
"""simple docstring"""


import copy
from typing import Dict, Optional

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


__A       						=       logging.get_logger(__name__)

__A       						=       {
    'shi-labs/oneformer_ade20k_swin_tiny': (
        'https://huggingface.co/shi-labs/oneformer_ade20k_swin_tiny/blob/main/config.json'
    ),
    # See all OneFormer models at https://huggingface.co/models?filter=oneformer
}







class 		lowerCamelCase__					(					__magic_name__				):





			'''simple docstring'''
			lowerCamelCase  					=		'''oneformer'''
			lowerCamelCase  					=		{'''hidden_size''': '''hidden_dim'''}



			def __init__(							self     ,     __UpperCAmelCase = None     ,     __UpperCAmelCase = 2_55     ,     __UpperCAmelCase = 1_50     ,     __UpperCAmelCase = 0.1     ,     __UpperCAmelCase = 2.0     ,     __UpperCAmelCase = 5.0     ,     __UpperCAmelCase = 5.0     ,     __UpperCAmelCase = 0.5     ,     __UpperCAmelCase = 0.0_7     ,     __UpperCAmelCase = 1_25_44     ,     __UpperCAmelCase = 3.0     ,     __UpperCAmelCase = 0.7_5     ,     __UpperCAmelCase = 0.0_2     ,     __UpperCAmelCase = 1.0     ,     __UpperCAmelCase = 1e-05     ,     __UpperCAmelCase = False     ,     __UpperCAmelCase = True     ,     __UpperCAmelCase = True     ,     __UpperCAmelCase = [4, 8, 16, 32]     ,     __UpperCAmelCase = 77     ,     __UpperCAmelCase = 2_56     ,     __UpperCAmelCase = 77     ,     __UpperCAmelCase = 6     ,     __UpperCAmelCase = 4_94_08     ,     __UpperCAmelCase = 2     ,     __UpperCAmelCase = 16     ,     __UpperCAmelCase = 2_56     ,     __UpperCAmelCase = 2_56     ,     __UpperCAmelCase = 2_56     ,     __UpperCAmelCase = 10_24     ,     __UpperCAmelCase = "GN"     ,     __UpperCAmelCase = 6     ,     __UpperCAmelCase = 10     ,     __UpperCAmelCase = True     ,     __UpperCAmelCase = 8     ,     __UpperCAmelCase = 0.1     ,     __UpperCAmelCase = 20_48     ,     __UpperCAmelCase = False     ,     __UpperCAmelCase = False     ,     __UpperCAmelCase = 2     ,     __UpperCAmelCase = 4     ,     **__UpperCAmelCase     ,     ) ->     Tuple:
				if backbone_config is None:
					logger.info("""`backbone_config` is unset. Initializing the config with the default `Swin` backbone."""  )
					_lowerCAmelCase								=CONFIG_MAPPING["""swin"""](
					    image_size=2_24     ,     in_channels=3     ,     patch_size=4     ,     embed_dim=96     ,     depths=[2, 2, 6, 2]     ,     num_heads=[3, 6, 12, 24]     ,     window_size=7     ,     drop_path_rate=0.3     ,     use_absolute_embeddings=__UpperCAmelCase     ,     out_features=["""stage1""", """stage2""", """stage3""", """stage4"""]     ,     )
				elif isinstance(__UpperCAmelCase     ,     __UpperCAmelCase  ):
					_lowerCAmelCase								=backbone_config.get("""model_type"""  )
					_lowerCAmelCase								=CONFIG_MAPPING[backbone_model_type]
					_lowerCAmelCase								=config_class.from_dict(__UpperCAmelCase  )

				_lowerCAmelCase								=backbone_config

				_lowerCAmelCase								=ignore_value
				_lowerCAmelCase								=num_queries
				_lowerCAmelCase								=no_object_weight
				_lowerCAmelCase								=class_weight
				_lowerCAmelCase								=mask_weight
				_lowerCAmelCase								=dice_weight
				_lowerCAmelCase								=contrastive_weight
				_lowerCAmelCase								=contrastive_temperature
				_lowerCAmelCase								=train_num_points
				_lowerCAmelCase								=oversample_ratio
				_lowerCAmelCase								=importance_sample_ratio
				_lowerCAmelCase								=init_std
				_lowerCAmelCase								=init_xavier_std
				_lowerCAmelCase								=layer_norm_eps
				_lowerCAmelCase								=is_training
				_lowerCAmelCase								=use_auxiliary_loss
				_lowerCAmelCase								=output_auxiliary_logits
				_lowerCAmelCase								=strides
				_lowerCAmelCase								=task_seq_len
				_lowerCAmelCase								=text_encoder_width
				_lowerCAmelCase								=text_encoder_context_length
				_lowerCAmelCase								=text_encoder_num_layers
				_lowerCAmelCase								=text_encoder_vocab_size
				_lowerCAmelCase								=text_encoder_proj_layers
				_lowerCAmelCase								=text_encoder_n_ctx
				_lowerCAmelCase								=conv_dim
				_lowerCAmelCase								=mask_dim
				_lowerCAmelCase								=hidden_dim
				_lowerCAmelCase								=encoder_feedforward_dim
				_lowerCAmelCase								=norm
				_lowerCAmelCase								=encoder_layers
				_lowerCAmelCase								=decoder_layers
				_lowerCAmelCase								=use_task_norm
				_lowerCAmelCase								=num_attention_heads
				_lowerCAmelCase								=dropout
				_lowerCAmelCase								=dim_feedforward
				_lowerCAmelCase								=pre_norm
				_lowerCAmelCase								=enforce_input_proj
				_lowerCAmelCase								=query_dec_layers
				_lowerCAmelCase								=common_stride
				_lowerCAmelCase								=decoder_layers

