

'''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        A_ (				A_       ,   unittest.TestCase							):






     '''simple docstring'''



     UpperCAmelCase_						:	int  		=  KandinskyImgaImgPipeline
     UpperCAmelCase_						:	List[Any]  		=  ["prompt", "image_embeds", "negative_image_embeds", "image"]
     UpperCAmelCase_						:	str  		=  [
         "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_						:	Optional[Any]  		=  False


     @property
     def    UpperCAmelCase_       (		self :     List[Any]       )						->	Optional[Any]:
      return 32


     @property
     def    UpperCAmelCase_       (		self :     Tuple       )						->	Dict:
      return 32


     @property
     def    UpperCAmelCase_       (		self :     List[Any]       )						->	List[str]:
      return self.time_input_dim


     @property
     def    UpperCAmelCase_       (		self :     int       )						->	Union[str, Any]:
      return self.time_input_dim * 4


     @property
     def    UpperCAmelCase_       (		self :     Optional[Any]       )						->	List[str]:
      return 100


     @property
     def    UpperCAmelCase_       (		self :     List[str]       )						->	str:
      UpperCAmelCase       :	Union[str, Any]											= XLMRobertaTokenizerFast.from_pretrained('YiYiXu/tiny-random-mclip-base'       )
      return tokenizer


     @property
     def    UpperCAmelCase_       (		self :     List[Any]       )						->	int:
      torch.manual_seed(0       )
      UpperCAmelCase       :	Union[str, 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=1_005     ,			)

      UpperCAmelCase       :	List[str]											= MultilingualCLIP(A_       )
      UpperCAmelCase       :	Optional[Any]											= text_encoder.eval()

      return text_encoder


     @property
     def    UpperCAmelCase_       (		self :     Optional[int]       )						->	int:
      torch.manual_seed(0       )

      UpperCAmelCase       :	Optional[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,
      }

      UpperCAmelCase       :	Tuple											= UNetaDConditionModel(**A_       )
      return model


     @property
     def    UpperCAmelCase_       (		self :     Tuple       )						->	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 :     int       )						->	int:
      torch.manual_seed(0       )
      UpperCAmelCase       :	Dict											= VQModel(**self.dummy_movq_kwargs       )
      return model


     def    UpperCAmelCase_       (		self :     List[str]       )						->	Any:
      UpperCAmelCase       :	List[str]											= self.dummy_text_encoder
      UpperCAmelCase       :	Optional[int]											= self.dummy_tokenizer
      UpperCAmelCase       :	List[Any]											= self.dummy_unet
      UpperCAmelCase       :	Union[str, Any]											= self.dummy_movq

      UpperCAmelCase       :	str											= {
          '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,
      }

      UpperCAmelCase       :	List[str]											= DDIMScheduler(**A_       )

      UpperCAmelCase       :	List[str]											= {
          'text_encoder': text_encoder,
          'tokenizer': tokenizer,
          'unet': unet,
          'scheduler': scheduler,
          'movq': movq,
      }

      return components


     def    UpperCAmelCase_       (		self :     Tuple     ,			lowercase_ :     Any     ,			lowercase_ :     List[str]=0       )						->	int:
      UpperCAmelCase       :	int											= floats_tensor((1, self.cross_attention_dim)     ,			rng=random.Random(A_       )       ).to(A_       )
      UpperCAmelCase       :	Dict											= floats_tensor((1, self.cross_attention_dim)     ,			rng=random.Random(seed + 1       )       ).to(A_       )
      # create init_image
      UpperCAmelCase       :	Optional[Any]											= floats_tensor((1, 3, 64, 64)     ,			rng=random.Random(A_       )       ).to(A_       )
      UpperCAmelCase       :	List[Any]											= image.cpu().permute(0     ,			2     ,			3     ,			1       )[0]
      UpperCAmelCase       :	Dict											= Image.fromarray(np.uinta(A_       )       ).convert('RGB'       ).resize((256, 256)       )

      if str(A_       ).startswith('mps'       ):
       UpperCAmelCase       :	List[str]											= torch.manual_seed(A_       )
      else:
       UpperCAmelCase       :	List[str]											= torch.Generator(device=A_       ).manual_seed(A_       )
      UpperCAmelCase       :	List[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 :     Optional[int]       )						->	Dict:
      UpperCAmelCase       :	List[Any]											= 'cpu'

