
import unittest

from transformers import JukeboxTokenizer
from transformers.testing_utils import require_torch


class 			lowercase_		(      unittest.TestCase       ):
       __lowerCamelCase				       =						JukeboxTokenizer
       __lowerCamelCase				       =						{
           "artist": "Zac Brown Band",
           "genres": "Country",
           "lyrics": "I met a traveller from an antique land,\n        Who said \"Two vast and trunkless legs of stone\n        Stand in the desert. . . . Near them, on the sand,\n        Half sunk a shattered visage lies, whose frown,\n        And wrinkled lip, and sneer of cold command,\n        Tell that its sculptor well those passions read\n        Which yet survive, stamped on these lifeless things,\n        The hand that mocked them, and the heart that fed;\n        And on the pedestal, these words appear:\n        My name is Ozymandias, King of Kings;\n        Look on my Works, ye Mighty, and despair!\n        Nothing beside remains. Round the decay\n        Of that colossal Wreck, boundless and bare\n        The lone and level sands stretch far away\n        ",
       }


       @require_torch
       def 	_snake_case				(    self				)						->    Dict:
              import torch

              SCREAMING_SNAKE_CASE_							:				str		=JukeboxTokenizer.from_pretrained('''openai/jukebox-1b-lyrics'''				)
              SCREAMING_SNAKE_CASE_							:				str		=tokenizer(**self.metas				)['''input_ids''']
              # fmt: off
              SCREAMING_SNAKE_CASE_							:				Any		=[
                  torch.tensor([[
                      0, 0, 0, 7_169, 507, 9, 76, 39, 31, 46, 76, 27,
                      76, 46, 44, 27, 48, 31, 38, 38, 31, 44, 76, 32,
                      44, 41, 39, 76, 27, 40, 76, 27, 40, 46, 35, 43,
                      47, 31, 76, 38, 27, 40, 30, 64, 78, 76, 76, 76,
                      76, 76, 76, 76, 76, 23, 34, 41, 76, 45, 27, 35,
                      30, 76, 71, 20, 49, 41, 76, 48, 27, 45, 46, 76,
                      27, 40, 30, 76, 46, 44, 47, 40, 37, 38, 31, 45,
                      45, 76, 38, 31, 33, 45, 76, 41, 32, 76, 45, 46,
                      41, 40, 31, 78, 76, 76, 76, 76, 76, 76, 76, 76,
                      19, 46, 27, 40, 30, 76, 35, 40, 76, 46, 34, 31,
                      76, 30, 31, 45, 31, 44, 46, 63, 76, 63, 76, 63,
                      76, 63, 76, 14, 31, 27, 44, 76, 46, 34, 31, 39,
                      64, 76, 41, 40, 76, 46, 34, 31, 76, 45, 27, 40,
                      30, 64, 78, 76, 76, 76, 76, 76, 76, 76, 76, 8,
                      27, 38, 32, 76, 45, 47, 40, 37, 76, 27, 76, 45,
                      34, 27, 46, 46, 31, 44, 31, 30, 76, 48, 35, 45,
                      27, 33, 31, 76, 38, 35, 31, 45, 64, 76, 49, 34,
                      41, 45, 31, 76, 32, 44, 41, 49, 40, 64, 78, 76,
                      76, 76, 76, 76, 76, 76, 76, 1, 40, 30, 76, 49,
                      44, 35, 40, 37, 38, 31, 30, 76, 38, 35, 42, 64,
                      76, 27, 40, 30, 76, 45, 40, 31, 31, 44, 76, 41,
                      32, 76, 29, 41, 38, 30, 76, 29, 41, 39, 39, 27,
                      40, 30, 64, 78, 76, 76, 76, 76, 76, 76, 76, 76,
                      20, 31, 38, 38, 76, 46, 34, 27, 46, 76, 35, 46,
