





from queue import Queue
from typing import TYPE_CHECKING, Optional


if TYPE_CHECKING:
     from ..models.auto import AutoTokenizer



class    _SCREAMING_SNAKE_CASE						:







 '''simple docstring'''

 def 							SCREAMING_SNAKE_CASE_					(self				:   Optional[Any]       , UpperCAmelCase_				:   Tuple)       ->Dict:



   '''simple docstring'''



   raise NotImplementedError()







 def 							SCREAMING_SNAKE_CASE_					(self				:   Tuple)       ->Any:



   '''simple docstring'''



   raise NotImplementedError()



class    _SCREAMING_SNAKE_CASE						(	__SCREAMING_SNAKE_CASE    ):







 '''simple docstring'''

 def __init__(self				:   Dict       , UpperCAmelCase_				:   "AutoTokenizer"       , UpperCAmelCase_				:   bool = False       , **UpperCAmelCase_				:   int)       ->List[Any]:



   '''simple docstring'''



   lowerCamelCase__:      Optional[int]			  =tokenizer
   lowerCamelCase__:      List[Any]			  =skip_prompt
   lowerCamelCase__:      Dict			  =decode_kwargs

   # variables used in the streaming process
   lowerCamelCase__:      Optional[int]			  =[]
   lowerCamelCase__:      List[str]			  =0
   lowerCamelCase__:      List[Any]			  =True

 def 							SCREAMING_SNAKE_CASE_					(self				:   Any       , UpperCAmelCase_				:   List[Any])       ->Any:



   '''simple docstring'''



   if len(value.shape) > 1 and value.shape[0] > 1:
     raise ValueError("TextStreamer only supports batch size 1")
   elif len(value.shape) > 1:
     lowerCamelCase__:      Optional[int]			  =value[0]

   if self.skip_prompt and self.next_tokens_are_prompt:
     lowerCamelCase__:      Any			  =False
     return

   # Add the new token to the cache and decodes the entire thing.
   self.token_cache.extend(value.tolist())
   lowerCamelCase__:      int			  =self.tokenizer.decode(self.token_cache       , **self.decode_kwargs)

   # After the symbol for a new line, we flush the cache.
   if text.endswith("\n"):
     lowerCamelCase__:      Any			  =text[self.print_len :]
     lowerCamelCase__:      Union[str, Any]			  =[]
     lowerCamelCase__:      str			  =0
   # If the last token is a CJK character, we print the characters.
   elif len(UpperCAmelCase_) > 0 and self._is_chinese_char(ord(text[-1])):
     lowerCamelCase__:      Any			  =text[self.print_len :]
     self.print_len += len(UpperCAmelCase_)
   # Otherwise, prints until the last space char (simple heuristic to avoid printing incomplete words,
   # which may change with the subsequent token -- there are probably smarter ways to do this!)
   else:
     lowerCamelCase__:      str			  =text[self.print_len : text.rfind(" ") + 1]
     self.print_len += len(UpperCAmelCase_)

   self.on_finalized_text(UpperCAmelCase_)

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



   '''simple docstring'''



   if len(self.token_cache) > 0:
     lowerCamelCase__:      Tuple			  =self.tokenizer.decode(self.token_cache       , **self.decode_kwargs)
     lowerCamelCase__:      Optional[int]			  =text[self.print_len :]
     lowerCamelCase__:      List[str]			  =[]
     lowerCamelCase__:      Tuple			  =0
   else:
     lowerCamelCase__:      str			  =""

   lowerCamelCase__:      List[str]			  =True
   self.on_finalized_text(UpperCAmelCase_       , stream_end=UpperCAmelCase_)

 def 							SCREAMING_SNAKE_CASE_					(self				:   List[Any]       , UpperCAmelCase_				:   str       , UpperCAmelCase_				:   bool = False)       ->int:



   '''simple docstring'''



   print(UpperCAmelCase_       , flush=UpperCAmelCase_       , end="" if not stream_end else None)







 def 							SCREAMING_SNAKE_CASE_					(self				:   Tuple       , UpperCAmelCase_				:   Optional[int])       ->int:



   '''simple docstring'''



   if (
       (cp >= 0x4E00 and cp <= 0x9FFF)
       or (cp >= 0x3400 and cp <= 0x4DBF)  #
       or (cp >= 0x2_0000 and cp <= 0x2_A6DF)  #
       or (cp >= 0x2_A700 and cp <= 0x2_B73F)  #
       or (cp >= 0x2_B740 and cp <= 0x2_B81F)  #
       or (cp >= 0x2_B820 and cp <= 0x2_CEAF)  #
       or (cp >= 0xF900 and cp <= 0xFAFF)
       or (cp >= 0x2_F800 and cp <= 0x2_FA1F)  #
   ):  #
     return True

   return False



class    _SCREAMING_SNAKE_CASE						(	__SCREAMING_SNAKE_CASE    ):







