





import os
import pathlib
import tempfile
import uuid

import numpy as np

from ..utils import is_soundfile_availble, is_torch_available, is_vision_available, logging


snake_case			:      Any 							=   logging.get_logger(__name__)

if is_vision_available():
							import PIL.Image
							from PIL import Image
							from PIL.Image import Image as ImageType
else:
							snake_case			:      Tuple 							=   object

if is_torch_available():
							import torch

if is_soundfile_availble():
							import soundfile as sf



class    __lowercase  :

							"""simple docstring"""


							def __init__(    self				, A_	)->	List[Any]:
											_SCREAMING_SNAKE_CASE      = value





							def __str__(    self	)->	List[str]:
											return self.to_string()





							def 		__magic_name__			(    self	)->	int:
											logger.error(
											    'This is a raw AgentType of unknown type. Display in notebooks and string conversion will be unreliable'	)
											return self._value





							def 		__magic_name__			(    self	)->	str:
											logger.error(
											    'This is a raw AgentType of unknown type. Display in notebooks and string conversion will be unreliable'	)
											return str(self._value	)







class    __lowercase  (       __UpperCAmelCase      ,     __UpperCAmelCase							):

							"""simple docstring"""


							def 		__magic_name__			(    self	)->	Optional[int]:
											return self._value





							def 		__magic_name__			(    self	)->	Dict:
											return self._value







class    __lowercase  (       __UpperCAmelCase      ,     __UpperCAmelCase							):

							"""simple docstring"""


							def __init__(    self				, A_	)->	Optional[Any]:
											super().__init__(lowerCamelCase__	)

											if not is_vision_available():
															raise ImportError('PIL must be installed in order to handle images.'	)

											_SCREAMING_SNAKE_CASE      = None
											_SCREAMING_SNAKE_CASE      = None
											_SCREAMING_SNAKE_CASE      = None

											if isinstance(lowerCamelCase__				, lowerCamelCase__	):
															_SCREAMING_SNAKE_CASE      = value
											elif isinstance(lowerCamelCase__				, (str, pathlib.Path)	):
															_SCREAMING_SNAKE_CASE      = value
											elif isinstance(lowerCamelCase__				, torch.Tensor	):
															_SCREAMING_SNAKE_CASE      = value
											else:
															raise ValueError(F'''Unsupported type for {self.__class__.__name__}: {type(lowerCamelCase__	)}'''	)





							def 		__magic_name__			(    self				, A_=None				, A_=None	)->	List[Any]:
											from IPython.display import Image, display

											display(Image(self.to_string()	)	)





							def 		__magic_name__			(    self	)->	Union[str, Any]:



											if self._raw is not None:
															return self._raw

											if self._path is not None:
															_SCREAMING_SNAKE_CASE      = Image.open(self._path	)
															return self._raw





							def 		__magic_name__			(    self	)->	Optional[Any]:



											if self._path is not None:
															return self._path

											if self._raw is not None:
															_SCREAMING_SNAKE_CASE      = tempfile.mkdtemp()
															_SCREAMING_SNAKE_CASE      = os.path.join(lowerCamelCase__				, str(uuid.uuida()	) + '.png'	)
															self._raw.save(self._path	)

															return self._path

											if self._tensor is not None:
															_SCREAMING_SNAKE_CASE      = self._tensor.cpu().detach().numpy()

															# There is likely simpler than load into image into save
															_SCREAMING_SNAKE_CASE      = Image.fromarray((array * 255).astype(np.uinta	)	)

															_SCREAMING_SNAKE_CASE      = tempfile.mkdtemp()
															_SCREAMING_SNAKE_CASE      = os.path.join(lowerCamelCase__				, str(uuid.uuida()	) + '.png'	)

															img.save(self._path	)

															return self._path







class    __lowercase  (       __UpperCAmelCase							):

							"""simple docstring"""


							def __init__(    self				, A_				, A_=16000	)->	Union[str, Any]:
											super().__init__(lowerCamelCase__	)

											if not is_soundfile_availble():
															raise ImportError('soundfile must be installed in order to handle audio.'	)

