

"""simple docstring"""




import time
from contextlib import contextmanager
from pathlib import Path

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


SCREAMING_SNAKE_CASE_     :			List[str]	  =			'''__DUMMY_TRANSFORMERS_USER__'''
SCREAMING_SNAKE_CASE_     :			Tuple	  =			'''Dummy User'''
SCREAMING_SNAKE_CASE_     :			Optional[int]	  =			'''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'''

SCREAMING_SNAKE_CASE_     :			List[Any]	  =			'''https://hub-ci.huggingface.co'''
SCREAMING_SNAKE_CASE_     :			Dict	  =			CI_HUB_ENDPOINT + '''/datasets/{repo_id}/resolve/{revision}/{path}'''
SCREAMING_SNAKE_CASE_     :			Dict	  =			CI_HUB_ENDPOINT + '''/{repo_id}/resolve/{revision}/{filename}'''
SCREAMING_SNAKE_CASE_     :			List[Any]	  =			Path('''~/.huggingface/hub_ci_token''').expanduser()



@pytest.fixture
def 	UpperCAmelCase__   (				A__   )      ->      Dict:




   """simple docstring"""
   monkeypatch.setattr(
       "huggingface_hub.file_download.HUGGINGFACE_CO_URL_TEMPLATE"	,    __SCREAMING_SNAKE_CASE   )



@pytest.fixture
def 	UpperCAmelCase__   (				A__   )      ->      Dict:




   """simple docstring"""
   monkeypatch.setattr("datasets.config.HF_ENDPOINT"	,    __SCREAMING_SNAKE_CASE   )
   monkeypatch.setattr("datasets.config.HUB_DATASETS_URL"	,    __SCREAMING_SNAKE_CASE   )



@pytest.fixture
def 	UpperCAmelCase__   (				A__   )      ->      Any:




   """simple docstring"""
   monkeypatch.setattr("huggingface_hub.hf_api.HfFolder.path_token"	,    __SCREAMING_SNAKE_CASE   )



@pytest.fixture
def 	UpperCAmelCase__   (				A__	,    A__   )      ->      int:




   """simple docstring"""
   HfFolder.save_token(__SCREAMING_SNAKE_CASE   )
   yield
   HfFolder.delete_token()



@pytest.fixture(scope="session"   )
def 	UpperCAmelCase__   (				)      ->      Union[str, Any]:




   """simple docstring"""
   return HfApi(endpoint=__SCREAMING_SNAKE_CASE   )



@pytest.fixture(scope="session"   )
def 	UpperCAmelCase__   (				A__   )      ->      Tuple:




   """simple docstring"""
   lowerCamelCase__						=			HfFolder.get_token()
   HfFolder.save_token(__SCREAMING_SNAKE_CASE   )
   yield CI_HUB_USER_TOKEN
   if previous_token is not None:
      HfFolder.save_token(__SCREAMING_SNAKE_CASE   )



@pytest.fixture
def 	UpperCAmelCase__   (				A__   )      ->      Tuple:




   """simple docstring"""
   def _cleanup_repo(A__   ):
      hf_api.delete_repo(__SCREAMING_SNAKE_CASE	,    token=__SCREAMING_SNAKE_CASE	,    repo_type="dataset"   )

   return _cleanup_repo



@pytest.fixture
def 	UpperCAmelCase__   (				A__   )      ->      Tuple:




   """simple docstring"""
   @contextmanager
   def _temporary_repo(A__   ):
      try:
         yield repo_id
      finally:
         cleanup_repo(__SCREAMING_SNAKE_CASE   )

   return _temporary_repo



@pytest.fixture(scope="session"   )
def 	UpperCAmelCase__   (				A__	,    A__	,    A__   )      ->      Any:




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



@pytest.fixture()
def 	UpperCAmelCase__   (				A__	,    A__	,    A__   )      ->      Optional[int]:




   """simple docstring"""
   return hf_private_dataset_repo_txt_data_



@pytest.fixture(scope="session"   )
def 	UpperCAmelCase__   (				A__	,    A__	,    A__   )      ->      Any:




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



@pytest.fixture()
def 	UpperCAmelCase__   (				A__	,    A__	,    A__   )      ->      Dict:




   """simple docstring"""
   return hf_private_dataset_repo_zipped_txt_data_



@pytest.fixture(scope="session"   )
def 	UpperCAmelCase__   (				A__	,    A__	,    A__   )      ->      str:




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



@pytest.fixture()
def 	UpperCAmelCase__   (				A__	,    A__	,    A__   )      ->      List[Any]:




   """simple docstring"""
   return hf_private_dataset_repo_zipped_img_data_






"""simple docstring"""




import gc
import unittest

from diffusers import FlaxControlNetModel, FlaxStableDiffusionControlNetPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax


if is_flax_available():
     import jax
     import jax.numpy as jnp
     from flax.jax_utils import replicate
     from flax.training.common_utils import shard


