



from copy import deepcopy
from typing import Optional, Union

import numpy as np

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, is_tf_available, is_torch_available


if is_torch_available():
							import torch

if is_tf_available():
							import tensorflow as tf



class 	lowerCamelCase__      (							lowerCamelCase__):




		'''simple docstring'''



		snake_case_     =["""image_processor"""]
		snake_case_     ="""SamImageProcessor"""



		def __init__(self		,__lowerCamelCase			)					->      Tuple:

						"""simple docstring"""






						super().__init__(__lowerCamelCase			)
						lowerCAmelCase__					:    int												=					self.image_processor
						lowerCAmelCase__					:    Tuple												=					-10
						lowerCAmelCase__					:    str												=					self.image_processor.size['''longest_edge''']



		def __call__(self		,__lowerCamelCase=None		,__lowerCamelCase=None		,__lowerCamelCase=None		,__lowerCamelCase=None		,__lowerCamelCase = None		,**__lowerCamelCase		,)					->      BatchEncoding:

						"""simple docstring"""






						lowerCAmelCase__					:    List[str]												=					self.image_processor(
						    __lowerCamelCase		,return_tensors=__lowerCamelCase		,**__lowerCamelCase		,)

						# pop arguments that are not used in the foward but used nevertheless
						lowerCAmelCase__					:    Union[str, Any]												=					encoding_image_processor['''original_sizes''']

						if hasattr(__lowerCamelCase		,'''numpy'''			):  # Checks if Torch or TF tensor
										lowerCAmelCase__					:    str												=					original_sizes.numpy()

						lowerCAmelCase__					:    Optional[int]												=					self._check_and_preprocess_points(
						    input_points=__lowerCamelCase		,input_labels=__lowerCamelCase		,input_boxes=__lowerCamelCase		,)

						lowerCAmelCase__					:    Optional[int]												=					self._normalize_and_convert(
						    __lowerCamelCase		,__lowerCamelCase		,input_points=__lowerCamelCase		,input_labels=__lowerCamelCase		,input_boxes=__lowerCamelCase		,return_tensors=__lowerCamelCase		,)

						return encoding_image_processor



		def 		lowerCAmelCase__				(self		,__lowerCamelCase		,__lowerCamelCase		,__lowerCamelCase=None		,__lowerCamelCase=None		,__lowerCamelCase=None		,__lowerCamelCase="pt"		,)					->      List[Any]:

						"""simple docstring"""






						if input_points is not None:
										if len(__lowerCamelCase			) != len(__lowerCamelCase			):
														lowerCAmelCase__					:    Tuple												=					[
														    self._normalize_coordinates(self.target_size		,__lowerCamelCase		,original_sizes[0]			) for point in input_points
														]
										else:
														lowerCAmelCase__					:    Dict												=					[
														    self._normalize_coordinates(self.target_size		,__lowerCamelCase		,__lowerCamelCase			)
														    for point, original_size in zip(__lowerCamelCase		,__lowerCamelCase			)
														]
										# check that all arrays have the same shape
										if not all(point.shape == input_points[0].shape for point in input_points			):
														if input_labels is not None:
																		lowerCAmelCase__					:    int												=					self._pad_points_and_labels(__lowerCamelCase		,__lowerCamelCase			)

										lowerCAmelCase__					:    str												=					np.array(__lowerCamelCase			)

						if input_labels is not None:
										lowerCAmelCase__					:    str												=					np.array(__lowerCamelCase			)

						if input_boxes is not None:
										if len(__lowerCamelCase			) != len(__lowerCamelCase			):
														lowerCAmelCase__					:    Union[str, Any]												=					[
														    self._normalize_coordinates(self.target_size		,__lowerCamelCase		,original_sizes[0]		,is_bounding_box=__lowerCamelCase			)
														    for box in input_boxes
														]
										else:
														lowerCAmelCase__					:    List[str]												=					[
														    self._normalize_coordinates(self.target_size		,__lowerCamelCase		,__lowerCamelCase		,is_bounding_box=__lowerCamelCase			)
														    for box, original_size in zip(__lowerCamelCase		,__lowerCamelCase			)
														]
										lowerCAmelCase__					:    int												=					np.array(__lowerCamelCase			)

