






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					:       List[str]							       = KandinskyImgaImgPipeline
				_UpperCamelCase					:       List[str]							       = ["prompt", "image_embeds", "negative_image_embeds", "image"]
				_UpperCamelCase					:       Dict							       = [
				    "prompt",
				    "negative_prompt",
				    "image_embeds",
				    "negative_image_embeds",
				    "image",
				]
				_UpperCamelCase					:       Optional[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					:       Tuple							       = False


				@property
				def 						__a					(					self      ):
											return 3_2


				@property
				def 						__a					(					self      ):
											return 3_2


				@property
				def 						__a					(					self      ):
											return self.time_input_dim


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


				@property
				def 						__a					(					self      ):
											return 1_0_0


				@property
				def 						__a					(					self      ):
											_lowercase							:   int 							=   XLMRobertaTokenizerFast.from_pretrained('YiYiXu/tiny-random-mclip-base'      )
											return tokenizer


				@property
				def 						__a					(					self      ):
											torch.manual_seed(0      )
											_lowercase							:   Tuple 							=   MCLIPConfig(
											    numDims=self.cross_attention_dim ,						transformerDimensions=self.text_embedder_hidden_size ,						hidden_size=self.text_embedder_hidden_size ,						intermediate_size=3_7 ,						num_attention_heads=4 ,						num_hidden_layers=5 ,						vocab_size=1_0_0_5 ,						)

											_lowercase							:   Any 							=   MultilingualCLIP(_lowerCAmelCase      )
											_lowercase							:   List[str] 							=   text_encoder.eval()

											return text_encoder


				@property
				def 						__a					(					self      ):
											torch.manual_seed(0      )

											_lowercase							:   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,
											}

											_lowercase							:   Tuple 							=   UNetaDConditionModel(**_lowerCAmelCase      )
											return model


				@property
				def 						__a					(					self      ):
											return {
											    "block_out_channels": [3_2, 6_4],
											    "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": 1_2,
											    "out_channels": 3,
											    "up_block_types": [
											        "AttnUpDecoderBlock2D",
											        "UpDecoderBlock2D",
											    ],
											    "vq_embed_dim": 4,
											}


				@property
				def 						__a					(					self      ):
											torch.manual_seed(0      )
											_lowercase							:   Optional[Any] 							=   VQModel(**self.dummy_movq_kwargs      )
											return model


				def 						__a					(					self      ):
											_lowercase							:   Dict 							=   self.dummy_text_encoder
											_lowercase							:   Dict 							=   self.dummy_tokenizer
											_lowercase							:   Any 							=   self.dummy_unet
											_lowercase							:   Optional[int] 							=   self.dummy_movq

											_lowercase							:   Tuple 							=   {
											    'num_train_timesteps': 1_0_0_0,
											    'beta_schedule': 'linear',
											    'beta_start': 0.0_00_85,
											    'beta_end': 0.0_12,
											    'clip_sample': False,
											    'set_alpha_to_one': False,
											    'steps_offset': 0,
											    'prediction_type': 'epsilon',
											    'thresholding': False,
											}

											_lowercase							:   int 							=   DDIMScheduler(**_lowerCAmelCase      )

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

											return components


				def 						__a					(					self ,						_lowerCAmelCase ,						_lowerCAmelCase=0      ):
											_lowercase							:   str 							=   floats_tensor((1, self.cross_attention_dim) ,						rng=random.Random(_lowerCAmelCase      )      ).to(_lowerCAmelCase      )
											_lowercase							:   Union[str, Any] 							=   floats_tensor((1, self.cross_attention_dim) ,						rng=random.Random(seed + 1      )      ).to(_lowerCAmelCase      )
											# create init_image
											_lowercase							:   Tuple 							=   floats_tensor((1, 3, 6_4, 6_4) ,						rng=random.Random(_lowerCAmelCase      )      ).to(_lowerCAmelCase      )
											_lowercase							:   Union[str, Any] 							=   image.cpu().permute(0 ,						2 ,						3 ,						1      )[0]
											_lowercase							:   Union[str, Any] 							=   Image.fromarray(np.uinta(_lowerCAmelCase      )      ).convert('RGB'      ).resize((2_5_6, 2_5_6)      )

