
import json
import os
import unittest

from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin







@require_tokenizers
class        lowerCAmelCase__      (   __lowercase		,	unittest.TestCase	):
						UpperCamelCase_							: str	=		MgpstrTokenizer
						UpperCamelCase_							: Union[str, Any]	=		False
						UpperCamelCase_							: str	=		{}
						UpperCamelCase_							: int	=		False

						def  A_				(			self				)					->      Tuple:


								'''simple docstring'''

								super().setUp()

								# fmt: off
								_UpperCamelCase						    =	["""[GO]""", """[s]""", """0""", """1""", """2""", """3""", """4""", """5""", """6""", """7""", """8""", """9""", """a""", """b""", """c""", """d""", """e""", """f""", """g""", """h""", """i""", """j""", """k""", """l""", """m""", """n""", """o""", """p""", """q""", """r""", """s""", """t""", """u""", """v""", """w""", """x""", """y""", """z"""]
								# fmt: on
								_UpperCamelCase						    =	dict(zip(a       ,							range(len(a				)				)				)				)

								_UpperCamelCase						    =	os.path.join(self.tmpdirname       ,							VOCAB_FILES_NAMES["""vocab_file"""]				)
								with open(self.vocab_file       ,							"""w"""       ,							encoding="""utf-8"""				) as fp:
										fp.write(json.dumps(a				) + """\n"""				)

						def  A_				(			self       ,							**a				)					->      Optional[int]:


								'''simple docstring'''

								return MgpstrTokenizer.from_pretrained(self.tmpdirname       ,							**a				)

						def  A_				(			self       ,							a				)					->      Optional[Any]:


								'''simple docstring'''

								_UpperCamelCase						    =	"""tester"""
								_UpperCamelCase						    =	"""tester"""
								return input_text, output_text

						@unittest.skip("""MGP-STR always lower cases letters."""				)
						def  A_				(			self				)					->      Dict:


								'''simple docstring'''

								pass

						def  A_				(			self				)					->      Tuple:


								'''simple docstring'''

								_UpperCamelCase						    =	self.get_tokenizers(do_lower_case=a				)
								for tokenizer in tokenizers:
										with self.subTest(F'{tokenizer.__class__.__name__}'				):
												_UpperCamelCase						    =	"""[SPECIAL_TOKEN]"""

												tokenizer.add_special_tokens({"""cls_token""": special_token}				)
												_UpperCamelCase						    =	tokenizer.encode([special_token]       ,							add_special_tokens=a				)
												self.assertEqual(len(a				)       ,							1				)

												_UpperCamelCase						    =	tokenizer.decode(a       ,							skip_special_tokens=a				)
												self.assertTrue(special_token not in decoded				)

						def  A_				(			self				)					->      Tuple:


								'''simple docstring'''

								_UpperCamelCase						    =	self.get_tokenizers()
								for tokenizer in tokenizers:
										with self.subTest(F'{tokenizer.__class__.__name__}'				):
												_UpperCamelCase    ,	_UpperCamelCase						    =	self.get_input_output_texts(a				)

												_UpperCamelCase						    =	tokenizer.tokenize(a				)
												_UpperCamelCase						    =	tokenizer.convert_tokens_to_ids(a				)
												_UpperCamelCase						    =	tokenizer.encode(a       ,							add_special_tokens=a				)
												self.assertListEqual(a       ,							a				)

												_UpperCamelCase						    =	tokenizer.convert_ids_to_tokens(a				)
												self.assertNotEqual(len(a				)       ,							0				)
												_UpperCamelCase						    =	tokenizer.decode(a				)
												self.assertIsInstance(a       ,							a				)

												self.assertEqual(text_a.replace(""" """       ,							""""""				)       ,							a				)

						@unittest.skip("""MGP-STR tokenizer only handles one sequence."""				)
						def  A_				(			self				)					->      int:


								'''simple docstring'''

								pass







						@unittest.skip("""inputs cannot be pretokenized in MgpstrTokenizer"""				)
						def  A_				(			self				)					->      Optional[Any]:


								'''simple docstring'''

								pass





import copy

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


lowerCamelCase__	=							logging.get_logger(__name__)

lowerCamelCase__	=							{
    "ut/deta": "https://huggingface.co/ut/deta/resolve/main/config.json",
}







class        lowerCAmelCase__      (   __lowercase	):
						UpperCamelCase_							: int	=		"deta"
						UpperCamelCase_							: Union[str, Any]	=		{
						    "hidden_size": "d_model",
						    "num_attention_heads": "encoder_attention_heads",
						}

						def __init__(			self       ,							a=None       ,							a=9_00       ,							a=20_48       ,							a=6       ,							a=20_48       ,							a=8       ,							a=6       ,							a=10_24       ,							a=8       ,							a=0.0       ,							a=True       ,							a="relu"       ,							a=2_56       ,							a=0.1       ,							a=0.0       ,							a=0.0       ,							a=0.02       ,							a=1.0       ,							a=True       ,							a=False       ,							a="sine"       ,							a=5       ,							a=4       ,							a=4       ,							a=True       ,							a=3_00       ,							a=True       ,							a=True       ,							a=1       ,							a=5       ,							a=2       ,							a=1       ,							a=1       ,							a=5       ,							a=2       ,							a=0.1       ,							a=0.25       ,							**a       ,							)					->      Dict:


								'''simple docstring'''

								if backbone_config is None:
										logger.info("""`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone."""				)
										_UpperCamelCase						    =	CONFIG_MAPPING["""resnet"""](out_features=["""stage2""", """stage3""", """stage4"""]				)
								else:
										if isinstance(a       ,							a				):
												_UpperCamelCase						    =	backbone_config.pop("""model_type"""				)
												_UpperCamelCase						    =	CONFIG_MAPPING[backbone_model_type]
												_UpperCamelCase						    =	config_class.from_dict(a				)

								_UpperCamelCase						    =	backbone_config
								_UpperCamelCase						    =	num_queries
								_UpperCamelCase						    =	max_position_embeddings
								_UpperCamelCase						    =	d_model
								_UpperCamelCase						    =	encoder_ffn_dim
								_UpperCamelCase						    =	encoder_layers
								_UpperCamelCase						    =	encoder_attention_heads
								_UpperCamelCase						    =	decoder_ffn_dim
								_UpperCamelCase						    =	decoder_layers
								_UpperCamelCase						    =	decoder_attention_heads
								_UpperCamelCase						    =	dropout
								_UpperCamelCase						    =	attention_dropout
								_UpperCamelCase						    =	activation_dropout
								_UpperCamelCase						    =	activation_function
								_UpperCamelCase						    =	init_std
								_UpperCamelCase						    =	init_xavier_std
								_UpperCamelCase						    =	encoder_layerdrop
								_UpperCamelCase						    =	auxiliary_loss
								_UpperCamelCase						    =	position_embedding_type
								# deformable attributes
								_UpperCamelCase						    =	num_feature_levels
								_UpperCamelCase						    =	encoder_n_points
								_UpperCamelCase						    =	decoder_n_points
								_UpperCamelCase						    =	two_stage
								_UpperCamelCase						    =	two_stage_num_proposals
								_UpperCamelCase						    =	with_box_refine
								_UpperCamelCase						    =	assign_first_stage
								if two_stage is True and with_box_refine is False:
										raise ValueError("""If two_stage is True, with_box_refine must be True."""				)
								# Hungarian matcher
								_UpperCamelCase						    =	class_cost
								_UpperCamelCase						    =	bbox_cost
								_UpperCamelCase						    =	giou_cost
								# Loss coefficients
								_UpperCamelCase						    =	mask_loss_coefficient
								_UpperCamelCase						    =	dice_loss_coefficient
								_UpperCamelCase						    =	bbox_loss_coefficient
								_UpperCamelCase						    =	giou_loss_coefficient
								_UpperCamelCase						    =	eos_coefficient
								_UpperCamelCase						    =	focal_alpha
								super().__init__(is_encoder_decoder=a       ,							**a				)

						@property
						def  A_				(			self				)					->      int:


								'''simple docstring'''

								return self.encoder_attention_heads

						@property
						def  A_				(			self				)					->      int:


								'''simple docstring'''

								return self.d_model







						def  A_				(			self				)					->      Tuple:


								'''simple docstring'''

								_UpperCamelCase						    =	copy.deepcopy(self.__dict__				)
								_UpperCamelCase						    =	self.backbone_config.to_dict()
								_UpperCamelCase						    =	self.__class__.model_type
								return output