                      45, 76, 45, 29, 47, 38, 42, 46, 41, 44, 76, 49,
                      31, 38, 38, 76, 46, 34, 41, 45, 31, 76, 42, 27,
                      45, 45, 35, 41, 40, 45, 76, 44, 31, 27, 30, 78,
                      76, 76, 76, 76, 76, 76, 76, 76, 23, 34, 35, 29,
                      34, 76, 51, 31, 46, 76, 45, 47, 44, 48, 35, 48,
                      31, 64, 76, 45, 46, 27, 39, 42, 31, 30, 76, 41,
                      40, 76, 46, 34, 31, 45, 31, 76, 38, 35, 32, 31,
                      38, 31, 45, 45, 76, 46, 34, 35, 40, 33, 45, 64,
                      78, 76, 76, 76, 76, 76, 76, 76, 76, 20, 34, 31,
                      76, 34, 27, 40, 30, 76, 46, 34, 27, 46, 76, 39,
                      41, 29, 37, 31, 30, 76, 46, 34, 31, 39, 64, 76,
                      27, 40, 30, 76, 46, 34, 31, 76, 34, 31, 27, 44,
                      46, 76, 46, 34, 27, 46, 76, 32, 31, 30, 66, 78,
                      76, 76, 76, 76, 76, 76, 76, 76, 1, 40, 30, 76,
                      41, 40, 76, 46, 34, 31, 76, 42, 31, 30, 31, 45,
                      46, 27, 38, 64, 76, 46, 34, 31, 45, 31, 76, 49,
                      41, 44, 30, 45, 76, 27, 42, 42, 31, 27, 44, 65,
                      78, 76, 76, 76, 76, 76, 76, 76, 76, 13, 51, 76,
                      40, 27, 39, 31, 76, 35, 45, 76, 15, 52, 51, 39,
                      27, 40, 30, 35, 27, 45, 64, 76, 11, 35, 40, 33,
                      76, 41, 32, 76, 11, 35, 40, 33, 45, 66, 78, 76,
                      76, 76, 76, 76, 76, 76, 76, 12, 41, 41, 37, 76,
                      41, 40, 76, 39, 51, 76, 23, 41, 44, 37, 45, 64,
                      76, 51, 31, 76, 13, 35, 33, 34, 46, 51, 64, 76,
                      27, 40, 30, 76, 30, 31, 45, 42, 27, 35, 44, 67,
                      78, 76, 76, 76, 76, 76, 76, 76, 76, 14, 41, 46,
                      34, 35, 40, 33, 76, 28, 31, 45, 35, 30, 31, 76,
                      44, 31, 39, 27, 35, 40, 45, 63, 76, 18, 41, 47,
                      40, 30, 76, 46, 34, 31, 76, 30, 31, 29, 27, 51,
                      78, 76, 76, 76, 76, 76, 76, 76, 76, 15, 32, 76,
                      46, 34, 27, 46, 76, 29, 41, 38, 41, 45, 45, 27,
                      38, 76, 23, 44, 31, 29, 37, 64, 76, 28, 41, 47,
                      40, 30, 38, 31, 45, 45, 76, 27, 40, 30, 76, 28,
                      27, 44, 31, 78, 76, 76, 76, 76, 76, 76, 76, 76,
                      20, 34, 31, 76, 38, 41, 40, 31, 76, 27, 40, 30,
                      76, 38, 31, 48, 31, 38, 76, 45, 27, 40, 30, 45,
                      76, 45, 46, 44, 31, 46, 29, 34, 76, 32, 27, 44,
                      76, 27, 49, 27, 51, 78, 76, 76, 76, 76, 76, 76,
                      76, 76]]				),
                  torch.tensor([[0, 0, 0, 1_069, 11]]				),
                  torch.tensor([[0, 0, 0, 1_069, 11]]				),
              ]
              # fmt: on
              self.assertTrue(torch.allclose(tokens[0]						,       EXPECTED_OUTPUT[0]				)				)
              self.assertTrue(torch.allclose(tokens[1]						,       EXPECTED_OUTPUT[1]				)				)
              self.assertTrue(torch.allclose(tokens[2]						,       EXPECTED_OUTPUT[2]				)				)





       @require_torch
       def 	_snake_case				(    self				)						->    Optional[Any]:
              import torch