						if input_boxes is not None:
										if return_tensors == "pt":
														lowerCAmelCase__					:    List[str]												=					torch.from_numpy(__lowerCamelCase			)
														# boxes batch size of 1 by default
														lowerCAmelCase__					:    Tuple												=					input_boxes.unsqueeze(1			) if len(input_boxes.shape			) != 3 else input_boxes
										elif return_tensors == "tf":
														lowerCAmelCase__					:    Union[str, Any]												=					tf.convert_to_tensor(__lowerCamelCase			)
														# boxes batch size of 1 by default
														lowerCAmelCase__					:    int												=					tf.expand_dims(__lowerCamelCase		,1			) if len(input_boxes.shape			) != 3 else input_boxes
										encoding_image_processor.update({'''input_boxes''': input_boxes}			)
						if input_points is not None:
										if return_tensors == "pt":
														lowerCAmelCase__					:    List[str]												=					torch.from_numpy(__lowerCamelCase			)
														# point batch size of 1 by default
														lowerCAmelCase__					:    Tuple												=					input_points.unsqueeze(1			) if len(input_points.shape			) != 4 else input_points
										elif return_tensors == "tf":
														lowerCAmelCase__					:    List[str]												=					tf.convert_to_tensor(__lowerCamelCase			)
														# point batch size of 1 by default
														lowerCAmelCase__					:    Tuple												=					tf.expand_dims(__lowerCamelCase		,1			) if len(input_points.shape			) != 4 else input_points
										encoding_image_processor.update({'''input_points''': input_points}			)
						if input_labels is not None:
										if return_tensors == "pt":
														lowerCAmelCase__					:    Any												=					torch.from_numpy(__lowerCamelCase			)
														# point batch size of 1 by default
														lowerCAmelCase__					:    Dict												=					input_labels.unsqueeze(1			) if len(input_labels.shape			) != 3 else input_labels
										elif return_tensors == "tf":
														lowerCAmelCase__					:    Dict												=					tf.convert_to_tensor(__lowerCamelCase			)
														# point batch size of 1 by default
														lowerCAmelCase__					:    Any												=					tf.expand_dims(__lowerCamelCase		,1			) if len(input_labels.shape			) != 3 else input_labels
										encoding_image_processor.update({'''input_labels''': input_labels}			)

						return encoding_image_processor



		def 		lowerCAmelCase__				(self		,__lowerCamelCase		,__lowerCamelCase			)					->      List[Any]:

						"""simple docstring"""






						lowerCAmelCase__					:    Dict												=					max([point.shape[0] for point in input_points]			)
						lowerCAmelCase__					:    Any												=					[]
						for i, point in enumerate(__lowerCamelCase			):
										if point.shape[0] != expected_nb_points:
														lowerCAmelCase__					:    int												=					np.concatenate(
														    [point, np.zeros((expected_nb_points - point.shape[0], 2)			) + self.point_pad_value]		,axis=0			)
														lowerCAmelCase__					:    Optional[Any]												=					np.append(input_labels[i]		,[self.point_pad_value]			)
										processed_input_points.append(__lowerCamelCase			)
						lowerCAmelCase__					:    Union[str, Any]												=					processed_input_points
						return input_points, input_labels



		def 		lowerCAmelCase__				(self		,__lowerCamelCase		,__lowerCamelCase		,__lowerCamelCase		,__lowerCamelCase=False			)					->      np.ndarray:

						"""simple docstring"""






						lowerCAmelCase__					:    Optional[Any]												=					original_size
						lowerCAmelCase__					:    str												=					self.image_processor._get_preprocess_shape(__lowerCamelCase		,longest_edge=__lowerCamelCase			)
						lowerCAmelCase__					:    Union[str, Any]												=					deepcopy(__lowerCamelCase			).astype(__lowerCamelCase			)

						if is_bounding_box:
										lowerCAmelCase__					:    Dict												=					coords.reshape(-1		,2		,2			)

						lowerCAmelCase__					:    Optional[Any]												=					coords[..., 0] * (new_w / old_w)
						lowerCAmelCase__					:    Optional[int]												=					coords[..., 1] * (new_h / old_h)

						if is_bounding_box:
										lowerCAmelCase__					:    str												=					coords.reshape(-1		,4			)

						return coords



		def 		lowerCAmelCase__				(self		,__lowerCamelCase=None		,__lowerCamelCase=None		,__lowerCamelCase=None		,)					->      List[str]:

						"""simple docstring"""






						if input_points is not None:
										if hasattr(__lowerCamelCase		,'''numpy'''			):  # Checks for TF or Torch tensor
														lowerCAmelCase__					:    Union[str, Any]												=					input_points.numpy().tolist()

										if not isinstance(__lowerCamelCase		,__lowerCamelCase			) or not isinstance(input_points[0]		,__lowerCamelCase			):
														raise ValueError('''Input points must be a list of list of floating points.'''			)
										lowerCAmelCase__					:    Dict												=					[np.array(__lowerCamelCase			) for input_point in input_points]
						else:
										lowerCAmelCase__					:    Dict												=					None

						if input_labels is not None:
										if hasattr(__lowerCamelCase		,'''numpy'''			):
														lowerCAmelCase__					:    Optional[Any]												=					input_labels.numpy().tolist()

										if not isinstance(__lowerCamelCase		,__lowerCamelCase			) or not isinstance(input_labels[0]		,__lowerCamelCase			):
														raise ValueError('''Input labels must be a list of list integers.'''			)
										lowerCAmelCase__					:    List[Any]												=					[np.array(__lowerCamelCase			) for label in input_labels]
						else:
										lowerCAmelCase__					:    Optional[int]												=					None

						if input_boxes is not None:
										if hasattr(__lowerCamelCase		,'''numpy'''			):
														lowerCAmelCase__					:    int												=					input_boxes.numpy().tolist()

										if (
										    not isinstance(__lowerCamelCase		,__lowerCamelCase			)
										    or not isinstance(input_boxes[0]		,__lowerCamelCase			)
										    or not isinstance(input_boxes[0][0]		,__lowerCamelCase			)
										):
														raise ValueError('''Input boxes must be a list of list of list of floating points.'''			)
										lowerCAmelCase__					:    Optional[Any]												=					[np.array(__lowerCamelCase			).astype(np.floataa			) for box in input_boxes]
						else:
										lowerCAmelCase__					:    Optional[Any]												=					None

						return input_points, input_labels, input_boxes



		@property
		def 		lowerCAmelCase__				(self			)					->      Any:

						"""simple docstring"""






						lowerCAmelCase__					:    Optional[Any]												=					self.image_processor.model_input_names
						return list(dict.fromkeys(__lowerCamelCase			)			)



		def 		lowerCAmelCase__				(self		,*__lowerCamelCase		,**__lowerCamelCase			)					->      List[Any]:

						"""simple docstring"""






						return self.image_processor.post_process_masks(*__lowerCamelCase		,**__lowerCamelCase			)






import unittest

import numpy as np
import torch
from torch import nn
from transformers import (
    CLIPImageProcessor,
    CLIPTextConfig,
    CLIPTextModelWithProjection,
    CLIPTokenizer,
    CLIPVisionConfig,
    CLIPVisionModelWithProjection,
)

from diffusers import KandinskyVaaPriorPipeline, PriorTransformer, UnCLIPScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import enable_full_determinism, skip_mps

from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()



class 	lowerCamelCase__      (							lowerCamelCase__				,    unittest.TestCase):




