


'''simple docstring'''


import copy
import inspect

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






@require_torch
class 						lowerCAmelCase__      :
			lowerCAmelCase			:						Any							 =  ()
			lowerCAmelCase			:						Tuple							 =  True




			def 				lowerCAmelCase__     (		self	:  List[str]			) ->Dict:



					'''simple docstring'''







					_UpperCAmelCase		:							Any             =   self.config_class

					# test default config
					_UpperCAmelCase		:							Optional[Any]             =   config_class()
					self.assertIsNotNone(lowerCamelCase__			)
					_UpperCAmelCase		:							Any             =   ["stem"] + [F"""stage{idx}""" for idx in range(1						,	len(config.depths			) + 1			)]
					self.assertEqual(config.stage_names						,	lowerCamelCase__			)
					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
					_UpperCAmelCase		:							Optional[int]             =   config_class(out_features=lowerCamelCase__						,	out_indices=lowerCamelCase__			)
					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
					_UpperCAmelCase		:							Tuple             =   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
					_UpperCAmelCase		:							Optional[int]             =   config_class(out_features=["stage1", "stage3"]			)
					self.assertEqual(config.out_features						,	["stage1", "stage3"]			)
					self.assertEqual(config.out_indices						,	[1, 3]			)

					# Only out_indices set
					_UpperCAmelCase		:							Optional[int]             =   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(lowerCamelCase__			):
							_UpperCAmelCase		:							Dict             =   config_class(out_features=["stage1", "stage2"]						,	out_indices=[0, 2]			)




			def 				lowerCAmelCase__     (		self	:  int			) ->Dict:



					'''simple docstring'''







					_UpperCAmelCase       ,     _UpperCAmelCase		:							Tuple             =   self.model_tester.prepare_config_and_inputs_for_common()

					for model_class in self.all_model_classes:
							_UpperCAmelCase		:							Dict             =   model_class(lowerCamelCase__			)
							_UpperCAmelCase		:							List[Any]             =   inspect.signature(model.forward			)
							# signature.parameters is an OrderedDict => so arg_names order is deterministic
							_UpperCAmelCase		:							Any             =   [*signature.parameters.keys()]
							_UpperCAmelCase		:							List[str]             =   ["pixel_values"]
							self.assertListEqual(arg_names[:1]						,	lowerCamelCase__			)




			def 				lowerCAmelCase__     (		self	:  Any			) ->str:



					'''simple docstring'''







					_UpperCAmelCase       ,     _UpperCAmelCase		:							str             =   self.model_tester.prepare_config_and_inputs_for_common()

					for model_class in self.all_model_classes:
							_UpperCAmelCase		:							Any             =   model_class(lowerCamelCase__			)
							self.assertEqual(len(model.channels			)						,	len(config.out_features			)			)
							_UpperCAmelCase		:							Any             =   model.num_features
							_UpperCAmelCase		:							List[str]             =   [config.stage_names.index(lowerCamelCase__			) for feat in config.out_features]
							_UpperCAmelCase		:							Optional[Any]             =   [num_features[idx] for idx in out_indices]
							self.assertListEqual(model.channels						,	lowerCamelCase__			)

							_UpperCAmelCase		:							Optional[int]             =   copy.deepcopy(lowerCamelCase__			)
							_UpperCAmelCase		:							int             =   None
							_UpperCAmelCase		:							Optional[int]             =   model_class(lowerCamelCase__			)
							self.assertEqual(len(model.channels			)						,	1			)
							self.assertListEqual(model.channels						,	[num_features[-1]]			)

							_UpperCAmelCase		:							List[str]             =   copy.deepcopy(lowerCamelCase__			)
							_UpperCAmelCase		:							List[str]             =   None
							_UpperCAmelCase		:							List[str]             =   model_class(lowerCamelCase__			)
							self.assertEqual(len(model.channels			)						,	1			)
							self.assertListEqual(model.channels						,	[num_features[-1]]			)




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



					'''simple docstring'''







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

					for model_class in self.all_model_classes:
							_UpperCAmelCase		:							str             =   model_class(lowerCamelCase__			)
							model.to(lowerCamelCase__			)
							model.eval()
							_UpperCAmelCase		:							Tuple             =   model(**lowerCamelCase__			)

							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
							_UpperCAmelCase		:							List[str]             =   copy.deepcopy(lowerCamelCase__			)
							_UpperCAmelCase		:							Union[str, Any]             =   None
							_UpperCAmelCase		:							int             =   model_class(lowerCamelCase__			)
							model.to(lowerCamelCase__			)
							model.eval()
							_UpperCAmelCase		:							Union[str, Any]             =   model(**lowerCamelCase__			)

