



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_xlnet import XLNetTokenizer
else:
 a_   :Optional[Any]        =      None


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

a_   :Union[str, Any]        =      {"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}

a_   :Optional[Any]        =      {
    "vocab_file": {
        "xlnet-base-cased": "https://huggingface.co/xlnet-base-cased/resolve/main/spiece.model",
        "xlnet-large-cased": "https://huggingface.co/xlnet-large-cased/resolve/main/spiece.model",
    },
    "tokenizer_file": {
        "xlnet-base-cased": "https://huggingface.co/xlnet-base-cased/resolve/main/tokenizer.json",
        "xlnet-large-cased": "https://huggingface.co/xlnet-large-cased/resolve/main/tokenizer.json",
    },
}

a_   :Optional[Any]        =      {
    "xlnet-base-cased": None,
    "xlnet-large-cased": None,
}

a_   :Optional[Any]        =      "▁"

# Segments (not really needed)
a_   :List[Any]        =      0
a_   :Optional[Any]        =      1
a_   :Tuple        =      2
a_   :Dict        =      3
a_   :Union[str, Any]        =      4




class 					snake_case__						(       _SCREAMING_SNAKE_CASE     ):






     """simple docstring"""


     _SCREAMING_SNAKE_CASE						=	VOCAB_FILES_NAMES
     _SCREAMING_SNAKE_CASE						=	PRETRAINED_VOCAB_FILES_MAP
     _SCREAMING_SNAKE_CASE						=	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     _SCREAMING_SNAKE_CASE						=	"left"
     _SCREAMING_SNAKE_CASE						=	XLNetTokenizer






     def __init__(       self  :  Union[str, Any], _snake_case  :  int=None, _snake_case  :  str=None, _snake_case  :  List[str]=False, _snake_case  :  Optional[Any]=True, _snake_case  :  Optional[int]=False, _snake_case  :  Any="<s>", _snake_case  :  List[str]="</s>", _snake_case  :  int="<unk>", _snake_case  :  List[Any]="<sep>", _snake_case  :  str="<pad>", _snake_case  :  int="<cls>", _snake_case  :  Optional[int]="<mask>", _snake_case  :  int=["<eop>", "<eod>"], **_snake_case  :  Optional[Any], )   ->Dict:
            snake_case__							:	str   	=	AddedToken(A_, lstrip=A_, rstrip=A_					) if isinstance(A_, A_					) else mask_token

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

            snake_case__							:	List[str]   	=	3
            snake_case__							:	Optional[int]   	=	do_lower_case
            snake_case__							:	int   	=	remove_space
            snake_case__							:	Tuple   	=	keep_accents
            snake_case__							:	Optional[int]   	=	vocab_file
            snake_case__							:	Tuple   	=	False if not self.vocab_file else True






     def    lowercase_   (       self  :  Optional[int], _snake_case  :  Dict, _snake_case  :  Any = None					)   ->List[int]:
            snake_case__							:	List[Any]   	=	[self.sep_token_id]
            snake_case__							:	Optional[Any]   	=	[self.cls_token_id]
            if token_ids_a is None:
                   return token_ids_a + sep + cls
            return token_ids_a + sep + token_ids_a + sep + cls






     def    lowercase_   (       self  :  Dict, _snake_case  :  Any, _snake_case  :  Union[str, Any] = None					)   ->List[int]:
            snake_case__							:	int   	=	[self.sep_token_id]
            snake_case__							:	int   	=	[2]

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






     def    lowercase_   (       self  :  Any, _snake_case  :  Optional[Any], _snake_case  :  List[Any] = None					)   ->Tuple[str]:
            if not self.can_save_slow_tokenizer:
                   raise ValueError(
                       'Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '
                       'tokenizer.'					)

            if not os.path.isdir(A_					):
                   logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''					)
                   return
            snake_case__							:	str   	=	os.path.join(
                A_, (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']					)

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

            return (out_vocab_file,)








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 				lowercase					(		_SCREAMING_SNAKE_CASE  ,   _SCREAMING_SNAKE_CASE  ,   unittest.TestCase   ):
		__lowercase       :       int	     =				IFInpaintingPipeline
		__lowercase       :       str	     =				TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"width", "height"}
		__lowercase       :       Optional[int]	     =				TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
		__lowercase       :       Optional[int]	     =				PipelineTesterMixin.required_optional_params - {"latents"}



		def __UpperCamelCase ( self       )  ->					List[str]:
				"""simple docstring"""
				return self._get_dummy_components()



		def __UpperCamelCase ( self				,      A_				,      A_=0       )  ->					List[Any]:
				"""simple docstring"""
				if str(A_       ).startswith('mps'       ):
						UpperCamelCase		=     torch.manual_seed(A_       )
				else:
						UpperCamelCase		=     torch.Generator(device=A_       ).manual_seed(A_       )

				UpperCamelCase		=     floats_tensor((1, 3, 32, 32)				,      rng=random.Random(A_       )       ).to(A_       )
				UpperCamelCase		=     floats_tensor((1, 3, 32, 32)				,      rng=random.Random(A_       )       ).to(A_       )

				UpperCamelCase		=     {
				    '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 __UpperCamelCase ( self       )  ->					str:
				"""simple docstring"""
				self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3       )



		def __UpperCamelCase ( self       )  ->					str:
				"""simple docstring"""
				self._test_save_load_optional_components()



		@unittest.skipIf(torch_device != 'cuda'				,      reason='float16 requires CUDA'       )
		def __UpperCamelCase ( self       )  ->					str:
				"""simple docstring"""
				# 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 __UpperCamelCase ( self       )  ->					Optional[Any]:
				"""simple docstring"""
				self._test_attention_slicing_forward_pass(expected_max_diff=1e-2       )



		def __UpperCamelCase ( self       )  ->					Dict:
				"""simple docstring"""
				self._test_save_load_local()



		def __UpperCamelCase ( self       )  ->					Dict:
				"""simple docstring"""
				self._test_inference_batch_single_identical(
				    expected_max_diff=1e-2				,      )
