



import copy
from typing import Dict, Optional

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
from ..detr import DetrConfig
from ..swin import SwinConfig


lowercase_													=      {
    """facebook/maskformer-swin-base-ade""": (
        """https://huggingface.co/facebook/maskformer-swin-base-ade/blob/main/config.json"""
    )
    # See all MaskFormer models at https://huggingface.co/models?filter=maskformer
}

lowercase_													=      logging.get_logger(__name__)






class     __a			(  a_					):
	SCREAMING_SNAKE_CASE  	=							"maskformer"
	SCREAMING_SNAKE_CASE  	=							{"hidden_size": "mask_feature_size"}
	SCREAMING_SNAKE_CASE  	=							["resnet", "swin"]
	SCREAMING_SNAKE_CASE  	=							["detr"]



	def __init__(    self				:     str					,						snake_case_				:     int = 2_56					,						snake_case_				:     int = 2_56					,						snake_case_				:     float = 0.1					,						snake_case_				:     bool = False					,						snake_case_				:     Optional[Dict] = None					,						snake_case_				:     Optional[Dict] = None					,						snake_case_				:     float = 0.0_2					,						snake_case_				:     float = 1.0					,						snake_case_				:     float = 1.0					,						snake_case_				:     float = 1.0					,						snake_case_				:     float = 2_0.0					,						snake_case_				:     Optional[bool] = None					,						**snake_case_				:     int					,						)->				List[Any]:
						if backbone_config is None:
											# fall back to https://huggingface.co/microsoft/swin-base-patch4-window12-384-in22k
											__lowerCAmelCase						 =SwinConfig(
											    image_size=3_84					,						in_channels=3					,						patch_size=4					,						embed_dim=1_28					,						depths=[2, 2, 18, 2]					,						num_heads=[4, 8, 16, 32]					,						window_size=12					,						drop_path_rate=0.3					,						out_features=["""stage1""", """stage2""", """stage3""", """stage4"""]					,						)

						if isinstance(__SCREAMING_SNAKE_CASE					,						__SCREAMING_SNAKE_CASE):
											__lowerCAmelCase						 =backbone_config.pop("""model_type""")
											__lowerCAmelCase						 =CONFIG_MAPPING[backbone_model_type]
											__lowerCAmelCase						 =config_class.from_dict(__SCREAMING_SNAKE_CASE)

						# verify that the backbone is supported
						if backbone_config.model_type not in self.backbones_supported:
											logger.warning_once(
											    F"""Backbone {backbone_config.model_type} is not a supported model and may not be compatible with MaskFormer. """
											    F"""Supported model types: {','.join(self.backbones_supported)}""")

						if decoder_config is None:
											# fall back to https://huggingface.co/facebook/detr-resnet-50
											__lowerCAmelCase						 =DetrConfig()
						else:
											# verify that the decoder is supported
											__lowerCAmelCase						 =(
											    decoder_config.pop("""model_type""") if isinstance(__SCREAMING_SNAKE_CASE					,						__SCREAMING_SNAKE_CASE) else decoder_config.model_type
											)
											if decoder_type not in self.decoders_supported:
																raise ValueError(
																    F"""Transformer Decoder {decoder_type} not supported, please use one of"""
																    F""" {','.join(self.decoders_supported)}""")
											if isinstance(__SCREAMING_SNAKE_CASE					,						__SCREAMING_SNAKE_CASE):
																__lowerCAmelCase						 =CONFIG_MAPPING[decoder_type]
																__lowerCAmelCase						 =config_class.from_dict(__SCREAMING_SNAKE_CASE)

						__lowerCAmelCase						 =backbone_config
						__lowerCAmelCase						 =decoder_config
						# main feature dimension for the model
						__lowerCAmelCase						 =fpn_feature_size
						__lowerCAmelCase						 =mask_feature_size
						# initializer
						__lowerCAmelCase						 =init_std
						__lowerCAmelCase						 =init_xavier_std
						# Hungarian matcher && loss
						__lowerCAmelCase						 =cross_entropy_weight
						__lowerCAmelCase						 =dice_weight
						__lowerCAmelCase						 =mask_weight
						__lowerCAmelCase						 =use_auxiliary_loss
						__lowerCAmelCase						 =no_object_weight
						__lowerCAmelCase						 =output_auxiliary_logits

