



'''simple docstring'''





import io
import os
import unicodedata
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

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


SCREAMING_SNAKE_CASE__          =  logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__          =  "▁"

SCREAMING_SNAKE_CASE__          =  {"vocab_file": "vocab.txt", "sentencepiece_model_ckpt": "sentencepiece.bpe.model"}

SCREAMING_SNAKE_CASE__          =  {
    "sentencepiece_model_file": "sentencepiece.bpe.model",
    "vocab_file": "vocab.txt",
}

SCREAMING_SNAKE_CASE__          =  {
    "vocab_file": {
        "ernie-m-base": "https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/vocab.txt",
        "ernie-m-large": "https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/vocab.txt",
    },
    "sentencepiece_model_file": {
        "ernie-m-base": "https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/sentencepiece.bpe.model",
        "ernie-m-large": "https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/sentencepiece.bpe.model",
    },
}

SCREAMING_SNAKE_CASE__          =  {
    "ernie-m-base": 514,
    "ernie-m-large": 514,
}

SCREAMING_SNAKE_CASE__          =  {
    "ernie-m-base": {"do_lower_case": False},
    "ernie-m-large": {"do_lower_case": False},
}


class 					snake_case			(UpperCamelCase ):
       lowerCAmelCase__	:List[str]     =       ["input_ids"]

       lowerCAmelCase__	:int     =       VOCAB_FILES_NAMES
       lowerCAmelCase__	:Tuple     =       PRETRAINED_INIT_CONFIGURATION
       lowerCAmelCase__	:Union[str, Any]     =       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       lowerCAmelCase__	:List[str]     =       PRETRAINED_VOCAB_FILES_MAP
       lowerCAmelCase__	:Dict     =       RESOURCE_FILES_NAMES



       def __init__(					self  ,UpperCAmelCase_  ,UpperCAmelCase_=None  ,UpperCAmelCase_=False  ,UpperCAmelCase_="utf8"  ,UpperCAmelCase_="[UNK]"  ,UpperCAmelCase_="[SEP]"  ,UpperCAmelCase_="[PAD]"  ,UpperCAmelCase_="[CLS]"  ,UpperCAmelCase_="[MASK]"  ,UpperCAmelCase_ = None  ,**UpperCAmelCase_  ,)	->				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.

           lowercase__									=    {} if sp_model_kwargs is None else sp_model_kwargs
           super().__init__(
               do_lower_case=UpperCAmelCase_  ,unk_token=UpperCAmelCase_  ,sep_token=UpperCAmelCase_  ,pad_token=UpperCAmelCase_  ,cls_token=UpperCAmelCase_  ,mask_token=UpperCAmelCase_  ,vocab_file=UpperCAmelCase_  ,encoding=UpperCAmelCase_  ,sp_model_kwargs=self.sp_model_kwargs  ,**UpperCAmelCase_  ,)
           lowercase__									=    do_lower_case
           lowercase__									=    sentencepiece_model_ckpt
           lowercase__									=    spm.SentencePieceProcessor(**self.sp_model_kwargs   )
           self.sp_model.Load(UpperCAmelCase_   )

           # to mimic paddlenlp.transformers.ernie_m.tokenizer.ErnieMTokenizer functioning
           if vocab_file is not None:
               lowercase__									=    self.load_vocab(filepath=UpperCAmelCase_   )
           else:
               lowercase__									=    {self.sp_model.id_to_piece(UpperCAmelCase_   ): id for id in range(self.sp_model.get_piece_size()   )}
           lowercase__									=    {v: k for k, v in self.vocab.items()}



       def 				_a   (					self  ,UpperCAmelCase_   )	->				List[str]:
           if text is None:
               return None

           lowercase__									=    self.tokenize(UpperCAmelCase_   )
           lowercase__							,							lowercase__									=    "", []

           for i, ch in enumerate(UpperCAmelCase_   ):
               if ch in self.SP_CHAR_MAPPING:
                   lowercase__									=    self.SP_CHAR_MAPPING.get(UpperCAmelCase_   )
               else:
                   lowercase__									=    unicodedata.normalize("NFKC"  ,UpperCAmelCase_   )
               if self.is_whitespace(UpperCAmelCase_   ):
                   continue
               normalized_text += ch
               char_mapping.extend([i] * len(UpperCAmelCase_   )   )

