
'''simple docstring'''





import concurrent.futures
import json
import os
import shutil
import tempfile
import unittest

from transformers import AutoTokenizer, PreTrainedTokenizerFast
from transformers.testing_utils import require_tokenizers

from ..test_tokenization_common import TokenizerTesterMixin



@require_tokenizers
class 			lowerCAmelCase__ (							UpperCamelCase       ,				unittest.TestCase							):



  '''simple docstring'''






  _UpperCamelCase							=			PreTrainedTokenizerFast
  _UpperCamelCase							=			False
  _UpperCamelCase							=			True
  _UpperCamelCase							=			'tokenizer_file'

  def 		_lowerCamelCase						(self	:			Any     ) ->							str:
        snake_case_    							=				False  # because we don't have pretrained_vocab_files_map
        super().setUp()
        snake_case_    							=				True

        snake_case_    							=				["robot-test/dummy-tokenizer-fast", "robot-test/dummy-tokenizer-wordlevel"]
        snake_case_    							=				"SaulLu/dummy-tokenizer-bytelevel-bpe"

        # Inclusion of 2 tokenizers to test different types of models (Unigram and WordLevel for the moment)
        snake_case_    							=				[(PreTrainedTokenizerFast, model_path, {}) for model_path in model_paths]

        snake_case_    							=				PreTrainedTokenizerFast.from_pretrained(model_paths[0]     )
        tokenizer.save_pretrained(self.tmpdirname     )

  def 		_lowerCamelCase						(self	:			Any     ) ->							List[Any]:
        # We disable this test for PreTrainedTokenizerFast because it is the only tokenizer that is not linked to any
        # model
        pass

  def 		_lowerCamelCase						(self	:			Tuple     ) ->							Dict:
        # We disable this test for PreTrainedTokenizerFast because it is the only tokenizer that is not linked to any
        # model
        pass

  def 		_lowerCamelCase						(self	:			Any     ) ->							Dict:
        # We disable this test for PreTrainedTokenizerFast because it is the only tokenizer that is not linked to any
        # model
        pass

  def 		_lowerCamelCase						(self	:			Any     ) ->							Tuple:
        # PreTrainedTokenizerFast doesn't have tokenizer_file in its signature
        pass

  def 		_lowerCamelCase						(self	:			List[str]     ) ->							List[str]:
        snake_case_    							=				self.tmpdirname
        # Here we want to test the 2 available tokenizers that use 2 different types of models: Unigram and WordLevel.
        for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
              with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''     ):
                    try:
                          snake_case_    							=				tempfile.mkdtemp()
                          snake_case_    							=				self.rust_tokenizer_class.from_pretrained(lowerCAmelCase_						,			**lowerCAmelCase_     )

                          tokenizer.save_pretrained(self.tmpdirname     )
                          super().test_training_new_tokenizer()
                    finally:
                          # Even if the test fails, we must be sure that the folder is deleted and that the default tokenizer
                          # is restored
                          shutil.rmtree(self.tmpdirname     )
                          snake_case_    							=				tmpdirname_orig

  def 		_lowerCamelCase						(self	:			Union[str, Any]     ) ->							Dict:
        snake_case_    							=				self.tmpdirname
        # Here we want to test the 2 available tokenizers that use 2 different types of models: Unigram and WordLevel.
        for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
              with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''     ):
                    try:
                          snake_case_    							=				tempfile.mkdtemp()
                          snake_case_    							=				self.rust_tokenizer_class.from_pretrained(lowerCAmelCase_						,			**lowerCAmelCase_     )

