




"""simple docstring"""







from collections import UserDict
from typing import List, Union

from ..utils import (
    add_end_docstrings,
    is_tf_available,
    is_torch_available,
    is_vision_available,
    logging,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
  from PIL import Image

  from ..image_utils import load_image

if is_torch_available():
  from ..models.auto.modeling_auto import MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING

if is_tf_available():
  from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING
  from ..tf_utils import stable_softmax

SCREAMING_SNAKE_CASE_	     =       logging.get_logger(__name__)

@add_end_docstrings(lowerCamelCase_			)
class 				snake_case_			(					lowerCamelCase_			):







   """simple docstring"""




   def __init__(		self       ,					**lowerCamelCase_)				->   Tuple:
       super().__init__(**lowerCamelCase_)

       requires_backends(self       ,					'''vision''')
       self.check_model_type(
           TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING
           if self.framework == '''tf'''
           else MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING)







   def __call__(		self       ,					lowerCamelCase_       ,					**lowerCamelCase_)				->   Optional[int]:
       return super().__call__(lowerCamelCase_       ,					**lowerCamelCase_)







   def   UpperCAmelCase__			(		self       ,					**lowerCamelCase_)				->   Any:
       UpperCamelCase              =	{}
       if "candidate_labels" in kwargs:
           UpperCamelCase              =	kwargs['''candidate_labels''']
       if "hypothesis_template" in kwargs:
           UpperCamelCase              =	kwargs['''hypothesis_template''']

       return preprocess_params, {}, {}







   def   UpperCAmelCase__			(		self       ,					lowerCamelCase_       ,					lowerCamelCase_=None       ,					lowerCamelCase_="This is a photo of {}.")				->   Union[str, Any]:
       UpperCamelCase              =	load_image(lowerCamelCase_)
       UpperCamelCase              =	self.image_processor(images=[image]       ,					return_tensors=self.framework)
       UpperCamelCase              =	candidate_labels
       UpperCamelCase              =	[hypothesis_template.format(lowerCamelCase_) for x in candidate_labels]
       UpperCamelCase              =	self.tokenizer(lowerCamelCase_       ,					return_tensors=self.framework       ,					padding=lowerCamelCase_)
       UpperCamelCase              =	[text_inputs]
       return inputs







   def   UpperCAmelCase__			(		self       ,					lowerCamelCase_)				->   Any:
       UpperCamelCase              =	model_inputs.pop('''candidate_labels''')
       UpperCamelCase              =	model_inputs.pop('''text_inputs''')
       if isinstance(text_inputs[0]       ,					lowerCamelCase_):
           UpperCamelCase              =	text_inputs[0]
       else:
           # Batching case.
           UpperCamelCase              =	text_inputs[0][0]

       UpperCamelCase              =	self.model(**lowerCamelCase_       ,					**lowerCamelCase_)

       UpperCamelCase              =	{
           '''candidate_labels''': candidate_labels,
           '''logits''': outputs.logits_per_image,
       }
       return model_outputs







   def   UpperCAmelCase__			(		self       ,					lowerCamelCase_)				->   Any:
       UpperCamelCase              =	model_outputs.pop('''candidate_labels''')
       UpperCamelCase              =	model_outputs['''logits'''][0]
       if self.framework == "pt":
           UpperCamelCase              =	logits.softmax(dim=-1).squeeze(-1)
           UpperCamelCase              =	probs.tolist()
           if not isinstance(lowerCamelCase_       ,					lowerCamelCase_):
               UpperCamelCase              =	[scores]
       elif self.framework == "tf":
           UpperCamelCase              =	stable_softmax(lowerCamelCase_       ,					axis=-1)
           UpperCamelCase              =	probs.numpy().tolist()
       else:
           raise ValueError(F'Unsupported framework: {self.framework}')

       UpperCamelCase              =	[
           {'''score''': score, '''label''': candidate_label}
           for score, candidate_label in sorted(zip(lowerCamelCase_       ,					lowerCamelCase_)       ,					key=lambda lowerCamelCase_: -x[0])
       ]
       return result


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 							UpperCAmelCase_						(     __lowercase							,			unittest.TestCase  ):
							"""simple docstring"""







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


							@property
							def 							__lowercase							(     self			)       ->       Optional[int]:
												return 3_2


							@property
							def 							__lowercase							(     self			)       ->       int:
												return 3_2


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


							@property
							def 							__lowercase							(     self			)       ->       List[str]:
												return self.time_input_dim * 4


							@property
							def 							__lowercase							(     self			)       ->       Optional[Any]:
												return 1_0_0


							@property
							def 							__lowercase							(     self			)       ->       Optional[Any]:
												_a      :       Any   =						XLMRobertaTokenizerFast.from_pretrained('''YiYiXu/tiny-random-mclip-base'''			)
												return tokenizer


							@property
							def 							__lowercase							(     self			)       ->       str:
												torch.manual_seed(0			)
												_a      :       List[Any]   =						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			,				)

												_a      :       Optional[int]   =						MultilingualCLIP(_a			)
												_a      :       Tuple   =						text_encoder.eval()

												return text_encoder


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

												_a      :       List[str]   =						{
												    '''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,
												}

