import copy
import inspect

from transformers.testing_utils import require_torch, torch_device
from transformers.utils.backbone_utils import BackboneType


@require_torch
class 						a__							:
			A__				:   Optional[int]								=			()
			A__				:   Any								=			True



			def __SCREAMING_SNAKE_CASE							(						self    ) ->    str:
								__a							=   self.config_class

								# test default config
								__a							=   config_class()
								self.assertIsNotNone(UpperCAmelCase    )
								__a							=   ['stem'] + [f'''stage{idx}''' for idx in range(1      ,						len(config.depths    ) + 1    )]
								self.assertEqual(config.stage_names      ,						UpperCAmelCase    )
								self.assertTrue(set(config.out_features    ).issubset(set(config.stage_names    )    )    )

								# Test out_features and out_indices are correctly set
								# out_features and out_indices both None
								__a							=   config_class(out_features=UpperCAmelCase      ,						out_indices=UpperCAmelCase    )
								self.assertEqual(config.out_features      ,						[config.stage_names[-1]]    )
								self.assertEqual(config.out_indices      ,						[len(config.stage_names    ) - 1]    )

								# out_features and out_indices both set
								__a							=   config_class(out_features=['stem', 'stage1']      ,						out_indices=[0, 1]    )
								self.assertEqual(config.out_features      ,						['stem', 'stage1']    )
								self.assertEqual(config.out_indices      ,						[0, 1]    )

								# Only out_features set
								__a							=   config_class(out_features=['stage1', 'stage3']    )
								self.assertEqual(config.out_features      ,						['stage1', 'stage3']    )
								self.assertEqual(config.out_indices      ,						[1, 3]    )

								# Only out_indices set
								__a							=   config_class(out_indices=[0, 2]    )
								self.assertEqual(config.out_features      ,						[config.stage_names[0], config.stage_names[2]]    )
								self.assertEqual(config.out_indices      ,						[0, 2]    )

								# Error raised when out_indices do not correspond to out_features
								with self.assertRaises(UpperCAmelCase    ):
													__a							=   config_class(out_features=['stage1', 'stage2']      ,						out_indices=[0, 2]    )



			def __SCREAMING_SNAKE_CASE							(						self    ) ->    Any:
								__a					,      __a							=   self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
													__a							=   model_class(UpperCAmelCase    )
													__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]      ,						UpperCAmelCase    )



			def __SCREAMING_SNAKE_CASE							(						self    ) ->    List[Any]:
								__a					,      __a							=   self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
													__a							=   model_class(UpperCAmelCase    )
													self.assertEqual(len(model.channels    )      ,						len(config.out_features    )    )
													__a							=   model.num_features
													__a							=   [config.stage_names.index(UpperCAmelCase    ) for feat in config.out_features]
													__a							=   [num_features[idx] for idx in out_indices]
													self.assertListEqual(model.channels      ,						UpperCAmelCase    )

													__a							=   copy.deepcopy(UpperCAmelCase    )
													__a							=   None
													__a							=   model_class(UpperCAmelCase    )
													self.assertEqual(len(model.channels    )      ,						1    )
													self.assertListEqual(model.channels      ,						[num_features[-1]]    )

													__a							=   copy.deepcopy(UpperCAmelCase    )
													__a							=   None
													__a							=   model_class(UpperCAmelCase    )
													self.assertEqual(len(model.channels    )      ,						1    )
													self.assertListEqual(model.channels      ,						[num_features[-1]]    )



			def __SCREAMING_SNAKE_CASE							(						self    ) ->    int:
								__a					,      __a							=   self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
													__a							=   model_class(UpperCAmelCase    )
													model.to(UpperCAmelCase    )
													model.eval()
													__a							=   model(**UpperCAmelCase    )

													self.assertEqual(len(result.feature_maps    )      ,						len(config.out_features    )    )
													self.assertEqual(len(model.channels    )      ,						len(config.out_features    )    )
													self.assertEqual(len(result.feature_maps    )      ,						len(config.out_indices    )    )
													self.assertEqual(len(model.channels    )      ,						len(config.out_indices    )    )

													# Check output of last stage is taken if out_features=None, out_indices=None
													__a							=   copy.deepcopy(UpperCAmelCase    )
													__a							=   None
													__a							=   model_class(UpperCAmelCase    )
													model.to(UpperCAmelCase    )
													model.eval()
													__a							=   model(**UpperCAmelCase    )

													self.assertEqual(len(result.feature_maps    )      ,						1    )
													self.assertEqual(len(model.channels    )      ,						1    )

													__a							=   copy.deepcopy(UpperCAmelCase    )
													__a							=   None
													__a							=   model_class(UpperCAmelCase    )
													model.to(UpperCAmelCase    )
													model.eval()
													__a							=   model(**UpperCAmelCase    )

													self.assertEqual(len(result.feature_maps    )      ,						1    )
													self.assertEqual(len(model.channels    )      ,						1    )

