

'''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, 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 		__SCREAMING_SNAKE_CASE							(			lowercase__						,							unittest.TestCase      ):
				lowerCamelCase_		       =    KandinskyInpaintPipeline
				lowerCamelCase_		       =    ['prompt', 'image_embeds', 'negative_image_embeds', 'image', 'mask_image']
				lowerCamelCase_		       =    [
				    'prompt',
				    'negative_prompt',
				    'image_embeds',
				    'negative_image_embeds',
				    'image',
				    'mask_image',
				]
				lowerCamelCase_		       =    [
				    'generator',
				    'height',
				    'width',
				    'latents',
				    'guidance_scale',
				    'negative_prompt',
				    'num_inference_steps',
				    'return_dict',
				    'guidance_scale',
				    'num_images_per_prompt',
				    'output_type',
				    'return_dict',
				]
				lowerCamelCase_		       =    False







				@property
				def  lowerCamelCase_	(       self     :		Optional[Any]   ):
										'''simple docstring'''
										return 32







				@property
				def  lowerCamelCase_	(       self     :		List[str]   ):
										'''simple docstring'''
										return 32







				@property
				def  lowerCamelCase_	(       self     :		str   ):
										'''simple docstring'''
										return self.time_input_dim







				@property
				def  lowerCamelCase_	(       self     :		Optional[int]   ):
										'''simple docstring'''
										return self.time_input_dim * 4







				@property
				def  lowerCamelCase_	(       self     :		Union[str, Any]   ):
										'''simple docstring'''
										return 100







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







				@property
				def  lowerCamelCase_	(       self     :		Union[str, Any]   ):
										'''simple docstring'''
										torch.manual_seed(0   )
										lowercase :							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		,					)

										lowercase :							Union[str, Any]    		=MultilingualCLIP(UpperCAmelCase__   )
										lowercase :							int    		=text_encoder.eval()

										return text_encoder







				@property
				def  lowerCamelCase_	(       self     :		int   ):
										'''simple docstring'''
										torch.manual_seed(0   )

										lowercase :							Optional[Any]    		={
										    '''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,
										}

										lowercase :							Optional[int]    		=UNetaDConditionModel(**UpperCAmelCase__   )
										return model







				@property
				def  lowerCamelCase_	(       self     :		int   ):
										'''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  lowerCamelCase_	(       self     :		Dict   ):
										'''simple docstring'''
										torch.manual_seed(0   )
										lowercase :							str    		=VQModel(**self.dummy_movq_kwargs   )
										return model







				def  lowerCamelCase_	(       self     :		Union[str, Any]   ):
										'''simple docstring'''
										lowercase :							int    		=self.dummy_text_encoder
										lowercase :							int    		=self.dummy_tokenizer
										lowercase :							Optional[int]    		=self.dummy_unet
										lowercase :							Optional[int]    		=self.dummy_movq

										lowercase :							Any    		=DDIMScheduler(
										    num_train_timesteps=1000		,					beta_schedule='''linear'''		,					beta_start=0.0_00_85		,					beta_end=0.0_12		,					clip_sample=UpperCAmelCase__		,					set_alpha_to_one=UpperCAmelCase__		,					steps_offset=1		,					prediction_type='''epsilon'''		,					thresholding=UpperCAmelCase__		,					)

										lowercase :							Dict    		={
										    '''text_encoder''': text_encoder,
										    '''tokenizer''': tokenizer,
										    '''unet''': unet,
										    '''scheduler''': scheduler,
										    '''movq''': movq,
										}

										return components







				def  lowerCamelCase_	(       self     :		str		,					UpperCAmelCase__     :		List[str]		,					UpperCAmelCase__     :		Any=0   ):
										'''simple docstring'''
										lowercase :							Optional[Any]    		=floats_tensor((1, self.cross_attention_dim)		,					rng=random.Random(UpperCAmelCase__   )   ).to(UpperCAmelCase__   )
										lowercase :							Tuple    		=floats_tensor((1, self.cross_attention_dim)		,					rng=random.Random(seed + 1   )   ).to(UpperCAmelCase__   )
										# create init_image
										lowercase :							Tuple    		=floats_tensor((1, 3, 64, 64)		,					rng=random.Random(UpperCAmelCase__   )   ).to(UpperCAmelCase__   )
										lowercase :							Any    		=image.cpu().permute(0		,					2		,					3		,					1   )[0]
										lowercase :							List[Any]    		=Image.fromarray(np.uinta(UpperCAmelCase__   )   ).convert('''RGB'''   ).resize((256, 256)   )
										# create mask
										lowercase :							List[str]    		=np.ones((64, 64)		,					dtype=np.floataa   )
										lowercase :							List[str]    		=0

										if str(UpperCAmelCase__   ).startswith('''mps'''   ):
																lowercase :							Optional[int]    		=torch.manual_seed(UpperCAmelCase__   )
										else:
																lowercase :							Dict    		=torch.Generator(device=UpperCAmelCase__   ).manual_seed(UpperCAmelCase__   )
										lowercase :							Optional[int]    		={
										    '''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  lowerCamelCase_	(       self     :		List[str]   ):
										'''simple docstring'''
										lowercase :							Dict    		='''cpu'''

