

import json
import os
import unittest

from transformers.models.roc_bert.tokenization_roc_bert import (
    VOCAB_FILES_NAMES,
    RoCBertBasicTokenizer,
    RoCBertTokenizer,
    RoCBertWordpieceTokenizer,
    _is_control,
    _is_punctuation,
    _is_whitespace,
)
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin, filter_non_english





@require_tokenizers
class     snake_case_			(				_a			, unittest.TestCase							):


							"""simple docstring"""





							__UpperCAmelCase       =RoCBertTokenizer
							__UpperCAmelCase       =None
							__UpperCAmelCase       =False
							__UpperCAmelCase       =True
							__UpperCAmelCase       =filter_non_english




							def        A__  ( self							):
												super().setUp()

												__lowerCAmelCase					=							['[UNK]', '[CLS]', '[SEP]', '[PAD]', '[MASK]', '你', '好', '是', '谁', 'a', 'b', 'c', 'd']
												__lowerCAmelCase					=							{}
												__lowerCAmelCase					=							{}
												for i, value in enumerate(_A							):
																	__lowerCAmelCase					=							i
																	__lowerCAmelCase					=							i
												__lowerCAmelCase					=							os.path.join(self.tmpdirname					,						VOCAB_FILES_NAMES['vocab_file']							)
												__lowerCAmelCase					=							os.path.join(self.tmpdirname					,						VOCAB_FILES_NAMES['word_shape_file']							)
												__lowerCAmelCase					=							os.path.join(self.tmpdirname					,						VOCAB_FILES_NAMES['word_pronunciation_file']							)
												with open(self.vocab_file					,						'w'					,						encoding='utf-8'							) as vocab_writer:
																	vocab_writer.write(''.join([x + '\n' for x in vocab_tokens]							)							)
												with open(self.word_shape_file					,						'w'					,						encoding='utf-8'							) as word_shape_writer:
																	json.dump(_A					,						_A					,						ensure_ascii=_A							)
												with open(self.word_pronunciation_file					,						'w'					,						encoding='utf-8'							) as word_pronunciation_writer:
																	json.dump(_A					,						_A					,						ensure_ascii=_A							)




							def        A__  ( self							):
												__lowerCAmelCase					=							self.tokenizer_class(self.vocab_file					,						self.word_shape_file					,						self.word_pronunciation_file							)

												__lowerCAmelCase					=							tokenizer.tokenize('你好[SEP]你是谁'							)
												self.assertListEqual(_A					,						['你', '好', '[SEP]', '你', '是', '谁']							)
												self.assertListEqual(tokenizer.convert_tokens_to_ids(_A							)					,						[5, 6, 2, 5, 7, 8]							)
												self.assertListEqual(tokenizer.convert_tokens_to_shape_ids(_A							)					,						[5, 6, 2, 5, 7, 8]							)
												self.assertListEqual(tokenizer.convert_tokens_to_pronunciation_ids(_A							)					,						[5, 6, 2, 5, 7, 8]							)




							def        A__  ( self							):
												__lowerCAmelCase					=							RoCBertBasicTokenizer()

												self.assertListEqual(tokenizer.tokenize('ah\u535A\u63A8zz'							)					,						['ah', '\u535A', '\u63A8', 'zz']							)




							def        A__  ( self							):
												__lowerCAmelCase					=							RoCBertBasicTokenizer(do_lower_case=_A							)

												self.assertListEqual(
												    tokenizer.tokenize(' \tHeLLo!how  \n Are yoU?  '							)					,						['hello', '!', 'how', 'are', 'you', '?']							)
												self.assertListEqual(tokenizer.tokenize('H\u00E9llo'							)					,						['hello']							)




							def        A__  ( self							):
												__lowerCAmelCase					=							RoCBertBasicTokenizer(do_lower_case=_A					,						strip_accents=_A							)