											if str(_lowerCAmelCase      ).startswith('mps'      ):
																		_lowercase							:   str 							=   torch.manual_seed(_lowerCAmelCase      )
											else:
																		_lowercase							:   Optional[int] 							=   torch.Generator(device=_lowerCAmelCase      ).manual_seed(_lowerCAmelCase      )
											_lowercase							:   Optional[int] 							=   {
											    'prompt': 'horse',
											    'image': init_image,
											    'image_embeds': image_embeds,
											    'negative_image_embeds': negative_image_embeds,
											    'generator': generator,
											    'height': 6_4,
											    'width': 6_4,
											    'num_inference_steps': 1_0,
											    'guidance_scale': 7.0,
											    'strength': 0.2,
											    'output_type': 'np',
											}
											return inputs


				def 						__a					(					self      ):
											_lowercase							:   List[Any] 							=   'cpu'

											_lowercase							:   Union[str, Any] 							=   self.get_dummy_components()

											_lowercase							:   str 							=   self.pipeline_class(**_lowerCAmelCase      )
											_lowercase							:   List[str] 							=   pipe.to(_lowerCAmelCase      )

											pipe.set_progress_bar_config(disable=_lowerCAmelCase      )

											_lowercase							:   Dict 							=   pipe(**self.get_dummy_inputs(_lowerCAmelCase      )      )
											_lowercase							:   Tuple 							=   output.images

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

											_lowercase							:   Optional[int] 							=   image[0, -3:, -3:, -1]
											_lowercase							:   Any 							=   image_from_tuple[0, -3:, -3:, -1]

											assert image.shape == (1, 6_4, 6_4, 3)

											_lowercase							:   Union[str, Any] 							=   np.array(
											    [0.61_47_49_43, 0.6_07_35_39, 0.43_30_85_44, 0.5_92_82_69, 0.47_49_35_95, 0.46_75_59_73, 0.4_61_38_38, 0.45_36_87_97, 0.50_11_92_33]      )
											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 						__a					(					self      ):
											# clean up the VRAM after each test
											super().tearDown()
											gc.collect()
											torch.cuda.empty_cache()


				def 						__a					(					self      ):
											_lowercase							:   Any 							=   load_numpy(
											    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
											    '/kandinsky/kandinsky_img2img_frog.npy'      )

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

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

											_lowercase							:   Tuple 							=   KandinskyImgaImgPipeline.from_pretrained(
											    'kandinsky-community/kandinsky-2-1' ,						torch_dtype=torch.floataa      )
											_lowercase							:   Tuple 							=   pipeline.to(_lowerCAmelCase      )

											pipeline.set_progress_bar_config(disable=_lowerCAmelCase      )

											_lowercase							:   Tuple 							=   torch.Generator(device='cpu'      ).manual_seed(0      )
											_lowercase							,							_lowercase							:   Any 							=   pipe_prior(
											    _lowerCAmelCase ,						generator=_lowerCAmelCase ,						num_inference_steps=5 ,						negative_prompt='' ,						).to_tuple()

											_lowercase							:   Any 							=   pipeline(
											    _lowerCAmelCase ,						image=_lowerCAmelCase ,						image_embeds=_lowerCAmelCase ,						negative_image_embeds=_lowerCAmelCase ,						generator=_lowerCAmelCase ,						num_inference_steps=1_0_0 ,						height=7_6_8 ,						width=7_6_8 ,						strength=0.2 ,						output_type='np' ,						)

											_lowercase							:   Union[str, Any] 							=   output.images[0]

											assert image.shape == (7_6_8, 7_6_8, 3)

											assert_mean_pixel_difference(_lowerCAmelCase ,						_lowerCAmelCase      )












from pathlib import Path

import fire
def 		lowercase__  (				_UpperCamelCase       ,							_UpperCamelCase       ,							_UpperCamelCase) ->			Optional[int]:
      """simple docstring"""

      UpperCamelCase					       =  Path(_UpperCamelCase)
      UpperCamelCase					       =  Path(_UpperCamelCase)
      dest_dir.mkdir(exist_ok=_UpperCamelCase)
      for path in src_dir.iterdir():
            UpperCamelCase					       =  [x.rstrip() for x in list(path.open().readlines())][:n]
            UpperCamelCase					       =  dest_dir.joinpath(path.name)
            print(_UpperCamelCase)
            dest_path.open('w').write('\n'.join(_UpperCamelCase))


if __name__ == "__main__":
       fire.Fire(minify)



