






import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ....tokenization_utils_fast import PreTrainedTokenizerFast
from ....utils import logging
from .tokenization_retribert import RetriBertTokenizer


__UpperCamelCase      :      Dict     						=						logging.get_logger(__name__)

__UpperCamelCase      :      str     						=						{"""vocab_file""": """vocab.txt""", """tokenizer_file""": """tokenizer.json"""}

__UpperCamelCase      :      List[str]     						=						{
    """vocab_file""": {
        """yjernite/retribert-base-uncased""": (
            """https://huggingface.co/yjernite/retribert-base-uncased/resolve/main/vocab.txt"""
        ),
    },
    """tokenizer_file""": {
        """yjernite/retribert-base-uncased""": (
            """https://huggingface.co/yjernite/retribert-base-uncased/resolve/main/tokenizer.json"""
        ),
    },
}

__UpperCamelCase      :      Optional[int]     						=						{
    """yjernite/retribert-base-uncased""": 512,
}


__UpperCamelCase      :      List[Any]     						=						{
    """yjernite/retribert-base-uncased""": {"""do_lower_case""": True},
}


class        __UpperCamelCase    (     _lowerCAmelCase   ):
	__snake_case	:Tuple   =       VOCAB_FILES_NAMES
	__snake_case	:int   =       PRETRAINED_VOCAB_FILES_MAP
	__snake_case	:Any   =       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	__snake_case	:List[Any]   =       PRETRAINED_INIT_CONFIGURATION
	__snake_case	:str   =       RetriBertTokenizer
	__snake_case	:Dict   =       ['input_ids', 'attention_mask']







	def __init__(  self      :							Tuple       ,				_lowerCAmelCase      :							Dict=None       ,				_lowerCAmelCase      :							Optional[int]=None       ,				_lowerCAmelCase      :							int=True       ,				_lowerCAmelCase      :							Union[str, Any]="[UNK]"       ,				_lowerCAmelCase      :							List[Any]="[SEP]"       ,				_lowerCAmelCase      :							Dict="[PAD]"       ,				_lowerCAmelCase      :							Optional[int]="[CLS]"       ,				_lowerCAmelCase      :							List[str]="[MASK]"       ,				_lowerCAmelCase      :							Any=True       ,				_lowerCAmelCase      :							Optional[int]=None       ,				**_lowerCAmelCase      :							Optional[int]       ,				)		-> List[Any]:
		"""simple docstring"""







		super().__init__(
		    _lowerCAmelCase       ,				tokenizer_file=_lowerCAmelCase       ,				do_lower_case=_lowerCAmelCase       ,				unk_token=_lowerCAmelCase       ,				sep_token=_lowerCAmelCase       ,				pad_token=_lowerCAmelCase       ,				cls_token=_lowerCAmelCase       ,				mask_token=_lowerCAmelCase       ,				tokenize_chinese_chars=_lowerCAmelCase       ,				strip_accents=_lowerCAmelCase       ,				**_lowerCAmelCase       ,				)

		__lowercase             =  json.loads(self.backend_tokenizer.normalizer.__getstate__()			)
		if (
		    normalizer_state.get("""lowercase"""       ,				_lowerCAmelCase			) != do_lower_case
		    or normalizer_state.get("""strip_accents"""       ,				_lowerCAmelCase			) != strip_accents
		    or normalizer_state.get("""handle_chinese_chars"""       ,				_lowerCAmelCase			) != tokenize_chinese_chars
		):
			__lowercase             =  getattr(_lowerCAmelCase       ,				normalizer_state.pop("""type"""			)			)
			__lowercase             =  do_lower_case
			__lowercase             =  strip_accents
			__lowercase             =  tokenize_chinese_chars
			__lowercase             =  normalizer_class(**_lowerCAmelCase			)

		__lowercase             =  do_lower_case







	def  _a   (  self      :							str       ,				_lowerCAmelCase      :							List[Any]       ,				_lowerCAmelCase      :							str=None			)		-> Optional[int]:
		"""simple docstring"""







		__lowercase             =  [self.cls_token_id] + token_ids_a + [self.sep_token_id]

		if token_ids_a:
			output += token_ids_a + [self.sep_token_id]

		return output







	def  _a   (  self      :							str       ,				_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			) * [0] + len(token_ids_a + sep			) * [1]







	def  _a   (  self      :							List[str]       ,				_lowerCAmelCase      :							str       ,				_lowerCAmelCase      :							Optional[str] = None			)		-> Tuple[str]:
		"""simple docstring"""







		__lowercase             =  self._tokenizer.model.save(_lowerCAmelCase       ,				name=_lowerCAmelCase			)
		return tuple(_lowerCAmelCase			)









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    (     _lowerCAmelCase   ,			_lowerCAmelCase   ,			unittest.TestCase   ):
	__snake_case	:Any   =       IFInpaintingPipeline
	__snake_case	:str   =       TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'width', 'height'}
	__snake_case	:Optional[int]   =       TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
	__snake_case	:str   =       PipelineTesterMixin.required_optional_params - {'latents'}







	def  _a   (  self      :							Union[str, Any]			)		-> Tuple:
		"""simple docstring"""







		return self._get_dummy_components()







	def  _a   (  self      :							Optional[int]       ,				_lowerCAmelCase      :							List[str]       ,				_lowerCAmelCase      :							Dict=0			)		-> Any:
		"""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, 32, 32)       ,				rng=random.Random(_lowerCAmelCase			)			).to(_lowerCAmelCase			)

		__lowercase             =  {
		    """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  _a   (  self      :							Tuple			)		-> Dict:
		"""simple docstring"""







		self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3			)







	def  _a   (  self      :							Union[str, Any]			)		-> Dict:
		"""simple docstring"""







		self._test_save_load_optional_components()







	@unittest.skipIf(torch_device != """cuda"""       ,				reason="""float16 requires CUDA"""			)
	def  _a   (  self      :							List[str]			)		-> Tuple:
		"""simple docstring"""







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







	def  _a   (  self      :							Tuple			)		-> Optional[Any]:
		"""simple docstring"""







		self._test_attention_slicing_forward_pass(expected_max_diff=1e-2			)







	def  _a   (  self      :							str			)		-> Optional[int]:
		"""simple docstring"""







		self._test_save_load_local()







	def  _a   (  self      :							List[str]			)		-> List[Any]:
		"""simple docstring"""







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

