




from typing import TYPE_CHECKING

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


_lowerCamelCase      :  Tuple      						=						{
    "configuration_mega": ["MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP", "MegaConfig", "MegaOnnxConfig"],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowerCamelCase      :  int      						=						[
	    "MEGA_PRETRAINED_MODEL_ARCHIVE_LIST",
	    "MegaForCausalLM",
	    "MegaForMaskedLM",
	    "MegaForMultipleChoice",
	    "MegaForQuestionAnswering",
	    "MegaForSequenceClassification",
	    "MegaForTokenClassification",
	    "MegaModel",
	    "MegaPreTrainedModel",
	]

if TYPE_CHECKING:
	from .configuration_mega import MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP, MegaConfig, MegaOnnxConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_mega import (
		    MEGA_PRETRAINED_MODEL_ARCHIVE_LIST,
		    MegaForCausalLM,
		    MegaForMaskedLM,
		    MegaForMultipleChoice,
		    MegaForQuestionAnswering,
		    MegaForSequenceClassification,
		    MegaForTokenClassification,
		    MegaModel,
		    MegaPreTrainedModel,
		)

else:
	import sys

	_lowerCamelCase      :  Any      						=						_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)







from __future__ import annotations

import inspect
import unittest

import numpy as np

from transformers import ResNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
	import tensorflow as tf

	from transformers import TFResNetForImageClassification, TFResNetModel
	from transformers.models.resnet.modeling_tf_resnet import TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
	from PIL import Image

	from transformers import AutoImageProcessor
class        __snake_case  :



				def __init__(      self      :		str		,	_UpperCAmelCase      :		Optional[Any]		,	_UpperCAmelCase      :		List[str]=3		,	_UpperCAmelCase      :		List[str]=32		,	_UpperCAmelCase      :		str=3		,	_UpperCAmelCase      :		Tuple=10		,	_UpperCAmelCase      :		Optional[Any]=[10, 20, 30, 40]		,	_UpperCAmelCase      :		int=[1, 1, 2, 1]		,	_UpperCAmelCase      :		str=True		,	_UpperCAmelCase      :		List[str]=True		,	_UpperCAmelCase      :		str="relu"		,	_UpperCAmelCase      :		int=3		,	_UpperCAmelCase      :		int=None		,	)  ->    Optional[Any]:





								'''simple docstring'''




								_lowerCAmelCase    :    Dict										=      parent
								_lowerCAmelCase    :    int										=      batch_size
								_lowerCAmelCase    :    List[str]										=      image_size
								_lowerCAmelCase    :    Optional[int]										=      num_channels
								_lowerCAmelCase    :    List[str]										=      embeddings_size
								_lowerCAmelCase    :    int										=      hidden_sizes
								_lowerCAmelCase    :    str										=      depths
								_lowerCAmelCase    :    int										=      is_training
								_lowerCAmelCase    :    Dict										=      use_labels
								_lowerCAmelCase    :    Dict										=      hidden_act
								_lowerCAmelCase    :    Dict										=      num_labels
								_lowerCAmelCase    :    List[Any]										=      scope
								_lowerCAmelCase    :    Optional[int]										=      len(_UpperCAmelCase      )



				def       SCREAMING_SNAKE_CASE       (      self      :		Dict      )  ->    int:





								'''simple docstring'''




								_lowerCAmelCase    :    List[str]										=      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]      )

								_lowerCAmelCase    :    Tuple										=      None
								if self.use_labels:
												_lowerCAmelCase    :    Optional[int]										=      ids_tensor([self.batch_size]		,	self.num_labels      )

								_lowerCAmelCase    :    Dict										=      self.get_config()

								return config, pixel_values, labels



				def       SCREAMING_SNAKE_CASE       (      self      :		List[str]      )  ->    int:





								'''simple docstring'''




								return ResNetConfig(
								    num_channels=self.num_channels		,	embeddings_size=self.embeddings_size		,	hidden_sizes=self.hidden_sizes		,	depths=self.depths		,	hidden_act=self.hidden_act		,	num_labels=self.num_labels		,	image_size=self.image_size		,	)



				def       SCREAMING_SNAKE_CASE       (      self      :		List[str]		,	_UpperCAmelCase      :		int		,	_UpperCAmelCase      :		str		,	_UpperCAmelCase      :		Union[str, Any]      )  ->    Optional[int]:





								'''simple docstring'''




								_lowerCAmelCase    :    Optional[Any]										=      TFResNetModel(config=_UpperCAmelCase      )
								_lowerCAmelCase    :    Union[str, Any]										=      model(_UpperCAmelCase      )
								# expected last hidden states: B, C, H // 32, W // 32
								self.parent.assertEqual(
								    result.last_hidden_state.shape		,	(self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)		,	)



				def       SCREAMING_SNAKE_CASE       (      self      :		Optional[Any]		,	_UpperCAmelCase      :		str		,	_UpperCAmelCase      :		Tuple		,	_UpperCAmelCase      :		Optional[Any]      )  ->    Optional[Any]:





								'''simple docstring'''




								_lowerCAmelCase    :    Optional[int]										=      self.num_labels
								_lowerCAmelCase    :    Any										=      TFResNetForImageClassification(_UpperCAmelCase      )
								_lowerCAmelCase    :    str										=      model(_UpperCAmelCase		,	labels=_UpperCAmelCase      )
								self.parent.assertEqual(result.logits.shape		,	(self.batch_size, self.num_labels)      )



				def       SCREAMING_SNAKE_CASE       (      self      :		int      )  ->    Optional[int]:





								'''simple docstring'''




								_lowerCAmelCase    :    Any										=      self.prepare_config_and_inputs()
								_lowerCAmelCase					,    _lowerCAmelCase					,    _lowerCAmelCase    :    List[str]										=      config_and_inputs
								_lowerCAmelCase    :    Tuple										=      {"""pixel_values""": pixel_values}
								return config, inputs_dict






@require_tf
class        __snake_case  (_a						,	_a						,	unittest.TestCase		):
				lowerCAmelCase__				=     (TFResNetModel, TFResNetForImageClassification) if is_tf_available() else ()
				lowerCAmelCase__				=     (
				    {"feature-extraction": TFResNetModel, "image-classification": TFResNetForImageClassification}
				    if is_tf_available()
				    else {}
				)

				lowerCAmelCase__				=     False
				lowerCAmelCase__				=     False
				lowerCAmelCase__				=     False
				lowerCAmelCase__				=     False
				lowerCAmelCase__				=     False



				def       SCREAMING_SNAKE_CASE       (      self      :		int      )  ->    int:





								'''simple docstring'''




								_lowerCAmelCase    :    Optional[int]										=      TFResNetModelTester(self      )
								_lowerCAmelCase    :    Union[str, Any]										=      ConfigTester(self		,	config_class=_UpperCAmelCase		,	has_text_modality=_UpperCAmelCase      )



				def       SCREAMING_SNAKE_CASE       (      self      :		List[Any]      )  ->    str:





								'''simple docstring'''




								self.create_and_test_config_common_properties()
								self.config_tester.create_and_test_config_to_json_string()
								self.config_tester.create_and_test_config_to_json_file()
								self.config_tester.create_and_test_config_from_and_save_pretrained()
								self.config_tester.create_and_test_config_with_num_labels()
								self.config_tester.check_config_can_be_init_without_params()
								self.config_tester.check_config_arguments_init()



				def       SCREAMING_SNAKE_CASE       (      self      :		Any      )  ->    Union[str, Any]:





								'''simple docstring'''




								return



				@unittest.skip(reason="""ResNet does not use inputs_embeds"""      )
				def       SCREAMING_SNAKE_CASE       (      self      :		int      )  ->    Union[str, Any]:





								'''simple docstring'''




								pass



				@unittest.skip(reason="""ResNet does not support input and output embeddings"""      )
				def       SCREAMING_SNAKE_CASE       (      self      :		Tuple      )  ->    Union[str, Any]:





								'''simple docstring'''




								pass



				def       SCREAMING_SNAKE_CASE       (      self      :		Tuple      )  ->    Any:





								'''simple docstring'''




								_lowerCAmelCase					,    _lowerCAmelCase    :    Dict										=      self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
												_lowerCAmelCase    :    Any										=      model_class(_UpperCAmelCase      )
												_lowerCAmelCase    :    Optional[Any]										=      inspect.signature(model.call      )
												# signature.parameters is an OrderedDict => so arg_names order is deterministic
												_lowerCAmelCase    :    Tuple										=      [*signature.parameters.keys()]

