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   _lowerCAmelCase		(     __SCREAMING_SNAKE_CASE      ,   unittest.TestCase				):
							_UpperCAmelCase						    =     KandinskyImgaImgPipeline
							_UpperCAmelCase						    =     ['prompt', 'image_embeds', 'negative_image_embeds', 'image']
							_UpperCAmelCase						    =     [
							    'prompt',
							    'negative_prompt',
							    'image_embeds',
							    'negative_image_embeds',
							    'image',
							]
							_UpperCAmelCase						    =     [
							    'generator',
							    'height',
							    'width',
							    'strength',
							    'guidance_scale',
							    'negative_prompt',
							    'num_inference_steps',
							    'return_dict',
							    'guidance_scale',
							    'num_images_per_prompt',
							    'output_type',
							    'return_dict',
							]
							_UpperCAmelCase						    =     False







							@property
							def 				snake_case  (				self     :    Optional[int]		):
										return 32







							@property
							def 				snake_case  (				self     :    Union[str, Any]		):
										return 32







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







							@property
							def 				snake_case  (				self     :    List[str]		):
										return self.time_input_dim * 4







							@property
							def 				snake_case  (				self     :    List[str]		):
										return 100







							@property
							def 				snake_case  (				self     :    List[str]		):
										lowerCamelCase						:int						  =     XLMRobertaTokenizerFast.from_pretrained('''YiYiXu/tiny-random-mclip-base'''		)
										return tokenizer







							@property
							def 				snake_case  (				self     :    int		):
										torch.manual_seed(0		)
										lowerCamelCase						: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	,					)

										lowerCamelCase						:Tuple						  =     MultilingualCLIP(__snake_case		)
										lowerCamelCase						:Dict						  =     text_encoder.eval()

										return text_encoder







							@property
							def 				snake_case  (				self     :    Optional[Any]		):
										torch.manual_seed(0		)

										lowerCamelCase						: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,
										}

										lowerCamelCase						:Dict						  =     UNetaDConditionModel(**__snake_case		)
										return model







							@property
							def 				snake_case  (				self     :    Dict		):
										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 				snake_case  (				self     :    int		):
										torch.manual_seed(0		)
										lowerCamelCase						:Union[str, Any]						  =     VQModel(**self.dummy_movq_kwargs		)
										return model







							def 				snake_case  (				self     :    Optional[Any]		):
										lowerCamelCase						:Union[str, Any]						  =     self.dummy_text_encoder
										lowerCamelCase						:Any						  =     self.dummy_tokenizer
										lowerCamelCase						:Optional[int]						  =     self.dummy_unet
										lowerCamelCase						:Union[str, Any]						  =     self.dummy_movq

										lowerCamelCase						:int						  =     {
										    '''num_train_timesteps''': 1000,
										    '''beta_schedule''': '''linear''',
										    '''beta_start''': 0.0_0_0_8_5,
										    '''beta_end''': 0.0_1_2,
										    '''clip_sample''': False,
										    '''set_alpha_to_one''': False,
										    '''steps_offset''': 0,
										    '''prediction_type''': '''epsilon''',
										    '''thresholding''': False,
										}

										lowerCamelCase						:List[str]						  =     DDIMScheduler(**__snake_case		)

										lowerCamelCase						:Tuple						  =     {
										    '''text_encoder''': text_encoder,
										    '''tokenizer''': tokenizer,
										    '''unet''': unet,
										    '''scheduler''': scheduler,
										    '''movq''': movq,
										}

										return components







							def 				snake_case  (				self     :    Tuple	,					__snake_case     :    str	,					__snake_case     :    Tuple=0		):
										lowerCamelCase						:Optional[int]						  =     floats_tensor((1, self.cross_attention_dim)	,					rng=random.Random(__snake_case		)		).to(__snake_case		)
										lowerCamelCase						:str						  =     floats_tensor((1, self.cross_attention_dim)	,					rng=random.Random(seed + 1		)		).to(__snake_case		)
										# create init_image
										lowerCamelCase						:List[Any]						  =     floats_tensor((1, 3, 64, 64)	,					rng=random.Random(__snake_case		)		).to(__snake_case		)
										lowerCamelCase						:Optional[int]						  =     image.cpu().permute(0	,					2	,					3	,					1		)[0]
										lowerCamelCase						:Optional[int]						  =     Image.fromarray(np.uinta(__snake_case		)		).convert('''RGB'''		).resize((256, 256)		)

										if str(__snake_case		).startswith('''mps'''		):
													lowerCamelCase						:Any						  =     torch.manual_seed(__snake_case		)
										else:
													lowerCamelCase						:Dict						  =     torch.Generator(device=__snake_case		).manual_seed(__snake_case		)
										lowerCamelCase						:Union[str, Any]						  =     {
										    '''prompt''': '''horse''',
										    '''image''': init_image,
										    '''image_embeds''': image_embeds,
										    '''negative_image_embeds''': negative_image_embeds,
										    '''generator''': generator,
										    '''height''': 64,
										    '''width''': 64,
										    '''num_inference_steps''': 10,
										    '''guidance_scale''': 7.0,
										    '''strength''': 0.2,
										    '''output_type''': '''np''',
										}
										return inputs







							def 				snake_case  (				self     :    Tuple		):
										lowerCamelCase						:List[Any]						  =     '''cpu'''

										lowerCamelCase						:List[str]						  =     self.get_dummy_components()

