
'''simple docstring'''






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							(  UpperCAmelCase__							:							Dict):
				lowerCamelCase					:			Tuple									= 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.''')
				lowerCamelCase					:			Optional[Any]									= 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.''')

				lowerCamelCase					:			List[Any]									= components[:-1] + [test_fn.replace('.py'       ,							'')]
				lowerCamelCase					:			List[str]									= '.'.join(UpperCAmelCase__)

				return test_module_path





def    UpperCAmelCase							(  UpperCAmelCase__							:							int):
				lowerCamelCase					:			List[str]									= get_module_path(UpperCAmelCase__)
				lowerCamelCase					:			Optional[int]									= importlib.import_module(UpperCAmelCase__)

				return test_module





def    UpperCAmelCase							(  UpperCAmelCase__							:							Any):
				lowerCamelCase					:			List[str]									= []
				lowerCamelCase					:			Optional[Any]									= get_test_module(UpperCAmelCase__)
				for attr in dir(UpperCAmelCase__):
								if attr.endswith('ModelTester'):
												tester_classes.append(getattr(UpperCAmelCase__       ,							UpperCAmelCase__))

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





def    UpperCAmelCase							(  UpperCAmelCase__							:							Dict):
				lowerCamelCase					:			Optional[int]									= []
				lowerCamelCase					:			Dict									= get_test_module(UpperCAmelCase__)
				for attr in dir(UpperCAmelCase__):
								lowerCamelCase					:			str									= getattr(UpperCAmelCase__       ,							UpperCAmelCase__)
								# (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).
								lowerCamelCase					:			int									= getattr(UpperCAmelCase__       ,							'all_model_classes'       ,							[])
								if len(UpperCAmelCase__) > 0:
												test_classes.append(UpperCAmelCase__)

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





def    UpperCAmelCase							(  UpperCAmelCase__							:							Optional[Any]):
				lowerCamelCase					:			List[str]									= get_test_classes(UpperCAmelCase__)
				lowerCamelCase					:			Optional[Any]									= set()
				for test_class in test_classes:
								model_classes.update(test_class.all_model_classes)

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





def    UpperCAmelCase							(  UpperCAmelCase__							:							List[str]):
				lowerCamelCase					:			int									= test_class()
				if hasattr(UpperCAmelCase__       ,							'setUp'):
								test.setUp()

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

				return model_tester





def    UpperCAmelCase							(  UpperCAmelCase__							:							Optional[Any]       ,							UpperCAmelCase__							:							str):
				lowerCamelCase					:			int									= get_test_classes(UpperCAmelCase__)

				lowerCamelCase					:			int									= []
				for test_class in test_classes:
								if model_class in test_class.all_model_classes:
												target_test_classes.append(UpperCAmelCase__)

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





def    UpperCAmelCase							(  UpperCAmelCase__							:							str       ,							UpperCAmelCase__							:							List[str]):
				lowerCamelCase					:			Tuple									= get_test_classes_for_model(UpperCAmelCase__       ,							UpperCAmelCase__)

				lowerCamelCase					:			Any									= []
				for test_class in test_classes:
								lowerCamelCase					:			List[str]									= get_model_tester_from_test_class(UpperCAmelCase__)
								if tester_class is not None:
												tester_classes.append(UpperCAmelCase__)

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





def    UpperCAmelCase							(  UpperCAmelCase__							:							Any):
				lowerCamelCase					:			Dict									= get_test_classes(UpperCAmelCase__)
				lowerCamelCase					:			Tuple									= {test_class: get_model_tester_from_test_class(UpperCAmelCase__) for test_class in test_classes}
				return test_tester_mapping





def    UpperCAmelCase							(  UpperCAmelCase__							:							Dict):
				lowerCamelCase					:			Tuple									= get_model_classes(UpperCAmelCase__)
				lowerCamelCase					:			Any									= {
				    model_class: get_test_classes_for_model(UpperCAmelCase__       ,							UpperCAmelCase__) for model_class in model_classes
				}
				return model_test_mapping





def    UpperCAmelCase							(  UpperCAmelCase__							:							int):
				lowerCamelCase					:			Union[str, Any]									= get_model_classes(UpperCAmelCase__)
				lowerCamelCase					:			Optional[int]									= {
				    model_class: get_tester_classes_for_model(UpperCAmelCase__       ,							UpperCAmelCase__) for model_class in model_classes
				}
				return model_to_tester_mapping





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


'''simple docstring'''






from typing import TYPE_CHECKING

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


A			      =      {
    'configuration_clip': [
        'CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'CLIPConfig',
        'CLIPOnnxConfig',
        'CLIPTextConfig',
        'CLIPVisionConfig',
    ],
    'processing_clip': ['CLIPProcessor'],
    'tokenization_clip': ['CLIPTokenizer'],
}

try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					A			      =      ['CLIPTokenizerFast']

try:
					if not is_vision_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					A			      =      ['CLIPFeatureExtractor']
					A			      =      ['CLIPImageProcessor']

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					A			      =      [
					    'CLIP_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'CLIPModel',
					    'CLIPPreTrainedModel',
					    'CLIPTextModel',
					    'CLIPTextModelWithProjection',
					    'CLIPVisionModel',
					    'CLIPVisionModelWithProjection',
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					A			      =      [
					    'TF_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'TFCLIPModel',
					    'TFCLIPPreTrainedModel',
					    'TFCLIPTextModel',
					    'TFCLIPVisionModel',
					]

try:
					if not is_flax_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					A			      =      [
					    'FlaxCLIPModel',
					    'FlaxCLIPPreTrainedModel',
					    'FlaxCLIPTextModel',
					    'FlaxCLIPTextPreTrainedModel',
					    'FlaxCLIPVisionModel',
					    'FlaxCLIPVisionPreTrainedModel',
					]


if TYPE_CHECKING:
					from .configuration_clip import (
					    CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    CLIPConfig,
					    CLIPOnnxConfig,
					    CLIPTextConfig,
					    CLIPVisionConfig,
					)
					from .processing_clip import CLIPProcessor
					from .tokenization_clip import CLIPTokenizer

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_clip_fast import CLIPTokenizerFast

					try:
										if not is_vision_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .feature_extraction_clip import CLIPFeatureExtractor
										from .image_processing_clip import CLIPImageProcessor

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_clip import (
										    CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
										    CLIPModel,
										    CLIPPreTrainedModel,
										    CLIPTextModel,
										    CLIPTextModelWithProjection,
										    CLIPVisionModel,
										    CLIPVisionModelWithProjection,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_clip import (
										    TF_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFCLIPModel,
										    TFCLIPPreTrainedModel,
										    TFCLIPTextModel,
										    TFCLIPVisionModel,
										)

					try:
										if not is_flax_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_flax_clip import (
										    FlaxCLIPModel,
										    FlaxCLIPPreTrainedModel,
										    FlaxCLIPTextModel,
										    FlaxCLIPTextPreTrainedModel,
										    FlaxCLIPVisionModel,
										    FlaxCLIPVisionPreTrainedModel,
										)


else:
					import sys

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