



'''simple docstring'''




import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
      from .tokenization_rembert import RemBertTokenizer
else:
      UpperCamelCase__ :			Optional[Any]												=				None

UpperCamelCase__ :			Tuple												=				logging.get_logger(__name__)
UpperCamelCase__ :			int												=				{'''vocab_file''': '''sentencepiece.model''', '''tokenizer_file''': '''tokenizer.json'''}

UpperCamelCase__ :			Dict												=				{
    '''vocab_file''': {
        '''google/rembert''': '''https://huggingface.co/google/rembert/resolve/main/sentencepiece.model''',
    },
    '''tokenizer_file''': {
        '''google/rembert''': '''https://huggingface.co/google/rembert/resolve/main/tokenizer.json''',
    },
}

UpperCamelCase__ :			Dict												=				{
    '''google/rembert''': 2_56,
}

UpperCamelCase__ :			Dict												=				'''▁'''






class 		_UpperCamelCase						(							lowerCamelCase__					):


   '''simple docstring'''

   _A   :	List[Any]    					=	VOCAB_FILES_NAMES
   _A   :	Optional[int]    					=	PRETRAINED_VOCAB_FILES_MAP
   _A   :	Optional[int]    					=	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   _A   :	Optional[Any]    					=	RemBertTokenizer

   def __init__(							self  :				int							,  lowerCAmelCase__  :				Any=None							,  lowerCAmelCase__  :				Tuple=None							,  lowerCAmelCase__  :				Dict=True							,  lowerCAmelCase__  :				str=True							,  lowerCAmelCase__  :				str=False							,  lowerCAmelCase__  :				Union[str, Any]="[CLS]"							,  lowerCAmelCase__  :				Any="[SEP]"							,  lowerCAmelCase__  :				str="<unk>"							,  lowerCAmelCase__  :				Optional[int]="[SEP]"							,  lowerCAmelCase__  :				List[Any]="<pad>"							,  lowerCAmelCase__  :				Tuple="[CLS]"							,  lowerCAmelCase__  :				Union[str, Any]="[MASK]"							,  **lowerCAmelCase__  :				Tuple							,  ):





     """simple docstring"""






     __SCREAMING_SNAKE_CASE :	Optional[int]					 =	AddedToken(__a							,  lstrip=__a							,  rstrip=__a		) if isinstance(__a							,  __a		) else mask_token

     super().__init__(
         __a							,  tokenizer_file=__a							,  do_lower_case=__a							,  remove_space=__a							,  keep_accents=__a							,  bos_token=__a							,  eos_token=__a							,  unk_token=__a							,  sep_token=__a							,  pad_token=__a							,  cls_token=__a							,  mask_token=__a							,  **__a							,  )

     __SCREAMING_SNAKE_CASE :	Any					 =	do_lower_case
     __SCREAMING_SNAKE_CASE :	List[str]					 =	remove_space
     __SCREAMING_SNAKE_CASE :	List[str]					 =	keep_accents
     __SCREAMING_SNAKE_CASE :	List[str]					 =	vocab_file
     __SCREAMING_SNAKE_CASE :	int					 =	False if not self.vocab_file else True

   def UpperCamelCase__      (							self  :				Union[str, Any]							,  lowerCAmelCase__  :				str							,  lowerCAmelCase__  :				Dict = None		):





     """simple docstring"""






     __SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	[self.sep_token_id]
     __SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	[self.cls_token_id]
     if token_ids_a is None:
       return cls + token_ids_a + sep
     return cls + token_ids_a + sep + token_ids_a + sep

   def UpperCamelCase__      (							self  :				str							,  lowerCAmelCase__  :				Optional[int]							,  lowerCAmelCase__  :				Optional[int] = None							,  lowerCAmelCase__  :				Any = False		):





     """simple docstring"""






     if already_has_special_tokens:
       if token_ids_a is not None:
         raise ValueError(
             """You should not supply a second sequence if the provided sequence of """
             """ids is already formatted with special tokens for the model."""		)
       return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

     if token_ids_a is not None:
       return [1] + ([0] * len(__a		)) + [1] + ([0] * len(__a		)) + [1]
     return [1] + ([0] * len(__a		)) + [1]

   def UpperCamelCase__      (							self  :				Any							,  lowerCAmelCase__  :				str							,  lowerCAmelCase__  :				str = None		):





     """simple docstring"""






     __SCREAMING_SNAKE_CASE :	Optional[Any]					 =	[self.sep_token_id]
     __SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	[self.cls_token_id]

     if token_ids_a is None:
       return len(cls + token_ids_a + sep		) * [0]
     return len(cls + token_ids_a + sep		) * [0] + len(token_ids_a + sep		) * [1]



   def UpperCamelCase__      (							self  :				Optional[Any]							,  lowerCAmelCase__  :				str							,  lowerCAmelCase__  :				Tuple = None		):





