

'''simple docstring'''



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

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


_UpperCamelCase						:  List[Any]							       =				logging.get_logger(__name__)

_UpperCamelCase						:  List[str]							       =				{"vocab_file": "spiece.model"}

_UpperCamelCase						:  Tuple							       =				{
    "vocab_file": {
        "bert_for_seq_generation": (
            "https://huggingface.co/google/bert_for_seq_generation_L-24_bbc_encoder/resolve/main/spiece.model"
        ),
    }
}

_UpperCamelCase						:  str							       =				{"bert_for_seq_generation": 512}



class 				_snake_case					(   a_			):
  SCREAMING_SNAKE_CASE			:       Optional[Any]				=     VOCAB_FILES_NAMES
  SCREAMING_SNAKE_CASE			:       Optional[Any]				=     PRETRAINED_VOCAB_FILES_MAP
  SCREAMING_SNAKE_CASE			:       str				=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  SCREAMING_SNAKE_CASE			:       List[int]				=     []
  SCREAMING_SNAKE_CASE			:       Tuple				=     ['''input_ids''', '''attention_mask''']

  def __init__(				self      , _SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE="<s>"      , _SCREAMING_SNAKE_CASE="</s>"      , _SCREAMING_SNAKE_CASE="<unk>"      , _SCREAMING_SNAKE_CASE="<pad>"      , _SCREAMING_SNAKE_CASE="<::::>"      , _SCREAMING_SNAKE_CASE = None      , **_SCREAMING_SNAKE_CASE      , ):





      '''simple docstring'''

      lowerCAmelCase 						=							{} if sp_model_kwargs is None else sp_model_kwargs

      # Add extra_ids to the special token list
      super().__init__(
          bos_token=_SCREAMING_SNAKE_CASE      , eos_token=_SCREAMING_SNAKE_CASE      , unk_token=_SCREAMING_SNAKE_CASE      , pad_token=_SCREAMING_SNAKE_CASE      , sep_token=_SCREAMING_SNAKE_CASE      , sp_model_kwargs=self.sp_model_kwargs      , **_SCREAMING_SNAKE_CASE      , )

      lowerCAmelCase 						=							vocab_file

      lowerCAmelCase 						=							spm.SentencePieceProcessor(**self.sp_model_kwargs	)
      self.sp_model.Load(_SCREAMING_SNAKE_CASE	)

  @property
  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      return self.sp_model.get_piece_size()

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      lowerCAmelCase 						=							{self.convert_ids_to_tokens(_SCREAMING_SNAKE_CASE	): i for i in range(self.vocab_size	)}
      vocab.update(self.added_tokens_encoder	)
      return vocab

  def __getstate__(				self	):





      '''simple docstring'''

      lowerCAmelCase 						=							self.__dict__.copy()
      lowerCAmelCase 						=							None
      return state

  def __setstate__(				self      , _SCREAMING_SNAKE_CASE	):





      '''simple docstring'''

      lowerCAmelCase 						=							d

      # for backward compatibility
      if not hasattr(self      , 'sp_model_kwargs'	):
          lowerCAmelCase 						=							{}

      lowerCAmelCase 						=							spm.SentencePieceProcessor(**self.sp_model_kwargs	)
      self.sp_model.Load(self.vocab_file	)

  def   _SCREAMING_SNAKE_CASE				(				self      , _SCREAMING_SNAKE_CASE	):





      '''simple docstring'''

      return self.sp_model.encode(_SCREAMING_SNAKE_CASE      , out_type=_SCREAMING_SNAKE_CASE	)

  def   _SCREAMING_SNAKE_CASE				(				self      , _SCREAMING_SNAKE_CASE	):





      '''simple docstring'''

      return self.sp_model.piece_to_id(_SCREAMING_SNAKE_CASE	)

  def   _SCREAMING_SNAKE_CASE				(				self      , _SCREAMING_SNAKE_CASE	):





      '''simple docstring'''

      lowerCAmelCase 						=							self.sp_model.IdToPiece(_SCREAMING_SNAKE_CASE	)
      return token

  def   _SCREAMING_SNAKE_CASE				(				self      , _SCREAMING_SNAKE_CASE	):