           lowercase__							,							lowercase__							,							lowercase__									=    normalized_text, [], 0

           if self.do_lower_case:
               lowercase__									=    text.lower()

           for token in split_tokens:
               if token[:1] == "▁":
                   lowercase__									=    token[1:]
               lowercase__									=    text[offset:].index(UpperCAmelCase_   ) + offset
               lowercase__									=    start + len(UpperCAmelCase_   )

               token_mapping.append((char_mapping[start], char_mapping[end - 1] + 1)   )
               lowercase__									=    end
           return token_mapping



       @property
       def 				_a   (					self   )	->				Any:
           return len(self.vocab   )



       def 				_a   (					self   )	->				Optional[int]:
           return dict(self.vocab  ,**self.added_tokens_encoder   )



       def __getstate__(					self   )	->				Any:
           lowercase__									=    self.__dict__.copy()
           lowercase__									=    None
           return state



       def __setstate__(					self  ,UpperCAmelCase_   )	->				Optional[Any]:
           lowercase__									=    d

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

           lowercase__									=    spm.SentencePieceProcessor(**self.sp_model_kwargs   )
           self.sp_model.Load(self.sentencepiece_model_ckpt   )



       def 				_a   (					self  ,UpperCAmelCase_   )	->				Optional[int]:
           return "".join((self.SP_CHAR_MAPPING.get(UpperCAmelCase_  ,UpperCAmelCase_   ) for c in text)   )



       def 				_a   (					self  ,UpperCAmelCase_  ,UpperCAmelCase_=False  ,UpperCAmelCase_=64  ,UpperCAmelCase_=0.1   )	->				str:

           if self.sp_model_kwargs.get("enable_sampling"   ) is True:
               lowercase__									=    True
           if self.sp_model_kwargs.get("alpha"   ) is not None:
               lowercase__									=    self.sp_model_kwargs.get("alpha"   )
           if self.sp_model_kwargs.get("nbest_size"   ) is not None:
               lowercase__									=    self.sp_model_kwargs.get("nbest_size"   )

           if not enable_sampling:
               lowercase__									=    self.sp_model.EncodeAsPieces(UpperCAmelCase_   )
           else:
               lowercase__									=    self.sp_model.SampleEncodeAsPieces(UpperCAmelCase_  ,UpperCAmelCase_  ,UpperCAmelCase_   )
           lowercase__									=    []
           for pi, piece in enumerate(UpperCAmelCase_   ):
               if piece == SPIECE_UNDERLINE:
                   if not pieces[pi + 1].startswith(UpperCAmelCase_   ) and pi != 0:
                       new_pieces.append(UpperCAmelCase_   )
                       continue
                   else:
                       continue
               lowercase__									=    0
               for i, chunk in enumerate(UpperCAmelCase_   ):
                   if chunk == SPIECE_UNDERLINE:
                       continue
                   if self.is_ch_char(UpperCAmelCase_   ) or self.is_punct(UpperCAmelCase_   ):
                       if i > lst_i and piece[lst_i:i] != SPIECE_UNDERLINE:
                           new_pieces.append(piece[lst_i:i]   )
                       new_pieces.append(UpperCAmelCase_   )
                       lowercase__									=    i + 1
                   elif chunk.isdigit() and i > 0 and not piece[i - 1].isdigit():
                       if i > lst_i and piece[lst_i:i] != SPIECE_UNDERLINE:
                           new_pieces.append(piece[lst_i:i]   )
                       lowercase__									=    i
                   elif not chunk.isdigit() and i > 0 and piece[i - 1].isdigit():
                       if i > lst_i and piece[lst_i:i] != SPIECE_UNDERLINE:
                           new_pieces.append(piece[lst_i:i]   )
                       lowercase__									=    i
               if len(UpperCAmelCase_   ) > lst_i:
                   new_pieces.append(piece[lst_i:]   )
           return new_pieces



