import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
   from transformers import AutoModelForSeqaSeqLM, AutoTokenizer







@require_torch
@require_sentencepiece
@require_tokenizers
class __snake_case    (						unittest.TestCase		):




   @slow
   def      __a						(   self      :    List[str]				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				AutoModelForSeqaSeqLM.from_pretrained("""google/mt5-small"""	,     return_dict=_lowercase				).to(_lowercase				)
     SCREAMING_SNAKE_CASE__      							=				AutoTokenizer.from_pretrained("""google/mt5-small"""				)

     SCREAMING_SNAKE_CASE__      							=				tokenizer("""Hello there"""	,     return_tensors="""pt"""				).input_ids
     SCREAMING_SNAKE_CASE__      							=				tokenizer("""Hi I am"""	,     return_tensors="""pt"""				).input_ids

     SCREAMING_SNAKE_CASE__      							=				model(input_ids.to(_lowercase				)	,     labels=labels.to(_lowercase				)				).loss
     SCREAMING_SNAKE_CASE__      							=				-(labels.shape[-1] * loss.item())

     SCREAMING_SNAKE_CASE__      							=				-84.91_27
     self.assertTrue(abs(mtf_score - EXPECTED_SCORE				) < 1E-4				)

import PIL.Image
import PIL.ImageOps
from packaging import version
from PIL import Image


if version.parse(version.parse(PIL.__version__).base_version) >= version.parse('''9.1.0'''):
   __lowerCamelCase      :			Optional[Any]							       =		{
       '''linear''': PIL.Image.Resampling.BILINEAR,
       '''bilinear''': PIL.Image.Resampling.BILINEAR,
       '''bicubic''': PIL.Image.Resampling.BICUBIC,
       '''lanczos''': PIL.Image.Resampling.LANCZOS,
       '''nearest''': PIL.Image.Resampling.NEAREST,
   }
else:
   __lowerCamelCase      :			int							       =		{
       '''linear''': PIL.Image.LINEAR,
       '''bilinear''': PIL.Image.BILINEAR,
       '''bicubic''': PIL.Image.BICUBIC,
       '''lanczos''': PIL.Image.LANCZOS,
       '''nearest''': PIL.Image.NEAREST,
   }



def      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	int     )							-> Optional[Any]:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__      							=				(images / 2 + 0.5).clamp(0			,					1     )
  SCREAMING_SNAKE_CASE__      							=				images.cpu().permute(0			,					2			,					3			,					1     ).float().numpy()
  SCREAMING_SNAKE_CASE__      							=				numpy_to_pil(__UpperCamelCase     )
  return images






def      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	int     )							-> int:







  """simple docstring"""





  if images.ndim == 3:
    SCREAMING_SNAKE_CASE__      							=				images[None, ...]
  SCREAMING_SNAKE_CASE__      							=				(images * 2_55).round().astype("""uint8"""     )
  if images.shape[-1] == 1:
    # special case for grayscale (single channel) images
    SCREAMING_SNAKE_CASE__      							=				[Image.fromarray(image.squeeze()			,					mode="""L"""     ) for image in images]
  else:
    SCREAMING_SNAKE_CASE__      							=				[Image.fromarray(__UpperCamelCase     ) for image in images]

  return pil_images
