





import gc
import unittest

from parameterized import parameterized

from diffusers import FlaxUNetaDConditionModel
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import load_hf_numpy, require_flax, slow


if is_flax_available():
      import jax
      import jax.numpy as jnp







@slow
@require_flax
class     __snake_case       (							unittest.TestCase			):





       '''simple docstring'''







       def 							UpperCAmelCase__ (  self :    Any							,				A :    Dict							,				A :    Any		):
         return f'''gaussian_noise_s={seed}_shape={'_'.join([str(A		) for s in shape]		)}.npy'''




       def 							UpperCAmelCase__ (  self :    Optional[Any]		):
         # clean up the VRAM after each test
         super().tearDown()
         gc.collect()




       def 							UpperCAmelCase__ (  self :    Optional[Any]							,				A :    Optional[int]=0							,				A :    Tuple=(4, 4, 64, 64)							,				A :    Tuple=False		):
         __snake_case:						Dict   =						jnp.bfloataa if fpaa else jnp.floataa
         __snake_case:						str   =						jnp.array(load_hf_numpy(self.get_file_format(A							,				A		)		)							,				dtype=A		)
         return image




       def 							UpperCAmelCase__ (  self :    Union[str, Any]							,				A :    Any=False							,				A :    Optional[Any]="CompVis/stable-diffusion-v1-4"		):
         __snake_case:						List[Any]   =						jnp.bfloataa if fpaa else jnp.floataa
         __snake_case:						Union[str, Any]   =						"""bf16""" if fpaa else None

         __snake_case  ,   __snake_case:						Optional[int]   =						FlaxUNetaDConditionModel.from_pretrained(
             A							,				subfolder="""unet"""							,				dtype=A							,				revision=A		)
         return model, params




       def 							UpperCAmelCase__ (  self :    Tuple							,				A :    Tuple=0							,				A :    str=(4, 77, 768)							,				A :    List[str]=False		):
         __snake_case:						Any   =						jnp.bfloataa if fpaa else jnp.floataa
         __snake_case:						Dict   =						jnp.array(load_hf_numpy(self.get_file_format(A							,				A		)		)							,				dtype=A		)
         return hidden_states




       @parameterized.expand(
           [
               # fmt: off
               [83, 4, [-0.2323, -0.1304, 0.0813, -0.3093, -0.0919, -0.1571, -0.1125, -0.5806]],
               [17, 0.55, [-0.0831, -0.2443, 0.0901, -0.0919, 0.3396, 0.0103, -0.3743, 0.0701]],
               [8, 0.89, [-0.4863, 0.0859, 0.0875, -0.1658, 0.9199, -0.0114, 0.4839, 0.4639]],
               [3, 1_000, [-0.5649, 0.2402, -0.5518, 0.1248, 1.1328, -0.2443, -0.0325, -1.0078]],
               # fmt: on
           ]		)
       def 							UpperCAmelCase__ (  self :    Optional[Any]							,				A :    Optional[Any]							,				A :    str							,				A :    Any		):
         __snake_case  ,   __snake_case:						Union[str, Any]   =						self.get_unet_model(model_id="""CompVis/stable-diffusion-v1-4"""							,				fpaa=A		)
         __snake_case:						Tuple   =						self.get_latents(A							,				fpaa=A		)
         __snake_case:						int   =						self.get_encoder_hidden_states(A							,				fpaa=A		)

         __snake_case:						List[Any]   =						model.apply(
             {"""params""": params}							,				A							,				jnp.array(A							,				dtype=jnp.intaa		)							,				encoder_hidden_states=A							,				).sample

         assert sample.shape == latents.shape

         __snake_case:						str   =						jnp.asarray(jax.device_get((sample[-1, -2:, -2:, :2].flatten())		)							,				dtype=jnp.floataa		)
         __snake_case:						Optional[int]   =						jnp.array(A							,				dtype=jnp.floataa		)

         # Found torch (float16) and flax (bfloat16) outputs to be within this tolerance, in the same hardware
         assert jnp.allclose(A							,				A							,				atol=1E-2		)






       @parameterized.expand(
           [
               # fmt: off
               [83, 4, [0.1514, 0.0807, 0.1624, 0.1016, -0.1896, 0.0263, 0.0677, 0.2310]],
               [17, 0.55, [0.1164, -0.0216, 0.0170, 0.1589, -0.3120, 0.1005, -0.0581, -0.1458]],
               [8, 0.89, [-0.1758, -0.0169, 0.1004, -0.1411, 0.1312, 0.1103, -0.1996, 0.2139]],
               [3, 1_000, [0.1214, 0.0352, -0.0731, -0.1562, -0.0994, -0.0906, -0.2340, -0.0539]],
               # fmt: on
           ]		)
       def 							UpperCAmelCase__ (  self :    Optional[Any]							,				A :    int							,				A :    Tuple							,				A :    List[str]		):
         __snake_case  ,   __snake_case:						Union[str, Any]   =						self.get_unet_model(model_id="""stabilityai/stable-diffusion-2"""							,				fpaa=A		)
         __snake_case:						Optional[int]   =						self.get_latents(A							,				shape=(4, 4, 96, 96)							,				fpaa=A		)
         __snake_case:						str   =						self.get_encoder_hidden_states(A							,				shape=(4, 77, 1_024)							,				fpaa=A		)

