'''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


SCREAMING_SNAKE_CASE_:	List[Any]    =logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_:	int    ={'tokenizer_file': 'tokenizer.json'}

SCREAMING_SNAKE_CASE_:	List[Any]    ={
    '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 			__A (					UpperCamelCase__      ):
    a__			:		Optional[Any]			  =    VOCAB_FILES_NAMES
    a__			:		str			  =    PRETRAINED_VOCAB_FILES_MAP
    a__			:		int			  =    ["""input_ids""", """attention_mask"""]
    a__			:		Optional[Any]			  =    None



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

         UpperCAmelCase_			       =  add_prefix_space



    def    _lowercase		(self       :       Optional[int]  ,     *__a       :       Union[str, Any]  ,     **__a       :       Optional[Any]					):
         UpperCAmelCase_			       =  kwargs.get("is_split_into_words"  ,     __a					)
         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(*__a  ,     **__a					)



    def    _lowercase		(self       :       Tuple  ,     *__a       :       List[str]  ,     **__a       :       Dict					):
         UpperCAmelCase_			       =  kwargs.get("is_split_into_words"  ,     __a					)

         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(*__a  ,     **__a					)



    def    _lowercase		(self       :       Optional[int]  ,     __a       :       str  ,     __a       :       Optional[str] = None					):
         UpperCAmelCase_			       =  self._tokenizer.model.save(__a  ,     name=__a					)
         return tuple(__a					)






    def    _lowercase		(self       :       Tuple  ,     __a       :       "Conversation"					):
         UpperCAmelCase_			       =  []
         for is_user, text in conversation.iter_texts():
              input_ids.extend(self.encode(__a  ,     add_special_tokens=__a					) + [self.eos_token_id]					)

         if len(__a					) > self.model_max_length:
              UpperCAmelCase_			       =  input_ids[-self.model_max_length :]
         return input_ids







'''simple docstring'''



import unittest

import numpy as np
import torch

from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()







class 		lowerCAmelCase__  ( unittest.TestCase   ):



 """simple docstring"""



 @property
 def  UpperCAmelCase__						(						self   :			int     )    ->					Optional[int]:


      """simple docstring"""




      torch.manual_seed(0     )
      __SCREAMING_SNAKE_CASE      = UNetaDModel(
          block_out_channels=(32, 64)   ,				layers_per_block=2   ,				sample_size=32   ,				in_channels=3   ,				out_channels=3   ,				down_block_types=("""DownBlock2D""", """AttnDownBlock2D""")   ,				up_block_types=("""AttnUpBlock2D""", """UpBlock2D""")   ,				)
      return model


 def  UpperCAmelCase__						(						self   :			Optional[Any]     )    ->					Tuple:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = self.dummy_uncond_unet
      __SCREAMING_SNAKE_CASE      = KarrasVeScheduler()

      __SCREAMING_SNAKE_CASE      = KarrasVePipeline(unet=__SCREAMING_SNAKE_CASE   ,				scheduler=__SCREAMING_SNAKE_CASE     )
      pipe.to(__SCREAMING_SNAKE_CASE     )
      pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE     )

      __SCREAMING_SNAKE_CASE      = torch.manual_seed(0     )
      __SCREAMING_SNAKE_CASE      = pipe(num_inference_steps=2   ,				generator=__SCREAMING_SNAKE_CASE   ,				output_type="""numpy"""     ).images

      __SCREAMING_SNAKE_CASE      = torch.manual_seed(0     )
      __SCREAMING_SNAKE_CASE      = pipe(num_inference_steps=2   ,				generator=__SCREAMING_SNAKE_CASE   ,				output_type="""numpy"""   ,				return_dict=__SCREAMING_SNAKE_CASE     )[0]

      __SCREAMING_SNAKE_CASE      = image[0, -3:, -3:, -1]
      __SCREAMING_SNAKE_CASE      = image_from_tuple[0, -3:, -3:, -1]

      assert image.shape == (1, 32, 32, 3)
      __SCREAMING_SNAKE_CASE      = np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]     )

      assert np.abs(image_slice.flatten() - expected_slice     ).max() < 1E-2
      assert np.abs(image_from_tuple_slice.flatten() - expected_slice     ).max() < 1E-2







@slow
@require_torch
class 		lowerCAmelCase__  ( unittest.TestCase   ):



 """simple docstring"""



 def  UpperCAmelCase__						(						self   :			List[Any]     )    ->					Union[str, Any]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = """google/ncsnpp-celebahq-256"""
      __SCREAMING_SNAKE_CASE      = UNetaDModel.from_pretrained(__SCREAMING_SNAKE_CASE     )
      __SCREAMING_SNAKE_CASE      = KarrasVeScheduler()

      __SCREAMING_SNAKE_CASE      = KarrasVePipeline(unet=__SCREAMING_SNAKE_CASE   ,				scheduler=__SCREAMING_SNAKE_CASE     )
      pipe.to(__SCREAMING_SNAKE_CASE     )
      pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE     )

      __SCREAMING_SNAKE_CASE      = torch.manual_seed(0     )
      __SCREAMING_SNAKE_CASE      = pipe(num_inference_steps=20   ,				generator=__SCREAMING_SNAKE_CASE   ,				output_type="""numpy"""     ).images

      __SCREAMING_SNAKE_CASE      = image[0, -3:, -3:, -1]
      assert image.shape == (1, 256, 256, 3)
      __SCREAMING_SNAKE_CASE      = np.array([0.578, 0.5811, 0.5924, 0.5809, 0.587, 0.5886, 0.5861, 0.5802, 0.586]     )

      assert np.abs(image_slice.flatten() - expected_slice     ).max() < 1E-2






