
"""simple docstring"""




import unittest

from transformers import AutoTokenizer, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, slow


if is_flax_available():
					import jax.numpy as jnp

					from transformers import FlaxXLMRobertaModel



@require_sentencepiece
@require_tokenizers
@require_flax
class 	lowerCAmelCase_						(unittest.TestCase  ):


				"""simple docstring"""



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



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       List[Any]           =   FlaxXLMRobertaModel.from_pretrained("""xlm-roberta-base"""    )
											SCREAMING_SNAKE_CASE__						:       str           =   AutoTokenizer.from_pretrained("""xlm-roberta-base"""    )
											SCREAMING_SNAKE_CASE__						:       int           =   """The dog is cute and lives in the garden house"""
											SCREAMING_SNAKE_CASE__						:       Any           =   jnp.array([tokenizer.encode(SCREAMING_SNAKE_CASE__    )]    )

											SCREAMING_SNAKE_CASE__						:       Dict           =   (1, 12, 7_68)  # batch_size, sequence_length, embedding_vector_dim
											SCREAMING_SNAKE_CASE__						:       Optional[int]           =   jnp.array(
											    [[-0.0101, 0.1218, -0.0803, 0.0801, 0.1327, 0.0776, -0.1215, 0.2383, 0.3338, 0.3106, 0.0300, 0.0252]]    )

											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   model(SCREAMING_SNAKE_CASE__    )["""last_hidden_state"""]
											self.assertEqual(output.shape   ,       SCREAMING_SNAKE_CASE__    )
											# compare the actual values for a slice of last dim
											self.assertTrue(jnp.allclose(output[:, :, -1]   ,       SCREAMING_SNAKE_CASE__   ,       atol=1E-3    )    )











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 					A__   (       __snake_case       ,   unittest.TestCase			):
      _UpperCAmelCase	:List[str]						    =   KandinskyInpaintPipeline
      _UpperCAmelCase	:List[str]						    =   ['prompt', 'image_embeds', 'negative_image_embeds', 'image', 'mask_image']
      _UpperCAmelCase	:Dict						    =   [
          'prompt',
          'negative_prompt',
          'image_embeds',
          'negative_image_embeds',
          'image',
          'mask_image',
      ]
      _UpperCAmelCase	:Optional[int]						    =   [
          'generator',
          'height',
          'width',
          'latents',
          'guidance_scale',
          'negative_prompt',
          'num_inference_steps',
          'return_dict',
          'guidance_scale',
          'num_images_per_prompt',
          'output_type',
          'return_dict',
      ]
      _UpperCAmelCase	:int						    =   False





      @property
      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       return 32





      @property
      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       return 32





      @property
      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       return self.time_input_dim





      @property
      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       return self.time_input_dim * 4





      @property
      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       return 100





      @property
      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       UpperCamelCase	:					Any       	=   XLMRobertaTokenizerFast.from_pretrained("YiYiXu/tiny-random-mclip-base"   )
       return tokenizer





      @property
      def        __UpperCamelCase( 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	:					Optional[int]       	=   MultilingualCLIP(A_   )
       UpperCamelCase	:					Union[str, Any]       	=   text_encoder.eval()

       return text_encoder





      @property
      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       torch.manual_seed(0   )

       UpperCamelCase	:					Optional[int]       	=   {
           "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(**A_   )
       return model





      @property
      def        __UpperCamelCase( 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        __UpperCamelCase( self   ):
       '''simple docstring'''




       torch.manual_seed(0   )
       UpperCamelCase	:					List[str]       	=   VQModel(**self.dummy_movq_kwargs   )
       return model





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       UpperCamelCase	:					Any       	=   self.dummy_text_encoder
       UpperCamelCase	:					str       	=   self.dummy_tokenizer
       UpperCamelCase	:					List[Any]       	=   self.dummy_unet
       UpperCamelCase	:					Optional[Any]       	=   self.dummy_movq