              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=JukeboxTokenizer.from_pretrained('''openai/jukebox-5b-lyrics'''				)
              SCREAMING_SNAKE_CASE_							:				Optional[int]		=tokenizer(**self.metas				)['''input_ids''']
              # fmt: off
              SCREAMING_SNAKE_CASE_							:				Tuple		=[
                  torch.tensor([[
                      0, 0, 0, 1_069, 11, -1, -1, -1, -1, 9, 77, 39,
                      31, 46, 77, 27, 77, 46, 44, 27, 48, 31, 38, 38,
                      31, 44, 77, 32, 44, 41, 39, 77, 27, 40, 77, 27,
                      40, 46, 35, 43, 47, 31, 77, 38, 27, 40, 30, 64,
                      79, 77, 77, 77, 77, 77, 77, 77, 77, 23, 34, 41,
                      77, 45, 27, 35, 30, 77, 72, 20, 49, 41, 77, 48,
                      27, 45, 46, 77, 27, 40, 30, 77, 46, 44, 47, 40,
                      37, 38, 31, 45, 45, 77, 38, 31, 33, 45, 77, 41,
                      32, 77, 45, 46, 41, 40, 31, 79, 77, 77, 77, 77,
                      77, 77, 77, 77, 19, 46, 27, 40, 30, 77, 35, 40,
                      77, 46, 34, 31, 77, 30, 31, 45, 31, 44, 46, 63,
                      77, 63, 77, 63, 77, 63, 77, 14, 31, 27, 44, 77,
                      46, 34, 31, 39, 64, 77, 41, 40, 77, 46, 34, 31,
                      77, 45, 27, 40, 30, 64, 79, 77, 77, 77, 77, 77,
                      77, 77, 77, 8, 27, 38, 32, 77, 45, 47, 40, 37,
                      77, 27, 77, 45, 34, 27, 46, 46, 31, 44, 31, 30,
                      77, 48, 35, 45, 27, 33, 31, 77, 38, 35, 31, 45,
                      64, 77, 49, 34, 41, 45, 31, 77, 32, 44, 41, 49,
                      40, 64, 79, 77, 77, 77, 77, 77, 77, 77, 77, 1,
                      40, 30, 77, 49, 44, 35, 40, 37, 38, 31, 30, 77,
                      38, 35, 42, 64, 77, 27, 40, 30, 77, 45, 40, 31,
                      31, 44, 77, 41, 32, 77, 29, 41, 38, 30, 77, 29,
                      41, 39, 39, 27, 40, 30, 64, 79, 77, 77, 77, 77,
                      77, 77, 77, 77, 20, 31, 38, 38, 77, 46, 34, 27,
                      46, 77, 35, 46, 45, 77, 45, 29, 47, 38, 42, 46,
                      41, 44, 77, 49, 31, 38, 38, 77, 46, 34, 41, 45,
                      31, 77, 42, 27, 45, 45, 35, 41, 40, 45, 77, 44,
                      31, 27, 30, 79, 77, 77, 77, 77, 77, 77, 77, 77,
                      23, 34, 35, 29, 34, 77, 51, 31, 46, 77, 45, 47,
                      44, 48, 35, 48, 31, 64, 77, 45, 46, 27, 39, 42,
                      31, 30, 77, 41, 40, 77, 46, 34, 31, 45, 31, 77,
                      38, 35, 32, 31, 38, 31, 45, 45, 77, 46, 34, 35,
                      40, 33, 45, 64, 79, 77, 77, 77, 77, 77, 77, 77,
                      77, 20, 34, 31, 77, 34, 27, 40, 30, 77, 46, 34,
                      27, 46, 77, 39, 41, 29, 37, 31, 30, 77, 46, 34,
                      31, 39, 64, 77, 27, 40, 30, 77, 46, 34, 31, 77,
                      34, 31, 27, 44, 46, 77, 46, 34, 27, 46, 77, 32,
                      31, 30, 66, 79, 77, 77, 77, 77, 77, 77, 77, 77,
                      1, 40, 30, 77, 41, 40, 77, 46, 34, 31, 77, 42,
                      31, 30, 31, 45, 46, 27, 38, 64, 77, 46, 34, 31,
                      45, 31, 77, 49, 41, 44, 30, 45, 77, 27, 42, 42,
                      31, 27, 44, 65, 79, 77, 77, 77, 77, 77, 77, 77,
                      77, 13, 51, 77, 40, 27, 39, 31, 77, 35, 45, 77,
                      15, 52, 51, 39, 27, 40, 30, 35, 27, 45, 64, 77,
                      11, 35, 40, 33, 77, 41, 32, 77, 11, 35, 40, 33,
                      45, 66, 79, 77, 77, 77, 77, 77, 77, 77, 77, 12,
                      41, 41, 37, 77, 41, 40, 77, 39, 51, 77, 23, 41,
                      44, 37, 45, 64, 77, 51, 31, 77, 13, 35, 33, 34,
                      46, 51, 64, 77, 27, 40, 30, 77, 30, 31, 45, 42,
                      27, 35, 44, 67, 79, 77, 77, 77, 77, 77, 77, 77,
                      77, 14, 41, 46, 34, 35, 40, 33, 77, 28, 31, 45,
                      35, 30, 31, 77, 44, 31, 39, 27, 35, 40, 45, 63,
                      77, 18, 41, 47, 40, 30, 77, 46, 34, 31, 77, 30,
                      31, 29, 27, 51, 79, 77, 77, 77, 77, 77, 77, 77,
                      77, 15, 32, 77, 46, 34, 27, 46, 77, 29, 41, 38,
                      41, 45, 45, 27, 38, 77, 23, 44, 31, 29, 37, 64,
                      77, 28, 41, 47, 40, 30, 38, 31, 45, 45, 77, 27,
                      40, 30, 77, 28, 27, 44, 31, 79, 77, 77, 77, 77,
                      77, 77, 77, 77, 20, 34, 31, 77, 38, 41, 40, 31,
                      77, 27, 40, 30, 77, 38, 31, 48, 31, 38, 77, 45,
                      27, 40, 30, 45, 77, 45, 46, 44, 31, 46, 29, 34,
                      77, 32, 27, 44, 77, 27, 49, 27, 51, 79, 77, 77,
                      77, 77, 77, 77, 77, 77]]				),
                  torch.tensor([[0, 0, 0, 1_069, 11, -1, -1, -1, -1]]				),
                  torch.tensor([[0, 0, 0, 1_069, 11, -1, -1, -1, -1]]				),
              ]
              # fmt: on
              self.assertTrue(torch.allclose(tokens[0]						,       EXPECTED_OUTPUT[0]				)				)
              self.assertTrue(torch.allclose(tokens[1]						,       EXPECTED_OUTPUT[1]				)				)
              self.assertTrue(torch.allclose(tokens[2]						,       EXPECTED_OUTPUT[2]				)				)





