



'''simple docstring'''





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


_lowerCAmelCase										=     logging.get_logger(__name__)

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

_lowerCAmelCase										=     {
    '''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_(    SCREAMING_SNAKE_CASE_					):
  '''simple docstring'''




  __lowercase							:				List[str]           =				VOCAB_FILES_NAMES
  __lowercase							:				Union[str, Any]           =				PRETRAINED_VOCAB_FILES_MAP
  __lowercase							:				str           =				['''input_ids''', '''attention_mask''']
  __lowercase							:				Dict           =				None




  def __init__(   self  ,__UpperCAmelCase=None  ,__UpperCAmelCase=None  ,__UpperCAmelCase=None  ,__UpperCAmelCase="<unk>"  ,__UpperCAmelCase="<s>"  ,__UpperCAmelCase="</s>"  ,__UpperCAmelCase="<pad>"  ,__UpperCAmelCase=False  ,__UpperCAmelCase=False  ,**__UpperCAmelCase  ,)   ->		List[str]:
      super().__init__(
          __UpperCAmelCase  ,__UpperCAmelCase  ,tokenizer_file=__UpperCAmelCase  ,unk_token=__UpperCAmelCase  ,bos_token=__UpperCAmelCase  ,eos_token=__UpperCAmelCase  ,pad_token=__UpperCAmelCase  ,add_prefix_space=__UpperCAmelCase  ,clean_up_tokenization_spaces=__UpperCAmelCase  ,**__UpperCAmelCase  ,)
      lowerCAmelCase__			:  str											= json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()				)
      if pre_tok_state.get("""add_prefix_space"""  ,__UpperCAmelCase				) != add_prefix_space:
          lowerCAmelCase__			:  Dict											= getattr(__UpperCAmelCase  ,pre_tok_state.pop("""type"""				)				)
          lowerCAmelCase__			:  Optional[int]											= add_prefix_space
          lowerCAmelCase__			:  Any											= pre_tok_class(**__UpperCAmelCase				)

      lowerCAmelCase__			:  int											= add_prefix_space




  def 							UpperCAmelCase_     (   self  ,*__UpperCAmelCase  ,**__UpperCAmelCase				)   ->		BatchEncoding:
      lowerCAmelCase__			:  Optional[Any]											= kwargs.get("""is_split_into_words"""  ,__UpperCAmelCase				)
      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(*__UpperCAmelCase  ,**__UpperCAmelCase				)




  def 							UpperCAmelCase_     (   self  ,*__UpperCAmelCase  ,**__UpperCAmelCase				)   ->		BatchEncoding:
      lowerCAmelCase__			:  Dict											= kwargs.get("""is_split_into_words"""  ,__UpperCAmelCase				)

      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(*__UpperCAmelCase  ,**__UpperCAmelCase				)




  def 							UpperCAmelCase_     (   self  ,__UpperCAmelCase  ,__UpperCAmelCase = None				)   ->		Tuple[str]:
      lowerCAmelCase__			:  int											= self._tokenizer.model.save(__UpperCAmelCase  ,name=__UpperCAmelCase				)
      return tuple(__UpperCAmelCase				)






  def 							UpperCAmelCase_     (   self  ,__UpperCAmelCase				)   ->		List[int]:
      lowerCAmelCase__			:  Dict											= []
      for is_user, text in conversation.iter_texts():
          input_ids.extend(self.encode(__UpperCAmelCase  ,add_special_tokens=__UpperCAmelCase				) + [self.eos_token_id]				)

      if len(__UpperCAmelCase				) > self.model_max_length:
          lowerCAmelCase__			:  Optional[int]											= input_ids[-self.model_max_length :]
      return input_ids







'''simple docstring'''





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_(    SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_						,			unittest.TestCase					):
  '''simple docstring'''




  __lowercase							:				List[str]           =				IFInpaintingSuperResolutionPipeline
  __lowercase							:				int           =				TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'''width''', '''height'''}
  __lowercase							:				List[str]           =				TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS.union({'''original_image'''}					)
  __lowercase							:				Optional[Any]           =				PipelineTesterMixin.required_optional_params - {'''latents'''}




  def 							UpperCAmelCase_     (   self				)   ->		Any:
      return self._get_superresolution_dummy_components()




  def 							UpperCAmelCase_     (   self  ,__UpperCAmelCase  ,__UpperCAmelCase=0				)   ->		List[Any]:
      if str(__UpperCAmelCase				).startswith("""mps"""				):
          lowerCAmelCase__			:  Any											= torch.manual_seed(__UpperCAmelCase				)
      else:
          lowerCAmelCase__			:  Dict											= torch.Generator(device=__UpperCAmelCase				).manual_seed(__UpperCAmelCase				)

      lowerCAmelCase__			:  Tuple											= floats_tensor((1, 3, 16, 16)  ,rng=random.Random(__UpperCAmelCase				)				).to(__UpperCAmelCase				)
      lowerCAmelCase__			:  Union[str, Any]											= floats_tensor((1, 3, 32, 32)  ,rng=random.Random(__UpperCAmelCase				)				).to(__UpperCAmelCase				)
      lowerCAmelCase__			:  Any											= floats_tensor((1, 3, 32, 32)  ,rng=random.Random(__UpperCAmelCase				)				).to(__UpperCAmelCase				)

      lowerCAmelCase__			:  Dict											= {
          """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 							UpperCAmelCase_     (   self				)   ->		Union[str, Any]:
      self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3				)




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




  @unittest.skipIf(torch_device != """cuda"""  ,reason="""float16 requires CUDA"""				)
  def 							UpperCAmelCase_     (   self				)   ->		List[Any]:
      # 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				)   ->		str:
      self._test_attention_slicing_forward_pass(expected_max_diff=1E-2				)




  def 							UpperCAmelCase_     (   self				)   ->		List[Any]:
      self._test_save_load_local()






  def 							UpperCAmelCase_     (   self				)   ->		int:
      self._test_inference_batch_single_identical(
          expected_max_diff=1E-2  ,)