                          tokenizer.save_pretrained(self.tmpdirname     )
                          super().test_training_new_tokenizer_with_special_tokens_change()
                    finally:
                          # Even if the test fails, we must be sure that the folder is deleted and that the default tokenizer
                          # is restored
                          shutil.rmtree(self.tmpdirname     )
                          snake_case_    							=				tmpdirname_orig


  def 		_lowerCamelCase						(self	:			Union[str, Any]     ) ->							Union[str, Any]:
        snake_case_    							=				self.rust_tokenizer_class.from_pretrained(self.bytelevel_bpe_model_name     )

        snake_case_    							=				("a" for _ in range(10_00     ))
        snake_case_    							=				tokenizer.train_new_from_iterator(text_iterator=lowerCAmelCase_						,			length=10_00						,			vocab_size=50     )

        snake_case_    							=				new_tokenizer.encode("a🤗"     )
        self.assertEqual(lowerCAmelCase_						,			[64, 1_72, 2_53, 97, 2_45]     )



@require_tokenizers
class 			lowerCAmelCase__ (							unittest.TestCase							):



  '''simple docstring'''






  def 		_lowerCamelCase						(self	:			Tuple     ) ->							Dict:
        snake_case_    							=				AutoTokenizer.from_pretrained("bert-base-cased"     )
        snake_case_    							=				json.loads(tokenizer._tokenizer.to_str()     )
        snake_case_    							=				len(lowerCAmelCase_     )

        with tempfile.TemporaryDirectory() as tmp_dir:
              # Hack to save this in the tokenizer_config.json
              snake_case_    							=				["tokenizer.4.0.0.json"]
              tokenizer.save_pretrained(lowerCAmelCase_     )
              json.dump(lowerCAmelCase_						,			open(os.path.join(lowerCAmelCase_						,			"tokenizer.4.0.0.json"     )						,			"w"     )     )

              # This should pick the new tokenizer file as the version of Transformers is > 4.0.0
              snake_case_    							=				AutoTokenizer.from_pretrained(lowerCAmelCase_     )
              self.assertEqual(len(lowerCAmelCase_     )						,			len(lowerCAmelCase_     ) + 1     )
              snake_case_    							=				json.loads(new_tokenizer._tokenizer.to_str()     )
              self.assertIn("huggingface"						,			json_tokenizer["model"]["vocab"]     )

              # Will need to be adjusted if we reach v42 and this test is still here.
              # Should pick the old tokenizer file as the version of Transformers is < 4.0.0
              shutil.move(os.path.join(lowerCAmelCase_						,			"tokenizer.4.0.0.json"     )						,			os.path.join(lowerCAmelCase_						,			"tokenizer.42.0.0.json"     )     )
              snake_case_    							=				["tokenizer.42.0.0.json"]
              tokenizer.save_pretrained(lowerCAmelCase_     )
              snake_case_    							=				AutoTokenizer.from_pretrained(lowerCAmelCase_     )
              self.assertEqual(len(lowerCAmelCase_     )						,			len(lowerCAmelCase_     )     )
              snake_case_    							=				json.loads(new_tokenizer._tokenizer.to_str()     )
              self.assertNotIn("huggingface"						,			json_tokenizer["model"]["vocab"]     )


  def 		_lowerCamelCase						(self	:			List[str]     ) ->							List[Any]:
        # This repo has two tokenizer files, one for v4.0.0 and above with an added token, one for versions lower.
        snake_case_    							=				"hf-internal-testing/test-two-tokenizers"

        # This should pick the new tokenizer file as the version of Transformers is > 4.0.0
        snake_case_    							=				AutoTokenizer.from_pretrained(lowerCAmelCase_     )
        self.assertEqual(len(lowerCAmelCase_     )						,			2_89_97     )
        snake_case_    							=				json.loads(tokenizer._tokenizer.to_str()     )
        self.assertIn("huggingface"						,			json_tokenizer["model"]["vocab"]     )

        # Testing an older version by monkey-patching the version in the module it's used.
        import transformers as old_transformers

        snake_case_    							=				"3.0.0"
        snake_case_    							=				old_transformers.models.auto.AutoTokenizer.from_pretrained(lowerCAmelCase_     )
        self.assertEqual(len(lowerCAmelCase_     )						,			2_89_96     )
        snake_case_    							=				json.loads(old_tokenizer._tokenizer.to_str()     )
        self.assertNotIn("huggingface"						,			json_tokenizer["model"]["vocab"]     )