         __snake_case:						str   =						model.apply(
             {"""params""": params}							,				A							,				jnp.array(A							,				dtype=jnp.intaa		)							,				encoder_hidden_states=A							,				).sample

         assert sample.shape == latents.shape

         __snake_case:						Dict   =						jnp.asarray(jax.device_get((sample[-1, -2:, -2:, :2].flatten())		)							,				dtype=jnp.floataa		)
         __snake_case:						Any   =						jnp.array(A							,				dtype=jnp.floataa		)

         # Found torch (float16) and flax (bfloat16) outputs to be within this tolerance, on the same hardware
         assert jnp.allclose(A							,				A							,				atol=1E-2		)













from . import __version__

# Backward compatibility imports, to make sure all those objects can be found in file_utils
from .utils import (
    CLOUDFRONT_DISTRIB_PREFIX,
    CONFIG_NAME,
    DISABLE_TELEMETRY,
    DUMMY_INPUTS,
    DUMMY_MASK,
    ENV_VARS_TRUE_AND_AUTO_VALUES,
    ENV_VARS_TRUE_VALUES,
    FEATURE_EXTRACTOR_NAME,
    FLAX_WEIGHTS_NAME,
    HF_MODULES_CACHE,
    HUGGINGFACE_CO_PREFIX,
    HUGGINGFACE_CO_RESOLVE_ENDPOINT,
    MODEL_CARD_NAME,
    MULTIPLE_CHOICE_DUMMY_INPUTS,
    PYTORCH_PRETRAINED_BERT_CACHE,
    PYTORCH_TRANSFORMERS_CACHE,
    S3_BUCKET_PREFIX,
    SENTENCEPIECE_UNDERLINE,
    SPIECE_UNDERLINE,
    TF2_WEIGHTS_NAME,
    TF_WEIGHTS_NAME,
    TORCH_FX_REQUIRED_VERSION,
    TRANSFORMERS_CACHE,
    TRANSFORMERS_DYNAMIC_MODULE_NAME,
    USE_JAX,
    USE_TF,
    USE_TORCH,
    WEIGHTS_INDEX_NAME,
    WEIGHTS_NAME,
    ContextManagers,
    DummyObject,
    EntryNotFoundError,
    ExplicitEnum,
    ModelOutput,
    PaddingStrategy,
    PushToHubMixin,
    RepositoryNotFoundError,
    RevisionNotFoundError,
    TensorType,
    _LazyModule,
    add_code_sample_docstrings,
    add_end_docstrings,
    add_start_docstrings,
    add_start_docstrings_to_model_forward,
    cached_property,
    copy_func,
    default_cache_path,
    define_sagemaker_information,
    get_cached_models,
    get_file_from_repo,
    get_full_repo_name,
    get_torch_version,
    has_file,
    http_user_agent,
    is_apex_available,
    is_bsa_available,
    is_coloredlogs_available,
    is_datasets_available,
    is_detectrona_available,
    is_faiss_available,
    is_flax_available,
    is_ftfy_available,
    is_in_notebook,
    is_ipex_available,
    is_librosa_available,
    is_offline_mode,
    is_onnx_available,
    is_pandas_available,
    is_phonemizer_available,
    is_protobuf_available,
    is_psutil_available,
    is_pyanvml_available,
    is_pyctcdecode_available,
    is_pytesseract_available,
    is_pytorch_quantization_available,
    is_rjieba_available,
    is_sagemaker_dp_enabled,
    is_sagemaker_mp_enabled,
    is_scipy_available,
    is_sentencepiece_available,
    is_seqio_available,
    is_sklearn_available,
    is_soundfile_availble,
    is_spacy_available,
    is_speech_available,
    is_tensor,
    is_tensorflow_probability_available,
    is_tfaonnx_available,
    is_tf_available,
    is_timm_available,
    is_tokenizers_available,
    is_torch_available,
    is_torch_bfaa_available,
    is_torch_cuda_available,
    is_torch_fx_available,
    is_torch_fx_proxy,
    is_torch_mps_available,
    is_torch_tfaa_available,
    is_torch_tpu_available,
    is_torchaudio_available,
    is_training_run_on_sagemaker,
    is_vision_available,
    replace_return_docstrings,
    requires_backends,
    to_numpy,
    to_py_obj,
    torch_only_method,
)






