import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
   import torch

if is_vision_available():
   from PIL import Image

   from transformers import LevitImageProcessor







class __snake_case    (						unittest.TestCase		):




   def __init__(   self      :    Any	,     _lowercase      :    Tuple	,     _lowercase      :    str=7	,     _lowercase      :    List[str]=3	,     _lowercase      :    Optional[int]=18	,     _lowercase      :    List[str]=30	,     _lowercase      :    Dict=4_00	,     _lowercase      :    Any=True	,     _lowercase      :    Dict=None	,     _lowercase      :    str=True	,     _lowercase      :    Dict=None	,     _lowercase      :    Optional[int]=True	,     _lowercase      :    Dict=[0.5, 0.5, 0.5]	,     _lowercase      :    Union[str, Any]=[0.5, 0.5, 0.5]	,     ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				size if size is not None else {"""shortest_edge""": 18}
     SCREAMING_SNAKE_CASE__      							=				crop_size if crop_size is not None else {"""height""": 18, """width""": 18}
     SCREAMING_SNAKE_CASE__      							=				parent
     SCREAMING_SNAKE_CASE__      							=				batch_size
     SCREAMING_SNAKE_CASE__      							=				num_channels
     SCREAMING_SNAKE_CASE__      							=				image_size
     SCREAMING_SNAKE_CASE__      							=				min_resolution
     SCREAMING_SNAKE_CASE__      							=				max_resolution
     SCREAMING_SNAKE_CASE__      							=				do_resize
     SCREAMING_SNAKE_CASE__      							=				size
     SCREAMING_SNAKE_CASE__      							=				do_center_crop
     SCREAMING_SNAKE_CASE__      							=				crop_size
     SCREAMING_SNAKE_CASE__      							=				do_normalize
     SCREAMING_SNAKE_CASE__      							=				image_mean
     SCREAMING_SNAKE_CASE__      							=				image_std




   def      __a						(   self      :    Optional[Any]				):



     """simple docstring"""






     return {
         "image_mean": self.image_mean,
         "image_std": self.image_std,
         "do_normalize": self.do_normalize,
         "do_resize": self.do_resize,
         "do_center_crop": self.do_center_crop,
         "size": self.size,
         "crop_size": self.crop_size,
     }







@require_torch
@require_vision
class __snake_case    (						lowerCamelCase_       ,		unittest.TestCase		):
   lowerCAmelCase_														=							LevitImageProcessor if is_vision_available() else None




   def      __a						(   self      :    Optional[Any]				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				LevitImageProcessingTester(self				)




   @property
   def      __a						(   self      :    Dict				):



     """simple docstring"""






     return self.image_processor_tester.prepare_image_processor_dict()




   def      __a						(   self      :    Optional[Any]				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				self.image_processing_class(**self.image_processor_dict				)
     self.assertTrue(hasattr(_lowercase	,     """image_mean"""				)				)
     self.assertTrue(hasattr(_lowercase	,     """image_std"""				)				)
     self.assertTrue(hasattr(_lowercase	,     """do_normalize"""				)				)
     self.assertTrue(hasattr(_lowercase	,     """do_resize"""				)				)
     self.assertTrue(hasattr(_lowercase	,     """do_center_crop"""				)				)
     self.assertTrue(hasattr(_lowercase	,     """size"""				)				)




   def      __a						(   self      :    Optional[Any]				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				self.image_processing_class.from_dict(self.image_processor_dict				)
     self.assertEqual(image_processor.size	,     {"""shortest_edge""": 18}				)
     self.assertEqual(image_processor.crop_size	,     {"""height""": 18, """width""": 18}				)

     SCREAMING_SNAKE_CASE__      							=				self.image_processing_class.from_dict(self.image_processor_dict	,     size=42	,     crop_size=84				)
     self.assertEqual(image_processor.size	,     {"""shortest_edge""": 42}				)
     self.assertEqual(image_processor.crop_size	,     {"""height""": 84, """width""": 84}				)




   def      __a						(   self      :    Union[str, Any]				):