 '''simple docstring'''

 def __init__(self				:   Union[str, Any]       , UpperCAmelCase_				:   "AutoTokenizer"       , UpperCAmelCase_				:   bool = False       , UpperCAmelCase_				:   Optional[float] = None       , **UpperCAmelCase_				:   Optional[int])       ->Optional[Any]:



   '''simple docstring'''



   super().__init__(UpperCAmelCase_       , UpperCAmelCase_       , **UpperCAmelCase_)
   lowerCamelCase__:      Any			  =Queue()
   lowerCamelCase__:      Any			  =None
   lowerCamelCase__:      int			  =timeout

 def 							SCREAMING_SNAKE_CASE_					(self				:   Optional[int]       , UpperCAmelCase_				:   str       , UpperCAmelCase_				:   bool = False)       ->Union[str, Any]:



   '''simple docstring'''



   self.text_queue.put(UpperCAmelCase_       , timeout=self.timeout)
   if stream_end:
     self.text_queue.put(self.stop_signal       , timeout=self.timeout)

 def __iter__(self				:   List[str])       ->Dict:



   '''simple docstring'''



   return self







 def 							SCREAMING_SNAKE_CASE_					(self				:   Any)       ->List[Any]:



   '''simple docstring'''



   lowerCamelCase__:      List[Any]			  =self.text_queue.get(timeout=self.timeout)
   if value == self.stop_signal:
     raise StopIteration()
   else:
     return value







import json
import sys
import tempfile
import unittest
from pathlib import Path

import transformers
from transformers import (
    CONFIG_MAPPING,
    IMAGE_PROCESSOR_MAPPING,
    AutoConfig,
    AutoImageProcessor,
    CLIPConfig,
    CLIPImageProcessor,
)
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER


sys.path.append(str(Path(__file__).parent.parent.parent.parent / 'utils'))

from test_module.custom_configuration import CustomConfig  # noqa E402
from test_module.custom_image_processing import CustomImageProcessor  # noqa E402





class        lowerCamelCase_							(      unittest.TestCase					):


 '''simple docstring'''







 def       lowerCAmelCase_  (							self  :		Any						):
       SCREAMING_SNAKE_CASE_							=			0



 def       lowerCAmelCase_  (							self  :		int						):
       SCREAMING_SNAKE_CASE_							=			AutoImageProcessor.from_pretrained('openai/clip-vit-base-patch32'						)
       self.assertIsInstance(_lowerCAmelCase				,				_lowerCAmelCase						)



 def       lowerCAmelCase_  (							self  :		Optional[int]						):
       with tempfile.TemporaryDirectory() as tmpdirname:
             SCREAMING_SNAKE_CASE_							=			Path(_lowerCAmelCase						) / 'preprocessor_config.json'
             SCREAMING_SNAKE_CASE_							=			Path(_lowerCAmelCase						) / 'config.json'
             json.dump(
                 {'image_processor_type': 'CLIPImageProcessor', 'processor_class': 'CLIPProcessor'}				,				open(_lowerCAmelCase				,				'w'						)				,				)
             json.dump({'model_type': 'clip'}				,				open(_lowerCAmelCase				,				'w'						)						)

             SCREAMING_SNAKE_CASE_							=			AutoImageProcessor.from_pretrained(_lowerCAmelCase						)
             self.assertIsInstance(_lowerCAmelCase				,				_lowerCAmelCase						)



 def       lowerCAmelCase_  (							self  :		str						):
       # Ensure we can load the image processor from the feature extractor config
       with tempfile.TemporaryDirectory() as tmpdirname:
             SCREAMING_SNAKE_CASE_							=			Path(_lowerCAmelCase						) / 'preprocessor_config.json'
             SCREAMING_SNAKE_CASE_							=			Path(_lowerCAmelCase						) / 'config.json'
             json.dump(
                 {'feature_extractor_type': 'CLIPFeatureExtractor', 'processor_class': 'CLIPProcessor'}				,				open(_lowerCAmelCase				,				'w'						)				,				)
             json.dump({'model_type': 'clip'}				,				open(_lowerCAmelCase				,				'w'						)						)

