"""simple docstring"""






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

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


_lowercase			:			int										=						logging.get_logger(__name__)
_lowercase			:			Any										=						{"vocab_file": "spiece.model"}

_lowercase			:			Optional[int]										=						{
    "vocab_file": {
        "albert-base-v1": "https://huggingface.co/albert-base-v1/resolve/main/spiece.model",
        "albert-large-v1": "https://huggingface.co/albert-large-v1/resolve/main/spiece.model",
        "albert-xlarge-v1": "https://huggingface.co/albert-xlarge-v1/resolve/main/spiece.model",
        "albert-xxlarge-v1": "https://huggingface.co/albert-xxlarge-v1/resolve/main/spiece.model",
        "albert-base-v2": "https://huggingface.co/albert-base-v2/resolve/main/spiece.model",
        "albert-large-v2": "https://huggingface.co/albert-large-v2/resolve/main/spiece.model",
        "albert-xlarge-v2": "https://huggingface.co/albert-xlarge-v2/resolve/main/spiece.model",
        "albert-xxlarge-v2": "https://huggingface.co/albert-xxlarge-v2/resolve/main/spiece.model",
    }
}

_lowercase			:			List[str]										=						{
    "albert-base-v1": 5_1_2,
    "albert-large-v1": 5_1_2,
    "albert-xlarge-v1": 5_1_2,
    "albert-xxlarge-v1": 5_1_2,
    "albert-base-v2": 5_1_2,
    "albert-large-v2": 5_1_2,
    "albert-xlarge-v2": 5_1_2,
    "albert-xxlarge-v2": 5_1_2,
}

_lowercase			:			List[Any]										=						"▁"

class 	__SCREAMING_SNAKE_CASE     (				lowerCAmelCase_       ):


					'''simple docstring'''





					_a             =							VOCAB_FILES_NAMES
					_a             =							PRETRAINED_VOCAB_FILES_MAP
					_a             =							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES



					def __init__(	self							:  Tuple,  lowerCamelCase							:  Tuple,  lowerCamelCase							:  Optional[Any]=True,  lowerCamelCase							:  Any=True,  lowerCamelCase							:  Union[str, Any]=False,  lowerCamelCase							:  List[str]="[CLS]",  lowerCamelCase							:  Dict="[SEP]",  lowerCamelCase							:  Optional[int]="<unk>",  lowerCamelCase							:  Tuple="[SEP]",  lowerCamelCase							:  Optional[Any]="<pad>",  lowerCamelCase							:  List[str]="[CLS]",  lowerCamelCase							:  int="[MASK]",  lowerCamelCase							:  Optional[Dict[str, Any]] = None,  **lowerCamelCase							:  Optional[Any],  )-> None:
								# Mask token behave like a normal word, i.e. include the space before it and
								# is included in the raw text, there should be a match in a non-normalized sentence.
								lowerCamelCase__		:		Optional[int]		     =(
								    AddedToken(lowerCamelCase,  lstrip=lowerCamelCase,  rstrip=lowerCamelCase,  normalized=lowerCamelCase					)
								    if isinstance(lowerCamelCase,  lowerCamelCase					)
								    else mask_token
								)

								lowerCamelCase__		:		List[Any]		     ={} if sp_model_kwargs is None else sp_model_kwargs

								super().__init__(
								    do_lower_case=lowerCamelCase,  remove_space=lowerCamelCase,  keep_accents=lowerCamelCase,  bos_token=lowerCamelCase,  eos_token=lowerCamelCase,  unk_token=lowerCamelCase,  sep_token=lowerCamelCase,  pad_token=lowerCamelCase,  cls_token=lowerCamelCase,  mask_token=lowerCamelCase,  sp_model_kwargs=self.sp_model_kwargs,  **lowerCamelCase,  )

								lowerCamelCase__		:		List[Any]		     =do_lower_case
								lowerCamelCase__		:		Union[str, Any]		     =remove_space
								lowerCamelCase__		:		str		     =keep_accents
								lowerCamelCase__		:		Tuple		     =vocab_file

								lowerCamelCase__		:		Any		     =spm.SentencePieceProcessor(**self.sp_model_kwargs					)
								self.sp_model.Load(lowerCamelCase					)



					@property
					def 		snake_case	(	self							:  List[Any]					)-> Optional[int]:
								return len(self.sp_model					)



					def 		snake_case	(	self							:  Any					)-> Any:
								lowerCamelCase__		:		Union[str, Any]		     ={self.convert_ids_to_tokens(lowerCamelCase					): i for i in range(self.vocab_size					)}
								vocab.update(self.added_tokens_encoder					)
								return vocab



					def __getstate__(	self							:  List[str]					)-> Union[str, Any]:
								lowerCamelCase__		:		Dict		     =self.__dict__.copy()
								lowerCamelCase__		:		Optional[Any]		     =None
								return state



					def __setstate__(	self							:  List[Any],  lowerCamelCase							:  Optional[int]					)-> Tuple:
								lowerCamelCase__		:		List[str]		     =d