				super().__init__(**__UpperCAmelCase  )



			def 					_lowerCAmelCase		(							self  ) ->     Dict[str, any]:
				_lowerCAmelCase								=copy.deepcopy(self.__dict__  )
				_lowerCAmelCase								=self.backbone_config.to_dict()
				_lowerCAmelCase								=self.__class__.model_type
				return output











"""simple docstring"""







import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device


if is_torch_available():
			import torch

			from transformers import XLMProphetNetForConditionalGeneration, XLMProphetNetTokenizer


@require_torch
class     __UpperCAmelCase(   unittest.TestCase							):




						"""simple docstring"""




						@slow
						def 		UpperCAmelCase_       (   self			):
												'''simple docstring'''
												lowercase__				:      Optional[int]=       XLMProphetNetForConditionalGeneration.from_pretrained("microsoft/xprophetnet-large-wiki100-cased"			)
												model.to(snake_case__			)

												# encoder-decoder outputs
												lowercase__				:      Dict=       torch.tensor([[17, 96208, 103471, 2]]			).to(snake_case__			)
												lowercase__				:      Optional[Any]=       torch.tensor(
												    [[2, 250, 9953, 34, 69489, 1620, 32, 118424, 624, 210, 105, 2913, 1032, 351]]			).to(snake_case__			)
												lowercase__				:      Dict=       model(
												    input_ids=snake_case__       ,  attention_mask=snake_case__       ,  encoder_outputs=snake_case__       ,  decoder_input_ids=snake_case__			)
												lowercase__				:      int=       output[0]
												lowercase__				:      str=       torch.Size((1, 14, 250012)			)
												self.assertEqual(output_predited_logis.shape       ,  snake_case__			)
												lowercase__				:      Union[str, Any]=       torch.tensor(
												    [[[-6.39_86, -8.23_91, 12.51_89], [-6.32_89, -8.08_64, 12.62_11], [-6.24_18, -8.04_45, 12.79_68]]]			).to(snake_case__			)
												self.assertTrue(torch.allclose(output_predited_logis[:, :3, :3]       ,  snake_case__       ,  atol=1e-4			)			)

												# encoder outputs
												lowercase__				:      Tuple=       model.prophetnet.encoder(snake_case__			)[0]
												lowercase__				:      Union[str, Any]=       torch.tensor(
												    [[[-1.42_60, -0.76_28, 0.84_53], [-1.47_19, -0.13_91, 0.78_07], [-1.76_78, 0.01_14, 0.46_46]]]			).to(snake_case__			)
												lowercase__				:      List[Any]=       torch.Size((1, 4, 1024)			)
												self.assertEqual(encoder_outputs.shape       ,  snake_case__			)
												self.assertTrue(torch.allclose(encoder_outputs[:, :3, :3]       ,  snake_case__       ,  atol=1e-4			)			)

												# decoder outputs
												lowercase__				:      int=       model.prophetnet.decoder(
												    snake_case__       ,  encoder_hidden_states=snake_case__       ,  )
												lowercase__				:      Union[str, Any]=       decoder_outputs[1].view(1       ,  model.config.ngram       ,  14       ,  -1			)
												lowercase__				:      List[Any]=       model.lm_head(snake_case__			)
												lowercase__				:      str=       predicting_streams_logits[:, 0]
												self.assertTrue(torch.allclose(next_first_stream_logits[:, :3, :3]       ,  snake_case__       ,  atol=1e-4			)			)




