






import time
from contextlib import contextmanager
from pathlib import Path

import pytest
import requests
from huggingface_hub.hf_api import HfApi, HfFolder


_a      	=  '''__DUMMY_TRANSFORMERS_USER__'''
_a      	=  '''Dummy User'''
_a      	=  '''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'''

_a      	=  '''https://hub-ci.huggingface.co'''
_a      	=  CI_HUB_ENDPOINT + '''/datasets/{repo_id}/resolve/{revision}/{path}'''
_a      	=  CI_HUB_ENDPOINT + '''/{repo_id}/resolve/{revision}/{filename}'''
_a      	=  Path('''~/.huggingface/hub_ci_token''').expanduser()


@pytest.fixture
def __A    (      __lowerCAmelCase	)->  Optional[Any]:





						"""simple docstring"""



						monkeypatch.setattr(
						    'huggingface_hub.file_download.HUGGINGFACE_CO_URL_TEMPLATE' ,		__lowerCAmelCase	)


@pytest.fixture
def __A    (      __lowerCAmelCase	)->  Union[str, Any]:





						"""simple docstring"""



						monkeypatch.setattr('datasets.config.HF_ENDPOINT' ,		__lowerCAmelCase	)
						monkeypatch.setattr('datasets.config.HUB_DATASETS_URL' ,		__lowerCAmelCase	)


@pytest.fixture
def __A    (      __lowerCAmelCase	)->  Optional[Any]:





						"""simple docstring"""



						monkeypatch.setattr('huggingface_hub.hf_api.HfFolder.path_token' ,		__lowerCAmelCase	)


@pytest.fixture
def __A    (      __lowerCAmelCase ,		__lowerCAmelCase	)->  Tuple:





						"""simple docstring"""



						HfFolder.save_token(__lowerCAmelCase	)
						yield
						HfFolder.delete_token()


@pytest.fixture(scope='session'	)
def __A    (      )->  Tuple:





						"""simple docstring"""



						return HfApi(endpoint=__lowerCAmelCase	)


@pytest.fixture(scope='session'	)
def __A    (      __lowerCAmelCase	)->  Union[str, Any]:





						"""simple docstring"""



						_UpperCAmelCase									=			HfFolder.get_token()
						HfFolder.save_token(__lowerCAmelCase	)
						yield CI_HUB_USER_TOKEN
						if previous_token is not None:
												HfFolder.save_token(__lowerCAmelCase	)


@pytest.fixture
def __A    (      __lowerCAmelCase	)->  Optional[Any]:





						"""simple docstring"""



						def _cleanup_repo(__lowerCAmelCase	):
												hf_api.delete_repo(__lowerCAmelCase ,		token=__lowerCAmelCase ,		repo_type='dataset'	)

						return _cleanup_repo


@pytest.fixture
def __A    (      __lowerCAmelCase	)->  Union[str, Any]:





						"""simple docstring"""



						@contextmanager
						def _temporary_repo(__lowerCAmelCase	):
												try:
																		yield repo_id
												finally:
																		cleanup_repo(__lowerCAmelCase	)

						return _temporary_repo


@pytest.fixture(scope='session'	)
def __A    (      __lowerCAmelCase ,		__lowerCAmelCase ,		__lowerCAmelCase	)->  Dict:





						"""simple docstring"""



						_UpperCAmelCase									=			F"""repo_txt_data-{int(time.time() * 10E3	)}"""
						_UpperCAmelCase									=			F"""{CI_HUB_USER}/{repo_name}"""
						hf_api.create_repo(__lowerCAmelCase ,		token=__lowerCAmelCase ,		repo_type='dataset' ,		private=__lowerCAmelCase	)
						hf_api.upload_file(
						    token=__lowerCAmelCase ,		path_or_fileobj=str(__lowerCAmelCase	) ,		path_in_repo='data/text_data.txt' ,		repo_id=__lowerCAmelCase ,		repo_type='dataset' ,		)
						yield repo_id
						try:
												hf_api.delete_repo(__lowerCAmelCase ,		token=__lowerCAmelCase ,		repo_type='dataset'	)
						except (requests.exceptions.HTTPError, ValueError):  # catch http error and token invalid error
												pass


@pytest.fixture()
def __A    (      __lowerCAmelCase ,		__lowerCAmelCase ,		__lowerCAmelCase	)->  List[Any]:





						"""simple docstring"""



						return hf_private_dataset_repo_txt_data_


@pytest.fixture(scope='session'	)
def __A    (      __lowerCAmelCase ,		__lowerCAmelCase ,		__lowerCAmelCase	)->  int:





						"""simple docstring"""



						_UpperCAmelCase									=			F"""repo_zipped_txt_data-{int(time.time() * 10E3	)}"""
						_UpperCAmelCase									=			F"""{CI_HUB_USER}/{repo_name}"""
						hf_api.create_repo(__lowerCAmelCase ,		token=__lowerCAmelCase ,		repo_type='dataset' ,		private=__lowerCAmelCase	)
						hf_api.upload_file(
						    token=__lowerCAmelCase ,		path_or_fileobj=str(__lowerCAmelCase	) ,		path_in_repo='data.zip' ,		repo_id=__lowerCAmelCase ,		repo_type='dataset' ,		)
						yield repo_id
						try:
												hf_api.delete_repo(__lowerCAmelCase ,		token=__lowerCAmelCase ,		repo_type='dataset'	)
						except (requests.exceptions.HTTPError, ValueError):  # catch http error and token invalid error
												pass


@pytest.fixture()
def __A    (      __lowerCAmelCase ,		__lowerCAmelCase ,		__lowerCAmelCase	)->  List[Any]:





						"""simple docstring"""



						return hf_private_dataset_repo_zipped_txt_data_


@pytest.fixture(scope='session'	)
def __A    (      __lowerCAmelCase ,		__lowerCAmelCase ,		__lowerCAmelCase	)->  Dict:





						"""simple docstring"""



						_UpperCAmelCase									=			F"""repo_zipped_img_data-{int(time.time() * 10E3	)}"""
						_UpperCAmelCase									=			F"""{CI_HUB_USER}/{repo_name}"""
						hf_api.create_repo(__lowerCAmelCase ,		token=__lowerCAmelCase ,		repo_type='dataset' ,		private=__lowerCAmelCase	)
						hf_api.upload_file(
						    token=__lowerCAmelCase ,		path_or_fileobj=str(__lowerCAmelCase	) ,		path_in_repo='data.zip' ,		repo_id=__lowerCAmelCase ,		repo_type='dataset' ,		)
						yield repo_id
						try:
												hf_api.delete_repo(__lowerCAmelCase ,		token=__lowerCAmelCase ,		repo_type='dataset'	)
						except (requests.exceptions.HTTPError, ValueError):  # catch http error and token invalid error
												pass


@pytest.fixture()
def __A    (      __lowerCAmelCase ,		__lowerCAmelCase ,		__lowerCAmelCase	)->  int:





						"""simple docstring"""



						return hf_private_dataset_repo_zipped_img_data_







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		)