						__lowerCAmelCase						 =self.decoder_config.encoder_attention_heads
						__lowerCAmelCase						 =self.decoder_config.num_hidden_layers
						super().__init__(**__SCREAMING_SNAKE_CASE)



	@classmethod
	def 		UpperCamelCase				(    cls				:     str					,						snake_case_				:     PretrainedConfig					,						snake_case_				:     PretrainedConfig					,						**snake_case_				:     Optional[Any])->				List[str]:
						return cls(
						    backbone_config=__SCREAMING_SNAKE_CASE					,						decoder_config=__SCREAMING_SNAKE_CASE					,						**__SCREAMING_SNAKE_CASE					,						)



	def 		UpperCamelCase				(    self				:     List[Any])->				List[str]:
						__lowerCAmelCase						 =copy.deepcopy(self.__dict__)
						__lowerCAmelCase						 =self.backbone_config.to_dict()
						__lowerCAmelCase						 =self.decoder_config.to_dict()
						__lowerCAmelCase						 =self.__class__.model_type
						return output





import unittest

from transformers import CamembertTokenizer, CamembertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import is_torch_available

from ...test_tokenization_common import TokenizerTesterMixin


SCREAMING_SNAKE_CASE			:						Union[str, Any]				  =		get_tests_dir("""fixtures/test_sentencepiece.model""")
SCREAMING_SNAKE_CASE			:						List[Any]				  =		get_tests_dir("""fixtures/test_sentencepiece_bpe.model""")

SCREAMING_SNAKE_CASE			:						Any				  =		"""pt""" if is_torch_available() else """tf"""
@require_sentencepiece
@require_tokenizers
class  A_			(							a_      ,							unittest.TestCase			):
  _SCREAMING_SNAKE_CASE     					=      CamembertTokenizer
  _SCREAMING_SNAKE_CASE     					=      CamembertTokenizerFast
  _SCREAMING_SNAKE_CASE     					=      True
  _SCREAMING_SNAKE_CASE     					=      True

  def 		_UpperCAmelCase (		self			:   Optional[int]							):
   super().setUp()

   # We have a SentencePiece fixture for testing
   __a							=	CamembertTokenizer(__SCREAMING_SNAKE_CASE							)
   tokenizer.save_pretrained(self.tmpdirname							)

  def 		_UpperCAmelCase (		self			:   Any							):
   __a							=	"<pad>"
   __a							=	1

   self.assertEqual(self.get_tokenizer()._convert_token_to_id(__SCREAMING_SNAKE_CASE							)  ,				__SCREAMING_SNAKE_CASE							)
   self.assertEqual(self.get_tokenizer()._convert_id_to_token(__SCREAMING_SNAKE_CASE							)  ,				__SCREAMING_SNAKE_CASE							)

  def 		_UpperCAmelCase (		self			:   Any							):
   __a							=	list(self.get_tokenizer().get_vocab().keys()							)

   self.assertEqual(vocab_keys[0]  ,				"<s>NOTUSED"							)
   self.assertEqual(vocab_keys[1]  ,				"<pad>"							)
   self.assertEqual(vocab_keys[-1]  ,				"<mask>"							)
   self.assertEqual(len(__SCREAMING_SNAKE_CASE							)  ,				10_04							)

  def 		_UpperCAmelCase (		self			:   Optional[int]							):
   self.assertEqual(self.get_tokenizer().vocab_size  ,				10_05							)

  def 		_UpperCAmelCase (		self			:   int							):
   __a							=	CamembertTokenizer(__SCREAMING_SNAKE_CASE							)
   tokenizer.save_pretrained(self.tmpdirname							)
   __a							=	CamembertTokenizerFast.from_pretrained(self.tmpdirname							)

