





'''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        UpperCAmelCase     (	UpperCamelCase__      ,	unittest.TestCase    ):
 __lowercase       		=    KandinskyImgaImgPipeline
 __lowercase       		=    ["""prompt""", """image_embeds""", """negative_image_embeds""", """image"""]
 __lowercase       		=    [
     """prompt""",
     """negative_prompt""",
     """image_embeds""",
     """negative_image_embeds""",
     """image""",
 ]
 __lowercase       		=    [
     """generator""",
     """height""",
     """width""",
     """strength""",
     """guidance_scale""",
     """negative_prompt""",
     """num_inference_steps""",
     """return_dict""",
     """guidance_scale""",
     """num_images_per_prompt""",
     """output_type""",
     """return_dict""",
 ]
 __lowercase       		=    False
 @property
 def 					UpperCAmelCase_							(    self				:Any							)->							int:
     return 32
 @property
 def 					UpperCAmelCase_							(    self				:Any							)->							Dict:
     return 32
 @property
 def 					UpperCAmelCase_							(    self				:Optional[int]							)->							List[str]:
     return self.time_input_dim
 @property
 def 					UpperCAmelCase_							(    self				:List[str]							)->							Tuple:
     return self.time_input_dim * 4
 @property
 def 					UpperCAmelCase_							(    self				:Optional[Any]							)->							List[Any]:
     return 1_00
 @property
 def 					UpperCAmelCase_							(    self				:Any							)->							Optional[int]:
     A__    =      XLMRobertaTokenizerFast.from_pretrained("YiYiXu/tiny-random-mclip-base"							)
     return tokenizer
 @property
 def 					UpperCAmelCase_							(    self				:Tuple							)->							List[Any]:
     torch.manual_seed(0							)
     A__    =      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						,	)

     A__    =      MultilingualCLIP(lowercase_							)
     A__    =      text_encoder.eval()

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

     A__    =      {
         "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,
     }

     A__    =      UNetaDConditionModel(**lowercase_							)
     return model
 @property
 def 					UpperCAmelCase_							(    self				:int							)->							Optional[int]:
     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				:Dict							)->							str:
     torch.manual_seed(0							)
     A__    =      VQModel(**self.dummy_movq_kwargs							)
     return model
 def 					UpperCAmelCase_							(    self				:Any							)->							List[str]:
     A__    =      self.dummy_text_encoder
     A__    =      self.dummy_tokenizer
     A__    =      self.dummy_unet
     A__    =      self.dummy_movq

     A__    =      {
         "num_train_timesteps": 10_00,
         "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,
     }

     A__    =      DDIMScheduler(**lowercase_							)

     A__    =      {
         "text_encoder": text_encoder,
         "tokenizer": tokenizer,
         "unet": unet,
         "scheduler": scheduler,
         "movq": movq,
     }

     return components
 def 					UpperCAmelCase_							(    self				:int						,	lowercase_				:Optional[Any]						,	lowercase_				:List[Any]=0							)->							str:
     A__    =      floats_tensor((1, self.cross_attention_dim)						,	rng=random.Random(lowercase_							)							).to(lowercase_							)
     A__    =      floats_tensor((1, self.cross_attention_dim)						,	rng=random.Random(seed + 1							)							).to(lowercase_							)
     # create init_image
     A__    =      floats_tensor((1, 3, 64, 64)						,	rng=random.Random(lowercase_							)							).to(lowercase_							)
     A__    =      image.cpu().permute(0						,	2						,	3						,	1							)[0]
     A__    =      Image.fromarray(np.uinta(lowercase_							)							).convert("RGB"							).resize((2_56, 2_56)							)

     if str(lowercase_							).startswith("mps"							):
         A__    =      torch.manual_seed(lowercase_							)
     else:
         A__    =      torch.Generator(device=lowercase_							).manual_seed(lowercase_							)
     A__    =      {
         "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				:Optional[int]							)->							str:
     A__    =      "cpu"

     A__    =      self.get_dummy_components()

     A__    =      self.pipeline_class(**lowercase_							)
     A__    =      pipe.to(lowercase_							)

     pipe.set_progress_bar_config(disable=lowercase_							)

     A__    =      pipe(**self.get_dummy_inputs(lowercase_							)							)
     A__    =      output.images

     A__    =      pipe(
         **self.get_dummy_inputs(lowercase_							)						,	return_dict=lowercase_						,	)[0]

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

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

     A__    =      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        UpperCAmelCase     (	unittest.TestCase    ):
 def 					UpperCAmelCase_							(    self				:str							)->							Union[str, Any]:
     # clean up the VRAM after each test
     super().tearDown()
     gc.collect()
     torch.cuda.empty_cache()



 def 					UpperCAmelCase_							(    self				:List[Any]							)->							Union[str, Any]:
     A__    =      load_numpy(
         "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
         "/kandinsky/kandinsky_img2img_frog.npy"							)

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

     A__    =      KandinskyPriorPipeline.from_pretrained(
         "kandinsky-community/kandinsky-2-1-prior"						,	torch_dtype=torch.floataa							)
     pipe_prior.to(lowercase_							)

     A__    =      KandinskyImgaImgPipeline.from_pretrained(
         "kandinsky-community/kandinsky-2-1"						,	torch_dtype=torch.floataa							)
     A__    =      pipeline.to(lowercase_							)

     pipeline.set_progress_bar_config(disable=lowercase_							)

     A__    =      torch.Generator(device="cpu"							).manual_seed(0							)
     A__,     A__    =      pipe_prior(
         lowercase_						,	generator=lowercase_						,	num_inference_steps=5						,	negative_prompt=""						,	).to_tuple()

     A__    =      pipeline(
         lowercase_						,	image=lowercase_						,	image_embeds=lowercase_						,	negative_image_embeds=lowercase_						,	generator=lowercase_						,	num_inference_steps=1_00						,	height=7_68						,	width=7_68						,	strength=0.2						,	output_type="np"						,	)

     A__    =      output.images[0]

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

     assert_mean_pixel_difference(lowercase_						,	lowercase_							)










'''simple docstring'''

__lowerCAmelCase							:     Dict		     ="\n# Installazione di Transformers\n! pip install transformers datasets\n# Per installare dalla fonte invece dell'ultima versione rilasciata, commenta il comando sopra e\n# rimuovi la modalità commento al comando seguente.\n# ! pip install git+https://github.com/huggingface/transformers.git\n"

__lowerCAmelCase							:     Optional[Any]		     =[{"type": "code", "content": INSTALL_CONTENT}]
__lowerCAmelCase							:     Tuple		     ={
    "{processor_class}": "FakeProcessorClass",
    "{model_class}": "FakeModelClass",
    "{object_class}": "FakeObjectClass",
}