		'''simple docstring'''



		snake_case_     =KandinskyVaaPriorPipeline
		snake_case_     =["""prompt"""]
		snake_case_     =["""prompt""", """negative_prompt"""]
		snake_case_     =[
		    """num_images_per_prompt""",
		    """generator""",
		    """num_inference_steps""",
		    """latents""",
		    """negative_prompt""",
		    """guidance_scale""",
		    """output_type""",
		    """return_dict""",
		]
		snake_case_     =False



		@property
		def 		lowerCAmelCase__				(self			)					->      Any:

						"""simple docstring"""






						return 32



		@property
		def 		lowerCAmelCase__				(self			)					->      List[str]:

						"""simple docstring"""






						return 32



		@property
		def 		lowerCAmelCase__				(self			)					->      Tuple:

						"""simple docstring"""






						return self.time_input_dim



		@property
		def 		lowerCAmelCase__				(self			)					->      Optional[int]:

						"""simple docstring"""






						return self.time_input_dim * 4



		@property
		def 		lowerCAmelCase__				(self			)					->      Dict:

						"""simple docstring"""






						return 1_00



		@property
		def 		lowerCAmelCase__				(self			)					->      Union[str, Any]:

						"""simple docstring"""






						lowerCAmelCase__					:    Any												=					CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''			)
						return tokenizer



		@property
		def 		lowerCAmelCase__				(self			)					->      str:

						"""simple docstring"""






						torch.manual_seed(0			)
						lowerCAmelCase__					:    str												=					CLIPTextConfig(
						    bos_token_id=0		,eos_token_id=2		,hidden_size=self.text_embedder_hidden_size		,projection_dim=self.text_embedder_hidden_size		,intermediate_size=37		,layer_norm_eps=1e-05		,num_attention_heads=4		,num_hidden_layers=5		,pad_token_id=1		,vocab_size=10_00		,)
						return CLIPTextModelWithProjection(__lowerCamelCase			)



		@property
		def 		lowerCAmelCase__				(self			)					->      int:

						"""simple docstring"""






						torch.manual_seed(0			)

						lowerCAmelCase__					:    List[str]												=					{
						    '''num_attention_heads''': 2,
						    '''attention_head_dim''': 12,
						    '''embedding_dim''': self.text_embedder_hidden_size,
						    '''num_layers''': 1,
						}

						lowerCAmelCase__					:    List[str]												=					PriorTransformer(**__lowerCamelCase			)
						# clip_std and clip_mean is initialized to be 0 so PriorTransformer.post_process_latents will always return 0 - set clip_std to be 1 so it won't return 0
						lowerCAmelCase__					:    int												=					nn.Parameter(torch.ones(model.clip_std.shape			)			)
						return model



		@property
		def 		lowerCAmelCase__				(self			)					->      List[str]:

						"""simple docstring"""






						torch.manual_seed(0			)
						lowerCAmelCase__					:    Any												=					CLIPVisionConfig(
						    hidden_size=self.text_embedder_hidden_size		,image_size=2_24		,projection_dim=self.text_embedder_hidden_size		,intermediate_size=37		,num_attention_heads=4		,num_channels=3		,num_hidden_layers=5		,patch_size=14		,)

						lowerCAmelCase__					:    int												=					CLIPVisionModelWithProjection(__lowerCamelCase			)
						return model



		@property
		def 		lowerCAmelCase__				(self			)					->      Any:

						"""simple docstring"""






						lowerCAmelCase__					:    Optional[Any]												=					CLIPImageProcessor(
						    crop_size=2_24		,do_center_crop=__lowerCamelCase		,do_normalize=__lowerCamelCase		,do_resize=__lowerCamelCase		,image_mean=[0.4814_5466, 0.457_8275, 0.4082_1073]		,image_std=[0.2686_2954, 0.2613_0258, 0.2757_7711]		,resample=3		,size=2_24		,)

						return image_processor



		def 		lowerCAmelCase__				(self			)					->      Any:

						"""simple docstring"""






						lowerCAmelCase__					:    List[Any]												=					self.dummy_prior
						lowerCAmelCase__					:    List[Any]												=					self.dummy_image_encoder
						lowerCAmelCase__					:    Optional[Any]												=					self.dummy_text_encoder
						lowerCAmelCase__					:    Optional[int]												=					self.dummy_tokenizer
						lowerCAmelCase__					:    str												=					self.dummy_image_processor

						lowerCAmelCase__					:    Union[str, Any]												=					UnCLIPScheduler(
						    variance_type='''fixed_small_log'''		,prediction_type='''sample'''		,num_train_timesteps=10_00		,clip_sample=__lowerCamelCase		,clip_sample_range=10.0		,)

						lowerCAmelCase__					:    int												=					{
						    '''prior''': prior,
						    '''image_encoder''': image_encoder,
						    '''text_encoder''': text_encoder,
						    '''tokenizer''': tokenizer,
						    '''scheduler''': scheduler,
						    '''image_processor''': image_processor,
						}

						return components



		def 		lowerCAmelCase__				(self		,__lowerCamelCase		,__lowerCamelCase=0			)					->      int:

						"""simple docstring"""






						if str(__lowerCamelCase			).startswith('''mps'''			):
										lowerCAmelCase__					:    Dict												=					torch.manual_seed(__lowerCamelCase			)
						else:
										lowerCAmelCase__					:    Any												=					torch.Generator(device=__lowerCamelCase			).manual_seed(__lowerCamelCase			)
						lowerCAmelCase__					:    Optional[Any]												=					{
						    '''prompt''': '''horse''',
						    '''generator''': generator,
						    '''guidance_scale''': 4.0,
						    '''num_inference_steps''': 2,
						    '''output_type''': '''np''',
						}
						return inputs



		def 		lowerCAmelCase__				(self			)					->      List[str]:

						"""simple docstring"""






						lowerCAmelCase__					:    str												=					'''cpu'''

						lowerCAmelCase__					:    Optional[int]												=					self.get_dummy_components()

						lowerCAmelCase__					:    int												=					self.pipeline_class(**__lowerCamelCase			)
						lowerCAmelCase__					:    int												=					pipe.to(__lowerCamelCase			)

						pipe.set_progress_bar_config(disable=__lowerCamelCase			)

						lowerCAmelCase__					:    List[Any]												=					pipe(**self.get_dummy_inputs(__lowerCamelCase			)			)
						lowerCAmelCase__					:    Union[str, Any]												=					output.image_embeds

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

						lowerCAmelCase__					:    Union[str, Any]												=					image[0, -10:]
						lowerCAmelCase__					:    str												=					image_from_tuple[0, -10:]

						assert image.shape == (1, 32)

						lowerCAmelCase__					:    int												=					np.array(
						    [-0.0532, 1.7120, 0.3656, -1.0852, -0.8946, -1.1756, 0.4348, 0.2482, 0.5146, -0.1156]			)

						assert np.abs(image_slice.flatten() - expected_slice			).max() < 1e-2
						assert np.abs(image_from_tuple_slice.flatten() - expected_slice			).max() < 1e-2



		@skip_mps
		def 		lowerCAmelCase__				(self			)					->      List[Any]:

						"""simple docstring"""






						lowerCAmelCase__					:    List[Any]												=					torch_device == '''cpu'''
						lowerCAmelCase__					:    List[Any]												=					True
						lowerCAmelCase__					:    Optional[int]												=					False

						self._test_inference_batch_single_identical(
						    test_max_difference=__lowerCamelCase		,relax_max_difference=__lowerCamelCase		,test_mean_pixel_difference=__lowerCamelCase		,)



		@skip_mps
		def 		lowerCAmelCase__				(self			)					->      Any:

						"""simple docstring"""






						lowerCAmelCase__					:    int												=					torch_device == '''cpu'''
						lowerCAmelCase__					:    int												=					False

						self._test_attention_slicing_forward_pass(
						    test_max_difference=__lowerCamelCase		,test_mean_pixel_difference=__lowerCamelCase		,)

