
import inspect
import os
import unittest

import torch

import accelerate
from accelerate import debug_launcher
from accelerate.test_utils import (
    execute_subprocess_async,
    require_cpu,
    require_huggingface_suite,
    require_multi_gpu,
    require_single_gpu,
)
from accelerate.utils import patch_environment



@require_huggingface_suite
class __lowerCAmelCase				(					unittest.TestCase      ):




			"""simple docstring"""







			def      lowerCamelCase__     (				self						:					Optional[Any]	)      -> str:







								"""simple docstring"""
								A_				 =						inspect.getfile(accelerate.test_utils	)
								A_				 =						os.path.sep.join(
								    mod_file.split(os.path.sep	)[:-1] + ["scripts", "external_deps", "test_metrics.py"]	)

								from accelerate.test_utils.scripts.external_deps import test_metrics  # noqa: F401

								A_				 =						test_metrics




			@require_cpu
			def      lowerCamelCase__     (				self						:					Tuple	)      -> Tuple:







								"""simple docstring"""
								debug_launcher(self.test_metrics.main       ,   num_processes=1	)




			@require_cpu
			def      lowerCamelCase__     (				self						:					str	)      -> Union[str, Any]:







								"""simple docstring"""
								debug_launcher(self.test_metrics.main	)




			@require_single_gpu
			def      lowerCamelCase__     (				self						:					Union[str, Any]	)      -> int:







								"""simple docstring"""
								self.test_metrics.main()






			@require_multi_gpu
			def      lowerCamelCase__     (				self						:					Optional[int]	)      -> int:







								"""simple docstring"""
								print(F'Found {torch.cuda.device_count()} devices.'	)
								A_				 =						["torchrun", F'--nproc_per_node={torch.cuda.device_count()}', self.test_file_path]
								with patch_environment(omp_num_threads=1	):
													execute_subprocess_async(lowerCAmelCase__       ,   env=os.environ.copy()	)






"""simple docstring"""





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 __lowerCAmelCase				:




			"""simple docstring"""







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







								"""simple docstring"""
								A_				 =						parent
								A_				 =						batch_size
								A_				 =						image_size
								A_				 =						num_channels
								A_				 =						embeddings_size
								A_				 =						hidden_sizes
								A_				 =						depths
								A_				 =						is_training
								A_				 =						use_labels
								A_				 =						hidden_act
								A_				 =						num_labels
								A_				 =						scope
								A_				 =						len(_snake_case	)




			def      lowerCamelCase__     (				self						:					Any	)      -> Optional[int]:







								"""simple docstring"""
								A_				 =						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]	)

								A_				 =						None
								if self.use_labels:
													A_				 =						ids_tensor([self.batch_size]       ,   self.num_labels	)

								A_				 =						self.get_config()

								return config, pixel_values, labels




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







								"""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      lowerCamelCase__     (				self						:					Union[str, Any]       ,   _snake_case						:					str       ,   _snake_case						:					Tuple       ,   _snake_case						:					Tuple	)      -> List[str]:







								"""simple docstring"""
								A_				 =						RegNetModel(config=_snake_case	)
								model.to(_snake_case	)
								model.eval()
								A_				 =						model(_snake_case	)
								# 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      lowerCamelCase__     (				self						:					Dict       ,   _snake_case						:					Tuple       ,   _snake_case						:					Optional[Any]       ,   _snake_case						:					Optional[Any]	)      -> Optional[Any]:







								"""simple docstring"""
								A_				 =						self.num_labels
								A_				 =						RegNetForImageClassification(_snake_case	)
								model.to(_snake_case	)
								model.eval()
								A_				 =						model(_snake_case       ,   labels=_snake_case	)
								self.parent.assertEqual(result.logits.shape       ,   (self.batch_size, self.num_labels)	)






			def      lowerCamelCase__     (				self						:					int	)      -> Optional[Any]:







								"""simple docstring"""
								A_				 =						self.prepare_config_and_inputs()
								A_ ,    A_ ,    A_				 =						config_and_inputs
								A_				 =						{"pixel_values": pixel_values}
								return config, inputs_dict





@require_torch
class __lowerCAmelCase				(					_lowercase     ,				_lowercase     ,				unittest.TestCase      ):




