


'''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, KandinskyImgaImgPipeline, 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 		UpperCAmelCase_      (  __lowercase       ,	unittest.TestCase		):
     lowerCamelCase				:	Optional[Any]		=     KandinskyImgaImgPipeline
     lowerCamelCase				:	Tuple		=     ['''prompt''', '''image_embeds''', '''negative_image_embeds''', '''image''']
     lowerCamelCase				:	Tuple		=     [
         '''prompt''',
         '''negative_prompt''',
         '''image_embeds''',
         '''negative_image_embeds''',
         '''image''',
     ]
     lowerCamelCase				:	int		=     [
         '''generator''',
         '''height''',
         '''width''',
         '''strength''',
         '''guidance_scale''',
         '''negative_prompt''',
         '''num_inference_steps''',
         '''return_dict''',
         '''guidance_scale''',
         '''num_images_per_prompt''',
         '''output_type''',
         '''return_dict''',
     ]
     lowerCamelCase				:	Dict		=     False





     @property
     def 							__UpperCAmelCase      ( self       :      Tuple    )				->  str:
        return 3_2





     @property
     def 							__UpperCAmelCase      ( self       :      List[str]    )				->  Optional[Any]:
        return 3_2





     @property
     def 							__UpperCAmelCase      ( self       :      Any    )				->  Dict:
        return self.time_input_dim





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





     @property
     def 							__UpperCAmelCase      ( self       :      Dict    )				->  List[str]:
        return 1_0_0





     @property
     def 							__UpperCAmelCase      ( self       :      int    )				->  Tuple:
        lowerCAmelCase     =			XLMRobertaTokenizerFast.from_pretrained('YiYiXu/tiny-random-mclip-base'    )
        return tokenizer





     @property
     def 							__UpperCAmelCase      ( self       :      List[str]    )				->  Optional[Any]:
        torch.manual_seed(0    )
        lowerCAmelCase     =			MCLIPConfig(
            numDims=self.cross_attention_dim    ,      transformerDimensions=self.text_embedder_hidden_size    ,      hidden_size=self.text_embedder_hidden_size    ,      intermediate_size=3_7    ,      num_attention_heads=4    ,      num_hidden_layers=5    ,      vocab_size=1_0_0_5    ,      )

        lowerCAmelCase     =			MultilingualCLIP(UpperCAmelCase__    )
        lowerCAmelCase     =			text_encoder.eval()

        return text_encoder





     @property
     def 							__UpperCAmelCase      ( self       :      Optional[Any]    )				->  Optional[Any]:
        torch.manual_seed(0    )

        lowerCAmelCase     =			{
            'in_channels': 4,
            # 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,
        }

        lowerCAmelCase     =			UNetaDConditionModel(**UpperCAmelCase__    )
        return model





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





     @property
     def 							__UpperCAmelCase      ( self       :      int    )				->  Tuple:
        torch.manual_seed(0    )
        lowerCAmelCase     =			VQModel(**self.dummy_movq_kwargs    )
        return model





     def 							__UpperCAmelCase      ( self       :      Optional[Any]    )				->  List[str]:
        lowerCAmelCase     =			self.dummy_text_encoder
        lowerCAmelCase     =			self.dummy_tokenizer
        lowerCAmelCase     =			self.dummy_unet
        lowerCAmelCase     =			self.dummy_movq

        lowerCAmelCase     =			{
            'num_train_timesteps': 1_0_0_0,
            'beta_schedule': 'linear',
            'beta_start': 0.00_085,
            'beta_end': 0.012,
            'clip_sample': False,
            'set_alpha_to_one': False,
            'steps_offset': 0,
            'prediction_type': 'epsilon',
            'thresholding': False,
        }

        lowerCAmelCase     =			DDIMScheduler(**UpperCAmelCase__    )

        lowerCAmelCase     =			{
            'text_encoder': text_encoder,
            'tokenizer': tokenizer,
            'unet': unet,
            'scheduler': scheduler,
            'movq': movq,
        }

