






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   __SCREAMING_SNAKE_CASE(	a_    ,  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 lowerCAmelCase_ (     self:      Union[str, Any]						)		->		Optional[int]:
											return 32
							@property
							def lowerCAmelCase_ (     self:      Dict						)		->		int:
											return 32
							@property
							def lowerCAmelCase_ (     self:      Optional[int]						)		->		Dict:
											return self.time_input_dim
							@property
							def lowerCAmelCase_ (     self:      str						)		->		Dict:
											return self.time_input_dim * 4
							@property
							def lowerCAmelCase_ (     self:      Tuple						)		->		Optional[int]:
											return 1_00
							@property
							def lowerCAmelCase_ (     self:      Tuple						)		->		int:
											snake_case__									=     XLMRobertaTokenizerFast.from_pretrained('YiYiXu/tiny-random-mclip-base'						)
											return tokenizer
							@property
							def lowerCAmelCase_ (     self:      Union[str, Any]						)		->		Tuple:
											torch.manual_seed(0						)
											snake_case__									=     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       ,	)

											snake_case__									=     MultilingualCLIP(UpperCamelCase						)
											snake_case__									=     text_encoder.eval()

											return text_encoder
							@property
							def lowerCAmelCase_ (     self:      List[Any]						)		->		Tuple:
											torch.manual_seed(0						)

											snake_case__									=     {
											    '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,
											}

											snake_case__									=     UNetaDConditionModel(**UpperCamelCase						)
											return model
							@property
							def lowerCAmelCase_ (     self:      int						)		->		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 lowerCAmelCase_ (     self:      Optional[int]						)		->		List[str]:
											torch.manual_seed(0						)
											snake_case__									=     VQModel(**self.dummy_movq_kwargs						)
											return model
							def lowerCAmelCase_ (     self:      Union[str, Any]						)		->		Tuple:
											snake_case__									=     self.dummy_text_encoder
											snake_case__									=     self.dummy_tokenizer
											snake_case__									=     self.dummy_unet
											snake_case__									=     self.dummy_movq

											snake_case__									=     {
											    'num_train_timesteps': 10_00,
											    'beta_schedule': 'linear',
											    'beta_start': 0.00_085,
											    'beta_end': 0.012,
											    'clip_sample': False,
											    'set_alpha_to_one': False,
											    'steps_offset': 0,
											    'prediction_type': 'epsilon',
											    'thresholding': False,
											}

											snake_case__									=     DDIMScheduler(**UpperCamelCase						)

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

											return components
							def lowerCAmelCase_ (     self:      Union[str, Any]       ,	UpperCamelCase:      Dict       ,	UpperCamelCase:      Union[str, Any]=0						)		->		Optional[Any]:
											snake_case__									=     floats_tensor((1, self.cross_attention_dim)       ,	rng=random.Random(UpperCamelCase						)						).to(UpperCamelCase						)
											snake_case__									=     floats_tensor((1, self.cross_attention_dim)       ,	rng=random.Random(seed + 1						)						).to(UpperCamelCase						)
											# create init_image
											snake_case__									=     floats_tensor((1, 3, 64, 64)       ,	rng=random.Random(UpperCamelCase						)						).to(UpperCamelCase						)
											snake_case__									=     image.cpu().permute(0       ,	2       ,	3       ,	1						)[0]
											snake_case__									=     Image.fromarray(np.uinta(UpperCamelCase						)						).convert('RGB'						).resize((2_56, 2_56)						)

											if str(UpperCamelCase						).startswith('mps'						):
															snake_case__									=     torch.manual_seed(UpperCamelCase						)
											else:
															snake_case__									=     torch.Generator(device=UpperCamelCase						).manual_seed(UpperCamelCase						)
											snake_case__									=     {
											    '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 lowerCAmelCase_ (     self:      Union[str, Any]						)		->		Optional[int]:
											snake_case__									=     'cpu'

											snake_case__									=     self.get_dummy_components()

											snake_case__									=     self.pipeline_class(**UpperCamelCase						)
											snake_case__									=     pipe.to(UpperCamelCase						)

											pipe.set_progress_bar_config(disable=UpperCamelCase						)

											snake_case__									=     pipe(**self.get_dummy_inputs(UpperCamelCase						)						)
											snake_case__									=     output.images

											snake_case__									=     pipe(
											    **self.get_dummy_inputs(UpperCamelCase						)       ,	return_dict=UpperCamelCase       ,	)[0]

											snake_case__									=     image[0, -3:, -3:, -1]
											snake_case__									=     image_from_tuple[0, -3:, -3:, -1]

