



import inspect
import unittest

from transformers import RegNetConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
	import torch
	from torch import nn

	from transformers import RegNetForImageClassification, RegNetModel
	from transformers.models.regnet.modeling_regnet import REGNET_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
	from PIL import Image

	from transformers import AutoImageProcessor


class 	a__     :


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

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Any      					= parent
					SCREAMING_SNAKE_CASE  :						Tuple      					= batch_size
					SCREAMING_SNAKE_CASE  :						Tuple      					= image_size
					SCREAMING_SNAKE_CASE  :						List[str]      					= num_channels
					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= embeddings_size
					SCREAMING_SNAKE_CASE  :						List[str]      					= hidden_sizes
					SCREAMING_SNAKE_CASE  :						Tuple      					= depths
					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= is_training
					SCREAMING_SNAKE_CASE  :						Any      					= use_labels
					SCREAMING_SNAKE_CASE  :						Any      					= hidden_act
					SCREAMING_SNAKE_CASE  :						int      					= num_labels
					SCREAMING_SNAKE_CASE  :						Tuple      					= scope
					SCREAMING_SNAKE_CASE  :						Any      					= len(__UpperCAmelCase      )


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

					"""simple docstring"""

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

					SCREAMING_SNAKE_CASE  :						Tuple      					= None
					if self.use_labels:
									SCREAMING_SNAKE_CASE  :						Any      					= ids_tensor([self.batch_size],					self.num_labels      )

					SCREAMING_SNAKE_CASE  :						Optional[int]      					= self.get_config()

					return config, pixel_values, labels


	def 				lowercase__  (self : Any      )     ->			Tuple:

					"""simple docstring"""

					return RegNetConfig(
					    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,					)


	def 				lowercase__  (self : List[str],					__UpperCAmelCase : str,					__UpperCAmelCase : Any,					__UpperCAmelCase : Tuple      )     ->			Optional[Any]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= RegNetModel(config=__UpperCAmelCase      )
					model.to(__UpperCAmelCase      )
					model.eval()
					SCREAMING_SNAKE_CASE  :						Tuple      					= 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 				lowercase__  (self : Optional[int],					__UpperCAmelCase : int,					__UpperCAmelCase : List[Any],					__UpperCAmelCase : Optional[Any]      )     ->			Dict:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						List[str]      					= self.num_labels
					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= RegNetForImageClassification(__UpperCAmelCase      )
					model.to(__UpperCAmelCase      )
					model.eval()
					SCREAMING_SNAKE_CASE  :						Tuple      					= model(__UpperCAmelCase,					labels=__UpperCAmelCase      )
					self.parent.assertEqual(result.logits.shape,					(self.batch_size, self.num_labels)      )






	def 				lowercase__  (self : Tuple      )     ->			Dict:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						List[str]      					= self.prepare_config_and_inputs()
					SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE  :						Optional[int]      					= config_and_inputs
					SCREAMING_SNAKE_CASE  :						Any      					= {'''pixel_values''': pixel_values}
					return config, inputs_dict



@require_torch
class 	a__     (				_lowercase,			_lowercase,			unittest.TestCase	):
	__magic_name__		:    Optional[int]	    =     (RegNetModel, RegNetForImageClassification) if is_torch_available() else ()
	__magic_name__		:    Any	    =     (
	    {"""feature-extraction""": RegNetModel, """image-classification""": RegNetForImageClassification}
	    if is_torch_available()
	    else {}
	)

	__magic_name__		:    List[Any]	    =     False
	__magic_name__		:    Optional[Any]	    =     False
	__magic_name__		:    str	    =     False
	__magic_name__		:    Optional[int]	    =     False


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

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Optional[int]      					= RegNetModelTester(self      )
					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= ConfigTester(self,					config_class=__UpperCAmelCase,					has_text_modality=__UpperCAmelCase      )


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

					"""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 				lowercase__  (self : Any      )     ->			str:

					"""simple docstring"""

					return


	@unittest.skip(reason='''RegNet does not use inputs_embeds'''      )
	def 				lowercase__  (self : List[str]      )     ->			Dict:

					"""simple docstring"""

					pass


	@unittest.skip(reason='''RegNet does not support input and output embeddings'''      )
	def 				lowercase__  (self : str      )     ->			Optional[Any]:

					"""simple docstring"""

					pass


	def 				lowercase__  (self : Any      )     ->			Dict:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE  :						int      					= self.model_tester.prepare_config_and_inputs_for_common()

					for model_class in self.all_model_classes:
									SCREAMING_SNAKE_CASE  :						Optional[int]      					= model_class(__UpperCAmelCase      )
									SCREAMING_SNAKE_CASE  :						Tuple      					= inspect.signature(model.forward      )
									# signature.parameters is an OrderedDict => so arg_names order is deterministic
									SCREAMING_SNAKE_CASE  :						Tuple      					= [*signature.parameters.keys()]

