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


if is_torch_available():
     import torch

if is_vision_available():
     from PIL import Image

     from transformers import SwinaSRImageProcessor
     from transformers.image_transforms import get_image_size
class SCREAMING_SNAKE_CASE_		(			unittest.TestCase		):







   def __init__(    self	:	Union[str, Any]      ,      lowerCamelCase_	:	Dict      ,      lowerCamelCase_	:	str=7      ,      lowerCamelCase_	:	Any=3      ,      lowerCamelCase_	:	Any=18      ,      lowerCamelCase_	:	Any=30      ,      lowerCamelCase_	:	Tuple=400      ,      lowerCamelCase_	:	str=True      ,      lowerCamelCase_	:	Optional[Any]=1 / 255      ,      lowerCamelCase_	:	Union[str, Any]=True      ,      lowerCamelCase_	:	Optional[Any]=8      ,      ):





    """simple docstring"""


    UpperCamelCase      =			parent
    UpperCamelCase      =			batch_size
    UpperCamelCase      =			num_channels
    UpperCamelCase      =			image_size
    UpperCamelCase      =			min_resolution
    UpperCamelCase      =			max_resolution
    UpperCamelCase      =			do_rescale
    UpperCamelCase      =			rescale_factor
    UpperCamelCase      =			do_pad
    UpperCamelCase      =			pad_size







   def 	lowerCamelCase_  (    self	:	str							):





    """simple docstring"""


    return {
        "do_rescale": self.do_rescale,
        "rescale_factor": self.rescale_factor,
        "do_pad": self.do_pad,
        "pad_size": self.pad_size,
    }







   def 	lowerCamelCase_  (    self	:	str      ,      lowerCamelCase_	:	str=False      ,      lowerCamelCase_	:	Union[str, Any]=False      ,      lowerCamelCase_	:	Tuple=False							):





    """simple docstring"""


    assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

    if equal_resolution:
     UpperCamelCase      =			[]
     for i in range(self.batch_size							):
      image_inputs.append(
          np.random.randint(
              255      ,      size=(self.num_channels, self.max_resolution, self.max_resolution)      ,      dtype=np.uinta							)							)
    else:
     UpperCamelCase      =			[]
     for i in range(self.batch_size							):
      UpperCamelCase     ,    UpperCamelCase      =			np.random.choice(np.arange(self.min_resolution      ,      self.max_resolution							)      ,      2							)
      image_inputs.append(np.random.randint(255      ,      size=(self.num_channels, width, height)      ,      dtype=np.uinta							)							)

    if not numpify and not torchify:
     # PIL expects the channel dimension as last dimension
     UpperCamelCase      =			[Image.fromarray(np.moveaxis(lowerCamelCase_      ,      0      ,      -1							)							) for x in image_inputs]

    if torchify:
     UpperCamelCase      =			[torch.from_numpy(lowerCamelCase_							) for x in image_inputs]

    return image_inputs





@require_torch
@require_vision
class SCREAMING_SNAKE_CASE_		(			__lowerCAmelCase     ,    unittest.TestCase		):
   __lowerCAmelCase											=							SwinaSRImageProcessor if is_vision_available() else None







   def 	lowerCamelCase_  (    self	:	Any							):





    """simple docstring"""


    UpperCamelCase      =			SwinaSRImageProcessingTester(self							)







   @property
   def 	lowerCamelCase_  (    self	:	int							):





    """simple docstring"""


    return self.image_processor_tester.prepare_image_processor_dict()







   def 	lowerCamelCase_  (    self	:	Optional[Any]							):





    """simple docstring"""


    UpperCamelCase      =			self.image_processing_class(**self.image_processor_dict							)
    self.assertTrue(hasattr(lowerCamelCase_      ,      """do_rescale"""							)							)
    self.assertTrue(hasattr(lowerCamelCase_      ,      """rescale_factor"""							)							)
    self.assertTrue(hasattr(lowerCamelCase_      ,      """do_pad"""							)							)
    self.assertTrue(hasattr(lowerCamelCase_      ,      """pad_size"""							)							)







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





    """simple docstring"""


    pass







   def 	lowerCamelCase_  (    self	:	List[Any]      ,      lowerCamelCase_	:	int							):





    """simple docstring"""


    UpperCamelCase     ,    UpperCamelCase      =			get_image_size(lowerCamelCase_							)
    UpperCamelCase      =			self.image_processor_tester.pad_size

