

"""simple docstring"""


import pytest


__A						       =      '''__dummy_dataset1__'''

__A						       =      '''
import json
import os

import datasets


REPO_URL = "https://huggingface.co/datasets/albertvillanova/tests-raw-jsonl/resolve/main/"
URLS = {"train": REPO_URL + "wikiann-bn-train.jsonl", "validation": REPO_URL + "wikiann-bn-validation.jsonl"}


class __DummyDataset1__(datasets.GeneratorBasedBuilder):

    def _info(self):
        features = datasets.Features(
            {
                "tokens": datasets.Sequence(datasets.Value("string")),
                "ner_tags": datasets.Sequence(
                    datasets.features.ClassLabel(
                        names=[
                            "O",
                            "B-PER",
                            "I-PER",
                            "B-ORG",
                            "I-ORG",
                            "B-LOC",
                            "I-LOC",
                        ]
                    )
                ),
                "langs": datasets.Sequence(datasets.Value("string")),
                "spans": datasets.Sequence(datasets.Value("string")),
            }
        )
        return datasets.DatasetInfo(features=features)

    def _split_generators(self, dl_manager):
        dl_path = dl_manager.download(URLS)
        return [
            datasets.SplitGenerator(datasets.Split.TRAIN, gen_kwargs={"filepath": dl_path["train"]}),
            datasets.SplitGenerator(datasets.Split.VALIDATION, gen_kwargs={"filepath": dl_path["validation"]}),
        ]

    def _generate_examples(self, filepath):
        with open(filepath, "r", encoding="utf-8") as f:
            for i, line in enumerate(f):
                yield i, json.loads(line)
'''






@pytest.fixture
def    a__   (   )     ->     Tuple:
		return DATASET_LOADING_SCRIPT_NAME






@pytest.fixture
def    a__   (   )     ->     Dict:
		return DATASET_LOADING_SCRIPT_CODE






@pytest.fixture
def    a__   (   __SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE			)     ->     Any:
		__lowerCAmelCase:	Any   				=      dataset_loading_script_name
		__lowerCAmelCase:	Dict   				=      tmp_path / '''datasets''' / script_name
		script_dir.mkdir(parents=a__			)
		__lowerCAmelCase:	Any   				=      script_dir / F"{script_name}.py"
		with open(a__ ,			"w"			) as f:
				f.write(a__			)
		return str(a__			)





"""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    snake_case     (    __snake_case,							unittest.TestCase  ):
				SCREAMING_SNAKE_CASE_       :				Tuple       				=  PriorTransformer
				SCREAMING_SNAKE_CASE_       :				List[str]       				=  """hidden_states"""

				@property
				def 							lowercase_					(     self							:    Dict)->       str:



						'''simple docstring'''


						__lowerCAmelCase:	str   				=      4
						__lowerCAmelCase:	int   				=      8
						__lowerCAmelCase:	int   				=      7

						__lowerCAmelCase:	str   				=      floats_tensor((batch_size, embedding_dim)).to(UpperCamelCase__)

						__lowerCAmelCase:	Optional[Any]   				=      floats_tensor((batch_size, embedding_dim)).to(UpperCamelCase__)
						__lowerCAmelCase:	Any   				=      floats_tensor((batch_size, num_embeddings, embedding_dim)).to(UpperCamelCase__)

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

				def 							lowercase_					(     self							:    Optional[int]					,  UpperCamelCase__							:    str=0)->       str:



						'''simple docstring'''


						torch.manual_seed(UpperCamelCase__)
						__lowerCAmelCase:	List[Any]   				=      4
						__lowerCAmelCase:	Dict   				=      8
						__lowerCAmelCase:	int   				=      7

						__lowerCAmelCase:	List[str]   				=      torch.randn((batch_size, embedding_dim)).to(UpperCamelCase__)

						__lowerCAmelCase:	Tuple   				=      torch.randn((batch_size, embedding_dim)).to(UpperCamelCase__)
						__lowerCAmelCase:	List[Any]   				=      torch.randn((batch_size, num_embeddings, embedding_dim)).to(UpperCamelCase__)

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

				@property
				def 							lowercase_					(     self							:    Dict)->       List[Any]:



						'''simple docstring'''


						return (4, 8)

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



						'''simple docstring'''


						return (4, 8)

				def 							lowercase_					(     self							:    Optional[int])->       Tuple:



						'''simple docstring'''