											_SCREAMING_SNAKE_CASE      = None
											_SCREAMING_SNAKE_CASE      = None

											_SCREAMING_SNAKE_CASE      = samplerate

											if isinstance(lowerCamelCase__				, (str, pathlib.Path)	):
															_SCREAMING_SNAKE_CASE      = value
											elif isinstance(lowerCamelCase__				, torch.Tensor	):
															_SCREAMING_SNAKE_CASE      = value
											else:
															raise ValueError(F'''Unsupported audio type: {type(lowerCamelCase__	)}'''	)





							def 		__magic_name__			(    self				, A_=None				, A_=None	)->	str:
											from IPython.display import Audio, display

											display(Audio(self.to_string()				, rate=self.samplerate	)	)





							def 		__magic_name__			(    self	)->	int:



											if self._tensor is not None:
															return self._tensor

											if self._path is not None:
															_SCREAMING_SNAKE_CASE      ,      _SCREAMING_SNAKE_CASE      = sf.read(self._path	)
															_SCREAMING_SNAKE_CASE      = torch.tensor(lowerCamelCase__	)
															return self._tensor





							def 		__magic_name__			(    self	)->	Dict:



											if self._path is not None:
															return self._path

											if self._tensor is not None:
															_SCREAMING_SNAKE_CASE      = tempfile.mkdtemp()
															_SCREAMING_SNAKE_CASE      = os.path.join(lowerCamelCase__				, str(uuid.uuida()	) + '.wav'	)
															sf.write(self._path				, self._tensor				, samplerate=self.samplerate	)
															return self._path







snake_case			:      List[Any] 							=   {'text': AgentText, 'image': AgentImage, 'audio': AgentAudio}
snake_case			:      List[Any] 							=   {str: AgentText}

if is_vision_available():
							snake_case			:      Optional[int] 							=   AgentImage


def 				SCREAMING_SNAKE_CASE (     *UpperCAmelCase__   ,**UpperCAmelCase__							):

				"""simple docstring"""






				_SCREAMING_SNAKE_CASE      = [(arg.to_raw() if isinstance(lowercase__   ,lowercase__							) else arg) for arg in args]
				_SCREAMING_SNAKE_CASE      = {k: (v.to_raw() if isinstance(lowercase__   ,lowercase__							) else v) for k, v in kwargs.items()}
				return args, kwargs



def 				SCREAMING_SNAKE_CASE (     UpperCAmelCase__   ,UpperCAmelCase__=None							):

				"""simple docstring"""






				if isinstance(lowercase__   ,lowercase__							):
								_SCREAMING_SNAKE_CASE      = {}
								for i, (k, v) in enumerate(outputs.items()							):
												if output_types is not None:
																# If the class has defined outputs, we can map directly according to the class definition
																if output_types[i] in AGENT_TYPE_MAPPING:
																				_SCREAMING_SNAKE_CASE      = AGENT_TYPE_MAPPING[output_types[i]](lowercase__							)
																else:
																				_SCREAMING_SNAKE_CASE      = AgentType(lowercase__							)

												else:
																# If the class does not have defined output, then we map according to the type
																for _k, _v in INSTANCE_TYPE_MAPPING.items():
																				if isinstance(lowercase__   ,_k							):
																								_SCREAMING_SNAKE_CASE      = _v(lowercase__							)
																if k not in decoded_outputs:
																				_SCREAMING_SNAKE_CASE      = AgentType[v]

				elif isinstance(lowercase__   ,(list, tuple)							):
								_SCREAMING_SNAKE_CASE      = type(lowercase__							)()
								for i, v in enumerate(lowercase__							):
												if output_types is not None:
																# If the class has defined outputs, we can map directly according to the class definition
																if output_types[i] in AGENT_TYPE_MAPPING:
																				decoded_outputs.append(AGENT_TYPE_MAPPING[output_types[i]](lowercase__							)							)
																else:
																				decoded_outputs.append(AgentType(lowercase__							)							)
												else:
																# If the class does not have defined output, then we map according to the type
																_SCREAMING_SNAKE_CASE      = False
																for _k, _v in INSTANCE_TYPE_MAPPING.items():
																				if isinstance(lowercase__   ,_k							):
																								decoded_outputs.append(_v(lowercase__							)							)
																								_SCREAMING_SNAKE_CASE      = True