   __a							=	"I was born in 92000, and this is falsé."

   __a							=	tokenizer.encode(__SCREAMING_SNAKE_CASE							)
   __a							=	rust_tokenizer.encode(__SCREAMING_SNAKE_CASE							)
   self.assertListEqual(__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE							)

   __a							=	tokenizer.encode(__SCREAMING_SNAKE_CASE  ,				add_special_tokens=__SCREAMING_SNAKE_CASE							)
   __a							=	rust_tokenizer.encode(__SCREAMING_SNAKE_CASE  ,				add_special_tokens=__SCREAMING_SNAKE_CASE							)
   self.assertListEqual(__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE							)

   # <unk> tokens are not the same for `rust` than for `slow`.
   # Because spm gives back raw token instead of `unk` in EncodeAsPieces
   # tokens = tokenizer.tokenize(sequence)
   __a							=	tokenizer.convert_ids_to_tokens(__SCREAMING_SNAKE_CASE							)
   __a							=	rust_tokenizer.tokenize(__SCREAMING_SNAKE_CASE							)
   self.assertListEqual(__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE							)

  def 		_UpperCAmelCase (		self			:   List[Any]							):
   if not self.test_rust_tokenizer:
    return

   __a							=	self.get_tokenizer()
   __a							=	self.get_rust_tokenizer()

   __a							=	"I was born in 92000, and this is falsé."

   __a							=	tokenizer.tokenize(__SCREAMING_SNAKE_CASE							)
   __a							=	rust_tokenizer.tokenize(__SCREAMING_SNAKE_CASE							)
   self.assertListEqual(__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE							)

   __a							=	tokenizer.encode(__SCREAMING_SNAKE_CASE  ,				add_special_tokens=__SCREAMING_SNAKE_CASE							)
   __a							=	rust_tokenizer.encode(__SCREAMING_SNAKE_CASE  ,				add_special_tokens=__SCREAMING_SNAKE_CASE							)
   self.assertListEqual(__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE							)

   __a							=	self.get_rust_tokenizer()
   __a							=	tokenizer.encode(__SCREAMING_SNAKE_CASE							)
   __a							=	rust_tokenizer.encode(__SCREAMING_SNAKE_CASE							)
   self.assertListEqual(__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE							)

  @slow
  def 		_UpperCAmelCase (		self			:   str							):
   # fmt: off
   __a							=	{"input_ids": [[5, 54, 71_96, 2_97, 30, 23, 7_76, 18, 11, 32_15, 37_05, 82_52, 22, 31_64, 11_81, 21_16, 29, 16, 8_13, 25, 7_91, 33_14, 20, 34_46, 38, 2_75_75, 1_20, 6, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [5, 4_68, 17, 11, 90_88, 20, 15_17, 8, 2_28_04, 1_88_18, 10, 38, 6_29, 6_07, 6_07, 1_42, 19, 71_96, 8_67, 56, 1_03_26, 24, 22_67, 20, 4_16, 50_72, 1_56_12, 2_33, 7_34, 7, 23_99, 27, 16, 30_15, 16_49, 7, 24, 20, 43_38, 23_99, 27, 13, 34_00, 14, 13, 61_89, 8, 9_30, 9, 6]], "attention_mask": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}  # noqa: E501
   # fmt: on

   # camembert is a french model. So we also use french texts.
   __a							=	[
       "Le transformeur est un modèle d'apprentissage profond introduit en 2017, "
       "utilisé principalement dans le domaine du traitement automatique des langues (TAL).",
       "À l'instar des réseaux de neurones récurrents (RNN), les transformeurs sont conçus "
       "pour gérer des données séquentielles, telles que le langage naturel, pour des tâches "
       "telles que la traduction et la synthèse de texte.",
   ]

   self.tokenizer_integration_test_util(
       expected_encoding=__SCREAMING_SNAKE_CASE  ,				model_name="camembert-base"  ,				revision="3a0641d9a1aeb7e848a74299e7e4c4bca216b4cf"  ,				sequences=__SCREAMING_SNAKE_CASE  ,				)


