


import os
import re
from shutil import copyfile
from typing import List, Optional, Tuple

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


A							:					Optional[Any]     						=     logging.get_logger(__name__)

A							:					Union[str, Any]     						=     {
    'vocab_file': 'vocab.txt',
    'merges_file': 'bpe.codes',
}

A							:					int     						=     {
    'vocab_file': {
        'vinai/phobert-base': 'https://huggingface.co/vinai/phobert-base/resolve/main/vocab.txt',
        'vinai/phobert-large': 'https://huggingface.co/vinai/phobert-large/resolve/main/vocab.txt',
    },
    'merges_file': {
        'vinai/phobert-base': 'https://huggingface.co/vinai/phobert-base/resolve/main/bpe.codes',
        'vinai/phobert-large': 'https://huggingface.co/vinai/phobert-large/resolve/main/bpe.codes',
    },
}

A							:					str     						=     {
    'vinai/phobert-base': 256,
    'vinai/phobert-large': 256,
}





def 	_lowerCAmelCase       (    _lowerCAmelCase )						->	int:


						'''simple docstring'''






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

						__snake_case				=		set(_lowerCAmelCase )
						return pairs






class     UpperCamelCase(  _a   ):
					snake_case_		:				List[str]										=	VOCAB_FILES_NAMES
					snake_case_		:				Optional[int]										=	PRETRAINED_VOCAB_FILES_MAP
					snake_case_		:				List[str]										=	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES


					def __init__(						self	:		Optional[Any]     ,    SCREAMING_SNAKE_CASE	:		str     ,    SCREAMING_SNAKE_CASE	:		Union[str, Any]     ,    SCREAMING_SNAKE_CASE	:		int="<s>"     ,    SCREAMING_SNAKE_CASE	:		Union[str, Any]="</s>"     ,    SCREAMING_SNAKE_CASE	:		List[Any]="</s>"     ,    SCREAMING_SNAKE_CASE	:		Optional[Any]="<s>"     ,    SCREAMING_SNAKE_CASE	:		Any="<unk>"     ,    SCREAMING_SNAKE_CASE	:		Dict="<pad>"     ,    SCREAMING_SNAKE_CASE	:		str="<mask>"     ,    **SCREAMING_SNAKE_CASE	:		List[str]     ,    )			->	Any:




											'''simple docstring'''

											super().__init__(
											    bos_token=SCREAMING_SNAKE_CASE     ,    eos_token=SCREAMING_SNAKE_CASE     ,    unk_token=SCREAMING_SNAKE_CASE     ,    sep_token=SCREAMING_SNAKE_CASE     ,    cls_token=SCREAMING_SNAKE_CASE     ,    pad_token=SCREAMING_SNAKE_CASE     ,    mask_token=SCREAMING_SNAKE_CASE     ,    **SCREAMING_SNAKE_CASE     ,    )

											__snake_case				=		vocab_file
											__snake_case				=		merges_file

											__snake_case				=		{}
											__snake_case				=		0
											__snake_case				=		1
											__snake_case				=		2
											__snake_case				=		3

											self.add_from_file(SCREAMING_SNAKE_CASE						)

											__snake_case				=		{v: k for k, v in self.encoder.items()}

											with open(SCREAMING_SNAKE_CASE     ,    encoding="utf-8"						) as merges_handle:
																	__snake_case				=		merges_handle.read().split("\n"						)[:-1]
											__snake_case				=		[tuple(merge.split()[:-1]						) for merge in merges]
											__snake_case				=		dict(zip(SCREAMING_SNAKE_CASE     ,    range(len(SCREAMING_SNAKE_CASE						)						)						)						)
											__snake_case				=		{}


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Any     ,    SCREAMING_SNAKE_CASE	:		List[int]     ,    SCREAMING_SNAKE_CASE	:		Optional[List[int]] = None						)			->	List[int]:




											'''simple docstring'''

											if token_ids_a is None:
																	return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
											__snake_case				=		[self.cls_token_id]
											__snake_case				=		[self.sep_token_id]
											return cls + token_ids_a + sep + sep + token_ids_a + sep


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Optional[int]     ,    SCREAMING_SNAKE_CASE	:		List[int]     ,    SCREAMING_SNAKE_CASE	:		Optional[List[int]] = None     ,    SCREAMING_SNAKE_CASE	:		bool = False						)			->	List[int]:




											'''simple docstring'''

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

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


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Any     ,    SCREAMING_SNAKE_CASE	:		List[int]     ,    SCREAMING_SNAKE_CASE	:		Optional[List[int]] = None						)			->	List[int]:




											'''simple docstring'''

											__snake_case				=		[self.sep_token_id]
											__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 + sep + token_ids_a + sep						) * [0]


					@property
					def 							SCREAMING_SNAKE_CASE_  (						self	:		Dict						)			->	Optional[Any]:




											'''simple docstring'''

											return len(self.encoder						)


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Any						)			->	List[Any]:




											'''simple docstring'''

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


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Dict     ,    SCREAMING_SNAKE_CASE	:		Any						)			->	Optional[int]:




											'''simple docstring'''

											if token in self.cache:
																	return self.cache[token]
											__snake_case				=		tuple(SCREAMING_SNAKE_CASE						)
											__snake_case				=		tuple(list(word[:-1]						) + [word[-1] + "</w>"]						)
											__snake_case				=		get_pairs(SCREAMING_SNAKE_CASE						)

											if not pairs:
																	return token

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

																							if word[i] == first and i < len(SCREAMING_SNAKE_CASE						) - 1 and word[i + 1] == second:
																													new_word.append(first + second						)
																													i += 2
																							else:
																													new_word.append(word[i]						)
																													i += 1
																	__snake_case				=		tuple(SCREAMING_SNAKE_CASE						)
																	__snake_case				=		new_word
																	if len(SCREAMING_SNAKE_CASE						) == 1:
																							break
																	else:
																							__snake_case				=		get_pairs(SCREAMING_SNAKE_CASE						)
											__snake_case				=		"@@ ".join(SCREAMING_SNAKE_CASE						)
											__snake_case				=		word[:-4]
											__snake_case				=		word
											return word


					def 							SCREAMING_SNAKE_CASE_  (						self	:		List[Any]     ,    SCREAMING_SNAKE_CASE	:		Optional[Any]						)			->	Union[str, Any]:




											'''simple docstring'''

											__snake_case				=		[]

											__snake_case				=		re.findall(R"\S+\n?"     ,    SCREAMING_SNAKE_CASE						)

											for token in words:
																	split_tokens.extend(list(self.bpe(SCREAMING_SNAKE_CASE						).split(" "						)						)						)
											return split_tokens


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Optional[Any]     ,    SCREAMING_SNAKE_CASE	:		Union[str, Any]						)			->	List[str]:




											'''simple docstring'''

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


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Optional[Any]     ,    SCREAMING_SNAKE_CASE	:		Tuple						)			->	Any:




											'''simple docstring'''

											return self.decoder.get(SCREAMING_SNAKE_CASE     ,    self.unk_token						)


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




											'''simple docstring'''

											__snake_case				=		" ".join(SCREAMING_SNAKE_CASE						).replace("@@ "     ,    ""						).strip()
											return out_string


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Optional[int]     ,    SCREAMING_SNAKE_CASE	:		str     ,    SCREAMING_SNAKE_CASE	:		Optional[str] = None						)			->	Tuple[str]:




											'''simple docstring'''

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

											if os.path.abspath(self.vocab_file						) != os.path.abspath(SCREAMING_SNAKE_CASE						):
																	copyfile(self.vocab_file     ,    SCREAMING_SNAKE_CASE						)

											if os.path.abspath(self.merges_file						) != os.path.abspath(SCREAMING_SNAKE_CASE						):
																	copyfile(self.merges_file     ,    SCREAMING_SNAKE_CASE						)

											return out_vocab_file, out_merge_file






					def 							SCREAMING_SNAKE_CASE_  (						self	:		Any     ,    SCREAMING_SNAKE_CASE	:		List[Any]						)			->	Optional[int]:




											'''simple docstring'''

											if isinstance(SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE						):
																	try:
																							with open(SCREAMING_SNAKE_CASE     ,    "r"     ,    encoding="utf-8"						) as fd:
																													self.add_from_file(SCREAMING_SNAKE_CASE						)
																	except FileNotFoundError as fnfe:
																							raise fnfe
																	except UnicodeError:
																							raise Exception(f'''Incorrect encoding detected in {f}, please rebuild the dataset'''						)
																	return

											__snake_case				=		f.readlines()
											for lineTmp in lines:
																	__snake_case				=		lineTmp.strip()
																	__snake_case				=		line.rfind(" "						)
																	if idx == -1:
																							raise ValueError("Incorrect dictionary format, expected '<token> <cnt>'"						)
																	__snake_case				=		line[:idx]
																	__snake_case				=		len(self.encoder						)









import torch
from torch import nn
from transformers import CLIPPreTrainedModel, CLIPVisionModel

from ...models.attention import BasicTransformerBlock
from ...utils import logging


A							:					int     						=     logging.get_logger(__name__)  # pylint: disable=invalid-name






class     UpperCamelCase(  _a   ):


					def __init__(						self	:		Optional[int]     ,    SCREAMING_SNAKE_CASE	:		Optional[int]     ,    SCREAMING_SNAKE_CASE	:		Dict=7_6_8						)			->	int:




											'''simple docstring'''

											super().__init__(SCREAMING_SNAKE_CASE						)
											__snake_case				=		proj_size

											__snake_case				=		CLIPVisionModel(SCREAMING_SNAKE_CASE						)
											__snake_case				=		PaintByExampleMapper(SCREAMING_SNAKE_CASE						)
											__snake_case				=		nn.LayerNorm(config.hidden_size						)
											__snake_case				=		nn.Linear(config.hidden_size     ,    self.proj_size						)

											# uncondition for scaling
											__snake_case				=		nn.Parameter(torch.randn((1, 1, self.proj_size)						)						)






					def 							SCREAMING_SNAKE_CASE_  (						self	:		Union[str, Any]     ,    SCREAMING_SNAKE_CASE	:		int     ,    SCREAMING_SNAKE_CASE	:		Optional[int]=False						)			->	Dict:




											'''simple docstring'''

											__snake_case				=		self.model(pixel_values=SCREAMING_SNAKE_CASE						)
											__snake_case				=		clip_output.pooler_output
											__snake_case				=		self.mapper(latent_states[:, None]						)
											__snake_case				=		self.final_layer_norm(SCREAMING_SNAKE_CASE						)
											__snake_case				=		self.proj_out(SCREAMING_SNAKE_CASE						)
											if return_uncond_vector:
																	return latent_states, self.uncond_vector

											return latent_states






class     UpperCamelCase(  nn.Module   ):


					def __init__(						self	:		List[Any]     ,    SCREAMING_SNAKE_CASE	:		List[str]						)			->	List[Any]:




											'''simple docstring'''

											super().__init__()
											__snake_case				=		(config.num_hidden_layers + 1) // 5
											__snake_case				=		config.hidden_size
											__snake_case				=		1
											__snake_case				=		nn.ModuleList(
											    [
											        BasicTransformerBlock(SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE     ,    activation_fn="gelu"     ,    attention_bias=SCREAMING_SNAKE_CASE						)
											        for _ in range(SCREAMING_SNAKE_CASE						)
											    ]						)






					def 							SCREAMING_SNAKE_CASE_  (						self	:		Union[str, Any]     ,    SCREAMING_SNAKE_CASE	:		str						)			->	Optional[int]:




											'''simple docstring'''

											for block in self.blocks:
																	__snake_case				=		block(SCREAMING_SNAKE_CASE						)

											return hidden_states





