
"""simple docstring"""







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 							__magic_name__     (		lowerCamelCase__      ,		unittest.TestCase   ):
					_SCREAMING_SNAKE_CASE       :					str     							=   KandinskyVaaPriorPipeline
					_SCREAMING_SNAKE_CASE       :					Optional[Any]     							=   ['prompt']
					_SCREAMING_SNAKE_CASE       :					Dict     							=   ['prompt', 'negative_prompt']
					_SCREAMING_SNAKE_CASE       :					List[Any]     							=   [
					    'num_images_per_prompt',
					    'generator',
					    'num_inference_steps',
					    'latents',
					    'negative_prompt',
					    'guidance_scale',
					    'output_type',
					    'return_dict',
					]
					_SCREAMING_SNAKE_CASE       :					Dict     							=   False





					@property
					def 							lowerCAmelCase      (				self   :							Union[str, Any]    ):
												return 32





					@property
					def 							lowerCAmelCase      (				self   :							Dict    ):
												return 32





					@property
					def 							lowerCAmelCase      (				self   :							Any    ):
												return self.time_input_dim





					@property
					def 							lowerCAmelCase      (				self   :							str    ):
												return self.time_input_dim * 4





					@property
					def 							lowerCAmelCase      (				self   :							Union[str, Any]    ):
												return 100





					@property
					def 							lowerCAmelCase      (				self   :							Any    ):
												__snake_case     			=       CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"    )
												return tokenizer





					@property
					def 							lowerCAmelCase      (				self   :							Dict    ):
												torch.manual_seed(0    )
												__snake_case     			=       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=1000							,						)
												return CLIPTextModelWithProjection(__lowerCamelCase    )





					@property
					def 							lowerCAmelCase      (				self   :							Optional[int]    ):
												torch.manual_seed(0    )

												__snake_case     			=       {
												    '''num_attention_heads''': 2,
												    '''attention_head_dim''': 12,
												    '''embedding_dim''': self.text_embedder_hidden_size,
												    '''num_layers''': 1,
												}

												__snake_case     			=       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
												__snake_case     			=       nn.Parameter(torch.ones(model.clip_std.shape    )    )
												return model





					@property
					def 							lowerCAmelCase      (				self   :							Tuple    ):
												torch.manual_seed(0    )
												__snake_case     			=       CLIPVisionConfig(
												    hidden_size=self.text_embedder_hidden_size							,						image_size=224							,						projection_dim=self.text_embedder_hidden_size							,						intermediate_size=37							,						num_attention_heads=4							,						num_channels=3							,						num_hidden_layers=5							,						patch_size=14							,						)

												__snake_case     			=       CLIPVisionModelWithProjection(__lowerCamelCase    )
												return model





					@property
					def 							lowerCAmelCase      (				self   :							Optional[int]    ):
												__snake_case     			=       CLIPImageProcessor(
												    crop_size=224							,						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=224							,						)

												return image_processor





					def 							lowerCAmelCase      (				self   :							Tuple    ):
												__snake_case     			=       self.dummy_prior
												__snake_case     			=       self.dummy_image_encoder
												__snake_case     			=       self.dummy_text_encoder
												__snake_case     			=       self.dummy_tokenizer
												__snake_case     			=       self.dummy_image_processor

												__snake_case     			=       UnCLIPScheduler(
												    variance_type="fixed_small_log"							,						prediction_type="sample"							,						num_train_timesteps=1000							,						clip_sample=__lowerCamelCase							,						clip_sample_range=10.0							,						)

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

												return components





					def 							lowerCAmelCase      (				self   :							Union[str, Any]							,						snake_case_   :							Optional[Any]							,						snake_case_   :							Any=0    ):






												if str(__lowerCamelCase    ).startswith("mps"    ):
																			__snake_case     			=       torch.manual_seed(__lowerCamelCase    )
												else:
																			__snake_case     			=       torch.Generator(device=__lowerCamelCase    ).manual_seed(__lowerCamelCase    )
												__snake_case     			=       {
												    '''prompt''': '''horse''',
												    '''generator''': generator,
												    '''guidance_scale''': 4.0,
												    '''num_inference_steps''': 2,
												    '''output_type''': '''np''',
												}
												return inputs





					def 							lowerCAmelCase      (				self   :							Tuple    ):
												__snake_case     			=       '''cpu'''

												__snake_case     			=       self.get_dummy_components()

												__snake_case     			=       self.pipeline_class(**__lowerCamelCase    )
												__snake_case     			=       pipe.to(__lowerCamelCase    )

												pipe.set_progress_bar_config(disable=__lowerCamelCase    )

												__snake_case     			=       pipe(**self.get_dummy_inputs(__lowerCamelCase    )    )
												__snake_case     			=       output.image_embeds

												__snake_case     			=       pipe(
												    **self.get_dummy_inputs(__lowerCamelCase    )							,						return_dict=__lowerCamelCase							,						)[0]

												__snake_case     			=       image[0, -10:]
												__snake_case     			=       image_from_tuple[0, -10:]

												assert image.shape == (1, 32)

												__snake_case     			=       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   :							str    ):
												__snake_case     			=       torch_device == '''cpu'''
												__snake_case     			=       True
												__snake_case     			=       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   :							List[Any]    ):
												__snake_case     			=       torch_device == '''cpu'''
												__snake_case     			=       False

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


"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


_SCREAMING_SNAKE_CASE	       =      {
    """configuration_mobilevit""": ["""MOBILEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MobileViTConfig""", """MobileViTOnnxConfig"""],
}

try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_SCREAMING_SNAKE_CASE	       =      ["""MobileViTFeatureExtractor"""]
		_SCREAMING_SNAKE_CASE	       =      ["""MobileViTImageProcessor"""]

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_SCREAMING_SNAKE_CASE	       =      [
		    """MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST""",
		    """MobileViTForImageClassification""",
		    """MobileViTForSemanticSegmentation""",
		    """MobileViTModel""",
		    """MobileViTPreTrainedModel""",
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_SCREAMING_SNAKE_CASE	       =      [
		    """TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST""",
		    """TFMobileViTForImageClassification""",
		    """TFMobileViTForSemanticSegmentation""",
		    """TFMobileViTModel""",
		    """TFMobileViTPreTrainedModel""",
		]

if TYPE_CHECKING:
		from .configuration_mobilevit import MOBILEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP, MobileViTConfig, MobileViTOnnxConfig

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .feature_extraction_mobilevit import MobileViTFeatureExtractor
				from .image_processing_mobilevit import MobileViTImageProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_mobilevit import (
				    MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    MobileViTForImageClassification,
				    MobileViTForSemanticSegmentation,
				    MobileViTModel,
				    MobileViTPreTrainedModel,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_mobilevit import (
				    TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFMobileViTForImageClassification,
				    TFMobileViTForSemanticSegmentation,
				    TFMobileViTModel,
				    TFMobileViTPreTrainedModel,
				)


else:
		import sys

		_SCREAMING_SNAKE_CASE	       =      _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
