"""simple docstring"""






import json
import os
from typing import Dict, List, Optional, Tuple

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


__lowerCAmelCase    :       List[str]					     =logging.get_logger(__name__)


__lowerCAmelCase    :       Dict					     ={
    """vocab_file""": """vocab.json""",
    """tokenizer_config_file""": """tokenizer_config.json""",
    """merges_file""": """merges.txt""",
}

__lowerCAmelCase    :       Optional[int]					     ={
    """vocab_file""": {
        """facebook/s2t-wav2vec2-large-en-de""": (
            """https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/vocab.json"""
        ),
    },
    """tokenizer_config_file""": {
        """facebook/s2t-wav2vec2-large-en-de""": (
            """https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/tokenizer_config.json"""
        ),
    },
    """merges_file""": {
        """facebook/s2t-wav2vec2-large-en-de""": (
            """https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/merges.txt"""
        ),
    },
}

__lowerCAmelCase    :       Union[str, Any]					     ="""</w>"""
__lowerCAmelCase    :       Optional[int]					     ="""@@ """


def 			UpperCAmelCase__       (				lowerCAmelCase__     :Optional[Any]		) ->    int:
      '''simple docstring'''


      lowercase														=	set()
      lowercase														=	word[0]
      for char in word[1:]:
            pairs.add((prev_char, char)		)
            lowercase														=	char
      return pairs


# Speech2Text2 has no max input length
__lowerCAmelCase    :       Tuple					     ={"""facebook/s2t-wav2vec2-large-en-de""": 1_0_2_4}






class  _A						(       lowerCAmelCase	):
      snake_case__			:   str							  =	VOCAB_FILES_NAMES
      snake_case__			:   Optional[Any]							  =	PRETRAINED_VOCAB_FILES_MAP
      snake_case__			:   str							  =	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      snake_case__			:   str							  =	['input_ids', 'attention_mask']

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







            """simple docstring"""



            super().__init__(
                unk_token=__lowerCAmelCase		,       bos_token=__lowerCAmelCase		,       eos_token=__lowerCAmelCase		,       pad_token=__lowerCAmelCase		,       do_lower_case=__lowerCAmelCase		,       **__lowerCAmelCase		,       )

            lowercase														=	do_lower_case

            with open(__lowerCAmelCase		,       encoding="""utf-8"""						) as vocab_handle:
                  lowercase														=	json.load(__lowerCAmelCase						)
            lowercase														=	{v: k for k, v in self.encoder.items()}

            if merges_file is None:
                  logger.info(f'No merges files provided. {self.__class__.__name__} can only be used for decoding.'						)

                  lowercase														=	None
                  lowercase														=	None
            else:
                  with open(__lowerCAmelCase		,       encoding="""utf-8"""						) as merges_handle:
                        lowercase														=	merges_handle.read().split("""\n"""						)[:-1]

                  lowercase														=	[tuple(merge.split()[:2]						) for merge in merges]
                  lowercase														=	dict(zip(__lowerCAmelCase		,       range(len(__lowerCAmelCase						)						)						)						)
                  lowercase														=	{}

      @property
      def 				A__       (						self						):







            """simple docstring"""



            return len(self.decoder						)

      def 				A__       (						self						):







            """simple docstring"""



            return dict(self.encoder		,       **self.added_tokens_encoder						)

      def 				A__       (						self		,       __lowerCAmelCase						):







            """simple docstring"""



            lowercase														=	tuple(token[:-1]						) + (token[-1] + BPE_TOKEN_MERGES,)
            if token in self.cache:
                  return self.cache[token]
            lowercase														=	get_pairs(__lowerCAmelCase						)

            if not pairs:
                  return token

            while True:
                  lowercase														=	min(__lowerCAmelCase		,       key=lambda __lowerCAmelCase						: self.bpe_ranks.get(__lowerCAmelCase		,       float("""inf"""						)						)						)
                  if bigram not in self.bpe_ranks:
                        break
                  lowercase							,							lowercase														=	bigram
                  lowercase														=	[]
                  lowercase														=	0
                  while i < len(__lowerCAmelCase						):
                        try:
                              lowercase														=	word.index(__lowerCAmelCase		,       __lowerCAmelCase						)
                        except ValueError:
                              new_word.extend(word[i:]						)
                              break
                        else:
                              new_word.extend(word[i:j]						)
                              lowercase														=	j

