



"""simple docstring"""




import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_mpnet import MPNetTokenizer


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

_UpperCamelCase:					Dict     				=	{'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}

_UpperCamelCase:					Dict     				=	{
    'vocab_file': {
        'microsoft/mpnet-base': 'https://huggingface.co/microsoft/mpnet-base/resolve/main/vocab.txt',
    },
    'tokenizer_file': {
        'microsoft/mpnet-base': 'https://huggingface.co/microsoft/mpnet-base/resolve/main/tokenizer.json',
    },
}

_UpperCamelCase:					str     				=	{
    'microsoft/mpnet-base': 5_1_2,
}

_UpperCamelCase:					Dict     				=	{
    'microsoft/mpnet-base': {'do_lower_case': True},
}


class 	a__		(    lowercase_  ):
   _lowerCamelCase       				=					VOCAB_FILES_NAMES
   _lowerCamelCase       				=					PRETRAINED_VOCAB_FILES_MAP
   _lowerCamelCase       				=					PRETRAINED_INIT_CONFIGURATION
   _lowerCamelCase       				=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   _lowerCamelCase       				=					MPNetTokenizer
   _lowerCamelCase       				=					['input_ids', 'attention_mask']


   def __init__(				self   :				int,  lowerCAmelCase   :				Dict=None,  lowerCAmelCase   :				str=None,  lowerCAmelCase   :				Any=True,  lowerCAmelCase   :				int="<s>",  lowerCAmelCase   :				Union[str, Any]="</s>",  lowerCAmelCase   :				List[str]="</s>",  lowerCAmelCase   :				str="<s>",  lowerCAmelCase   :				Optional[Any]="[UNK]",  lowerCAmelCase   :				Tuple="<pad>",  lowerCAmelCase   :				Optional[Any]="<mask>",  lowerCAmelCase   :				int=True,  lowerCAmelCase   :				List[Any]=None,  **lowerCAmelCase   :				Dict,  )     ->							Optional[int]:
         super().__init__(
             _lowerCAmelCase,  tokenizer_file=_lowerCAmelCase,  do_lower_case=_lowerCAmelCase,  bos_token=_lowerCAmelCase,  eos_token=_lowerCAmelCase,  sep_token=_lowerCAmelCase,  cls_token=_lowerCAmelCase,  unk_token=_lowerCAmelCase,  pad_token=_lowerCAmelCase,  mask_token=_lowerCAmelCase,  tokenize_chinese_chars=_lowerCAmelCase,  strip_accents=_lowerCAmelCase,  **_lowerCAmelCase,  )

         lowercase	:   str											= json.loads(self.backend_tokenizer.normalizer.__getstate__()      )
         if (
             pre_tok_state.get('lowercase',  _lowerCAmelCase      ) != do_lower_case
             or pre_tok_state.get('strip_accents',  _lowerCAmelCase      ) != strip_accents
         ):
               lowercase	:   int											= getattr(_lowerCAmelCase,  pre_tok_state.pop('type'      )      )
               lowercase	:   Tuple											= do_lower_case
               lowercase	:   Optional[int]											= strip_accents
               lowercase	:   Union[str, Any]											= pre_tok_class(**_lowerCAmelCase      )

         lowercase	:   Any											= do_lower_case


   @property
   def 							lowercase					(				self   :				str      )     ->							Dict:
         if self._mask_token is None:
               if self.verbose:
                     logger.error('Using mask_token, but it is not set yet.'      )
               return None
         return str(self._mask_token      )


   @mask_token.setter
   def 							lowercase					(				self   :				str,  lowerCAmelCase   :				Dict      )     ->							List[str]:
         lowercase	:   str											= AddedToken(_lowerCAmelCase,  lstrip=_lowerCAmelCase,  rstrip=_lowerCAmelCase      ) if isinstance(_lowerCAmelCase,  _lowerCAmelCase      ) else value
         lowercase	:   Union[str, Any]											= value


   def 							lowercase					(				self   :				str,  lowerCAmelCase   :				List[str],  lowerCAmelCase   :				List[Any]=None      )     ->							str:
         lowercase	:   Optional[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 							lowercase					(				self   :				List[str],  lowerCAmelCase   :				List[Any],  lowerCAmelCase   :				List[str] = None      )     ->							Optional[int]:
         lowercase	:   Optional[Any]											= [self.sep_token_id]
         lowercase	:   Tuple											= [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]






   def 							lowercase					(				self   :				List[str],  lowerCAmelCase   :				Any,  lowerCAmelCase   :				Tuple = None      )     ->							Optional[int]:
         lowercase	:   Tuple											= self._tokenizer.model.save(_lowerCAmelCase,  name=_lowerCAmelCase      )
         return tuple(_lowerCAmelCase      )







import unittest

import numpy as np
import torch

from diffusers import DDPMPipeline, DDPMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device


enable_full_determinism()

class SCREAMING_SNAKE_CASE_			(       unittest.TestCase ):





