






"""simple docstring"""
import os


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_doctest_list.py
_lowerCAmelCase         =							"."


if __name__ == "__main__":
							_lowerCAmelCase         =							os.path.join(REPO_PATH, "utils/documentation_tests.txt")
							_lowerCAmelCase         =							[]
							_lowerCAmelCase         =							[]
							with open(doctest_file_path) as fp:
														for line in fp:
																					_lowerCAmelCase         =							line.strip()
																					_lowerCAmelCase         =							os.path.join(REPO_PATH, line)
																					if not (os.path.isfile(path) or os.path.isdir(path)):
																												non_existent_paths.append(line)
																					all_paths.append(path)
							if len(non_existent_paths) > 0:
														_lowerCAmelCase         =							"\n".join(non_existent_paths)
														raise ValueError(F'`utils/documentation_tests.txt` contains non-existent paths:\n{non_existent_paths}')
							if all_paths != sorted(all_paths):
														raise ValueError("Files in `utils/documentation_tests.txt` are not in alphabetical order.")









"""simple docstring"""
import gc
import inspect
import unittest

import torch
from parameterized import parameterized

from diffusers import PriorTransformer
from diffusers.utils import floats_tensor, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin


enable_full_determinism()



class      SCREAMING_SNAKE_CASE  (				_SCREAMING_SNAKE_CASE       ,       unittest.TestCase						):





	"""simple docstring"""




	_A       :				int    =				PriorTransformer
	_A       :				Dict    =				"""hidden_states"""





	@property
	def    lowerCamelCase(self							):
		A_		:						Tuple	    =   4
		A_		:						Dict	    =   8
		A_		:						str	    =   7

		A_		:						Dict	    =   floats_tensor((batch_size, embedding_dim)							).to(lowerCAmelCase_							)

		A_		:						str	    =   floats_tensor((batch_size, embedding_dim)							).to(lowerCAmelCase_							)
		A_		:						List[str]	    =   floats_tensor((batch_size, num_embeddings, embedding_dim)							).to(lowerCAmelCase_							)

		return {
		    "hidden_states": hidden_states,
		    "timestep": 2,
		    "proj_embedding": proj_embedding,
		    "encoder_hidden_states": encoder_hidden_states,
		}





	def    lowerCamelCase(self     ,     lowerCAmelCase_=0							):
		torch.manual_seed(lowerCAmelCase_							)
		A_		:						Optional[Any]	    =   4
		A_		:						Tuple	    =   8
		A_		:						Tuple	    =   7

		A_		:						int	    =   torch.randn((batch_size, embedding_dim)							).to(lowerCAmelCase_							)

		A_		:						List[str]	    =   torch.randn((batch_size, embedding_dim)							).to(lowerCAmelCase_							)
		A_		:						Tuple	    =   torch.randn((batch_size, num_embeddings, embedding_dim)							).to(lowerCAmelCase_							)

		return {
		    "hidden_states": hidden_states,
		    "timestep": 2,
		    "proj_embedding": proj_embedding,
		    "encoder_hidden_states": encoder_hidden_states,
		}





	@property
	def    lowerCamelCase(self							):
		return (4, 8)





	@property
	def    lowerCamelCase(self							):
		return (4, 8)





	def    lowerCamelCase(self							):
		A_		:						int	    =   {
		    """num_attention_heads""": 2,
		    """attention_head_dim""": 4,
		    """num_layers""": 2,
		    """embedding_dim""": 8,
		    """num_embeddings""": 7,
		    """additional_embeddings""": 4,
		}
		A_		:						Dict	    =   self.dummy_input
		return init_dict, inputs_dict





	def    lowerCamelCase(self							):
		A_    ,     A_		:						Optional[int]	    =   PriorTransformer.from_pretrained(
		    """hf-internal-testing/prior-dummy"""     ,     output_loading_info=lowerCAmelCase_							)
		self.assertIsNotNone(lowerCAmelCase_							)
		self.assertEqual(len(loading_info["""missing_keys"""]							)     ,     0							)

		model.to(lowerCAmelCase_							)
		A_		:						List[Any]	    =   model(**self.dummy_input							)[0]

		assert hidden_states is not None, "Make sure output is not None"





	def    lowerCamelCase(self							):
		A_    ,     A_		:						Union[str, Any]	    =   self.prepare_init_args_and_inputs_for_common()

