


"""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, 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       (   snake_case_					,							unittest.TestCase			):
   __UpperCAmelCase				: int													=						KandinskyImgaImgPipeline
   __UpperCAmelCase				: int													=						['''prompt''', '''image_embeds''', '''negative_image_embeds''', '''image''']
   __UpperCAmelCase				: Optional[Any]													=						[
       '''prompt''',
       '''negative_prompt''',
       '''image_embeds''',
       '''negative_image_embeds''',
       '''image''',
   ]
   __UpperCAmelCase				: int													=						[
       '''generator''',
       '''height''',
       '''width''',
       '''strength''',
       '''guidance_scale''',
       '''negative_prompt''',
       '''num_inference_steps''',
       '''return_dict''',
       '''guidance_scale''',
       '''num_images_per_prompt''',
       '''output_type''',
       '''return_dict''',
   ]
   __UpperCAmelCase				: int													=						False







   @property
   def 				lowerCamelCase	(			self		)			->	Union[str, Any]:

       '''simple docstring'''




       return 32







   @property
   def 				lowerCamelCase	(			self		)			->	Optional[int]:

       '''simple docstring'''




       return 32







   @property
   def 				lowerCamelCase	(			self		)			->	Any:

       '''simple docstring'''




       return self.time_input_dim







   @property
   def 				lowerCamelCase	(			self		)			->	int:

       '''simple docstring'''




       return self.time_input_dim * 4







   @property
   def 				lowerCamelCase	(			self		)			->	List[str]:

       '''simple docstring'''




       return 100







   @property
   def 				lowerCamelCase	(			self		)			->	List[str]:

       '''simple docstring'''




       snake_case					:   Tuple         =					XLMRobertaTokenizerFast.from_pretrained("YiYiXu/tiny-random-mclip-base"		)
       return tokenizer







   @property
   def 				lowerCamelCase	(			self		)			->	Union[str, Any]:

       '''simple docstring'''




       torch.manual_seed(0		)
       snake_case					:   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		,			)

       snake_case					:   str         =					MultilingualCLIP(UpperCamelCase__		)
       snake_case					:   Optional[Any]         =					text_encoder.eval()

       return text_encoder







   @property
   def 				lowerCamelCase	(			self		)			->	str:

       '''simple docstring'''




       torch.manual_seed(0		)

       snake_case					:   int         =					{
           "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					:   Optional[int]         =					UNetaDConditionModel(**UpperCamelCase__		)
       return model







   @property
   def 				lowerCamelCase	(			self		)			->	Any:

       '''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		)			->	Optional[Any]:

       '''simple docstring'''




       torch.manual_seed(0		)
       snake_case					:   List[Any]         =					VQModel(**self.dummy_movq_kwargs		)
       return model







   def 				lowerCamelCase	(			self		)			->	Optional[int]:

       '''simple docstring'''




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

       snake_case					:   Optional[Any]         =					{
           "num_train_timesteps": 1000,
           "beta_schedule": "linear",
           "beta_start": 0.00085,
           "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(**UpperCamelCase__		)

       snake_case					:   List[str]         =					{
           "text_encoder": text_encoder,
           "tokenizer": tokenizer,
           "unet": unet,
           "scheduler": scheduler,
           "movq": movq,
       }

       return components







   def 				lowerCamelCase	(			self		,			UpperCamelCase__		,			UpperCamelCase__=0		)			->	str:

       '''simple docstring'''




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

       if str(UpperCamelCase__		).startswith("mps"		):
           snake_case					:   List[str]         =					torch.manual_seed(UpperCamelCase__		)
       else:
           snake_case					:   List[str]         =					torch.Generator(device=UpperCamelCase__		).manual_seed(UpperCamelCase__		)
       snake_case					:   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 				lowerCamelCase	(			self		)			->	Union[str, Any]:

       '''simple docstring'''




       snake_case					:   Any         =					"cpu"

       snake_case					:   Any         =					self.get_dummy_components()

       snake_case					:   Dict         =					self.pipeline_class(**UpperCamelCase__		)
       snake_case					:   Optional[int]         =					pipe.to(UpperCamelCase__		)

       pipe.set_progress_bar_config(disable=UpperCamelCase__		)

       snake_case					:   Optional[Any]         =					pipe(**self.get_dummy_inputs(UpperCamelCase__		)		)
       snake_case					:   Any         =					output.images

       snake_case					:   str         =					pipe(
           **self.get_dummy_inputs(UpperCamelCase__		)		,			return_dict=UpperCamelCase__		,			)[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					:   List[Any]         =					np.array(
           [0.61474943, 0.6073539, 0.43308544, 0.5928269, 0.47493595, 0.46755973, 0.4613838, 0.45368797, 0.50119233]		)
       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 				lowerCamelCase	(			self		)			->	List[Any]:

       '''simple docstring'''




       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()







   def 				lowerCamelCase	(			self		)			->	Dict:

       '''simple docstring'''




       snake_case					:   Any         =					load_numpy(
           "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
           "/kandinsky/kandinsky_img2img_frog.npy"		)

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

       snake_case					:   Optional[Any]         =					KandinskyPriorPipeline.from_pretrained(
           "kandinsky-community/kandinsky-2-1-prior"		,			torch_dtype=torch.floataa		)
       pipe_prior.to(UpperCamelCase__		)

       snake_case					:   List[Any]         =					KandinskyImgaImgPipeline.from_pretrained(
           "kandinsky-community/kandinsky-2-1"		,			torch_dtype=torch.floataa		)
       snake_case					:   Optional[int]         =					pipeline.to(UpperCamelCase__		)

       pipeline.set_progress_bar_config(disable=UpperCamelCase__		)

       snake_case					:   Optional[int]         =					torch.Generator(device="cpu"		).manual_seed(0		)
       snake_case      ,snake_case					:   List[str]         =					pipe_prior(
           UpperCamelCase__		,			generator=UpperCamelCase__		,			num_inference_steps=5		,			negative_prompt=""		,			).to_tuple()

       snake_case					:   Optional[Any]         =					pipeline(
           UpperCamelCase__		,			image=UpperCamelCase__		,			image_embeds=UpperCamelCase__		,			negative_image_embeds=UpperCamelCase__		,			generator=UpperCamelCase__		,			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(UpperCamelCase__		,			UpperCamelCase__		)









"""simple docstring"""



from typing import Any







def 						__lowerCAmelCase		(		lowercase						:						list		,							lowercase						:						list		,							lowercase						:						dict		,							lowercase						:						dict		,							lowercase						:						dict		,							)			->		list:


    """simple docstring"""





    _validation(
        lowercase		,							lowercase		,							lowercase		,							lowercase		,							lowercase		,							)
    # Creates data structures and fill initial step
    snake_case					:   dict         =					{}
    snake_case					:   dict         =					{}
    for state in states_space:
        snake_case					:   int         =					observations_space[0]
        snake_case					:   Any         =					(
            initial_probabilities[state] * emission_probabilities[state][observation]
        )
        snake_case					:   Union[str, Any]         =					None

    # Fills the data structure with the probabilities of
    # different transitions and pointers to previous states
    for o in range(1		,							len(lowercase				)				):
        snake_case					:   Optional[Any]         =					observations_space[o]
        snake_case					:   str         =					observations_space[o - 1]
        for state in states_space:
            # Calculates the argmax for probability function
            snake_case					:   str         =					""
            snake_case					:   List[Any]         =					-1
            for k_state in states_space:
                snake_case					:   Tuple         =					(
                    probabilities[(k_state, prior_observation)]
                    * transition_probabilities[k_state][state]
                    * emission_probabilities[state][observation]
                )
                if probability > max_probability:
                    snake_case					:   Optional[Any]         =					probability
                    snake_case					:   int         =					k_state

