
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 						UpperCamelCase__    (			SCREAMING_SNAKE_CASE_       ,unittest.TestCase       ):
   _SCREAMING_SNAKE_CASE :				Tuple      			= KandinskyInpaintPipeline
   _SCREAMING_SNAKE_CASE :				Tuple      			= ["prompt", "image_embeds", "negative_image_embeds", "image", "mask_image"]
   _SCREAMING_SNAKE_CASE :				Optional[Any]      			= [
       "prompt",
       "negative_prompt",
       "image_embeds",
       "negative_image_embeds",
       "image",
       "mask_image",
   ]
   _SCREAMING_SNAKE_CASE :				List[str]      			= [
       "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 :				str      			= False
   @property
   def  lowerCAmelCase     (self					:   Dict  ):
    return 3_2
   @property
   def  lowerCAmelCase     (self					:   Optional[int]  ):
    return 3_2
   @property
   def  lowerCAmelCase     (self					:   Dict  ):
    return self.time_input_dim
   @property
   def  lowerCAmelCase     (self					:   Dict  ):
    return self.time_input_dim * 4
   @property
   def  lowerCAmelCase     (self					:   List[Any]  ):
    return 1_0_0
   @property
   def  lowerCAmelCase     (self					:   int  ):
    __a					:    List[str]													=    XLMRobertaTokenizerFast.from_pretrained('''YiYiXu/tiny-random-mclip-base'''  )
    return tokenizer
   @property
   def  lowerCAmelCase     (self					:   Tuple  ):
    torch.manual_seed(0  )
    __a					:    str													=    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   ,							)

    __a					:    List[Any]													=    MultilingualCLIP(__a  )
    __a					:    List[str]													=    text_encoder.eval()

    return text_encoder
   @property
   def  lowerCAmelCase     (self					:   Dict  ):
    torch.manual_seed(0  )

    __a					:    Tuple													=    {
        '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,
    }

    __a					:    Any													=    UNetaDConditionModel(**__a  )
    return model
   @property
   def  lowerCAmelCase     (self					:   List[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  lowerCAmelCase     (self					:   int  ):
    torch.manual_seed(0  )
    __a					:    Optional[int]													=    VQModel(**self.dummy_movq_kwargs  )
    return model
   def  lowerCAmelCase     (self					:   Dict  ):
    __a					:    Tuple													=    self.dummy_text_encoder
    __a					:    str													=    self.dummy_tokenizer
    __a					:    Any													=    self.dummy_unet
    __a					:    Optional[Any]													=    self.dummy_movq

    __a					:    Optional[Any]													=    DDIMScheduler(
        num_train_timesteps=1_0_0_0   ,							beta_schedule='''linear'''   ,							beta_start=0.0_0085   ,							beta_end=0.012   ,							clip_sample=__a   ,							set_alpha_to_one=__a   ,							steps_offset=1   ,							prediction_type='''epsilon'''   ,							thresholding=__a   ,							)

    __a					:    List[str]													=    {
        'text_encoder': text_encoder,
        'tokenizer': tokenizer,
        'unet': unet,
        'scheduler': scheduler,
        'movq': movq,
    }

    return components
   def  lowerCAmelCase     (self					:   List[str]   ,							snake_case_					:   Dict   ,							snake_case_					:   int=0  ):
    __a					:    int													=    floats_tensor((1, self.cross_attention_dim)   ,							rng=random.Random(__a  )  ).to(__a  )
    __a					:    str													=    floats_tensor((1, self.cross_attention_dim)   ,							rng=random.Random(seed + 1  )  ).to(__a  )
    # create init_image
    __a					:    Any													=    floats_tensor((1, 3, 6_4, 6_4)   ,							rng=random.Random(__a  )  ).to(__a  )
    __a					:    Optional[int]													=    image.cpu().permute(0   ,							2   ,							3   ,							1  )[0]
    __a					:    List[str]													=    Image.fromarray(np.uinta(__a  )  ).convert('''RGB'''  ).resize((2_5_6, 2_5_6)  )
    # create mask
    __a					:    Optional[int]													=    np.ones((6_4, 6_4)   ,							dtype=np.floataa  )
    __a					:    List[Any]													=    0

    if str(__a  ).startswith('''mps'''  ):
     __a					:    Any													=    torch.manual_seed(__a  )
    else:
     __a					:    Optional[int]													=    torch.Generator(device=__a  ).manual_seed(__a  )
    __a					:    int													=    {
        'prompt': 'horse',
        'image': init_image,
        'mask_image': mask,
        'image_embeds': image_embeds,
        'negative_image_embeds': negative_image_embeds,
        'generator': generator,
        'height': 6_4,
        'width': 6_4,
        'num_inference_steps': 2,
        'guidance_scale': 4.0,
        'output_type': 'np',
    }
    return inputs
   def  lowerCAmelCase     (self					:   Any  ):
    __a					:    List[str]													=    'cpu'

