
import copy
from typing import Dict, Optional

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


__lowerCamelCase							= logging.get_logger(__name__)

__lowerCamelCase							= {
    """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 						UpperCAmelCase	(		A_				):
   A__      :	List[str]		  =  "oneformer"
   A__      :	Any		  =  {"hidden_size": "hidden_dim"}



   def __init__(self	:      Any					,		snake_case__	:      Optional[Dict] = None					,		snake_case__	:      int = 2_55					,		snake_case__	:      int = 1_50					,		snake_case__	:      int = 0.1					,		snake_case__	:      float = 2.0					,		snake_case__	:      float = 5.0					,		snake_case__	:      float = 5.0					,		snake_case__	:      float = 0.5					,		snake_case__	:      float = 0.07					,		snake_case__	:      int = 1_25_44					,		snake_case__	:      float = 3.0					,		snake_case__	:      float = 0.75					,		snake_case__	:      float = 0.02					,		snake_case__	:      float = 1.0					,		snake_case__	:      float = 1e-05					,		snake_case__	:      bool = False					,		snake_case__	:      bool = True					,		snake_case__	:      bool = True					,		snake_case__	:      Optional[list] = [4, 8, 16, 32]					,		snake_case__	:      int = 77					,		snake_case__	:      int = 2_56					,		snake_case__	:      int = 77					,		snake_case__	:      int = 6					,		snake_case__	:      int = 4_94_08					,		snake_case__	:      int = 2					,		snake_case__	:      int = 16					,		snake_case__	:      int = 2_56					,		snake_case__	:      int = 2_56					,		snake_case__	:      int = 2_56					,		snake_case__	:      int = 10_24					,		snake_case__	:      str = "GN"					,		snake_case__	:      int = 6					,		snake_case__	:      int = 10					,		snake_case__	:      bool = True					,		snake_case__	:      int = 8					,		snake_case__	:      float = 0.1					,		snake_case__	:      int = 20_48					,		snake_case__	:      bool = False					,		snake_case__	:      bool = False					,		snake_case__	:      int = 2					,		snake_case__	:      int = 4					,		**snake_case__	:      Optional[int]					,		)	->			Tuple:


          '''simple docstring'''


          if backbone_config is None:
                 logger.info("`backbone_config` is unset. Initializing the config with the default `Swin` backbone."				)
                 snake_case :     str           =   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=snake_case__					,		out_features=["stage1", "stage2", "stage3", "stage4"]					,		)
          elif isinstance(snake_case__					,		snake_case__				):
                 snake_case :     int           =   backbone_config.get("model_type"				)
                 snake_case :     Dict           =   CONFIG_MAPPING[backbone_model_type]
                 snake_case :     List[str]           =   config_class.from_dict(snake_case__				)

          snake_case :     Optional[Any]           =   backbone_config

          snake_case :     int           =   ignore_value
          snake_case :     int           =   num_queries
          snake_case :     int           =   no_object_weight
          snake_case :     int           =   class_weight
          snake_case :     Optional[Any]           =   mask_weight
          snake_case :     Optional[int]           =   dice_weight
          snake_case :     Optional[Any]           =   contrastive_weight
          snake_case :     Union[str, Any]           =   contrastive_temperature
          snake_case :     Union[str, Any]           =   train_num_points
          snake_case :     str           =   oversample_ratio
          snake_case :     Optional[Any]           =   importance_sample_ratio
          snake_case :     Optional[int]           =   init_std
          snake_case :     str           =   init_xavier_std
          snake_case :     Dict           =   layer_norm_eps
          snake_case :     Dict           =   is_training
          snake_case :     Union[str, Any]           =   use_auxiliary_loss
          snake_case :     List[str]           =   output_auxiliary_logits
          snake_case :     Tuple           =   strides
          snake_case :     Any           =   task_seq_len
          snake_case :     Optional[Any]           =   text_encoder_width
          snake_case :     Optional[int]           =   text_encoder_context_length
          snake_case :     Union[str, Any]           =   text_encoder_num_layers
          snake_case :     Dict           =   text_encoder_vocab_size
          snake_case :     Union[str, Any]           =   text_encoder_proj_layers
          snake_case :     Dict           =   text_encoder_n_ctx
          snake_case :     Dict           =   conv_dim
          snake_case :     List[Any]           =   mask_dim
          snake_case :     Dict           =   hidden_dim
          snake_case :     str           =   encoder_feedforward_dim
          snake_case :     int           =   norm
          snake_case :     Union[str, Any]           =   encoder_layers
          snake_case :     List[Any]           =   decoder_layers
          snake_case :     Dict           =   use_task_norm
          snake_case :     int           =   num_attention_heads
          snake_case :     int           =   dropout
          snake_case :     Optional[Any]           =   dim_feedforward
          snake_case :     Optional[int]           =   pre_norm
          snake_case :     str           =   enforce_input_proj
          snake_case :     int           =   query_dec_layers
          snake_case :     Any           =   common_stride
          snake_case :     Optional[int]           =   decoder_layers