									SCREAMING_SNAKE_CASE  :						int      					= ['''pixel_values''']
									self.assertListEqual(arg_names[:1],					__UpperCAmelCase      )


	def 				lowercase__  (self : str      )     ->			str:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_model(*__UpperCAmelCase      )


	def 				lowercase__  (self : List[Any]      )     ->			Tuple:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE  :						int      					= self.model_tester.prepare_config_and_inputs_for_common()

					for model_class in self.all_model_classes:
									SCREAMING_SNAKE_CASE  :						Any      					= model_class(config=__UpperCAmelCase      )
									for name, module in model.named_modules():
													if isinstance(__UpperCAmelCase,					(nn.BatchNormad, nn.GroupNorm)      ):
																	self.assertTrue(
																	    torch.all(module.weight == 1      ),					msg=F'''Parameter {name} of model {model_class} seems not properly initialized''',					)
																	self.assertTrue(
																	    torch.all(module.bias == 0      ),					msg=F'''Parameter {name} of model {model_class} seems not properly initialized''',					)


	def 				lowercase__  (self : int      )     ->			Any:

					"""simple docstring"""

					def check_hidden_states_output(__UpperCAmelCase : List[Any],					__UpperCAmelCase : List[Any],					__UpperCAmelCase : List[Any]      ):
									SCREAMING_SNAKE_CASE  :						Optional[int]      					= model_class(__UpperCAmelCase      )
									model.to(__UpperCAmelCase      )
									model.eval()

									with torch.no_grad():
													SCREAMING_SNAKE_CASE  :						Any      					= model(**self._prepare_for_class(__UpperCAmelCase,					__UpperCAmelCase      )      )

									SCREAMING_SNAKE_CASE  :						Any      					= outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

									SCREAMING_SNAKE_CASE  :						str      					= self.model_tester.num_stages
									self.assertEqual(len(__UpperCAmelCase      ),					expected_num_stages + 1      )

									# RegNet'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 // 2, self.model_tester.image_size // 2],					)

					SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE  :						List[str]      					= self.model_tester.prepare_config_and_inputs_for_common()
					SCREAMING_SNAKE_CASE  :						int      					= ['''basic''', '''bottleneck''']
					for model_class in self.all_model_classes:
									for layer_type in layers_type:
													SCREAMING_SNAKE_CASE  :						Optional[int]      					= layer_type
													SCREAMING_SNAKE_CASE  :						int      					= True
													check_hidden_states_output(__UpperCAmelCase,					__UpperCAmelCase,					__UpperCAmelCase      )

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

													check_hidden_states_output(__UpperCAmelCase,					__UpperCAmelCase,					__UpperCAmelCase      )


	def 				lowercase__  (self : Optional[Any]      )     ->			Optional[Any]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Any      					= self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_image_classification(*__UpperCAmelCase      )






	@slow
	def 				lowercase__  (self : Optional[Any]      )     ->			int:

					"""simple docstring"""

					for model_name in REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									SCREAMING_SNAKE_CASE  :						str      					= RegNetModel.from_pretrained(__UpperCAmelCase      )
									self.assertIsNotNone(__UpperCAmelCase      )






def       __lowercase  ():
				SCREAMING_SNAKE_CASE  :						str      					= Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''       )
				return image




@require_torch
@require_vision
class 	a__     (				unittest.TestCase	):


	@cached_property
	def 				lowercase__  (self : Union[str, Any]      )     ->			Tuple:

					"""simple docstring"""

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






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

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Optional[int]      					= RegNetForImageClassification.from_pretrained(REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]      ).to(__UpperCAmelCase      )

					SCREAMING_SNAKE_CASE  :						Any      					= self.default_image_processor
					SCREAMING_SNAKE_CASE  :						Dict      					= prepare_img()
					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= image_processor(images=__UpperCAmelCase,					return_tensors='''pt'''      ).to(__UpperCAmelCase      )

					# forward pass
					with torch.no_grad():
									SCREAMING_SNAKE_CASE  :						Tuple      					= model(**__UpperCAmelCase      )

					# verify the logits
					SCREAMING_SNAKE_CASE  :						Optional[int]      					= torch.Size((1, 1000)      )
					self.assertEqual(outputs.logits.shape,					__UpperCAmelCase      )

					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= torch.tensor([-0.4180, -1.5051, -3.4836]      ).to(__UpperCAmelCase      )

					self.assertTrue(torch.allclose(outputs.logits[0, :3],					__UpperCAmelCase,					atol=1e-4      )      )









'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


snake_case_			=     {
    """configuration_timesformer""": ["""TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """TimesformerConfig"""],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	snake_case_			=     [
	    """TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """TimesformerModel""",
	    """TimesformerForVideoClassification""",
	    """TimesformerPreTrainedModel""",
	]

if TYPE_CHECKING:
	from .configuration_timesformer import TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, TimesformerConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_timesformer import (
		    TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TimesformerForVideoClassification,
		    TimesformerModel,
		    TimesformerPreTrainedModel,
		)

else:
	import sys

	snake_case_			=     _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




