import copy
import json
import os
import tempfile

from transformers import is_torch_available

from .test_configuration_utils import config_common_kwargs

class 					_lowercase    (     UpperCAmelCase__			):






    '''simple docstring'''




    def __init__(    self       ,						lowerCamelCase__       ,						lowerCamelCase__=None       ,						lowerCamelCase__=True       ,						lowerCamelCase__=None       ,						**lowerCamelCase__ ):
       lowerCAmelCase_:							str       =	parent
       lowerCAmelCase_:							Optional[int]       =	config_class
       lowerCAmelCase_:							Union[str, Any]       =	has_text_modality
       lowerCAmelCase_:							Optional[Any]       =	kwargs
       lowerCAmelCase_:							str       =	common_properties




    def      _a    (    self ):
       lowerCAmelCase_:							Optional[Any]       =	self.config_class(**self.inputs_dict )
       lowerCAmelCase_:							List[str]       =	(
           ["hidden_size", "num_attention_heads", "num_hidden_layers"]
           if self.common_properties is None
           else self.common_properties
       )

       # Add common fields for text models
       if self.has_text_modality:
          common_properties.extend(["vocab_size"] )

       # Test that config has the common properties as getters
       for prop in common_properties:
          self.parent.assertTrue(hasattr(lowerCamelCase__       ,						lowerCamelCase__ )       ,						msg=F'''`{prop}` does not exist''' )

       # Test that config has the common properties as setter
       for idx, name in enumerate(lowerCamelCase__ ):
          try:
             setattr(lowerCamelCase__       ,						lowerCamelCase__       ,						lowerCamelCase__ )
             self.parent.assertEqual(
                 getattr(lowerCamelCase__       ,						lowerCamelCase__ )       ,						lowerCamelCase__       ,						msg=F'''`{name} value {idx} expected, but was {getattr(lowerCamelCase__       ,						lowerCamelCase__ )}''' )
          except NotImplementedError:
             # Some models might not be able to implement setters for common_properties
             # In that case, a NotImplementedError is raised
             pass

        # Test if config class can be called with Config(prop_name=..)
       for idx, name in enumerate(lowerCamelCase__ ):
          try:
             lowerCAmelCase_:							Optional[Any]       =	self.config_class(**{name: idx} )
             self.parent.assertEqual(
                 getattr(lowerCamelCase__       ,						lowerCamelCase__ )       ,						lowerCamelCase__       ,						msg=F'''`{name} value {idx} expected, but was {getattr(lowerCamelCase__       ,						lowerCamelCase__ )}''' )
          except NotImplementedError:
             # Some models might not be able to implement setters for common_properties
             # In that case, a NotImplementedError is raised
             pass




    def      _a    (    self ):
       lowerCAmelCase_:							Union[str, Any]       =	self.config_class(**self.inputs_dict )
       lowerCAmelCase_:							Optional[Any]       =	json.loads(config.to_json_string() )
       for key, value in self.inputs_dict.items():
          self.parent.assertEqual(obj[key]       ,						lowerCamelCase__ )




    def      _a    (    self ):
       lowerCAmelCase_:							int       =	self.config_class(**self.inputs_dict )

       with tempfile.TemporaryDirectory() as tmpdirname:
          lowerCAmelCase_:							Any       =	os.path.join(lowerCamelCase__       ,						"config.json" )
          config_first.to_json_file(lowerCamelCase__ )
          lowerCAmelCase_:							Any       =	self.config_class.from_json_file(lowerCamelCase__ )

       self.parent.assertEqual(config_second.to_dict()       ,						config_first.to_dict() )




    def      _a    (    self ):
       lowerCAmelCase_:							List[Any]       =	self.config_class(**self.inputs_dict )

       with tempfile.TemporaryDirectory() as tmpdirname:
          config_first.save_pretrained(lowerCamelCase__ )
          lowerCAmelCase_:							Tuple       =	self.config_class.from_pretrained(lowerCamelCase__ )

       self.parent.assertEqual(config_second.to_dict()       ,						config_first.to_dict() )




    def      _a    (    self ):
       lowerCAmelCase_:							Optional[Any]       =	self.config_class(**self.inputs_dict )