@require_tokenizers
class 			lowerCAmelCase__ (							unittest.TestCase							):



  '''simple docstring'''






  def 		_lowerCamelCase						(self	:			List[Any]     ) ->							List[str]:
        # See https://github.com/huggingface/transformers/pull/12550
        # and https://github.com/huggingface/tokenizers/issues/537
        snake_case_    							=				PreTrainedTokenizerFast.from_pretrained("robot-test/dummy-tokenizer-wordlevel"     )
        snake_case_    							=				"The Matrix is a 1999 science fiction action film."

        with concurrent.futures.ThreadPoolExecutor() as executor:
              snake_case_    							=				[executor.submit(self.fetch						,			lowerCAmelCase_						,			lowerCAmelCase_     ) for i in range(10     )]
              snake_case_    							=				[future.result() for future in futures]
              self.assertEqual(lowerCAmelCase_						,			[[1, 10, 0, 8, 0, 18, 0, 0, 0, 2] for i in range(10     )]     )


  def 		_lowerCamelCase						(self	:			Dict						,			lowerCAmelCase_	:			Optional[int]						,			lowerCAmelCase_	:			Optional[Any]     ) ->							Union[str, Any]:
        return tokenizer.encode(lowerCAmelCase_						,			truncation="longest_first"						,			padding="longest"     )





'''simple docstring'''






import copy
from typing import Dict, Optional

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


a_       :     List[str] 							=     logging.get_logger(__name__)

a_       :     Union[str, Any] 							=     {
    "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 			a  (  _SCREAMING_SNAKE_CASE							):
 _lowerCAmelCase			       =						"""oneformer"""
 _lowerCAmelCase			       =						{"""hidden_size""": """hidden_dim"""}
 def __init__(			self   ,					__magic_name__ = None   ,					__magic_name__ = 2_55   ,					__magic_name__ = 1_50   ,					__magic_name__ = 0.1   ,					__magic_name__ = 2.0   ,					__magic_name__ = 5.0   ,					__magic_name__ = 5.0   ,					__magic_name__ = 0.5   ,					__magic_name__ = 0.0_7   ,					__magic_name__ = 1_25_44   ,					__magic_name__ = 3.0   ,					__magic_name__ = 0.7_5   ,					__magic_name__ = 0.0_2   ,					__magic_name__ = 1.0   ,					__magic_name__ = 1e-05   ,					__magic_name__ = False   ,					__magic_name__ = True   ,					__magic_name__ = True   ,					__magic_name__ = [4, 8, 16, 32]   ,					__magic_name__ = 77   ,					__magic_name__ = 2_56   ,					__magic_name__ = 77   ,					__magic_name__ = 6   ,					__magic_name__ = 4_94_08   ,					__magic_name__ = 2   ,					__magic_name__ = 16   ,					__magic_name__ = 2_56   ,					__magic_name__ = 2_56   ,					__magic_name__ = 2_56   ,					__magic_name__ = 10_24   ,					__magic_name__ = "GN"   ,					__magic_name__ = 6   ,					__magic_name__ = 10   ,					__magic_name__ = True   ,					__magic_name__ = 8   ,					__magic_name__ = 0.1   ,					__magic_name__ = 20_48   ,					__magic_name__ = False   ,					__magic_name__ = False   ,					__magic_name__ = 2   ,					__magic_name__ = 4   ,					**__magic_name__   ,					)       ->				Dict:
      if backbone_config is None:
           logger.info('`backbone_config` is unset. Initializing the config with the default `Swin` backbone.'    )
           _a         =				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=__magic_name__   ,					out_features=['stage1', 'stage2', 'stage3', 'stage4']   ,					)
      elif isinstance(__magic_name__   ,					__magic_name__    ):
           _a         =				backbone_config.get('model_type'    )
           _a         =				CONFIG_MAPPING[backbone_model_type]
           _a         =				config_class.from_dict(__magic_name__    )

      _a         =				backbone_config

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

      super().__init__(**__magic_name__    )




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

