"""simple docstring"""







import random
import unittest

import torch

from diffusers import IFSuperResolutionPipeline
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     _SCREAMING_SNAKE_CASE       ( UpperCAmelCase						,							UpperCAmelCase						,							unittest.TestCase							):



   '''simple docstring'''



   __UpperCAmelCase  :       List[str]						=						IFSuperResolutionPipeline
   __UpperCAmelCase  :       Any						=						TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"width", "height"}
   __UpperCAmelCase  :       List[Any]						=						TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
   __UpperCAmelCase  :       int						=						PipelineTesterMixin.required_optional_params - {"latents"}







   def       _A							(  self  ):


      '''simple docstring'''

      return self._get_superresolution_dummy_components()







   def       _A							(  self   ,							_SCREAMING_SNAKE_CASE   ,							_SCREAMING_SNAKE_CASE=0  ):


      '''simple docstring'''

      if str(_SCREAMING_SNAKE_CASE  ).startswith('mps'  ):
         __lowerCamelCase								=				torch.manual_seed(_SCREAMING_SNAKE_CASE  )
      else:
         __lowerCamelCase								=				torch.Generator(device=_SCREAMING_SNAKE_CASE  ).manual_seed(_SCREAMING_SNAKE_CASE  )

      __lowerCamelCase								=				floats_tensor((1, 3, 32, 32)   ,							rng=random.Random(_SCREAMING_SNAKE_CASE  )  ).to(_SCREAMING_SNAKE_CASE  )

      __lowerCamelCase								=				{
          'prompt': 'A painting of a squirrel eating a burger',
          'image': 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       _A							(  self  ):


      '''simple docstring'''

      self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3  )







   def       _A							(  self  ):


      '''simple docstring'''

      self._test_save_load_optional_components()







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


      '''simple docstring'''

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







   def       _A							(  self  ):


      '''simple docstring'''

      self._test_attention_slicing_forward_pass(expected_max_diff=1E-2  )







   def       _A							(  self  ):


      '''simple docstring'''

      self._test_save_load_local()







   def       _A							(  self  ):


      '''simple docstring'''

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







"""simple docstring"""







import sys

from transformers import AutoTokenizer


_SCREAMING_SNAKE_CASE  :		List[str]        =      sys.argv[1]
_SCREAMING_SNAKE_CASE  :		List[str]        =      sys.argv[2]
_SCREAMING_SNAKE_CASE  :		Tuple        =      int(sys.argv[3])

_SCREAMING_SNAKE_CASE  :		List[str]        =      0

_SCREAMING_SNAKE_CASE  :		Optional[int]        =      AutoTokenizer.from_pretrained(model_name_or_path)
max_len -= tokenizer.num_special_tokens_to_add()

with open(dataset, '''rt''') as f_p:
     for line in f_p:
          _SCREAMING_SNAKE_CASE  :		List[Any]        =      line.rstrip()

          if not line:
               print(line)
               _SCREAMING_SNAKE_CASE  :		Union[str, Any]        =      0
               continue

          _SCREAMING_SNAKE_CASE  :		Dict        =      line.split()[0]

          _SCREAMING_SNAKE_CASE  :		Tuple        =      len(tokenizer.tokenize(token))

          # Token contains strange control characters like \x96 or \x95
          # Just filter out the complete line
          if current_subwords_len == 0:
               continue

          if (subword_len_counter + current_subwords_len) > max_len:
               print('''''')
               print(line)
               _SCREAMING_SNAKE_CASE  :		List[Any]        =      current_subwords_len
               continue

          subword_len_counter += current_subwords_len

          print(line)






