"""simple docstring"""





import gc
import random
import unittest

import numpy as np
import torch

from diffusers import (
    DDIMScheduler,
    KandinskyVaaControlnetPipeline,
    KandinskyVaaPriorPipeline,
    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   UpperCAmelCase_			(					_a				,							unittest.TestCase):
      lowerCamelCase__	:					List[str]										=						KandinskyVaaControlnetPipeline
      lowerCamelCase__	:					List[str]										=						["image_embeds", "negative_image_embeds", "hint"]
      lowerCamelCase__	:					Tuple										=						["image_embeds", "negative_image_embeds", "hint"]
      lowerCamelCase__	:					List[str]										=						[
          "generator",
          "height",
          "width",
          "latents",
          "guidance_scale",
          "num_inference_steps",
          "return_dict",
          "guidance_scale",
          "num_images_per_prompt",
          "output_type",
          "return_dict",
      ]
      lowerCamelCase__	:					Optional[Any]										=						False


      @property
      def 	_UpperCAmelCase (     self							) ->      Union[str, Any]:
             return 3_2


      @property
      def 	_UpperCAmelCase (     self							) ->      Optional[int]:
             return 3_2


      @property
      def 	_UpperCAmelCase (     self							) ->      Union[str, Any]:
             return self.time_input_dim


      @property
      def 	_UpperCAmelCase (     self							) ->      Union[str, Any]:
             return self.time_input_dim * 4


      @property
      def 	_UpperCAmelCase (     self							) ->      Any:
             return 1_0_0


      @property
      def 	_UpperCAmelCase (     self							) ->      str:
             torch.manual_seed(0							)

             lowercase__    :	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,
             }

             lowercase__    :	List[Any]     					=			UNetaDConditionModel(**a							)
             return model


      @property
      def 	_UpperCAmelCase (     self							) ->      List[Any]:
             return {
                 "block_out_channels": [3_2, 3_2, 6_4, 6_4],
                 "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": 1_2,
                 "out_channels": 3,
                 "up_block_types": ["AttnUpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D"],
                 "vq_embed_dim": 4,
             }


      @property
      def 	_UpperCAmelCase (     self							) ->      str:
             torch.manual_seed(0							)
             lowercase__    :	Tuple     					=			VQModel(**self.dummy_movq_kwargs							)
             return model


      def 	_UpperCAmelCase (     self							) ->      int:
             lowercase__    :	Tuple     					=			self.dummy_unet
             lowercase__    :	Tuple     					=			self.dummy_movq

             lowercase__    :	Dict     					=			DDIMScheduler(
                 num_train_timesteps=1_0_0_0	,    beta_schedule='linear'	,    beta_start=0.00_085	,    beta_end=0.012	,    clip_sample=a	,    set_alpha_to_one=a	,    steps_offset=1	,    prediction_type='epsilon'	,    thresholding=a	,    )

             lowercase__    :	Any     					=			{
                 'unet': unet,
                 'scheduler': scheduler,
                 'movq': movq,
             }
             return components


      def 	_UpperCAmelCase (     self	,    a	,    a=0							) ->      List[str]:
             lowercase__    :	Tuple     					=			floats_tensor((1, self.text_embedder_hidden_size)	,    rng=random.Random(a							)							).to(a							)
             lowercase__    :	Any     					=			floats_tensor((1, self.text_embedder_hidden_size)	,    rng=random.Random(seed + 1							)							).to(
                 a							)

             # create hint
             lowercase__    :	Union[str, Any]     					=			floats_tensor((1, 3, 6_4, 6_4)	,    rng=random.Random(a							)							).to(a							)

             if str(a							).startswith('mps'							):
                    lowercase__    :	str     					=			torch.manual_seed(a							)
             else:
                    lowercase__    :	str     					=			torch.Generator(device=a							).manual_seed(a							)
             lowercase__    :	List[Any]     					=			{
                 'image_embeds': image_embeds,
                 'negative_image_embeds': negative_image_embeds,
                 'hint': hint,
                 'generator': generator,
                 'height': 6_4,
                 'width': 6_4,
                 'guidance_scale': 4.0,
                 'num_inference_steps': 2,
                 'output_type': 'np',
             }
             return inputs


      def 	_UpperCAmelCase (     self							) ->      List[str]:
             lowercase__    :	List[str]     					=			'cpu'

             lowercase__    :	Any     					=			self.get_dummy_components()

