





import unittest

import numpy as np
import torch

from diffusers import PNDMPipeline, PNDMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()

class 		SCREAMING_SNAKE_CASE__    (	unittest.TestCase  ):


      @property
      def        a			(self	:				Optional[int]				):





         """simple docstring"""



         torch.manual_seed(0				)
         __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        a			(self	:				Union[str, Any]				):





         """simple docstring"""



         __snake_case								=       self.dummy_uncond_unet
         __snake_case								=       PNDMScheduler()

         __snake_case								=       PNDMPipeline(unet=a__    ,      scheduler=a__				)
         pndm.to(a__				)
         pndm.set_progress_bar_config(disable=a__				)

         __snake_case								=       torch.manual_seed(0				)
         __snake_case								=       pndm(generator=a__    ,      num_inference_steps=20    ,      output_type='''numpy'''				).images

         __snake_case								=       torch.manual_seed(0				)
         __snake_case								=       pndm(generator=a__    ,      num_inference_steps=20    ,      output_type='''numpy'''    ,      return_dict=a__				)[0]

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

         assert image.shape == (1, 32, 32, 3)
         __snake_case								=       np.array([1.0, 1.0, 0.0, 1.0, 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 		SCREAMING_SNAKE_CASE__    (	unittest.TestCase  ):


      def        a			(self	:				List[Any]				):





         """simple docstring"""



         __snake_case								=       '''google/ddpm-cifar10-32'''

         __snake_case								=       UNetaDModel.from_pretrained(a__				)
         __snake_case								=       PNDMScheduler()

         __snake_case								=       PNDMPipeline(unet=a__    ,      scheduler=a__				)
         pndm.to(a__				)
         pndm.set_progress_bar_config(disable=a__				)
         __snake_case								=       torch.manual_seed(0				)
         __snake_case								=       pndm(generator=a__    ,      output_type='''numpy'''				).images

         __snake_case								=       image[0, -3:, -3:, -1]

         assert image.shape == (1, 32, 32, 3)
         __snake_case								=       np.array([0.1_5_6_4, 0.1_4_6_4_5, 0.1_4_0_6, 0.1_4_7_1_5, 0.1_2_4_2_5, 0.1_4_0_4_5, 0.1_3_1_1_5, 0.1_2_1_7_5, 0.1_2_5]				)

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








from typing import List, Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_herbert import HerbertTokenizer


snake_case_           =      logging.get_logger(__name__)

snake_case_           =      {'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

snake_case_           =      {
    'vocab_file': {
        'allegro/herbert-base-cased': 'https://huggingface.co/allegro/herbert-base-cased/resolve/main/vocab.json'
    },
    'merges_file': {
        'allegro/herbert-base-cased': 'https://huggingface.co/allegro/herbert-base-cased/resolve/main/merges.txt'
    },
}

snake_case_           =      {'allegro/herbert-base-cased': 514}
snake_case_           =      {}

class 		SCREAMING_SNAKE_CASE__    (	_UpperCAmelCase  ):
      A_						:    Dict							=   VOCAB_FILES_NAMES
      A_						:    Optional[Any]							=   PRETRAINED_VOCAB_FILES_MAP
      A_						:    Optional[int]							=   PRETRAINED_INIT_CONFIGURATION
      A_						:    int							=   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      A_						:    Any							=   HerbertTokenizer


      def __init__(self	:				Dict    ,      a__	:				Tuple=None    ,      a__	:				Optional[int]=None    ,      a__	:				List[str]=None    ,      a__	:				Optional[int]="<s>"    ,      a__	:				Optional[Any]="<unk>"    ,      a__	:				Any="<pad>"    ,      a__	:				List[Any]="<mask>"    ,      a__	:				Any="</s>"    ,      **a__	:				Tuple    ,      ):





         """simple docstring"""



         super().__init__(
             a__    ,      a__    ,      tokenizer_file=a__    ,      cls_token=a__    ,      unk_token=a__    ,      pad_token=a__    ,      mask_token=a__    ,      sep_token=a__    ,      **a__    ,      )


      def        a			(self	:				List[str]    ,      a__	:				List[int]    ,      a__	:				Optional[List[int]] = None				):





         """simple docstring"""



         __snake_case								=       [self.cls_token_id]
         __snake_case								=       [self.sep_token_id]
         if token_ids_a is None:
            return cls + token_ids_a + sep

         return cls + token_ids_a + sep + token_ids_a + sep


      def        a			(self	:				List[str]    ,      a__	:				List[int]    ,      a__	:				Optional[List[int]] = None    ,      a__	:				bool = False				):





         """simple docstring"""



         if already_has_special_tokens:
            return super().get_special_tokens_mask(
                token_ids_a=a__    ,      token_ids_a=a__    ,      already_has_special_tokens=a__				)

         if token_ids_a is None:
            return [1] + ([0] * len(a__				)) + [1]
         return [1] + ([0] * len(a__				)) + [1] + ([0] * len(a__				)) + [1]


      def        a			(self	:				Optional[int]    ,      a__	:				List[int]    ,      a__	:				Optional[List[int]] = None				):





         """simple docstring"""



         __snake_case								=       [self.sep_token_id]
         __snake_case								=       [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        a			(self	:				int    ,      a__	:				str    ,      a__	:				Optional[str] = None				):





         """simple docstring"""



         __snake_case								=       self._tokenizer.model.save(a__    ,      name=a__				)
         return tuple(a__				)