      UpperCAmelCase       :	Any											= self.get_dummy_components()

      UpperCAmelCase       :	int											= self.pipeline_class(**A_       )
      UpperCAmelCase       :	Tuple											= pipe.to(A_       )

      pipe.set_progress_bar_config(disable=A_       )

      UpperCAmelCase       :	Tuple											= pipe(**self.get_dummy_inputs(A_       )       )
      UpperCAmelCase       :	Union[str, Any]											= output.images

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

      UpperCAmelCase       :	List[Any]											= image[0, -3:, -3:, -1]
      UpperCAmelCase       :	Union[str, Any]											= image_from_tuple[0, -3:, -3:, -1]

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

      UpperCAmelCase       :	Any											= 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        A_ (				unittest.TestCase							):






     '''simple docstring'''



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




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

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

      UpperCAmelCase       :	Tuple											= KandinskyPriorPipeline.from_pretrained(
          'kandinsky-community/kandinsky-2-1-prior'     ,			torch_dtype=torch.floataa       )
      pipe_prior.to(A_       )

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

      pipeline.set_progress_bar_config(disable=A_       )

      UpperCAmelCase       :	List[str]											= torch.Generator(device='cpu'       ).manual_seed(0       )
      UpperCAmelCase					, UpperCAmelCase       :	str											= pipe_prior(
          A_     ,			generator=A_     ,			num_inference_steps=5     ,			negative_prompt=''     ,			).to_tuple()

      UpperCAmelCase       :	Optional[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'     ,			)

      UpperCAmelCase       :	List[str]											= output.images[0]

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

      assert_mean_pixel_difference(A_     ,			A_       )








from collections import OrderedDict
from typing import Mapping

from packaging import version

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


_A		       =						logging.get_logger(__name__)

_A		       =						{
    'hustvl/yolos-small': 'https://huggingface.co/hustvl/yolos-small/resolve/main/config.json',
    # See all YOLOS models at https://huggingface.co/models?filter=yolos
}






class     UpperCAmelCase__       (					A_		):



    """simple docstring"""






    UpperCAmelCase__						:		Optional[int]      =  "yolos"






    def __init__(			self					,   A_=768					,   A_=12					,   A_=12					,   A_=3072					,   A_="gelu"					,   A_=0.0					,   A_=0.0					,   A_=0.02					,   A_=1E-12					,   A_=[512, 864]					,   A_=16					,   A_=3					,   A_=True					,   A_=100					,   A_=True					,   A_=False					,   A_=1					,   A_=5					,   A_=2					,   A_=5					,   A_=2					,   A_=0.1					,   **A_					,   )  ->       Any:
         super().__init__(**A_    )

         __UpperCamelCase    							=hidden_size
         __UpperCamelCase    							=num_hidden_layers
         __UpperCamelCase    							=num_attention_heads
         __UpperCamelCase    							=intermediate_size
         __UpperCamelCase    							=hidden_act
         __UpperCamelCase    							=hidden_dropout_prob
         __UpperCamelCase    							=attention_probs_dropout_prob
         __UpperCamelCase    							=initializer_range
         __UpperCamelCase    							=layer_norm_eps
         __UpperCamelCase    							=image_size
         __UpperCamelCase    							=patch_size
         __UpperCamelCase    							=num_channels
         __UpperCamelCase    							=qkv_bias
         __UpperCamelCase    							=num_detection_tokens
         __UpperCamelCase    							=use_mid_position_embeddings
         __UpperCamelCase    							=auxiliary_loss
         # Hungarian matcher
         __UpperCamelCase    							=class_cost
         __UpperCamelCase    							=bbox_cost
         __UpperCamelCase    							=giou_cost
         # Loss coefficients
         __UpperCamelCase    							=bbox_loss_coefficient
         __UpperCamelCase    							=giou_loss_coefficient
         __UpperCamelCase    							=eos_coefficient






class     UpperCAmelCase__       (					A_		):



    """simple docstring"""






    UpperCAmelCase__						:		str      =  version.parse("1.11"		)






    @property
    def _a					(			self    )  ->       Mapping[str, Mapping[int, str]]:
         return OrderedDict(
             [
                 ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
             ]    )






    @property
    def _a					(			self    )  ->       float:
         return 1E-4






    @property
    def _a					(			self    )  ->       int:
         return 12