             SCREAMING_SNAKE_CASE_							=			AutoImageProcessor.from_pretrained(_lowerCAmelCase						)
             self.assertIsInstance(_lowerCAmelCase				,				_lowerCAmelCase						)



 def       lowerCAmelCase_  (							self  :		Union[str, Any]						):
       with tempfile.TemporaryDirectory() as tmpdirname:
             SCREAMING_SNAKE_CASE_							=			CLIPConfig()

             # Create a dummy config file with image_proceesor_type
             SCREAMING_SNAKE_CASE_							=			Path(_lowerCAmelCase						) / 'preprocessor_config.json'
             SCREAMING_SNAKE_CASE_							=			Path(_lowerCAmelCase						) / 'config.json'
             json.dump(
                 {'image_processor_type': 'CLIPImageProcessor', 'processor_class': 'CLIPProcessor'}				,				open(_lowerCAmelCase				,				'w'						)				,				)
             json.dump({'model_type': 'clip'}				,				open(_lowerCAmelCase				,				'w'						)						)

             # remove image_processor_type to make sure config.json alone is enough to load image processor locally
             SCREAMING_SNAKE_CASE_							=			AutoImageProcessor.from_pretrained(_lowerCAmelCase						).to_dict()

             config_dict.pop('image_processor_type'						)
             SCREAMING_SNAKE_CASE_							=			CLIPImageProcessor(**_lowerCAmelCase						)

             # save in new folder
             model_config.save_pretrained(_lowerCAmelCase						)
             config.save_pretrained(_lowerCAmelCase						)

             SCREAMING_SNAKE_CASE_							=			AutoImageProcessor.from_pretrained(_lowerCAmelCase						)

             # make sure private variable is not incorrectly saved
             SCREAMING_SNAKE_CASE_							=			json.loads(config.to_json_string()						)
             self.assertTrue('_processor_class' not in dict_as_saved						)

       self.assertIsInstance(_lowerCAmelCase				,				_lowerCAmelCase						)



 def       lowerCAmelCase_  (							self  :		str						):
       with tempfile.TemporaryDirectory() as tmpdirname:
             SCREAMING_SNAKE_CASE_							=			Path(_lowerCAmelCase						) / 'preprocessor_config.json'
             json.dump(
                 {'image_processor_type': 'CLIPImageProcessor', 'processor_class': 'CLIPProcessor'}				,				open(_lowerCAmelCase				,				'w'						)				,				)

             SCREAMING_SNAKE_CASE_							=			AutoImageProcessor.from_pretrained(_lowerCAmelCase						)
             self.assertIsInstance(_lowerCAmelCase				,				_lowerCAmelCase						)



 def       lowerCAmelCase_  (							self  :		List[Any]						):
       with self.assertRaisesRegex(
           _lowerCAmelCase				,				'clip-base is not a local folder and is not a valid model identifier'						):
             SCREAMING_SNAKE_CASE_							=			AutoImageProcessor.from_pretrained('clip-base'						)



 def       lowerCAmelCase_  (							self  :		List[Any]						):
       with self.assertRaisesRegex(
           _lowerCAmelCase				,				R'aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)'						):
             SCREAMING_SNAKE_CASE_							=			AutoImageProcessor.from_pretrained(_lowerCAmelCase				,				revision='aaaaaa'						)



 def       lowerCAmelCase_  (							self  :		str						):
       with self.assertRaisesRegex(
           _lowerCAmelCase				,				'hf-internal-testing/config-no-model does not appear to have a file named preprocessor_config.json.'				,				):
             SCREAMING_SNAKE_CASE_							=			AutoImageProcessor.from_pretrained('hf-internal-testing/config-no-model'						)



 def       lowerCAmelCase_  (							self  :		Tuple						):
       # If remote code is not set, we will time out when asking whether to load the model.
       with self.assertRaises(_lowerCAmelCase						):
             SCREAMING_SNAKE_CASE_							=			AutoImageProcessor.from_pretrained('hf-internal-testing/test_dynamic_image_processor'						)
       # If remote code is disabled, we can't load this config.
       with self.assertRaises(_lowerCAmelCase						):
             SCREAMING_SNAKE_CASE_							=			AutoImageProcessor.from_pretrained(
                 'hf-internal-testing/test_dynamic_image_processor'				,				trust_remote_code=_lowerCAmelCase						)

       SCREAMING_SNAKE_CASE_							=			AutoImageProcessor.from_pretrained(
           'hf-internal-testing/test_dynamic_image_processor'				,				trust_remote_code=_lowerCAmelCase						)
       self.assertEqual(image_processor.__class__.__name__				,				'NewImageProcessor'						)

