






"""simple docstring"""






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     __lowerCAmelCase (      unittest.TestCase							):







  '''simple docstring'''


  def __init__(   self:		List[str]					,      UpperCamelCase_:		Tuple					,      UpperCamelCase_:		Tuple=7					,      UpperCamelCase_:		Tuple=3					,      UpperCamelCase_:		int=18					,      UpperCamelCase_:		Union[str, Any]=30					,      UpperCamelCase_:		Optional[Any]=400					,      UpperCamelCase_:		Optional[Any]=True					,      UpperCamelCase_:		Union[str, Any]=None					,      UpperCamelCase_:		Optional[Any]=True					,      UpperCamelCase_:		List[Any]=[0.5, 0.5, 0.5]					,      UpperCamelCase_:		List[str]=[0.5, 0.5, 0.5]					,      ):
     UpperCamelCase_			     =size if size is not None else {"height": 18, "width": 18}
     UpperCamelCase_			     =parent
     UpperCamelCase_			     =batch_size
     UpperCamelCase_			     =num_channels
     UpperCamelCase_			     =image_size
     UpperCamelCase_			     =min_resolution
     UpperCamelCase_			     =max_resolution
     UpperCamelCase_			     =do_resize
     UpperCamelCase_			     =size
     UpperCamelCase_			     =do_normalize
     UpperCamelCase_			     =image_mean
     UpperCamelCase_			     =image_std





  def        UpperCamelCase__ (   self:		Optional[int]       ):
     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     __lowerCAmelCase (      UpperCAmelCase__							, unittest.TestCase							):







  '''simple docstring'''


  __lowerCamelCase							:     Tuple  						=      DPTImageProcessor if is_vision_available() else None
  def        UpperCamelCase__ (   self:		Optional[int]       ):
     UpperCamelCase_			     =DPTImageProcessingTester(self       )
  @property
  def        UpperCamelCase__ (   self:		str       ):
     return self.image_processor_tester.prepare_image_processor_dict()
  def        UpperCamelCase__ (   self:		Any       ):
     UpperCamelCase_			     =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        UpperCamelCase__ (   self:		Tuple       ):
     UpperCamelCase_			     =self.image_processing_class.from_dict(self.image_processor_dict       )
     self.assertEqual(image_processor.size					,      {"height": 18, "width": 18}       )

     UpperCamelCase_			     =self.image_processing_class.from_dict(self.image_processor_dict					,      size=42       )
     self.assertEqual(image_processor.size					,      {"height": 42, "width": 42}       )
  def        UpperCamelCase__ (   self:		str       ):
     UpperCamelCase_			     =self.image_processing_class(**self.image_processor_dict       )
     # create random PIL images
     UpperCamelCase_			     =prepare_image_inputs(self.image_processor_tester					,      equal_resolution=_UpperCAmelCase       )
     for image in image_inputs:
        self.assertIsInstance(_UpperCAmelCase					,      Image.Image       )

     # Test not batched input
     UpperCamelCase_			     =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
     UpperCamelCase_			     =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        UpperCamelCase__ (   self:		Any       ):
     UpperCamelCase_			     =self.image_processing_class(**self.image_processor_dict       )
     # create random numpy tensors
     UpperCamelCase_			     =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
     UpperCamelCase_			     =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
     UpperCamelCase_			     =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        UpperCamelCase__ (   self:		Optional[int]       ):
     UpperCamelCase_			     =self.image_processing_class(**self.image_processor_dict       )
     # create random PyTorch tensors
     UpperCamelCase_			     =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
     UpperCamelCase_			     =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
     UpperCamelCase_			     =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"],
         )					,      )




import os
import sys

A			:						Tuple        =  os.path.join(os.path.dirname(__file__), 'src')
sys.path.append(SRC_DIR)


from transformers import (
    AutoConfig,
    AutoModel,
    AutoModelForCausalLM,
    AutoModelForMaskedLM,
    AutoModelForQuestionAnswering,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    add_start_docstrings,
)


A			:						Dict        =  [
    'torch',
    'numpy',
    'tokenizers',
    'filelock',
    'requests',
    'tqdm',
    'regex',
    'sentencepiece',
    'sacremoses',
    'importlib_metadata',
    'huggingface_hub',
]






@add_start_docstrings(AutoConfig.__doc__	)
def 					UpperCamelCase   (      *__magic_name__	:				str       ,       **__magic_name__	:				List[Any]	)  ->    str:
		"""simple docstring"""




		return AutoConfig.from_pretrained(*__magic_name__       ,       **__magic_name__	)






@add_start_docstrings(AutoTokenizer.__doc__	)
def 					UpperCamelCase   (      *__magic_name__	:				Any       ,       **__magic_name__	:				Optional[Any]	)  ->    Optional[Any]:
		"""simple docstring"""




		return AutoTokenizer.from_pretrained(*__magic_name__       ,       **__magic_name__	)






@add_start_docstrings(AutoModel.__doc__	)
def 					UpperCamelCase   (      *__magic_name__	:				str       ,       **__magic_name__	:				Optional[Any]	)  ->    Tuple:
		"""simple docstring"""




		return AutoModel.from_pretrained(*__magic_name__       ,       **__magic_name__	)






@add_start_docstrings(AutoModelForCausalLM.__doc__	)
def 					UpperCamelCase   (      *__magic_name__	:				str       ,       **__magic_name__	:				Tuple	)  ->    List[str]:
		"""simple docstring"""




		return AutoModelForCausalLM.from_pretrained(*__magic_name__       ,       **__magic_name__	)






@add_start_docstrings(AutoModelForMaskedLM.__doc__	)
def 					UpperCamelCase   (      *__magic_name__	:				Optional[Any]       ,       **__magic_name__	:				Any	)  ->    Optional[int]:
		"""simple docstring"""




		return AutoModelForMaskedLM.from_pretrained(*__magic_name__       ,       **__magic_name__	)






@add_start_docstrings(AutoModelForSequenceClassification.__doc__	)
def 					UpperCamelCase   (      *__magic_name__	:				Any       ,       **__magic_name__	:				List[str]	)  ->    Union[str, Any]:
		"""simple docstring"""




		return AutoModelForSequenceClassification.from_pretrained(*__magic_name__       ,       **__magic_name__	)







@add_start_docstrings(AutoModelForQuestionAnswering.__doc__	)
def 					UpperCamelCase   (      *__magic_name__	:				Dict       ,       **__magic_name__	:				List[Any]	)  ->    int:
		"""simple docstring"""




		return AutoModelForQuestionAnswering.from_pretrained(*__magic_name__       ,       **__magic_name__	)

