





"""simple docstring"""




import unittest

import numpy as np

from transformers.testing_utils import is_flaky, 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 DonutImageProcessor
class     SCREAMING_SNAKE_CASE       (unittest.TestCase				):


   def __init__(   self      :    List[Any]      ,					a      :    Dict      ,					a      :    Optional[int]=7      ,					a      :    Optional[int]=3      ,					a      :    Optional[int]=18      ,					a      :    Optional[Any]=30      ,					a      :    Any=400      ,					a      :    Dict=True      ,					a      :    str=None      ,					a      :    Union[str, Any]=True      ,					a      :    List[Any]=False      ,					a      :    List[str]=True      ,					a      :    Any=True      ,					a      :    List[str]=[0.5, 0.5, 0.5]      ,					a      :    Any=[0.5, 0.5, 0.5]      ,					)->       Optional[int]:





     """simple docstring"""







     lowercase__           =  parent
     lowercase__           =  batch_size
     lowercase__           =  num_channels
     lowercase__           =  image_size
     lowercase__           =  min_resolution
     lowercase__           =  max_resolution
     lowercase__           =  do_resize
     lowercase__           =  size if size is not None else {"height": 18, "width": 20}
     lowercase__           =  do_thumbnail
     lowercase__           =  do_align_axis
     lowercase__           =  do_pad
     lowercase__           =  do_normalize
     lowercase__           =  image_mean
     lowercase__           =  image_std


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Tuple	)->       Union[str, Any]:





     """simple docstring"""







     return {
         "do_resize": self.do_resize,
         "size": self.size,
         "do_thumbnail": self.do_thumbnail,
         "do_align_long_axis": self.do_align_axis,
         "do_pad": self.do_pad,
         "do_normalize": self.do_normalize,
         "image_mean": self.image_mean,
         "image_std": self.image_std,
     }






@require_torch
@require_vision
class     SCREAMING_SNAKE_CASE       (lowerCamelCase__      ,     unittest.TestCase				):
   _UpperCamelCase   :		Any			 =	DonutImageProcessor if is_vision_available() else None


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Dict	)->       Any:





     """simple docstring"""







     lowercase__           =  DonutImageProcessingTester(self	)


   @property
   def 	SCREAMING_SNAKE_CASE_							(   self      :    Any	)->       Optional[Any]:





     """simple docstring"""







     return self.image_processor_tester.prepare_image_processor_dict()


   def 	SCREAMING_SNAKE_CASE_							(   self      :    int	)->       Optional[int]:





     """simple docstring"""







     lowercase__           =  self.image_processing_class(**self.image_processor_dict	)
     self.assertTrue(hasattr(a      ,					'do_resize'	)	)
     self.assertTrue(hasattr(a      ,					'size'	)	)
     self.assertTrue(hasattr(a      ,					'do_thumbnail'	)	)
     self.assertTrue(hasattr(a      ,					'do_align_long_axis'	)	)
     self.assertTrue(hasattr(a      ,					'do_pad'	)	)
     self.assertTrue(hasattr(a      ,					'do_normalize'	)	)
     self.assertTrue(hasattr(a      ,					'image_mean'	)	)
     self.assertTrue(hasattr(a      ,					'image_std'	)	)


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Optional[Any]	)->       Optional[Any]:





     """simple docstring"""







     lowercase__           =  self.image_processing_class.from_dict(self.image_processor_dict	)
     self.assertEqual(image_processor.size      ,					{'height': 18, 'width': 20}	)

     lowercase__           =  self.image_processing_class.from_dict(self.image_processor_dict      ,					size=42	)
     self.assertEqual(image_processor.size      ,					{'height': 42, 'width': 42}	)

     # Previous config had dimensions in (width, height) order
     lowercase__           =  self.image_processing_class.from_dict(self.image_processor_dict      ,					size=(42, 84)	)
     self.assertEqual(image_processor.size      ,					{'height': 84, 'width': 42}	)


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Union[str, Any]	)->       int:





     """simple docstring"""







     pass


   @is_flaky()
   def 	SCREAMING_SNAKE_CASE_							(   self      :    int	)->       int:





     """simple docstring"""







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

     # Test not batched input
     lowercase__           =  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.size['height'],
             self.image_processor_tester.size['width'],
         )      ,					)

     # Test batched
     lowercase__           =  image_processing(a      ,					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.size['height'],
             self.image_processor_tester.size['width'],
         )      ,					)


   @is_flaky()
   def 	SCREAMING_SNAKE_CASE_							(   self      :    int	)->       Dict:





     """simple docstring"""







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

     # Test not batched input
     lowercase__           =  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.size['height'],
             self.image_processor_tester.size['width'],
         )      ,					)

     # Test batched
     lowercase__           =  image_processing(a      ,					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.size['height'],
             self.image_processor_tester.size['width'],
         )      ,					)


