
'''simple docstring'''

import gc
import random
import unittest

import numpy as np
import torch
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
from diffusers.utils import floats_tensor, 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     (							SCREAMING_SNAKE_CASE			,    unittest.TestCase  ):







   """simple docstring"""




   A		= KandinskyPipeline
   A		= [
       'prompt',
       'image_embeds',
       'negative_image_embeds',
   ]
   A		= ['prompt', 'negative_prompt', 'image_embeds', 'negative_image_embeds']
   A		= [
       'generator',
       'height',
       'width',
       'latents',
       'guidance_scale',
       'negative_prompt',
       'num_inference_steps',
       'return_dict',
       'guidance_scale',
       'num_images_per_prompt',
       'output_type',
       'return_dict',
   ]
   A		= False






   @property
   def 					__a			(       self      ):
    return 32






   @property
   def 					__a			(       self      ):
    return 32






   @property
   def 					__a			(       self      ):
    return self.time_input_dim






   @property
   def 					__a			(       self      ):
    return self.time_input_dim * 4






   @property
   def 					__a			(       self      ):
    return 100






   @property
   def 					__a			(       self      ):
    SCREAMING_SNAKE_CASE  :  List[Any]								=		XLMRobertaTokenizerFast.from_pretrained('YiYiXu/tiny-random-mclip-base'      )
    return tokenizer






   @property
   def 					__a			(       self      ):
    torch.manual_seed(0      )
    SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		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    ,)

    SCREAMING_SNAKE_CASE  :  Any								=		MultilingualCLIP(__SCREAMING_SNAKE_CASE      )
    SCREAMING_SNAKE_CASE  :  Optional[Any]								=		text_encoder.eval()

    return text_encoder






   @property
   def 					__a			(       self      ):
    torch.manual_seed(0      )

    SCREAMING_SNAKE_CASE  :  Tuple								=		{
        '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,
    }

    SCREAMING_SNAKE_CASE  :  List[str]								=		UNetaDConditionModel(**__SCREAMING_SNAKE_CASE      )
    return model






   @property
   def 					__a			(       self      ):
    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      ):
    torch.manual_seed(0      )
    SCREAMING_SNAKE_CASE  :  List[Any]								=		VQModel(**self.dummy_movq_kwargs      )
    return model






   def 					__a			(       self      ):
    SCREAMING_SNAKE_CASE  :  Optional[int]								=		self.dummy_text_encoder
    SCREAMING_SNAKE_CASE  :  int								=		self.dummy_tokenizer
    SCREAMING_SNAKE_CASE  :  str								=		self.dummy_unet
    SCREAMING_SNAKE_CASE  :  Dict								=		self.dummy_movq

    SCREAMING_SNAKE_CASE  :  Optional[Any]								=		DDIMScheduler(
        num_train_timesteps=1000    ,beta_schedule='linear'    ,beta_start=0.0_0085    ,beta_end=0.012    ,clip_sample=__SCREAMING_SNAKE_CASE    ,set_alpha_to_one=__SCREAMING_SNAKE_CASE    ,steps_offset=1    ,prediction_type='epsilon'    ,thresholding=__SCREAMING_SNAKE_CASE    ,)

    SCREAMING_SNAKE_CASE  :  List[Any]								=		{
        'text_encoder': text_encoder,
        'tokenizer': tokenizer,
        'unet': unet,
        'scheduler': scheduler,
        'movq': movq,
    }
    return components






   def 					__a			(       self    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE=0      ):
    SCREAMING_SNAKE_CASE  :  Optional[Any]								=		floats_tensor((1, self.cross_attention_dim)    ,rng=random.Random(__SCREAMING_SNAKE_CASE      )      ).to(__SCREAMING_SNAKE_CASE      )
    SCREAMING_SNAKE_CASE  :  List[Any]								=		floats_tensor((1, self.cross_attention_dim)    ,rng=random.Random(seed + 1      )      ).to(__SCREAMING_SNAKE_CASE      )
    if str(__SCREAMING_SNAKE_CASE      ).startswith('mps'      ):
     SCREAMING_SNAKE_CASE  :  int								=		torch.manual_seed(__SCREAMING_SNAKE_CASE      )
    else:
     SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		torch.Generator(device=__SCREAMING_SNAKE_CASE      ).manual_seed(__SCREAMING_SNAKE_CASE      )
    SCREAMING_SNAKE_CASE  :  Tuple								=		{
        'prompt': 'horse',
        'image_embeds': image_embeds,
        'negative_image_embeds': negative_image_embeds,
        'generator': generator,
        'height': 64,
        'width': 64,
        'guidance_scale': 4.0,
        'num_inference_steps': 2,
        'output_type': 'np',
    }
    return inputs






   def 					__a			(       self      ):
    SCREAMING_SNAKE_CASE  :  List[Any]								=		'cpu'

    SCREAMING_SNAKE_CASE  :  Dict								=		self.get_dummy_components()

    SCREAMING_SNAKE_CASE  :  Any								=		self.pipeline_class(**__SCREAMING_SNAKE_CASE      )
    SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		pipe.to(__SCREAMING_SNAKE_CASE      )

    pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE      )

    SCREAMING_SNAKE_CASE  :  Any								=		pipe(**self.get_dummy_inputs(__SCREAMING_SNAKE_CASE      )      )
    SCREAMING_SNAKE_CASE  :  Optional[int]								=		output.images

    SCREAMING_SNAKE_CASE  :  Any								=		pipe(
        **self.get_dummy_inputs(__SCREAMING_SNAKE_CASE      )    ,return_dict=__SCREAMING_SNAKE_CASE    ,)[0]

