


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__    (							UpperCamelCase__    ,    unittest.TestCase  ):





							'''simple docstring'''






							lowercase__      :       Dict     						=				KandinskyInpaintPipeline
							lowercase__      :       List[str]     						=				['''prompt''', '''image_embeds''', '''negative_image_embeds''', '''image''', '''mask_image''']
							lowercase__      :       Tuple     						=				[
							    '''prompt''',
							    '''negative_prompt''',
							    '''image_embeds''',
							    '''negative_image_embeds''',
							    '''image''',
							    '''mask_image''',
							]
							lowercase__      :       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''',
							]
							lowercase__      :       str     						=				False







							@property
							def   __SCREAMING_SNAKE_CASE							(		self       )	->							List[str]:
														return 32







							@property
							def   __SCREAMING_SNAKE_CASE							(		self       )	->							Optional[int]:
														return 32







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







							@property
							def   __SCREAMING_SNAKE_CASE							(		self       )	->							List[Any]:
														return self.time_input_dim * 4







							@property
							def   __SCREAMING_SNAKE_CASE							(		self       )	->							Dict:
														return 1_00







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







							@property
							def   __SCREAMING_SNAKE_CASE							(		self       )	->							Dict:
														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=37 ,   num_attention_heads=4 ,   num_hidden_layers=5 ,   vocab_size=10_05 ,   )

														lowerCAmelCase__	       =     MultilingualCLIP(UpperCamelCase_       )
														lowerCAmelCase__	       =     text_encoder.eval()

														return text_encoder







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

														lowerCAmelCase__	       =     {
														    '''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,
														}

														lowerCAmelCase__	       =     UNetaDConditionModel(**UpperCamelCase_       )
														return model







							@property
							def   __SCREAMING_SNAKE_CASE							(		self       )	->							List[str]:
														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   __SCREAMING_SNAKE_CASE							(		self       )	->							Tuple:
														torch.manual_seed(0       )
														lowerCAmelCase__	       =     VQModel(**self.dummy_movq_kwargs       )
														return model







							def   __SCREAMING_SNAKE_CASE							(		self       )	->							Optional[Any]:
														lowerCAmelCase__	       =     self.dummy_text_encoder
														lowerCAmelCase__	       =     self.dummy_tokenizer
														lowerCAmelCase__	       =     self.dummy_unet
														lowerCAmelCase__	       =     self.dummy_movq

														lowerCAmelCase__	       =     DDIMScheduler(
														    num_train_timesteps=10_00 ,   beta_schedule='''linear''' ,   beta_start=0.00_085 ,   beta_end=0.012 ,   clip_sample=UpperCamelCase_ ,   set_alpha_to_one=UpperCamelCase_ ,   steps_offset=1 ,   prediction_type='''epsilon''' ,   thresholding=UpperCamelCase_ ,   )

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

														return components







							def   __SCREAMING_SNAKE_CASE							(		self ,   lowerCamelCase_ ,   lowerCamelCase_=0       )	->							List[Any]:
														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, 64, 64) ,   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_56, 2_56)       )
														# create mask
														lowerCAmelCase__	       =     np.ones((64, 64) ,   dtype=np.floataa       )
														lowerCAmelCase__	       =     0

														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,
														    '''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   __SCREAMING_SNAKE_CASE							(		self       )	->							List[Any]:
														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]

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

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

														lowerCAmelCase__	       =     np.array(
														    [0.8_326_919, 0.73_790_467, 0.20_918_581, 0.9_309_612, 0.5_511_791, 0.43_713_328, 0.5_513_321, 0.49_922_934, 0.59_497_786]       )

														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   __SCREAMING_SNAKE_CASE							(		self       )	->							Union[str, Any]:
														super().test_inference_batch_single_identical(expected_max_diff=3e-3       )




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





							'''simple docstring'''






							def   __SCREAMING_SNAKE_CASE							(		self       )	->							int:
														super().tearDown()
														gc.collect()
														torch.cuda.empty_cache()







							def   __SCREAMING_SNAKE_CASE							(		self       )	->							Optional[Any]:
														lowerCAmelCase__	       =     load_numpy(
														    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
														    '''/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy'''       )

														lowerCAmelCase__	       =     load_image(
														    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/kandinsky/cat.png'''       )
														lowerCAmelCase__	       =     np.ones((7_68, 7_68) ,   dtype=np.floataa       )
														lowerCAmelCase__	       =     0

														lowerCAmelCase__	       =     '''a hat'''