													# Check backbone can be initialized with fresh weights
													__a							=   copy.deepcopy(UpperCAmelCase    )
													__a							=   False
													__a							=   model_class(UpperCAmelCase    )
													model.to(UpperCAmelCase    )
													model.eval()
													__a							=   model(**UpperCAmelCase    )



			def __SCREAMING_SNAKE_CASE							(						self    ) ->    Union[str, Any]:
								__a					,      __a							=   self.model_tester.prepare_config_and_inputs_for_common()

								for backbone_class in self.all_model_classes:
													__a							=   backbone_class(UpperCAmelCase    )

													self.assertTrue(hasattr(UpperCAmelCase      ,						'backbone_type'    )    )
													self.assertTrue(hasattr(UpperCAmelCase      ,						'stage_names'    )    )
													self.assertTrue(hasattr(UpperCAmelCase      ,						'num_features'    )    )
													self.assertTrue(hasattr(UpperCAmelCase      ,						'out_indices'    )    )
													self.assertTrue(hasattr(UpperCAmelCase      ,						'out_features'    )    )
													self.assertTrue(hasattr(UpperCAmelCase      ,						'out_feature_channels'    )    )
													self.assertTrue(hasattr(UpperCAmelCase      ,						'channels'    )    )

													self.assertIsInstance(backbone.backbone_type      ,						UpperCAmelCase    )
													# Verify num_features has been initialized in the backbone init
													self.assertIsNotNone(backbone.num_features    )
													self.assertTrue(len(backbone.channels    ) == len(backbone.out_indices    )    )
													self.assertTrue(len(backbone.stage_names    ) == len(backbone.num_features    )    )
													self.assertTrue(len(backbone.channels    ) <= len(backbone.num_features    )    )
													self.assertTrue(len(backbone.out_feature_channels    ) == len(backbone.stage_names    )    )







			def __SCREAMING_SNAKE_CASE							(						self    ) ->    List[Any]:
								__a					,      __a							=   self.model_tester.prepare_config_and_inputs_for_common()
								__a							=   inputs_dict['pixel_values'].shape[0]

								for backbone_class in self.all_model_classes:
													__a							=   backbone_class(UpperCAmelCase    )
													backbone.to(UpperCAmelCase    )
													backbone.eval()

													__a							=   backbone(**UpperCAmelCase    )

													# Test default outputs and verify feature maps
													self.assertIsInstance(outputs.feature_maps      ,						UpperCAmelCase    )
													self.assertTrue(len(outputs.feature_maps    ) == len(backbone.channels    )    )
													for feature_map, n_channels in zip(outputs.feature_maps      ,						backbone.channels    ):
																		self.assertTrue(feature_map.shape[:2]      ,						(batch_size, n_channels)    )
													self.assertIsNone(outputs.hidden_states    )
													self.assertIsNone(outputs.attentions    )

													# Test output_hidden_states=True
													__a							=   backbone(**UpperCAmelCase      ,						output_hidden_states=UpperCAmelCase    )
													self.assertIsNotNone(outputs.hidden_states    )
													self.assertTrue(len(outputs.hidden_states    )      ,						len(backbone.stage_names    )    )
													for hidden_state, n_channels in zip(outputs.hidden_states      ,						backbone.channels    ):
																		self.assertTrue(hidden_state.shape[:2]      ,						(batch_size, n_channels)    )

													# Test output_attentions=True
													if self.has_attentions:
																		__a							=   backbone(**UpperCAmelCase      ,						output_attentions=UpperCAmelCase    )
																		self.assertIsNotNone(outputs.attentions    )





# as due to their complexity multi-gpu tests could impact other tests, and to aid debug we have those in a separate module.

import os
import sys

from transformers.testing_utils import TestCasePlus, execute_subprocess_async, get_gpu_count, require_torch_gpu, slow

from .utils import load_json


class 						a__							(   __snake_case		):



			@classmethod
			def __SCREAMING_SNAKE_CASE							(						cls    ) ->    Optional[int]:
								return cls







			@slow
			@require_torch_gpu
			def __SCREAMING_SNAKE_CASE							(						self    ) ->    Dict:
								__a							=   self.get_auto_remove_tmp_dir()
								__a							=   f'''
            --model_name Helsinki-NLP/opus-mt-en-ro
            --save_dir {output_dir}
            --data_dir {self.test_file_dir_str}/test_data/wmt_en_ro
            --num_beams 2
            --task translation
        '''.split()

								# we want this test to run even if there is only one GPU, but if there are more we use them all
								__a							=   get_gpu_count()
								__a							=   f'''
            -m torch.distributed.launch
            --nproc_per_node={n_gpu}
            {self.test_file_dir}/run_distributed_eval.py
        '''.split()
								__a							=   [sys.executable] + distributed_args + args
								execute_subprocess_async(UpperCAmelCase      ,						env=self.get_env()    )

								__a							=   os.path.join(UpperCAmelCase      ,						'test_bleu.json'    )
								__a							=   load_json(UpperCAmelCase    )
								# print(metrics)
								self.assertGreaterEqual(metrics['bleu']      ,						2_5    )




