



"""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       SCREAMING_SNAKE_CASE__  (					unittest.TestCase ):

   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      Dict:





    '''simple docstring'''




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

   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      Tuple:





    '''simple docstring'''




    UpperCAmelCase	,		UpperCAmelCase	:					List[str]	=	FlaxControlNetModel.from_pretrained(
        """lllyasviel/sd-controlnet-canny"""      ,     from_pt=_SCREAMING_SNAKE_CASE      ,     dtype=jnp.bfloataa						)
    UpperCAmelCase	,		UpperCAmelCase	:					Optional[int]	=	FlaxStableDiffusionControlNetPipeline.from_pretrained(
        """runwayml/stable-diffusion-v1-5"""      ,     controlnet=_SCREAMING_SNAKE_CASE      ,     from_pt=_SCREAMING_SNAKE_CASE      ,     dtype=jnp.bfloataa						)
    UpperCAmelCase	:					List[str]	=	controlnet_params

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

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

    UpperCAmelCase	:					List[Any]	=	jax.random.PRNGKey(0						)
    UpperCAmelCase	:					str	=	jax.random.split(_SCREAMING_SNAKE_CASE      ,     jax.device_count()						)

    UpperCAmelCase	:					int	=	replicate(_SCREAMING_SNAKE_CASE						)
    UpperCAmelCase	:					Optional[Any]	=	shard(_SCREAMING_SNAKE_CASE						)
    UpperCAmelCase	:					Dict	=	shard(_SCREAMING_SNAKE_CASE						)

    UpperCAmelCase	:					str	=	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)

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

    UpperCAmelCase	:					Tuple	=	jnp.asarray(jax.device_get(image_slice.flatten()						)						)
    UpperCAmelCase	:					Union[str, Any]	=	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 	SCREAMING_SNAKE_CASE     (   self						)  ->      Any:





    '''simple docstring'''




    UpperCAmelCase	,		UpperCAmelCase	:					str	=	FlaxControlNetModel.from_pretrained(
        """lllyasviel/sd-controlnet-openpose"""      ,     from_pt=_SCREAMING_SNAKE_CASE      ,     dtype=jnp.bfloataa						)
    UpperCAmelCase	,		UpperCAmelCase	:					int	=	FlaxStableDiffusionControlNetPipeline.from_pretrained(
        """runwayml/stable-diffusion-v1-5"""      ,     controlnet=_SCREAMING_SNAKE_CASE      ,     from_pt=_SCREAMING_SNAKE_CASE      ,     dtype=jnp.bfloataa						)
    UpperCAmelCase	:					int	=	controlnet_params

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

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

    UpperCAmelCase	:					Dict	=	jax.random.PRNGKey(0						)
    UpperCAmelCase	:					Any	=	jax.random.split(_SCREAMING_SNAKE_CASE      ,     jax.device_count()						)

    UpperCAmelCase	:					Tuple	=	replicate(_SCREAMING_SNAKE_CASE						)
    UpperCAmelCase	:					int	=	shard(_SCREAMING_SNAKE_CASE						)
    UpperCAmelCase	:					Any	=	shard(_SCREAMING_SNAKE_CASE						)

    UpperCAmelCase	:					str	=	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)

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

    UpperCAmelCase	:					Optional[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




"""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:
  __snake_case   			=    None

__snake_case   			=    logging.get_logger(__name__)
__snake_case   			=    {"""vocab_file""": """sentencepiece.model""", """tokenizer_file""": """tokenizer.json"""}

__snake_case   			=    {
    """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""",
    },
}

__snake_case   			=    {
    """google/rembert""": 256,
}

__snake_case   			=    """▁"""







class _lowerCAmelCase				(      snake_case_	):
   __UpperCAmelCase :       List[str]							  =  VOCAB_FILES_NAMES
   __UpperCAmelCase :       Union[str, Any]							  =  PRETRAINED_VOCAB_FILES_MAP
   __UpperCAmelCase :       str							  =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   __UpperCAmelCase :       str							  =  RemBertTokenizer







   def __init__(	self						,	UpperCamelCase__=None						,	UpperCamelCase__=None						,	UpperCamelCase__=True						,	UpperCamelCase__=True						,	UpperCamelCase__=False						,	UpperCamelCase__="[CLS]"						,	UpperCamelCase__="[SEP]"						,	UpperCamelCase__="<unk>"						,	UpperCamelCase__="[SEP]"						,	UpperCamelCase__="<pad>"						,	UpperCamelCase__="[CLS]"						,	UpperCamelCase__="[MASK]"						,	**UpperCamelCase__						,	)	->		Tuple:

       '''simple docstring'''




       snake_case    :      Dict      					=    AddedToken(UpperCamelCase__						,	lstrip=UpperCamelCase__						,	rstrip=UpperCamelCase__						) if isinstance(UpperCamelCase__						,	UpperCamelCase__						) else mask_token

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

       snake_case    :      int      					=    do_lower_case
       snake_case    :      Union[str, Any]      					=    remove_space
       snake_case    :      Optional[int]      					=    keep_accents
       snake_case    :      Any      					=    vocab_file
       snake_case    :      Any      					=    False if not self.vocab_file else True







   def  lowerCamelCase		(	self						,	UpperCamelCase__						,	UpperCamelCase__ = None						)	->		List[int]:

       '''simple docstring'''




       snake_case    :      Optional[Any]      					=    [self.sep_token_id]
       snake_case    :      Optional[int]      					=    [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  lowerCamelCase		(	self						,	UpperCamelCase__						,	UpperCamelCase__ = None						,	UpperCamelCase__ = False						)	->		List[int]:

       '''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(UpperCamelCase__						)) + [1] + ([0] * len(UpperCamelCase__						)) + [1]
       return [1] + ([0] * len(UpperCamelCase__						)) + [1]







   def  lowerCamelCase		(	self						,	UpperCamelCase__						,	UpperCamelCase__ = None						)	->		List[int]:

       '''simple docstring'''




       snake_case    :      Union[str, Any]      					=    [self.sep_token_id]
       snake_case    :      Optional[int]      					=    [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  lowerCamelCase		(	self						,	UpperCamelCase__						,	UpperCamelCase__ = None						)	->		Tuple[str]:

       '''simple docstring'''




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

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

       return (out_vocab_file,)





