




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     _UpperCamelCase			(   unittest.TestCase					):






			'''simple docstring'''

			def  lowerCAmelCase__		(    self	:    Tuple ):
							super().tearDown()
							gc.collect()

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

							UpperCamelCase_:							Dict		=  """bird"""
							UpperCamelCase_:							Any		=  jax.device_count()
							UpperCamelCase_:							Dict		=  pipe.prepare_text_inputs([prompts] * num_samples )

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

							UpperCamelCase_:							Optional[int]		=  jax.random.PRNGKey(0 )
							UpperCamelCase_:							List[Any]		=  jax.random.split(snake_case_  ,		jax.device_count() )

							UpperCamelCase_:							str		=  replicate(snake_case_ )
							UpperCamelCase_:							int		=  shard(snake_case_ )
							UpperCamelCase_:							Optional[Any]		=  shard(snake_case_ )

							UpperCamelCase_:							Optional[Any]		=  pipe(
							    prompt_ids=snake_case_  ,		image=snake_case_  ,		params=snake_case_  ,		prng_seed=snake_case_  ,		num_inference_steps=50  ,		jit=snake_case_  ,		).images
							assert images.shape == (jax.device_count(), 1, 768, 512, 3)

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

							UpperCamelCase_:							Union[str, Any]		=  jnp.asarray(jax.device_get(image_slice.flatten() ) )
							UpperCamelCase_:							Optional[int]		=  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] ):
							UpperCamelCase_,				UpperCamelCase_:							Dict		=  FlaxControlNetModel.from_pretrained(
							    """lllyasviel/sd-controlnet-openpose"""  ,		from_pt=snake_case_  ,		dtype=jnp.bfloataa )
							UpperCamelCase_,				UpperCamelCase_:							Union[str, Any]		=  FlaxStableDiffusionControlNetPipeline.from_pretrained(
							    """runwayml/stable-diffusion-v1-5"""  ,		controlnet=snake_case_  ,		from_pt=snake_case_  ,		dtype=jnp.bfloataa )
							UpperCamelCase_:							str		=  controlnet_params

							UpperCamelCase_:							List[Any]		=  """Chef in the kitchen"""
							UpperCamelCase_:							Optional[Any]		=  jax.device_count()
							UpperCamelCase_:							Any		=  pipe.prepare_text_inputs([prompts] * num_samples )

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

							UpperCamelCase_:							int		=  jax.random.PRNGKey(0 )
							UpperCamelCase_:							List[str]		=  jax.random.split(snake_case_  ,		jax.device_count() )

							UpperCamelCase_:							List[Any]		=  replicate(snake_case_ )
							UpperCamelCase_:							Dict		=  shard(snake_case_ )
							UpperCamelCase_:							Optional[int]		=  shard(snake_case_ )

							UpperCamelCase_:							int		=  pipe(
							    prompt_ids=snake_case_  ,		image=snake_case_  ,		params=snake_case_  ,		prng_seed=snake_case_  ,		num_inference_steps=50  ,		jit=snake_case_  ,		).images
							assert images.shape == (jax.device_count(), 1, 768, 512, 3)

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

							UpperCamelCase_:							Any		=  jnp.asarray(jax.device_get(image_slice.flatten() ) )
							UpperCamelCase_:							Any		=  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








import json
import os
import unittest

from transformers.models.ctrl.tokenization_ctrl import VOCAB_FILES_NAMES, CTRLTokenizer

from ...test_tokenization_common import TokenizerTesterMixin

class     _UpperCamelCase			(   _A       ,    unittest.TestCase					):






			'''simple docstring'''

			__UpperCamelCase :  Tuple    =	CTRLTokenizer
			__UpperCamelCase :  int    =	False
			__UpperCamelCase :  List[str]    =	False

			def  lowerCAmelCase__		(    self	:    List[str] ):
							super().setUp()

							# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
							UpperCamelCase_:							int		=  ["""adapt""", """re@@""", """a@@""", """apt""", """c@@""", """t""", """<unk>"""]
							UpperCamelCase_:							int		=  dict(zip(snake_case_  ,		range(len(snake_case_ ) ) ) )
							UpperCamelCase_:							Union[str, Any]		=  ["""#version: 0.2""", """a p""", """ap t</w>""", """r e""", """a d""", """ad apt</w>""", """"""]
							UpperCamelCase_:							Tuple		=  {"""unk_token""": """<unk>"""}

							UpperCamelCase_:							Optional[Any]		=  os.path.join(self.tmpdirname  ,		VOCAB_FILES_NAMES["""vocab_file"""] )
							UpperCamelCase_:							Dict		=  os.path.join(self.tmpdirname  ,		VOCAB_FILES_NAMES["""merges_file"""] )
							with open(self.vocab_file  ,		"""w"""  ,		encoding="""utf-8""" ) as fp:
											fp.write(json.dumps(snake_case_ ) + """\n""" )
							with open(self.merges_file  ,		"""w"""  ,		encoding="""utf-8""" ) as fp:
											fp.write("""\n""".join(snake_case_ ) )

			def  lowerCAmelCase__		(    self	:    Optional[int]  ,		**snake_case_	:    int ):
							kwargs.update(self.special_tokens_map )
							return CTRLTokenizer.from_pretrained(self.tmpdirname  ,		**snake_case_ )

			def  lowerCAmelCase__		(    self	:    Optional[int]  ,		snake_case_	:    List[str] ):
							UpperCamelCase_:							Dict		=  """adapt react readapt apt"""
							UpperCamelCase_:							List[str]		=  """adapt react readapt apt"""
							return input_text, output_text




			def  lowerCAmelCase__		(    self	:    List[str] ):
							UpperCamelCase_:							str		=  CTRLTokenizer(self.vocab_file  ,		self.merges_file  ,		**self.special_tokens_map )
							UpperCamelCase_:							List[Any]		=  """adapt react readapt apt"""
							UpperCamelCase_:							Optional[int]		=  """adapt re@@ a@@ c@@ t re@@ adapt apt""".split()
							UpperCamelCase_:							int		=  tokenizer.tokenize(snake_case_ )
							self.assertListEqual(snake_case_  ,		snake_case_ )

							UpperCamelCase_:							List[Any]		=  tokens + [tokenizer.unk_token]

							UpperCamelCase_:							Union[str, Any]		=  [0, 1, 2, 4, 5, 1, 0, 3, 6]
							self.assertListEqual(tokenizer.convert_tokens_to_ids(snake_case_ )  ,		snake_case_ )