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

										pipe.set_progress_bar_config(disable=__snake_case		)

										lowerCamelCase						:Dict						  =     pipe(**self.get_dummy_inputs(__snake_case		)		)
										lowerCamelCase						:List[str]						  =     output.images

										lowerCamelCase						:Any						  =     pipe(
										    **self.get_dummy_inputs(__snake_case		)	,					return_dict=__snake_case	,					)[0]

										lowerCamelCase						:Tuple						  =     image[0, -3:, -3:, -1]
										lowerCamelCase						:Dict						  =     image_from_tuple[0, -3:, -3:, -1]

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

										lowerCamelCase						:Dict						  =     np.array(
										    [0.6_1_4_7_4_9_4_3, 0.6_0_7_3_5_3_9, 0.4_3_3_0_8_5_4_4, 0.5_9_2_8_2_6_9, 0.4_7_4_9_3_5_9_5, 0.4_6_7_5_5_9_7_3, 0.4_6_1_3_8_3_8, 0.4_5_3_6_8_7_9_7, 0.5_0_1_1_9_2_3_3]		)
										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   _lowerCAmelCase		(     unittest.TestCase				):







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







							def 				snake_case  (				self     :    Any		):
										lowerCamelCase						:Union[str, Any]						  =     load_numpy(
										    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
										    '''/kandinsky/kandinsky_img2img_frog.npy'''		)

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

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

										lowerCamelCase						:Optional[Any]						  =     KandinskyImgaImgPipeline.from_pretrained(
										    '''kandinsky-community/kandinsky-2-1'''	,					torch_dtype=torch.floataa		)
										lowerCamelCase						:Optional[Any]						  =     pipeline.to(__snake_case		)

										pipeline.set_progress_bar_config(disable=__snake_case		)

										lowerCamelCase						:int						  =     torch.Generator(device='''cpu'''		).manual_seed(0		)
										lowerCamelCase  ,    lowerCamelCase						:int						  =     pipe_prior(
										    __snake_case	,					generator=__snake_case	,					num_inference_steps=5	,					negative_prompt=''''''	,					).to_tuple()

										lowerCamelCase						:int						  =     pipeline(
										    __snake_case	,					image=__snake_case	,					image_embeds=__snake_case	,					negative_image_embeds=__snake_case	,					generator=__snake_case	,					num_inference_steps=100	,					height=768	,					width=768	,					strength=0.2	,					output_type='''np'''	,					)

										lowerCamelCase						:Dict						  =     output.images[0]

										assert image.shape == (768, 768, 3)

										assert_mean_pixel_difference(__snake_case	,					__snake_case		)




from collections import OrderedDict
from typing import Mapping

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


A__           =							logging.get_logger(__name__)

A__           =							{
    """andreasmadsen/efficient_mlm_m0.40""": (
        """https://huggingface.co/andreasmadsen/efficient_mlm_m0.40/resolve/main/config.json"""
    ),
}
class   _lowerCAmelCase		(     __SCREAMING_SNAKE_CASE				):
							_UpperCAmelCase						    =     'roberta-prelayernorm'







							def __init__(				self     :    str	,					__snake_case     :    List[str]=50265	,					__snake_case     :    Union[str, Any]=768	,					__snake_case     :    Tuple=12	,					__snake_case     :    int=12	,					__snake_case     :    Any=3072	,					__snake_case     :    Optional[int]="gelu"	,					__snake_case     :    List[Any]=0.1	,					__snake_case     :    int=0.1	,					__snake_case     :    Union[str, Any]=512	,					__snake_case     :    Dict=2	,					__snake_case     :    int=0.0_2	,					__snake_case     :    Any=1e-1_2	,					__snake_case     :    Optional[int]=1	,					__snake_case     :    Dict=0	,					__snake_case     :    Optional[int]=2	,					__snake_case     :    Any="absolute"	,					__snake_case     :    Union[str, Any]=True	,					__snake_case     :    List[str]=None	,					**__snake_case     :    Optional[int]	,					):
										super().__init__(pad_token_id=__snake_case	,					bos_token_id=__snake_case	,					eos_token_id=__snake_case	,					**__snake_case		)

										lowerCamelCase						:Optional[int]						  =     vocab_size
										lowerCamelCase						:Dict						  =     hidden_size
										lowerCamelCase						:Tuple						  =     num_hidden_layers
										lowerCamelCase						:Optional[int]						  =     num_attention_heads
										lowerCamelCase						:Any						  =     hidden_act
										lowerCamelCase						:List[Any]						  =     intermediate_size
										lowerCamelCase						:Union[str, Any]						  =     hidden_dropout_prob
										lowerCamelCase						:str						  =     attention_probs_dropout_prob
										lowerCamelCase						:Tuple						  =     max_position_embeddings
										lowerCamelCase						:int						  =     type_vocab_size
										lowerCamelCase						:Optional[Any]						  =     initializer_range
										lowerCamelCase						:Union[str, Any]						  =     layer_norm_eps
										lowerCamelCase						:Dict						  =     position_embedding_type
										lowerCamelCase						:List[Any]						  =     use_cache
										lowerCamelCase						:Optional[int]						  =     classifier_dropout


class   _lowerCAmelCase		(     __SCREAMING_SNAKE_CASE				):







							@property
							def 				snake_case  (				self     :    Any		):
										if self.task == "multiple-choice":
													lowerCamelCase						:Optional[Any]						  =     {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
										else:
													lowerCamelCase						:List[str]						  =     {0: '''batch''', 1: '''sequence'''}
										return OrderedDict(
										    [
										        ('''input_ids''', dynamic_axis),
										        ('''attention_mask''', dynamic_axis),
										    ]		)



