



'''simple docstring'''
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union

import numpy as np
import PIL
from PIL import Image

from ...utils import BaseOutput, is_torch_available, is_transformers_available


@dataclass
class   SCREAMING_SNAKE_CASE__				(   snake_case_					):
	"""simple docstring"""


	A__    :    Dict						=				{
	    '''sld_warmup_steps''': 15,
	    '''sld_guidance_scale''': 20,
	    '''sld_threshold''': 0.0,
	    '''sld_momentum_scale''': 0.0,
	    '''sld_mom_beta''': 0.0,
	}
	A__    :    Dict						=				{
	    '''sld_warmup_steps''': 10,
	    '''sld_guidance_scale''': 1000,
	    '''sld_threshold''': 0.01,
	    '''sld_momentum_scale''': 0.3,
	    '''sld_mom_beta''': 0.4,
	}
	A__    :    Optional[int]						=				{
	    '''sld_warmup_steps''': 7,
	    '''sld_guidance_scale''': 2000,
	    '''sld_threshold''': 0.025,
	    '''sld_momentum_scale''': 0.5,
	    '''sld_mom_beta''': 0.7,
	}
	A__    :    Any						=				{
	    '''sld_warmup_steps''': 0,
	    '''sld_guidance_scale''': 5000,
	    '''sld_threshold''': 1.0,
	    '''sld_momentum_scale''': 0.5,
	    '''sld_mom_beta''': 0.7,
	}







@dataclass
class   SCREAMING_SNAKE_CASE__				(   snake_case_					):
	"""simple docstring"""


	A__    :    Union[List[PIL.Image.Image], np.ndarray]
	A__    :    Optional[List[bool]]
	A__    :    Optional[Union[List[PIL.Image.Image], np.ndarray]]
	A__    :    Optional[str]


if is_transformers_available() and is_torch_available():
					from .pipeline_stable_diffusion_safe import StableDiffusionPipelineSafe
					from .safety_checker import SafeStableDiffusionSafetyChecker










'''simple docstring'''
import json
import os
from typing import List, Optional, Tuple

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


__SCREAMING_SNAKE_CASE :      Tuple             =logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE :      Any             ={
    'vocab_file': 'vocab.json',
    'merges_file': 'merges.txt',
}

__SCREAMING_SNAKE_CASE :      List[str]             ={
    'vocab_file': {
        'microsoft/biogpt': 'https://huggingface.co/microsoft/biogpt/resolve/main/vocab.json',
    },
    'merges_file': {'microsoft/biogpt': 'https://huggingface.co/microsoft/biogpt/resolve/main/merges.txt'},
}

__SCREAMING_SNAKE_CASE :      Optional[int]             ={
    'microsoft/biogpt': 1024,
}



def 		_SCREAMING_SNAKE_CASE							(				lowerCamelCase__   :      int				):


			'''simple docstring'''







			A:      str														=   set()
			A:      Any														=   word[0]
			for char in word[1:]:
						pairs.add((prev_char, char)				)
						A:      Union[str, Any]														=   char
			return pairs


class   SCREAMING_SNAKE_CASE__				(   snake_case_					):
	"""simple docstring"""


	A__    :    Dict						=				VOCAB_FILES_NAMES
	A__    :    Dict						=				PRETRAINED_VOCAB_FILES_MAP
	A__    :    Any						=				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	A__    :    Tuple						=				['''input_ids''', '''attention_mask''']


	def __init__(		self					,   A					,   A					,   A="<unk>"					,   A="<s>"					,   A="</s>"					,   A="</s>"					,   A="<pad>"					,   **A					,   ) ->							Dict:
				super().__init__(
				    bos_token=A					,   eos_token=A					,   sep_token=A					,   unk_token=A					,   pad_token=A					,   **A					,   )

				try:
							import sacremoses
				except ImportError:
							raise ImportError(
							    """You need to install sacremoses to use BioGptTokenizer. """
							    """See https://pypi.org/project/sacremoses/ for installation."""			)

				A:      int														=   """en"""
				A:      List[Any]														=   sacremoses
				# cache of sm.MosesTokenizer instance
				A:      str														=   {}
				A:      Any														=   {}
				with open(A					,   encoding="""utf-8"""			) as vocab_handle:
							A:      int														=   json.load(A			)
				A:      Any														=   {v: k for k, v in self.encoder.items()}
				with open(A					,   encoding="""utf-8"""			) as merges_handle:
							A:      str														=   merges_handle.read().split("""\n"""			)[:-1]
				A:      List[str]														=   [tuple(merge.split()[:2]			) for merge in merges]
				A:      Optional[Any]														=   dict(zip(A					,   range(len(A			)			)			)			)
				A:      int														=   {}