																if not found:
																				decoded_outputs.append(AgentType(lowercase__							)							)

				else:
								if output_types[0] in AGENT_TYPE_MAPPING:
												# If the class has defined outputs, we can map directly according to the class definition
												_SCREAMING_SNAKE_CASE      = AGENT_TYPE_MAPPING[output_types[0]](lowercase__							)

								else:
												# If the class does not have defined output, then we map according to the type
												for _k, _v in INSTANCE_TYPE_MAPPING.items():
																if isinstance(lowercase__   ,_k							):
																				return _v(lowercase__							)
												return AgentType(lowercase__							)

				return decoded_outputs




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

import regex as re

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


if TYPE_CHECKING:
							from transformers.pipelines.conversational import Conversation

snake_case			:      Any 							=   logging.get_logger(__name__)

snake_case			:      str 							=   {'vocab_file': 'vocab.json', 'merges_file': 'merges.txt'}

snake_case			:      Optional[int] 							=   {
    'vocab_file': {
        'microsoft/deberta-base': 'https://huggingface.co/microsoft/deberta-base/resolve/main/vocab.json',
        'microsoft/deberta-large': 'https://huggingface.co/microsoft/deberta-large/resolve/main/vocab.json',
        'microsoft/deberta-xlarge': 'https://huggingface.co/microsoft/deberta-xlarge/resolve/main/vocab.json',
        'microsoft/deberta-base-mnli': 'https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/vocab.json',
        'microsoft/deberta-large-mnli': 'https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/vocab.json',
        'microsoft/deberta-xlarge-mnli': (
            'https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/vocab.json'
        ),
    },
    'merges_file': {
        'microsoft/deberta-base': 'https://huggingface.co/microsoft/deberta-base/resolve/main/merges.txt',
        'microsoft/deberta-large': 'https://huggingface.co/microsoft/deberta-large/resolve/main/merges.txt',
        'microsoft/deberta-xlarge': 'https://huggingface.co/microsoft/deberta-xlarge/resolve/main/merges.txt',
        'microsoft/deberta-base-mnli': 'https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/merges.txt',
        'microsoft/deberta-large-mnli': 'https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/merges.txt',
        'microsoft/deberta-xlarge-mnli': (
            'https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/merges.txt'
        ),
    },
}

snake_case			:      Tuple 							=   {
    'microsoft/deberta-base': 5_12,
    'microsoft/deberta-large': 5_12,
    'microsoft/deberta-xlarge': 5_12,
    'microsoft/deberta-base-mnli': 5_12,
    'microsoft/deberta-large-mnli': 5_12,
    'microsoft/deberta-xlarge-mnli': 5_12,
}

snake_case			:      Any 							=   {
    'microsoft/deberta-base': {'do_lower_case': False},
    'microsoft/deberta-large': {'do_lower_case': False},
}


def 				SCREAMING_SNAKE_CASE (     ):

				"""simple docstring"""






				_SCREAMING_SNAKE_CASE      = (
				    list(range(ord('!'							)   ,ord('~'							) + 1							)							) + list(range(ord('¡'							)   ,ord('¬'							) + 1							)							) + list(range(ord('®'							)   ,ord('ÿ'							) + 1							)							)
				)
				_SCREAMING_SNAKE_CASE      = bs[:]
				_SCREAMING_SNAKE_CASE      = 0
				for b in range(2**8							):
								if b not in bs:
												bs.append(UpperCAmelCase__							)
												cs.append(2**8 + n							)
												n += 1
				_SCREAMING_SNAKE_CASE      = [chr(UpperCAmelCase__							) for n in cs]
				return dict(zip(UpperCAmelCase__   ,UpperCAmelCase__							)							)


def 				SCREAMING_SNAKE_CASE (     UpperCAmelCase__							):

				"""simple docstring"""






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



class    __lowercase  (       UpperCamelCase							):

							"""simple docstring"""