        return components





     def 							__UpperCAmelCase      ( self       :      List[str]    ,      UpperCAmelCase__       :      Optional[int]    ,      UpperCAmelCase__       :      List[Any]=0    )				->  Tuple:
        lowerCAmelCase     =			floats_tensor((1, self.cross_attention_dim)    ,      rng=random.Random(UpperCAmelCase__    )    ).to(UpperCAmelCase__    )
        lowerCAmelCase     =			floats_tensor((1, self.cross_attention_dim)    ,      rng=random.Random(seed + 1    )    ).to(UpperCAmelCase__    )
        # create init_image
        lowerCAmelCase     =			floats_tensor((1, 3, 6_4, 6_4)    ,      rng=random.Random(UpperCAmelCase__    )    ).to(UpperCAmelCase__    )
        lowerCAmelCase     =			image.cpu().permute(0    ,      2    ,      3    ,      1    )[0]
        lowerCAmelCase     =			Image.fromarray(np.uinta(UpperCAmelCase__    )    ).convert('RGB'    ).resize((2_5_6, 2_5_6)    )

        if str(UpperCAmelCase__    ).startswith('mps'    ):
           lowerCAmelCase     =			torch.manual_seed(UpperCAmelCase__    )
        else:
           lowerCAmelCase     =			torch.Generator(device=UpperCAmelCase__    ).manual_seed(UpperCAmelCase__    )
        lowerCAmelCase     =			{
            'prompt': 'horse',
            'image': init_image,
            'image_embeds': image_embeds,
            'negative_image_embeds': negative_image_embeds,
            'generator': generator,
            'height': 6_4,
            'width': 6_4,
            'num_inference_steps': 1_0,
            'guidance_scale': 7.0,
            'strength': 0.2,
            'output_type': 'np',
        }
        return inputs





     def 							__UpperCAmelCase      ( self       :      Dict    )				->  List[str]:
        lowerCAmelCase     =			'cpu'

        lowerCAmelCase     =			self.get_dummy_components()

        lowerCAmelCase     =			self.pipeline_class(**UpperCAmelCase__    )
        lowerCAmelCase     =			pipe.to(UpperCAmelCase__    )

        pipe.set_progress_bar_config(disable=UpperCAmelCase__    )

        lowerCAmelCase     =			pipe(**self.get_dummy_inputs(UpperCAmelCase__    )    )
        lowerCAmelCase     =			output.images

        lowerCAmelCase     =			pipe(
            **self.get_dummy_inputs(UpperCAmelCase__    )    ,      return_dict=UpperCAmelCase__    ,      )[0]

        lowerCAmelCase     =			image[0, -3:, -3:, -1]
        lowerCAmelCase     =			image_from_tuple[0, -3:, -3:, -1]

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

        lowerCAmelCase     =			np.array(
            [0.61_474_943, 0.6_073_539, 0.43_308_544, 0.5_928_269, 0.47_493_595, 0.46_755_973, 0.4_613_838, 0.45_368_797, 0.50_119_233]    )
        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]    )				->  Tuple:
        # clean up the VRAM after each test
        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()





     def 							__UpperCAmelCase      ( self       :      List[str]    )				->  List[str]:
        lowerCAmelCase     =			load_numpy(
            'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
            '/kandinsky/kandinsky_img2img_frog.npy'    )

        lowerCAmelCase     =			load_image(
            'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main' '/kandinsky/cat.png'    )
        lowerCAmelCase     =			'A red cartoon frog, 4k'

        lowerCAmelCase     =			KandinskyPriorPipeline.from_pretrained(
            'kandinsky-community/kandinsky-2-1-prior'    ,      torch_dtype=torch.floataa    )
        pipe_prior.to(UpperCAmelCase__    )

        lowerCAmelCase     =			KandinskyImgaImgPipeline.from_pretrained(
            'kandinsky-community/kandinsky-2-1'    ,      torch_dtype=torch.floataa    )
        lowerCAmelCase     =			pipeline.to(UpperCAmelCase__    )

        pipeline.set_progress_bar_config(disable=UpperCAmelCase__    )

