


'''simple docstring'''


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 GLPNImageProcessor





class     lowercase__  (						unittest.TestCase					):





							'''simple docstring'''


							def __init__(    self    ,					__snake_case    ,					__snake_case=7    ,					__snake_case=3    ,					__snake_case=18    ,					__snake_case=30    ,					__snake_case=400    ,					__snake_case=True    ,					__snake_case=32    ,					__snake_case=True    ,					):
											_SCREAMING_SNAKE_CASE :       int     		=      parent
											_SCREAMING_SNAKE_CASE :       str     		=      batch_size
											_SCREAMING_SNAKE_CASE :       Optional[int]     		=      num_channels
											_SCREAMING_SNAKE_CASE :       int     		=      image_size
											_SCREAMING_SNAKE_CASE :       Optional[int]     		=      min_resolution
											_SCREAMING_SNAKE_CASE :       List[str]     		=      max_resolution
											_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      do_resize
											_SCREAMING_SNAKE_CASE :       Optional[int]     		=      size_divisor
											_SCREAMING_SNAKE_CASE :       int     		=      do_rescale






							def UpperCAmelCase_	(    self  ):
											return {
											    "do_resize": self.do_resize,
											    "size_divisor": self.size_divisor,
											    "do_rescale": self.do_rescale,
											}





@require_torch
@require_vision
class     lowercase__  (						_snake_case     ,     unittest.TestCase					):





							'''simple docstring'''


							A_							:      List[Any]      	=  GLPNImageProcessor if is_vision_available() else None






							def UpperCAmelCase_	(    self  ):
											_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      GLPNImageProcessingTester(self  )






							@property
							def UpperCAmelCase_	(    self  ):
											return self.image_processor_tester.prepare_image_processor_dict()






							def UpperCAmelCase_	(    self  ):
											_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      self.image_processing_class(**self.image_processor_dict  )
											self.assertTrue(hasattr(__snake_case    ,					"""do_resize"""  )  )
											self.assertTrue(hasattr(__snake_case    ,					"""size_divisor"""  )  )
											self.assertTrue(hasattr(__snake_case    ,					"""resample"""  )  )
											self.assertTrue(hasattr(__snake_case    ,					"""do_rescale"""  )  )






							def UpperCAmelCase_	(    self  ):
											pass






							def UpperCAmelCase_	(    self  ):
											# Initialize image_processing
											_SCREAMING_SNAKE_CASE :       Any     		=      self.image_processing_class(**self.image_processor_dict  )
											# create random PIL images
											_SCREAMING_SNAKE_CASE :       List[Any]     		=      prepare_image_inputs(self.image_processor_tester    ,					equal_resolution=__snake_case  )
											for image in image_inputs:
															self.assertIsInstance(__snake_case    ,					Image.Image  )

											# Test not batched input (GLPNImageProcessor doesn't support batching)
											_SCREAMING_SNAKE_CASE :       List[Any]     		=      image_processing(image_inputs[0]    ,					return_tensors="""pt"""  ).pixel_values
											self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0  )
											self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0  )






							def UpperCAmelCase_	(    self  ):
											# Initialize image_processing
											_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      self.image_processing_class(**self.image_processor_dict  )
											# create random numpy tensors
											_SCREAMING_SNAKE_CASE :       str     		=      prepare_image_inputs(self.image_processor_tester    ,					equal_resolution=__snake_case    ,					numpify=__snake_case  )
											for image in image_inputs:
															self.assertIsInstance(__snake_case    ,					np.ndarray  )

											# Test not batched input (GLPNImageProcessor doesn't support batching)
											_SCREAMING_SNAKE_CASE :       Optional[int]     		=      image_processing(image_inputs[0]    ,					return_tensors="""pt"""  ).pixel_values
											self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0  )
											self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0  )






							def UpperCAmelCase_	(    self  ):
											# Initialize image_processing
											_SCREAMING_SNAKE_CASE :       Any     		=      self.image_processing_class(**self.image_processor_dict  )
											# create random PyTorch tensors
											_SCREAMING_SNAKE_CASE :       List[Any]     		=      prepare_image_inputs(self.image_processor_tester    ,					equal_resolution=__snake_case    ,					torchify=__snake_case  )
											for image in image_inputs:
															self.assertIsInstance(__snake_case    ,					torch.Tensor  )