												self.assertListEqual(
												    tokenizer.tokenize(' \tHäLLo!how  \n Are yoU?  '							)					,						['hällo', '!', 'how', 'are', 'you', '?']							)
												self.assertListEqual(tokenizer.tokenize('H\u00E9llo'							)					,						['h\u00E9llo']							)




							def        A__  ( self							):
												__lowerCAmelCase					=							RoCBertBasicTokenizer(do_lower_case=_A					,						strip_accents=_A							)

												self.assertListEqual(
												    tokenizer.tokenize(' \tHäLLo!how  \n Are yoU?  '							)					,						['hallo', '!', 'how', 'are', 'you', '?']							)
												self.assertListEqual(tokenizer.tokenize('H\u00E9llo'							)					,						['hello']							)




							def        A__  ( self							):
												__lowerCAmelCase					=							RoCBertBasicTokenizer(do_lower_case=_A							)

												self.assertListEqual(
												    tokenizer.tokenize(' \tHäLLo!how  \n Are yoU?  '							)					,						['hallo', '!', 'how', 'are', 'you', '?']							)
												self.assertListEqual(tokenizer.tokenize('H\u00E9llo'							)					,						['hello']							)




							def        A__  ( self							):
												__lowerCAmelCase					=							RoCBertBasicTokenizer(do_lower_case=_A							)

												self.assertListEqual(
												    tokenizer.tokenize(' \tHeLLo!how  \n Are yoU?  '							)					,						['HeLLo', '!', 'how', 'Are', 'yoU', '?']							)




							def        A__  ( self							):
												__lowerCAmelCase					=							RoCBertBasicTokenizer(do_lower_case=_A					,						strip_accents=_A							)

												self.assertListEqual(
												    tokenizer.tokenize(' \tHäLLo!how  \n Are yoU?  '							)					,						['HäLLo', '!', 'how', 'Are', 'yoU', '?']							)




							def        A__  ( self							):
												__lowerCAmelCase					=							RoCBertBasicTokenizer(do_lower_case=_A					,						strip_accents=_A							)

												self.assertListEqual(
												    tokenizer.tokenize(' \tHäLLo!how  \n Are yoU?  '							)					,						['HaLLo', '!', 'how', 'Are', 'yoU', '?']							)




							def        A__  ( self							):
												__lowerCAmelCase					=							RoCBertBasicTokenizer(do_lower_case=_A					,						never_split=['[UNK]']							)

												self.assertListEqual(
												    tokenizer.tokenize(' \tHeLLo!how  \n Are yoU? [UNK]'							)					,						['HeLLo', '!', 'how', 'Are', 'yoU', '?', '[UNK]']							)




							def        A__  ( self							):
												__lowerCAmelCase					=							['[UNK]', '[CLS]', '[SEP]', 'want', '##want', '##ed', 'wa', 'un', 'runn', '##ing']

												__lowerCAmelCase					=							{}
												for i, token in enumerate(_A							):
																	__lowerCAmelCase					=							i
												__lowerCAmelCase					=							RoCBertWordpieceTokenizer(vocab=_A					,						unk_token='[UNK]'							)

												self.assertListEqual(tokenizer.tokenize(''							)					,						[]							)

												self.assertListEqual(tokenizer.tokenize('unwanted running'							)					,						['un', '##want', '##ed', 'runn', '##ing']							)

												self.assertListEqual(tokenizer.tokenize('unwantedX running'							)					,						['[UNK]', 'runn', '##ing']							)




							def        A__  ( self							):
												self.assertTrue(_is_whitespace(' '							)							)
												self.assertTrue(_is_whitespace('\t'							)							)
												self.assertTrue(_is_whitespace('\r'							)							)
												self.assertTrue(_is_whitespace('\n'							)							)
												self.assertTrue(_is_whitespace('\u00A0'							)							)

												self.assertFalse(_is_whitespace('A'							)							)
												self.assertFalse(_is_whitespace('-'							)							)




							def        A__  ( self							):
												self.assertTrue(_is_control('\u0005'							)							)

