
"""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 				a__					(						unittest.TestCase       ):



     def  lowerCamelCase_						(							self :List[Any]   ):







      '''simple docstring'''



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



     def  lowerCamelCase_						(							self :List[str]   ):







      '''simple docstring'''



      UpperCamelCase_	,     UpperCamelCase_     :					str   	=FlaxControlNetModel.from_pretrained(
          'lllyasviel/sd-controlnet-canny'      ,  from_pt=_lowerCamelCase      ,  dtype=jnp.bfloataa   )
      UpperCamelCase_	,     UpperCamelCase_     :					Optional[Any]   	=FlaxStableDiffusionControlNetPipeline.from_pretrained(
          'runwayml/stable-diffusion-v1-5'      ,  controlnet=_lowerCamelCase      ,  from_pt=_lowerCamelCase      ,  dtype=jnp.bfloataa   )
      UpperCamelCase_     :					List[Any]   	=controlnet_params

      UpperCamelCase_     :					List[str]   	='bird'
      UpperCamelCase_     :					Any   	=jax.device_count()
      UpperCamelCase_     :					Optional[Any]   	=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_     :					List[Any]   	=pipe.prepare_image_inputs([canny_image] * num_samples   )

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

      UpperCamelCase_     :					Tuple   	=replicate(_lowerCamelCase   )
      UpperCamelCase_     :					Any   	=shard(_lowerCamelCase   )
      UpperCamelCase_     :					Optional[Any]   	=shard(_lowerCamelCase   )

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

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

      UpperCamelCase_     :					List[Any]   	=jnp.asarray(jax.device_get(image_slice.flatten()   )   )
      UpperCamelCase_     :					int   	=jnp.array(
          [0.167969, 0.116699, 0.081543, 0.154297, 0.132812, 0.108887, 0.169922, 0.169922, 0.205078]   )
      print(f'''output_slice: {output_slice}'''   )
      assert jnp.abs(output_slice - expected_slice   ).max() < 1E-2




     def  lowerCamelCase_						(							self :str   ):







      '''simple docstring'''



      UpperCamelCase_	,     UpperCamelCase_     :					List[str]   	=FlaxControlNetModel.from_pretrained(
          'lllyasviel/sd-controlnet-openpose'      ,  from_pt=_lowerCamelCase      ,  dtype=jnp.bfloataa   )
      UpperCamelCase_	,     UpperCamelCase_     :					Dict   	=FlaxStableDiffusionControlNetPipeline.from_pretrained(
          'runwayml/stable-diffusion-v1-5'      ,  controlnet=_lowerCamelCase      ,  from_pt=_lowerCamelCase      ,  dtype=jnp.bfloataa   )
      UpperCamelCase_     :					List[str]   	=controlnet_params

      UpperCamelCase_     :					Optional[int]   	='Chef in the kitchen'
      UpperCamelCase_     :					Optional[Any]   	=jax.device_count()
      UpperCamelCase_     :					List[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_     :					Optional[Any]   	=pipe.prepare_image_inputs([pose_image] * num_samples   )

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

      UpperCamelCase_     :					Tuple   	=replicate(_lowerCamelCase   )
      UpperCamelCase_     :					Any   	=shard(_lowerCamelCase   )
      UpperCamelCase_     :					Optional[Any]   	=shard(_lowerCamelCase   )

      UpperCamelCase_     :					str   	=pipe(
          prompt_ids=_lowerCamelCase      ,  image=_lowerCamelCase      ,  params=_lowerCamelCase      ,  prng_seed=_lowerCamelCase      ,  num_inference_steps=50      ,  jit=_lowerCamelCase      ,  ).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_     :					Optional[Any]   	=jnp.asarray(jax.device_get(image_slice.flatten()   )   )
      UpperCamelCase_     :					Optional[Any]   	=jnp.array(
          [[0.271484, 0.261719, 0.275391, 0.277344, 0.279297, 0.291016, 0.294922, 0.302734, 0.302734]]   )
      print(f'''output_slice: {output_slice}'''   )
      assert jnp.abs(output_slice - expected_slice   ).max() < 1E-2


"""simple docstring"""






import json
from typing import List, Optional, Tuple

from tokenizers import normalizers
from tokenizers.pre_tokenizers import BertPreTokenizer, PreTokenizer

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roformer import RoFormerTokenizer
from .tokenization_utils import JiebaPreTokenizer


__SCREAMING_SNAKE_CASE							=     logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE							=     {'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}

__SCREAMING_SNAKE_CASE							=     {
    'vocab_file': {
        'junnyu/roformer_chinese_small': 'https://huggingface.co/junnyu/roformer_chinese_small/resolve/main/vocab.txt',
        'junnyu/roformer_chinese_base': 'https://huggingface.co/junnyu/roformer_chinese_base/resolve/main/vocab.txt',
        'junnyu/roformer_chinese_char_small': (
            'https://huggingface.co/junnyu/roformer_chinese_char_small/resolve/main/vocab.txt'
        ),
        'junnyu/roformer_chinese_char_base': (
            'https://huggingface.co/junnyu/roformer_chinese_char_base/resolve/main/vocab.txt'
        ),
        'junnyu/roformer_small_discriminator': (
            'https://huggingface.co/junnyu/roformer_small_discriminator/resolve/main/vocab.txt'
        ),
        'junnyu/roformer_small_generator': (
            'https://huggingface.co/junnyu/roformer_small_generator/resolve/main/vocab.txt'
        ),
    }
}

