


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

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


lowerCamelCase__							:						Union[str, Any]							     =   logging.get_logger(__name__)

lowerCamelCase__							:						int							     =   {
    """vocab_file""": """vocab.txt""",
    """merges_file""": """bpe.codes""",
}

lowerCamelCase__							:						Optional[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""",
    },
}

lowerCamelCase__							:						List[str]							     =   {
    """vinai/phobert-base""": 2_5_6,
    """vinai/phobert-large""": 2_5_6,
}







def SCREAMING_SNAKE_CASE	(			__lowerCAmelCase				)    ->				Dict:
  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 		__magic_name__						(snake_case_				):





     '''simple docstring'''



     __lowercase				:							Optional[int]	   =       VOCAB_FILES_NAMES
     __lowercase				:							Optional[Any]	   =       PRETRAINED_VOCAB_FILES_MAP
     __lowercase				:							List[Any]	   =       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES

     def __init__(					self:Union[str, Any]     ,		_a:Optional[int]     ,		_a:Tuple     ,		_a:Optional[Any]="<s>"     ,		_a:Optional[int]="</s>"     ,		_a:Tuple="</s>"     ,		_a:Tuple="<s>"     ,		_a:List[Any]="<unk>"     ,		_a:Tuple="<pad>"     ,		_a:Dict="<mask>"     ,		**_a:Tuple     ,		):
       super().__init__(
           bos_token=_a     ,		eos_token=_a     ,		unk_token=_a     ,		sep_token=_a     ,		cls_token=_a     ,		pad_token=_a     ,		mask_token=_a     ,		**_a     ,		)

       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(_a   )

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

       with open(_a     ,		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(_a     ,		range(len(_a   )   )   )   )
       snake_case__						= {}

     def 	SCREAMING_SNAKE_CASE__						(					self:int     ,		_a:List[int]     ,		_a:Optional[List[int]] = None   ):

       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:List[str]     ,		_a:List[int]     ,		_a:Optional[List[int]] = None     ,		_a:bool = False   ):

       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, 1] + ([0] * len(_a   )) + [1]

     def 	SCREAMING_SNAKE_CASE__						(					self:str     ,		_a:List[int]     ,		_a:Optional[List[int]] = None   ):
       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:List[str]   ):
       return len(self.encoder   )

     def 	SCREAMING_SNAKE_CASE__						(					self:List[str]   ):
       return dict(self.encoder     ,		**self.added_tokens_encoder   )

     def 	SCREAMING_SNAKE_CASE__						(					self:str     ,		_a:str   ):
       if token in self.cache:
         return self.cache[token]
       snake_case__						= tuple(_a   )
       snake_case__						= tuple(list(word[:-1]   ) + [word[-1] + '''</w>''']   )
       snake_case__						= get_pairs(_a   )

       if not pairs:
         return token

       while True:
         snake_case__						= min(_a     ,		key=lambda _a   : self.bpe_ranks.get(_a     ,		float('''inf'''   )   )   )
         if bigram not in self.bpe_ranks:
           break
         snake_case__				,				snake_case__						= bigram
         snake_case__						= []
         snake_case__						= 0
         while i < len(_a   ):
           try:
             snake_case__						= word.index(_a     ,		_a   )
           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(_a   ) - 1 and word[i + 1] == second:
             new_word.append(first + second   )
             i += 2
           else:
             new_word.append(word[i]   )
             i += 1
         snake_case__						= tuple(_a   )
         snake_case__						= new_word
         if len(_a   ) == 1:
           break
         else:
           snake_case__						= get_pairs(_a   )
       snake_case__						= '''@@ '''.join(_a   )
       snake_case__						= word[:-4]
       snake_case__						= word
       return word

     def 	SCREAMING_SNAKE_CASE__						(					self:List[str]     ,		_a:List[str]   ):
       snake_case__						= []

       snake_case__						= re.findall(r'''\S+\n?'''     ,		_a   )

       for token in words:
         split_tokens.extend(list(self.bpe(_a   ).split(''' '''   )   )   )
       return split_tokens