			"""simple docstring"""







			snake_case	      =				(RegNetModel, RegNetForImageClassification) if is_torch_available() else ()
			snake_case	      =				(
			    {"feature-extraction": RegNetModel, "image-classification": RegNetForImageClassification}
			    if is_torch_available()
			    else {}
			)

			snake_case	      =				False
			snake_case	      =				False
			snake_case	      =				False
			snake_case	      =				False




			def      lowerCamelCase__     (				self						:					str	)      -> str:







								"""simple docstring"""
								A_				 =						RegNetModelTester(self	)
								A_				 =						ConfigTester(self       ,   config_class=_snake_case       ,   has_text_modality=_snake_case	)




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







								"""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      lowerCamelCase__     (				self						:					int	)      -> List[Any]:







								"""simple docstring"""
								return




			@unittest.skip(reason="RegNet does not use inputs_embeds"	)
			def      lowerCamelCase__     (				self						:					int	)      -> Optional[Any]:







								"""simple docstring"""
								pass




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







								"""simple docstring"""
								pass




			def      lowerCamelCase__     (				self						:					Optional[int]	)      -> List[Any]:







								"""simple docstring"""
								A_ ,    A_				 =						self.model_tester.prepare_config_and_inputs_for_common()

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

													A_				 =						["pixel_values"]
													self.assertListEqual(arg_names[:1]       ,   _snake_case	)




			def      lowerCamelCase__     (				self						:					Any	)      -> int:







								"""simple docstring"""
								A_				 =						self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*_snake_case	)




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







								"""simple docstring"""
								A_ ,    A_				 =						self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
													A_				 =						model_class(config=_snake_case	)
													for name, module in model.named_modules():
																		if isinstance(_snake_case       ,   (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      lowerCamelCase__     (				self						:					List[Any]	)      -> List[str]:







								"""simple docstring"""
								def check_hidden_states_output(_snake_case						:					Tuple       ,   _snake_case						:					Optional[int]       ,   _snake_case						:					Optional[int]	):
													A_				 =						model_class(_snake_case	)
													model.to(_snake_case	)
													model.eval()

													with torch.no_grad():
																		A_				 =						model(**self._prepare_for_class(_snake_case       ,   _snake_case	)	)

													A_				 =						outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

													A_				 =						self.model_tester.num_stages
													self.assertEqual(len(_snake_case	)       ,   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_				 =						self.model_tester.prepare_config_and_inputs_for_common()
								A_				 =						["basic", "bottleneck"]
								for model_class in self.all_model_classes:
													for layer_type in layers_type:
																		A_				 =						layer_type
																		A_				 =						True
																		check_hidden_states_output(_snake_case       ,   _snake_case       ,   _snake_case	)

																		# check that output_hidden_states also work using config
																		del inputs_dict["output_hidden_states"]
																		A_				 =						True

																		check_hidden_states_output(_snake_case       ,   _snake_case       ,   _snake_case	)




			def      lowerCamelCase__     (				self						:					str	)      -> List[str]:







								"""simple docstring"""
								A_				 =						self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_image_classification(*_snake_case	)






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







								"""simple docstring"""
								for model_name in REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
													A_				 =						RegNetModel.from_pretrained(_snake_case	)
													self.assertIsNotNone(_snake_case	)





def 							A_     ():

					'''simple docstring'''





					A_				 =						Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"				)
					return image




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




			"""simple docstring"""







			@cached_property
			def      lowerCamelCase__     (				self						:					Union[str, Any]	)      -> int:







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






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







								"""simple docstring"""
								A_				 =						RegNetForImageClassification.from_pretrained(REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]	).to(_snake_case	)

								A_				 =						self.default_image_processor
								A_				 =						prepare_img()
								A_				 =						image_processor(images=_snake_case       ,   return_tensors="pt"	).to(_snake_case	)

								# forward pass
								with torch.no_grad():
													A_				 =						model(**_snake_case	)

								# verify the logits
								A_				 =						torch.Size((1, 1_000)	)
								self.assertEqual(outputs.logits.shape       ,   _snake_case	)

								A_				 =						torch.tensor([-0.4_1_8_0, -1.5_0_5_1, -3.4_8_3_6]	).to(_snake_case	)

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