							SCREAMING_SNAKE_CASE   :					int        =						VOCAB_FILES_NAMES
							SCREAMING_SNAKE_CASE   :					List[Any]        =						PRETRAINED_VOCAB_FILES_MAP
							SCREAMING_SNAKE_CASE   :					Any        =						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							SCREAMING_SNAKE_CASE   :					Optional[Any]        =						["input_ids", "attention_mask", "token_type_ids"]





							def __init__(    self				, A_				, A_				, A_="replace"				, A_="[CLS]"				, A_="[SEP]"				, A_="[SEP]"				, A_="[CLS]"				, A_="[UNK]"				, A_="[PAD]"				, A_="[MASK]"				, A_=False				, A_=False				, **A_				, )->	List[str]:
											_SCREAMING_SNAKE_CASE      = AddedToken(A_				, lstrip=A_				, rstrip=A_	) if isinstance(A_				, A_	) else bos_token
											_SCREAMING_SNAKE_CASE      = AddedToken(A_				, lstrip=A_				, rstrip=A_	) if isinstance(A_				, A_	) else eos_token
											_SCREAMING_SNAKE_CASE      = AddedToken(A_				, lstrip=A_				, rstrip=A_	) if isinstance(A_				, A_	) else sep_token
											_SCREAMING_SNAKE_CASE      = AddedToken(A_				, lstrip=A_				, rstrip=A_	) if isinstance(A_				, A_	) else cls_token
											_SCREAMING_SNAKE_CASE      = AddedToken(A_				, lstrip=A_				, rstrip=A_	) if isinstance(A_				, A_	) else unk_token
											_SCREAMING_SNAKE_CASE      = AddedToken(A_				, lstrip=A_				, rstrip=A_	) if isinstance(A_				, A_	) else pad_token

											# Mask token behave like a normal word, i.e. include the space before it
											_SCREAMING_SNAKE_CASE      = AddedToken(A_				, lstrip=A_				, rstrip=A_	) if isinstance(A_				, A_	) else mask_token

											super().__init__(
											    errors=A_				, bos_token=A_				, eos_token=A_				, unk_token=A_				, sep_token=A_				, cls_token=A_				, pad_token=A_				, mask_token=A_				, add_prefix_space=A_				, add_bos_token=A_				, **A_				, )
											_SCREAMING_SNAKE_CASE      = add_bos_token

											with open(A_				, encoding='utf-8'	) as vocab_handle:
															_SCREAMING_SNAKE_CASE      = json.load(A_	)
											_SCREAMING_SNAKE_CASE      = {v: k for k, v in self.encoder.items()}
											_SCREAMING_SNAKE_CASE      = errors  # how to handle errors in decoding
											_SCREAMING_SNAKE_CASE      = bytes_to_unicode()
											_SCREAMING_SNAKE_CASE      = {v: k for k, v in self.byte_encoder.items()}
											with open(A_				, encoding='utf-8'	) as merges_handle:
															_SCREAMING_SNAKE_CASE      = merges_handle.read().split('\n'	)[1:-1]
											_SCREAMING_SNAKE_CASE      = [tuple(merge.split()	) for merge in bpe_merges]
											_SCREAMING_SNAKE_CASE      = dict(zip(A_				, range(len(A_	)	)	)	)
											_SCREAMING_SNAKE_CASE      = {}
											_SCREAMING_SNAKE_CASE      = add_prefix_space

											# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
											_SCREAMING_SNAKE_CASE      = re.compile(r'\'s|\'t|\'re|\'ve|\'m|\'ll|\'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+'	)





							@property
							# Copied from transformers.models.gpt2.tokenization_gpt2.GPT2Tokenizer.vocab_size
							def 		__magic_name__			(    self	)->	int:
											return len(self.encoder	)





							def 		__magic_name__			(    self	)->	Tuple:
											return dict(self.encoder				, **self.added_tokens_encoder	)





							def 		__magic_name__			(    self				, A_	)->	Optional[int]:
											if token in self.cache:
															return self.cache[token]
											_SCREAMING_SNAKE_CASE      = tuple(A_	)
											_SCREAMING_SNAKE_CASE      = get_pairs(A_	)