		A_		:						Union[str, Any]	    =   self.model_class(**lowerCAmelCase_							)
		A_		:						Tuple	    =   inspect.signature(model.forward							)
		# signature.parameters is an OrderedDict => so arg_names order is deterministic
		A_		:						Tuple	    =   [*signature.parameters.keys()]

		A_		:						List[str]	    =   ["""hidden_states""", """timestep"""]
		self.assertListEqual(arg_names[:2]     ,     lowerCAmelCase_							)





	def    lowerCamelCase(self							):
		A_		:						int	    =   PriorTransformer.from_pretrained("""hf-internal-testing/prior-dummy"""							)
		A_		:						Optional[Any]	    =   model.to(lowerCAmelCase_							)

		if hasattr(lowerCAmelCase_     ,     """set_default_attn_processor"""							):
			model.set_default_attn_processor()

		A_		:						int	    =   self.get_dummy_seed_input()

		with torch.no_grad():
			A_		:						int	    =   model(**lowerCAmelCase_							)[0]

		A_		:						Any	    =   output[0, :5].flatten().cpu()
		print(lowerCAmelCase_							)

		# Since the VAE Gaussian prior's generator is seeded on the appropriate device,
		# the expected output slices are not the same for CPU and GPU.
		A_		:						Optional[int]	    =   torch.tensor([-1.3436, -0.2870, 0.7538, 0.4368, -0.0239]							)
		self.assertTrue(torch_all_close(lowerCAmelCase_     ,     lowerCAmelCase_     ,     rtol=1e-2							)							)






@slow
class      SCREAMING_SNAKE_CASE  (				unittest.TestCase						):





	"""simple docstring"""




	def    lowerCamelCase(self     ,     lowerCAmelCase_=1     ,     lowerCAmelCase_=768     ,     lowerCAmelCase_=77     ,     lowerCAmelCase_=0							):
		torch.manual_seed(lowerCAmelCase_							)
		A_		:						Tuple	    =   batch_size
		A_		:						Any	    =   embedding_dim
		A_		:						Tuple	    =   num_embeddings

		A_		:						Any	    =   torch.randn((batch_size, embedding_dim)							).to(lowerCAmelCase_							)

		A_		:						Union[str, Any]	    =   torch.randn((batch_size, embedding_dim)							).to(lowerCAmelCase_							)
		A_		:						Tuple	    =   torch.randn((batch_size, num_embeddings, embedding_dim)							).to(lowerCAmelCase_							)

		return {
		    "hidden_states": hidden_states,
		    "timestep": 2,
		    "proj_embedding": proj_embedding,
		    "encoder_hidden_states": encoder_hidden_states,
		}





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





	@parameterized.expand(
	    [
	        # fmt: off
	        [13, [-0.5861,  0.1283, -0.0931,  0.0882,  0.4476,  0.1329, -0.0498,  0.0640]],
	        [37, [-0.4913,  0.0110, -0.0483,  0.0541,  0.4954, -0.0170,  0.0354,  0.1651]],
	        # fmt: on
	    ]							)
	def    lowerCamelCase(self     ,     lowerCAmelCase_     ,     lowerCAmelCase_							):
		A_		:						List[Any]	    =   PriorTransformer.from_pretrained("""kandinsky-community/kandinsky-2-1-prior"""     ,     subfolder="""prior"""							)
		model.to(lowerCAmelCase_							)
		A_		:						int	    =   self.get_dummy_seed_input(seed=lowerCAmelCase_							)

		with torch.no_grad():
			A_		:						Tuple	    =   model(**lowerCAmelCase_							)[0]

		assert list(sample.shape							) == [1, 768]

		A_		:						Tuple	    =   sample[0, :8].flatten().cpu()
		print(lowerCAmelCase_							)
		A_		:						Optional[int]	    =   torch.tensor(lowerCAmelCase_							)

		assert torch_all_close(lowerCAmelCase_     ,     lowerCAmelCase_     ,     atol=1e-3							)