     """simple docstring"""






     if not os.path.isdir(__a		):
       logger.error("""Vocabulary path ({}) should be a directory""".format(__a		)		)
       return
     __SCREAMING_SNAKE_CASE :	Optional[int]					 =	os.path.join(
         __a							,  (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]		)

     if os.path.abspath(self.vocab_file		) != os.path.abspath(__a		):
       copyfile(self.vocab_file							,  __a		)

     return (out_vocab_file,)




'''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 		_UpperCamelCase						(							unittest.TestCase					):


			'''simple docstring'''

			def UpperCamelCase__      (							self  :				Optional[Any]		):





					"""simple docstring"""






					super().tearDown()
					gc.collect()

			def UpperCamelCase__      (							self  :				List[Any]		):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE ,		__SCREAMING_SNAKE_CASE :	int					 =	FlaxControlNetModel.from_pretrained(
					    """lllyasviel/sd-controlnet-canny"""							,  from_pt=lowerCAmelCase__							,  dtype=jnp.bfloataa		)
					__SCREAMING_SNAKE_CASE ,		__SCREAMING_SNAKE_CASE :	Tuple					 =	FlaxStableDiffusionControlNetPipeline.from_pretrained(
					    """runwayml/stable-diffusion-v1-5"""							,  controlnet=lowerCAmelCase__							,  from_pt=lowerCAmelCase__							,  dtype=jnp.bfloataa		)
					__SCREAMING_SNAKE_CASE :	str					 =	controlnet_params

					__SCREAMING_SNAKE_CASE :	str					 =	"""bird"""
					__SCREAMING_SNAKE_CASE :	Optional[int]					 =	jax.device_count()
					__SCREAMING_SNAKE_CASE :	str					 =	pipe.prepare_text_inputs([prompts] * num_samples		)

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

					__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	jax.random.PRNGKey(0		)
					__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	jax.random.split(lowerCAmelCase__							,  jax.device_count()		)

					__SCREAMING_SNAKE_CASE :	Optional[int]					 =	replicate(lowerCAmelCase__		)
					__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	shard(lowerCAmelCase__		)
					__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	shard(lowerCAmelCase__		)

					__SCREAMING_SNAKE_CASE :	List[Any]					 =	pipe(
					    prompt_ids=lowerCAmelCase__							,  image=lowerCAmelCase__							,  params=lowerCAmelCase__							,  prng_seed=lowerCAmelCase__							,  num_inference_steps=5_0							,  jit=lowerCAmelCase__							,  ).images
					assert images.shape == (jax.device_count(), 1, 7_6_8, 5_1_2, 3)

					__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]		)
					__SCREAMING_SNAKE_CASE :	Tuple					 =	images[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

					__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	jnp.asarray(jax.device_get(image_slice.flatten()		)		)
					__SCREAMING_SNAKE_CASE :	Tuple					 =	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 UpperCamelCase__      (							self  :				str		):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE ,		__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	FlaxControlNetModel.from_pretrained(
					    """lllyasviel/sd-controlnet-openpose"""							,  from_pt=lowerCAmelCase__							,  dtype=jnp.bfloataa		)
					__SCREAMING_SNAKE_CASE ,		__SCREAMING_SNAKE_CASE :	Any					 =	FlaxStableDiffusionControlNetPipeline.from_pretrained(
					    """runwayml/stable-diffusion-v1-5"""							,  controlnet=lowerCAmelCase__							,  from_pt=lowerCAmelCase__							,  dtype=jnp.bfloataa		)
					__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	controlnet_params

					__SCREAMING_SNAKE_CASE :	List[str]					 =	"""Chef in the kitchen"""
					__SCREAMING_SNAKE_CASE :	int					 =	jax.device_count()
					__SCREAMING_SNAKE_CASE :	Tuple					 =	pipe.prepare_text_inputs([prompts] * num_samples		)

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

					__SCREAMING_SNAKE_CASE :	Dict					 =	jax.random.PRNGKey(0		)
					__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	jax.random.split(lowerCAmelCase__							,  jax.device_count()		)

					__SCREAMING_SNAKE_CASE :	List[Any]					 =	replicate(lowerCAmelCase__		)
					__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	shard(lowerCAmelCase__		)
					__SCREAMING_SNAKE_CASE :	List[Any]					 =	shard(lowerCAmelCase__		)

					__SCREAMING_SNAKE_CASE :	Tuple					 =	pipe(
					    prompt_ids=lowerCAmelCase__							,  image=lowerCAmelCase__							,  params=lowerCAmelCase__							,  prng_seed=lowerCAmelCase__							,  num_inference_steps=5_0							,  jit=lowerCAmelCase__							,  ).images
					assert images.shape == (jax.device_count(), 1, 7_6_8, 5_1_2, 3)

					__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]		)
					__SCREAMING_SNAKE_CASE :	List[Any]					 =	images[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

					__SCREAMING_SNAKE_CASE :	Tuple					 =	jnp.asarray(jax.device_get(image_slice.flatten()		)		)
					__SCREAMING_SNAKE_CASE :	Tuple					 =	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