       UpperCamelCase	:					Union[str, Any]       	=   DDIMScheduler(
           num_train_timesteps=1000       ,							beta_schedule="linear"       ,							beta_start=0.0_00_85       ,							beta_end=0.0_12       ,							clip_sample=A_       ,							set_alpha_to_one=A_       ,							steps_offset=1       ,							prediction_type="epsilon"       ,							thresholding=A_       ,							)

       UpperCamelCase	:					Optional[Any]       	=   {
           "text_encoder": text_encoder,
           "tokenizer": tokenizer,
           "unet": unet,
           "scheduler": scheduler,
           "movq": movq,
       }

       return components





      def        __UpperCamelCase( self       ,							A_       ,							A_=0   ):
       '''simple docstring'''




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

       if str(A_   ).startswith("mps"   ):
        UpperCamelCase	:					int       	=   torch.manual_seed(A_   )
       else:
        UpperCamelCase	:					Tuple       	=   torch.Generator(device=A_   ).manual_seed(A_   )
       UpperCamelCase	:					Union[str, 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        __UpperCamelCase( self   ):
       '''simple docstring'''




       UpperCamelCase	:					Optional[int]       	=   "cpu"

       UpperCamelCase	:					Tuple       	=   self.get_dummy_components()

       UpperCamelCase	:					str       	=   self.pipeline_class(**A_   )
       UpperCamelCase	:					Tuple       	=   pipe.to(A_   )

       pipe.set_progress_bar_config(disable=A_   )

       UpperCamelCase	:					Any       	=   pipe(**self.get_dummy_inputs(A_   )   )
       UpperCamelCase	:					List[Any]       	=   output.images

       UpperCamelCase	:					List[Any]       	=   pipe(
           **self.get_dummy_inputs(A_   )       ,							return_dict=A_       ,							)[0]

       UpperCamelCase	:					List[Any]       	=   image[0, -3:, -3:, -1]
       UpperCamelCase	:					Any       	=   image_from_tuple[0, -3:, -3:, -1]

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

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

       UpperCamelCase	:					Union[str, Any]       	=   np.array(
           [0.8_32_69_19, 0.73_79_04_67, 0.20_91_85_81, 0.9_30_96_12, 0.5_51_17_91, 0.43_71_33_28, 0.5_51_33_21, 0.49_92_29_34, 0.59_49_77_86]   )

       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        __UpperCamelCase( self   ):
       '''simple docstring'''




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



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





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




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





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       UpperCamelCase	:					Optional[Any]       	=   load_numpy(
           "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
           "/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy"   )

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

       UpperCamelCase	:					List[Any]       	=   "a hat"

       UpperCamelCase	:					Tuple       	=   KandinskyPriorPipeline.from_pretrained(
           "kandinsky-community/kandinsky-2-1-prior"       ,							torch_dtype=torch.floataa   )
       pipe_prior.to(A_   )

       UpperCamelCase	:					Union[str, Any]       	=   KandinskyInpaintPipeline.from_pretrained(
           "kandinsky-community/kandinsky-2-1-inpaint"       ,							torch_dtype=torch.floataa   )
       UpperCamelCase	:					Optional[Any]       	=   pipeline.to(A_   )
       pipeline.set_progress_bar_config(disable=A_   )

       UpperCamelCase	:					List[Any]       	=   torch.Generator(device="cpu"   ).manual_seed(0   )
       UpperCamelCase	,    UpperCamelCase	:					Optional[Any]       	=   pipe_prior(
           A_       ,							generator=A_       ,							num_inference_steps=5       ,							negative_prompt=""       ,							).to_tuple()

       UpperCamelCase	:					Dict       	=   pipeline(
           A_       ,							image=A_       ,							mask_image=A_       ,							image_embeds=A_       ,							negative_image_embeds=A_       ,							generator=A_       ,							num_inference_steps=100       ,							height=768       ,							width=768       ,							output_type="np"       ,							)

       UpperCamelCase	:					List[str]       	=   output.images[0]

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

       assert_mean_pixel_difference(A_       ,							A_   )
