






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 				UpperCAmelCase_  (		__lowerCamelCase							,      __lowerCamelCase							,      unittest.TestCase		):
     __lowerCamelCase      				=  IFInpaintingPipeline
     __lowerCamelCase      				=  TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'width', 'height'}
     __lowerCamelCase      				=  TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
     __lowerCamelCase      				=  PipelineTesterMixin.required_optional_params - {'latents'}




     def   __UpperCAmelCase  (				self		):
          return self._get_dummy_components()




     def   __UpperCAmelCase  (				self	,						_lowerCAmelCase	,						_lowerCAmelCase=0		):
          if str(_lowerCAmelCase		).startswith("""mps"""		):
               UpperCAmelCase__      :  Optional[int]				      =			torch.manual_seed(_lowerCAmelCase		)
          else:
               UpperCAmelCase__      :  Any				      =			torch.Generator(device=_lowerCAmelCase		).manual_seed(_lowerCAmelCase		)

          UpperCAmelCase__      :  List[Any]				      =			floats_tensor((1, 3, 32, 32)	,						rng=random.Random(_lowerCAmelCase		)		).to(_lowerCAmelCase		)
          UpperCAmelCase__      :  str				      =			floats_tensor((1, 3, 32, 32)	,						rng=random.Random(_lowerCAmelCase		)		).to(_lowerCAmelCase		)

          UpperCAmelCase__      :  Optional[int]				      =			{
              """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		):
          self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3		)




     def   __UpperCAmelCase  (				self		):
          self._test_save_load_optional_components()




     @unittest.skipIf(torch_device != """cuda"""	,						reason="""float16 requires CUDA"""		)
     def   __UpperCAmelCase  (				self		):
          # Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
          super().test_save_load_floataa(expected_max_diff=1e-1		)




     def   __UpperCAmelCase  (				self		):
          self._test_attention_slicing_forward_pass(expected_max_diff=1e-2		)




     def   __UpperCAmelCase  (				self		):
          self._test_save_load_local()




     def   __UpperCAmelCase  (				self		):
          self._test_inference_batch_single_identical(
              expected_max_diff=1e-2	,						)













import json
import os
import unittest

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

from ...test_tokenization_common import TokenizerTesterMixin




class        SCREAMING_SNAKE_CASE__					(		UpperCamelCase__							,   unittest.TestCase ):
 __SCREAMING_SNAKE_CASE							=      CTRLTokenizer
 __SCREAMING_SNAKE_CASE							=      False
 __SCREAMING_SNAKE_CASE							=      False



 def 							UpperCamelCase     (				self					):
        super().setUp()

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

        A__		   =		os.path.join(self.tmpdirname,VOCAB_FILES_NAMES['''vocab_file''']					)
        A__		   =		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(__lowerCamelCase					) + '''\n'''					)
        with open(self.merges_file,'''w''',encoding='''utf-8'''					) as fp:
               fp.write('''\n'''.join(__lowerCamelCase					)					)



 def 							UpperCamelCase     (				self,**__lowerCamelCase					):
        kwargs.update(self.special_tokens_map					)
        return CTRLTokenizer.from_pretrained(self.tmpdirname,**__lowerCamelCase					)



 def 							UpperCamelCase     (				self,__lowerCamelCase					):
        A__		   =		'''adapt react readapt apt'''
        A__		   =		'''adapt react readapt apt'''
        return input_text, output_text




 def 							UpperCamelCase     (				self					):
        A__		   =		CTRLTokenizer(self.vocab_file,self.merges_file,**self.special_tokens_map					)
        A__		   =		'''adapt react readapt apt'''
        A__		   =		'''adapt re@@ a@@ c@@ t re@@ adapt apt'''.split()
        A__		   =		tokenizer.tokenize(__lowerCamelCase					)
        self.assertListEqual(__lowerCamelCase,__lowerCamelCase					)

        A__		   =		tokens + [tokenizer.unk_token]

        A__		   =		[0, 1, 2, 4, 5, 1, 0, 3, 6]
        self.assertListEqual(tokenizer.convert_tokens_to_ids(__lowerCamelCase					),__lowerCamelCase					)