						__lowerCAmelCase:	str   				=      {
						    "num_attention_heads": 2,
						    "attention_head_dim": 4,
						    "num_layers": 2,
						    "embedding_dim": 8,
						    "num_embeddings": 7,
						    "additional_embeddings": 4,
						}
						__lowerCAmelCase:	Any   				=      self.dummy_input
						return init_dict, inputs_dict

				def 							lowercase_					(     self							:    List[Any])->       int:



						'''simple docstring'''


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

						model.to(UpperCamelCase__)
						__lowerCAmelCase:	Dict   				=      model(**self.dummy_input)[0]

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

				def 							lowercase_					(     self							:    List[str])->       Tuple:



						'''simple docstring'''


						__lowerCAmelCase				,			__lowerCAmelCase:	Optional[Any]   				=      self.prepare_init_args_and_inputs_for_common()

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

						__lowerCAmelCase:	Any   				=      ["hidden_states", "timestep"]
						self.assertListEqual(arg_names[:2]					,  UpperCamelCase__)




				def 							lowercase_					(     self							:    Optional[int])->       List[str]:



						'''simple docstring'''


						__lowerCAmelCase:	int   				=      PriorTransformer.from_pretrained("hf-internal-testing/prior-dummy")
						__lowerCAmelCase:	Union[str, Any]   				=      model.to(UpperCamelCase__)

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

						__lowerCAmelCase:	str   				=      self.get_dummy_seed_input()

						with torch.no_grad():
								__lowerCAmelCase:	Dict   				=      model(**UpperCamelCase__)[0]

						__lowerCAmelCase:	Dict   				=      output[0, :5].flatten().cpu()
						print(UpperCamelCase__)

						# 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.
						__lowerCAmelCase:	List[str]   				=      torch.tensor([-1.3436, -0.2870, 0.7538, 0.4368, -0.0239])
						self.assertTrue(torch_all_close(UpperCamelCase__					,  UpperCamelCase__					,  rtol=1e-2))





@slow
class    snake_case     (    unittest.TestCase  ):

				def 							lowercase_					(     self							:    int					,  UpperCamelCase__							:    Dict=1					,  UpperCamelCase__							:    str=7_6_8					,  UpperCamelCase__							:    int=7_7					,  UpperCamelCase__							:    Any=0)->       Union[str, Any]:



						'''simple docstring'''


						torch.manual_seed(UpperCamelCase__)
						__lowerCAmelCase:	List[Any]   				=      batch_size
						__lowerCAmelCase:	Any   				=      embedding_dim
						__lowerCAmelCase:	Dict   				=      num_embeddings

						__lowerCAmelCase:	Dict   				=      torch.randn((batch_size, embedding_dim)).to(UpperCamelCase__)

						__lowerCAmelCase:	str   				=      torch.randn((batch_size, embedding_dim)).to(UpperCamelCase__)
						__lowerCAmelCase:	int   				=      torch.randn((batch_size, num_embeddings, embedding_dim)).to(UpperCamelCase__)

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

				def 							lowercase_					(     self							:    List[Any])->       Union[str, Any]:



						'''simple docstring'''


						super().tearDown()
						gc.collect()
						torch.cuda.empty_cache()




				@parameterized.expand(
				    [
				        # fmt: off
				        [1_3, [-0.5861,  0.1283, -0.0931,  0.0882,  0.4476,  0.1329, -0.0498,  0.0640]],
				        [3_7, [-0.4913,  0.0110, -0.0483,  0.0541,  0.4954, -0.0170,  0.0354,  0.1651]],
				        # fmt: on
				    ])
				def 							lowercase_					(     self							:    Optional[int]					,  UpperCamelCase__							:    List[str]					,  UpperCamelCase__							:    int)->       List[Any]:



						'''simple docstring'''


						__lowerCAmelCase:	List[str]   				=      PriorTransformer.from_pretrained("kandinsky-community/kandinsky-2-1-prior"					,  subfolder="prior")
						model.to(UpperCamelCase__)
						__lowerCAmelCase:	Dict   				=      self.get_dummy_seed_input(seed=UpperCamelCase__)

						with torch.no_grad():
								__lowerCAmelCase:	Optional[Any]   				=      model(**UpperCamelCase__)[0]

						assert list(sample.shape) == [1, 7_6_8]

						__lowerCAmelCase:	Dict   				=      sample[0, :8].flatten().cpu()
						print(UpperCamelCase__)
						__lowerCAmelCase:	Union[str, Any]   				=      torch.tensor(UpperCamelCase__)

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