    UpperCamelCase      =			(old_height // size + 1) * size - old_height
    UpperCamelCase      =			(old_width // size + 1) * size - old_width
    return old_height + pad_height, old_width + pad_width







   def 	lowerCamelCase_  (    self	:	Optional[int]							):





    """simple docstring"""


    UpperCamelCase      =			self.image_processing_class(**self.image_processor_dict							)
    # create random PIL images
    UpperCamelCase      =			self.image_processor_tester.prepare_inputs(equal_resolution=lowerCamelCase_							)
    for image in image_inputs:
     self.assertIsInstance(lowerCamelCase_      ,      Image.Image							)

    # Test not batched input
    UpperCamelCase      =			image_processor(image_inputs[0]      ,      return_tensors="""pt"""							).pixel_values
    UpperCamelCase     ,    UpperCamelCase      =			self.calculate_expected_size(np.array(image_inputs[0]							)							)
    self.assertEqual(
        encoded_images.shape      ,      (
            1,
            self.image_processor_tester.num_channels,
            expected_height,
            expected_width,
        )      ,      )







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





    """simple docstring"""


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

    # Test not batched input
    UpperCamelCase      =			image_processor(image_inputs[0]      ,      return_tensors="""pt"""							).pixel_values
    UpperCamelCase     ,    UpperCamelCase      =			self.calculate_expected_size(image_inputs[0]							)
    self.assertEqual(
        encoded_images.shape      ,      (
            1,
            self.image_processor_tester.num_channels,
            expected_height,
            expected_width,
        )      ,      )







   def 	lowerCamelCase_  (    self	:	Any							):





    """simple docstring"""


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

    # Test not batched input
    UpperCamelCase      =			image_processor(image_inputs[0]      ,      return_tensors="""pt"""							).pixel_values
    UpperCamelCase     ,    UpperCamelCase      =			self.calculate_expected_size(image_inputs[0]							)
    self.assertEqual(
        encoded_images.shape      ,      (
            1,
            self.image_processor_tester.num_channels,
            expected_height,
            expected_width,
        )      ,      )

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


_SCREAMING_SNAKE_CASE   		=			logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE   		=			{
    """tau/splinter-base""": """https://huggingface.co/tau/splinter-base/resolve/main/config.json""",
    """tau/splinter-base-qass""": """https://huggingface.co/tau/splinter-base-qass/resolve/main/config.json""",
    """tau/splinter-large""": """https://huggingface.co/tau/splinter-large/resolve/main/config.json""",
    """tau/splinter-large-qass""": """https://huggingface.co/tau/splinter-large-qass/resolve/main/config.json""",
    # See all Splinter models at https://huggingface.co/models?filter=splinter
}
class SCREAMING_SNAKE_CASE_		(			__lowerCAmelCase		):
   __lowerCAmelCase											=							"""splinter"""







   def __init__(    self	:	List[Any]      ,      lowerCamelCase_	:	int=3_0522      ,      lowerCamelCase_	:	Any=768      ,      lowerCamelCase_	:	List[Any]=12      ,      lowerCamelCase_	:	Tuple=12      ,      lowerCamelCase_	:	Tuple=3072      ,      lowerCamelCase_	:	Any="gelu"      ,      lowerCamelCase_	:	Union[str, Any]=0.1      ,      lowerCamelCase_	:	str=0.1      ,      lowerCamelCase_	:	int=512      ,      lowerCamelCase_	:	List[Any]=2      ,      lowerCamelCase_	:	List[Any]=0.0_2      ,      lowerCamelCase_	:	Dict=1E-12      ,      lowerCamelCase_	:	Tuple=True      ,      lowerCamelCase_	:	str=0      ,      lowerCamelCase_	:	Any=104      ,      **lowerCamelCase_	:	Dict      ,      ):





    """simple docstring"""


    super().__init__(pad_token_id=lowerCamelCase_      ,      **lowerCamelCase_							)

    UpperCamelCase      =			vocab_size
    UpperCamelCase      =			max_position_embeddings
    UpperCamelCase      =			hidden_size
    UpperCamelCase      =			num_hidden_layers
    UpperCamelCase      =			num_attention_heads
    UpperCamelCase      =			intermediate_size
    UpperCamelCase      =			hidden_act
    UpperCamelCase      =			hidden_dropout_prob
    UpperCamelCase      =			attention_probs_dropout_prob
    UpperCamelCase      =			initializer_range
    UpperCamelCase      =			type_vocab_size
    UpperCamelCase      =			layer_norm_eps
    UpperCamelCase      =			use_cache
    UpperCamelCase      =			question_token_id
