



import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging


if TYPE_CHECKING:
						from transformers.pipelines.conversational import Conversation


SCREAMING_SNAKE_CASE__       =      logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__       =      {'''tokenizer_file''': '''tokenizer.json'''}

SCREAMING_SNAKE_CASE__       =      {
    '''tokenizer_file''': {
        '''bigscience/tokenizer''': '''https://huggingface.co/bigscience/tokenizer/blob/main/tokenizer.json''',
        '''bigscience/bloom-560m''': '''https://huggingface.co/bigscience/bloom-560m/blob/main/tokenizer.json''',
        '''bigscience/bloom-1b1''': '''https://huggingface.co/bigscience/bloom-1b1/blob/main/tokenizer.json''',
        '''bigscience/bloom-1b7''': '''https://huggingface.co/bigscience/bloom-1b7/blob/main/tokenizer.json''',
        '''bigscience/bloom-3b''': '''https://huggingface.co/bigscience/bloom-3b/blob/main/tokenizer.json''',
        '''bigscience/bloom-7b1''': '''https://huggingface.co/bigscience/bloom-7b1/blob/main/tokenizer.json''',
        '''bigscience/bloom''': '''https://huggingface.co/bigscience/bloom/blob/main/tokenizer.json''',
    },
}


class      __lowerCAmelCase     (		UpperCAmelCase_      ):
					"""simple docstring"""






					A__				:		Tuple			       =    VOCAB_FILES_NAMES
					A__				:		int			       =    PRETRAINED_VOCAB_FILES_MAP
					A__				:		Dict			       =    ["input_ids", "attention_mask"]
					A__				:		Optional[Any]			       =    None



					def __init__(     self					:       Dict				,					_snake_case					:       str=None				,					_snake_case					:       int=None				,					_snake_case					:       str=None				,					_snake_case					:       Union[str, Any]="<unk>"				,					_snake_case					:       str="<s>"				,					_snake_case					:       int="</s>"				,					_snake_case					:       Tuple="<pad>"				,					_snake_case					:       Dict=False				,					_snake_case					:       int=False				,					**_snake_case					:       str				,					):






								"""simple docstring"""


								super().__init__(
								    _snake_case				,					_snake_case				,					tokenizer_file=_snake_case				,					unk_token=_snake_case				,					bos_token=_snake_case				,					eos_token=_snake_case				,					pad_token=_snake_case				,					add_prefix_space=_snake_case				,					clean_up_tokenization_spaces=_snake_case				,					**_snake_case				,					)
								A__       =    json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()					)
								if pre_tok_state.get('add_prefix_space'				,					_snake_case					) != add_prefix_space:
											A__       =    getattr(_snake_case				,					pre_tok_state.pop('type'					)					)
											A__       =    add_prefix_space
											A__       =    pre_tok_class(**_snake_case					)

								A__       =    add_prefix_space



					def     _a							(     self					:       Union[str, Any]				,					*_snake_case					:       List[Any]				,					**_snake_case					:       Union[str, Any]					):






								"""simple docstring"""


								A__       =    kwargs.get('is_split_into_words'				,					_snake_case					)
								if not (self.add_prefix_space or not is_split_into_words):
											raise Exception(
											    F'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True to use it with'''
											    ' pretokenized inputs.'					)

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



					def     _a							(     self					:       str				,					*_snake_case					:       Optional[int]				,					**_snake_case					:       Any					):






								"""simple docstring"""


								A__       =    kwargs.get('is_split_into_words'				,					_snake_case					)

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

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



					def     _a							(     self					:       int				,					_snake_case					:       str				,					_snake_case					:       Optional[str] = None					):






								"""simple docstring"""


								A__       =    self._tokenizer.model.save(_snake_case				,					name=_snake_case					)
								return tuple(_snake_case					)



					def     _a							(     self					:       Tuple				,					_snake_case					:       "Conversation"					):






								"""simple docstring"""


								A__       =    []
								for is_user, text in conversation.iter_texts():
											input_ids.extend(self.encode(_snake_case				,					add_special_tokens=_snake_case					) + [self.eos_token_id]					)

								if len(_snake_case					) > self.model_max_length:
											A__       =    input_ids[-self.model_max_length :]
								return input_ids









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      __lowerCAmelCase     (		UpperCAmelCase_							,     UpperCAmelCase_							,     unittest.TestCase      ):
					"""simple docstring"""






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



					def     _a							(     self					:       Any					):






								"""simple docstring"""


								return self._get_dummy_components()



					def     _a							(     self					:       Optional[int]				,					_snake_case					:       Any				,					_snake_case					:       str=0					):






								"""simple docstring"""


								if str(_snake_case					).startswith('mps'					):
											A__       =    torch.manual_seed(_snake_case					)
								else:
											A__       =    torch.Generator(device=_snake_case					).manual_seed(_snake_case					)

								A__       =    floats_tensor((1, 3, 32, 32)				,					rng=random.Random(_snake_case					)					).to(_snake_case					)
								A__       =    floats_tensor((1, 3, 32, 32)				,					rng=random.Random(_snake_case					)					).to(_snake_case					)

								A__       =    {
								    '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					:       Dict					):






								"""simple docstring"""


								self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3					)



					def     _a							(     self					:       int					):






								"""simple docstring"""


								self._test_save_load_optional_components()



					@unittest.skipIf(torch_device != 'cuda'				,					reason='float16 requires CUDA'					)
					def     _a							(     self					:       Optional[int]					):






								"""simple docstring"""


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



					def     _a							(     self					:       List[str]					):






								"""simple docstring"""


								self._test_attention_slicing_forward_pass(expected_max_diff=1E-2					)



					def     _a							(     self					:       Dict					):






								"""simple docstring"""


								self._test_save_load_local()



					def     _a							(     self					:       Optional[int]					):






								"""simple docstring"""


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