										lowercase :							Optional[int]    		=self.get_dummy_components()

										lowercase :							Any    		=self.pipeline_class(**UpperCAmelCase__   )
										lowercase :							Any    		=pipe.to(UpperCAmelCase__   )

										pipe.set_progress_bar_config(disable=UpperCAmelCase__   )

										lowercase :							Tuple    		=pipe(**self.get_dummy_inputs(UpperCAmelCase__   )   )
										lowercase :							Any    		=output.images

										lowercase :							List[str]    		=pipe(
										    **self.get_dummy_inputs(UpperCAmelCase__   )		,					return_dict=UpperCAmelCase__		,					)[0]

										lowercase :							Dict    		=image[0, -3:, -3:, -1]
										lowercase :							str    		=image_from_tuple[0, -3:, -3:, -1]

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

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

										lowercase :							List[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  lowerCamelCase_	(       self     :		List[str]   ):
										'''simple docstring'''
										super().test_inference_batch_single_identical(expected_max_diff=3E-3   )





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







				def  lowerCamelCase_	(       self     :		Optional[int]   ):
										'''simple docstring'''
										# clean up the VRAM after each test
										super().tearDown()
										gc.collect()
										torch.cuda.empty_cache()







				def  lowerCamelCase_	(       self     :		int   ):
										'''simple docstring'''
										lowercase :							str    		=load_numpy(
										    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
										    '''/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy'''   )

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

										lowercase :							Tuple    		='''a hat'''

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

										lowercase :							List[str]    		=KandinskyInpaintPipeline.from_pretrained(
										    '''kandinsky-community/kandinsky-2-1-inpaint'''		,					torch_dtype=torch.floataa   )
										lowercase :							List[str]    		=pipeline.to(UpperCAmelCase__   )
										pipeline.set_progress_bar_config(disable=UpperCAmelCase__   )

										lowercase :							Dict    		=torch.Generator(device='''cpu'''   ).manual_seed(0   )
										lowercase		,	lowercase :							Optional[Any]    		=pipe_prior(
										    UpperCAmelCase__		,					generator=UpperCAmelCase__		,					num_inference_steps=5		,					negative_prompt=''''''		,					).to_tuple()

										lowercase :							List[str]    		=pipeline(
										    UpperCAmelCase__		,					image=UpperCAmelCase__		,					mask_image=UpperCAmelCase__		,					image_embeds=UpperCAmelCase__		,					negative_image_embeds=UpperCAmelCase__		,					generator=UpperCAmelCase__		,					num_inference_steps=100		,					height=768		,					width=768		,					output_type='''np'''		,					)

										lowercase :							List[Any]    		=output.images[0]

										assert image.shape == (768, 768, 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


__lowerCAmelCase				:      Optional[Any]						      =logging.get_logger(__name__)

__lowerCAmelCase				:      str						      ={
    "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   UpperCAmelCase      (			UpperCamelCase__							):
 __lowercase  						=							"""distilbert"""
 __lowercase  						=							{
     """hidden_size""": """dim""",
     """num_attention_heads""": """n_heads""",
     """num_hidden_layers""": """n_layers""",
 }
 def __init__(							self :Tuple		,			lowercase_ :List[str]=3_05_22		,			lowercase_ :Tuple=5_12		,			lowercase_ :List[str]=False		,			lowercase_ :int=6		,			lowercase_ :Optional[int]=12		,			lowercase_ :Optional[int]=7_68		,			lowercase_ :str=4 * 7_68		,			lowercase_ :List[Any]=0.1		,			lowercase_ :str=0.1		,			lowercase_ :Optional[int]="gelu"		,			lowercase_ :Optional[Any]=0.0_2		,			lowercase_ :Optional[Any]=0.1		,			lowercase_ :List[Any]=0.2		,			lowercase_ :str=0		,			**lowercase_ :Union[str, Any]		,			)->				str:
     A__							=     vocab_size
     A__							=     max_position_embeddings
     A__							=     sinusoidal_pos_embds
     A__							=     n_layers
     A__							=     n_heads
     A__							=     dim
     A__							=     hidden_dim
     A__							=     dropout
     A__							=     attention_dropout
     A__							=     activation
     A__							=     initializer_range
     A__							=     qa_dropout
     A__							=     seq_classif_dropout
     super().__init__(**lowercase_		,			pad_token_id=lowercase_				)






class   UpperCAmelCase      (			UpperCamelCase__							):
 @property
 def 	UpperCAmelCase_				(							self :List[str]				)->				Mapping[str, Mapping[int, str]]:
     if self.task == "multiple-choice":
         A__							=     {0: "batch", 1: "choice", 2: "sequence"}
     else:
         A__							=     {0: "batch", 1: "sequence"}
     return OrderedDict(
         [
             ("input_ids", dynamic_axis),
             ("attention_mask", dynamic_axis),
         ]				)



