





"""simple docstring"""




import json
import os
import unittest

from transformers.models.ctrl.tokenization_ctrl import VOCAB_FILES_NAMES, CTRLTokenizer

from ...test_tokenization_common import TokenizerTesterMixin

class  _UpperCAmelCase  (   UpperCAmelCase__			,   unittest.TestCase		):



      '''simple docstring'''
      lowercase_      : Dict	    =   CTRLTokenizer
      lowercase_      : str	    =   False
      lowercase_      : Any	    =   False

      def 					lowerCamelCase_			(     self  ):

        """simple docstring"""

        super().setUp()

        # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
        A_  :       str										=  ['adapt', 're@@', 'a@@', 'apt', 'c@@', 't', '<unk>']
        A_  :       Union[str, Any]										=  dict(zip(snake_case_					,      range(len(snake_case_  )  )  )  )
        A_  :       int										=  ['#version: 0.2', 'a p', 'ap t</w>', 'r e', 'a d', 'ad apt</w>', '']
        A_  :       int										=  {'unk_token': '<unk>'}

        A_  :       Any										=  os.path.join(self.tmpdirname					,      VOCAB_FILES_NAMES['vocab_file']  )
        A_  :       Union[str, Any]										=  os.path.join(self.tmpdirname					,      VOCAB_FILES_NAMES['merges_file']  )
        with open(self.vocab_file					,      'w'					,      encoding='utf-8'  ) as fp:
          fp.write(json.dumps(snake_case_  ) + '\n'  )
        with open(self.merges_file					,      'w'					,      encoding='utf-8'  ) as fp:
          fp.write('\n'.join(snake_case_  )  )

      def 					lowerCamelCase_			(     self					,      **snake_case_  ):

        """simple docstring"""

        kwargs.update(self.special_tokens_map  )
        return CTRLTokenizer.from_pretrained(self.tmpdirname					,      **snake_case_  )

      def 					lowerCamelCase_			(     self					,      snake_case_  ):

        """simple docstring"""

        A_  :       Optional[Any]										=  'adapt react readapt apt'
        A_  :       List[Any]										=  'adapt react readapt apt'
        return input_text, output_text

      def 					lowerCamelCase_			(     self  ):

        """simple docstring"""

        A_  :       Union[str, Any]										=  CTRLTokenizer(self.vocab_file					,      self.merges_file					,      **self.special_tokens_map  )
        A_  :       Optional[int]										=  'adapt react readapt apt'
        A_  :       Dict										=  'adapt re@@ a@@ c@@ t re@@ adapt apt'.split()
        A_  :       Tuple										=  tokenizer.tokenize(snake_case_  )
        self.assertListEqual(snake_case_					,      snake_case_  )

        A_  :       str										=  tokens + [tokenizer.unk_token]

        A_  :       Union[str, Any]										=  [0, 1, 2, 4, 5, 1, 0, 3, 6]
        self.assertListEqual(tokenizer.convert_tokens_to_ids(snake_case_  )					,      snake_case_  )






"""simple docstring"""




import torch

from diffusers import DiffusionPipeline

class  _UpperCAmelCase  (   UpperCAmelCase__		):



      '''simple docstring'''

      def __init__(     self					,      snake_case_					,      snake_case_  ):

        """simple docstring"""

        super().__init__()

        self.register_modules(unet=snake_case_					,      scheduler=snake_case_  )

      def __call__(     self  ):

        """simple docstring"""

        A_  :       Optional[Any]										=  torch.randn(
            (1, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)					,      )
        A_  :       List[str]										=  1

        A_  :       List[str]										=  self.unet(snake_case_					,      snake_case_  ).sample
        A_  :       Optional[int]										=  self.scheduler.step(snake_case_					,      snake_case_					,      snake_case_  ).prev_sample

        A_  :       List[Any]										=  scheduler_output - scheduler_output + torch.ones_like(snake_case_  )

        return result