    __a					:    Tuple													=    self.get_dummy_components()

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

    pipe.set_progress_bar_config(disable=__a  )

    __a					:    Optional[Any]													=    pipe(**self.get_dummy_inputs(__a  )  )
    __a					:    Dict													=    output.images

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

    __a					:    int													=    image[0, -3:, -3:, -1]
    __a					:    Optional[int]													=    image_from_tuple[0, -3:, -3:, -1]

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

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

    __a					:    List[Any]													=    np.array(
        [0.832_6919, 0.7379_0467, 0.2091_8581, 0.930_9612, 0.551_1791, 0.4371_3328, 0.551_3321, 0.4992_2934, 0.5949_7786]  )

    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  lowerCAmelCase     (self					:   List[Any]  ):
    super().test_inference_batch_single_identical(expected_max_diff=3E-3  )





@slow
@require_torch_gpu
class 						UpperCamelCase__    (			unittest.TestCase       ):
   def  lowerCAmelCase     (self					:   Dict  ):
    super().tearDown()
    gc.collect()
    torch.cuda.empty_cache()







   def  lowerCAmelCase     (self					:   Optional[int]  ):
    __a					:    Union[str, Any]													=    load_numpy(
        '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
        '''/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy'''  )

    __a					:    Tuple													=    load_image(
        '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/kandinsky/cat.png'''  )
    __a					:    Tuple													=    np.ones((7_6_8, 7_6_8)   ,							dtype=np.floataa  )
    __a					:    Tuple													=    0

    __a					:    Tuple													=    'a hat'

    __a					:    Optional[Any]													=    KandinskyPriorPipeline.from_pretrained(
        '''kandinsky-community/kandinsky-2-1-prior'''   ,							torch_dtype=torch.floataa  )
    pipe_prior.to(__a  )

    __a					:    int													=    KandinskyInpaintPipeline.from_pretrained(
        '''kandinsky-community/kandinsky-2-1-inpaint'''   ,							torch_dtype=torch.floataa  )
    __a					:    Optional[Any]													=    pipeline.to(__a  )
    pipeline.set_progress_bar_config(disable=__a  )

    __a					:    Any													=    torch.Generator(device='''cpu'''  ).manual_seed(0  )
    __a					:    Union[str, Any]													=    pipe_prior(
        __a   ,							generator=__a   ,							num_inference_steps=5   ,							negative_prompt=''''''   ,							).to_tuple()

    __a					:    List[str]													=    pipeline(
        __a   ,							image=__a   ,							mask_image=__a   ,							image_embeds=__a   ,							negative_image_embeds=__a   ,							generator=__a   ,							num_inference_steps=1_0_0   ,							height=7_6_8   ,							width=7_6_8   ,							output_type='''np'''   ,							)

    __a					:    int													=    output.images[0]

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

    assert_mean_pixel_difference(__a   ,							__a  )





'''simple docstring'''





import json
import os
import unittest

from transformers import DebertaTokenizer, DebertaTokenizerFast
from transformers.models.deberta.tokenization_deberta import VOCAB_FILES_NAMES
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin



class  snake_case__						(    SCREAMING_SNAKE_CASE_  ,     unittest.TestCase				):
 A__				= DebertaTokenizer
 A__				= True
 A__				= DebertaTokenizerFast
 def A_   (   self						:							Optional[int]  )      ->   str:







       '''simple docstring'''
       super().setUp()

       # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
       __snake_case :   Union[str, Any]													=    [
           'l',
           'o',
           'w',
           'e',
           'r',
           's',
           't',
           'i',
           'd',
           'n',
           '\u0120',
           '\u0120l',
           '\u0120n',
           '\u0120lo',
           '\u0120low',
           'er',
           '\u0120lowest',
           '\u0120newer',
           '\u0120wider',
           '[UNK]',
       ]
       __snake_case :   List[str]													=    dict(zip(__a      ,     range(len(__a  )  )  )  )
       __snake_case :   int													=    ['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
       __snake_case :   Any													=    {'unk_token': '[UNK]'}

       __snake_case :   Union[str, Any]													=    os.path.join(self.tmpdirname      ,     VOCAB_FILES_NAMES['vocab_file']  )
       __snake_case :   Dict													=    os.path.join(self.tmpdirname      ,     VOCAB_FILES_NAMES['merges_file']  )
       with open(self.vocab_file      ,     'w'      ,     encoding='utf-8'  ) as fp:
             fp.write(json.dumps(__a  ) + '\n'  )
       with open(self.merges_file      ,     'w'      ,     encoding='utf-8'  ) as fp:
             fp.write('\n'.join(__a  )  )
 def A_   (   self						:							Optional[int]      ,     **__a						:							List[Any]  )      ->   Optional[Any]:







       '''simple docstring'''
       kwargs.update(self.special_tokens_map  )
       return self.tokenizer_class.from_pretrained(self.tmpdirname      ,     **__a  )
 def A_   (   self						:							Optional[int]      ,     __a						:							Dict  )      ->   List[str]:







       '''simple docstring'''
       __snake_case :   Union[str, Any]													=    'lower newer'
       __snake_case :   List[Any]													=    'lower newer'
       return input_text, output_text
 def A_   (   self						:							Dict  )      ->   Union[str, Any]:







       '''simple docstring'''
       __snake_case :   str													=    self.get_tokenizer()
       __snake_case :   List[str]													=    'lower newer'
       __snake_case :   Tuple													=    ['l', 'o', 'w', 'er', '\u0120', 'n', 'e', 'w', 'er']
       __snake_case :   Optional[int]													=    tokenizer.tokenize(__a  )
       self.assertListEqual(__a      ,     __a  )

       __snake_case :   str													=    tokens + [tokenizer.unk_token]
       __snake_case :   Tuple													=    [0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
       self.assertListEqual(tokenizer.convert_tokens_to_ids(__a  )      ,     __a  )
 def A_   (   self						:							List[Any]  )      ->   Dict:







       '''simple docstring'''
       __snake_case :   List[str]													=    self.get_tokenizer()
       __snake_case :   List[str]													=    tokenizer('Hello'      ,     'World'  )
       __snake_case :   int													=    [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1]
       self.assertListEqual(tokd['token_type_ids']      ,     __a  )
 @slow
 def A_   (   self						:							Dict  )      ->   str:







       '''simple docstring'''
       __snake_case :   str													=    self.tokenizer_class.from_pretrained('microsoft/deberta-base'  )

       __snake_case :   str													=    tokenizer.encode('sequence builders'      ,     add_special_tokens=__a  )
       __snake_case :   Any													=    tokenizer.encode('multi-sequence build'      ,     add_special_tokens=__a  )

       __snake_case :   List[Any]													=    tokenizer.encode(
           'sequence builders'      ,     add_special_tokens=__a      ,     add_prefix_space=__a  )
       __snake_case :   Union[str, Any]													=    tokenizer.encode(
           'sequence builders'      ,     'multi-sequence build'      ,     add_special_tokens=__a      ,     add_prefix_space=__a  )

       __snake_case :   int													=    tokenizer.build_inputs_with_special_tokens(__a  )
       __snake_case :   Tuple													=    tokenizer.build_inputs_with_special_tokens(__a      ,     __a  )

       assert encoded_sentence == encoded_text_from_decode
       assert encoded_pair == encoded_pair_from_decode





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







       '''simple docstring'''
       __snake_case :   int													=    [self.tokenizer_class]
       if self.test_rust_tokenizer:
             tokenizer_classes.append(self.rust_tokenizer_class  )

       for tokenizer_class in tokenizer_classes:
             __snake_case :   List[Any]													=    tokenizer_class.from_pretrained('microsoft/deberta-base'  )

             __snake_case :   int													=    [
                 'ALBERT: A Lite BERT for Self-supervised Learning of Language Representations',
                 'ALBERT incorporates two parameter reduction techniques',
                 'The first one is a factorized embedding parameterization. By decomposing the large vocabulary'
                 ' embedding matrix into two small matrices, we separate the size of the hidden layers from the size of'
                 ' vocabulary embedding.',
             ]

             __snake_case :   Union[str, Any]													=    tokenizer(__a      ,     padding=__a  )
             __snake_case :   List[str]													=    [tokenizer.decode(__a      ,     skip_special_tokens=__a  ) for seq in encoding['input_ids']]

             # fmt: off
             __snake_case :   Optional[Any]													=    {
                 'input_ids': [
                     [1, 2118, 11126, 565, 35, 83, 25191, 163, 18854, 13, 12156, 12, 16101, 25376, 13807, 9, 22205, 27893, 1635, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                     [1, 2118, 11126, 565, 24536, 80, 43797, 4878, 7373, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                     [1, 133, 78, 65, 16, 10, 3724, 1538, 33183, 11303, 43797, 1938, 4, 870, 24165, 29105, 5, 739, 32644, 33183, 11303, 36173, 88, 80, 650, 7821, 45940, 6, 52, 2559, 5, 1836, 9, 5, 7397, 13171, 31, 5, 1836, 9, 32644, 33183, 11303, 4, 2]
                 ],
                 'token_type_ids': [
                     [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                     [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                     [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
                 ],
                 'attention_mask': [
                     [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                     [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                     [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
                 ]
             }
             # fmt: on

             __snake_case :   int													=    [
                 'ALBERT: A Lite BERT for Self-supervised Learning of Language Representations',
                 'ALBERT incorporates two parameter reduction techniques',
                 'The first one is a factorized embedding parameterization. By decomposing the large vocabulary'
                 ' embedding matrix into two small matrices, we separate the size of the hidden layers from the size of'
                 ' vocabulary embedding.',
             ]

             self.assertDictEqual(encoding.data      ,     __a  )

             for expected, decoded in zip(__a      ,     __a  ):
                   self.assertEqual(__a      ,     __a  )



