
import random
import unittest

import torch

from diffusers import IFImgaImgPipeline
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_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin






@skip_mps
class 						UpperCAmelCase	(		A_ ,A_ ,unittest.TestCase				):
   A__      :	str		  =  IFImgaImgPipeline
   A__      :	Optional[Any]		  =  TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"width", "height"}
   A__      :	int		  =  TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
   A__      :	str		  =  PipelineTesterMixin.required_optional_params - {"latents"}



   def        _SCREAMING_SNAKE_CASE     (self	:      int				)	->			str:


          '''simple docstring'''


          return self._get_dummy_components()



   def        _SCREAMING_SNAKE_CASE     (self	:      Union[str, Any]					,		snake_case__	:      Optional[Any]					,		snake_case__	:      int=0				)	->			Dict:


          '''simple docstring'''


          if str(snake_case__				).startswith("mps"				):
                 snake_case :     Tuple           =   torch.manual_seed(snake_case__				)
          else:
                 snake_case :     int           =   torch.Generator(device=snake_case__				).manual_seed(snake_case__				)

          snake_case :     Union[str, Any]           =   floats_tensor((1, 3, 32, 32)					,		rng=random.Random(snake_case__				)				).to(snake_case__				)

          snake_case :     int           =   {
              "prompt": "A painting of a squirrel eating a burger",
              "image": image,
              "generator": generator,
              "num_inference_steps": 2,
              "output_type": "numpy",
          }

          return inputs



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


          '''simple docstring'''


          self._test_save_load_optional_components()



   @unittest.skipIf(
       torch_device != "cuda" or not is_xformers_available()					,		reason="XFormers attention is only available with CUDA and `xformers` installed"					,		)
   def        _SCREAMING_SNAKE_CASE     (self	:      Optional[int]				)	->			int:


          '''simple docstring'''


          self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3				)



   @unittest.skipIf(torch_device != "cuda"					,		reason="float16 requires CUDA"				)
   def        _SCREAMING_SNAKE_CASE     (self	:      int				)	->			str:


          '''simple docstring'''


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



   @unittest.skipIf(torch_device != "cuda"					,		reason="float16 requires CUDA"				)
   def        _SCREAMING_SNAKE_CASE     (self	:      Optional[Any]				)	->			Any:


          '''simple docstring'''


          super().test_floataa_inference(expected_max_diff=1e-1				)



   def        _SCREAMING_SNAKE_CASE     (self	:      str				)	->			str:


          '''simple docstring'''


          self._test_attention_slicing_forward_pass(expected_max_diff=1e-2				)



   def        _SCREAMING_SNAKE_CASE     (self	:      List[Any]				)	->			Tuple:


          '''simple docstring'''


          self._test_save_load_local()



   def        _SCREAMING_SNAKE_CASE     (self	:      Tuple				)	->			Dict:


          '''simple docstring'''


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







from transformers.models.cpm.tokenization_cpm import CpmTokenizer
from transformers.testing_utils import custom_tokenizers

from ..xlnet.test_modeling_xlnet import XLNetModelTest






@custom_tokenizers
class 						UpperCAmelCase	(		A_				):



   def        _SCREAMING_SNAKE_CASE     (self	:      List[str]					,		snake_case__	:      Dict					,		snake_case__	:      Tuple					,		snake_case__	:      Union[str, Any]					,		snake_case__	:      List[str]					,		snake_case__	:      List[Any]				)	->			Any:


          '''simple docstring'''


          return True



   def        _SCREAMING_SNAKE_CASE     (self	:      str				)	->			Dict:


          '''simple docstring'''


          snake_case :     Union[str, Any]           =   CpmTokenizer.from_pretrained("TsinghuaAI/CPM-Generate"				)
          snake_case :     str           =   "Hugging Face大法好，谁用谁知道。"
          snake_case :     Optional[Any]           =   "Hugging Face大法好,谁用谁知道。<unk>"
          snake_case :     int           =   "▁Hu gg ing ▁ ▂ ▁F ace ▁大法 ▁好 ▁ , ▁谁 ▁用 ▁谁 ▁知 道 ▁ 。".split()

          snake_case :     Union[str, Any]           =   tokenizer.tokenize(snake_case__				)
          self.assertListEqual(snake_case__					,		snake_case__				)

          snake_case :     Tuple           =   tokens + [tokenizer.unk_token]

          snake_case :     int           =   [1_37_89, 1_32_83, 14_21, 8, 10, 11_64, 1_36_08, 1_65_28, 63, 8, 9, 4_40, 1_08, 4_40, 1_21, 90, 8, 12, 0]
          self.assertListEqual(tokenizer.convert_tokens_to_ids(snake_case__				)					,		snake_case__				)

          snake_case :     Dict           =   tokenizer.decode(snake_case__				)
          self.assertEqual(snake_case__					,		snake_case__				)





