



import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bart import BartTokenizer


a__   :					Optional[int] 						=      logging.get_logger(__name__)


a__   :					int 						=      {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}

# See all BART models at https://huggingface.co/models?filter=bart
a__   :					Dict 						=      {
    "vocab_file": {
        "facebook/bart-base": "https://huggingface.co/facebook/bart-base/resolve/main/vocab.json",
        "facebook/bart-large": "https://huggingface.co/facebook/bart-large/resolve/main/vocab.json",
        "facebook/bart-large-mnli": "https://huggingface.co/facebook/bart-large-mnli/resolve/main/vocab.json",
        "facebook/bart-large-cnn": "https://huggingface.co/facebook/bart-large-cnn/resolve/main/vocab.json",
        "facebook/bart-large-xsum": "https://huggingface.co/facebook/bart-large-xsum/resolve/main/vocab.json",
        "yjernite/bart_eli5": "https://huggingface.co/yjernite/bart_eli5/resolve/main/vocab.json",
    },
    "merges_file": {
        "facebook/bart-base": "https://huggingface.co/facebook/bart-base/resolve/main/merges.txt",
        "facebook/bart-large": "https://huggingface.co/facebook/bart-large/resolve/main/merges.txt",
        "facebook/bart-large-mnli": "https://huggingface.co/facebook/bart-large-mnli/resolve/main/merges.txt",
        "facebook/bart-large-cnn": "https://huggingface.co/facebook/bart-large-cnn/resolve/main/merges.txt",
        "facebook/bart-large-xsum": "https://huggingface.co/facebook/bart-large-xsum/resolve/main/merges.txt",
        "yjernite/bart_eli5": "https://huggingface.co/yjernite/bart_eli5/resolve/main/merges.txt",
    },
    "tokenizer_file": {
        "facebook/bart-base": "https://huggingface.co/facebook/bart-base/resolve/main/tokenizer.json",
        "facebook/bart-large": "https://huggingface.co/facebook/bart-large/resolve/main/tokenizer.json",
        "facebook/bart-large-mnli": "https://huggingface.co/facebook/bart-large-mnli/resolve/main/tokenizer.json",
        "facebook/bart-large-cnn": "https://huggingface.co/facebook/bart-large-cnn/resolve/main/tokenizer.json",
        "facebook/bart-large-xsum": "https://huggingface.co/facebook/bart-large-xsum/resolve/main/tokenizer.json",
        "yjernite/bart_eli5": "https://huggingface.co/yjernite/bart_eli5/resolve/main/tokenizer.json",
    },
}

a__   :					Any 						=      {
    "facebook/bart-base": 10_24,
    "facebook/bart-large": 10_24,
    "facebook/bart-large-mnli": 10_24,
    "facebook/bart-large-cnn": 10_24,
    "facebook/bart-large-xsum": 10_24,
    "yjernite/bart_eli5": 10_24,
}



class    UpperCAmelCase__(				lowerCamelCase						):

   '''simple docstring'''




   A     :	Dict												=     VOCAB_FILES_NAMES
   A     :	int												=     PRETRAINED_VOCAB_FILES_MAP
   A     :	Any												=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   A     :	Optional[int]												=     ["input_ids", "attention_mask"]
   A     :	int												=     BartTokenizer


   def __init__(    self	:      Dict      ,					lowerCAmelCase	:      Tuple=None      ,					lowerCAmelCase	:      Any=None      ,					lowerCAmelCase	:      int=None      ,					lowerCAmelCase	:      List[Any]="replace"      ,					lowerCAmelCase	:      int="<s>"      ,					lowerCAmelCase	:      Dict="</s>"      ,					lowerCAmelCase	:      Optional[int]="</s>"      ,					lowerCAmelCase	:      List[str]="<s>"      ,					lowerCAmelCase	:      Dict="<unk>"      ,					lowerCAmelCase	:      Optional[int]="<pad>"      ,					lowerCAmelCase	:      List[str]="<mask>"      ,					lowerCAmelCase	:      Any=False      ,					lowerCAmelCase	:      Optional[int]=True      ,					**lowerCAmelCase	:      int      ,					)						->					int:




         """simple docstring"""

         super().__init__(
             lowerCAmelCase      ,					lowerCAmelCase      ,					tokenizer_file=lowerCAmelCase      ,					errors=lowerCAmelCase      ,					bos_token=lowerCAmelCase      ,					eos_token=lowerCAmelCase      ,					sep_token=lowerCAmelCase      ,					cls_token=lowerCAmelCase      ,					unk_token=lowerCAmelCase      ,					pad_token=lowerCAmelCase      ,					mask_token=lowerCAmelCase      ,					add_prefix_space=lowerCAmelCase      ,					trim_offsets=lowerCAmelCase      ,					**lowerCAmelCase      ,					)

         lowercase__  =	json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__())
         if pre_tok_state.get('add_prefix_space'      ,					lowerCAmelCase) != add_prefix_space:
               lowercase__  =	getattr(lowerCAmelCase      ,					pre_tok_state.pop('type'))
               lowercase__  =	add_prefix_space
               lowercase__  =	pre_tok_class(**lowerCAmelCase)

         lowercase__  =	add_prefix_space

         # the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
         lowercase__  =	'post_processor'
         lowercase__  =	getattr(self.backend_tokenizer      ,					lowerCAmelCase      ,					lowerCAmelCase)
         if tokenizer_component_instance:
               lowercase__  =	json.loads(tokenizer_component_instance.__getstate__())

