Optional[int]							=    XLMRobertaTokenizerFast.from_pretrained("""YiYiXu/tiny-random-mclip-base"""	)
        return tokenizer



  @property
  def  _a      (self	):


        """simple docstring"""

        torch.manual_seed(0	)
        UpperCAmelCase__			:							Optional[int]							=    MCLIPConfig(
            numDims=self.cross_attention_dim							,       transformerDimensions=self.text_embedder_hidden_size							,       hidden_size=self.text_embedder_hidden_size							,       intermediate_size=37							,       num_attention_heads=4							,       num_hidden_layers=5							,       vocab_size=1005							,       )

        UpperCAmelCase__			:							List[Any]							=    MultilingualCLIP(_lowerCamelCase	)
        UpperCAmelCase__			:							Optional[Any]							=    text_encoder.eval()

        return text_encoder



  @property
  def  _a      (self	):


        """simple docstring"""

        torch.manual_seed(0	)

        UpperCAmelCase__			:							Dict							=    {
            """in_channels""": 9,
            # Out channels is double in channels because predicts mean and variance
            """out_channels""": 8,
            """addition_embed_type""": """text_image""",
            """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""": """text_image_proj""",
            """cross_attention_dim""": self.cross_attention_dim,
            """attention_head_dim""": 4,
            """resnet_time_scale_shift""": """scale_shift""",
            """class_embed_type""": None,
        }

        UpperCAmelCase__			:							List[Any]							=    UNetaDConditionModel(**_lowerCamelCase	)
        return model



  @property
  def  _a      (self	):


        """simple docstring"""

        return {
            "block_out_channels": [32, 64],
            "down_block_types": ["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",
            ],
            "vq_embed_dim": 4,
        }



  @property
  def  _a      (self	):


        """simple docstring"""

        torch.manual_seed(0	)
        UpperCAmelCase__			:							Any							=    VQModel(**self.dummy_movq_kwargs	)
        return model



  def  _a      (self	):


        """simple docstring"""

        UpperCAmelCase__			:							Dict							=    self.dummy_text_encoder
        UpperCAmelCase__			:							Optional[Any]							=    self.dummy_tokenizer
        UpperCAmelCase__			:							Optional[Any]							=    self.dummy_unet
        UpperCAmelCase__			:							List[Any]							=    self.dummy_movq

        UpperCAmelCase__			:							Union[str, Any]							=    DDIMScheduler(
            num_train_timesteps=1000							,       beta_schedule="""linear"""							,       beta_start=0.00_085							,       beta_end=0.012							,       clip_sample=_lowerCamelCase							,       set_alpha_to_one=_lowerCamelCase							,       steps_offset=1							,       prediction_type="""epsilon"""							,       thresholding=_lowerCamelCase							,       )

        UpperCAmelCase__			:							Any							=    {
            """text_encoder""": text_encoder,
            """tokenizer""": tokenizer,
            """unet""": unet,
            """scheduler""": scheduler,
            """movq""": movq,
        }

        return components



  def  _a      (self							,       _lowerCamelCase							,       _lowerCamelCase=0	):


        """simple docstring"""

        UpperCAmelCase__			:							Dict							=    floats_tensor((1, self.cross_attention_dim)							,       rng=random.Random(_lowerCamelCase	)	).to(_lowerCamelCase	)
        UpperCAmelCase__			:							Optional[int]							=    floats_tensor((1, self.cross_attention_dim)							,       rng=random.Random(seed + 1	)	).to(_lowerCamelCase	)
        # create init_image
        UpperCAmelCase__			:							Optional[int]							=    floats_tensor((1, 3, 64, 64)							,       rng=random.Random(_lowerCamelCase	)	).to(_lowerCamelCase	)
        UpperCAmelCase__			:							Optional[int]							=    image.cpu().permute(0							,       2							,       3							,       1	)[0]
        UpperCAmelCase__			:							Optional[Any]							=    Image.fromarray(np.uinta(_lowerCamelCase	)	).convert("""RGB"""	).resize((256, 256)	)
        # create mask
        UpperCAmelCase__			:							List[Any]							=    np.ones((64, 64)							,       dtype=np.floataa	)
        UpperCAmelCase__			:							Optional[int]							=    0

        if str(_lowerCamelCase	).startswith("""mps"""	):
              UpperCAmelCase__			:							Tuple							=    torch.manual_seed(_lowerCamelCase	)
        else:
              UpperCAmelCase__			:							Optional[int]							=    torch.Generator(device=_lowerCamelCase	).manual_seed(_lowerCamelCase	)
        UpperCAmelCase__			:							Any							=    {
            """prompt""": """horse""",
            """image""": init_image,
            """mask_image""": mask,
            """image_embeds""": image_embeds,
            """negative_image_embeds""": negative_image_embeds,
            """generator""": generator,
            """height""": 64,
            """width""": 64,
            """num_inference_steps""": 2,
            """guidance_scale""": 4.0,
            """output_type""": """np""",
        }
        return inputs



  def  _a      (self	):


