



import unittest

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

from diffusers import KandinskyPriorPipeline, 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_     (			_A    ,unittest.TestCase    ):






						'''simple docstring'''
						a__ =	KandinskyPriorPipeline
						a__ =	["prompt"]
						a__ =	["prompt", "negative_prompt"]
						a__ =	[
						    "num_images_per_prompt",
						    "generator",
						    "num_inference_steps",
						    "latents",
						    "negative_prompt",
						    "guidance_scale",
						    "output_type",
						    "return_dict",
						]
						a__ =	False







						@property
						def       SCREAMING_SNAKE_CASE__		(						self   :      Union[str, Any]			)   ->					Tuple:
									return 32







						@property
						def       SCREAMING_SNAKE_CASE__		(						self   :      str			)   ->					str:
									return 32







						@property
						def       SCREAMING_SNAKE_CASE__		(						self   :      Optional[int]			)   ->					int:
									return self.time_input_dim







						@property
						def       SCREAMING_SNAKE_CASE__		(						self   :      int			)   ->					int:
									return self.time_input_dim * 4







						@property
						def       SCREAMING_SNAKE_CASE__		(						self   :      str			)   ->					Union[str, Any]:
									return 1_00







						@property
						def       SCREAMING_SNAKE_CASE__		(						self   :      List[str]			)   ->					Tuple:
									A				:       Optional[Any]														=    CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"			)
									return tokenizer







						@property
						def       SCREAMING_SNAKE_CASE__		(						self   :      Tuple			)   ->					Tuple:
									torch.manual_seed(0			)
									A				:       Tuple														=    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       SCREAMING_SNAKE_CASE__		(						self   :      Tuple			)   ->					Tuple:
									torch.manual_seed(0			)

									A				:       Tuple														=    {
									    "num_attention_heads": 2,
									    "attention_head_dim": 12,
									    "embedding_dim": self.text_embedder_hidden_size,
									    "num_layers": 1,
									}

									A				:       Optional[int]														=    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
									A				:       Any														=    nn.Parameter(torch.ones(model.clip_std.shape			)			)
									return model







						@property
						def       SCREAMING_SNAKE_CASE__		(						self   :      Tuple			)   ->					Union[str, Any]:
									torch.manual_seed(0			)
									A				:       Optional[int]														=    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						,						)

									A				:       Optional[int]														=    CLIPVisionModelWithProjection(__lowerCamelCase			)
									return model







						@property
						def       SCREAMING_SNAKE_CASE__		(						self   :      List[Any]			)   ->					Union[str, Any]:
									A				:       List[str]														=    CLIPImageProcessor(
									    crop_size=2_24						,						do_center_crop=__lowerCamelCase						,						do_normalize=__lowerCamelCase						,						do_resize=__lowerCamelCase						,						image_mean=[0.48145466, 0.4578275, 0.40821073]						,						image_std=[0.26862954, 0.26130258, 0.27577711]						,						resample=3						,						size=2_24						,						)

									return image_processor







						def       SCREAMING_SNAKE_CASE__		(						self   :      Tuple			)   ->					Dict:
									A				:       int														=    self.dummy_prior
									A				:       Any														=    self.dummy_image_encoder
									A				:       Tuple														=    self.dummy_text_encoder
									A				:       int														=    self.dummy_tokenizer
									A				:       List[str]														=    self.dummy_image_processor

									A				:       Any														=    UnCLIPScheduler(
									    variance_type="fixed_small_log"						,						prediction_type="sample"						,						num_train_timesteps=10_00						,						clip_sample=__lowerCamelCase						,						clip_sample_range=10.0						,						)

									A				:       Tuple														=    {
									    "prior": prior,
									    "image_encoder": image_encoder,
									    "text_encoder": text_encoder,
									    "tokenizer": tokenizer,
									    "scheduler": scheduler,
									    "image_processor": image_processor,
									}

									return components







						def       SCREAMING_SNAKE_CASE__		(						self   :      Dict						,						__lowerCamelCase   :      int						,						__lowerCamelCase   :      List[str]=0			)   ->					Union[str, Any]:
									if str(__lowerCamelCase			).startswith("mps"			):
												A				:       Optional[int]														=    torch.manual_seed(__lowerCamelCase			)
									else:
												A				:       Union[str, Any]														=    torch.Generator(device=__lowerCamelCase			).manual_seed(__lowerCamelCase			)
									A				:       Optional[Any]														=    {
									    "prompt": "horse",
									    "generator": generator,
									    "guidance_scale": 4.0,
									    "num_inference_steps": 2,
									    "output_type": "np",
									}
									return inputs







						def       SCREAMING_SNAKE_CASE__		(						self   :      Dict			)   ->					Any:
									A				:       Dict														=    "cpu"

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

									A				:       List[Any]														=    self.pipeline_class(**__lowerCamelCase			)
									A				:       Dict														=    pipe.to(__lowerCamelCase			)

									pipe.set_progress_bar_config(disable=__lowerCamelCase			)

									A				:       List[Any]														=    pipe(**self.get_dummy_inputs(__lowerCamelCase			)			)
									A				:       int														=    output.image_embeds

									A				:       List[str]														=    pipe(
									    **self.get_dummy_inputs(__lowerCamelCase			)						,						return_dict=__lowerCamelCase						,						)[0]

									A				:       Dict														=    image[0, -10:]
									A				:       Any														=    image_from_tuple[0, -10:]

									assert image.shape == (1, 32)

									A				:       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       SCREAMING_SNAKE_CASE__		(						self   :      Any			)   ->					List[Any]:
									A				:       Union[str, Any]														=    torch_device == "cpu"
									A				:       Dict														=    True
									A				:       Any														=    False

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







						@skip_mps
						def       SCREAMING_SNAKE_CASE__		(						self   :      int			)   ->					List[Any]:
									A				:       List[Any]														=    torch_device == "cpu"
									A				:       str														=    False

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




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


__SCREAMING_SNAKE_CASE     =	{"""configuration_yoso""": ["""YOSO_PRETRAINED_CONFIG_ARCHIVE_MAP""", """YosoConfig"""]}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__SCREAMING_SNAKE_CASE     =	[
							    """YOSO_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """YosoForMaskedLM""",
							    """YosoForMultipleChoice""",
							    """YosoForQuestionAnswering""",
							    """YosoForSequenceClassification""",
							    """YosoForTokenClassification""",
							    """YosoLayer""",
							    """YosoModel""",
							    """YosoPreTrainedModel""",
							]


if TYPE_CHECKING:
							from .configuration_yoso import YOSO_PRETRAINED_CONFIG_ARCHIVE_MAP, YosoConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_yoso import (
														    YOSO_PRETRAINED_MODEL_ARCHIVE_LIST,
														    YosoForMaskedLM,
														    YosoForMultipleChoice,
														    YosoForQuestionAnswering,
														    YosoForSequenceClassification,
														    YosoForTokenClassification,
														    YosoLayer,
														    YosoModel,
														    YosoPreTrainedModel,
														)


else:
							import sys

							__SCREAMING_SNAKE_CASE     =	_LazyModule(__name__, globals()["""__file__"""], _import_structure)