
'''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  UpperCAmelCase__						(       unittest.TestCase ):



    @property
    def   lowerCamelCase_  (	self      :       List[Any]     ):
          torch.manual_seed(0     )
          _lowerCamelCase				:   Any								=   UNetaDModel(
              block_out_channels=(3_2, 6_4),layers_per_block=2,sample_size=3_2,in_channels=3,out_channels=3,down_block_types=("DownBlock2D", "AttnDownBlock2D"),up_block_types=("AttnUpBlock2D", "UpBlock2D"),)
          return model



    def   lowerCamelCase_  (	self      :       List[Any]     ):
          _lowerCamelCase				:   Tuple								=   self.dummy_uncond_unet
          _lowerCamelCase				:   List[str]								=   KarrasVeScheduler()

          _lowerCamelCase				:   Union[str, Any]								=   KarrasVePipeline(unet=__A,scheduler=__A     )
          pipe.to(__A     )
          pipe.set_progress_bar_config(disable=__A     )

          _lowerCamelCase				:   str								=   torch.manual_seed(0     )
          _lowerCamelCase				:   Optional[Any]								=   pipe(num_inference_steps=2,generator=__A,output_type="numpy"     ).images

          _lowerCamelCase				:   List[Any]								=   torch.manual_seed(0     )
          _lowerCamelCase				:   str								=   pipe(num_inference_steps=2,generator=__A,output_type="numpy",return_dict=__A     )[0]

          _lowerCamelCase				:   List[Any]								=   image[0, -3:, -3:, -1]
          _lowerCamelCase				:   Optional[int]								=   image_from_tuple[0, -3:, -3:, -1]

          assert image.shape == (1, 3_2, 3_2, 3)
          _lowerCamelCase				:   Any								=   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  UpperCAmelCase__						(       unittest.TestCase ):



    def   lowerCamelCase_  (	self      :       List[Any]     ):
          _lowerCamelCase				:   int								=   "google/ncsnpp-celebahq-256"
          _lowerCamelCase				:   Optional[Any]								=   UNetaDModel.from_pretrained(__A     )
          _lowerCamelCase				:   Union[str, Any]								=   KarrasVeScheduler()

          _lowerCamelCase				:   Dict								=   KarrasVePipeline(unet=__A,scheduler=__A     )
          pipe.to(__A     )
          pipe.set_progress_bar_config(disable=__A     )

          _lowerCamelCase				:   Optional[Any]								=   torch.manual_seed(0     )
          _lowerCamelCase				:   Any								=   pipe(num_inference_steps=2_0,generator=__A,output_type="numpy"     ).images

          _lowerCamelCase				:   List[Any]								=   image[0, -3:, -3:, -1]
          assert image.shape == (1, 2_5_6, 2_5_6, 3)
          _lowerCamelCase				:   Optional[int]								=   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

'''simple docstring'''






from typing import List, Optional

from tokenizers import ByteLevelBPETokenizer

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot_small import BlenderbotSmallTokenizer


UpperCAmelCase_       :   Union[str, Any]   =       logging.get_logger(__name__)

UpperCAmelCase_       :   Optional[int]   =       {
    'vocab_file': 'vocab.json',
    'merges_file': 'merges.txt',
    'tokenizer_config_file': 'tokenizer_config.json',
}

UpperCAmelCase_       :   Union[str, Any]   =       {
    'vocab_file': {
        'facebook/blenderbot_small-90M': 'https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/vocab.json'
    },
    'merges_file': {
        'facebook/blenderbot_small-90M': 'https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/merges.txt'
    },
    'tokenizer_config_file': {
        'facebook/blenderbot_small-90M': (
            'https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/tokenizer_config.json'
        )
    },
}

UpperCAmelCase_       :   Union[str, Any]   =       {
    'facebook/blenderbot_small-90M': 512,
}

class  UpperCAmelCase__						(       A ):
    lowerCAmelCase_				    =		VOCAB_FILES_NAMES
    lowerCAmelCase_				    =		PRETRAINED_VOCAB_FILES_MAP
    lowerCAmelCase_				    =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    lowerCAmelCase_				    =		BlenderbotSmallTokenizer



    def __init__(	self      :       Optional[Any],__A      :       str=None,__A      :       str=None,__A      :       int="<|endoftext|>",__A      :       List[str]="<|endoftext|>",__A      :       Optional[Any]="<|endoftext|>",__A      :       Union[str, Any]=False,__A      :       List[Any]=True,**__A      :       str,):
          super().__init__(
              ByteLevelBPETokenizer(
                  vocab=__A,merges=__A,add_prefix_space=__A,trim_offsets=__A,),bos_token=__A,eos_token=__A,unk_token=__A,**__A,)
          _lowerCamelCase				:   List[Any]								=   add_prefix_space



    def   lowerCamelCase_  (	self      :       Any,__A      :       Optional[Any],__A      :       Optional[int]=None     ):
          _lowerCamelCase				:   Any								=   [self.bos_token_id] + token_ids_a + [self.eos_token_id]
          if token_ids_a is None:
                return output

          return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]



    def   lowerCamelCase_  (	self      :       List[Any],__A      :       List[int],__A      :       Optional[List[int]] = None     ):
          _lowerCamelCase				:   Any								=   [self.sep_token_id]
          _lowerCamelCase				:   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 + sep + token_ids_a + sep     ) * [0]