       # Test image processor can be reloaded.
       with tempfile.TemporaryDirectory() as tmp_dir:
             image_processor.save_pretrained(_lowerCAmelCase						)
             SCREAMING_SNAKE_CASE_							=			AutoImageProcessor.from_pretrained(_lowerCAmelCase				,				trust_remote_code=_lowerCAmelCase						)
       self.assertEqual(reloaded_image_processor.__class__.__name__				,				'NewImageProcessor'						)



 def       lowerCAmelCase_  (							self  :		Optional[Any]						):
       try:
             AutoConfig.register('custom'				,				_lowerCAmelCase						)
             AutoImageProcessor.register(_lowerCAmelCase				,				_lowerCAmelCase						)
             # Trying to register something existing in the Transformers library will raise an error
             with self.assertRaises(_lowerCAmelCase						):
                   AutoImageProcessor.register(_lowerCAmelCase				,				_lowerCAmelCase						)

             with tempfile.TemporaryDirectory() as tmpdirname:
                   SCREAMING_SNAKE_CASE_							=			Path(_lowerCAmelCase						) / 'preprocessor_config.json'
                   SCREAMING_SNAKE_CASE_							=			Path(_lowerCAmelCase						) / 'config.json'
                   json.dump(
                       {'feature_extractor_type': 'CLIPFeatureExtractor', 'processor_class': 'CLIPProcessor'}				,				open(_lowerCAmelCase				,				'w'						)				,				)
                   json.dump({'model_type': 'clip'}				,				open(_lowerCAmelCase				,				'w'						)						)

                   SCREAMING_SNAKE_CASE_							=			CustomImageProcessor.from_pretrained(_lowerCAmelCase						)

             # Now that the config is registered, it can be used as any other config with the auto-API
             with tempfile.TemporaryDirectory() as tmp_dir:
                   image_processor.save_pretrained(_lowerCAmelCase						)
                   SCREAMING_SNAKE_CASE_							=			AutoImageProcessor.from_pretrained(_lowerCAmelCase						)
                   self.assertIsInstance(_lowerCAmelCase				,				_lowerCAmelCase						)

       finally:
             if "custom" in CONFIG_MAPPING._extra_content:
                   del CONFIG_MAPPING._extra_content["custom"]
             if CustomConfig in IMAGE_PROCESSOR_MAPPING._extra_content:
                   del IMAGE_PROCESSOR_MAPPING._extra_content[CustomConfig]




 def       lowerCAmelCase_  (							self  :		Union[str, Any]						):





       class        lowerCamelCase_							(      _SCREAMING_SNAKE_CASE					):


        '''simple docstring'''







        lowercase_				    =    True

       try:
             AutoConfig.register('custom'				,				_lowerCAmelCase						)
             AutoImageProcessor.register(_lowerCAmelCase				,				_lowerCAmelCase						)
             # If remote code is not set, the default is to use local
             SCREAMING_SNAKE_CASE_							=			AutoImageProcessor.from_pretrained('hf-internal-testing/test_dynamic_image_processor'						)
             self.assertEqual(image_processor.__class__.__name__				,				'NewImageProcessor'						)
             self.assertTrue(image_processor.is_local						)

             # If remote code is disabled, we load the local one.
             SCREAMING_SNAKE_CASE_							=			AutoImageProcessor.from_pretrained(
                 'hf-internal-testing/test_dynamic_image_processor'				,				trust_remote_code=_lowerCAmelCase						)
             self.assertEqual(image_processor.__class__.__name__				,				'NewImageProcessor'						)
             self.assertTrue(image_processor.is_local						)

             # If remote is enabled, we load from the Hub
             SCREAMING_SNAKE_CASE_							=			AutoImageProcessor.from_pretrained(
                 'hf-internal-testing/test_dynamic_image_processor'				,				trust_remote_code=_lowerCAmelCase						)
             self.assertEqual(image_processor.__class__.__name__				,				'NewImageProcessor'						)
             self.assertTrue(not hasattr(_lowerCAmelCase				,				'is_local'						)						)

       finally:
             if "custom" in CONFIG_MAPPING._extra_content:
                   del CONFIG_MAPPING._extra_content["custom"]
             if CustomConfig in IMAGE_PROCESSOR_MAPPING._extra_content:
                   del IMAGE_PROCESSOR_MAPPING._extra_content[CustomConfig]