





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 PoolFormerImageProcessor






class        snake_case_    (		unittest.TestCase						):






  def __init__(							self			:					List[str]       ,  _snake_case			:					Union[str, Any]       ,  _snake_case			:					List[Any]=7       ,  _snake_case			:					str=3       ,  _snake_case			:					Optional[int]=30       ,  _snake_case			:					Any=400       ,  _snake_case			:					Dict=True       ,  _snake_case			:					Any=None       ,  _snake_case			:					Optional[int]=0.9       ,  _snake_case			:					Union[str, Any]=None       ,  _snake_case			:					str=True       ,  _snake_case			:					List[Any]=[0.5, 0.5, 0.5]       ,  _snake_case			:					Dict=[0.5, 0.5, 0.5]       ,  )->Optional[Any]:






   '''simple docstring'''

   __lowerCAmelCase					:	Optional[int]      =       size if size is not None else {"""shortest_edge""": 30}
   __lowerCAmelCase					:	int      =       crop_size if crop_size is not None else {"""height""": 30, """width""": 30}
   __lowerCAmelCase					:	Dict      =       parent
   __lowerCAmelCase					:	Tuple      =       batch_size
   __lowerCAmelCase					:	Union[str, Any]      =       num_channels
   __lowerCAmelCase					:	List[str]      =       min_resolution
   __lowerCAmelCase					:	int      =       max_resolution
   __lowerCAmelCase					:	Union[str, Any]      =       do_resize_and_center_crop
   __lowerCAmelCase					:	Dict      =       size
   __lowerCAmelCase					:	Any      =       crop_pct
   __lowerCAmelCase					:	List[str]      =       crop_size
   __lowerCAmelCase					:	List[Any]      =       do_normalize
   __lowerCAmelCase					:	List[Any]      =       image_mean
   __lowerCAmelCase					:	Any      =       image_std






  def 				UpperCAmelCase__						(							self			:					List[str]      )->int:






   '''simple docstring'''

   return {
       "size": self.size,
       "do_resize_and_center_crop": self.do_resize_and_center_crop,
       "crop_pct": self.crop_pct,
       "crop_size": self.crop_size,
       "do_normalize": self.do_normalize,
       "image_mean": self.image_mean,
       "image_std": self.image_std,
   }






@require_torch
@require_vision
class        snake_case_    (		__lowercase					,unittest.TestCase						):
  A_      							=   PoolFormerImageProcessor if is_vision_available() else None






  def 				UpperCAmelCase__						(							self			:					Union[str, Any]      )->List[str]:






   '''simple docstring'''

   __lowerCAmelCase					:	Optional[int]      =       PoolFormerImageProcessingTester(self      )






  @property
  def 				UpperCAmelCase__						(							self			:					Dict      )->Any:






   '''simple docstring'''

   return self.image_processor_tester.prepare_image_processor_dict()






  def 				UpperCAmelCase__						(							self			:					Any      )->str:






   '''simple docstring'''

   __lowerCAmelCase					:	Union[str, Any]      =       self.image_processing_class(**self.image_processor_dict      )
   self.assertTrue(hasattr(_snake_case       ,  """do_resize_and_center_crop"""      )      )
   self.assertTrue(hasattr(_snake_case       ,  """size"""      )      )
   self.assertTrue(hasattr(_snake_case       ,  """crop_pct"""      )      )
   self.assertTrue(hasattr(_snake_case       ,  """do_normalize"""      )      )
   self.assertTrue(hasattr(_snake_case       ,  """image_mean"""      )      )
   self.assertTrue(hasattr(_snake_case       ,  """image_std"""      )      )






  def 				UpperCAmelCase__						(							self			:					List[str]      )->Union[str, Any]:






   '''simple docstring'''

   __lowerCAmelCase					:	List[str]      =       self.image_processing_class.from_dict(self.image_processor_dict      )
   self.assertEqual(image_processor.size       ,  {"""shortest_edge""": 30}      )
   self.assertEqual(image_processor.crop_size       ,  {"""height""": 30, """width""": 30}      )