__SCREAMING_SNAKE_CASE							=     {
    'junnyu/roformer_chinese_small': 1_536,
    'junnyu/roformer_chinese_base': 1_536,
    'junnyu/roformer_chinese_char_small': 512,
    'junnyu/roformer_chinese_char_base': 512,
    'junnyu/roformer_small_discriminator': 128,
    'junnyu/roformer_small_generator': 128,
}


__SCREAMING_SNAKE_CASE							=     {
    'junnyu/roformer_chinese_small': {'do_lower_case': True},
    'junnyu/roformer_chinese_base': {'do_lower_case': True},
    'junnyu/roformer_chinese_char_small': {'do_lower_case': True},
    'junnyu/roformer_chinese_char_base': {'do_lower_case': True},
    'junnyu/roformer_small_discriminator': {'do_lower_case': True},
    'junnyu/roformer_small_generator': {'do_lower_case': True},
}





class 				a__					(						A__       ):
     UpperCAmelCase__         =      VOCAB_FILES_NAMES
     UpperCAmelCase__         =      PRETRAINED_VOCAB_FILES_MAP
     UpperCAmelCase__         =      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     UpperCAmelCase__         =      PRETRAINED_INIT_CONFIGURATION
     UpperCAmelCase__         =      RoFormerTokenizer



     def __init__(							self :Tuple      ,  _lowerCamelCase :Dict=None      ,  _lowerCamelCase :Dict=None      ,  _lowerCamelCase :List[Any]=True      ,  _lowerCamelCase :Dict="[UNK]"      ,  _lowerCamelCase :List[str]="[SEP]"      ,  _lowerCamelCase :str="[PAD]"      ,  _lowerCamelCase :Optional[Any]="[CLS]"      ,  _lowerCamelCase :Optional[int]="[MASK]"      ,  _lowerCamelCase :str=True      ,  _lowerCamelCase :Tuple=None      ,  **_lowerCamelCase :Any      ,  ):







      '''simple docstring'''



      super().__init__(
          _lowerCamelCase      ,  tokenizer_file=_lowerCamelCase      ,  do_lower_case=_lowerCamelCase      ,  unk_token=_lowerCamelCase      ,  sep_token=_lowerCamelCase      ,  pad_token=_lowerCamelCase      ,  cls_token=_lowerCamelCase      ,  mask_token=_lowerCamelCase      ,  tokenize_chinese_chars=_lowerCamelCase      ,  strip_accents=_lowerCamelCase      ,  **_lowerCamelCase      ,  )

      UpperCamelCase_     :					Any   	=json.loads(self.backend_tokenizer.normalizer.__getstate__()   )
      if (
          pre_tok_state.get('lowercase'      ,  _lowerCamelCase   ) != do_lower_case
          or pre_tok_state.get('strip_accents'      ,  _lowerCamelCase   ) != strip_accents
      ):
       UpperCamelCase_     :					Optional[Any]   	=getattr(_lowerCamelCase      ,  pre_tok_state.pop('type'   )   )
       UpperCamelCase_     :					Tuple   	=do_lower_case
       UpperCamelCase_     :					Union[str, Any]   	=strip_accents
       UpperCamelCase_     :					Union[str, Any]   	=pre_tok_class(**_lowerCamelCase   )

      UpperCamelCase_     :					List[str]   	=do_lower_case



     def __getstate__(							self :Any   ):







      '''simple docstring'''



      UpperCamelCase_     :					str   	=self.__dict__.copy()
      UpperCamelCase_     :					Union[str, Any]   	=BertPreTokenizer()
      return state



     def __setstate__(							self :str      ,  _lowerCamelCase :Optional[Any]   ):







      '''simple docstring'''



      UpperCamelCase_     :					int   	=d
      UpperCamelCase_     :					Optional[int]   	=self.__dict__['_tokenizer'].get_vocab()
      UpperCamelCase_     :					Any   	=PreTokenizer.custom(JiebaPreTokenizer(_lowerCamelCase   )   )



     def  lowerCamelCase_						(							self :Dict      ,  _lowerCamelCase :Optional[Any]      ,  _lowerCamelCase :Any=None   ):







      '''simple docstring'''



      UpperCamelCase_     :					int   	=[self.cls_token_id] + token_ids_a + [self.sep_token_id]

      if token_ids_a:
       output += token_ids_a + [self.sep_token_id]

      return output



     def  lowerCamelCase_						(							self :Union[str, Any]      ,  _lowerCamelCase :List[int]      ,  _lowerCamelCase :Optional[List[int]] = None   ):







      '''simple docstring'''



      UpperCamelCase_     :					List[Any]   	=[self.sep_token_id]
      UpperCamelCase_     :					List[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  lowerCamelCase_						(							self :Optional[int]      ,  _lowerCamelCase :str      ,  _lowerCamelCase :Optional[str] = None   ):







      '''simple docstring'''



      UpperCamelCase_     :					List[Any]   	=self._tokenizer.model.save(_lowerCamelCase      ,  name=_lowerCamelCase   )
      return tuple(_lowerCamelCase   )




     def  lowerCamelCase_						(							self :str      ,  _lowerCamelCase :List[str]      ,  _lowerCamelCase :List[Any]=None      ,  _lowerCamelCase :Optional[Any]=None      ,  _lowerCamelCase :int=False      ,  **_lowerCamelCase :Optional[int]      ,  ):







      '''simple docstring'''



      UpperCamelCase_     :					str   	=BertPreTokenizer()
      return super().save_pretrained(_lowerCamelCase      ,  _lowerCamelCase      ,  _lowerCamelCase      ,  _lowerCamelCase      ,  **_lowerCamelCase   )
