




"""simple docstring"""







import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging


if TYPE_CHECKING:
  from transformers.pipelines.conversational import Conversation


SCREAMING_SNAKE_CASE_	     =       logging.get_logger(__name__)

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

SCREAMING_SNAKE_CASE_	     =       {
    'tokenizer_file': {
        'EleutherAI/gpt-neox-20b': 'https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/tokenizer.json',
    },
}

SCREAMING_SNAKE_CASE_	     =       {
    'gpt-neox-20b': 2048,
}

class 				snake_case_			(					lowerCamelCase_			):







   """simple docstring"""




   A_				      =       VOCAB_FILES_NAMES
   A_				      =       PRETRAINED_VOCAB_FILES_MAP
   A_				      =       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   A_				      =       ['''input_ids''', '''attention_mask''']







   def __init__(		self       ,					lowerCamelCase_=None       ,					lowerCamelCase_=None       ,					lowerCamelCase_=None       ,					lowerCamelCase_="<|endoftext|>"       ,					lowerCamelCase_="<|endoftext|>"       ,					lowerCamelCase_="<|endoftext|>"       ,					lowerCamelCase_=False       ,					**lowerCamelCase_       ,					)				->   List[str]:
       super().__init__(
           lowerCamelCase_       ,					lowerCamelCase_       ,					tokenizer_file=lowerCamelCase_       ,					unk_token=lowerCamelCase_       ,					bos_token=lowerCamelCase_       ,					eos_token=lowerCamelCase_       ,					add_prefix_space=lowerCamelCase_       ,					**lowerCamelCase_       ,					)

       UpperCamelCase              =	json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__())
       if pre_tok_state.get('''add_prefix_space'''       ,					lowerCamelCase_) != add_prefix_space:
           UpperCamelCase              =	getattr(lowerCamelCase_       ,					pre_tok_state.pop('''type'''))
           UpperCamelCase              =	add_prefix_space
           UpperCamelCase              =	pre_tok_class(**lowerCamelCase_)

       UpperCamelCase              =	add_prefix_space







   def   UpperCAmelCase__			(		self       ,					lowerCamelCase_       ,					lowerCamelCase_ = None)				->   Tuple[str]:
       UpperCamelCase              =	self._tokenizer.model.save(lowerCamelCase_       ,					name=lowerCamelCase_)
       return tuple(lowerCamelCase_)







   def   UpperCAmelCase__			(		self       ,					lowerCamelCase_)				->   List[int]:
       UpperCamelCase              =	[]
       for is_user, text in conversation.iter_texts():
           input_ids.extend(self.encode(lowerCamelCase_       ,					add_special_tokens=lowerCamelCase_) + [self.eos_token_id])

       if len(lowerCamelCase_) > self.model_max_length:
           UpperCamelCase              =	input_ids[-self.model_max_length :]
       return input_ids






"""simple docstring"""
import random
import unittest

import torch

from diffusers import IFInpaintingPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import (
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin







@skip_mps
class       a							(   a__   , a__   , unittest.TestCase  ):
 snake_case__             =       IFInpaintingPipeline
 snake_case__             =       TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'''width''', '''height'''}
 snake_case__             =       TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
 snake_case__             =       PipelineTesterMixin.required_optional_params - {'''latents'''}



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  return self._get_dummy_components()



 def    UpperCamelCase__  (     self				,       _snake_case				,       _snake_case=0     ):







  """simple docstring"""






  if str(_snake_case     ).startswith('mps'     ):
   lowerCAmelCase    				= torch.manual_seed(_snake_case     )
  else:
   lowerCAmelCase    				= torch.Generator(device=_snake_case     ).manual_seed(_snake_case     )

  lowerCAmelCase    				= floats_tensor((1, 3, 32, 32)				,       rng=random.Random(_snake_case     )     ).to(_snake_case     )
  lowerCAmelCase    				= floats_tensor((1, 3, 32, 32)				,       rng=random.Random(_snake_case     )     ).to(_snake_case     )

  lowerCAmelCase    				= {
      'prompt': 'A painting of a squirrel eating a burger',
      'image': image,
      'mask_image': mask_image,
      'generator': generator,
      'num_inference_steps': 2,
      'output_type': 'numpy',
  }

  return inputs



 @unittest.skipIf(
     torch_device != 'cuda' or not is_xformers_available()				,       reason='XFormers attention is only available with CUDA and `xformers` installed'				,       )
 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3     )



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  self._test_save_load_optional_components()



 @unittest.skipIf(torch_device != 'cuda'				,       reason='float16 requires CUDA'     )
 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  super().test_save_load_floataa(expected_max_diff=1E-1     )



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  self._test_attention_slicing_forward_pass(expected_max_diff=1E-2     )



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  self._test_save_load_local()



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  self._test_inference_batch_single_identical(
      expected_max_diff=1E-2				,       )