     def 	SCREAMING_SNAKE_CASE__						(					self:List[str]     ,		_a:Dict   ):
       return self.encoder.get(_a     ,		self.encoder.get(self.unk_token   )   )

     def 	SCREAMING_SNAKE_CASE__						(					self:Tuple     ,		_a:Union[str, Any]   ):
       return self.decoder.get(_a     ,		self.unk_token   )

     def 	SCREAMING_SNAKE_CASE__						(					self:int     ,		_a:str   ):
       snake_case__						= ''' '''.join(_a   ).replace('''@@ '''     ,		''''''   ).strip()
       return out_string

     def 	SCREAMING_SNAKE_CASE__						(					self:Optional[Any]     ,		_a:str     ,		_a:Optional[str] = None   ):
       if not os.path.isdir(_a   ):
         logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""   )
         return
       snake_case__						= os.path.join(
           _a     ,		(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']   )
       snake_case__						= os.path.join(
           _a     ,		(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''merges_file''']   )

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

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

       return out_vocab_file, out_merge_file







     def 	SCREAMING_SNAKE_CASE__						(					self:Any     ,		_a:str   ):
       if isinstance(_a     ,		_a   ):
         try:
           with open(_a     ,		'''r'''     ,		encoding='''utf-8'''   ) as fd:
             self.add_from_file(_a   )
         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 random
import unittest

import torch

from diffusers import IFImgaImgSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin
@skip_mps
class 		__magic_name__						(snake_case_   ,snake_case_   ,unittest.TestCase				):





     '''simple docstring'''



     __lowercase				:							str	   =       IFImgaImgSuperResolutionPipeline
     __lowercase				:							Optional[Any]	   =       TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'width', 'height'}
     __lowercase				:							int	   =       TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({'original_image'}				)
     __lowercase				:							List[str]	   =       PipelineTesterMixin.required_optional_params - {'latents'}

     def 	SCREAMING_SNAKE_CASE__						(					self:Dict   ):
       return self._get_superresolution_dummy_components()

     def 	SCREAMING_SNAKE_CASE__						(					self:Dict     ,		_a:int     ,		_a:Optional[Any]=0   ):
       if str(_a   ).startswith('''mps'''   ):
         snake_case__						= torch.manual_seed(_a   )
       else:
         snake_case__						= torch.Generator(device=_a   ).manual_seed(_a   )

       snake_case__						= floats_tensor((1, 3, 32, 32)     ,		rng=random.Random(_a   )   ).to(_a   )
       snake_case__						= floats_tensor((1, 3, 16, 16)     ,		rng=random.Random(_a   )   ).to(_a   )

       snake_case__						= {
           '''prompt''': '''A painting of a squirrel eating a burger''',
           '''image''': image,
           '''original_image''': original_image,
           '''generator''': generator,
           '''num_inference_steps''': 2,
           '''output_type''': '''numpy''',
       }

       return inputs

     @unittest.skipIf(
         torch_device != '''cuda''' or not is_xformers_available()     ,		reason='''XFormers attention is only available with CUDA and `xformers` installed'''     ,		)
     def 	SCREAMING_SNAKE_CASE__						(					self:Union[str, Any]   ):
       self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3   )

     def 	SCREAMING_SNAKE_CASE__						(					self:Tuple   ):
       self._test_save_load_optional_components()

     @unittest.skipIf(torch_device != '''cuda'''     ,		reason='''float16 requires CUDA'''   )
     def 	SCREAMING_SNAKE_CASE__						(					self:Dict   ):
       # Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
       super().test_save_load_floataa(expected_max_diff=1e-1   )

     def 	SCREAMING_SNAKE_CASE__						(					self:Optional[int]   ):
       self._test_attention_slicing_forward_pass(expected_max_diff=1e-2   )

     def 	SCREAMING_SNAKE_CASE__						(					self:str   ):
       self._test_save_load_local()







     def 	SCREAMING_SNAKE_CASE__						(					self:Union[str, Any]   ):
       self._test_inference_batch_single_identical(
           expected_max_diff=1e-2     ,		)