          super().__init__(**snake_case__				)



   def        _SCREAMING_SNAKE_CASE     (self	:      List[str]				)	->			Dict[str, any]:


          '''simple docstring'''


          snake_case :     Optional[Any]           =   copy.deepcopy(self.__dict__				)
          snake_case :     Union[str, Any]           =   self.backbone_config.to_dict()
          snake_case :     Dict           =   self.__class__.model_type
          return output







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 						UpperCAmelCase	(		A_ ,unittest.TestCase				):
   A__      :	Dict		  =  PreTrainedTokenizerFast
   A__      :	str		  =  False
   A__      :	List[Any]		  =  True
   A__      :	List[str]		  =  "tokenizer_file"



   def        _SCREAMING_SNAKE_CASE     (self	:      Dict				)	->			Union[str, Any]:


          '''simple docstring'''


          snake_case :     Union[str, Any]           =   False  # because we don't have pretrained_vocab_files_map
          super().setUp()
          snake_case :     Optional[int]           =   True

          snake_case :     List[str]           =   ["robot-test/dummy-tokenizer-fast", "robot-test/dummy-tokenizer-wordlevel"]
          snake_case :     Optional[Any]           =   "SaulLu/dummy-tokenizer-bytelevel-bpe"

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

          snake_case :     List[Any]           =   PreTrainedTokenizerFast.from_pretrained(model_paths[0]				)
          tokenizer.save_pretrained(self.tmpdirname				)



   def        _SCREAMING_SNAKE_CASE     (self	:      Dict				)	->			int:


          '''simple docstring'''


          pass



   def        _SCREAMING_SNAKE_CASE     (self	:      Any				)	->			int:


          '''simple docstring'''


          pass



   def        _SCREAMING_SNAKE_CASE     (self	:      Any				)	->			Union[str, Any]:


          '''simple docstring'''


          pass



   def        _SCREAMING_SNAKE_CASE     (self	:      Tuple				)	->			List[str]:


          '''simple docstring'''


          pass



   def        _SCREAMING_SNAKE_CASE     (self	:      Optional[int]				)	->			List[str]:


          '''simple docstring'''


          snake_case :     Optional[int]           =   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 :     Any           =   tempfile.mkdtemp()
                               snake_case :     int           =   self.rust_tokenizer_class.from_pretrained(snake_case__					,		**snake_case__				)

                               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 :     int           =   tmpdirname_orig



   def        _SCREAMING_SNAKE_CASE     (self	:      Tuple				)	->			Dict:


          '''simple docstring'''


          snake_case :     Optional[int]           =   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 :     Union[str, Any]           =   tempfile.mkdtemp()
                               snake_case :     str           =   self.rust_tokenizer_class.from_pretrained(snake_case__					,		**snake_case__				)

                               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 :     Dict           =   tmpdirname_orig



   def        _SCREAMING_SNAKE_CASE     (self	:      int				)	->			Union[str, Any]:


          '''simple docstring'''


          snake_case :     int           =   self.rust_tokenizer_class.from_pretrained(self.bytelevel_bpe_model_name				)

          snake_case :     int           =   ("a" for _ in range(10_00				))
          snake_case :     Optional[int]           =   tokenizer.train_new_from_iterator(text_iterator=snake_case__					,		length=10_00					,		vocab_size=50				)

          snake_case :     str           =   new_tokenizer.encode("a🤗"				)
          self.assertEqual(snake_case__					,		[64, 1_72, 2_53, 97, 2_45]				)






@require_tokenizers
class 						UpperCAmelCase	(		unittest.TestCase				):



   def        _SCREAMING_SNAKE_CASE     (self	:      Union[str, Any]				)	->			List[str]:


          '''simple docstring'''


          snake_case :     List[str]           =   AutoTokenizer.from_pretrained("bert-base-cased"				)
          snake_case :     Union[str, Any]           =   json.loads(tokenizer._tokenizer.to_str()				)
          snake_case :     Tuple           =   len(snake_case__				)

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

                 # This should pick the new tokenizer file as the version of Transformers is > 4.0.0
                 snake_case :     Optional[Any]           =   AutoTokenizer.from_pretrained(snake_case__				)
                 self.assertEqual(len(snake_case__				)					,		len(snake_case__				) + 1				)
                 snake_case :     Union[str, Any]           =   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(snake_case__					,		"tokenizer.4.0.0.json"				)					,		os.path.join(snake_case__					,		"tokenizer.42.0.0.json"				)				)
                 snake_case :     Optional[Any]           =   ["tokenizer.42.0.0.json"]
                 tokenizer.save_pretrained(snake_case__				)
                 snake_case :     Dict           =   AutoTokenizer.from_pretrained(snake_case__				)
                 self.assertEqual(len(snake_case__				)					,		len(snake_case__				)				)
                 snake_case :     Optional[int]           =   json.loads(new_tokenizer._tokenizer.to_str()				)
                 self.assertNotIn("huggingface"					,		json_tokenizer["model"]["vocab"]				)



   def        _SCREAMING_SNAKE_CASE     (self	:      Optional[int]				)	->			List[str]:


          '''simple docstring'''


          snake_case :     int           =   "hf-internal-testing/test-two-tokenizers"

          # This should pick the new tokenizer file as the version of Transformers is > 4.0.0
          snake_case :     int           =   AutoTokenizer.from_pretrained(snake_case__				)
          self.assertEqual(len(snake_case__				)					,		2_89_97				)
          snake_case :     Optional[int]           =   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 :     Dict           =   "3.0.0"
          snake_case :     Union[str, Any]           =   old_transformers.models.auto.AutoTokenizer.from_pretrained(snake_case__				)
          self.assertEqual(len(snake_case__				)					,		2_89_96				)
          snake_case :     Optional[Any]           =   json.loads(old_tokenizer._tokenizer.to_str()				)
          self.assertNotIn("huggingface"					,		json_tokenizer["model"]["vocab"]				)






@require_tokenizers
class 						UpperCAmelCase	(		unittest.TestCase				):



   def        _SCREAMING_SNAKE_CASE     (self	:      Optional[int]				)	->			List[Any]:


          '''simple docstring'''


          snake_case :     Tuple           =   PreTrainedTokenizerFast.from_pretrained("robot-test/dummy-tokenizer-wordlevel"				)
          snake_case :     List[Any]           =   "The Matrix is a 1999 science fiction action film."

          with concurrent.futures.ThreadPoolExecutor() as executor:
                 snake_case :     Union[str, Any]           =   [executor.submit(self.fetch					,		snake_case__					,		snake_case__				) for i in range(10				)]
                 snake_case :     Optional[Any]           =   [future.result() for future in futures]
                 self.assertEqual(snake_case__					,		[[1, 10, 0, 8, 0, 18, 0, 0, 0, 2] for i in range(10				)]				)



   def        _SCREAMING_SNAKE_CASE     (self	:      str					,		snake_case__	:      List[str]					,		snake_case__	:      str				)	->			List[str]:


          '''simple docstring'''


          return tokenizer.encode(snake_case__					,		truncation="longest_first"					,		padding="longest"				)





