





import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_mvp import MvpTokenizer


lowerCAmelCase_    =    logging.get_logger(__name__)


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

# See all MVP models at https://huggingface.co/models?filter=mvp
lowerCAmelCase_    =    {
    '''vocab_file''': {
        '''RUCAIBox/mvp''': '''https://huggingface.co/RUCAIBox/mvp/resolve/main/vocab.json''',
    },
    '''added_tokens.json''': {
        '''RUCAIBox/mvp''': '''https://huggingface.co/RUCAIBox/mvp/resolve/main/added_tokens.json''',
    },
    '''merges_file''': {
        '''RUCAIBox/mvp''': '''https://huggingface.co/RUCAIBox/mvp/resolve/main/merges.txt''',
    },
    '''tokenizer_file''': {
        '''RUCAIBox/mvp''': '''https://huggingface.co/RUCAIBox/mvp/resolve/main/tokenizer.json''',
    },
}

lowerCAmelCase_    =    {
    '''RUCAIBox/mvp''': 1_0_2_4,
}



class  __lowerCAmelCase	(      _a					):
			lowerCamelCase_					:					Tuple	      =				VOCAB_FILES_NAMES
			lowerCamelCase_					:					List[str]	      =				PRETRAINED_VOCAB_FILES_MAP
			lowerCamelCase_					:					Dict	      =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			lowerCamelCase_					:					Dict	      =				['''input_ids''', '''attention_mask''']
			lowerCamelCase_					:					Optional[int]	      =				MvpTokenizer






			def __init__(self ,							__magic_name__=None ,							__magic_name__=None ,							__magic_name__=None ,							__magic_name__="replace" ,							__magic_name__="<s>" ,							__magic_name__="</s>" ,							__magic_name__="</s>" ,							__magic_name__="<s>" ,							__magic_name__="<unk>" ,							__magic_name__="<pad>" ,							__magic_name__="<mask>" ,							__magic_name__=False ,							__magic_name__=True ,							**__magic_name__ ,							)					->       List[str]:


							'''simple docstring'''



							super().__init__(
							    __magic_name__ ,							__magic_name__ ,							tokenizer_file=__magic_name__ ,							errors=__magic_name__ ,							bos_token=__magic_name__ ,							eos_token=__magic_name__ ,							sep_token=__magic_name__ ,							cls_token=__magic_name__ ,							unk_token=__magic_name__ ,							pad_token=__magic_name__ ,							mask_token=__magic_name__ ,							add_prefix_space=__magic_name__ ,							trim_offsets=__magic_name__ ,							**__magic_name__ ,							)

							snake_case_						:   Dict           =  json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()   )
							if pre_tok_state.get('''add_prefix_space''' ,							__magic_name__   ) != add_prefix_space:
											snake_case_						:   Union[str, Any]           =  getattr(__magic_name__ ,							pre_tok_state.pop('''type'''   )   )
											snake_case_						:   Union[str, Any]           =  add_prefix_space
											snake_case_						:   int           =  pre_tok_class(**__magic_name__   )

							snake_case_						:   Union[str, Any]           =  add_prefix_space

							# the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
							snake_case_						:   Union[str, Any]           =  '''post_processor'''
							snake_case_						:   int           =  getattr(self.backend_tokenizer ,							__magic_name__ ,							__magic_name__   )
							if tokenizer_component_instance:
											snake_case_						:   Any           =  json.loads(tokenizer_component_instance.__getstate__()   )

											# The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
											if "sep" in state:
															snake_case_						:   int           =  tuple(state['''sep''']   )
											if "cls" in state:
															snake_case_						:   Optional[Any]           =  tuple(state['''cls''']   )

											snake_case_						:   List[str]           =  False

											if state.get('''add_prefix_space''' ,							__magic_name__   ) != add_prefix_space:
															snake_case_						:   List[str]           =  add_prefix_space
															snake_case_						:   Optional[int]           =  True

											if state.get('''trim_offsets''' ,							__magic_name__   ) != trim_offsets:
															snake_case_						:   Tuple           =  trim_offsets
															snake_case_						:   int           =  True

											if changes_to_apply:
															snake_case_						:   Any           =  getattr(__magic_name__ ,							state.pop('''type'''   )   )
															snake_case_						:   Optional[Any]           =  component_class(**__magic_name__   )
															setattr(self.backend_tokenizer ,							__magic_name__ ,							__magic_name__   )






			@property
			def       lowerCamelCase  (self   )					->       str:


							'''simple docstring'''



							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       lowerCamelCase  (self ,							__magic_name__   )					->       Tuple:


							'''simple docstring'''



							snake_case_						:   Union[str, Any]           =  AddedToken(__magic_name__ ,							lstrip=__magic_name__ ,							rstrip=__magic_name__   ) if isinstance(__magic_name__ ,							__magic_name__   ) else value
							snake_case_						:   str           =  value






			def       lowerCamelCase  (self ,							*__magic_name__ ,							**__magic_name__   )					->       BatchEncoding:


							'''simple docstring'''



							snake_case_						:   Optional[int]           =  kwargs.get('''is_split_into_words''' ,							__magic_name__   )

							if is_split_into_words and not self.add_prefix_space:
											raise ValueError(
											    F'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
											    '''to use it with pretokenized inputs.'''   )

							return super()._batch_encode_plus(*__magic_name__ ,							**__magic_name__   )






			def       lowerCamelCase  (self ,							*__magic_name__ ,							**__magic_name__   )					->       BatchEncoding:


							'''simple docstring'''



							snake_case_						:   Optional[Any]           =  kwargs.get('''is_split_into_words''' ,							__magic_name__   )

							if is_split_into_words and not self.add_prefix_space:
											raise ValueError(
											    F'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
											    '''to use it with pretokenized inputs.'''   )

							return super()._encode_plus(*__magic_name__ ,							**__magic_name__   )






			def       lowerCamelCase  (self ,							__magic_name__ ,							__magic_name__ = None   )					->       Tuple[str]:


							'''simple docstring'''



							snake_case_						:   List[Any]           =  self._tokenizer.model.save(__magic_name__ ,							name=__magic_name__   )
							return tuple(__magic_name__   )






			def       lowerCamelCase  (self ,							__magic_name__ ,							__magic_name__=None   )					->       Optional[int]:


							'''simple docstring'''



							snake_case_						:   List[str]           =  [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 ,							__magic_name__ ,							__magic_name__ = None   )					->       List[int]:


							'''simple docstring'''



							snake_case_						:   str           =  [self.sep_token_id]
							snake_case_						:   List[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]







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  __lowerCAmelCase	(      unittest.TestCase					):






			@property
			def       lowerCamelCase  (self   )					->       Union[str, Any]:


							'''simple docstring'''



							torch.manual_seed(0   )
							snake_case_						:   int           =  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       lowerCamelCase  (self   )					->       Dict:


							'''simple docstring'''



							snake_case_						:   Any           =  self.dummy_uncond_unet
							snake_case_						:   Optional[Any]           =  PNDMScheduler()

							snake_case_						:   Optional[Any]           =  PNDMPipeline(unet=__magic_name__ ,							scheduler=__magic_name__   )
							pndm.to(__magic_name__   )
							pndm.set_progress_bar_config(disable=__magic_name__   )

							snake_case_						:   str           =  torch.manual_seed(0   )
							snake_case_						:   Dict           =  pndm(generator=__magic_name__ ,							num_inference_steps=20 ,							output_type='''numpy'''   ).images

							snake_case_						:   str           =  torch.manual_seed(0   )
							snake_case_						:   str           =  pndm(generator=__magic_name__ ,							num_inference_steps=20 ,							output_type='''numpy''' ,							return_dict=__magic_name__   )[0]

							snake_case_						:   Any           =  image[0, -3:, -3:, -1]
							snake_case_						:   str           =  image_from_tuple[0, -3:, -3:, -1]

							assert image.shape == (1, 32, 32, 3)
							snake_case_						:   str           =  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  __lowerCAmelCase	(      unittest.TestCase					):






			def       lowerCamelCase  (self   )					->       Any:


							'''simple docstring'''



							snake_case_						:   Tuple           =  '''google/ddpm-cifar10-32'''

							snake_case_						:   Tuple           =  UNetaDModel.from_pretrained(__magic_name__   )
							snake_case_						:   Optional[Any]           =  PNDMScheduler()

							snake_case_						:   Any           =  PNDMPipeline(unet=__magic_name__ ,							scheduler=__magic_name__   )
							pndm.to(__magic_name__   )
							pndm.set_progress_bar_config(disable=__magic_name__   )
							snake_case_						:   int           =  torch.manual_seed(0   )
							snake_case_						:   Tuple           =  pndm(generator=__magic_name__ ,							output_type='''numpy'''   ).images

							snake_case_						:   List[str]           =  image[0, -3:, -3:, -1]

							assert image.shape == (1, 32, 32, 3)
							snake_case_						:   str           =  np.array([0.1_564, 0.14_645, 0.1_406, 0.14_715, 0.12_425, 0.14_045, 0.13_115, 0.12_175, 0.125]   )

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