												_a      :       Dict   =						UNetaDConditionModel(**_a			)
												return model


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


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

												_a      :       Tuple   =						DDIMScheduler(
												    num_train_timesteps=1_0_0_0			,				beta_schedule='''linear'''			,				beta_start=0.0_0085			,				beta_end=0.012			,				clip_sample=_a			,				set_alpha_to_one=_a			,				steps_offset=1			,				prediction_type='''epsilon'''			,				thresholding=_a			,				)

												_a      :       str   =						{
												    '''text_encoder''': text_encoder,
												    '''tokenizer''': tokenizer,
												    '''unet''': unet,
												    '''scheduler''': scheduler,
												    '''movq''': movq,
												}

												return components


							def 							__lowercase							(     self			,				_a			,				_a=0			)       ->       int:
												_a      :       Union[str, Any]   =						floats_tensor((1, self.cross_attention_dim)			,				rng=random.Random(_a			)			).to(_a			)
												_a      :       List[str]   =						floats_tensor((1, self.cross_attention_dim)			,				rng=random.Random(seed + 1			)			).to(_a			)
												# create init_image
												_a      :       Tuple   =						floats_tensor((1, 3, 6_4, 6_4)			,				rng=random.Random(_a			)			).to(_a			)
												_a      :       Dict   =						image.cpu().permute(0			,				2			,				3			,				1			)[0]
												_a      :       Optional[int]   =						Image.fromarray(np.uinta(_a			)			).convert('''RGB'''			).resize((2_5_6, 2_5_6)			)
												# create mask
												_a      :       Union[str, Any]   =						np.ones((6_4, 6_4)			,				dtype=np.floataa			)
												_a      :       List[str]   =						0

												if str(_a			).startswith('''mps'''			):
																	_a      :       Tuple   =						torch.manual_seed(_a			)
												else:
																	_a      :       Any   =						torch.Generator(device=_a			).manual_seed(_a			)
												_a      :       Any   =						{
												    '''prompt''': '''horse''',
												    '''image''': init_image,
												    '''mask_image''': mask,
												    '''image_embeds''': image_embeds,
												    '''negative_image_embeds''': negative_image_embeds,
												    '''generator''': generator,
												    '''height''': 6_4,
												    '''width''': 6_4,
												    '''num_inference_steps''': 2,
												    '''guidance_scale''': 4.0,
												    '''output_type''': '''np''',
												}
												return inputs


							def 							__lowercase							(     self			)       ->       Optional[Any]:
												_a      :       Optional[Any]   =						'''cpu'''

												_a      :       List[Any]   =						self.get_dummy_components()

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

												pipe.set_progress_bar_config(disable=_a			)

												_a      :       Any   =						pipe(**self.get_dummy_inputs(_a			)			)
												_a      :       str   =						output.images

												_a      :       Tuple   =						pipe(
												    **self.get_dummy_inputs(_a			)			,				return_dict=_a			,				)[0]

												_a      :       Union[str, Any]   =						image[0, -3:, -3:, -1]
												_a      :       Tuple   =						image_from_tuple[0, -3:, -3:, -1]

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

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

												_a      :       str   =						np.array(
												    [0.832_6919, 0.7379_0467, 0.2091_8581, 0.930_9612, 0.551_1791, 0.4371_3328, 0.551_3321, 0.4992_2934, 0.5949_7786]			)

												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 							__lowercase							(     self			)       ->       Dict:
												super().test_inference_batch_single_identical(expected_max_diff=3e-3			)



@slow
@require_torch_gpu
class 							UpperCAmelCase_						(     unittest.TestCase  ):
							"""simple docstring"""







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




							def 							__lowercase							(     self			)       ->       Union[str, Any]:
												_a      :       Tuple   =						load_numpy(
												    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
												    '''/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy'''			)

												_a      :       str   =						load_image(
												    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/kandinsky/cat.png'''			)
												_a      :       Tuple   =						np.ones((7_6_8, 7_6_8)			,				dtype=np.floataa			)
												_a      :       Any   =						0

												_a      :       Optional[Any]   =						'''a hat'''

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

												_a      :       Tuple   =						KandinskyInpaintPipeline.from_pretrained(
												    '''kandinsky-community/kandinsky-2-1-inpaint'''			,				torch_dtype=torch.floataa			)
												_a      :       Union[str, Any]   =						pipeline.to(_a			)
												pipeline.set_progress_bar_config(disable=_a			)

												_a      :       Union[str, Any]   =						torch.Generator(device='''cpu'''			).manual_seed(0			)
												_a ,   _a      :       Dict   =						pipe_prior(
												    _a			,				generator=_a			,				num_inference_steps=5			,				negative_prompt=''''''			,				).to_tuple()

												_a      :       Optional[int]   =						pipeline(
												    _a			,				image=_a			,				mask_image=_a			,				image_embeds=_a			,				negative_image_embeds=_a			,				generator=_a			,				num_inference_steps=1_0_0			,				height=7_6_8			,				width=7_6_8			,				output_type='''np'''			,				)

												_a      :       Optional[int]   =						output.images[0]

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

												assert_mean_pixel_difference(_a			,				_a			)