												self.assertFalse(_is_control('A'							)							)
												self.assertFalse(_is_control(' '							)							)
												self.assertFalse(_is_control('\t'							)							)
												self.assertFalse(_is_control('\r'							)							)




							def        A__  ( self							):
												self.assertTrue(_is_punctuation('-'							)							)
												self.assertTrue(_is_punctuation('$'							)							)
												self.assertTrue(_is_punctuation('`'							)							)
												self.assertTrue(_is_punctuation('.'							)							)

												self.assertFalse(_is_punctuation('A'							)							)
												self.assertFalse(_is_punctuation(' '							)							)




							def        A__  ( self							):
												__lowerCAmelCase					=							self.get_tokenizer()

												# Example taken from the issue https://github.com/huggingface/tokenizers/issues/340
												self.assertListEqual([tokenizer.tokenize(_A							) for t in ['Test', '\xad', 'test']]					,						[['[UNK]'], [], ['[UNK]']]							)

												if self.test_rust_tokenizer:
																	__lowerCAmelCase					=							self.get_rust_tokenizer()
																	self.assertListEqual(
																	    [rust_tokenizer.tokenize(_A							) for t in ['Test', '\xad', 'test']]					,						[['[UNK]'], [], ['[UNK]']]							)




							def        A__  ( self							):
												for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
																	with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""							):
																						__lowerCAmelCase					=							self.rust_tokenizer_class.from_pretrained(_A					,						**_A							)

																						__lowerCAmelCase					=							F"""A, naïve {tokenizer_r.mask_token} AllenNLP sentence."""
																						__lowerCAmelCase					=							tokenizer_r.encode_plus(
																						    _A					,						return_attention_mask=_A					,						return_token_type_ids=_A					,						return_offsets_mapping=_A					,						add_special_tokens=_A					,						)

																						__lowerCAmelCase					=							tokenizer_r.do_lower_case if hasattr(_A					,						'do_lower_case'							) else False
																						__lowerCAmelCase					=							(
																						    [
																						        ((0, 0), tokenizer_r.cls_token),
																						        ((0, 1), 'A'),
																						        ((1, 2), ','),
																						        ((3, 5), 'na'),
																						        ((5, 6), '##ï'),
																						        ((6, 8), '##ve'),
																						        ((9, 1_5), tokenizer_r.mask_token),
																						        ((1_6, 2_1), 'Allen'),
																						        ((2_1, 2_3), '##NL'),
																						        ((2_3, 2_4), '##P'),
																						        ((2_5, 3_3), 'sentence'),
																						        ((3_3, 3_4), '.'),
																						        ((0, 0), tokenizer_r.sep_token),
																						    ]
																						    if not do_lower_case
																						    else [
																						        ((0, 0), tokenizer_r.cls_token),
																						        ((0, 1), 'a'),
																						        ((1, 2), ','),
																						        ((3, 8), 'naive'),
																						        ((9, 1_5), tokenizer_r.mask_token),
																						        ((1_6, 2_1), 'allen'),
																						        ((2_1, 2_3), '##nl'),
																						        ((2_3, 2_4), '##p'),
																						        ((2_5, 3_3), 'sentence'),
																						        ((3_3, 3_4), '.'),
																						        ((0, 0), tokenizer_r.sep_token),
																						    ]
																						)

																						self.assertEqual(
																						    [e[1] for e in expected_results]					,						tokenizer_r.convert_ids_to_tokens(tokens['input_ids']							)							)
																						self.assertEqual([e[0] for e in expected_results]					,						tokens['offset_mapping']							)




							def        A__  ( self							):
												__lowerCAmelCase					=							['的', '人', '有']
												__lowerCAmelCase					=							''.join(_A							)
												for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
																	with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""							):
																						__lowerCAmelCase					=							True
																						__lowerCAmelCase					=							self.tokenizer_class.from_pretrained(_A					,						**_A							)
																						__lowerCAmelCase					=							self.rust_tokenizer_class.from_pretrained(_A					,						**_A							)

