



'''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


if is_torch_available():
   import torch

if is_vision_available():
   from PIL import Image

   from transformers import ChineseCLIPImageProcessor






class 					lowerCamelCase_    (				unittest.TestCase     ):
  """simple docstring"""




  def __init__( self      :	Any			,	_a      :	Any			,	_a      :	Dict=7			,	_a      :	str=3			,	_a      :	Tuple=18			,	_a      :	List[str]=30			,	_a      :	Tuple=400			,	_a      :	List[Any]=True			,	_a      :	str=None			,	_a      :	List[Any]=True			,	_a      :	Optional[int]=None			,	_a      :	int=True			,	_a      :	str=[0.48145466, 0.4578275, 0.40821073]			,	_a      :	int=[0.26862954, 0.26130258, 0.27577711]			,	_a      :	List[str]=True			,	)					->					List[Any]:
    __lowerCamelCase       :       Tuple           = size if size is not None else {'height': 224, 'width': 224}
    __lowerCamelCase       :       List[Any]           = crop_size if crop_size is not None else {'height': 18, 'width': 18}
    __lowerCamelCase       :       Tuple           = parent
    __lowerCamelCase       :       Union[str, Any]           = batch_size
    __lowerCamelCase       :       int           = num_channels
    __lowerCamelCase       :       int           = image_size
    __lowerCamelCase       :       Optional[Any]           = min_resolution
    __lowerCamelCase       :       List[str]           = max_resolution
    __lowerCamelCase       :       List[Any]           = do_resize
    __lowerCamelCase       :       Union[str, Any]           = size
    __lowerCamelCase       :       List[str]           = do_center_crop
    __lowerCamelCase       :       Tuple           = crop_size
    __lowerCamelCase       :       Optional[Any]           = do_normalize
    __lowerCamelCase       :       Optional[Any]           = image_mean
    __lowerCamelCase       :       Optional[int]           = image_std
    __lowerCamelCase       :       Dict           = do_convert_rgb




  def 						_lowercase     ( self      :	Dict   )					->					List[str]:
    return {
        "do_resize": self.do_resize,
        "size": self.size,
        "do_center_crop": self.do_center_crop,
        "crop_size": self.crop_size,
        "do_normalize": self.do_normalize,
        "image_mean": self.image_mean,
        "image_std": self.image_std,
        "do_convert_rgb": self.do_convert_rgb,
    }




  def 						_lowercase     ( self      :	Union[str, Any]			,	_a      :	str=False			,	_a      :	List[str]=False			,	_a      :	Optional[int]=False   )					->					Optional[int]:
    assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

    if equal_resolution:
      __lowerCamelCase       :       int           = []
      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:
      __lowerCamelCase       :       str           = []
      for i in range(self.batch_size   ):
        __lowerCamelCase       ,__lowerCamelCase       :       Tuple           = 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
      __lowerCamelCase       :       Any           = [Image.fromarray(np.moveaxis(_a			,	0			,	-1   )   ) for x in image_inputs]

    if torchify:
      __lowerCamelCase       :       int           = [torch.from_numpy(_a   ) for x in image_inputs]

    return image_inputs






@require_torch
@require_vision
class 					lowerCamelCase_    (				SCREAMING_SNAKE_CASE_   ,   unittest.TestCase     ):
  """simple docstring"""




  a_												=ChineseCLIPImageProcessor if is_vision_available() else None




  def 						_lowercase     ( self      :	Tuple   )					->					Union[str, Any]:
    __lowerCamelCase       :       Dict           = ChineseCLIPImageProcessingTester(self			,	do_center_crop=_a   )




  @property
  def 						_lowercase     ( self      :	Any   )					->					Any:
    return self.image_processor_tester.prepare_image_processor_dict()




  def 						_lowercase     ( self      :	List[str]   )					->					Tuple:
    __lowerCamelCase       :       Tuple           = self.image_processing_class(**self.image_processor_dict   )
    self.assertTrue(hasattr(_a			,	'do_resize'   )   )
    self.assertTrue(hasattr(_a			,	'size'   )   )
    self.assertTrue(hasattr(_a			,	'do_center_crop'   )   )
    self.assertTrue(hasattr(_a			,	'center_crop'   )   )
    self.assertTrue(hasattr(_a			,	'do_normalize'   )   )
    self.assertTrue(hasattr(_a			,	'image_mean'   )   )
    self.assertTrue(hasattr(_a			,	'image_std'   )   )
    self.assertTrue(hasattr(_a			,	'do_convert_rgb'   )   )




  def 						_lowercase     ( self      :	int   )					->					List[str]:
    __lowerCamelCase       :       str           = self.image_processing_class.from_dict(self.image_processor_dict   )
    self.assertEqual(image_processor.size			,	{'height': 224, 'width': 224}   )
    self.assertEqual(image_processor.crop_size			,	{'height': 18, 'width': 18}   )

