import importlib
import os
import sys


# This is required to make the module import works (when the python process is running from the root of the repo)
sys.path.append('''.''')







def 			__UpperCamelCase     (    A				):
     UpperCamelCase__					    =   test_file.split(os.path.sep				)
     if components[0:2] != ["tests", "models"]:
          raise ValueError(
              '''`test_file` should start with `tests/models/` (with `/` being the OS specific path separator). Got '''
              f"{test_file} instead."				)
     UpperCamelCase__					    =   components[-1]
     if not test_fn.endswith('''py'''				):
          raise ValueError(f"`test_file` should be a python file. Got {test_fn} instead."				)
     if not test_fn.startswith('''test_modeling_'''				):
          raise ValueError(
              f"`test_file` should point to a file name of the form `test_modeling_*.py`. Got {test_fn} instead."				)

     UpperCamelCase__					    =   components[:-1] + [test_fn.replace('''.py'''			,				''''''				)]
     UpperCamelCase__					    =   """.""".join(lowercase_				)

     return test_module_path







def 			__UpperCamelCase     (    A				):
     UpperCamelCase__					    =   get_module_path(lowercase_				)
     UpperCamelCase__					    =   importlib.import_module(lowercase_				)

     return test_module







def 			__UpperCamelCase     (    A				):
     UpperCamelCase__					    =   []
     UpperCamelCase__					    =   get_test_module(lowercase_				)
     for attr in dir(lowercase_				):
          if attr.endswith('''ModelTester'''				):
               tester_classes.append(getattr(lowercase_			,				lowercase_				)				)

    # sort with class names
     return sorted(lowercase_			,				key=lambda A				: x.__name__				)







def 			__UpperCamelCase     (    A				):
     UpperCamelCase__					    =   []
     UpperCamelCase__					    =   get_test_module(lowercase_				)
     for attr in dir(lowercase_				):
          UpperCamelCase__					    =   getattr(lowercase_			,				lowercase_				)
          # (TF/Flax)ModelTesterMixin is also an attribute in specific model test module. Let's exclude them by checking
          # `all_model_classes` is not empty (which also excludes other special classes).
          UpperCamelCase__					    =   getattr(lowercase_			,				'''all_model_classes'''			,				[]				)
          if len(lowercase_				) > 0:
               test_classes.append(lowercase_				)

    # sort with class names
     return sorted(lowercase_			,				key=lambda A				: x.__name__				)







def 			__UpperCamelCase     (    A				):
     UpperCamelCase__					    =   get_test_classes(lowercase_				)
     UpperCamelCase__					    =   set()
     for test_class in test_classes:
          model_classes.update(test_class.all_model_classes				)

     # sort with class names
     return sorted(lowercase_			,				key=lambda A				: x.__name__				)







def 			__UpperCamelCase     (    A				):
     UpperCamelCase__					    =   test_class()
     if hasattr(lowercase_			,				'''setUp'''				):
          test.setUp()

     UpperCamelCase__					    =   None
     if hasattr(lowercase_			,				'''model_tester'''				):
          # `(TF/Flax)ModelTesterMixin` has this attribute default to `None`. Let's skip this case.
          if test.model_tester is not None:
               UpperCamelCase__					    =   test.model_tester.__class__

     return model_tester







def 			__UpperCamelCase     (    A			,				A				):
     UpperCamelCase__					    =   get_test_classes(lowercase_				)

     UpperCamelCase__					    =   []
     for test_class in test_classes:
          if model_class in test_class.all_model_classes:
               target_test_classes.append(lowercase_				)

    # sort with class names
     return sorted(lowercase_			,				key=lambda A				: x.__name__				)







def 			__UpperCamelCase     (    A			,				A				):
     UpperCamelCase__					    =   get_test_classes_for_model(lowercase_			,				lowercase_				)

     UpperCamelCase__					    =   []
     for test_class in test_classes:
          UpperCamelCase__					    =   get_model_tester_from_test_class(lowercase_				)
          if tester_class is not None:
               tester_classes.append(lowercase_				)

    # sort with class names
     return sorted(lowercase_			,				key=lambda A				: x.__name__				)







def 			__UpperCamelCase     (    A				):
     UpperCamelCase__					    =   get_test_classes(lowercase_				)
     UpperCamelCase__					    =   {test_class: get_model_tester_from_test_class(lowercase_				) for test_class in test_classes}
     return test_tester_mapping







def 			__UpperCamelCase     (    A				):
     UpperCamelCase__					    =   get_model_classes(lowercase_				)
     UpperCamelCase__					    =   {
         model_class: get_test_classes_for_model(lowercase_			,				lowercase_				) for model_class in model_classes
     }
     return model_test_mapping







def 			__UpperCamelCase     (    A				):
     UpperCamelCase__					    =   get_model_classes(lowercase_				)
     UpperCamelCase__					    =   {
         model_class: get_tester_classes_for_model(lowercase_			,				lowercase_				) for model_class in model_classes
     }
     return model_to_tester_mapping







def 			__UpperCamelCase     (    A				):
     if isinstance(lowercase_			,				lowercase_				):
          return o
     elif isinstance(lowercase_			,				lowercase_				):
          return o.__name__
     elif isinstance(lowercase_			,				(list, tuple)				):
          return [to_json(lowercase_				) for x in o]
     elif isinstance(lowercase_			,				lowercase_				):
          return {to_json(lowercase_				): to_json(lowercase_				) for k, v in o.items()}
     else:
          return o






from typing import TYPE_CHECKING

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


A_			:					Tuple    			=     {
    'configuration_xlm': ['XLM_PRETRAINED_CONFIG_ARCHIVE_MAP', 'XLMConfig', 'XLMOnnxConfig'],
    'tokenization_xlm': ['XLMTokenizer'],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A_			:					Dict    			=     [
	    'XLM_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'XLMForMultipleChoice',
	    'XLMForQuestionAnswering',
	    'XLMForQuestionAnsweringSimple',
	    'XLMForSequenceClassification',
	    'XLMForTokenClassification',
	    'XLMModel',
	    'XLMPreTrainedModel',
	    'XLMWithLMHeadModel',
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A_			:					str    			=     [
	    'TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'TFXLMForMultipleChoice',
	    'TFXLMForQuestionAnsweringSimple',
	    'TFXLMForSequenceClassification',
	    'TFXLMForTokenClassification',
	    'TFXLMMainLayer',
	    'TFXLMModel',
	    'TFXLMPreTrainedModel',
	    'TFXLMWithLMHeadModel',
	]


if TYPE_CHECKING:
	from .configuration_xlm import XLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMConfig, XLMOnnxConfig
	from .tokenization_xlm import XLMTokenizer

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_xlm import (
		    XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
		    XLMForMultipleChoice,
		    XLMForQuestionAnswering,
		    XLMForQuestionAnsweringSimple,
		    XLMForSequenceClassification,
		    XLMForTokenClassification,
		    XLMModel,
		    XLMPreTrainedModel,
		    XLMWithLMHeadModel,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_xlm import (
		    TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFXLMForMultipleChoice,
		    TFXLMForQuestionAnsweringSimple,
		    TFXLMForSequenceClassification,
		    TFXLMForTokenClassification,
		    TFXLMMainLayer,
		    TFXLMModel,
		    TFXLMPreTrainedModel,
		    TFXLMWithLMHeadModel,
		)

else:
	import sys

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



