



from collections import OrderedDict
from typing import List, Mapping

from packaging import version

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


__A       =						logging.get_logger(__name__)

__A       =						{
    '''google/efficientnet-b7''': '''https://huggingface.co/google/efficientnet-b7/resolve/main/config.json''',
}






class 		lowercase						(			snake_case__):






	"""simple docstring"""


	a__				: Tuple		       =							"efficientnet"




	def __init__(     self     :						List[Any]						,  __UpperCAmelCase     :						int = 3						,  __UpperCAmelCase     :						int = 600						,  __UpperCAmelCase     :						float = 2.0						,  __UpperCAmelCase     :						float = 3.1						,  __UpperCAmelCase     :						int = 8						,  __UpperCAmelCase     :						List[int] = [3, 3, 5, 3, 5, 5, 3]						,  __UpperCAmelCase     :						List[int] = [32, 16, 24, 40, 80, 112, 192]						,  __UpperCAmelCase     :						List[int] = [16, 24, 40, 80, 112, 192, 320]						,  __UpperCAmelCase     :						List[int] = []						,  __UpperCAmelCase     :						List[int] = [1, 2, 2, 2, 1, 2, 1]						,  __UpperCAmelCase     :						List[int] = [1, 2, 2, 3, 3, 4, 1]						,  __UpperCAmelCase     :						List[int] = [1, 6, 6, 6, 6, 6, 6]						,  __UpperCAmelCase     :						float = 0.25						,  __UpperCAmelCase     :						str = "swish"						,  __UpperCAmelCase     :						int = 2_560						,  __UpperCAmelCase     :						str = "mean"						,  __UpperCAmelCase     :						float = 0.02						,  __UpperCAmelCase     :						float = 0.001						,  __UpperCAmelCase     :						float = 0.99						,  __UpperCAmelCase     :						float = 0.5						,  __UpperCAmelCase     :						float = 0.2						,  **__UpperCAmelCase     :						int						,  )      ->			List[Any]:
							super().__init__(**__UpperCAmelCase					)

							UpperCAmelCase_=       num_channels
							UpperCAmelCase_=       image_size
							UpperCAmelCase_=       width_coefficient
							UpperCAmelCase_=       depth_coefficient
							UpperCAmelCase_=       depth_divisor
							UpperCAmelCase_=       kernel_sizes
							UpperCAmelCase_=       in_channels
							UpperCAmelCase_=       out_channels
							UpperCAmelCase_=       depthwise_padding
							UpperCAmelCase_=       strides
							UpperCAmelCase_=       num_block_repeats
							UpperCAmelCase_=       expand_ratios
							UpperCAmelCase_=       squeeze_expansion_ratio
							UpperCAmelCase_=       hidden_act
							UpperCAmelCase_=       hidden_dim
							UpperCAmelCase_=       pooling_type
							UpperCAmelCase_=       initializer_range
							UpperCAmelCase_=       batch_norm_eps
							UpperCAmelCase_=       batch_norm_momentum
							UpperCAmelCase_=       dropout_rate
							UpperCAmelCase_=       drop_connect_rate
							UpperCAmelCase_=       sum(__UpperCAmelCase					) * 4






class 		lowercase						(			snake_case__):






	"""simple docstring"""


	a__				: int		       =							version.parse("1.11")




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




	@property
	def   _SCREAMING_SNAKE_CASE		(     self     :						Tuple					)      ->			float:
							return 1E-5









import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyInpaintPipeline, 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 		lowercase						(			snake_case__			,   unittest.TestCase):






	"""simple docstring"""


	a__				: Tuple		       =							KandinskyInpaintPipeline
	a__				: Tuple		       =							["prompt", "image_embeds", "negative_image_embeds", "image", "mask_image"]
	a__				: str		       =							[
	    "prompt",
	    "negative_prompt",
	    "image_embeds",
	    "negative_image_embeds",
	    "image",
	    "mask_image",
	]
	a__				: List[Any]		       =							[
	    "generator",
	    "height",
	    "width",
	    "latents",
	    "guidance_scale",
	    "negative_prompt",
	    "num_inference_steps",
	    "return_dict",
	    "guidance_scale",
	    "num_images_per_prompt",
	    "output_type",
	    "return_dict",
	]
	a__				: Union[str, Any]		       =							False




	@property
	def   _SCREAMING_SNAKE_CASE		(     self     :						Any					)      ->			Any:
							return 32




	@property
	def   _SCREAMING_SNAKE_CASE		(     self     :						Optional[int]					)      ->			Union[str, Any]:
							return 32




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




	@property
	def   _SCREAMING_SNAKE_CASE		(     self     :						Dict					)      ->			Tuple:
							return self.time_input_dim * 4




	@property
	def   _SCREAMING_SNAKE_CASE		(     self     :						int					)      ->			List[str]:
							return 100




	@property
	def   _SCREAMING_SNAKE_CASE		(     self     :						Tuple					)      ->			List[str]:
							UpperCAmelCase_=       XLMRobertaTokenizerFast.from_pretrained("""YiYiXu/tiny-random-mclip-base"""					)
							return tokenizer




	@property
	def   _SCREAMING_SNAKE_CASE		(     self     :						Any					)      ->			List[str]:
							torch.manual_seed(0					)
							UpperCAmelCase_=       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_=       MultilingualCLIP(__UpperCAmelCase					)
							UpperCAmelCase_=       text_encoder.eval()

							return text_encoder




	@property
	def   _SCREAMING_SNAKE_CASE		(     self     :						str					)      ->			Dict:
							torch.manual_seed(0					)

							UpperCAmelCase_=       {
							    """in_channels""": 9,
							    # 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_=       UNetaDConditionModel(**__UpperCAmelCase					)
							return model