   @is_flaky()
   def 	SCREAMING_SNAKE_CASE_							(   self      :    Dict	)->       Optional[int]:





     """simple docstring"""







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

     # Test not batched input
     lowercase__           =  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.size['height'],
             self.image_processor_tester.size['width'],
         )      ,					)

     # Test batched
     lowercase__           =  image_processing(a      ,					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.size['height'],
             self.image_processor_tester.size['width'],
         )      ,					)









import warnings

from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowercase_          =		logging.get_logger(__name__)

lowercase_          =		{
    """xlnet-base-cased""": """https://huggingface.co/xlnet-base-cased/resolve/main/config.json""",
    """xlnet-large-cased""": """https://huggingface.co/xlnet-large-cased/resolve/main/config.json""",
}
class     SCREAMING_SNAKE_CASE       (UpperCAmelCase				):
   _UpperCamelCase   :		str			 =	'xlnet'
   _UpperCamelCase   :		Optional[Any]			 =	['mems']
   _UpperCamelCase   :		Union[str, Any]			 =	{
       'n_token': 'vocab_size',  # Backward compatibility
       'hidden_size': 'd_model',
       'num_attention_heads': 'n_head',
       'num_hidden_layers': 'n_layer',
   }


   def __init__(   self      :    str      ,					a      :    int=32_000      ,					a      :    Any=1_024      ,					a      :    Optional[Any]=24      ,					a      :    str=16      ,					a      :    int=4_096      ,					a      :    List[str]="gelu"      ,					a      :    Any=True      ,					a      :    Dict="bi"      ,					a      :    str=0.02      ,					a      :    List[str]=1E-1_2      ,					a      :    Tuple=0.1      ,					a      :    Optional[Any]=512      ,					a      :    Tuple=None      ,					a      :    Union[str, Any]=True      ,					a      :    int=False      ,					a      :    int=False      ,					a      :    Tuple=-1      ,					a      :    Any=False      ,					a      :    Tuple="last"      ,					a      :    List[str]=True      ,					a      :    Optional[Any]="tanh"      ,					a      :    List[Any]=0.1      ,					a      :    int=5      ,					a      :    List[Any]=5      ,					a      :    Optional[int]=5      ,					a      :    Dict=1      ,					a      :    Optional[Any]=2      ,					**a      :    List[Any]      ,					)->       List[str]:





     """simple docstring"""







     lowercase__           =  vocab_size
     lowercase__           =  d_model
     lowercase__           =  n_layer
     lowercase__           =  n_head
     if d_model % n_head != 0:
       raise ValueError(f"""'d_model % n_head' ({d_model % n_head}) should be equal to 0"""	)
     if "d_head" in kwargs:
       if kwargs["d_head"] != d_model // n_head:
         raise ValueError(
             f"""`d_head` ({kwargs["d_head"]}) should be equal to `d_model // n_head` ({d_model // n_head})"""	)
     lowercase__           =  d_model // n_head
     lowercase__           =  ff_activation
     lowercase__           =  d_inner
     lowercase__           =  untie_r
     lowercase__           =  attn_type

     lowercase__           =  initializer_range
     lowercase__           =  layer_norm_eps

     lowercase__           =  dropout
     lowercase__           =  mem_len
     lowercase__           =  reuse_len
     lowercase__           =  bi_data
     lowercase__           =  clamp_len
     lowercase__           =  same_length

     lowercase__           =  summary_type
     lowercase__           =  summary_use_proj
     lowercase__           =  summary_activation
     lowercase__           =  summary_last_dropout
     lowercase__           =  start_n_top
     lowercase__           =  end_n_top

     lowercase__           =  bos_token_id
     lowercase__           =  pad_token_id
     lowercase__           =  eos_token_id

     if "use_cache" in kwargs:
       warnings.warn(
           'The `use_cache` argument is deprecated and will be removed in a future version, use `use_mems_eval`'
           ' instead.'      ,					a      ,					)
       lowercase__           =  kwargs['use_cache']

     lowercase__           =  use_mems_eval
     lowercase__           =  use_mems_train
     super().__init__(pad_token_id=a      ,					bos_token_id=a      ,					eos_token_id=a      ,					**a	)


   @property
   def 	SCREAMING_SNAKE_CASE_							(   self      :    Optional[Any]	)->       List[str]:





     """simple docstring"""







     logger.info(f"""The model {self.model_type} is one of the few models that has no sequence length limit."""	)
     return -1


   @max_position_embeddings.setter
   def 	SCREAMING_SNAKE_CASE_							(   self      :    Tuple      ,					a      :    Union[str, Any]	)->       List[Any]:





     """simple docstring"""







     raise NotImplementedError(
         f"""The model {self.model_type} is one of the few models that has no sequence length limit."""	)