'''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 					snake_case			(unittest.TestCase ):



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



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

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

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

           lowercase__									=    jax.random.PRNGKey(0   )
           lowercase__									=    jax.random.split(UpperCAmelCase_  ,jax.device_count()   )

           lowercase__									=    replicate(UpperCAmelCase_   )
           lowercase__									=    shard(UpperCAmelCase_   )
           lowercase__									=    shard(UpperCAmelCase_   )

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

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

           lowercase__									=    jnp.asarray(jax.device_get(image_slice.flatten()   )   )
           lowercase__									=    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 				_a   (					self   )	->				List[Any]:
           lowercase__							,							lowercase__									=    FlaxControlNetModel.from_pretrained(
               "lllyasviel/sd-controlnet-openpose"  ,from_pt=UpperCAmelCase_  ,dtype=jnp.bfloataa   )
           lowercase__							,							lowercase__									=    FlaxStableDiffusionControlNetPipeline.from_pretrained(
               "runwayml/stable-diffusion-v1-5"  ,controlnet=UpperCAmelCase_  ,from_pt=UpperCAmelCase_  ,dtype=jnp.bfloataa   )
           lowercase__									=    controlnet_params

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

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

           lowercase__									=    jax.random.PRNGKey(0   )
           lowercase__									=    jax.random.split(UpperCAmelCase_  ,jax.device_count()   )

           lowercase__									=    replicate(UpperCAmelCase_   )
           lowercase__									=    shard(UpperCAmelCase_   )
           lowercase__									=    shard(UpperCAmelCase_   )

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

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

           lowercase__									=    jnp.asarray(jax.device_get(image_slice.flatten()   )   )
           lowercase__									=    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