     """simple docstring"""






     pass




   def      __a						(   self      :    List[Any]				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				self.image_processing_class(**self.image_processor_dict				)
     # create random PIL images
     SCREAMING_SNAKE_CASE__      							=				prepare_image_inputs(self.image_processor_tester	,     equal_resolution=_lowercase				)
     for image in image_inputs:
       self.assertIsInstance(_lowercase	,     Image.Image				)

     # Test not batched input
     SCREAMING_SNAKE_CASE__      							=				image_processing(image_inputs[0]	,     return_tensors="""pt"""				).pixel_values
     self.assertEqual(
         encoded_images.shape	,     (
             1,
             self.image_processor_tester.num_channels,
             self.image_processor_tester.crop_size["""height"""],
             self.image_processor_tester.crop_size["""width"""],
         )	,     )

     # Test batched
     SCREAMING_SNAKE_CASE__      							=				image_processing(_lowercase	,     return_tensors="""pt"""				).pixel_values
     self.assertEqual(
         encoded_images.shape	,     (
             self.image_processor_tester.batch_size,
             self.image_processor_tester.num_channels,
             self.image_processor_tester.crop_size["""height"""],
             self.image_processor_tester.crop_size["""width"""],
         )	,     )




   def      __a						(   self      :    List[str]				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				self.image_processing_class(**self.image_processor_dict				)
     # create random numpy tensors
     SCREAMING_SNAKE_CASE__      							=				prepare_image_inputs(self.image_processor_tester	,     equal_resolution=_lowercase	,     numpify=_lowercase				)
     for image in image_inputs:
       self.assertIsInstance(_lowercase	,     np.ndarray				)

     # Test not batched input
     SCREAMING_SNAKE_CASE__      							=				image_processing(image_inputs[0]	,     return_tensors="""pt"""				).pixel_values
     self.assertEqual(
         encoded_images.shape	,     (
             1,
             self.image_processor_tester.num_channels,
             self.image_processor_tester.crop_size["""height"""],
             self.image_processor_tester.crop_size["""width"""],
         )	,     )

     # Test batched
     SCREAMING_SNAKE_CASE__      							=				image_processing(_lowercase	,     return_tensors="""pt"""				).pixel_values
     self.assertEqual(
         encoded_images.shape	,     (
             self.image_processor_tester.batch_size,
             self.image_processor_tester.num_channels,
             self.image_processor_tester.crop_size["""height"""],
             self.image_processor_tester.crop_size["""width"""],
         )	,     )




   def      __a						(   self      :    Dict				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				self.image_processing_class(**self.image_processor_dict				)
     # create random PyTorch tensors
     SCREAMING_SNAKE_CASE__      							=				prepare_image_inputs(self.image_processor_tester	,     equal_resolution=_lowercase	,     torchify=_lowercase				)
     for image in image_inputs:
       self.assertIsInstance(_lowercase	,     torch.Tensor				)

     # Test not batched input
     SCREAMING_SNAKE_CASE__      							=				image_processing(image_inputs[0]	,     return_tensors="""pt"""				).pixel_values
     self.assertEqual(
         encoded_images.shape	,     (
             1,
             self.image_processor_tester.num_channels,
             self.image_processor_tester.crop_size["""height"""],
             self.image_processor_tester.crop_size["""width"""],
         )	,     )

     # Test batched
     SCREAMING_SNAKE_CASE__      							=				image_processing(_lowercase	,     return_tensors="""pt"""				).pixel_values
     self.assertEqual(
         encoded_images.shape	,     (
             self.image_processor_tester.batch_size,
             self.image_processor_tester.num_channels,
             self.image_processor_tester.crop_size["""height"""],
             self.image_processor_tester.crop_size["""width"""],
         )	,     )

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				)