             lowercase__    :	int     					=			self.pipeline_class(**a							)
             lowercase__    :	str     					=			pipe.to(a							)

             pipe.set_progress_bar_config(disable=a							)

             lowercase__    :	Tuple     					=			pipe(**self.get_dummy_inputs(a							)							)
             lowercase__    :	Optional[Any]     					=			output.images

             lowercase__    :	Dict     					=			pipe(
                 **self.get_dummy_inputs(a							)	,    return_dict=a	,    )[0]

             lowercase__    :	Dict     					=			image[0, -3:, -3:, -1]
             lowercase__    :	Any     					=			image_from_tuple[0, -3:, -3:, -1]

             assert image.shape == (1, 6_4, 6_4, 3)

             lowercase__    :	str     					=			np.array(
                 [0.6_959_826, 0.868_279, 0.7_558_092, 0.68_769_467, 0.85_805_804, 0.65_977_496, 0.44_885_302, 0.5_959_111, 0.4_251_595]							)

             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   UpperCAmelCase_			(					unittest.TestCase):


      def 	_UpperCAmelCase (     self							) ->      List[str]:
             # clean up the VRAM after each test
             super().tearDown()
             gc.collect()
             torch.cuda.empty_cache()


      def 	_UpperCAmelCase (     self							) ->      Tuple:
             lowercase__    :	str     					=			load_numpy(
                 'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
                 '/kandinskyv22/kandinskyv22_controlnet_robotcat_fp16.npy'							)

             lowercase__    :	Tuple     					=			load_image(
                 'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
                 '/kandinskyv22/hint_image_cat.png'							)
             lowercase__    :	Optional[int]     					=			torch.from_numpy(np.array(a							)							).float() / 255.0
             lowercase__    :	str     					=			hint.permute(2	,    0	,    1							).unsqueeze(0							)

             lowercase__    :	str     					=			KandinskyVaaPriorPipeline.from_pretrained(
                 'kandinsky-community/kandinsky-2-2-prior'	,    torch_dtype=torch.floataa							)
             pipe_prior.to(a							)

             lowercase__    :	Optional[Any]     					=			KandinskyVaaControlnetPipeline.from_pretrained(
                 'kandinsky-community/kandinsky-2-2-controlnet-depth'	,    torch_dtype=torch.floataa							)
             lowercase__    :	str     					=			pipeline.to(a							)
             pipeline.set_progress_bar_config(disable=a							)

             lowercase__    :	Optional[int]     					=			'A robot, 4k photo'

             lowercase__    :	int     					=			torch.Generator(device='cuda'							).manual_seed(0							)
             lowercase__    :	Optional[int]     					=			pipe_prior(
                 a	,    generator=a	,    num_inference_steps=5	,    negative_prompt=''	,    ).to_tuple()

             lowercase__    :	Tuple     					=			torch.Generator(device='cuda'							).manual_seed(0							)
             lowercase__    :	Dict     					=			pipeline(
                 image_embeds=a	,    negative_image_embeds=a	,    hint=a	,    generator=a	,    num_inference_steps=1_0_0	,    output_type='np'	,    )

             lowercase__    :	Dict     					=			output.images[0]

             assert image.shape == (5_1_2, 5_1_2, 3)

             assert_mean_pixel_difference(a	,    a							)



"""simple docstring"""





import unittest

import numpy as np

from transformers import AlbertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
       import jax.numpy as jnp

       from transformers.models.albert.modeling_flax_albert import (
           FlaxAlbertForMaskedLM,
           FlaxAlbertForMultipleChoice,
           FlaxAlbertForPreTraining,
           FlaxAlbertForQuestionAnswering,
           FlaxAlbertForSequenceClassification,
           FlaxAlbertForTokenClassification,
           FlaxAlbertModel,
       )


class   UpperCAmelCase_			(					unittest.TestCase):


