

"""simple docstring"""




import os
import unittest

from huggingface_hub.utils import are_progress_bars_disabled

import transformers.models.bart.tokenization_bart
from transformers import logging
from transformers.testing_utils import CaptureLogger, mockenv, mockenv_context
from transformers.utils.logging import disable_progress_bar, enable_progress_bar


class 		_A       (			unittest.TestCase					):





      def 					_lowerCamelCase  (  self		)   ->					int:
         lowerCamelCase__						=			logging.get_logger()

         # the current default level is logging.WARNING
         lowerCamelCase__						=			logging.get_verbosity()

         logging.set_verbosity_error()
         self.assertEqual(logger.getEffectiveLevel()    ,						logging.get_verbosity()		)

         logging.set_verbosity_warning()
         self.assertEqual(logger.getEffectiveLevel()    ,						logging.get_verbosity()		)

         logging.set_verbosity_info()
         self.assertEqual(logger.getEffectiveLevel()    ,						logging.get_verbosity()		)

         logging.set_verbosity_debug()
         self.assertEqual(logger.getEffectiveLevel()    ,						logging.get_verbosity()		)

         # restore to the original level
         logging.set_verbosity(SCREAMING_SNAKE_CASE__		)





      def 					_lowerCamelCase  (  self		)   ->					Tuple:
         lowerCamelCase__						=			logging.get_verbosity()

         lowerCamelCase__						=			logging.get_logger("transformers.models.bart.tokenization_bart"		)
         lowerCamelCase__						=			"Testing 1, 2, 3"

         # should be able to log warnings (if default settings weren't overridden by `pytest --log-level-all`)
         if level_origin <= logging.WARNING:
            with CaptureLogger(SCREAMING_SNAKE_CASE__		) as cl:
               logger.warning(SCREAMING_SNAKE_CASE__		)
            self.assertEqual(cl.out    ,						msg + "\n"		)

         # this is setting the level for all of `transformers.*` loggers
         logging.set_verbosity_error()

         # should not be able to log warnings
         with CaptureLogger(SCREAMING_SNAKE_CASE__		) as cl:
            logger.warning(SCREAMING_SNAKE_CASE__		)
         self.assertEqual(cl.out    ,						""		)

         # should be able to log warnings again
         logging.set_verbosity_warning()
         with CaptureLogger(SCREAMING_SNAKE_CASE__		) as cl:
            logger.warning(SCREAMING_SNAKE_CASE__		)
         self.assertEqual(cl.out    ,						msg + "\n"		)

         # restore to the original level
         logging.set_verbosity(SCREAMING_SNAKE_CASE__		)





      @mockenv(TRANSFORMERS_VERBOSITY="error"		)
      def 					_lowerCamelCase  (  self		)   ->					Optional[Any]:
         # reset for the env var to take effect, next time some logger call is made
         transformers.utils.logging._reset_library_root_logger()
         # this action activates the env var
         lowerCamelCase__						=			logging.get_logger("transformers.models.bart.tokenization_bart"		)

         lowerCamelCase__						=			os.getenv("TRANSFORMERS_VERBOSITY"    ,						SCREAMING_SNAKE_CASE__		)
         lowerCamelCase__						=			logging.log_levels[env_level_str]

         lowerCamelCase__						=			logging.get_verbosity()
         self.assertEqual(
             SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						f'TRANSFORMERS_VERBOSITY={env_level_str}/{env_level}, but internal verbosity is {current_level}'    ,						)

         # restore to the original level
         lowerCamelCase__						=			""
         transformers.utils.logging._reset_library_root_logger()





      @mockenv(TRANSFORMERS_VERBOSITY="super-error"		)
      def 					_lowerCamelCase  (  self		)   ->					int:
         # reset for the env var to take effect, next time some logger call is made
         transformers.utils.logging._reset_library_root_logger()
         lowerCamelCase__						=			logging.logging.getLogger()
         with CaptureLogger(SCREAMING_SNAKE_CASE__		) as cl:
            # this action activates the env var
            logging.get_logger("transformers.models.bart.tokenization_bart"		)
         self.assertIn("Unknown option TRANSFORMERS_VERBOSITY=super-error"    ,						cl.out		)

         # no need to restore as nothing was changed





      def 					_lowerCamelCase  (  self		)   ->					Optional[int]:
         # testing `logger.warning_advice()`
         transformers.utils.logging._reset_library_root_logger()

         lowerCamelCase__						=			logging.get_logger("transformers.models.bart.tokenization_bart"		)
         lowerCamelCase__						=			"Testing 1, 2, 3"

         with mockenv_context(TRANSFORMERS_NO_ADVISORY_WARNINGS="1"		):
            # nothing should be logged as env var disables this method
            with CaptureLogger(SCREAMING_SNAKE_CASE__		) as cl:
               logger.warning_advice(SCREAMING_SNAKE_CASE__		)
            self.assertEqual(cl.out    ,						""		)

         with mockenv_context(TRANSFORMERS_NO_ADVISORY_WARNINGS=""		):
            # should log normally as TRANSFORMERS_NO_ADVISORY_WARNINGS is unset
            with CaptureLogger(SCREAMING_SNAKE_CASE__		) as cl:
               logger.warning_advice(SCREAMING_SNAKE_CASE__		)
            self.assertEqual(cl.out    ,						msg + "\n"		)






def 	UpperCAmelCase__   (				)      ->      Optional[Any]:




   """simple docstring"""
   disable_progress_bar()
   assert are_progress_bars_disabled()

   enable_progress_bar()
   assert not are_progress_bars_disabled()







"""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