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

											snake_case__									=     np.array(
											    [0.61_474_943, 0.6_073_539, 0.43_308_544, 0.5_928_269, 0.47_493_595, 0.46_755_973, 0.4_613_838, 0.45_368_797, 0.50_119_233]						)
											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   __SCREAMING_SNAKE_CASE(	unittest.TestCase	):
							def lowerCAmelCase_ (     self:      List[str]						)		->		List[str]:
											# clean up the VRAM after each test
											super().tearDown()
											gc.collect()
											torch.cuda.empty_cache()


							def lowerCAmelCase_ (     self:      Dict						)		->		int:
											snake_case__									=     load_numpy(
											    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
											    '/kandinsky/kandinsky_img2img_frog.npy'						)

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

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

											snake_case__									=     KandinskyImgaImgPipeline.from_pretrained(
											    'kandinsky-community/kandinsky-2-1'       ,	torch_dtype=torch.floataa						)
											snake_case__									=     pipeline.to(UpperCamelCase						)

											pipeline.set_progress_bar_config(disable=UpperCamelCase						)

											snake_case__									=     torch.Generator(device='cpu'						).manual_seed(0						)
											snake_case__							,						snake_case__									=     pipe_prior(
											    UpperCamelCase       ,	generator=UpperCamelCase       ,	num_inference_steps=5       ,	negative_prompt=''       ,	).to_tuple()

											snake_case__									=     pipeline(
											    UpperCamelCase       ,	image=UpperCamelCase       ,	image_embeds=UpperCamelCase       ,	negative_image_embeds=UpperCamelCase       ,	generator=UpperCamelCase       ,	num_inference_steps=1_00       ,	height=7_68       ,	width=7_68       ,	strength=0.2       ,	output_type='np'       ,	)

											snake_case__									=     output.images[0]

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

											assert_mean_pixel_difference(UpperCamelCase       ,	UpperCamelCase						)












from collections import OrderedDict
from typing import Mapping

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


__UpperCamelCase					:		Any	  =						logging.get_logger(__name__)

__UpperCamelCase					:		int	  =						{
    """junnyu/roformer_chinese_small""": """https://huggingface.co/junnyu/roformer_chinese_small/resolve/main/config.json""",
    """junnyu/roformer_chinese_base""": """https://huggingface.co/junnyu/roformer_chinese_base/resolve/main/config.json""",
    """junnyu/roformer_chinese_char_small""": (
        """https://huggingface.co/junnyu/roformer_chinese_char_small/resolve/main/config.json"""
    ),
    """junnyu/roformer_chinese_char_base""": (
        """https://huggingface.co/junnyu/roformer_chinese_char_base/resolve/main/config.json"""
    ),
    """junnyu/roformer_small_discriminator""": (
        """https://huggingface.co/junnyu/roformer_small_discriminator/resolve/main/config.json"""
    ),
    """junnyu/roformer_small_generator""": (
        """https://huggingface.co/junnyu/roformer_small_generator/resolve/main/config.json"""
    ),
    # See all RoFormer models at https://huggingface.co/models?filter=roformer
}


class   __SCREAMING_SNAKE_CASE(	a_	):
							_UpperCAmelCase     =       "roformer"
							def __init__(     self:      Tuple       ,	UpperCamelCase:      Optional[Any]=5_00_00       ,	UpperCamelCase:      str=None       ,	UpperCamelCase:      Any=7_68       ,	UpperCamelCase:      Dict=12       ,	UpperCamelCase:      List[Any]=12       ,	UpperCamelCase:      List[str]=30_72       ,	UpperCamelCase:      int="gelu"       ,	UpperCamelCase:      str=0.1       ,	UpperCamelCase:      Union[str, Any]=0.1       ,	UpperCamelCase:      Any=15_36       ,	UpperCamelCase:      Dict=2       ,	UpperCamelCase:      Dict=0.02       ,	UpperCamelCase:      List[str]=1e-12       ,	UpperCamelCase:      int=0       ,	UpperCamelCase:      Any=False       ,	UpperCamelCase:      int=True       ,	**UpperCamelCase:      List[Any]       ,	)		->		List[str]:
											super().__init__(pad_token_id=UpperCamelCase       ,	**UpperCamelCase						)

											snake_case__									=     vocab_size
											snake_case__									=     hidden_size if embedding_size is None else embedding_size
											snake_case__									=     hidden_size
											snake_case__									=     num_hidden_layers
											snake_case__									=     num_attention_heads
											snake_case__									=     hidden_act
											snake_case__									=     intermediate_size
											snake_case__									=     hidden_dropout_prob
											snake_case__									=     attention_probs_dropout_prob
											snake_case__									=     max_position_embeddings
											snake_case__									=     type_vocab_size
											snake_case__									=     initializer_range
											snake_case__									=     layer_norm_eps
											snake_case__									=     rotary_value
											snake_case__									=     use_cache


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




