






import unittest

from transformers.image_processing_utils import get_size_dict




class        _lowerCAmelCase( unittest.TestCase      ):





       """simple docstring"""







       def     _a					(      self							):
         # Test a dict with the wrong keys raises an error
         UpperCamelCase_:      Optional[int]					   =       {'wrong_key': 2_2_4}
         with self.assertRaises(_lowerCamelCase							):
           get_size_dict(_lowerCamelCase							)

         UpperCamelCase_:      Optional[int]					   =       {'height': 2_2_4}
         with self.assertRaises(_lowerCamelCase							):
           get_size_dict(_lowerCamelCase							)

         UpperCamelCase_:      Union[str, Any]					   =       {'width': 2_2_4, 'shortest_edge': 2_2_4}
         with self.assertRaises(_lowerCamelCase							):
           get_size_dict(_lowerCamelCase							)

         # Test a dict with the correct keys is returned as is
         UpperCamelCase_:      int					   =       {'height': 2_2_4, 'width': 2_2_4}
         UpperCamelCase_:      Optional[Any]					   =       get_size_dict(_lowerCamelCase							)
         self.assertEqual(_lowerCamelCase						,    _lowerCamelCase							)

         UpperCamelCase_:      Optional[Any]					   =       {'shortest_edge': 2_2_4}
         UpperCamelCase_:      List[Any]					   =       get_size_dict(_lowerCamelCase							)
         self.assertEqual(_lowerCamelCase						,    {'shortest_edge': 2_2_4}							)

         UpperCamelCase_:      Dict					   =       {'longest_edge': 2_2_4, 'shortest_edge': 2_2_4}
         UpperCamelCase_:      Optional[int]					   =       get_size_dict(_lowerCamelCase							)
         self.assertEqual(_lowerCamelCase						,    {'longest_edge': 2_2_4, 'shortest_edge': 2_2_4}							)

         # Test a single int value which  represents (size, size)
         UpperCamelCase_:      str					   =       get_size_dict(2_2_4							)
         self.assertEqual(_lowerCamelCase						,    {'height': 2_2_4, 'width': 2_2_4}							)

         # Test a single int value which represents the shortest edge
         UpperCamelCase_:      str					   =       get_size_dict(2_2_4						,    default_to_square=_lowerCamelCase							)
         self.assertEqual(_lowerCamelCase						,    {'shortest_edge': 2_2_4}							)

         # Test a tuple of ints which represents (height, width)
         UpperCamelCase_:      Optional[Any]					   =       get_size_dict((1_5_0, 2_0_0)							)
         self.assertEqual(_lowerCamelCase						,    {'height': 1_5_0, 'width': 2_0_0}							)

         # Test a tuple of ints which represents (width, height)
         UpperCamelCase_:      Any					   =       get_size_dict((1_5_0, 2_0_0)						,    height_width_order=_lowerCamelCase							)
         self.assertEqual(_lowerCamelCase						,    {'height': 2_0_0, 'width': 1_5_0}							)

         # Test an int representing the shortest edge and max_size which represents the longest edge
         UpperCamelCase_:      Union[str, Any]					   =       get_size_dict(2_2_4						,    max_size=2_5_6						,    default_to_square=_lowerCamelCase							)
         self.assertEqual(_lowerCamelCase						,    {'shortest_edge': 2_2_4, 'longest_edge': 2_5_6}							)

         # Test int with default_to_square=True and max_size fails
         with self.assertRaises(_lowerCamelCase							):
           get_size_dict(2_2_4						,    max_size=2_5_6						,    default_to_square=_lowerCamelCase							)







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


A_	:	List[Any]				=				{
    'configuration_openai': ['OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'OpenAIGPTConfig'],
    'tokenization_openai': ['OpenAIGPTTokenizer'],
}

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   A_	:	List[str]				=				['OpenAIGPTTokenizerFast']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   A_	:	Any				=				[
       'OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST',
       'OpenAIGPTDoubleHeadsModel',
       'OpenAIGPTForSequenceClassification',
       'OpenAIGPTLMHeadModel',
       'OpenAIGPTModel',
       'OpenAIGPTPreTrainedModel',
       'load_tf_weights_in_openai_gpt',
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   A_	:	str				=				[
       'TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST',
       'TFOpenAIGPTDoubleHeadsModel',
       'TFOpenAIGPTForSequenceClassification',
       'TFOpenAIGPTLMHeadModel',
       'TFOpenAIGPTMainLayer',
       'TFOpenAIGPTModel',
       'TFOpenAIGPTPreTrainedModel',
   ]


if TYPE_CHECKING:
   from .configuration_openai import OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP, OpenAIGPTConfig
   from .tokenization_openai import OpenAIGPTTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_openai_fast import OpenAIGPTTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_openai import (
          OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
          OpenAIGPTDoubleHeadsModel,
          OpenAIGPTForSequenceClassification,
          OpenAIGPTLMHeadModel,
          OpenAIGPTModel,
          OpenAIGPTPreTrainedModel,
          load_tf_weights_in_openai_gpt,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_openai import (
          TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFOpenAIGPTDoubleHeadsModel,
          TFOpenAIGPTForSequenceClassification,
          TFOpenAIGPTLMHeadModel,
          TFOpenAIGPTMainLayer,
          TFOpenAIGPTModel,
          TFOpenAIGPTPreTrainedModel,
      )

else:
   import sys

   A_	:	str				=				_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)