       def 				_a   (					self  ,UpperCAmelCase_   )	->				str:
           lowercase__									=    "".join(UpperCAmelCase_   ).replace(UpperCAmelCase_  ," "   ).strip()
           return out_string



       def 				_a   (					self  ,UpperCAmelCase_   )	->				Optional[Any]:
           lowercase__									=    self.convert_ids_to_tokens(UpperCAmelCase_   )
           lowercase__									=    "".join(UpperCAmelCase_   ).replace(UpperCAmelCase_  ," "   ).strip()
           return out_string



       def 				_a   (					self  ,UpperCAmelCase_   )	->				Optional[Any]:
           return self.vocab.get(UpperCAmelCase_  ,self.vocab.get(self.unk_token   )   )



       def 				_a   (					self  ,UpperCAmelCase_   )	->				int:
           return self.reverse_vocab.get(UpperCAmelCase_  ,self.unk_token   )



       def 				_a   (					self  ,UpperCAmelCase_  ,UpperCAmelCase_=None   )	->				Union[str, Any]:
           if token_ids_a is None:
               return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
           lowercase__									=    [self.cls_token_id]
           lowercase__									=    [self.sep_token_id]
           return _cls + token_ids_a + _sep + _sep + token_ids_a + _sep



       def 				_a   (					self  ,UpperCAmelCase_  ,UpperCAmelCase_=None   )	->				Any:
           if offset_mapping_a is None:
               return [(0, 0)] + offset_mapping_a + [(0, 0)]

           return [(0, 0)] + offset_mapping_a + [(0, 0), (0, 0)] + offset_mapping_a + [(0, 0)]



       def 				_a   (					self  ,UpperCAmelCase_  ,UpperCAmelCase_=None  ,UpperCAmelCase_=False   )	->				Dict:

           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, 1] + ([0] * len(UpperCAmelCase_   )) + [1]
           return [1] + ([0] * len(UpperCAmelCase_   )) + [1]



       def 				_a   (					self  ,UpperCAmelCase_  ,UpperCAmelCase_ = None   )	->				List[int]:
           # called when `add_special_tokens` is True, so align with `build_inputs_with_special_tokens` method
           if token_ids_a is None:
               # [CLS] X [SEP]
               return (len(UpperCAmelCase_   ) + 2) * [0]

           # [CLS] A [SEP] [SEP] B [SEP]
           return [0] * (len(UpperCAmelCase_   ) + 1) + [1] * (len(UpperCAmelCase_   ) + 3)



       def 				_a   (					self  ,UpperCAmelCase_   )	->				int:
           if "\u4e00" <= char <= "\u9fff":
               return True
           return False



       def 				_a   (					self  ,UpperCAmelCase_   )	->				Any:
           if ("a" <= char <= "z") or ("A" <= char <= "Z"):
               return True
           return False



       def 				_a   (					self  ,UpperCAmelCase_   )	->				Tuple:
           if char in ",;:.?!~，；：。？！《》【】":
               return True
           return False



       def 				_a   (					self  ,UpperCAmelCase_   )	->				Optional[Any]:
           if char == " " or char == "\t" or char == "\n" or char == "\r":
               return True
           if len(UpperCAmelCase_   ) == 1:
               lowercase__									=    unicodedata.category(UpperCAmelCase_   )
               if cat == "Zs":
                   return True
           return False



       def 				_a   (					self  ,UpperCAmelCase_   )	->				Tuple:
           lowercase__									=    {}
           with io.open(UpperCAmelCase_  ,"r"  ,encoding="utf-8"   ) as f:
               for index, line in enumerate(UpperCAmelCase_   ):
                   lowercase__									=    line.rstrip("\n"   )
                   lowercase__									=    int(UpperCAmelCase_   )

           return token_to_idx







       def 				_a   (					self  ,UpperCAmelCase_  ,UpperCAmelCase_ = None   )	->				Tuple[str]:
           lowercase__									=    0
           if os.path.isdir(UpperCAmelCase_   ):
               lowercase__									=    os.path.join(
                   UpperCAmelCase_  ,(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]   )
           else:
               lowercase__									=    (filename_prefix + "-" if filename_prefix else "") + save_directory
           with open(UpperCAmelCase_  ,"w"  ,encoding="utf-8"   ) as writer:
               for token, token_index in sorted(self.vocab.items()  ,key=lambda UpperCAmelCase_   : kv[1]   ):
                   if index != token_index:
                       logger.warning(
                           F'''Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive.'''
                           " Please check that the vocabulary is not corrupted!"   )
                       lowercase__									=    token_index
                   writer.write(token + "\n"   )
                   index += 1

