



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


snake_case__       :  Optional[int]     		= logging.get_logger(__name__)

snake_case__       :  Optional[int]     		= {"""tokenizer_file""": """tokenizer.json"""}

snake_case__       :  Optional[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 (			lowerCamelCase__						):


						'''simple docstring'''





						_snake_case      :  List[str]   				=   VOCAB_FILES_NAMES
						_snake_case      :  Optional[Any]   				=   PRETRAINED_VOCAB_FILES_MAP
						_snake_case      :  List[Any]   				=   ["""input_ids""", """attention_mask"""]
						_snake_case      :  int   				=   None



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






							'''simple docstring'''



							super().__init__(
							    SCREAMING_SNAKE_CASE__				,   SCREAMING_SNAKE_CASE__				,   tokenizer_file=SCREAMING_SNAKE_CASE__				,   unk_token=SCREAMING_SNAKE_CASE__				,   bos_token=SCREAMING_SNAKE_CASE__				,   eos_token=SCREAMING_SNAKE_CASE__				,   pad_token=SCREAMING_SNAKE_CASE__				,   add_prefix_space=SCREAMING_SNAKE_CASE__				,   clean_up_tokenization_spaces=SCREAMING_SNAKE_CASE__				,   **SCREAMING_SNAKE_CASE__				,   )
							__lowercase									=      json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()						)
							if pre_tok_state.get("add_prefix_space"				,   SCREAMING_SNAKE_CASE__						) != add_prefix_space:
								__lowercase									=      getattr(SCREAMING_SNAKE_CASE__				,   pre_tok_state.pop("type"						)						)
								__lowercase									=      add_prefix_space
								__lowercase									=      pre_tok_class(**SCREAMING_SNAKE_CASE__						)

							__lowercase									=      add_prefix_space



						def      _snake_case						(						self						:				Optional[Any]				,   *lowerCamelCase						:				List[Any]				,   **lowerCamelCase						:				Tuple						):






							'''simple docstring'''



							__lowercase									=      kwargs.get("is_split_into_words"				,   SCREAMING_SNAKE_CASE__						)
							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(*SCREAMING_SNAKE_CASE__				,   **SCREAMING_SNAKE_CASE__						)



						def      _snake_case						(						self						:				Tuple				,   *lowerCamelCase						:				Optional[int]				,   **lowerCamelCase						:				Dict						):






							'''simple docstring'''



							__lowercase									=      kwargs.get("is_split_into_words"				,   SCREAMING_SNAKE_CASE__						)

							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(*SCREAMING_SNAKE_CASE__				,   **SCREAMING_SNAKE_CASE__						)



						def      _snake_case						(						self						:				int				,   lowerCamelCase						:				Union[str, Any]				,   lowerCamelCase						:				str = None						):






							'''simple docstring'''



							__lowercase									=      self._tokenizer.model.save(SCREAMING_SNAKE_CASE__				,   name=SCREAMING_SNAKE_CASE__						)
							return tuple(SCREAMING_SNAKE_CASE__						)



						def      _snake_case						(						self						:				Optional[int]				,   lowerCamelCase						:				Optional[int]						):






							'''simple docstring'''



							__lowercase									=      []
							for is_user, text in conversation.iter_texts():
								input_ids.extend(self.encode(SCREAMING_SNAKE_CASE__				,   add_special_tokens=SCREAMING_SNAKE_CASE__						) + [self.eos_token_id]						)

							if len(SCREAMING_SNAKE_CASE__						) > self.model_max_length:
								__lowercase									=      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      A(  unittest.TestCase		):

		"""simple docstring"""





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

					"""simple docstring"""



					torch.manual_seed(0   )
					_UpperCamelCase					:str					     =	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   )      ->	str:

					"""simple docstring"""



					_UpperCamelCase					:Dict					     =	self.dummy_uncond_unet
					_UpperCamelCase					:List[Any]					     =	KarrasVeScheduler()

					_UpperCamelCase					:Optional[Any]					     =	KarrasVePipeline(unet=SCREAMING_SNAKE_CASE__					,				scheduler=SCREAMING_SNAKE_CASE__   )
					pipe.to(SCREAMING_SNAKE_CASE__   )
					pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__   )

					_UpperCamelCase					:Dict					     =	torch.manual_seed(0   )
					_UpperCamelCase					:List[Any]					     =	pipe(num_inference_steps=2					,				generator=SCREAMING_SNAKE_CASE__					,				output_type='''numpy'''   ).images

					_UpperCamelCase					:List[Any]					     =	torch.manual_seed(0   )
					_UpperCamelCase					:Dict					     =	pipe(num_inference_steps=2					,				generator=SCREAMING_SNAKE_CASE__					,				output_type='''numpy'''					,				return_dict=SCREAMING_SNAKE_CASE__   )[0]

					_UpperCamelCase					:Dict					     =	image[0, -3:, -3:, -1]
					_UpperCamelCase					:List[Any]					     =	image_from_tuple[0, -3:, -3:, -1]

					assert image.shape == (1, 32, 32, 3)
					_UpperCamelCase					:List[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      A(  unittest.TestCase		):

		"""simple docstring"""





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

					"""simple docstring"""



					_UpperCamelCase					:List[Any]					     =	'''google/ncsnpp-celebahq-256'''
					_UpperCamelCase					:Any					     =	UNetaDModel.from_pretrained(SCREAMING_SNAKE_CASE__   )
					_UpperCamelCase					:Optional[int]					     =	KarrasVeScheduler()

					_UpperCamelCase					:Optional[int]					     =	KarrasVePipeline(unet=SCREAMING_SNAKE_CASE__					,				scheduler=SCREAMING_SNAKE_CASE__   )
					pipe.to(SCREAMING_SNAKE_CASE__   )
					pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__   )

					_UpperCamelCase					:List[str]					     =	torch.manual_seed(0   )
					_UpperCamelCase					:List[Any]					     =	pipe(num_inference_steps=20					,				generator=SCREAMING_SNAKE_CASE__					,				output_type='''numpy'''   ).images

					_UpperCamelCase					:Dict					     =	image[0, -3:, -3:, -1]
					assert image.shape == (1, 2_56, 2_56, 3)
					_UpperCamelCase					:Any					     =	np.array([0.5_7_8, 0.5_8_1_1, 0.5_9_2_4, 0.5_8_0_9, 0.5_8_7, 0.5_8_8_6, 0.5_8_6_1, 0.5_8_0_2, 0.5_8_6]   )

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