																						__lowerCAmelCase					=							tokenizer_p.encode(_A					,						add_special_tokens=_A							)
																						__lowerCAmelCase					=							tokenizer_r.encode(_A					,						add_special_tokens=_A							)

																						__lowerCAmelCase					=							tokenizer_r.convert_ids_to_tokens(_A							)
																						__lowerCAmelCase					=							tokenizer_p.convert_ids_to_tokens(_A							)

																						# it is expected that each Chinese character is not preceded by "##"
																						self.assertListEqual(_A					,						_A							)
																						self.assertListEqual(_A					,						_A							)

																						__lowerCAmelCase					=							False
																						__lowerCAmelCase					=							self.rust_tokenizer_class.from_pretrained(_A					,						**_A							)
																						__lowerCAmelCase					=							self.tokenizer_class.from_pretrained(_A					,						**_A							)

																						__lowerCAmelCase					=							tokenizer_r.encode(_A					,						add_special_tokens=_A							)
																						__lowerCAmelCase					=							tokenizer_p.encode(_A					,						add_special_tokens=_A							)

																						__lowerCAmelCase					=							tokenizer_r.convert_ids_to_tokens(_A							)
																						__lowerCAmelCase					=							tokenizer_p.convert_ids_to_tokens(_A							)

																						# it is expected that only the first Chinese character is not preceded by "##".
																						__lowerCAmelCase					=							[
																						    F"""##{token}""" if idx != 0 else token for idx, token in enumerate(_A							)
																						]
																						self.assertListEqual(_A					,						_A							)
																						self.assertListEqual(_A					,						_A							)




							@slow
							def        A__  ( self							):
												__lowerCAmelCase					=							self.tokenizer_class(self.vocab_file					,						self.word_shape_file					,						self.word_pronunciation_file							)

												__lowerCAmelCase					=							tokenizer.encode('你好'					,						add_special_tokens=_A							)
												__lowerCAmelCase					=							tokenizer.encode('你是谁'					,						add_special_tokens=_A							)

												__lowerCAmelCase					=							tokenizer.build_inputs_with_special_tokens(_A							)
												__lowerCAmelCase					=							tokenizer.build_inputs_with_special_tokens(_A					,						_A							)

												assert encoded_sentence == [1] + text + [2]
												assert encoded_pair == [1] + text + [2] + text_a + [2]




							def        A__  ( self							):
												__lowerCAmelCase					=							self.get_tokenizers(do_lower_case=_A							)
												for tokenizer in tokenizers:
																	with self.subTest(F"""{tokenizer.__class__.__name__}"""							):
																						__lowerCAmelCase					=							'你好，你是谁'
																						__lowerCAmelCase					=							tokenizer.tokenize(_A							)
																						__lowerCAmelCase					=							tokenizer.convert_tokens_to_ids(_A							)
																						__lowerCAmelCase					=							tokenizer.convert_tokens_to_shape_ids(_A							)
																						__lowerCAmelCase					=							tokenizer.convert_tokens_to_pronunciation_ids(_A							)
																						__lowerCAmelCase					=							tokenizer.prepare_for_model(
																						    _A					,						_A					,						_A					,						add_special_tokens=_A							)

																						__lowerCAmelCase					=							tokenizer.encode_plus(_A					,						add_special_tokens=_A							)

																						self.assertEqual(_A					,						_A							)









import copy
import inspect
import unittest

from transformers import AutoBackbone
from transformers.configuration_utils import PretrainedConfig
from transformers.testing_utils import require_timm, require_torch, torch_device
from transformers.utils.import_utils import is_torch_available

from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor


if is_torch_available():
		import torch

		from transformers import TimmBackbone, TimmBackboneConfig

from ...test_pipeline_mixin import PipelineTesterMixin





class     snake_case_			:


							"""simple docstring"""





							def __init__( self					,						_A					,						_A=None					,						_A=None					,						_A=None					,						_A="resnet50"					,						_A=3					,						_A=3_2					,						_A=3					,						_A=True					,						_A=True					,						):
												__lowerCAmelCase					=							parent
												__lowerCAmelCase					=							out_indices if out_indices is not None else [4]
												__lowerCAmelCase					=							stage_names
												__lowerCAmelCase					=							out_features
												__lowerCAmelCase					=							backbone
												__lowerCAmelCase					=							batch_size
												__lowerCAmelCase					=							image_size
												__lowerCAmelCase					=							num_channels
												__lowerCAmelCase					=							use_pretrained_backbone
												__lowerCAmelCase					=							is_training




							def        A__  ( self							):
												__lowerCAmelCase					=							floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)
												__lowerCAmelCase					=							self.get_config()

												return config, pixel_values




							def        A__  ( self							):
												return TimmBackboneConfig(
												    image_size=self.image_size					,						num_channels=self.num_channels					,						out_features=self.out_features					,						out_indices=self.out_indices					,						stage_names=self.stage_names					,						use_pretrained_backbone=self.use_pretrained_backbone					,						backbone=self.backbone					,						)




							def        A__  ( self					,						_A					,						_A							):
												__lowerCAmelCase					=							TimmBackbone(config=_A							)
												model.to(_A							)
												model.eval()
												with torch.no_grad():
																	__lowerCAmelCase					=							model(_A							)
												self.parent.assertEqual(
												    result.feature_map[-1].shape					,						(self.batch_size, model.channels[-1], 1_4, 1_4)					,						)




							def        A__  ( self							):
												__lowerCAmelCase					=							self.prepare_config_and_inputs()
												__lowerCAmelCase,	__lowerCAmelCase					=							config_and_inputs
												__lowerCAmelCase					=							{'pixel_values': pixel_values}
												return config, inputs_dict





@require_torch
@require_timm
class     snake_case_			(				_a			, _a			, _a			, unittest.TestCase							):


							"""simple docstring"""





							__UpperCAmelCase       =(TimmBackbone,) if is_torch_available() else ()
							__UpperCAmelCase       ={"""feature-extraction""": TimmBackbone} if is_torch_available() else {}
							__UpperCAmelCase       =False
							__UpperCAmelCase       =False
							__UpperCAmelCase       =False
							__UpperCAmelCase       =False




							def        A__  ( self							):
												__lowerCAmelCase					=							TimmBackboneModelTester(self							)
												__lowerCAmelCase					=							ConfigTester(self					,						config_class=_A					,						has_text_modality=_A							)




							def        A__  ( self							):
												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							):
												__lowerCAmelCase					=							'resnet18'
												__lowerCAmelCase					=							'microsoft/resnet-18'

												__lowerCAmelCase					=							AutoBackbone.from_pretrained(_A					,						use_timm_backbone=_A							)
												__lowerCAmelCase					=							AutoBackbone.from_pretrained(_A							)

												self.assertEqual(len(timm_model.out_features							)					,						len(transformers_model.out_features							)							)
												self.assertEqual(len(timm_model.stage_names							)					,						len(transformers_model.stage_names							)							)
												self.assertEqual(timm_model.channels					,						transformers_model.channels							)
												# Out indices are set to the last layer by default. For timm models, we don't know
												# the number of layers in advance, so we set it to (-1,), whereas for transformers
												# models, we set it to [len(stage_names) - 1] (kept for backward compatibility).
												self.assertEqual(timm_model.out_indices					,						(-1,)							)
												self.assertEqual(transformers_model.out_indices					,						[len(timm_model.stage_names							) - 1]							)

												__lowerCAmelCase					=							AutoBackbone.from_pretrained(_A					,						use_timm_backbone=_A					,						out_indices=[1, 2, 3]							)
												__lowerCAmelCase					=							AutoBackbone.from_pretrained(_A					,						out_indices=[1, 2, 3]							)

												self.assertEqual(timm_model.out_indices					,						transformers_model.out_indices							)
												self.assertEqual(len(timm_model.out_features							)					,						len(transformers_model.out_features							)							)
												self.assertEqual(timm_model.channels					,						transformers_model.channels							)