								# for backward compatibility
								if not hasattr(self,  '''sp_model_kwargs'''					):
											lowerCamelCase__		:		Union[str, Any]		     ={}

								lowerCamelCase__		:		Optional[Any]		     =spm.SentencePieceProcessor(**self.sp_model_kwargs					)
								self.sp_model.Load(self.vocab_file					)



					def 		snake_case	(	self							:  Dict,  lowerCamelCase							:  Any					)-> Union[str, Any]:
								if self.remove_space:
											lowerCamelCase__		:		List[Any]		     =''' '''.join(inputs.strip().split()					)
								else:
											lowerCamelCase__		:		Union[str, Any]		     =inputs
								lowerCamelCase__		:		str		     =outputs.replace('''``''',  '''"'''					).replace('''\'\'''',  '''"'''					)

								if not self.keep_accents:
											lowerCamelCase__		:		Dict		     =unicodedata.normalize('''NFKD''',  lowerCamelCase					)
											lowerCamelCase__		:		Optional[int]		     =''''''.join([c for c in outputs if not unicodedata.combining(lowerCamelCase					)]					)
								if self.do_lower_case:
											lowerCamelCase__		:		Dict		     =outputs.lower()

								return outputs



					def 		snake_case	(	self							:  Tuple,  lowerCamelCase							:  str					)-> List[str]:
								lowerCamelCase__		:		List[Any]		     =self.preprocess_text(lowerCamelCase					)
								lowerCamelCase__		:		Tuple		     =self.sp_model.encode(lowerCamelCase,  out_type=lowerCamelCase					)
								lowerCamelCase__		:		Any		     =[]
								for piece in pieces:
											if len(lowerCamelCase					) > 1 and piece[-1] == str(''','''					) and piece[-2].isdigit():
														lowerCamelCase__		:		Union[str, Any]		     =self.sp_model.EncodeAsPieces(piece[:-1].replace(lowerCamelCase,  ''''''					)					)
														if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
																	if len(cur_pieces[0]					) == 1:
																				lowerCamelCase__		:		Optional[Any]		     =cur_pieces[1:]
																	else:
																				lowerCamelCase__		:		int		     =cur_pieces[0][1:]
														cur_pieces.append(piece[-1]					)
														new_pieces.extend(lowerCamelCase					)
											else:
														new_pieces.append(lowerCamelCase					)

								return new_pieces



					def 		snake_case	(	self							:  Union[str, Any],  lowerCamelCase							:  Tuple					)-> Union[str, Any]:
								return self.sp_model.PieceToId(lowerCamelCase					)



					def 		snake_case	(	self							:  Dict,  lowerCamelCase							:  Optional[Any]					)-> List[Any]:
								return self.sp_model.IdToPiece(lowerCamelCase					)



					def 		snake_case	(	self							:  List[Any],  lowerCamelCase							:  Dict					)-> str:
								lowerCamelCase__		:		Optional[Any]		     =[]
								lowerCamelCase__		:		Tuple		     =''''''
								lowerCamelCase__		:		Optional[int]		     =False
								for token in tokens:
											# make sure that special tokens are not decoded using sentencepiece model
											if token in self.all_special_tokens:
														if not prev_is_special:
																	out_string += " "
														out_string += self.sp_model.decode(lowerCamelCase					) + token
														lowerCamelCase__		:		Union[str, Any]		     =True
														lowerCamelCase__		:		List[Any]		     =[]
											else:
														current_sub_tokens.append(lowerCamelCase					)
														lowerCamelCase__		:		Union[str, Any]		     =False
								out_string += self.sp_model.decode(lowerCamelCase					)
								return out_string.strip()



					def 		snake_case	(	self							:  int,  lowerCamelCase							:  List[int],  lowerCamelCase							:  Optional[List[int]] = None					)-> List[int]:
								lowerCamelCase__		:		Tuple		     =[self.sep_token_id]
								lowerCamelCase__		:		Optional[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 		snake_case	(	self							:  Union[str, Any],  lowerCamelCase							:  List[int],  lowerCamelCase							:  Optional[List[int]] = None,  lowerCamelCase							:  bool = False					)-> List[int]:

								if already_has_special_tokens:
											return super().get_special_tokens_mask(
											    token_ids_a=lowerCamelCase,  token_ids_a=lowerCamelCase,  already_has_special_tokens=lowerCamelCase					)