            # Update probabilities and pointers dicts
            snake_case					:   List[str]         =					(
                probabilities[(arg_max, prior_observation)]
                * transition_probabilities[arg_max][state]
                * emission_probabilities[state][observation]
            )

            snake_case					:   List[Any]         =					arg_max

    # The final observation
    snake_case					:   Dict         =					observations_space[len(lowercase				) - 1]

    # argmax for given final observation
    snake_case					:   str         =					""
    snake_case					:   Optional[int]         =					-1
    for k_state in states_space:
        snake_case					:   int         =					probabilities[(k_state, final_observation)]
        if probability > max_probability:
            snake_case					:   Optional[int]         =					probability
            snake_case					:   List[Any]         =					k_state
    snake_case					:   str         =					arg_max

    # Process pointers backwards
    snake_case					:   List[str]         =					last_state
    snake_case					:   Optional[int]         =					[]
    for o in range(len(lowercase				) - 1		,							-1		,							-1				):
        result.append(lowercase				)
        snake_case					:   List[str]         =					pointers[previous, observations_space[o]]
    result.reverse()

    return result







def 						__lowerCAmelCase		(		lowercase						:						Any		,							lowercase						:						Any		,							lowercase						:						Any		,							lowercase						:						Any		,							lowercase						:						Any		,							)			->		None:


    """simple docstring"""





    _validate_not_empty(
        lowercase		,							lowercase		,							lowercase		,							lowercase		,							lowercase		,							)
    _validate_lists(lowercase		,							lowercase				)
    _validate_dicts(
        lowercase		,							lowercase		,							lowercase				)







def 						__lowerCAmelCase		(		lowercase						:						Any		,							lowercase						:						Any		,							lowercase						:						Any		,							lowercase						:						Any		,							lowercase						:						Any		,							)			->		None:


    """simple docstring"""





    if not all(
        [
            observations_space,
            states_space,
            initial_probabilities,
            transition_probabilities,
            emission_probabilities,
        ]				):
        raise ValueError("There's an empty parameter"				)







def 						__lowerCAmelCase		(		lowercase						:						Any		,							lowercase						:						Any				)			->		None:


    """simple docstring"""





    _validate_list(lowercase		,							"observations_space"				)
    _validate_list(lowercase		,							"states_space"				)







def 						__lowerCAmelCase		(		lowercase						:						Any		,							lowercase						:						str				)			->		None:


    """simple docstring"""





    if not isinstance(_object		,							lowercase				):
        snake_case					:   List[str]         =					F'{var_name} must be a list'
        raise ValueError(lowercase				)
    else:
        for x in _object:
            if not isinstance(lowercase		,							lowercase				):
                snake_case					:   Tuple         =					F'{var_name} must be a list of strings'
                raise ValueError(lowercase				)







def 						__lowerCAmelCase		(		lowercase						:						Any		,							lowercase						:						Any		,							lowercase						:						Any		,							)			->		None:


    """simple docstring"""





    _validate_dict(lowercase		,							"initial_probabilities"		,							lowercase				)
    _validate_nested_dict(lowercase		,							"transition_probabilities"				)
    _validate_nested_dict(lowercase		,							"emission_probabilities"				)







def 						__lowerCAmelCase		(		lowercase						:						Any		,							lowercase						:						str				)			->		None:


    """simple docstring"""





    _validate_dict(_object		,							lowercase		,							lowercase				)
    for x in _object.values():
        _validate_dict(lowercase		,							lowercase		,							lowercase		,							lowercase				)







def 						__lowerCAmelCase		(		lowercase						:						Any		,							lowercase						:						str		,							lowercase						:						type		,							lowercase						:						bool = False				)			->		None:


    """simple docstring"""





    if not isinstance(_object		,							lowercase				):
        snake_case					:   int         =					F'{var_name} must be a dict'
        raise ValueError(lowercase				)
    if not all(isinstance(lowercase		,							lowercase				) for x in _object				):
        snake_case					:   Optional[Any]         =					F'{var_name} all keys must be strings'
        raise ValueError(lowercase				)
    if not all(isinstance(lowercase		,							lowercase				) for x in _object.values()				):
        snake_case					:   Optional[int]         =					"nested dictionary " if nested else ""
        snake_case					:   int         =					F'{var_name} {nested_text}all values must be {value_type.__name__}'
        raise ValueError(lowercase				)


if __name__ == "__main__":
  from doctest import testmod

  testmod()





