

import unittest

import numpy as np

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

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 DPTImageProcessor







class        a__       (   unittest.TestCase       ):







  """simple docstring"""




  def __init__(			self    : List[Any]				,				UpperCAmelCase__    : str				,				UpperCAmelCase__    : Optional[int]=7				,				UpperCAmelCase__    : List[str]=3				,				UpperCAmelCase__    : int=1_8				,				UpperCAmelCase__    : List[Any]=3_0				,				UpperCAmelCase__    : Optional[int]=4_0_0				,				UpperCAmelCase__    : Tuple=True				,				UpperCAmelCase__    : Tuple=None				,				UpperCAmelCase__    : str=True				,				UpperCAmelCase__    : Union[str, Any]=[0.5, 0.5, 0.5]				,				UpperCAmelCase__    : List[Any]=[0.5, 0.5, 0.5]				,				)					->Any:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      Optional[Any]       =   size if size is not None else {"""height""": 1_8, """width""": 1_8}
        SCREAMING_SNAKE_CASE :      Optional[int]       =   parent
        SCREAMING_SNAKE_CASE :      List[Any]       =   batch_size
        SCREAMING_SNAKE_CASE :      Tuple       =   num_channels
        SCREAMING_SNAKE_CASE :      str       =   image_size
        SCREAMING_SNAKE_CASE :      List[Any]       =   min_resolution
        SCREAMING_SNAKE_CASE :      Optional[int]       =   max_resolution
        SCREAMING_SNAKE_CASE :      List[Any]       =   do_resize
        SCREAMING_SNAKE_CASE :      Dict       =   size
        SCREAMING_SNAKE_CASE :      List[str]       =   do_normalize
        SCREAMING_SNAKE_CASE :      Tuple       =   image_mean
        SCREAMING_SNAKE_CASE :      Optional[Any]       =   image_std




  def  _lowercase      (			self    : int		)					->Dict:
        """simple docstring"""







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







@require_torch
@require_vision
class        a__       (   UpperCAmelCase	,			unittest.TestCase       ):







  """simple docstring"""




  UpperCAmelCase__      : str			       =DPTImageProcessor if is_vision_available() else None


  def  _lowercase      (			self    : Union[str, Any]		)					->Any:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      List[Any]       =   DPTImageProcessingTester(self		)


  @property
  def  _lowercase      (			self    : int		)					->Dict:
        """simple docstring"""







        return self.image_processor_tester.prepare_image_processor_dict()


  def  _lowercase      (			self    : int		)					->List[str]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      Tuple       =   self.image_processing_class(**self.image_processor_dict		)
        self.assertTrue(hasattr(UpperCAmelCase__				,				"""image_mean"""		)		)
        self.assertTrue(hasattr(UpperCAmelCase__				,				"""image_std"""		)		)
        self.assertTrue(hasattr(UpperCAmelCase__				,				"""do_normalize"""		)		)
        self.assertTrue(hasattr(UpperCAmelCase__				,				"""do_resize"""		)		)
        self.assertTrue(hasattr(UpperCAmelCase__				,				"""size"""		)		)


  def  _lowercase      (			self    : List[str]		)					->Tuple:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      Dict       =   self.image_processing_class.from_dict(self.image_processor_dict		)
        self.assertEqual(image_processor.size				,				{"""height""": 1_8, """width""": 1_8}		)

        SCREAMING_SNAKE_CASE :      Optional[Any]       =   self.image_processing_class.from_dict(self.image_processor_dict				,				size=4_2		)
        self.assertEqual(image_processor.size				,				{"""height""": 4_2, """width""": 4_2}		)


  def  _lowercase      (			self    : Tuple		)					->Optional[int]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      int       =   self.image_processing_class(**self.image_processor_dict		)
        # create random PIL images
        SCREAMING_SNAKE_CASE :      Optional[int]       =   prepare_image_inputs(self.image_processor_tester				,				equal_resolution=UpperCAmelCase__		)
        for image in image_inputs:
              self.assertIsInstance(UpperCAmelCase__				,				Image.Image		)

        # Test not batched input
        SCREAMING_SNAKE_CASE :      Optional[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.size["""height"""],
                self.image_processor_tester.size["""width"""],
            )				,				)