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





      def 					_lowerCamelCase  (  self		)   ->					Tuple:
         # clean up the VRAM after each test
         super().tearDown()
         gc.collect()





      def 					_lowerCamelCase  (  self		)   ->					Optional[Any]:
         lowerCamelCase__					,       lowerCamelCase__						=			FlaxControlNetModel.from_pretrained(
             "lllyasviel/sd-controlnet-canny"    ,						from_pt=SCREAMING_SNAKE_CASE__    ,						dtype=jnp.bfloataa		)
         lowerCamelCase__					,       lowerCamelCase__						=			FlaxStableDiffusionControlNetPipeline.from_pretrained(
             "runwayml/stable-diffusion-v1-5"    ,						controlnet=SCREAMING_SNAKE_CASE__    ,						from_pt=SCREAMING_SNAKE_CASE__    ,						dtype=jnp.bfloataa		)
         lowerCamelCase__						=			controlnet_params

         lowerCamelCase__						=			"bird"
         lowerCamelCase__						=			jax.device_count()
         lowerCamelCase__						=			pipe.prepare_text_inputs([prompts] * num_samples		)

         lowerCamelCase__						=			load_image(
             "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png"		)
         lowerCamelCase__						=			pipe.prepare_image_inputs([canny_image] * num_samples		)

         lowerCamelCase__						=			jax.random.PRNGKey(0		)
         lowerCamelCase__						=			jax.random.split(SCREAMING_SNAKE_CASE__    ,						jax.device_count()		)

         lowerCamelCase__						=			replicate(SCREAMING_SNAKE_CASE__		)
         lowerCamelCase__						=			shard(SCREAMING_SNAKE_CASE__		)
         lowerCamelCase__						=			shard(SCREAMING_SNAKE_CASE__		)

         lowerCamelCase__						=			pipe(
             prompt_ids=SCREAMING_SNAKE_CASE__    ,						image=SCREAMING_SNAKE_CASE__    ,						params=SCREAMING_SNAKE_CASE__    ,						prng_seed=SCREAMING_SNAKE_CASE__    ,						num_inference_steps=50    ,						jit=SCREAMING_SNAKE_CASE__    ,						).images
         assert images.shape == (jax.device_count(), 1, 768, 512, 3)

         lowerCamelCase__						=			images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]		)
         lowerCamelCase__						=			images[0, 253:256, 253:256, -1]

         lowerCamelCase__						=			jnp.asarray(jax.device_get(image_slice.flatten()		)		)
         lowerCamelCase__						=			jnp.array(
             [0.16_79_69, 0.11_66_99, 0.08_15_43, 0.15_42_97, 0.13_28_12, 0.10_88_87, 0.16_99_22, 0.16_99_22, 0.20_50_78]		)
         print(f'output_slice: {output_slice}'		)
         assert jnp.abs(output_slice - expected_slice		).max() < 1e-2





      def 					_lowerCamelCase  (  self		)   ->					str:
         lowerCamelCase__					,       lowerCamelCase__						=			FlaxControlNetModel.from_pretrained(
             "lllyasviel/sd-controlnet-openpose"    ,						from_pt=SCREAMING_SNAKE_CASE__    ,						dtype=jnp.bfloataa		)
         lowerCamelCase__					,       lowerCamelCase__						=			FlaxStableDiffusionControlNetPipeline.from_pretrained(
             "runwayml/stable-diffusion-v1-5"    ,						controlnet=SCREAMING_SNAKE_CASE__    ,						from_pt=SCREAMING_SNAKE_CASE__    ,						dtype=jnp.bfloataa		)
         lowerCamelCase__						=			controlnet_params

         lowerCamelCase__						=			"Chef in the kitchen"
         lowerCamelCase__						=			jax.device_count()
         lowerCamelCase__						=			pipe.prepare_text_inputs([prompts] * num_samples		)

         lowerCamelCase__						=			load_image(
             "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/pose.png"		)
         lowerCamelCase__						=			pipe.prepare_image_inputs([pose_image] * num_samples		)

         lowerCamelCase__						=			jax.random.PRNGKey(0		)
         lowerCamelCase__						=			jax.random.split(SCREAMING_SNAKE_CASE__    ,						jax.device_count()		)

         lowerCamelCase__						=			replicate(SCREAMING_SNAKE_CASE__		)
         lowerCamelCase__						=			shard(SCREAMING_SNAKE_CASE__		)
         lowerCamelCase__						=			shard(SCREAMING_SNAKE_CASE__		)

         lowerCamelCase__						=			pipe(
             prompt_ids=SCREAMING_SNAKE_CASE__    ,						image=SCREAMING_SNAKE_CASE__    ,						params=SCREAMING_SNAKE_CASE__    ,						prng_seed=SCREAMING_SNAKE_CASE__    ,						num_inference_steps=50    ,						jit=SCREAMING_SNAKE_CASE__    ,						).images
         assert images.shape == (jax.device_count(), 1, 768, 512, 3)

         lowerCamelCase__						=			images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]		)
         lowerCamelCase__						=			images[0, 253:256, 253:256, -1]

         lowerCamelCase__						=			jnp.asarray(jax.device_get(image_slice.flatten()		)		)
         lowerCamelCase__						=			jnp.array(
             [[0.27_14_84, 0.26_17_19, 0.27_53_91, 0.27_73_44, 0.27_92_97, 0.29_10_16, 0.29_49_22, 0.30_27_34, 0.30_27_34]]		)
         print(f'output_slice: {output_slice}'		)
         assert jnp.abs(output_slice - expected_slice		).max() < 1e-2