	@property
	def      a__ (		self			) ->							Optional[Any]:
				return len(self.encoder			)


	def      a__ (		self			) ->							Optional[Any]:
				return dict(self.encoder					,   **self.added_tokens_encoder			)


	def      a__ (		self					,   A					,   A			) ->							List[Any]:
				if lang not in self.cache_moses_tokenizer:
							A:      Union[str, Any]														=   self.sm.MosesTokenizer(lang=A			)
							A:      List[str]														=   moses_tokenizer
				return self.cache_moses_tokenizer[lang].tokenize(
				    A					,   aggressive_dash_splits=A					,   return_str=A					,   escape=A			)


	def      a__ (		self					,   A					,   A			) ->							str:
				if lang not in self.cache_moses_detokenizer:
							A:      Optional[int]														=   self.sm.MosesDetokenizer(lang=A			)
							A:      Dict														=   moses_detokenizer
				return self.cache_moses_detokenizer[lang].detokenize(A			)


	def      a__ (		self					,   A			) ->							Optional[int]:
				A:      Union[str, Any]														=   tuple(token[:-1]			) + (token[-1] + """</w>""",)
				if token in self.cache:
							return self.cache[token]
				A:      List[str]														=   get_pairs(A			)

				if not pairs:
							return token + "</w>"

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

										if word[i] == first and i < len(A			) - 1 and word[i + 1] == second:
													new_word.append(first + second			)
													i += 2
										else:
													new_word.append(word[i]			)
													i += 1
							A:      Dict														=   tuple(A			)
							A:      Any														=   new_word
							if len(A			) == 1:
										break
							else:
										A:      Union[str, Any]														=   get_pairs(A			)
				A:      Dict														=   """ """.join(A			)
				if word == "\n  </w>":
							A:      List[Any]														=   """\n</w>"""
				A:      Tuple														=   word
				return word


	def      a__ (		self					,   A					,   A=False			) ->							Any:
				if bypass_tokenizer:
							A:      str														=   text.split()
				else:
							A:      str														=   self.moses_tokenize(A					,   self.lang			)

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

				return split_tokens


	def      a__ (		self					,   A			) ->							Dict:
				return self.encoder.get(A					,   self.encoder.get(self.unk_token			)			)


	def      a__ (		self					,   A			) ->							Any:
				return self.decoder.get(A					,   self.unk_token			)


	def      a__ (		self					,   A			) ->							Tuple:
				A:      Any														=   [t.replace(""" """					,   """"""			).replace("""</w>"""					,   """ """			) for t in tokens]
				A:      Union[str, Any]														=   """""".join(A			).split()
				# detokenize
				A:      List[str]														=   self.moses_detokenize(A					,   self.lang			)
				return text


	def      a__ (		self					,   A					,   A = None			) ->							List[int]:
				if token_ids_a is None:
							return [self.sep_token_id] + token_ids_a
				A:      Tuple														=   [self.sep_token_id]
				return sep + token_ids_a + sep + token_ids_a


	def      a__ (		self					,   A					,   A = None					,   A = False			) ->							List[int]:
				if already_has_special_tokens:
							return super().get_special_tokens_mask(
							    token_ids_a=A					,   token_ids_a=A					,   already_has_special_tokens=A			)
				# no bos used in fairseq
				if token_ids_a is not None:
							return [1] + ([0] * len(A			)) + [1] + ([0] * len(A			))
				return [1] + ([0] * len(A			))


	def      a__ (		self					,   A					,   A = None			) ->							List[int]:
				A:      Any														=   [self.sep_token_id]

				# no bos used in fairseq
				if token_ids_a is None:
							return len(token_ids_a + sep			) * [0]
				return len(token_ids_a + sep			) * [0] + len(token_ids_a + sep			) * [1]


	def      a__ (		self					,   A					,   A = None			) ->							Tuple[str]:
				if not os.path.isdir(A			):
							logger.error(f'Vocabulary path ({save_directory}) should be a directory'			)
							return
				A:      Dict														=   os.path.join(
				    A					,   (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]			)
				A:      Any														=   os.path.join(
				    A					,   (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""merges_file"""]			)

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

				A:      Tuple														=   0
				with open(A					,   """w"""					,   encoding="""utf-8"""			) as writer:
							for bpe_tokens, token_index in sorted(self.bpe_ranks.items()					,   key=lambda A			: kv[1]			):
										if index != token_index:
													logger.warning(
													    f'Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'
													    """ Please check that the tokenizer is not corrupted!"""			)
													A:      Optional[Any]														=   token_index
										writer.write(""" """.join(A			) + """\n"""			)
										index += 1

				return vocab_file, merge_file


	def __getstate__(		self			) ->							List[str]:
				A:      List[str]														=   self.__dict__.copy()
				A:      Any														=   None
				return state





	def __setstate__(		self					,   A			) ->							Optional[int]:
				A:      List[str]														=   d

				try:
							import sacremoses
				except ImportError:
							raise ImportError(
							    """You need to install sacremoses to use XLMTokenizer. """
							    """See https://pypi.org/project/sacremoses/ for installation."""			)

				A:      Dict														=   sacremoses