        # Test batched
        SCREAMING_SNAKE_CASE :      Dict       =   image_processing(UpperCAmelCase__				,				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"""],
            )				,				)


  def  _lowercase      (			self    : int		)					->Optional[Any]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      Tuple       =   self.image_processing_class(**self.image_processor_dict		)
        # create random numpy tensors
        SCREAMING_SNAKE_CASE :      Optional[int]       =   prepare_image_inputs(self.image_processor_tester				,				equal_resolution=UpperCAmelCase__				,				numpify=UpperCAmelCase__		)
        for image in image_inputs:
              self.assertIsInstance(UpperCAmelCase__				,				np.ndarray		)

        # Test not batched input
        SCREAMING_SNAKE_CASE :      Optional[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.size["""height"""],
                self.image_processor_tester.size["""width"""],
            )				,				)

        # Test batched
        SCREAMING_SNAKE_CASE :      Dict       =   image_processing(UpperCAmelCase__				,				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"""],
            )				,				)




  def  _lowercase      (			self    : Optional[int]		)					->str:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      int       =   self.image_processing_class(**self.image_processor_dict		)
        # create random PyTorch tensors
        SCREAMING_SNAKE_CASE :      Optional[int]       =   prepare_image_inputs(self.image_processor_tester				,				equal_resolution=UpperCAmelCase__				,				torchify=UpperCAmelCase__		)
        for image in image_inputs:
              self.assertIsInstance(UpperCAmelCase__				,				torch.Tensor		)

        # Test not batched input
        SCREAMING_SNAKE_CASE :      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.size["""height"""],
                self.image_processor_tester.size["""width"""],
            )				,				)

        # Test batched
        SCREAMING_SNAKE_CASE :      Optional[Any]       =   image_processing(UpperCAmelCase__				,				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"""],
            )				,				)







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


UpperCAmelCase__ :     Dict				    =    logging.get_logger(__name__)

UpperCAmelCase__ :     int				    =    {
    """abeja/gpt-neox-japanese-2.7b""": """https://huggingface.co/abeja/gpt-neox-japanese-2.7b/resolve/main/config.json""",
}







class        a__       (   UpperCAmelCase       ):







  """simple docstring"""




  UpperCAmelCase__      : Any			       ="""gpt_neox_japanese"""


  def __init__(			self    : Any				,				UpperCAmelCase__    : Any=3_2_0_0_0				,				UpperCAmelCase__    : Dict=2_5_6_0				,				UpperCAmelCase__    : List[str]=3_2				,				UpperCAmelCase__    : Optional[int]=3_2				,				UpperCAmelCase__    : List[str]=4				,				UpperCAmelCase__    : List[str]="gelu"				,				UpperCAmelCase__    : Optional[int]=1.00				,				UpperCAmelCase__    : List[Any]=1_0_0_0_0				,				UpperCAmelCase__    : Tuple=2_0_4_8				,				UpperCAmelCase__    : Optional[int]=0.02				,				UpperCAmelCase__    : Dict=1e-5				,				UpperCAmelCase__    : Dict=True				,				UpperCAmelCase__    : Tuple=3_1_9_9_6				,				UpperCAmelCase__    : Tuple=3_1_9_9_9				,				UpperCAmelCase__    : Dict=0.1				,				UpperCAmelCase__    : Union[str, Any]=0.0				,				**UpperCAmelCase__    : Optional[Any]				,				)					->Optional[Any]:
        """simple docstring"""







        super().__init__(bos_token_id=UpperCAmelCase__				,				eos_token_id=UpperCAmelCase__				,				**UpperCAmelCase__		)
        SCREAMING_SNAKE_CASE :      List[str]       =   vocab_size
        SCREAMING_SNAKE_CASE :      List[str]       =   max_position_embeddings
        SCREAMING_SNAKE_CASE :      Tuple       =   hidden_size
        SCREAMING_SNAKE_CASE :      Union[str, Any]       =   num_hidden_layers
        SCREAMING_SNAKE_CASE :      Optional[int]       =   num_attention_heads
        SCREAMING_SNAKE_CASE :      Any       =   intermediate_multiple_size
        SCREAMING_SNAKE_CASE :      Tuple       =   hidden_act
        SCREAMING_SNAKE_CASE :      Any       =   rotary_pct
        SCREAMING_SNAKE_CASE :      Tuple       =   rotary_emb_base
        SCREAMING_SNAKE_CASE :      Union[str, Any]       =   initializer_range
        SCREAMING_SNAKE_CASE :      Optional[Any]       =   layer_norm_eps
        SCREAMING_SNAKE_CASE :      str       =   use_cache
        SCREAMING_SNAKE_CASE :      Any       =   attention_dropout
        SCREAMING_SNAKE_CASE :      str       =   hidden_dropout