               # The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
               if "sep" in state:
                     lowercase__  =	tuple(state['sep'])
               if "cls" in state:
                     lowercase__  =	tuple(state['cls'])

               lowercase__  =	False

               if state.get('add_prefix_space'      ,					lowerCAmelCase) != add_prefix_space:
                     lowercase__  =	add_prefix_space
                     lowercase__  =	True

               if state.get('trim_offsets'      ,					lowerCAmelCase) != trim_offsets:
                     lowercase__  =	trim_offsets
                     lowercase__  =	True

               if changes_to_apply:
                     lowercase__  =	getattr(lowerCAmelCase      ,					state.pop('type'))
                     lowercase__  =	component_class(**lowerCAmelCase)
                     setattr(self.backend_tokenizer      ,					lowerCAmelCase      ,					lowerCAmelCase)


   @property
   def       UpperCAmelCase		(    self	:      int)						->					str:




         """simple docstring"""

         if self._mask_token is None:
               if self.verbose:
                     logger.error('Using mask_token, but it is not set yet.')
               return None
         return str(self._mask_token)


   @mask_token.setter
   def       UpperCAmelCase		(    self	:      List[Any]      ,					lowerCAmelCase	:      Optional[Any])						->					Union[str, Any]:




         """simple docstring"""

         lowercase__  =	AddedToken(lowerCAmelCase      ,					lstrip=lowerCAmelCase      ,					rstrip=lowerCAmelCase) if isinstance(lowerCAmelCase      ,					lowerCAmelCase) else value
         lowercase__  =	value


   def       UpperCAmelCase		(    self	:      List[Any]      ,					*lowerCAmelCase	:      Any      ,					**lowerCAmelCase	:      Union[str, Any])						->					BatchEncoding:




         """simple docstring"""

         lowercase__  =	kwargs.get('is_split_into_words'      ,					lowerCAmelCase)

         if is_split_into_words and not self.add_prefix_space:
               raise ValueError(
                   f'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
                   'to use it with pretokenized inputs.')

         return super()._batch_encode_plus(*lowerCAmelCase      ,					**lowerCAmelCase)


   def       UpperCAmelCase		(    self	:      Optional[Any]      ,					*lowerCAmelCase	:      int      ,					**lowerCAmelCase	:      List[Any])						->					BatchEncoding:




         """simple docstring"""

         lowercase__  =	kwargs.get('is_split_into_words'      ,					lowerCAmelCase)

         if is_split_into_words and not self.add_prefix_space:
               raise ValueError(
                   f'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
                   'to use it with pretokenized inputs.')

         return super()._encode_plus(*lowerCAmelCase      ,					**lowerCAmelCase)


   def       UpperCAmelCase		(    self	:      Tuple      ,					lowerCAmelCase	:      str      ,					lowerCAmelCase	:      Optional[str] = None)						->					Tuple[str]:




         """simple docstring"""

         lowercase__  =	self._tokenizer.model.save(lowerCAmelCase      ,					name=lowerCAmelCase)
         return tuple(lowerCAmelCase)


   def       UpperCAmelCase		(    self	:      Tuple      ,					lowerCAmelCase	:      int      ,					lowerCAmelCase	:      Union[str, Any]=None)						->					Union[str, Any]:




         """simple docstring"""

         lowercase__  =	[self.bos_token_id] + token_ids_a + [self.eos_token_id]
         if token_ids_a is None:
               return output

         return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]



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




         """simple docstring"""

         lowercase__  =	[self.sep_token_id]
         lowercase__  =	[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]





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    UpperCAmelCase__(				lowerCamelCase  ,				lowerCamelCase  ,				unittest.TestCase						):

   '''simple docstring'''




   A     :	str												=     IFImgaImgSuperResolutionPipeline
   A     :	Dict												=     TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"width", "height"}
   A     :	Any												=     TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({"original_image"}						)
   A     :	str												=     PipelineTesterMixin.required_optional_params - {"latents"}


   def       UpperCAmelCase		(    self	:      Dict)						->					Dict:




         """simple docstring"""

         return self._get_superresolution_dummy_components()


   def       UpperCAmelCase		(    self	:      Optional[Any]      ,					lowerCAmelCase	:      Tuple      ,					lowerCAmelCase	:      Union[str, Any]=0)						->					str:




         """simple docstring"""

         if str(lowerCAmelCase).startswith('mps'):
               lowercase__  =	torch.manual_seed(lowerCAmelCase)
         else:
               lowercase__  =	torch.Generator(device=lowerCAmelCase).manual_seed(lowerCAmelCase)

         lowercase__  =	floats_tensor((1, 3, 32, 32)      ,					rng=random.Random(lowerCAmelCase)).to(lowerCAmelCase)
         lowercase__  =	floats_tensor((1, 3, 16, 16)      ,					rng=random.Random(lowerCAmelCase)).to(lowerCAmelCase)

         lowercase__  =	{
             '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       UpperCAmelCase		(    self	:      str)						->					Dict:




         """simple docstring"""

         self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3)


   def       UpperCAmelCase		(    self	:      List[Any])						->					Dict:




         """simple docstring"""

         self._test_save_load_optional_components()


   @unittest.skipIf(torch_device != 'cuda'      ,					reason='float16 requires CUDA')
   def       UpperCAmelCase		(    self	:      Optional[Any])						->					List[Any]:




         """simple docstring"""

         super().test_save_load_floataa(expected_max_diff=1E-1)


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




         """simple docstring"""

         self._test_attention_slicing_forward_pass(expected_max_diff=1E-2)


   def       UpperCAmelCase		(    self	:      Tuple)						->					Tuple:




         """simple docstring"""

         self._test_save_load_local()



   def       UpperCAmelCase		(    self	:      List[str])						->					Tuple:




         """simple docstring"""

         self._test_inference_batch_single_identical(
             expected_max_diff=1E-2      ,					)