							@unittest.skip('TimmBackbone doesn\'t support feed forward chunking'							)
							def        A__  ( self							):
												pass




							@unittest.skip('TimmBackbone doesn\'t have num_hidden_layers attribute'							)
							def        A__  ( self							):
												pass




							@unittest.skip('TimmBackbone initialization is managed on the timm side'							)
							def        A__  ( self							):
												pass




							@unittest.skip('TimmBackbone models doesn\'t have inputs_embeds'							)
							def        A__  ( self							):
												pass




							@unittest.skip('TimmBackbone models doesn\'t have inputs_embeds'							)
							def        A__  ( self							):
												pass




							@unittest.skip('TimmBackbone model cannot be created without specifying a backbone checkpoint'							)
							def        A__  ( self							):
												pass




							@unittest.skip('Only checkpoints on timm can be loaded into TimmBackbone'							)
							def        A__  ( self							):
												pass




							@unittest.skip('model weights aren\'t tied in TimmBackbone.'							)
							def        A__  ( self							):
												pass




							@unittest.skip('model weights aren\'t tied in TimmBackbone.'							)
							def        A__  ( self							):
												pass




							@unittest.skip('Only checkpoints on timm can be loaded into TimmBackbone'							)
							def        A__  ( self							):
												pass




							@unittest.skip('Only checkpoints on timm can be loaded into TimmBackbone'							)
							def        A__  ( self							):
												pass




							@unittest.skip('TimmBackbone doesn\'t have hidden size info in its configuration.'							)
							def        A__  ( self							):
												pass




							@unittest.skip('TimmBackbone doesn\'t support output_attentions.'							)
							def        A__  ( self							):
												pass




							@unittest.skip('Safetensors is not supported by timm.'							)
							def        A__  ( self							):
												pass




							@unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.'							)
							def        A__  ( self							):
												pass




							def        A__  ( self							):
												__lowerCAmelCase,	__lowerCAmelCase					=							self.model_tester.prepare_config_and_inputs_for_common()

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

																	__lowerCAmelCase					=							['pixel_values']
																	self.assertListEqual(arg_names[:1]					,						_A							)




							def        A__  ( self							):
												__lowerCAmelCase,	__lowerCAmelCase					=							self.model_tester.prepare_config_and_inputs_for_common()
												__lowerCAmelCase					=							True
												__lowerCAmelCase					=							self.has_attentions

												# no need to test all models as different heads yield the same functionality
												__lowerCAmelCase					=							self.all_model_classes[0]
												__lowerCAmelCase					=							model_class(_A							)
												model.to(_A							)

												__lowerCAmelCase					=							self._prepare_for_class(_A					,						_A							)
												__lowerCAmelCase					=							model(**_A							)
												__lowerCAmelCase					=							outputs[0][-1]

												# Encoder-/Decoder-only models
												__lowerCAmelCase					=							outputs.hidden_states[0]
												hidden_states.retain_grad()

												if self.has_attentions:
																	__lowerCAmelCase					=							outputs.attentions[0]
																	attentions.retain_grad()

												output.flatten()[0].backward(retain_graph=_A							)

												self.assertIsNotNone(hidden_states.grad							)

												if self.has_attentions:
																	self.assertIsNotNone(attentions.grad							)




							def        A__  ( self							):
												__lowerCAmelCase,	__lowerCAmelCase					=							self.model_tester.prepare_config_and_inputs_for_common()

												for model_class in self.all_model_classes:
																	__lowerCAmelCase					=							model_class(_A							)
																	model.to(_A							)
																	model.eval()
																	__lowerCAmelCase					=							model(**_A							)

																	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
																	__lowerCAmelCase					=							copy.deepcopy(_A							)
																	__lowerCAmelCase					=							None
																	__lowerCAmelCase					=							model_class(_A							)
																	model.to(_A							)
																	model.eval()
																	__lowerCAmelCase					=							model(**_A							)

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

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