					"""simple docstring"""


					@property
					def        __magic_name__			(			self						):
										torch.manual_seed(0						)
										lowerCamelCase__      =							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        __magic_name__			(			self						):
										lowerCamelCase__      =							"cpu"
										lowerCamelCase__      =							self.dummy_uncond_unet
										lowerCamelCase__      =							DDPMScheduler()

										lowerCamelCase__      =							DDPMPipeline(unet=_lowerCAmelCase						,	scheduler=_lowerCAmelCase						)
										ddpm.to(_lowerCAmelCase						)
										ddpm.set_progress_bar_config(disable=_lowerCAmelCase						)

										lowerCamelCase__      =							torch.Generator(device=_lowerCAmelCase						).manual_seed(0						)
										lowerCamelCase__      =							ddpm(generator=_lowerCAmelCase						,	num_inference_steps=2						,	output_type="numpy"						).images

										lowerCamelCase__      =							torch.Generator(device=_lowerCAmelCase						).manual_seed(0						)
										lowerCamelCase__      =							ddpm(generator=_lowerCAmelCase						,	num_inference_steps=2						,	output_type="numpy"						,	return_dict=_lowerCAmelCase						)[0]

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

										assert image.shape == (1, 32, 32, 3)
										lowerCamelCase__      =							np.array(
										    [9.9_56E-01, 5.7_85E-01, 4.6_75E-01, 9.9_30E-01, 0.0, 1.000, 1.1_99E-03, 2.6_48E-04, 5.1_01E-04]						)

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






					def        __magic_name__			(			self						):
										lowerCamelCase__      =							self.dummy_uncond_unet
										lowerCamelCase__      =							DDPMScheduler(prediction_type="sample"						)

										lowerCamelCase__      =							DDPMPipeline(unet=_lowerCAmelCase						,	scheduler=_lowerCAmelCase						)
										ddpm.to(_lowerCAmelCase						)
										ddpm.set_progress_bar_config(disable=_lowerCAmelCase						)

										lowerCamelCase__      =							torch.manual_seed(0						)
										lowerCamelCase__      =							ddpm(generator=_lowerCAmelCase						,	num_inference_steps=2						,	output_type="numpy"						).images

										lowerCamelCase__      =							torch.manual_seed(0						)
										lowerCamelCase__      =							ddpm(generator=_lowerCAmelCase						,	num_inference_steps=2						,	output_type="numpy"						)[0]

										lowerCamelCase__      =							image[0, -3:, -3:, -1]
										lowerCamelCase__      =							image_eps[0, -3:, -3:, -1]

										assert image.shape == (1, 32, 32, 3)
										lowerCamelCase__      =							1E-2 if torch_device != "mps" else 3E-2
										assert np.abs(image_slice.flatten() - image_eps_slice.flatten()						).max() < tolerance

@slow
@require_torch_gpu
class SCREAMING_SNAKE_CASE_			(       unittest.TestCase ):





					"""simple docstring"""


					def        __magic_name__			(			self						):
										lowerCamelCase__      =							"google/ddpm-cifar10-32"

										lowerCamelCase__      =							UNetaDModel.from_pretrained(_lowerCAmelCase						)
										lowerCamelCase__      =							DDPMScheduler.from_pretrained(_lowerCAmelCase						)

										lowerCamelCase__      =							DDPMPipeline(unet=_lowerCAmelCase						,	scheduler=_lowerCAmelCase						)
										ddpm.to(_lowerCAmelCase						)
										ddpm.set_progress_bar_config(disable=_lowerCAmelCase						)

										lowerCamelCase__      =							torch.manual_seed(0						)
										lowerCamelCase__      =							ddpm(generator=_lowerCAmelCase						,	output_type="numpy"						).images

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

										assert image.shape == (1, 32, 32, 3)
										lowerCamelCase__      =							np.array([0.4_200, 0.3_588, 0.1_939, 0.3_847, 0.3_382, 0.2_647, 0.4_155, 0.3_582, 0.3_385]						)
										assert np.abs(image_slice.flatten() - expected_slice						).max() < 1E-2