           lowercase__									=    os.path.join(UpperCAmelCase_  ,"sentencepiece.bpe.model"   )
           with open(UpperCAmelCase_  ,"wb"   ) as fi:
               lowercase__									=    self.sp_model.serialized_model_proto()
               fi.write(UpperCAmelCase_   )

           return (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 				_a   (					self   )	->				Tuple:
           # clean up the VRAM after each test
           super().tearDown()
           gc.collect()



       def 				_a   (					self   )	->				List[Any]:
           lowercase__							,							lowercase__									=    FlaxControlNetModel.from_pretrained(
               "lllyasviel/sd-controlnet-canny"  ,from_pt=UpperCAmelCase_  ,dtype=jnp.bfloataa   )
           lowercase__							,							lowercase__									=    FlaxStableDiffusionControlNetPipeline.from_pretrained(
               "runwayml/stable-diffusion-v1-5"  ,controlnet=UpperCAmelCase_  ,from_pt=UpperCAmelCase_  ,dtype=jnp.bfloataa   )
           lowercase__									=    controlnet_params

           lowercase__									=    "bird"
           lowercase__									=    jax.device_count()
           lowercase__									=    pipe.prepare_text_inputs([prompts] * num_samples   )

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

           lowercase__									=    jax.random.PRNGKey(0   )
           lowercase__									=    jax.random.split(UpperCAmelCase_  ,jax.device_count()   )

           lowercase__									=    replicate(UpperCAmelCase_   )
           lowercase__									=    shard(UpperCAmelCase_   )
           lowercase__									=    shard(UpperCAmelCase_   )

           lowercase__									=    pipe(
               prompt_ids=UpperCAmelCase_  ,image=UpperCAmelCase_  ,params=UpperCAmelCase_  ,prng_seed=UpperCAmelCase_  ,num_inference_steps=50  ,jit=UpperCAmelCase_  ,).images
           assert images.shape == (jax.device_count(), 1, 768, 512, 3)

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

           lowercase__									=    jnp.asarray(jax.device_get(image_slice.flatten()   )   )
           lowercase__									=    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 				_a   (					self   )	->				List[Any]:
           lowercase__							,							lowercase__									=    FlaxControlNetModel.from_pretrained(
               "lllyasviel/sd-controlnet-openpose"  ,from_pt=UpperCAmelCase_  ,dtype=jnp.bfloataa   )
           lowercase__							,							lowercase__									=    FlaxStableDiffusionControlNetPipeline.from_pretrained(
               "runwayml/stable-diffusion-v1-5"  ,controlnet=UpperCAmelCase_  ,from_pt=UpperCAmelCase_  ,dtype=jnp.bfloataa   )
           lowercase__									=    controlnet_params

           lowercase__									=    "Chef in the kitchen"
           lowercase__									=    jax.device_count()
           lowercase__									=    pipe.prepare_text_inputs([prompts] * num_samples   )

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

           lowercase__									=    jax.random.PRNGKey(0   )
           lowercase__									=    jax.random.split(UpperCAmelCase_  ,jax.device_count()   )

           lowercase__									=    replicate(UpperCAmelCase_   )
           lowercase__									=    shard(UpperCAmelCase_   )
           lowercase__									=    shard(UpperCAmelCase_   )

           lowercase__									=    pipe(
               prompt_ids=UpperCAmelCase_  ,image=UpperCAmelCase_  ,params=UpperCAmelCase_  ,prng_seed=UpperCAmelCase_  ,num_inference_steps=50  ,jit=UpperCAmelCase_  ,).images
           assert images.shape == (jax.device_count(), 1, 768, 512, 3)

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

           lowercase__									=    jnp.asarray(jax.device_get(image_slice.flatten()   )   )
           lowercase__									=    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




