





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    __snake_case	(							__lowerCamelCase       ,      unittest.TestCase      ):





       '''simple docstring'''







       lowerCAmelCase__						=					KandinskyImgaImgPipeline
       lowerCAmelCase__						=					["""prompt""", """image_embeds""", """negative_image_embeds""", """image"""]
       lowerCAmelCase__						=					[
           """prompt""",
           """negative_prompt""",
           """image_embeds""",
           """negative_image_embeds""",
           """image""",
       ]
       lowerCAmelCase__						=					[
           """generator""",
           """height""",
           """width""",
           """strength""",
           """guidance_scale""",
           """negative_prompt""",
           """num_inference_steps""",
           """return_dict""",
           """guidance_scale""",
           """num_images_per_prompt""",
           """output_type""",
           """return_dict""",
       ]
       lowerCAmelCase__						=					False




       @property
       def 							UpperCAmelCase__			(  self			:   Dict		):
         return 32




       @property
       def 							UpperCAmelCase__			(  self			:   Any		):
         return 32




       @property
       def 							UpperCAmelCase__			(  self			:   str		):
         return self.time_input_dim




       @property
       def 							UpperCAmelCase__			(  self			:   Any		):
         return self.time_input_dim * 4




       @property
       def 							UpperCAmelCase__			(  self			:   List[Any]		):
         return 100




       @property
       def 							UpperCAmelCase__			(  self			:   Any		):
         __snake_case:						Optional[Any]			  =						XLMRobertaTokenizerFast.from_pretrained("""YiYiXu/tiny-random-mclip-base"""		)
         return tokenizer




       @property
       def 							UpperCAmelCase__			(  self			:   Tuple		):
         torch.manual_seed(0		)
         __snake_case:						str			  =						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=1_005							,    )

         __snake_case:						Any			  =						MultilingualCLIP(A		)
         __snake_case:						List[Any]			  =						text_encoder.eval()

         return text_encoder




       @property
       def 							UpperCAmelCase__			(  self			:   Union[str, Any]		):
         torch.manual_seed(0		)

         __snake_case:						str			  =						{
             """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:						Union[str, Any]			  =						UNetaDConditionModel(**A		)
         return model




       @property
       def 							UpperCAmelCase__			(  self			:   Optional[Any]		):
         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 							UpperCAmelCase__			(  self			:   Optional[Any]		):
         torch.manual_seed(0		)
         __snake_case:						Union[str, Any]			  =						VQModel(**self.dummy_movq_kwargs		)
         return model




       def 							UpperCAmelCase__			(  self			:   Union[str, Any]		):
         __snake_case:						Any			  =						self.dummy_text_encoder
         __snake_case:						Optional[Any]			  =						self.dummy_tokenizer
         __snake_case:						Dict			  =						self.dummy_unet
         __snake_case:						Optional[int]			  =						self.dummy_movq

         __snake_case:						Dict			  =						{
             """num_train_timesteps""": 1_000,
             """beta_schedule""": """linear""",
             """beta_start""": 0.0_0085,
             """beta_end""": 0.012,
             """clip_sample""": False,
             """set_alpha_to_one""": False,
             """steps_offset""": 0,
             """prediction_type""": """epsilon""",
             """thresholding""": False,
         }

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

         __snake_case:						Union[str, Any]			  =						{
             """text_encoder""": text_encoder,
             """tokenizer""": tokenizer,
             """unet""": unet,
             """scheduler""": scheduler,
             """movq""": movq,
         }

         return components




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

         if str(A		).startswith("""mps"""		):
           __snake_case:						List[Any]			  =						torch.manual_seed(A		)
         else:
           __snake_case:						Union[str, Any]			  =						torch.Generator(device=A		).manual_seed(A		)
         __snake_case:						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 							UpperCAmelCase__			(  self			:   Tuple		):
         __snake_case:						Optional[Any]			  =						"""cpu"""

         __snake_case:						int			  =						self.get_dummy_components()

         __snake_case:						List[str]			  =						self.pipeline_class(**A		)
         __snake_case:						List[Any]			  =						pipe.to(A		)

         pipe.set_progress_bar_config(disable=A		)

         __snake_case:						int			  =						pipe(**self.get_dummy_inputs(A		)		)
         __snake_case:						Optional[int]			  =						output.images

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

         __snake_case:						Tuple			  =						image[0, -3:, -3:, -1]
         __snake_case:						int			  =						image_from_tuple[0, -3:, -3:, -1]

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

         __snake_case:						int			  =						np.array(
             [0.6147_4943, 0.607_3539, 0.4330_8544, 0.592_8269, 0.4749_3595, 0.4675_5973, 0.461_3838, 0.4536_8797, 0.5011_9233]		)
         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    __snake_case	(							unittest.TestCase      ):