        """simple docstring"""

        UpperCAmelCase__			:							Union[str, Any]							=    """cpu"""

        UpperCAmelCase__			:							Optional[Any]							=    self.get_dummy_components()

        UpperCAmelCase__			:							Optional[Any]							=    self.pipeline_class(**_lowerCamelCase	)
        UpperCAmelCase__			:							Union[str, Any]							=    pipe.to(_lowerCamelCase	)

        pipe.set_progress_bar_config(disable=_lowerCamelCase	)

        UpperCAmelCase__			:							List[str]							=    pipe(**self.get_dummy_inputs(_lowerCamelCase	)	)
        UpperCAmelCase__			:							Optional[int]							=    output.images

        UpperCAmelCase__			:							Union[str, Any]							=    pipe(
            **self.get_dummy_inputs(_lowerCamelCase	)							,       return_dict=_lowerCamelCase							,       )[0]

        UpperCAmelCase__			:							Union[str, Any]							=    image[0, -3:, -3:, -1]
        UpperCAmelCase__			:							Optional[Any]							=    image_from_tuple[0, -3:, -3:, -1]

        print(F"""image.shape {image.shape}"""	)

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

        UpperCAmelCase__			:							Any							=    np.array(
            [0.8_326_919, 0.73_790_467, 0.20_918_581, 0.9_309_612, 0.5_511_791, 0.43_713_328, 0.5_513_321, 0.49_922_934, 0.59_497_786]	)

        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()}"""




  def  _a      (self	):


        """simple docstring"""

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




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


  '''simple docstring'''







  def  _a      (self	):


        """simple docstring"""

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




  def  _a      (self	):


        """simple docstring"""

        UpperCAmelCase__			:							str							=    load_numpy(
            """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
            """/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy"""	)

        UpperCAmelCase__			:							Union[str, Any]							=    load_image(
            """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main""" """/kandinsky/cat.png"""	)
        UpperCAmelCase__			:							List[str]							=    np.ones((768, 768)							,       dtype=np.floataa	)
        UpperCAmelCase__			:							Optional[int]							=    0

        UpperCAmelCase__			:							int							=    """a hat"""

        UpperCAmelCase__			:							List[Any]							=    KandinskyPriorPipeline.from_pretrained(
            """kandinsky-community/kandinsky-2-1-prior"""							,       torch_dtype=torch.floataa	)
        pipe_prior.to(_lowerCamelCase	)

        UpperCAmelCase__			:							str							=    KandinskyInpaintPipeline.from_pretrained(
            """kandinsky-community/kandinsky-2-1-inpaint"""							,       torch_dtype=torch.floataa	)
        UpperCAmelCase__			:							int							=    pipeline.to(_lowerCamelCase	)
        pipeline.set_progress_bar_config(disable=_lowerCamelCase	)

        UpperCAmelCase__			:							int							=    torch.Generator(device="""cpu"""	).manual_seed(0	)
        UpperCAmelCase__					,		UpperCAmelCase__			:							List[str]							=    pipe_prior(
            _lowerCamelCase							,       generator=_lowerCamelCase							,       num_inference_steps=5							,       negative_prompt=""""""							,       ).to_tuple()

        UpperCAmelCase__			:							Union[str, Any]							=    pipeline(
            _lowerCamelCase							,       image=_lowerCamelCase							,       mask_image=_lowerCamelCase							,       image_embeds=_lowerCamelCase							,       negative_image_embeds=_lowerCamelCase							,       generator=_lowerCamelCase							,       num_inference_steps=100							,       height=768							,       width=768							,       output_type="""np"""							,       )

        UpperCAmelCase__			:							Any							=    output.images[0]

        assert image.shape == (768, 768, 3)

        assert_mean_pixel_difference(_lowerCamelCase							,       _lowerCamelCase	)