   __lowerCAmelCase					:	List[str]      =       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 				UpperCAmelCase__						(							self			:					List[Any]      )->Any:






   '''simple docstring'''

   pass






  def 				UpperCAmelCase__						(							self			:					Any      )->List[str]:






   '''simple docstring'''

   __lowerCAmelCase					:	List[Any]      =       self.image_processing_class(**self.image_processor_dict      )
   # create random PIL images
   __lowerCAmelCase					:	Optional[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
   __lowerCAmelCase					:	str      =       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
   __lowerCAmelCase					:	Dict      =       image_processing(_snake_case       ,  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 				UpperCAmelCase__						(							self			:					Dict      )->str:






   '''simple docstring'''

   __lowerCAmelCase					:	Optional[int]      =       self.image_processing_class(**self.image_processor_dict      )
   # create random numpy tensors
   __lowerCAmelCase					:	Dict      =       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
   __lowerCAmelCase					:	Optional[int]      =       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
   __lowerCAmelCase					:	List[Any]      =       image_processing(_snake_case       ,  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 				UpperCAmelCase__						(							self			:					Any      )->Optional[int]:






   '''simple docstring'''

   __lowerCAmelCase					:	Union[str, Any]      =       self.image_processing_class(**self.image_processor_dict      )
   # create random PyTorch tensors
   __lowerCAmelCase					:	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
   __lowerCAmelCase					:	Union[str, Any]      =       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
   __lowerCAmelCase					:	Optional[int]      =       image_processing(_snake_case       ,  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"""],
       )       ,  )






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


_UpperCAmelCase             =							logging.get_logger(__name__)

_UpperCAmelCase             =							{
    'microsoft/biogpt': 'https://huggingface.co/microsoft/biogpt/resolve/main/config.json',
    # See all BioGPT models at https://huggingface.co/models?filter=biogpt
}






class        snake_case_    (		__lowercase						):
  A_      							=   'biogpt'






  def __init__(							self			:					int       ,  _snake_case			:					Any=42384       ,  _snake_case			:					Any=1024       ,  _snake_case			:					List[Any]=24       ,  _snake_case			:					Any=16       ,  _snake_case			:					List[str]=4096       ,  _snake_case			:					Dict="gelu"       ,  _snake_case			:					Tuple=0.1       ,  _snake_case			:					str=0.1       ,  _snake_case			:					Tuple=1024       ,  _snake_case			:					Tuple=0.02       ,  _snake_case			:					Tuple=1E-12       ,  _snake_case			:					Optional[int]=True       ,  _snake_case			:					Optional[int]=True       ,  _snake_case			:					Any=0.0       ,  _snake_case			:					Tuple=0.0       ,  _snake_case			:					str=1       ,  _snake_case			:					Dict=0       ,  _snake_case			:					str=2       ,  **_snake_case			:					Union[str, Any]       ,  )->Dict:






   '''simple docstring'''

   __lowerCAmelCase					:	List[Any]      =       vocab_size
   __lowerCAmelCase					:	Dict      =       max_position_embeddings
   __lowerCAmelCase					:	str      =       hidden_size
   __lowerCAmelCase					:	Dict      =       num_hidden_layers
   __lowerCAmelCase					:	List[Any]      =       num_attention_heads
   __lowerCAmelCase					:	Optional[Any]      =       intermediate_size
   __lowerCAmelCase					:	int      =       hidden_act
   __lowerCAmelCase					:	Any      =       hidden_dropout_prob
   __lowerCAmelCase					:	Any      =       attention_probs_dropout_prob
   __lowerCAmelCase					:	Any      =       initializer_range
   __lowerCAmelCase					:	int      =       layer_norm_eps
   __lowerCAmelCase					:	Optional[int]      =       scale_embedding
   __lowerCAmelCase					:	List[Any]      =       use_cache
   __lowerCAmelCase					:	str      =       layerdrop
   __lowerCAmelCase					:	Dict      =       activation_dropout
   super().__init__(pad_token_id=_snake_case       ,  bos_token_id=_snake_case       ,  eos_token_id=_snake_case       ,  **_snake_case      )