

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						:


					'''simple docstring'''






					def __init__(			self   ,	A__   ,	A__=3   ,	A__=32   ,	A__=3   ,	A__=10   ,	A__=[10, 20, 30, 40]   ,	A__=[1, 1, 2, 1]   ,	A__=True   ,	A__=True   ,	A__="relu"   ,	A__=3   ,	A__=None   ,	):
						A__							:   Optional[int]					       =		parent
						A__							:   int					       =		batch_size
						A__							:   Dict					       =		image_size
						A__							:   Dict					       =		num_channels
						A__							:   Any					       =		embeddings_size
						A__							:   Optional[Any]					       =		hidden_sizes
						A__							:   List[str]					       =		depths
						A__							:   List[Any]					       =		is_training
						A__							:   Optional[Any]					       =		use_labels
						A__							:   int					       =		hidden_act
						A__							:   int					       =		num_labels
						A__							:   Tuple					       =		scope
						A__							:   Optional[Any]					       =		len(A__      )






					def   __A      (			self      ):
						A__							:   List[Any]					       =		floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]      )

						A__							:   Optional[Any]					       =		None
						if self.use_labels:
							A__							:   Union[str, Any]					       =		ids_tensor([self.batch_size]   ,	self.num_labels      )

						A__							:   Optional[Any]					       =		self.get_config()

						return config, pixel_values, labels






					def   __A      (			self      ):
						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   __A      (			self   ,	A__   ,	A__   ,	A__      ):
						A__							:   str					       =		RegNetModel(config=A__      )
						model.to(A__      )
						model.eval()
						A__							:   Any					       =		model(A__      )
						# 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   __A      (			self   ,	A__   ,	A__   ,	A__      ):
						A__							:   int					       =		self.num_labels
						A__							:   int					       =		RegNetForImageClassification(A__      )
						model.to(A__      )
						model.eval()
						A__							:   Tuple					       =		model(A__   ,	labels=A__      )
						self.parent.assertEqual(result.logits.shape   ,	(self.batch_size, self.num_labels)      )






					def   __A      (			self      ):
						A__							:   Any					       =		self.prepare_config_and_inputs()
						A__       ,       A__       ,       A__							:   int					       =		config_and_inputs
						A__							:   Any					       =		{"""pixel_values""": pixel_values}
						return config, inputs_dict



@require_torch
class    _a						(__magic_name__		,       __magic_name__		,       unittest.TestCase    ):


					'''simple docstring'''






					UpperCAmelCase__:       Tuple  							=	(RegNetModel, RegNetForImageClassification) if is_torch_available() else ()
					UpperCAmelCase__:       List[Any]  							=	(
					    {'''feature-extraction''': RegNetModel, '''image-classification''': RegNetForImageClassification}
					    if is_torch_available()
					    else {}
					)

					UpperCAmelCase__:       Any  							=	False
					UpperCAmelCase__:       Tuple  							=	False
					UpperCAmelCase__:       Optional[Any]  							=	False
					UpperCAmelCase__:       str  							=	False






					def   __A      (			self      ):
						A__							:   int					       =		RegNetModelTester(self      )
						A__							:   List[str]					       =		ConfigTester(self   ,	config_class=A__   ,	has_text_modality=A__      )






					def   __A      (			self      ):
						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   __A      (			self      ):
						return






					@unittest.skip(reason="""RegNet does not use inputs_embeds"""      )
					def   __A      (			self      ):
						pass






					@unittest.skip(reason="""RegNet does not support input and output embeddings"""      )
					def   __A      (			self      ):
						pass






					def   __A      (			self      ):
						A__       ,       A__							:   Dict					       =		self.model_tester.prepare_config_and_inputs_for_common()

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

							A__							:   List[Any]					       =		["""pixel_values"""]
							self.assertListEqual(arg_names[:1]   ,	A__      )






					def   __A      (			self      ):
						A__							:   List[str]					       =		self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*A__      )






					def   __A      (			self      ):
						A__       ,       A__							:   Dict					       =		self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
							A__							:   List[Any]					       =		model_class(config=A__      )
							for name, module in model.named_modules():
								if isinstance(A__   ,	(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   __A      (			self      ):
						def check_hidden_states_output(A__   ,	A__   ,	A__      ):
							A__							:   int					       =		model_class(A__      )
							model.to(A__      )
							model.eval()

							with torch.no_grad():
								A__							:   Tuple					       =		model(**self._prepare_for_class(A__   ,	A__      )      )

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

							A__							:   Optional[Any]					       =		self.model_tester.num_stages
							self.assertEqual(len(A__      )   ,	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]   ,	)

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

								# check that output_hidden_states also work using config
								del inputs_dict["output_hidden_states"]
								A__							:   Union[str, Any]					       =		True

								check_hidden_states_output(A__   ,	A__   ,	A__      )






					def   __A      (			self      ):
						A__							:   List[str]					       =		self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_image_classification(*A__      )






					@slow
					def   __A      (			self      ):
						for model_name in REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
							A__							:   Tuple					       =		RegNetModel.from_pretrained(A__      )
							self.assertIsNotNone(A__      )



def 						UpperCamelCase ()					->				Optional[Any]:
	A__							:   int					       =		Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
	return image



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


					'''simple docstring'''






					@cached_property
					def   __A      (			self      ):
						return (
						    AutoImageProcessor.from_pretrained(REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]      )
						    if is_vision_available()
						    else None
						)






					@slow
					def   __A      (			self      ):
						A__							:   Optional[Any]					       =		RegNetForImageClassification.from_pretrained(REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]      ).to(A__      )

						A__							:   Union[str, Any]					       =		self.default_image_processor
						A__							:   List[str]					       =		prepare_img()
						A__							:   Tuple					       =		image_processor(images=A__   ,	return_tensors="""pt"""      ).to(A__      )

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

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

						A__							:   Optional[int]					       =		torch.tensor([-0.4_1_8_0, -1.5_0_5_1, -3.4_8_3_6]      ).to(A__      )

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






from typing import TYPE_CHECKING

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


A_	:			Optional[int]  	=      {'configuration_fnet': ['FNET_PRETRAINED_CONFIG_ARCHIVE_MAP', 'FNetConfig']}

try:
	if not is_sentencepiece_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A_	:			Optional[Any]  	=      ['FNetTokenizer']

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A_	:			Tuple  	=      ['FNetTokenizerFast']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A_	:			List[Any]  	=      [
	    'FNET_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'FNetForMaskedLM',
	    'FNetForMultipleChoice',
	    'FNetForNextSentencePrediction',
	    'FNetForPreTraining',
	    'FNetForQuestionAnswering',
	    'FNetForSequenceClassification',
	    'FNetForTokenClassification',
	    'FNetLayer',
	    'FNetModel',
	    'FNetPreTrainedModel',
	]


if TYPE_CHECKING:
	from .configuration_fnet import FNET_PRETRAINED_CONFIG_ARCHIVE_MAP, FNetConfig

	try:
		if not is_sentencepiece_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_fnet import FNetTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_fnet_fast import FNetTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_fnet import (
		    FNET_PRETRAINED_MODEL_ARCHIVE_LIST,
		    FNetForMaskedLM,
		    FNetForMultipleChoice,
		    FNetForNextSentencePrediction,
		    FNetForPreTraining,
		    FNetForQuestionAnswering,
		    FNetForSequenceClassification,
		    FNetForTokenClassification,
		    FNetLayer,
		    FNetModel,
		    FNetPreTrainedModel,
		)


else:
	import sys

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



