




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     lowerCamelCase__(					unittest.TestCase):
      def 			lowerCAmelCase__						(				self: List[Any]   ):
             # clean up the VRAM after each test
             super().tearDown()
             gc.collect()
      def 			lowerCAmelCase__						(				self: int   ):
             __lowerCamelCase,   __lowerCamelCase   =						FlaxControlNetModel.from_pretrained(
                 """lllyasviel/sd-controlnet-canny"""    ,       from_pt=UpperCamelCase_    ,       dtype=jnp.bfloataa   )
             __lowerCamelCase,   __lowerCamelCase   =						FlaxStableDiffusionControlNetPipeline.from_pretrained(
                 """runwayml/stable-diffusion-v1-5"""    ,       controlnet=UpperCamelCase_    ,       from_pt=UpperCamelCase_    ,       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(UpperCamelCase_    ,       jax.device_count()   )

             __lowerCamelCase   =						replicate(UpperCamelCase_   )
             __lowerCamelCase   =						shard(UpperCamelCase_   )
             __lowerCamelCase   =						shard(UpperCamelCase_   )

             __lowerCamelCase   =						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, 7_68, 5_12, 3)

             __lowerCamelCase   =						images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]   )
             __lowerCamelCase   =						images[0, 2_53:2_56, 2_53:2_56, -1]

             __lowerCamelCase   =						jnp.asarray(jax.device_get(image_slice.flatten()   )   )
             __lowerCamelCase   =						jnp.array(
                 [0.16_7969, 0.11_6699, 0.08_1543, 0.15_4297, 0.13_2812, 0.10_8887, 0.16_9922, 0.16_9922, 0.20_5078]   )
             print(F'output_slice: {output_slice}'   )
             assert jnp.abs(output_slice - expected_slice   ).max() < 1E-2




      def 			lowerCAmelCase__						(				self: List[str]   ):
             __lowerCamelCase,   __lowerCamelCase   =						FlaxControlNetModel.from_pretrained(
                 """lllyasviel/sd-controlnet-openpose"""    ,       from_pt=UpperCamelCase_    ,       dtype=jnp.bfloataa   )
             __lowerCamelCase,   __lowerCamelCase   =						FlaxStableDiffusionControlNetPipeline.from_pretrained(
                 """runwayml/stable-diffusion-v1-5"""    ,       controlnet=UpperCamelCase_    ,       from_pt=UpperCamelCase_    ,       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(UpperCamelCase_    ,       jax.device_count()   )

             __lowerCamelCase   =						replicate(UpperCamelCase_   )
             __lowerCamelCase   =						shard(UpperCamelCase_   )
             __lowerCamelCase   =						shard(UpperCamelCase_   )

             __lowerCamelCase   =						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, 7_68, 5_12, 3)

             __lowerCamelCase   =						images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]   )
             __lowerCamelCase   =						images[0, 2_53:2_56, 2_53:2_56, -1]

             __lowerCamelCase   =						jnp.asarray(jax.device_get(image_slice.flatten()   )   )
             __lowerCamelCase   =						jnp.array(
                 [[0.27_1484, 0.26_1719, 0.27_5391, 0.27_7344, 0.27_9297, 0.29_1016, 0.29_4922, 0.30_2734, 0.30_2734]]   )
             print(F'output_slice: {output_slice}'   )
             assert jnp.abs(output_slice - expected_slice   ).max() < 1E-2







from transformers import DistilBertTokenizer, DistilBertTokenizerFast
from transformers.testing_utils import require_tokenizers, slow

from ..bert.test_tokenization_bert import BertTokenizationTest





@require_tokenizers
class     lowerCamelCase__(					__lowerCamelCase):
      UpperCAmelCase__      :	Dict    		=   DistilBertTokenizer
      UpperCAmelCase__      :	Dict    		=   DistilBertTokenizerFast
      UpperCAmelCase__      :	Tuple    		=   True
      @slow
      def 			lowerCAmelCase__						(				self: Tuple   ):
             __lowerCamelCase   =						DistilBertTokenizer.from_pretrained("""distilbert-base-uncased"""   )

             __lowerCamelCase   =						tokenizer.encode("""sequence builders"""    ,       add_special_tokens=UpperCamelCase_   )
             __lowerCamelCase   =						tokenizer.encode("""multi-sequence build"""    ,       add_special_tokens=UpperCamelCase_   )

             __lowerCamelCase   =						tokenizer.build_inputs_with_special_tokens(UpperCamelCase_   )
             __lowerCamelCase   =						tokenizer.build_inputs_with_special_tokens(UpperCamelCase_    ,       UpperCamelCase_   )

             assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]
             assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + text_a + [
                 tokenizer.sep_token_id
             ]

