import unittest

from transformers.image_processing_utils import get_size_dict







class 			__snake_case   (     unittest.TestCase   ):
 def 	UpperCAmelCase__       (  self						)	->							Optional[Any]:
       '''simple docstring'''





       UpperCAmelCase    : Optional[int]      ={'''wrong_key''': 224}
       with self.assertRaises(snake_case__						):
             get_size_dict(snake_case__						)

       UpperCAmelCase    : Optional[int]      ={'''height''': 224}
       with self.assertRaises(snake_case__						):
             get_size_dict(snake_case__						)

       UpperCAmelCase    : List[str]      ={'''width''': 224, '''shortest_edge''': 224}
       with self.assertRaises(snake_case__						):
             get_size_dict(snake_case__						)

       # Test a dict with the correct keys is returned as is
       UpperCAmelCase    : List[Any]      ={'''height''': 224, '''width''': 224}
       UpperCAmelCase    : Dict      =get_size_dict(snake_case__						)
       self.assertEqual(snake_case__		,   snake_case__						)

       UpperCAmelCase    : Optional[Any]      ={'''shortest_edge''': 224}
       UpperCAmelCase    : Optional[int]      =get_size_dict(snake_case__						)
       self.assertEqual(snake_case__		,   {'''shortest_edge''': 224}						)

       UpperCAmelCase    : List[str]      ={'''longest_edge''': 224, '''shortest_edge''': 224}
       UpperCAmelCase    : List[str]      =get_size_dict(snake_case__						)
       self.assertEqual(snake_case__		,   {'''longest_edge''': 224, '''shortest_edge''': 224}						)

       # Test a single int value which  represents (size, size)
       UpperCAmelCase    : Dict      =get_size_dict(224						)
       self.assertEqual(snake_case__		,   {'''height''': 224, '''width''': 224}						)

       # Test a single int value which represents the shortest edge
       UpperCAmelCase    : Any      =get_size_dict(224		,   default_to_square=snake_case__						)
       self.assertEqual(snake_case__		,   {'''shortest_edge''': 224}						)

       # Test a tuple of ints which represents (height, width)
       UpperCAmelCase    : Dict      =get_size_dict((150, 200)						)
       self.assertEqual(snake_case__		,   {'''height''': 150, '''width''': 200}						)

       # Test a tuple of ints which represents (width, height)
       UpperCAmelCase    : List[Any]      =get_size_dict((150, 200)		,   height_width_order=snake_case__						)
       self.assertEqual(snake_case__		,   {'''height''': 200, '''width''': 150}						)

       # Test an int representing the shortest edge and max_size which represents the longest edge
       UpperCAmelCase    : List[Any]      =get_size_dict(224		,   max_size=256		,   default_to_square=snake_case__						)
       self.assertEqual(snake_case__		,   {'''shortest_edge''': 224, '''longest_edge''': 256}						)

       # Test int with default_to_square=True and max_size fails
       with self.assertRaises(snake_case__						):
             get_size_dict(224		,   max_size=256		,   default_to_square=snake_case__						)

from typing import TYPE_CHECKING

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


__snake_case			      =    {
    '''configuration_esm''': ['''ESM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''EsmConfig'''],
    '''tokenization_esm''': ['''EsmTokenizer'''],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __snake_case			      =    [
         '''ESM_PRETRAINED_MODEL_ARCHIVE_LIST''',
         '''EsmForMaskedLM''',
         '''EsmForSequenceClassification''',
         '''EsmForTokenClassification''',
         '''EsmModel''',
         '''EsmPreTrainedModel''',
     ]
     __snake_case			      =    ['''EsmForProteinFolding''', '''EsmFoldPreTrainedModel''']

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __snake_case			      =    [
         '''TF_ESM_PRETRAINED_MODEL_ARCHIVE_LIST''',
         '''TFEsmForMaskedLM''',
         '''TFEsmForSequenceClassification''',
         '''TFEsmForTokenClassification''',
         '''TFEsmModel''',
         '''TFEsmPreTrainedModel''',
     ]

if TYPE_CHECKING:
     from .configuration_esm import ESM_PRETRAINED_CONFIG_ARCHIVE_MAP, EsmConfig
     from .tokenization_esm import EsmTokenizer

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_esm import (
              ESM_PRETRAINED_MODEL_ARCHIVE_LIST,
              EsmForMaskedLM,
              EsmForSequenceClassification,
              EsmForTokenClassification,
              EsmModel,
              EsmPreTrainedModel,
          )
          from .modeling_esmfold import EsmFoldPreTrainedModel, EsmForProteinFolding

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_esm import (
              TF_ESM_PRETRAINED_MODEL_ARCHIVE_LIST,
              TFEsmForMaskedLM,
              TFEsmForSequenceClassification,
              TFEsmForTokenClassification,
              TFEsmModel,
              TFEsmPreTrainedModel,
          )


else:
     import sys

     __snake_case			      =    _LazyModule(__name__, globals()['''__file__'''], _import_structure)