											# Test not batched input (GLPNImageProcessor doesn't support batching)
											_SCREAMING_SNAKE_CASE :       int     		=      image_processing(image_inputs[0]    ,					return_tensors="""pt"""  ).pixel_values
											self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0  )
											self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0  )








'''simple docstring'''


from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


UpperCAmelCase_		:   Union[str, Any]							   =		logging.get_logger(__name__)

UpperCAmelCase_		:   Optional[int]							   =		{
    'google/bigbird-roberta-base': 'https://huggingface.co/google/bigbird-roberta-base/resolve/main/config.json',
    'google/bigbird-roberta-large': 'https://huggingface.co/google/bigbird-roberta-large/resolve/main/config.json',
    'google/bigbird-base-trivia-itc': 'https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/config.json',
    # See all BigBird models at https://huggingface.co/models?filter=big_bird
}





class     lowercase__  (						_snake_case					):





							'''simple docstring'''


							A_							:      str      	=  """big_bird"""






							def __init__(    self    ,					__snake_case=5_0358    ,					__snake_case=768    ,					__snake_case=12    ,					__snake_case=12    ,					__snake_case=3072    ,					__snake_case="gelu_new"    ,					__snake_case=0.1    ,					__snake_case=0.1    ,					__snake_case=4096    ,					__snake_case=2    ,					__snake_case=0.02    ,					__snake_case=1e-12    ,					__snake_case=True    ,					__snake_case=0    ,					__snake_case=1    ,					__snake_case=2    ,					__snake_case=66    ,					__snake_case="block_sparse"    ,					__snake_case=True    ,					__snake_case=False    ,					__snake_case=64    ,					__snake_case=3    ,					__snake_case=None    ,					**__snake_case    ,					):
											super().__init__(
											    pad_token_id=__snake_case    ,					bos_token_id=__snake_case    ,					eos_token_id=__snake_case    ,					sep_token_id=__snake_case    ,					**__snake_case    ,					)

											_SCREAMING_SNAKE_CASE :       str     		=      vocab_size
											_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      max_position_embeddings
											_SCREAMING_SNAKE_CASE :       List[str]     		=      hidden_size
											_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      num_hidden_layers
											_SCREAMING_SNAKE_CASE :       Any     		=      num_attention_heads
											_SCREAMING_SNAKE_CASE :       Optional[int]     		=      intermediate_size
											_SCREAMING_SNAKE_CASE :       List[Any]     		=      hidden_act
											_SCREAMING_SNAKE_CASE :       Optional[int]     		=      hidden_dropout_prob
											_SCREAMING_SNAKE_CASE :       Optional[int]     		=      attention_probs_dropout_prob
											_SCREAMING_SNAKE_CASE :       Tuple     		=      initializer_range
											_SCREAMING_SNAKE_CASE :       Any     		=      type_vocab_size
											_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      layer_norm_eps
											_SCREAMING_SNAKE_CASE :       List[Any]     		=      use_cache

											_SCREAMING_SNAKE_CASE :       List[Any]     		=      rescale_embeddings
											_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      attention_type
											_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      use_bias
											_SCREAMING_SNAKE_CASE :       int     		=      block_size
											_SCREAMING_SNAKE_CASE :       Any     		=      num_random_blocks
											_SCREAMING_SNAKE_CASE :       List[str]     		=      classifier_dropout





class     lowercase__  (						_snake_case					):





							'''simple docstring'''


							@property
							def UpperCAmelCase_	(    self  ):
											if self.task == "multiple-choice":
															_SCREAMING_SNAKE_CASE :       int     		=      {0: """batch""", 1: """choice""", 2: """sequence"""}
											else:
															_SCREAMING_SNAKE_CASE :       Optional[int]     		=      {0: """batch""", 1: """sequence"""}
											return OrderedDict(
											    [
											        ("""input_ids""", dynamic_axis),
											        ("""attention_mask""", dynamic_axis),
											    ]  )