    __lowerCamelCase       :       List[Any]           = 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 						_lowercase     ( self      :	List[str]   )					->					List[Any]:
    pass




  def 						_lowercase     ( self      :	Tuple   )					->					Dict:
    # Initialize image_processing
    __lowerCamelCase       :       List[Any]           = self.image_processing_class(**self.image_processor_dict   )
    # create random PIL images
    __lowerCamelCase       :       Optional[Any]           = self.image_processor_tester.prepare_inputs(equal_resolution=_a   )
    for image in image_inputs:
      self.assertIsInstance(_a			,	Image.Image   )

    # 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[str]           = image_processing(_a			,	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 						_lowercase     ( self      :	Any   )					->					int:
    # Initialize image_processing
    __lowerCamelCase       :       str           = self.image_processing_class(**self.image_processor_dict   )
    # create random numpy tensors
    __lowerCamelCase       :       str           = self.image_processor_tester.prepare_inputs(equal_resolution=_a			,	numpify=_a   )
    for image in image_inputs:
      self.assertIsInstance(_a			,	np.ndarray   )

    # Test not batched input
    __lowerCamelCase       :       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       :       int           = image_processing(_a			,	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 						_lowercase     ( self      :	List[Any]   )					->					Tuple:
    # Initialize image_processing
    __lowerCamelCase       :       List[str]           = self.image_processing_class(**self.image_processor_dict   )
    # create random PyTorch tensors
    __lowerCamelCase       :       Dict           = self.image_processor_tester.prepare_inputs(equal_resolution=_a			,	torchify=_a   )
    for image in image_inputs:
      self.assertIsInstance(_a			,	torch.Tensor   )

    # Test not batched input
    __lowerCamelCase       :       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       :       str           = image_processing(_a			,	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'],
        )			,	)






@require_torch
@require_vision
class 					lowerCamelCase_    (				SCREAMING_SNAKE_CASE_   ,   unittest.TestCase     ):
  """simple docstring"""




  a_												=ChineseCLIPImageProcessor if is_vision_available() else None




  def 						_lowercase     ( self      :	str   )					->					str:
    __lowerCamelCase       :       Dict           = ChineseCLIPImageProcessingTester(self			,	num_channels=4			,	do_center_crop=_a   )
    __lowerCamelCase       :       List[str]           = 3




  @property
  def 						_lowercase     ( self      :	str   )					->					List[str]:
    return self.image_processor_tester.prepare_image_processor_dict()




  def 						_lowercase     ( self      :	Dict   )					->					Union[str, Any]:
    __lowerCamelCase       :       List[Any]           = self.image_processing_class(**self.image_processor_dict   )
    self.assertTrue(hasattr(_a			,	'do_resize'   )   )
    self.assertTrue(hasattr(_a			,	'size'   )   )
    self.assertTrue(hasattr(_a			,	'do_center_crop'   )   )
    self.assertTrue(hasattr(_a			,	'center_crop'   )   )
    self.assertTrue(hasattr(_a			,	'do_normalize'   )   )
    self.assertTrue(hasattr(_a			,	'image_mean'   )   )
    self.assertTrue(hasattr(_a			,	'image_std'   )   )
    self.assertTrue(hasattr(_a			,	'do_convert_rgb'   )   )




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




  def 						_lowercase     ( self      :	Tuple   )					->					Optional[int]:
    # Initialize image_processing
    __lowerCamelCase       :       int           = self.image_processing_class(**self.image_processor_dict   )
    # create random PIL images
    __lowerCamelCase       :       Union[str, Any]           = self.image_processor_tester.prepare_inputs(equal_resolution=_a   )
    for image in image_inputs:
      self.assertIsInstance(_a			,	Image.Image   )

    # Test not batched input
    __lowerCamelCase       :       Optional[Any]           = image_processing(image_inputs[0]			,	return_tensors='pt'   ).pixel_values
    self.assertEqual(
        encoded_images.shape			,	(
            1,
            self.expected_encoded_image_num_channels,
            self.image_processor_tester.crop_size['height'],
            self.image_processor_tester.crop_size['width'],
        )			,	)

    # Test batched
    __lowerCamelCase       :       Optional[Any]           = image_processing(_a			,	return_tensors='pt'   ).pixel_values
    self.assertEqual(
        encoded_images.shape			,	(
            self.image_processor_tester.batch_size,
            self.expected_encoded_image_num_channels,
            self.image_processor_tester.crop_size['height'],
            self.image_processor_tester.crop_size['width'],
        )			,	)











'''simple docstring'''





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


_UpperCamelCase											=						{
    'configuration_biogpt': ['BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'BioGptConfig'],
    'tokenization_biogpt': ['BioGptTokenizer'],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCamelCase											=						[
       'BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST',
       'BioGptForCausalLM',
       'BioGptForTokenClassification',
       'BioGptForSequenceClassification',
       'BioGptModel',
       'BioGptPreTrainedModel',
   ]


if TYPE_CHECKING:
   from .configuration_biogpt import BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, BioGptConfig
   from .tokenization_biogpt import BioGptTokenizer

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_biogpt import (
          BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
          BioGptForCausalLM,
          BioGptForSequenceClassification,
          BioGptForTokenClassification,
          BioGptModel,
          BioGptPreTrainedModel,
      )


else:
   import sys

   _UpperCamelCase											=						_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)