	@property
	def   _SCREAMING_SNAKE_CASE		(     self     :						List[Any]					)      ->			str:
							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   _SCREAMING_SNAKE_CASE		(     self     :						Any					)      ->			Optional[int]:
							torch.manual_seed(0					)
							UpperCAmelCase_=       VQModel(**self.dummy_movq_kwargs					)
							return model




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

							UpperCAmelCase_=       DDIMScheduler(
							    num_train_timesteps=1_000						,  beta_schedule="""linear"""						,  beta_start=0.00_085						,  beta_end=0.012						,  clip_sample=__UpperCAmelCase						,  set_alpha_to_one=__UpperCAmelCase						,  steps_offset=1						,  prediction_type="""epsilon"""						,  thresholding=__UpperCAmelCase						,  )

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

							return components




	def   _SCREAMING_SNAKE_CASE		(     self     :						Union[str, Any]						,  __UpperCAmelCase     :						Tuple						,  __UpperCAmelCase     :						Dict=0					)      ->			Dict:
							UpperCAmelCase_=       floats_tensor((1, self.cross_attention_dim)						,  rng=random.Random(__UpperCAmelCase					)					).to(__UpperCAmelCase					)
							UpperCAmelCase_=       floats_tensor((1, self.cross_attention_dim)						,  rng=random.Random(seed + 1					)					).to(__UpperCAmelCase					)
							# create init_image
							UpperCAmelCase_=       floats_tensor((1, 3, 64, 64)						,  rng=random.Random(__UpperCAmelCase					)					).to(__UpperCAmelCase					)
							UpperCAmelCase_=       image.cpu().permute(0						,  2						,  3						,  1					)[0]
							UpperCAmelCase_=       Image.fromarray(np.uinta(__UpperCAmelCase					)					).convert("""RGB"""					).resize((256, 256)					)
							# create mask
							UpperCAmelCase_=       np.ones((64, 64)						,  dtype=np.floataa					)
							UpperCAmelCase_=       0

							if str(__UpperCAmelCase					).startswith("""mps"""					):
													UpperCAmelCase_=       torch.manual_seed(__UpperCAmelCase					)
							else:
													UpperCAmelCase_=       torch.Generator(device=__UpperCAmelCase					).manual_seed(__UpperCAmelCase					)
							UpperCAmelCase_=       {
							    """prompt""": """horse""",
							    """image""": init_image,
							    """mask_image""": mask,
							    """image_embeds""": image_embeds,
							    """negative_image_embeds""": negative_image_embeds,
							    """generator""": generator,
							    """height""": 64,
							    """width""": 64,
							    """num_inference_steps""": 2,
							    """guidance_scale""": 4.0,
							    """output_type""": """np""",
							}
							return inputs




	def   _SCREAMING_SNAKE_CASE		(     self     :						str					)      ->			List[str]:
							UpperCAmelCase_=       """cpu"""

							UpperCAmelCase_=       self.get_dummy_components()

							UpperCAmelCase_=       self.pipeline_class(**__UpperCAmelCase					)
							UpperCAmelCase_=       pipe.to(__UpperCAmelCase					)

							pipe.set_progress_bar_config(disable=__UpperCAmelCase					)

							UpperCAmelCase_=       pipe(**self.get_dummy_inputs(__UpperCAmelCase					)					)
							UpperCAmelCase_=       output.images

							UpperCAmelCase_=       pipe(
							    **self.get_dummy_inputs(__UpperCAmelCase					)						,  return_dict=__UpperCAmelCase						,  )[0]

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

							print(F"""image.shape {image.shape}"""					)

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

							UpperCAmelCase_=       np.array(
							    [0.8_326_919, 0.73_790_467, 0.20_918_581, 0.9_309_612, 0.5_511_791, 0.43_713_328, 0.5_513_321, 0.49_922_934, 0.59_497_786]					)

							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()}"""




	def   _SCREAMING_SNAKE_CASE		(     self     :						List[str]					)      ->			Any:
							super().test_inference_batch_single_identical(expected_max_diff=3E-3					)






@slow
@require_torch_gpu
class 		lowercase						(			unittest.TestCase):






	"""simple docstring"""


	def   _SCREAMING_SNAKE_CASE		(     self     :						Dict					)      ->			Any:
							# clean up the VRAM after each test
							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()




	def   _SCREAMING_SNAKE_CASE		(     self     :						Dict					)      ->			Optional[Any]:
							UpperCAmelCase_=       load_numpy(
							    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
							    """/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy"""					)

							UpperCAmelCase_=       load_image(
							    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main""" """/kandinsky/cat.png"""					)
							UpperCAmelCase_=       np.ones((768, 768)						,  dtype=np.floataa					)
							UpperCAmelCase_=       0

							UpperCAmelCase_=       """a hat"""

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

							UpperCAmelCase_=       KandinskyInpaintPipeline.from_pretrained(
							    """kandinsky-community/kandinsky-2-1-inpaint"""						,  torch_dtype=torch.floataa					)
							UpperCAmelCase_=       pipeline.to(__UpperCAmelCase					)
							pipeline.set_progress_bar_config(disable=__UpperCAmelCase					)

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

							UpperCAmelCase_=       pipeline(
							    __UpperCAmelCase						,  image=__UpperCAmelCase						,  mask_image=__UpperCAmelCase						,  image_embeds=__UpperCAmelCase						,  negative_image_embeds=__UpperCAmelCase						,  generator=__UpperCAmelCase						,  num_inference_steps=100						,  height=768						,  width=768						,  output_type="""np"""						,  )

							UpperCAmelCase_=       output.images[0]

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

							assert_mean_pixel_difference(__UpperCAmelCase						,  __UpperCAmelCase					)




