
import random
import unittest

import torch

from diffusers import IFInpaintingSuperResolutionPipeline
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 	_lowerCAmelCase					(	_UpperCAmelCase					,      _UpperCAmelCase					,      unittest.TestCase       ):
				_lowercase					=IFInpaintingSuperResolutionPipeline
				_lowercase					=TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"width", "height"}
				_lowercase					=TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS.union({'''original_image'''}       )
				_lowercase					=PipelineTesterMixin.required_optional_params - {"latents"}




				def      __a						(					self     )				->      Dict:
							return self._get_superresolution_dummy_components()




				def      __a						(					self				,				_UpperCamelCase				,				_UpperCamelCase=0     )				->      List[Any]:
							if str(_UpperCAmelCase     ).startswith("mps"     ):
										lowerCAmelCase_						=	torch.manual_seed(_UpperCAmelCase     )
							else:
										lowerCAmelCase_						=	torch.Generator(device=_UpperCAmelCase     ).manual_seed(_UpperCAmelCase     )

							lowerCAmelCase_						=	floats_tensor((1, 3, 16, 16)				,				rng=random.Random(_UpperCAmelCase     )     ).to(_UpperCAmelCase     )
							lowerCAmelCase_						=	floats_tensor((1, 3, 32, 32)				,				rng=random.Random(_UpperCAmelCase     )     ).to(_UpperCAmelCase     )
							lowerCAmelCase_						=	floats_tensor((1, 3, 32, 32)				,				rng=random.Random(_UpperCAmelCase     )     ).to(_UpperCAmelCase     )

							lowerCAmelCase_						=	{
							    '''prompt''': '''A painting of a squirrel eating a burger''',
							    '''image''': image,
							    '''original_image''': original_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     )				->      Optional[Any]:
							self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3     )




				def      __a						(					self     )				->      Optional[int]:
							self._test_save_load_optional_components()




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




				def      __a						(					self     )				->      Optional[int]:
							self._test_save_load_local()




				def      __a						(					self     )				->      Dict:
							self._test_inference_batch_single_identical(
							    expected_max_diff=1e-2				,				)




"""simple docstring"""






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


__A			   = logging.get_logger(__name__)

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

__A			   = {
    "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"
        ),
    },
}

__A			   = {
    "yjernite/retribert-base-uncased": 5_1_2,
}


__A			   = {
    "yjernite/retribert-base-uncased": {"do_lower_case": True},
}


class     UpperCAmelCase						(_UpperCAmelCase  ):



    """simple docstring"""

    _UpperCAmelCase	:Dict						    = VOCAB_FILES_NAMES
    _UpperCAmelCase	:str						    = PRETRAINED_VOCAB_FILES_MAP
    _UpperCAmelCase	:Optional[int]						    = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    _UpperCAmelCase	:Optional[int]						    = PRETRAINED_INIT_CONFIGURATION
    _UpperCAmelCase	:str						    = RetriBertTokenizer
    _UpperCAmelCase	:List[str]						    = ["input_ids", "attention_mask"]






    def __init__(   self    ,			_UpperCAmelCase=None    ,			_UpperCAmelCase=None    ,			_UpperCAmelCase=True    ,			_UpperCAmelCase="[UNK]"    ,			_UpperCAmelCase="[SEP]"    ,			_UpperCAmelCase="[PAD]"    ,			_UpperCAmelCase="[CLS]"    ,			_UpperCAmelCase="[MASK]"    ,			_UpperCAmelCase=True    ,			_UpperCAmelCase=None    ,			**_UpperCAmelCase    ,			):
       super().__init__(
           _UpperCAmelCase    ,			tokenizer_file=_UpperCAmelCase    ,			do_lower_case=_UpperCAmelCase    ,			unk_token=_UpperCAmelCase    ,			sep_token=_UpperCAmelCase    ,			pad_token=_UpperCAmelCase    ,			cls_token=_UpperCAmelCase    ,			mask_token=_UpperCAmelCase    ,			tokenize_chinese_chars=_UpperCAmelCase    ,			strip_accents=_UpperCAmelCase    ,			**_UpperCAmelCase    ,			)

       lowercase__:   Optional[Any]					=      json.loads(self.backend_tokenizer.normalizer.__getstate__()							)
       if (
           normalizer_state.get('''lowercase'''    ,			_UpperCAmelCase							) != do_lower_case
           or normalizer_state.get('''strip_accents'''    ,			_UpperCAmelCase							) != strip_accents
           or normalizer_state.get('''handle_chinese_chars'''    ,			_UpperCAmelCase							) != tokenize_chinese_chars
       ):
          lowercase__:   str					=      getattr(_UpperCAmelCase    ,			normalizer_state.pop('''type'''							)							)
          lowercase__:   Optional[Any]					=      do_lower_case
          lowercase__:   Tuple					=      strip_accents
          lowercase__:   str					=      tokenize_chinese_chars
          lowercase__:   Union[str, Any]					=      normalizer_class(**_UpperCAmelCase							)

       lowercase__:   List[str]					=      do_lower_case






    def _snake_case			(   self    ,			_UpperCAmelCase    ,			_UpperCAmelCase=None							):
       lowercase__:   Union[str, Any]					=      [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 _snake_case			(   self    ,			_UpperCAmelCase    ,			_UpperCAmelCase = None							):
       lowercase__:   Optional[int]					=      [self.sep_token_id]
       lowercase__:   Union[str, Any]					=      [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 _snake_case			(   self    ,			_UpperCAmelCase    ,			_UpperCAmelCase = None							):
       lowercase__:   List[str]					=      self._tokenizer.model.save(_UpperCAmelCase    ,			name=_UpperCAmelCase							)
       return tuple(_UpperCAmelCase							)