       lowerCAmelCase_:							str       =	"test"
       with tempfile.TemporaryDirectory() as tmpdirname:
          lowerCAmelCase_:							Optional[int]       =	os.path.join(lowerCamelCase__       ,						lowerCamelCase__ )
          config_first.save_pretrained(lowerCamelCase__ )
          lowerCAmelCase_:							Union[str, Any]       =	self.config_class.from_pretrained(lowerCamelCase__       ,						subfolder=lowerCamelCase__ )

       self.parent.assertEqual(config_second.to_dict()       ,						config_first.to_dict() )




    def      _a    (    self ):
       lowerCAmelCase_:							Union[str, Any]       =	self.config_class(**self.inputs_dict       ,						num_labels=5 )
       self.parent.assertEqual(len(config.idalabel )       ,						5 )
       self.parent.assertEqual(len(config.labelaid )       ,						5 )

       lowerCAmelCase_:							List[str]       =	3
       self.parent.assertEqual(len(config.idalabel )       ,						3 )
       self.parent.assertEqual(len(config.labelaid )       ,						3 )




    def      _a    (    self ):
       if self.config_class.is_composition:
          return
       lowerCAmelCase_:							Optional[Any]       =	self.config_class()
       self.parent.assertIsNotNone(lowerCamelCase__ )




    def      _a    (    self ):
       lowerCAmelCase_:							str       =	copy.deepcopy(lowerCamelCase__ )
       lowerCAmelCase_:							Dict       =	self.config_class(**lowerCamelCase__ )
       lowerCAmelCase_:							Dict       =	[]
       for key, value in config_common_kwargs.items():
          if key == "torch_dtype":
             if not is_torch_available():
                continue
             else:
                import torch

                if config.torch_dtype != torch.floataa:
                   wrong_values.append(("torch_dtype", config.torch_dtype, torch.floataa) )
          elif getattr(lowerCamelCase__       ,						lowerCamelCase__ ) != value:
             wrong_values.append((key, getattr(lowerCamelCase__       ,						lowerCamelCase__ ), value) )

       if len(lowerCamelCase__ ) > 0:
          lowerCAmelCase_:							Optional[int]       =	"\n".join([F'''- {v[0]}: got {v[1]} instead of {v[2]}''' for v in wrong_values] )
          raise ValueError(F'''The following keys were not properly set in the config:\n{errors}''' )




    def      _a    (    self ):
       self.create_and_test_config_common_properties()
       self.create_and_test_config_to_json_string()
       self.create_and_test_config_to_json_file()
       self.create_and_test_config_from_and_save_pretrained()
       self.create_and_test_config_from_and_save_pretrained_subfolder()
       self.create_and_test_config_with_num_labels()
       self.check_config_can_be_init_without_params()
       self.check_config_arguments_init()
from typing import TYPE_CHECKING

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


a  :      List[str]            =    {
    """configuration_efficientformer""": [
        """EFFICIENTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """EfficientFormerConfig""",
    ]
}

try:
 if not is_vision_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a  :      str            =    ["""EfficientFormerImageProcessor"""]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a  :      List[str]            =    [
     """EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """EfficientFormerForImageClassification""",
     """EfficientFormerForImageClassificationWithTeacher""",
     """EfficientFormerModel""",
     """EfficientFormerPreTrainedModel""",
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a  :      List[Any]            =    [
     """TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """TFEfficientFormerForImageClassification""",
     """TFEfficientFormerForImageClassificationWithTeacher""",
     """TFEfficientFormerModel""",
     """TFEfficientFormerPreTrainedModel""",
 ]

if TYPE_CHECKING:
 from .configuration_efficientformer import EFFICIENTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, EfficientFormerConfig

 try:
  if not is_vision_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .image_processing_efficientformer import EfficientFormerImageProcessor

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_efficientformer import (
      EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
      EfficientFormerForImageClassification,
      EfficientFormerForImageClassificationWithTeacher,
      EfficientFormerModel,
      EfficientFormerPreTrainedModel,
  )
 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_efficientformer import (
      TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFEfficientFormerForImageClassification,
      TFEfficientFormerForImageClassificationWithTeacher,
      TFEfficientFormerModel,
      TFEfficientFormerPreTrainedModel,
  )

else:
 import sys

 a  :      Tuple            =    _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)