						@slow
						def 		UpperCAmelCase_       (   self			):
												'''simple docstring'''
												lowercase__				:      Union[str, Any]=       XLMProphetNetForConditionalGeneration.from_pretrained(
												    "microsoft/xprophetnet-large-wiki100-cased-xglue-ntg"			)
												model.to(snake_case__			)

												lowercase__				:      Dict=       torch.tensor([[17, 96208, 103471, 2]]			).to(snake_case__			)
												lowercase__				:      List[str]=       torch.tensor(
												    [[2, 250, 9953, 34, 69489, 1620, 32, 118424, 624, 210, 105, 2913, 1032, 351]]			).to(snake_case__			)
												lowercase__				:      List[Any]=       model(
												    input_ids=snake_case__       ,  attention_mask=snake_case__       ,  encoder_outputs=snake_case__       ,  decoder_input_ids=snake_case__			)
												lowercase__				:      str=       output[0]
												lowercase__				:      Optional[Any]=       torch.Size((1, 14, 250012)			)
												self.assertEqual(output_predited_logis.shape       ,  snake_case__			)
												# compare the actual values for a slice.
												lowercase__				:      str=       torch.tensor(
												    [[[-9.22_53, -9.71_73, -6.35_29], [-7.67_01, -9.01_45, -1.93_82], [-8.01_95, -7.00_04, -0.15_23]]]			).to(snake_case__			)

												self.assertTrue(torch.allclose(output_predited_logis[:, :3, :3]       ,  snake_case__       ,  atol=1e-4			)			)




						@slow
						def 		UpperCAmelCase_       (   self			):
												'''simple docstring'''
												lowercase__				:      Tuple=       XLMProphetNetForConditionalGeneration.from_pretrained(
												    "microsoft/xprophetnet-large-wiki100-cased-xglue-ntg"			)
												model.to(snake_case__			)
												lowercase__				:      Optional[int]=       512

												lowercase__				:      int=       XLMProphetNetTokenizer.from_pretrained("microsoft/xprophetnet-large-wiki100-cased-xglue-ntg"			)

												lowercase__				:      str=       (
												    "Microsoft Corporation intends to officially end free support for the Windows 7 operating system after"
												    " January 14, 2020, according to the official portal of the organization. From that day, users of this"
												    " system will not be able to receive security updates, which could make their computers vulnerable to"
												    " cyber attacks."
												)
												lowercase__				:      Tuple=       (
												    "орпорация Microsoft намерена официально прекратить бесплатную поддержку операционной системы Windows 7"
												    " после 14 января 2020 года, сообщается на официальном портале организации . С указанного дня пользователи"
												    " этой системы не смогут получать обновления безопасности, из-за чего их компьютеры могут стать уязвимыми"
												    " к кибератакам."
												)
												lowercase__				:      Optional[int]=       (
												    "根据该组织的官方门户网站，微软公司打算在2020年1月14日之后正式终止对Windows 7操作系统的免费支持。从那时起，该系统的用户将无法接收安全更新，这可能会使他们的计算机容易受到网络攻击。"
												)

												lowercase__				:      Dict=       tokenizer(
												    [EN_SENTENCE, RU_SENTENCE, ZH_SENTENCE]       ,  padding=snake_case__       ,  max_length=255       ,  return_tensors="pt"			).input_ids
												lowercase__				:      Any=       input_ids.to(snake_case__			)

												lowercase__				:      List[Any]=       model.generate(
												    snake_case__       ,  num_beams=10       ,  length_penalty=1.0       ,  no_repeat_ngram_size=3       ,  early_stopping=snake_case__			)
												lowercase__				:      str=       [tokenizer.decode(snake_case__       ,  skip_special_tokens=snake_case__			) for g in summary_ids]
												lowercase__				:      Dict=       "Microsoft to end Windows 7 free support after January 14, 2020"
												lowercase__				:      int=       "Microsoft намерена прекратить бесплатную поддержку Windows 7 после 14 января 2020 года"
												lowercase__				:      List[str]=       "微软打算终止对Windows 7操作系统的免费支持"
												self.assertListEqual(
												    [EXPECTED_TITLE_EN, EXPECTED_TITLE_RU, EXPECTED_TITLE_ZH]       ,  snake_case__       ,  )

												lowercase__				:      List[str]=       model.generate(
												    snake_case__       ,  num_beams=1       ,  length_penalty=1.0       ,  no_repeat_ngram_size=3       ,  early_stopping=snake_case__			)
												lowercase__				:      Optional[Any]=       [
												    tokenizer.convert_ids_to_tokens(snake_case__       ,  skip_special_tokens=snake_case__			) for g in summary_ids_beama
												]
												lowercase__				:      List[str]=       "▁Microsoft ▁to ▁end ▁free ▁support ▁for ▁Windows ▁7".split(" "			)
												lowercase__				:      List[Any]=       "▁Microsoft ▁намерен а ▁прекрати ть ▁бес плат ную ▁поддержку ▁Windows ▁7 ▁после ▁14 ▁января ▁2020 ▁года".split(
												    " "			)
												lowercase__				:      int=       "微软 公司 打算 终止 对 Windows ▁7 操作 系统的 免费 支持".split(" "			)
												self.assertListEqual(
												    [EXPECTED_TITLE_EN_BEAM1_TOK, EXPECTED_TITLE_RU_BEAM1_TOK, EXPECTED_TITLE_ZH_BEAM1_TOK]       ,  snake_case__       ,  )