      def __init__(     self	,    a	,    a=1_3	,    a=7	,    a=True	,    a=True	,    a=True	,    a=True	,    a=9_9	,    a=3_2	,    a=5	,    a=4	,    a=3_7	,    a="gelu"	,    a=0.1	,    a=0.1	,    a=5_1_2	,    a=1_6	,    a=2	,    a=0.02	,    a=4	,    ) ->      Dict:
             lowercase__    :	Optional[Any]     					=			parent
             lowercase__    :	Dict     					=			batch_size
             lowercase__    :	List[Any]     					=			seq_length
             lowercase__    :	int     					=			is_training
             lowercase__    :	str     					=			use_attention_mask
             lowercase__    :	Dict     					=			use_token_type_ids
             lowercase__    :	Optional[int]     					=			use_labels
             lowercase__    :	Tuple     					=			vocab_size
             lowercase__    :	List[str]     					=			hidden_size
             lowercase__    :	Union[str, Any]     					=			num_hidden_layers
             lowercase__    :	int     					=			num_attention_heads
             lowercase__    :	Dict     					=			intermediate_size
             lowercase__    :	List[str]     					=			hidden_act
             lowercase__    :	Dict     					=			hidden_dropout_prob
             lowercase__    :	Tuple     					=			attention_probs_dropout_prob
             lowercase__    :	List[str]     					=			max_position_embeddings
             lowercase__    :	int     					=			type_vocab_size
             lowercase__    :	List[str]     					=			type_sequence_label_size
             lowercase__    :	Union[str, Any]     					=			initializer_range
             lowercase__    :	Optional[int]     					=			num_choices


      def 	_UpperCAmelCase (     self							) ->      Tuple:
             lowercase__    :	List[Any]     					=			ids_tensor([self.batch_size, self.seq_length]	,    self.vocab_size							)

             lowercase__    :	str     					=			None
             if self.use_attention_mask:
                    lowercase__    :	Optional[Any]     					=			random_attention_mask([self.batch_size, self.seq_length]							)

             lowercase__    :	List[str]     					=			None
             if self.use_token_type_ids:
                    lowercase__    :	Optional[int]     					=			ids_tensor([self.batch_size, self.seq_length]	,    self.type_vocab_size							)

             lowercase__    :	Any     					=			AlbertConfig(
                 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=a	,    initializer_range=self.initializer_range	,    )

             return config, input_ids, token_type_ids, attention_mask


      def 	_UpperCAmelCase (     self							) ->      Any:
             lowercase__    :	Optional[int]     					=			self.prepare_config_and_inputs()
             lowercase__					,   lowercase__					,   lowercase__					,   lowercase__    :	Tuple     					=			config_and_inputs
             lowercase__    :	Union[str, Any]     					=			{'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': attention_mask}
             return config, inputs_dict





@require_flax
class   UpperCAmelCase_			(					_a				,							unittest.TestCase):
      lowerCamelCase__	:					Tuple										=						(
          (
              FlaxAlbertModel,
              FlaxAlbertForPreTraining,
              FlaxAlbertForMaskedLM,
              FlaxAlbertForMultipleChoice,
              FlaxAlbertForQuestionAnswering,
              FlaxAlbertForSequenceClassification,
              FlaxAlbertForTokenClassification,
              FlaxAlbertForQuestionAnswering,
          )
          if is_flax_available()
          else ()
      )


      def 	_UpperCAmelCase (     self							) ->      List[Any]:
             lowercase__    :	Union[str, Any]     					=			FlaxAlbertModelTester(self							)


      @slow
      def 	_UpperCAmelCase (     self							) ->      str:
             for model_class_name in self.all_model_classes:
                    lowercase__    :	str     					=			model_class_name.from_pretrained('albert-base-v2'							)
                    lowercase__    :	Tuple     					=			model(np.ones((1, 1)							)							)
                    self.assertIsNotNone(a							)





@require_flax
class   UpperCAmelCase_			(					unittest.TestCase):


      @slow
      def 	_UpperCAmelCase (     self							) ->      Tuple:
             lowercase__    :	str     					=			FlaxAlbertModel.from_pretrained('albert-base-v2'							)
             lowercase__    :	Optional[int]     					=			np.array([[0, 3_4_5, 2_3_2, 3_2_8, 7_4_0, 1_4_0, 1_6_9_5, 6_9, 6_0_7_8, 1_5_8_8, 2]]							)
             lowercase__    :	Optional[Any]     					=			np.array([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]							)
             lowercase__    :	Any     					=			model(a	,    attention_mask=a							)[0]
             lowercase__    :	Tuple     					=			(1, 1_1, 7_6_8)
             self.assertEqual(output.shape	,    a							)
             lowercase__    :	Optional[Any]     					=			np.array(
                 [[[-0.6_513, 1.5_035, -0.2_766], [-0.6_515, 1.5_046, -0.2_780], [-0.6_512, 1.5_049, -0.2_784]]]							)

             self.assertTrue(jnp.allclose(output[:, 1:4, 1:4]	,    a	,    atol=1e-4							)							)