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



					def 		snake_case	(	self							:  Optional[int],  lowerCamelCase							:  List[int],  lowerCamelCase							:  Optional[List[int]] = None					)-> List[int]:
								lowerCamelCase__		:		str		     =[self.sep_token_id]
								lowerCamelCase__		:		Dict		     =[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 		snake_case	(	self							:  Any,  lowerCamelCase							:  str,  lowerCamelCase							:  Optional[str] = None					)-> Tuple[str]:
								if not os.path.isdir(lowerCamelCase					):
											logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''					)
											return
								lowerCamelCase__		:		Union[str, Any]		     =os.path.join(
								    lowerCamelCase,  (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']					)

								if os.path.abspath(self.vocab_file					) != os.path.abspath(lowerCamelCase					) and os.path.isfile(self.vocab_file					):
											copyfile(self.vocab_file,  lowerCamelCase					)
								elif not os.path.isfile(self.vocab_file					):
											with open(lowerCamelCase,  '''wb'''					) as fi:
														lowerCamelCase__		:		List[str]		     =self.sp_model.serialized_model_proto()
														fi.write(lowerCamelCase					)

								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 	__SCREAMING_SNAKE_CASE     (				unittest.TestCase       ):


					'''simple docstring'''





					def 		snake_case	(	self							:  Union[str, Any]					)-> Dict:
								# clean up the VRAM after each test
								super().tearDown()
								gc.collect()



					def 		snake_case	(	self							:  str					)-> Any:
								lowerCamelCase__     ,       lowerCamelCase__		:		Optional[Any]		     =FlaxControlNetModel.from_pretrained(
								    '''lllyasviel/sd-controlnet-canny''',  from_pt=lowerCamelCase,  dtype=jnp.bfloataa					)
								lowerCamelCase__     ,       lowerCamelCase__		:		Optional[int]		     =FlaxStableDiffusionControlNetPipeline.from_pretrained(
								    '''runwayml/stable-diffusion-v1-5''',  controlnet=lowerCamelCase,  from_pt=lowerCamelCase,  dtype=jnp.bfloataa					)
								lowerCamelCase__		:		Optional[int]		     =controlnet_params

								lowerCamelCase__		:		Dict		     ='''bird'''
								lowerCamelCase__		:		List[str]		     =jax.device_count()
								lowerCamelCase__		:		Optional[Any]		     =pipe.prepare_text_inputs([prompts] * num_samples					)

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

								lowerCamelCase__		:		Optional[int]		     =jax.random.PRNGKey(0					)
								lowerCamelCase__		:		Dict		     =jax.random.split(lowerCamelCase,  jax.device_count()					)

								lowerCamelCase__		:		Tuple		     =replicate(lowerCamelCase					)
								lowerCamelCase__		:		Tuple		     =shard(lowerCamelCase					)
								lowerCamelCase__		:		Optional[int]		     =shard(lowerCamelCase					)

								lowerCamelCase__		:		Tuple		     =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)

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

								lowerCamelCase__		:		Optional[Any]		     =jnp.asarray(jax.device_get(image_slice.flatten()					)					)
								lowerCamelCase__		:		Dict		     =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 		snake_case	(	self							:  Optional[int]					)-> Optional[int]:
								lowerCamelCase__     ,       lowerCamelCase__		:		Dict		     =FlaxControlNetModel.from_pretrained(
								    '''lllyasviel/sd-controlnet-openpose''',  from_pt=lowerCamelCase,  dtype=jnp.bfloataa					)
								lowerCamelCase__     ,       lowerCamelCase__		:		List[Any]		     =FlaxStableDiffusionControlNetPipeline.from_pretrained(
								    '''runwayml/stable-diffusion-v1-5''',  controlnet=lowerCamelCase,  from_pt=lowerCamelCase,  dtype=jnp.bfloataa					)
								lowerCamelCase__		:		Optional[Any]		     =controlnet_params

								lowerCamelCase__		:		int		     ='''Chef in the kitchen'''
								lowerCamelCase__		:		Optional[Any]		     =jax.device_count()
								lowerCamelCase__		:		Any		     =pipe.prepare_text_inputs([prompts] * num_samples					)

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

								lowerCamelCase__		:		Tuple		     =jax.random.PRNGKey(0					)
								lowerCamelCase__		:		Optional[Any]		     =jax.random.split(lowerCamelCase,  jax.device_count()					)

								lowerCamelCase__		:		int		     =replicate(lowerCamelCase					)
								lowerCamelCase__		:		List[Any]		     =shard(lowerCamelCase					)
								lowerCamelCase__		:		int		     =shard(lowerCamelCase					)

								lowerCamelCase__		:		Tuple		     =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)

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

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