                        if word[i] == first and i < len(__lowerCAmelCase						) - 1 and word[i + 1] == second:
                              new_word.append(first + second						)
                              i += 2
                        else:
                              new_word.append(word[i]						)
                              i += 1
                  lowercase														=	tuple(__lowerCAmelCase						)
                  lowercase														=	new_word
                  if len(__lowerCAmelCase						) == 1:
                        break
                  else:
                        lowercase														=	get_pairs(__lowerCAmelCase						)
            lowercase														=	""" """.join(__lowerCAmelCase						)
            if word == "\n  " + BPE_TOKEN_MERGES:
                  lowercase														=	"""\n""" + BPE_TOKEN_MERGES

            if word.endswith(__lowerCAmelCase						):
                  lowercase														=	word.replace(__lowerCAmelCase		,       """"""						)

            lowercase														=	word.replace(""" """		,       __lowerCAmelCase						)
            lowercase														=	word
            return word

      def 				A__       (						self		,       __lowerCAmelCase						):







            """simple docstring"""



            if self.bpe_ranks is None:
                  raise ValueError(
                      """This tokenizer was instantiated without a `merges.txt` file, so"""
                      """ that it can only be used for decoding, not for encoding."""
                      """Make sure to provide `merges.txt` file at instantiation to enable """
                      """encoding."""						)

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

            lowercase														=	text.split()

            lowercase														=	[]
            for token in text:
                  if token:
                        split_tokens.extend(list(self.bpe(__lowerCAmelCase						).split(""" """						)						)						)

            return split_tokens

      def 				A__       (						self		,       __lowerCAmelCase						):







            """simple docstring"""



            return self.encoder.get(__lowerCAmelCase		,       self.encoder.get(self.unk_token						)						)

      def 				A__       (						self		,       __lowerCAmelCase						):







            """simple docstring"""



            lowercase														=	self.decoder.get(__lowerCAmelCase		,       self.unk_token						)
            return result

      def 				A__       (						self		,       __lowerCAmelCase						):







            """simple docstring"""



            lowercase														=	""" """.join(__lowerCAmelCase						)

            # make sure @@ tokens are concatenated
            lowercase														=	"""""".join(string.split(__lowerCAmelCase						)						)

            return string







      def 				A__       (						self		,       __lowerCAmelCase		,       __lowerCAmelCase = None						):







            """simple docstring"""



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

            with open(__lowerCAmelCase		,       """w"""		,       encoding="""utf-8"""						) as f:
                  f.write(json.dumps(self.encoder		,       indent=2		,       sort_keys=__lowerCAmelCase		,       ensure_ascii=__lowerCAmelCase						) + """\n"""						)

            lowercase														=	0
            if self.bpe_ranks is None:
                  return (vocab_file,)

            with open(__lowerCAmelCase		,       """w"""		,       encoding="""utf-8"""						) as writer:
                  for bpe_tokens, token_index in sorted(self.bpe_ranks.items()		,       key=lambda __lowerCAmelCase						: kv[1]						):
                        if index != token_index:
                              logger.warning(
                                  f'Saving vocabulary to {merges_file}: BPE merge indices are not consecutive.'
                                  """ Please check that the tokenizer is not corrupted!"""						)
                              lowercase														=	token_index
                        writer.write(""" """.join(__lowerCAmelCase						) + """\n"""						)
                        index += 1

            return (vocab_file, merges_file)




"""simple docstring"""






import numpy as np
import torch
from imwatermark import WatermarkEncoder


# Copied from https://github.com/Stability-AI/generative-models/blob/613af104c6b85184091d42d374fef420eddb356d/scripts/demo/streamlit_helpers.py#L66
__lowerCAmelCase    :       Union[str, Any]					     =0B1011_0011_1110_1100_1001_0000_0111_1011_1011_0001_1001_1110
# bin(x)[2:] gives bits of x as str, use int to convert them to 0/1
__lowerCAmelCase    :       Optional[int]					     =[int(bit) for bit in bin(WATERMARK_MESSAGE)[2:]]





class  _A						:

      def __init__(						self						):







            """simple docstring"""



            lowercase														=	WATERMARK_BITS
            lowercase														=	WatermarkEncoder()

            self.encoder.set_watermark("""bits"""		,       self.watermark						)







      def 				A__       (						self		,       __lowerCAmelCase						):







            """simple docstring"""



            if images.shape[-1] < 256:
                  return images

            lowercase														=	(255 * (images / 2 + 0.5)).cpu().permute(0		,       2		,       3		,       1						).float().numpy()

            lowercase														=	[self.encoder.encode(__lowerCAmelCase		,       """dwtDct"""						) for image in images]

            lowercase														=	torch.from_numpy(np.array(__lowerCAmelCase						)						).permute(0		,       3		,       1		,       2						)

            lowercase														=	torch.clamp(2 * (images / 255 - 0.5)		,       min=-1.0		,       max=1.0						)
            return images