        lowerCAmelCase     =			torch.Generator(device='cpu'    ).manual_seed(0    )
        lowerCAmelCase     ,  lowerCAmelCase     =			pipe_prior(
            UpperCAmelCase__    ,      generator=UpperCAmelCase__    ,      num_inference_steps=5    ,      negative_prompt=''    ,      ).to_tuple()

        lowerCAmelCase     =			pipeline(
            UpperCAmelCase__    ,      image=UpperCAmelCase__    ,      image_embeds=UpperCAmelCase__    ,      negative_image_embeds=UpperCAmelCase__    ,      generator=UpperCAmelCase__    ,      num_inference_steps=1_0_0    ,      height=7_6_8    ,      width=7_6_8    ,      strength=0.2    ,      output_type='np'    ,      )

        lowerCAmelCase     =			output.images[0]

        assert image.shape == (7_6_8, 7_6_8, 3)

        assert_mean_pixel_difference(UpperCAmelCase__    ,      UpperCAmelCase__    )









'''simple docstring'''




import unittest

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


if is_torch_available():
       import torch

       from transformers import XLMRobertaModel

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





     @slow
     def 							__UpperCAmelCase      ( self       :      str    )				->  List[str]:
        lowerCAmelCase     =			XLMRobertaModel.from_pretrained('xlm-roberta-base'    )
        lowerCAmelCase     =			torch.tensor([[0, 5_8_1, 1_0_2_6_9, 8_3, 9_9_9_4_2, 1_3_6, 6_0_7_4_2, 2_3, 7_0, 8_0_5_8_3, 1_8_2_7_6, 2]]    )
        # The dog is cute and lives in the garden house

        lowerCAmelCase     =			torch.Size((1, 1_2, 7_6_8)    )  # batch_size, sequence_length, embedding_vector_dim
        lowerCAmelCase     =			torch.tensor(
            [[-0.0_101, 0.1_218, -0.0_803, 0.0_801, 0.1_327, 0.0_776, -0.1_215, 0.2_383, 0.3_338, 0.3_106, 0.0_300, 0.0_252]]    )
        #  xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')
        #  xlmr.eval()
        #  expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1]
        with torch.no_grad():
           lowerCAmelCase     =			model(UpperCAmelCase__    )['last_hidden_state'].detach()
        self.assertEqual(output.shape    ,      UpperCAmelCase__    )
        # compare the actual values for a slice of last dim
        self.assertTrue(torch.allclose(output[:, :, -1]    ,      UpperCAmelCase__    ,      atol=1E-3    )    )





     @slow
     def 							__UpperCAmelCase      ( self       :      List[Any]    )				->  Tuple:
        lowerCAmelCase     =			XLMRobertaModel.from_pretrained('xlm-roberta-large'    )
        lowerCAmelCase     =			torch.tensor([[0, 5_8_1, 1_0_2_6_9, 8_3, 9_9_9_4_2, 1_3_6, 6_0_7_4_2, 2_3, 7_0, 8_0_5_8_3, 1_8_2_7_6, 2]]    )
        # The dog is cute and lives in the garden house

        lowerCAmelCase     =			torch.Size((1, 1_2, 1_0_2_4)    )  # batch_size, sequence_length, embedding_vector_dim
        lowerCAmelCase     =			torch.tensor(
            [[-0.0_699, -0.0_318, 0.0_705, -0.1_241, 0.0_999, -0.0_520, 0.1_004, -0.1_838, -0.4_704, 0.1_437, 0.0_821, 0.0_126]]    )
        #  xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.large')
        #  xlmr.eval()
        #  expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1]
        with torch.no_grad():
           lowerCAmelCase     =			model(UpperCAmelCase__    )['last_hidden_state'].detach()
        self.assertEqual(output.shape    ,      UpperCAmelCase__    )
        # compare the actual values for a slice of last dim
        self.assertTrue(torch.allclose(output[:, :, -1]    ,      UpperCAmelCase__    ,      atol=1E-3    )    )





