
"""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 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     (   UpperCamelCase__	,  unittest.TestCase ):
				a__:			List[Any]									=						KandinskyPriorPipeline
				a__:			Any									=						["""prompt"""]
				a__:			int									=						["""prompt""", """negative_prompt"""]
				a__:			Any									=						[
				    """num_images_per_prompt""",
				    """generator""",
				    """num_inference_steps""",
				    """latents""",
				    """negative_prompt""",
				    """guidance_scale""",
				    """output_type""",
				    """return_dict""",
				]
				a__:			List[Any]									=						False




				@property
				def 					UpperCAmelCase__	(							self      ):
							return 32




				@property
				def 					UpperCAmelCase__	(							self      ):
							return 32




				@property
				def 					UpperCAmelCase__	(							self      ):
							return self.time_input_dim




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




				@property
				def 					UpperCAmelCase__	(							self      ):
							return 100




				@property
				def 					UpperCAmelCase__	(							self      ):
							lowerCamelCase_ 		=     CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''      )
							return tokenizer




				@property
				def 					UpperCAmelCase__	(							self      ):
							torch.manual_seed(0      )
							lowerCamelCase_ 		=     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-0_5       ,	num_attention_heads=4       ,	num_hidden_layers=5       ,	pad_token_id=1       ,	vocab_size=1000       ,	)
							return CLIPTextModelWithProjection(lowerCAmelCase__      )




				@property
				def 					UpperCAmelCase__	(							self      ):
							torch.manual_seed(0      )

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

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




				@property
				def 					UpperCAmelCase__	(							self      ):
							torch.manual_seed(0      )
							lowerCamelCase_ 		=     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       ,	)

							lowerCamelCase_ 		=     CLIPVisionModelWithProjection(lowerCAmelCase__      )
							return model




				@property
				def 					UpperCAmelCase__	(							self      ):
							lowerCamelCase_ 		=     CLIPImageProcessor(
							    crop_size=224       ,	do_center_crop=lowerCAmelCase__       ,	do_normalize=lowerCAmelCase__       ,	do_resize=lowerCAmelCase__       ,	image_mean=[0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3]       ,	image_std=[0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1]       ,	resample=3       ,	size=224       ,	)

							return image_processor




				def 					UpperCAmelCase__	(							self      ):
							lowerCamelCase_ 		=     self.dummy_prior
							lowerCamelCase_ 		=     self.dummy_image_encoder
							lowerCamelCase_ 		=     self.dummy_text_encoder
							lowerCamelCase_ 		=     self.dummy_tokenizer
							lowerCamelCase_ 		=     self.dummy_image_processor

							lowerCamelCase_ 		=     UnCLIPScheduler(
							    variance_type='''fixed_small_log'''       ,	prediction_type='''sample'''       ,	num_train_timesteps=1000       ,	clip_sample=lowerCAmelCase__       ,	clip_sample_range=1_0.0       ,	)

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

							return components




				def 					UpperCAmelCase__	(							self       ,	UpperCAmelCase       ,	UpperCAmelCase=0      ):




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




				def 					UpperCAmelCase__	(							self      ):
							lowerCamelCase_ 		=     "cpu"

							lowerCamelCase_ 		=     self.get_dummy_components()

							lowerCamelCase_ 		=     self.pipeline_class(**lowerCAmelCase__      )
							lowerCamelCase_ 		=     pipe.to(lowerCAmelCase__      )

							pipe.set_progress_bar_config(disable=lowerCAmelCase__      )

							lowerCamelCase_ 		=     pipe(**self.get_dummy_inputs(lowerCAmelCase__      )      )
							lowerCamelCase_ 		=     output.image_embeds

							lowerCamelCase_ 		=     pipe(
							    **self.get_dummy_inputs(lowerCAmelCase__      )       ,	return_dict=lowerCAmelCase__       ,	)[0]

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

							assert image.shape == (1, 32)

							lowerCamelCase_ 		=     np.array(
							    [-0.0_5_3_2, 1.7_1_2_0, 0.3_6_5_6, -1.0_8_5_2, -0.8_9_4_6, -1.1_7_5_6, 0.4_3_4_8, 0.2_4_8_2, 0.5_1_4_6, -0.1_1_5_6]      )

							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 					UpperCAmelCase__	(							self      ):
							lowerCamelCase_ 		=     torch_device == "cpu"
							lowerCamelCase_ 		=     True
							lowerCamelCase_ 		=     False

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





				@skip_mps
				def 					UpperCAmelCase__	(							self      ):
							lowerCamelCase_ 		=     torch_device == "cpu"
							lowerCamelCase_ 		=     False

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





"""simple docstring"""


import glob
import os

from get_test_info import get_tester_classes


if __name__ == "__main__":
						A_						       =				[]

						A_						       =				os.path.join("""tests""", """models""", """**""", """test_modeling_*.py""")
						A_						       =				glob.glob(pattern)
						# TODO: deal with TF/Flax too
						A_						       =				[
						    x for x in test_files if not (x.startswith("""test_modeling_tf_""") or x.startswith("""test_modeling_flax_"""))
						]

						for test_file in test_files:
												A_						       =				get_tester_classes(test_file)
												for tester_class in tester_classes:
																		# A few tester classes don't have `parent` parameter in `__init__`.
																		# TODO: deal this better
																		try:
																								A_						       =				tester_class(parent=None)
																		except Exception:
																								continue
																		if hasattr(tester, """get_config"""):
																								A_						       =				tester.get_config()
																								for k, v in config.to_dict().items():
																														if isinstance(v, int):
																																				A_						       =				None
																																				if k in ["vocab_size"]:
																																										A_						       =				100
																																				elif k in ["max_position_embeddings"]:
																																										A_						       =				128
																																				elif k in ["hidden_size", "d_model"]:
																																										A_						       =				40
																																				elif k == ["num_layers", "num_hidden_layers", "num_encoder_layers", "num_decoder_layers"]:
																																										A_						       =				5
																																				if target is not None and v > target:
																																										failures.append(
																																										    f"{tester_class.__name__} will produce a `config` of type `{config.__class__.__name__}`"
																																										    f" with config[\"{k}\"] = {v} which is too large for testing! Set its value to be smaller"
																																										    f" than {target}."
																																										)

						if len(failures) > 0:
												raise Exception(f"There were {len(failures)} failures:\n" + """\n""".join(failures))