    SCREAMING_SNAKE_CASE  :  Optional[Any]								=		image[0, -3:, -3:, -1]
    SCREAMING_SNAKE_CASE  :  Optional[Any]								=		image_from_tuple[0, -3:, -3:, -1]

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

    SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		np.array(
        [0.32_8663, 1.0, 0.2321_6873, 1.0, 0.9271_7564, 0.463_9046, 0.9689_4777, 0.3171_3378, 0.629_3953]      )

    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 		_a     (							unittest.TestCase  ):







   """simple docstring"""




   def 					__a			(       self      ):
    # clean up the VRAM after each test
    super().tearDown()
    gc.collect()
    torch.cuda.empty_cache()






   def 					__a			(       self      ):
    SCREAMING_SNAKE_CASE  :  List[Any]								=		load_numpy(
        'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
        '/kandinsky/kandinsky_text2img_cat_fp16.npy'      )

    SCREAMING_SNAKE_CASE  :  Dict								=		KandinskyPriorPipeline.from_pretrained(
        'kandinsky-community/kandinsky-2-1-prior'    ,torch_dtype=torch.floataa      )
    pipe_prior.to(__SCREAMING_SNAKE_CASE      )

    SCREAMING_SNAKE_CASE  :  int								=		KandinskyPipeline.from_pretrained('kandinsky-community/kandinsky-2-1'    ,torch_dtype=torch.floataa      )
    SCREAMING_SNAKE_CASE  :  Dict								=		pipeline.to(__SCREAMING_SNAKE_CASE      )
    pipeline.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE      )

    SCREAMING_SNAKE_CASE  :  Any								=		'red cat, 4k photo'

    SCREAMING_SNAKE_CASE  :  int								=		torch.Generator(device='cuda'      ).manual_seed(0      )
    SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE  :  Any								=		pipe_prior(
        __SCREAMING_SNAKE_CASE    ,generator=__SCREAMING_SNAKE_CASE    ,num_inference_steps=5    ,negative_prompt=''    ,).to_tuple()

    SCREAMING_SNAKE_CASE  :  List[str]								=		torch.Generator(device='cuda'      ).manual_seed(0      )
    SCREAMING_SNAKE_CASE  :  Optional[Any]								=		pipeline(
        __SCREAMING_SNAKE_CASE    ,image_embeds=__SCREAMING_SNAKE_CASE    ,negative_image_embeds=__SCREAMING_SNAKE_CASE    ,generator=__SCREAMING_SNAKE_CASE    ,num_inference_steps=100    ,output_type='np'    ,)

    SCREAMING_SNAKE_CASE  :  int								=		output.images[0]

    assert image.shape == (512, 512, 3)

    assert_mean_pixel_difference(__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE      )




'''simple docstring'''

import os
import unittest

from transformers import MPNetTokenizerFast
from transformers.models.mpnet.tokenization_mpnet import VOCAB_FILES_NAMES, MPNetTokenizer
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


@require_tokenizers
class 		_a     (							SCREAMING_SNAKE_CASE			,    unittest.TestCase  ):







   """simple docstring"""




   A		= MPNetTokenizer
   A		= MPNetTokenizerFast
   A		= True
   A		= True






   def 					__a			(       self      ):
    super().setUp()

    SCREAMING_SNAKE_CASE  :  Tuple								=		[
        '[UNK]',
        '[CLS]',
        '[SEP]',
        '[PAD]',
        '[MASK]',
        'want',
        '##want',
        '##ed',
        'wa',
        'un',
        'runn',
        '##ing',
        ',',
        'low',
        'lowest',
    ]
    SCREAMING_SNAKE_CASE  :  Optional[Any]								=		os.path.join(self.tmpdirname    ,VOCAB_FILES_NAMES['vocab_file']      )
    with open(self.vocab_file    ,'w'    ,encoding='utf-8'      ) as vocab_writer:
     vocab_writer.write(''.join([x + '\n' for x in vocab_tokens]      )      )






   def 					__a			(       self    ,__SCREAMING_SNAKE_CASE      ):
    SCREAMING_SNAKE_CASE  :  List[str]								=		'UNwant\u00E9d,running'
    SCREAMING_SNAKE_CASE  :  Any								=		'unwanted, running'
    return input_text, output_text






   def 					__a			(       self      ):
    SCREAMING_SNAKE_CASE  :  int								=		self.tokenizer_class(self.vocab_file      )

    SCREAMING_SNAKE_CASE  :  List[str]								=		tokenizer.tokenize('UNwant\u00E9d,running'      )
    self.assertListEqual(__SCREAMING_SNAKE_CASE    ,['un', '##want', '##ed', ',', 'runn', '##ing']      )
    self.assertListEqual(tokenizer.convert_tokens_to_ids(__SCREAMING_SNAKE_CASE      )    ,[9, 6, 7, 12, 10, 11]      )






   @slow
   def 					__a			(       self      ):
    SCREAMING_SNAKE_CASE  :  Tuple								=		self.tokenizer_class.from_pretrained('microsoft/mpnet-base'      )

    SCREAMING_SNAKE_CASE  :  Dict								=		tokenizer.encode('sequence builders'    ,add_special_tokens=__SCREAMING_SNAKE_CASE      )
    SCREAMING_SNAKE_CASE  :  List[Any]								=		tokenizer.encode('multi-sequence build'    ,add_special_tokens=__SCREAMING_SNAKE_CASE      )

    SCREAMING_SNAKE_CASE  :  Tuple								=		tokenizer.build_inputs_with_special_tokens(__SCREAMING_SNAKE_CASE      )
    SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		tokenizer.build_inputs_with_special_tokens(__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE      )

    assert encoded_sentence == [0] + text + [2]
    assert encoded_pair == [0] + text + [2] + [2] + text_a + [2]