											if not pairs:
															return token

											while True:
															_SCREAMING_SNAKE_CASE      = min(A_				, key=lambda A_	: self.bpe_ranks.get(A_				, float('inf'	)	)	)
															if bigram not in self.bpe_ranks:
																			break
															_SCREAMING_SNAKE_CASE      ,      _SCREAMING_SNAKE_CASE      = bigram
															_SCREAMING_SNAKE_CASE      = []
															_SCREAMING_SNAKE_CASE      = 0
															while i < len(A_	):
																			try:
																							_SCREAMING_SNAKE_CASE      = word.index(A_				, A_	)
																			except ValueError:
																							new_word.extend(word[i:]	)
																							break
																			else:
																							new_word.extend(word[i:j]	)
																							_SCREAMING_SNAKE_CASE      = 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
															_SCREAMING_SNAKE_CASE      = tuple(A_	)
															_SCREAMING_SNAKE_CASE      = new_word
															if len(A_	) == 1:
																			break
															else:
																			_SCREAMING_SNAKE_CASE      = get_pairs(A_	)
											_SCREAMING_SNAKE_CASE      = ' '.join(A_	)
											_SCREAMING_SNAKE_CASE      = word
											return word





							def 		__magic_name__			(    self				, A_				, A_ = None	)->	List[int]:
											if token_ids_a is None:
															return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
											_SCREAMING_SNAKE_CASE      = [self.cls_token_id]
											_SCREAMING_SNAKE_CASE      = [self.sep_token_id]
											return cls + token_ids_a + sep + token_ids_a + sep





							def 		__magic_name__			(    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_	)

											if token_ids_a is None:
															return [1] + ([0] * len(A_	)) + [1]
											return [1] + ([0] * len(A_	)) + [1] + ([0] * len(A_	)) + [1]





							def 		__magic_name__			(    self				, A_				, A_ = None	)->	List[int]:
											_SCREAMING_SNAKE_CASE      = [self.sep_token_id]
											_SCREAMING_SNAKE_CASE      = [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 		__magic_name__			(    self				, A_	)->	Dict:
											_SCREAMING_SNAKE_CASE      = []
											for token in re.findall(self.pat				, A_	):
															_SCREAMING_SNAKE_CASE      = ''.join(
															    self.byte_encoder[b] for b in token.encode('utf-8'	)	)  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
															bpe_tokens.extend(bpe_token for bpe_token in self.bpe(A_	).split(' '	)	)
											return bpe_tokens





							def 		__magic_name__			(    self				, A_	)->	Optional[Any]:
											return self.encoder.get(A_				, self.encoder.get(self.unk_token	)	)





							def 		__magic_name__			(    self				, A_	)->	Dict:
											return self.decoder.get(A_	)





							def 		__magic_name__			(    self				, A_	)->	Dict:
											_SCREAMING_SNAKE_CASE      = ''.join(A_	)
											_SCREAMING_SNAKE_CASE      = bytearray([self.byte_decoder[c] for c in text]	).decode('utf-8'				, errors=self.errors	)
											return text





							def 		__magic_name__			(    self				, A_				, A_ = None	)->	Tuple[str]:
											if not os.path.isdir(A_	):
															logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''	)
															return
											_SCREAMING_SNAKE_CASE      = os.path.join(
											    A_				, (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']	)
											_SCREAMING_SNAKE_CASE      = 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'	)

											_SCREAMING_SNAKE_CASE      = 0
											with open(A_				, 'w'				, encoding='utf-8'	) as writer:
															writer.write('#version: 0.2\n'	)
															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!'	)
																							_SCREAMING_SNAKE_CASE      = token_index
																			writer.write(' '.join(A_	) + '\n'	)
																			index += 1

											return vocab_file, merge_file





							def 		__magic_name__			(    self				, A_				, A_=False				, **A_	)->	str:
											_SCREAMING_SNAKE_CASE      = kwargs.pop('add_prefix_space'				, self.add_prefix_space	)
											if (is_split_into_words or add_prefix_space) and (len(A_	) > 0 and not text[0].isspace()):
															_SCREAMING_SNAKE_CASE      = ' ' + text
											return (text, kwargs)





							def 		__magic_name__			(    self				, A_	)->	List[int]:
											_SCREAMING_SNAKE_CASE      = []
											for is_user, text in conversation.iter_texts():
															input_ids.extend(self.encode(A_				, add_special_tokens=A_	) + [self.eos_token_id]	)
											if len(A_	) > self.model_max_length:
															_SCREAMING_SNAKE_CASE      = input_ids[-self.model_max_length :]
											return input_ids
