
'''simple docstring'''







import random
import unittest

import torch

from diffusers import IFInpaintingPipeline
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_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin







@skip_mps
class UpperCAmelCase__	( __snake_case    ,				__snake_case    ,				unittest.TestCase	):





  """simple docstring"""

  __UpperCAmelCase				:				int   =					IFInpaintingPipeline
  __UpperCAmelCase				:				List[str]   =					TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"""width""", """height"""}
  __UpperCAmelCase				:				Dict   =					TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
  __UpperCAmelCase				:				Any   =					PipelineTesterMixin.required_optional_params - {"""latents"""}


  def   __lowercase		(		self      :     str       ):


     '''simple docstring'''






     return self._get_dummy_components()


  def   __lowercase		(		self      :     Any							,_a      :     Optional[Any]							,_a      :     Tuple=0       ):


     '''simple docstring'''






     if str(UpperCamelCase__       ).startswith('mps'       ):
        _a				:	Union[str, Any]			=   torch.manual_seed(UpperCamelCase__       )
     else:
        _a				:	str			=   torch.Generator(device=UpperCamelCase__       ).manual_seed(UpperCamelCase__       )

     _a				:	List[str]			=   floats_tensor((1, 3, 32, 32)							,rng=random.Random(UpperCamelCase__       )       ).to(UpperCamelCase__       )
     _a				:	str			=   floats_tensor((1, 3, 32, 32)							,rng=random.Random(UpperCamelCase__       )       ).to(UpperCamelCase__       )

     _a				:	Union[str, Any]			=   {
         "prompt": "A painting of a squirrel eating a burger",
         "image": image,
         "mask_image": mask_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   __lowercase		(		self      :     Union[str, Any]       ):


     '''simple docstring'''






     self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3       )


  def   __lowercase		(		self      :     Optional[Any]       ):


     '''simple docstring'''






     self._test_save_load_optional_components()


  @unittest.skipIf(torch_device != 'cuda'							,reason='float16 requires CUDA'       )
  def   __lowercase		(		self      :     Union[str, Any]       ):


     '''simple docstring'''






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


  def   __lowercase		(		self      :     Optional[Any]       ):


     '''simple docstring'''






     self._test_attention_slicing_forward_pass(expected_max_diff=1E-2       )


  def   __lowercase		(		self      :     Optional[int]       ):


     '''simple docstring'''






     self._test_save_load_local()


  def   __lowercase		(		self      :     str       ):


     '''simple docstring'''






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






"""simple docstring"""


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

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
			from .tokenization_rembert import RemBertTokenizer
else:
			__A						   =			None

__A						   =			logging.get_logger(__name__)
__A						   =			{"vocab_file": "sentencepiece.model", "tokenizer_file": "tokenizer.json"}

__A						   =			{
    "vocab_file": {
        "google/rembert": "https://huggingface.co/google/rembert/resolve/main/sentencepiece.model",
    },
    "tokenizer_file": {
        "google/rembert": "https://huggingface.co/google/rembert/resolve/main/tokenizer.json",
    },
}

__A						   =			{
    "google/rembert": 256,
}

__A						   =			"▁"





class     snake_case	(				__snake_case  ):
				SCREAMING_SNAKE_CASE_   :      Tuple         =  VOCAB_FILES_NAMES
				SCREAMING_SNAKE_CASE_   :      Any         =  PRETRAINED_VOCAB_FILES_MAP
				SCREAMING_SNAKE_CASE_   :      Tuple         =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				SCREAMING_SNAKE_CASE_   :      Dict         =  RemBertTokenizer

				def __init__(	self							:				Tuple     ,  UpperCamelCase__							:				Dict=None     ,  UpperCamelCase__							:				List[Any]=None     ,  UpperCamelCase__							:				str=True     ,  UpperCamelCase__							:				Optional[int]=True     ,  UpperCamelCase__							:				Optional[Any]=False     ,  UpperCamelCase__							:				int="[CLS]"     ,  UpperCamelCase__							:				Optional[Any]="[SEP]"     ,  UpperCamelCase__							:				List[str]="<unk>"     ,  UpperCamelCase__							:				Dict="[SEP]"     ,  UpperCamelCase__							:				int="<pad>"     ,  UpperCamelCase__							:				Any="[CLS]"     ,  UpperCamelCase__							:				str="[MASK]"     ,  **UpperCamelCase__							:				Optional[Any]     ,  )->   List[Any]:



						'''simple docstring'''


						__lowerCAmelCase:       int          =			AddedToken(UpperCamelCase__     ,  lstrip=UpperCamelCase__     ,  rstrip=UpperCamelCase__) if isinstance(UpperCamelCase__     ,  UpperCamelCase__) else mask_token

						super().__init__(
						    UpperCamelCase__     ,  tokenizer_file=UpperCamelCase__     ,  do_lower_case=UpperCamelCase__     ,  remove_space=UpperCamelCase__     ,  keep_accents=UpperCamelCase__     ,  bos_token=UpperCamelCase__     ,  eos_token=UpperCamelCase__     ,  unk_token=UpperCamelCase__     ,  sep_token=UpperCamelCase__     ,  pad_token=UpperCamelCase__     ,  cls_token=UpperCamelCase__     ,  mask_token=UpperCamelCase__     ,  **UpperCamelCase__     ,  )

						__lowerCAmelCase:       Optional[int]          =			do_lower_case
						__lowerCAmelCase:       int          =			remove_space
						__lowerCAmelCase:       int          =			keep_accents
						__lowerCAmelCase:       str          =			vocab_file
						__lowerCAmelCase:       Tuple          =			False if not self.vocab_file else True

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



						'''simple docstring'''


						__lowerCAmelCase:       Optional[int]          =			[self.sep_token_id]
						__lowerCAmelCase:       Any          =			[self.cls_token_id]
						if token_ids_a is None:
								return cls + token_ids_a + sep
						return cls + token_ids_a + sep + token_ids_a + sep

				def 							lowercase_     (	self							:				Optional[Any]     ,  UpperCamelCase__							:				List[int]     ,  UpperCamelCase__							:				Optional[List[int]] = None     ,  UpperCamelCase__							:				bool = False)->   List[int]:



						'''simple docstring'''


						if already_has_special_tokens:
								if token_ids_a is not None:
										raise ValueError(
										    "You should not supply a second sequence if the provided sequence of "
										    "ids is already formatted with special tokens for the model.")
								return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

						if token_ids_a is not None:
								return [1] + ([0] * len(UpperCamelCase__)) + [1] + ([0] * len(UpperCamelCase__)) + [1]
						return [1] + ([0] * len(UpperCamelCase__)) + [1]

				def 							lowercase_     (	self							:				Tuple     ,  UpperCamelCase__							:				List[int]     ,  UpperCamelCase__							:				Optional[List[int]] = None)->   List[int]:



						'''simple docstring'''


						__lowerCAmelCase:       Optional[int]          =			[self.sep_token_id]
						__lowerCAmelCase:       Dict          =			[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 							lowercase_     (	self							:				Union[str, Any]     ,  UpperCamelCase__							:				str     ,  UpperCamelCase__							:				Optional[str] = None)->   Tuple[str]:



						'''simple docstring'''


						if not os.path.isdir(UpperCamelCase__):
								logger.error("Vocabulary path ({}) should be a directory".format(UpperCamelCase__))
								return
						__lowerCAmelCase:       Optional[Any]          =			os.path.join(
						    UpperCamelCase__     ,  (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"])

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

						return (out_vocab_file,)