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

							_UpperCAmelCase		:							Dict             =   copy.deepcopy(lowerCamelCase__			)
							_UpperCAmelCase		:							List[str]             =   None
							_UpperCAmelCase		:							Dict             =   model_class(lowerCamelCase__			)
							model.to(lowerCamelCase__			)
							model.eval()
							_UpperCAmelCase		:							Union[str, Any]             =   model(**lowerCamelCase__			)

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

							# Check backbone can be initialized with fresh weights
							_UpperCAmelCase		:							str             =   copy.deepcopy(lowerCamelCase__			)
							_UpperCAmelCase		:							Dict             =   False
							_UpperCAmelCase		:							Optional[int]             =   model_class(lowerCamelCase__			)
							model.to(lowerCamelCase__			)
							model.eval()
							_UpperCAmelCase		:							List[Any]             =   model(**lowerCamelCase__			)




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



					'''simple docstring'''







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

					for backbone_class in self.all_model_classes:
							_UpperCAmelCase		:							Optional[Any]             =   backbone_class(lowerCamelCase__			)

							self.assertTrue(hasattr(lowerCamelCase__						,	"backbone_type"			)			)
							self.assertTrue(hasattr(lowerCamelCase__						,	"stage_names"			)			)
							self.assertTrue(hasattr(lowerCamelCase__						,	"num_features"			)			)
							self.assertTrue(hasattr(lowerCamelCase__						,	"out_indices"			)			)
							self.assertTrue(hasattr(lowerCamelCase__						,	"out_features"			)			)
							self.assertTrue(hasattr(lowerCamelCase__						,	"out_feature_channels"			)			)
							self.assertTrue(hasattr(lowerCamelCase__						,	"channels"			)			)

							self.assertIsInstance(backbone.backbone_type						,	lowerCamelCase__			)
							# 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 				lowerCAmelCase__     (		self	:  Dict			) ->Dict:



					'''simple docstring'''







					_UpperCAmelCase       ,     _UpperCAmelCase		:							int             =   self.model_tester.prepare_config_and_inputs_for_common()
					_UpperCAmelCase		:							Tuple             =   inputs_dict["pixel_values"].shape[0]

					for backbone_class in self.all_model_classes:
							_UpperCAmelCase		:							int             =   backbone_class(lowerCamelCase__			)
							backbone.to(lowerCamelCase__			)
							backbone.eval()

							_UpperCAmelCase		:							str             =   backbone(**lowerCamelCase__			)

							# Test default outputs and verify feature maps
							self.assertIsInstance(outputs.feature_maps						,	lowerCamelCase__			)
							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
							_UpperCAmelCase		:							Dict             =   backbone(**lowerCamelCase__						,	output_hidden_states=lowerCamelCase__			)
							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:
									_UpperCAmelCase		:							Optional[int]             =   backbone(**lowerCamelCase__						,	output_attentions=lowerCamelCase__			)
									self.assertIsNotNone(outputs.attentions			)







'''simple docstring'''


import io
import json
import subprocess


lowerCamelCase__     						=  [
    ['en', 'ru'],
    ['ru', 'en'],
    ['en', 'de'],
    ['de', 'en'],
]

lowerCamelCase__     						=  8






def 			__lowerCAmelCase			(__lowerCAmelCase   ,			__lowerCAmelCase ):
		_UpperCAmelCase		:							List[str]             =   {}
		for src, tgt in pairs:
				_UpperCAmelCase		:							Tuple             =   F"""{src}-{tgt}"""
				_UpperCAmelCase		:							Tuple             =   F"""sacrebleu -t wmt19 -l {pair} --echo src""".split()
				_UpperCAmelCase		:							List[Any]             =   subprocess.run(__lowerCAmelCase   ,			stdout=subprocess.PIPE ).stdout.decode("utf-8" ).splitlines()
				_UpperCAmelCase		:							Tuple             =   F"""sacrebleu -t wmt19 -l {pair} --echo ref""".split()
				_UpperCAmelCase		:							List[str]             =   subprocess.run(__lowerCAmelCase   ,			stdout=subprocess.PIPE ).stdout.decode("utf-8" ).splitlines()
				_UpperCAmelCase		:							Tuple             =   {"src": src_lines[:n_objs], "tgt": tgt_lines[:n_objs]}
		return text


lowerCamelCase__     						=  get_all_data(pairs, n_objs)
lowerCamelCase__     						=  './fsmt_val_data.json'
with io.open(filename, 'w', encoding='utf-8') as f:
						lowerCamelCase__     						=  json.dump(text, f, indent=2, ensure_ascii=False)