												_lowerCAmelCase    :    Optional[Any]										=      ["""pixel_values"""]
												self.assertListEqual(arg_names[:1]		,	_UpperCAmelCase      )



				def       SCREAMING_SNAKE_CASE       (      self      :		Optional[int]      )  ->    Tuple:





								'''simple docstring'''




								_lowerCAmelCase    :    int										=      self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*_UpperCAmelCase      )



				def       SCREAMING_SNAKE_CASE       (      self      :		Union[str, Any]      )  ->    str:





								'''simple docstring'''




								def check_hidden_states_output(_UpperCAmelCase      :		Tuple		,	_UpperCAmelCase      :		Tuple		,	_UpperCAmelCase      :		Tuple      ):
												_lowerCAmelCase    :    Dict										=      model_class(_UpperCAmelCase      )
												_lowerCAmelCase    :    Optional[int]										=      model(**self._prepare_for_class(_UpperCAmelCase		,	_UpperCAmelCase      )      )

												_lowerCAmelCase    :    Optional[Any]										=      outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

												_lowerCAmelCase    :    Dict										=      self.model_tester.num_stages
												self.assertEqual(len(_UpperCAmelCase      )		,	expected_num_stages + 1      )

												# ResNet's feature maps are of shape (batch_size, num_channels, height, width)
												self.assertListEqual(
												    list(hidden_states[0].shape[-2:]      )		,	[self.model_tester.image_size // 4, self.model_tester.image_size // 4]		,	)

								_lowerCAmelCase					,    _lowerCAmelCase    :    Any										=      self.model_tester.prepare_config_and_inputs_for_common()
								_lowerCAmelCase    :    str										=      ["""basic""", """bottleneck"""]
								for model_class in self.all_model_classes:
												for layer_type in layers_type:
																_lowerCAmelCase    :    Tuple										=      layer_type
																_lowerCAmelCase    :    Any										=      True
																check_hidden_states_output(_UpperCAmelCase		,	_UpperCAmelCase		,	_UpperCAmelCase      )

																# check that output_hidden_states also work using config
																del inputs_dict["output_hidden_states"]
																_lowerCAmelCase    :    Any										=      True

																check_hidden_states_output(_UpperCAmelCase		,	_UpperCAmelCase		,	_UpperCAmelCase      )



				def       SCREAMING_SNAKE_CASE       (      self      :		Any      )  ->    Any:





								'''simple docstring'''




								_lowerCAmelCase    :    int										=      self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_image_classification(*_UpperCAmelCase      )



				@slow
				def       SCREAMING_SNAKE_CASE       (      self      :		Union[str, Any]      )  ->    str:





								'''simple docstring'''




								for model_name in TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
												_lowerCAmelCase    :    Dict										=      TFResNetModel.from_pretrained(_UpperCAmelCase      )
												self.assertIsNotNone(_UpperCAmelCase      )






def 							_UpperCAmelCase  ():
				'''simple docstring'''
				_lowerCAmelCase    :    Optional[int]										=      Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""       )
				return image





@require_tf
@require_vision
class        __snake_case  (unittest.TestCase		):



				@cached_property
				def       SCREAMING_SNAKE_CASE       (      self      :		List[Any]      )  ->    str:





								'''simple docstring'''




								return (
								    AutoImageProcessor.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]      )
								    if is_vision_available()
								    else None
								)



				@slow
				def       SCREAMING_SNAKE_CASE       (      self      :		Any      )  ->    List[Any]:





								'''simple docstring'''




								_lowerCAmelCase    :    Dict										=      TFResNetForImageClassification.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]      )

								_lowerCAmelCase    :    Optional[int]										=      self.default_image_processor
								_lowerCAmelCase    :    int										=      prepare_img()
								_lowerCAmelCase    :    int										=      image_processor(images=_UpperCAmelCase		,	return_tensors="""tf"""      )

								# forward pass
								_lowerCAmelCase    :    int										=      model(**_UpperCAmelCase      )

								# verify the logits
								_lowerCAmelCase    :    Union[str, Any]										=      tf.TensorShape((1, 1000)      )
								self.assertEqual(outputs.logits.shape		,	_UpperCAmelCase      )

								_lowerCAmelCase    :    Any										=      tf.constant([-11.1069, -9.7877, -8.3777]      )

								self.assertTrue(np.allclose(outputs.logits[0, :3].numpy()		,	_UpperCAmelCase		,	atol=1E-4      )      )