      '''simple docstring'''

      lowerCAmelCase 						=							[]
      lowerCAmelCase 						=							''
      for token in tokens:
          # make sure that special tokens are not decoded using sentencepiece model
          if token in self.all_special_tokens:
              out_string += self.sp_model.decode(_SCREAMING_SNAKE_CASE	) + token
              lowerCAmelCase 						=							[]
          else:
              current_sub_tokens.append(_SCREAMING_SNAKE_CASE	)
      out_string += self.sp_model.decode(_SCREAMING_SNAKE_CASE	)
      return out_string.strip()


  def   _SCREAMING_SNAKE_CASE				(				self      , _SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE = None	):





      '''simple docstring'''

      if not os.path.isdir(_SCREAMING_SNAKE_CASE	):
          logger.error(F'Vocabulary path ({save_directory}) should be a directory'	)
          return
      lowerCAmelCase 						=							os.path.join(
          _SCREAMING_SNAKE_CASE      , (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']	)

      if os.path.abspath(self.vocab_file	) != os.path.abspath(_SCREAMING_SNAKE_CASE	) and os.path.isfile(self.vocab_file	):
          copyfile(self.vocab_file      , _SCREAMING_SNAKE_CASE	)
      elif not os.path.isfile(self.vocab_file	):
          with open(_SCREAMING_SNAKE_CASE      , 'wb'	) as fi:
              lowerCAmelCase 						=							self.sp_model.serialized_model_proto()
              fi.write(_SCREAMING_SNAKE_CASE	)

      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 				_snake_case					(   unittest.TestCase			):

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

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

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      lowerCAmelCase						,   lowerCAmelCase 						=							FlaxControlNetModel.from_pretrained(
          'lllyasviel/sd-controlnet-canny'      , from_pt=_SCREAMING_SNAKE_CASE      , dtype=jnp.bfloataa	)
      lowerCAmelCase						,   lowerCAmelCase 						=							FlaxStableDiffusionControlNetPipeline.from_pretrained(
          'runwayml/stable-diffusion-v1-5'      , controlnet=_SCREAMING_SNAKE_CASE      , from_pt=_SCREAMING_SNAKE_CASE      , 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(_SCREAMING_SNAKE_CASE      , jax.device_count()	)

      lowerCAmelCase 						=							replicate(_SCREAMING_SNAKE_CASE	)
      lowerCAmelCase 						=							shard(_SCREAMING_SNAKE_CASE	)
      lowerCAmelCase 						=							shard(_SCREAMING_SNAKE_CASE	)

      lowerCAmelCase 						=							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, 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(
          [0.167_969, 0.116_699, 0.081_543, 0.154_297, 0.132_812, 0.108_887, 0.169_922, 0.169_922, 0.205_078]	)
      print(F'output_slice: {output_slice}'	)
      assert jnp.abs(output_slice - expected_slice	).max() < 1e-2


  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      lowerCAmelCase						,   lowerCAmelCase 						=							FlaxControlNetModel.from_pretrained(
          'lllyasviel/sd-controlnet-openpose'      , from_pt=_SCREAMING_SNAKE_CASE      , dtype=jnp.bfloataa	)
      lowerCAmelCase						,   lowerCAmelCase 						=							FlaxStableDiffusionControlNetPipeline.from_pretrained(
          'runwayml/stable-diffusion-v1-5'      , controlnet=_SCREAMING_SNAKE_CASE      , from_pt=_SCREAMING_SNAKE_CASE      , 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(_SCREAMING_SNAKE_CASE      , jax.device_count()	)

      lowerCAmelCase 						=							replicate(_SCREAMING_SNAKE_CASE	)
      lowerCAmelCase 						=							shard(_SCREAMING_SNAKE_CASE	)
      lowerCAmelCase 						=							shard(_SCREAMING_SNAKE_CASE	)

      lowerCAmelCase 						=							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, 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(
          [[0.271_484, 0.261_719, 0.275_391, 0.277_344, 0.279_297, 0.291_016, 0.294_922, 0.302_734, 0.302_734]]	)
      print(F'output_slice: {output_slice}'	)
      assert jnp.abs(output_slice - expected_slice	).max() < 1e-2