       '''simple docstring'''







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






       def 							UpperCAmelCase__			(  self			:   Optional[Any]		):
         __snake_case:						Optional[Any]			  =						load_numpy(
             """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
             """/kandinsky/kandinsky_img2img_frog.npy"""		)

         __snake_case:						List[Any]			  =						load_image(
             """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main""" """/kandinsky/cat.png"""		)
         __snake_case:						int			  =						"""A red cartoon frog, 4k"""

         __snake_case:						Any			  =						KandinskyPriorPipeline.from_pretrained(
             """kandinsky-community/kandinsky-2-1-prior"""							,    torch_dtype=torch.floataa		)
         pipe_prior.to(A		)

         __snake_case:						List[Any]			  =						KandinskyImgaImgPipeline.from_pretrained(
             """kandinsky-community/kandinsky-2-1"""							,    torch_dtype=torch.floataa		)
         __snake_case:						List[str]			  =						pipeline.to(A		)

         pipeline.set_progress_bar_config(disable=A		)

         __snake_case:						Tuple			  =						torch.Generator(device="""cpu"""		).manual_seed(0		)
         __snake_case  ,       __snake_case:						Tuple			  =						pipe_prior(
             A							,    generator=A							,    num_inference_steps=5							,    negative_prompt=""""""							,    ).to_tuple()

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

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

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

         assert_mean_pixel_difference(A							,    A		)













from ...configuration_utils import PretrainedConfig
from ...utils import logging


__UpperCAmelCase	:       str    	=							logging.get_logger(__name__)

__UpperCAmelCase	:       int    	=							{
    "RWKV/rwkv-4-169m-pile": "https://huggingface.co/RWKV/rwkv-4-169m-pile/resolve/main/config.json",
    "RWKV/rwkv-4-430m-pile": "https://huggingface.co/RWKV/rwkv-4-430m-pile/resolve/main/config.json",
    "RWKV/rwkv-4-1b5-pile": "https://huggingface.co/RWKV/rwkv-4-1b5-pile/resolve/main/config.json",
    "RWKV/rwkv-4-3b-pile": "https://huggingface.co/RWKV/rwkv-4-3b-pile/resolve/main/config.json",
    "RWKV/rwkv-4-7b-pile": "https://huggingface.co/RWKV/rwkv-4-7b-pile/resolve/main/config.json",
    "RWKV/rwkv-4-14b-pile": "https://huggingface.co/RWKV/rwkv-4-14b-pile/resolve/main/config.json",
    "RWKV/rwkv-raven-1b5": "https://huggingface.co/RWKV/rwkv-raven-1b5/resolve/main/config.json",
    "RWKV/rwkv-raven-3b": "https://huggingface.co/RWKV/rwkv-raven-3b/resolve/main/config.json",
    "RWKV/rwkv-raven-7b": "https://huggingface.co/RWKV/rwkv-raven-7b/resolve/main/config.json",
    "RWKV/rwkv-raven-14b": "https://huggingface.co/RWKV/rwkv-raven-14b/resolve/main/config.json",
}







class    __snake_case	(							__lowerCamelCase      ):





       '''simple docstring'''







       lowerCAmelCase__						=					"""rwkv"""
       lowerCAmelCase__						=					{"""max_position_embeddings""": """context_length"""}




       def __init__(  self			:   Dict							,    A			:   List[Any]=50_277							,    A			:   List[Any]=1_024							,    A			:   Union[str, Any]=4_096							,    A			:   Tuple=32							,    A			:   List[Any]=None							,    A			:   Tuple=None							,    A			:   Tuple=1E-5							,    A			:   int=0							,    A			:   Optional[int]=0							,    A			:   Dict=6							,    A			:   Dict=False							,    A			:   int=True							,    **A			:   List[Any]							,    ):
         __snake_case:						Tuple			  =						vocab_size
         __snake_case:						Any			  =						context_length
         __snake_case:						Dict			  =						hidden_size
         __snake_case:						Dict			  =						num_hidden_layers
         __snake_case:						Union[str, Any]			  =						attention_hidden_size if attention_hidden_size is not None else hidden_size
         __snake_case:						str			  =						intermediate_size if intermediate_size is not None else 4 * hidden_size
         __snake_case:						Any			  =						layer_norm_epsilon
         __snake_case:						int			  =						rescale_every
         __snake_case:						str			  =						use_cache

         __snake_case:						Dict			  =						bos_token_id
         __snake_case:						Union[str, Any]			  =						eos_token_id

         super().__init__(
             tie_word_embeddings=A							,    bos_token_id=A							,    eos_token_id=A							,    **A		)