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

														lowerCAmelCase__	       =     KandinskyInpaintPipeline.from_pretrained(
														    '''kandinsky-community/kandinsky-2-1-inpaint''' ,   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_ ,   mask_image=UpperCamelCase_ ,   image_embeds=UpperCamelCase_ ,   negative_image_embeds=UpperCamelCase_ ,   generator=UpperCamelCase_ ,   num_inference_steps=1_00 ,   height=7_68 ,   width=7_68 ,   output_type='''np''' ,   )

														lowerCAmelCase__	       =     output.images[0]

														assert image.shape == (7_68, 7_68, 3)

														assert_mean_pixel_difference(UpperCamelCase_ ,   UpperCamelCase_       )



'''simple docstring'''






from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__UpperCAmelCase       =							logging.get_logger(__name__)

__UpperCAmelCase       =							{
    '''distilbert-base-uncased''': '''https://huggingface.co/distilbert-base-uncased/resolve/main/config.json''',
    '''distilbert-base-uncased-distilled-squad''': (
        '''https://huggingface.co/distilbert-base-uncased-distilled-squad/resolve/main/config.json'''
    ),
    '''distilbert-base-cased''': '''https://huggingface.co/distilbert-base-cased/resolve/main/config.json''',
    '''distilbert-base-cased-distilled-squad''': (
        '''https://huggingface.co/distilbert-base-cased-distilled-squad/resolve/main/config.json'''
    ),
    '''distilbert-base-german-cased''': '''https://huggingface.co/distilbert-base-german-cased/resolve/main/config.json''',
    '''distilbert-base-multilingual-cased''': (
        '''https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/config.json'''
    ),
    '''distilbert-base-uncased-finetuned-sst-2-english''': (
        '''https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english/resolve/main/config.json'''
    ),
}




class 		a__    (							a__  ):





							'''simple docstring'''






							lowercase__      :       Optional[Any]     						=				"distilbert"
							lowercase__      :       Tuple     						=				{
							    "hidden_size": "dim",
							    "num_attention_heads": "n_heads",
							    "num_hidden_layers": "n_layers",
							}







							def __init__(		self ,   lowerCamelCase_=3_05_22 ,   lowerCamelCase_=5_12 ,   lowerCamelCase_=False ,   lowerCamelCase_=6 ,   lowerCamelCase_=12 ,   lowerCamelCase_=7_68 ,   lowerCamelCase_=4 * 7_68 ,   lowerCamelCase_=0.1 ,   lowerCamelCase_=0.1 ,   lowerCamelCase_="gelu" ,   lowerCamelCase_=0.02 ,   lowerCamelCase_=0.1 ,   lowerCamelCase_=0.2 ,   lowerCamelCase_=0 ,   **lowerCamelCase_ ,   )	->							Any:
														lowerCAmelCase__	       =     vocab_size
														lowerCAmelCase__	       =     max_position_embeddings
														lowerCAmelCase__	       =     sinusoidal_pos_embds
														lowerCAmelCase__	       =     n_layers
														lowerCAmelCase__	       =     n_heads
														lowerCAmelCase__	       =     dim
														lowerCAmelCase__	       =     hidden_dim
														lowerCAmelCase__	       =     dropout
														lowerCAmelCase__	       =     attention_dropout
														lowerCAmelCase__	       =     activation
														lowerCAmelCase__	       =     initializer_range
														lowerCAmelCase__	       =     qa_dropout
														lowerCAmelCase__	       =     seq_classif_dropout
														super().__init__(**lowerCamelCase_ ,   pad_token_id=lowerCamelCase_       )




class 		a__    (							a__  ):





							'''simple docstring'''






							@property
							def   __SCREAMING_SNAKE_CASE							(		self       )	->							Mapping[str, Mapping[int, str]]:
														if self.task == "multiple-choice":
																					lowerCAmelCase__	       =     {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
														else:
																					lowerCAmelCase__	       =     {0: '''batch''', 1: '''sequence'''}
														return OrderedDict(
														    [
														        ('''input_ids''', dynamic_axis),
														        ('''attention_mask''', dynamic_axis),
														    ]       )