

"""simple docstring"""




import json
import os
import sys
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile

from huggingface_hub import HfFolder, Repository, create_repo, delete_repo
from requests.exceptions import HTTPError

import transformers
from transformers import (
    CONFIG_MAPPING,
    FEATURE_EXTRACTOR_MAPPING,
    PROCESSOR_MAPPING,
    TOKENIZER_MAPPING,
    AutoConfig,
    AutoFeatureExtractor,
    AutoProcessor,
    AutoTokenizer,
    BertTokenizer,
    ProcessorMixin,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
)
from transformers.testing_utils import TOKEN, USER, get_tests_dir, is_staging_test
from transformers.tokenization_utils import TOKENIZER_CONFIG_FILE
from transformers.utils import FEATURE_EXTRACTOR_NAME, is_tokenizers_available


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

from test_module.custom_configuration import CustomConfig  # noqa E402
from test_module.custom_feature_extraction import CustomFeatureExtractor  # noqa E402
from test_module.custom_processing import CustomProcessor  # noqa E402
from test_module.custom_tokenization import CustomTokenizer  # noqa E402


SCREAMING_SNAKE_CASE_     :			Tuple	  =			get_tests_dir('''fixtures/dummy_feature_extractor_config.json''')
SCREAMING_SNAKE_CASE_     :			str	  =			get_tests_dir('''fixtures/vocab.json''')
SCREAMING_SNAKE_CASE_     :			Optional[int]	  =			get_tests_dir('''fixtures''')


class 		_A       (			unittest.TestCase					):
      __a								=	['[UNK]', '[CLS]', '[SEP]', '[PAD]', '[MASK]', 'bla', 'blou']





      def 					_lowerCamelCase  (  self		)   ->					Optional[int]:
         lowerCamelCase__						=			0





      def 					_lowerCamelCase  (  self		)   ->					Dict:
         lowerCamelCase__						=			AutoProcessor.from_pretrained("facebook/wav2vec2-base-960h"		)
         self.assertIsInstance(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__		)





      def 					_lowerCamelCase  (  self		)   ->					Optional[Any]:
         with tempfile.TemporaryDirectory() as tmpdirname:
            lowerCamelCase__						=			WavaVecaConfig()
            lowerCamelCase__						=			AutoProcessor.from_pretrained("facebook/wav2vec2-base-960h"		)

            # save in new folder
            model_config.save_pretrained(SCREAMING_SNAKE_CASE__		)
            processor.save_pretrained(SCREAMING_SNAKE_CASE__		)

            lowerCamelCase__						=			AutoProcessor.from_pretrained(SCREAMING_SNAKE_CASE__		)

         self.assertIsInstance(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__		)





      def 					_lowerCamelCase  (  self		)   ->					str:
         with tempfile.TemporaryDirectory() as tmpdirname:
            # copy relevant files
            copyfile(SCREAMING_SNAKE_CASE__    ,						os.path.join(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__		)		)
            copyfile(SCREAMING_SNAKE_CASE__    ,						os.path.join(SCREAMING_SNAKE_CASE__    ,						"vocab.json"		)		)

            lowerCamelCase__						=			AutoProcessor.from_pretrained(SCREAMING_SNAKE_CASE__		)

         self.assertIsInstance(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__		)





      def 					_lowerCamelCase  (  self		)   ->					Optional[int]:
         with tempfile.TemporaryDirectory() as tmpdirname:
            lowerCamelCase__						=			WavaVecaFeatureExtractor()
            lowerCamelCase__						=			AutoTokenizer.from_pretrained("facebook/wav2vec2-base-960h"		)

            lowerCamelCase__						=			WavaVecaProcessor(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__		)

            # save in new folder
            processor.save_pretrained(SCREAMING_SNAKE_CASE__		)

            # drop `processor_class` in tokenizer
            with open(os.path.join(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__		)    ,						"r"		) as f:
               lowerCamelCase__						=			json.load(SCREAMING_SNAKE_CASE__		)
               config_dict.pop("processor_class"		)

            with open(os.path.join(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__		)    ,						"w"		) as f:
               f.write(json.dumps(SCREAMING_SNAKE_CASE__		)		)

            lowerCamelCase__						=			AutoProcessor.from_pretrained(SCREAMING_SNAKE_CASE__		)

         self.assertIsInstance(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__		)





      def 					_lowerCamelCase  (  self		)   ->					str:
         with tempfile.TemporaryDirectory() as tmpdirname:
            lowerCamelCase__						=			WavaVecaFeatureExtractor()
            lowerCamelCase__						=			AutoTokenizer.from_pretrained("facebook/wav2vec2-base-960h"		)

            lowerCamelCase__						=			WavaVecaProcessor(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__		)

            # save in new folder
            processor.save_pretrained(SCREAMING_SNAKE_CASE__		)

            # drop `processor_class` in feature extractor
            with open(os.path.join(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__		)    ,						"r"		) as f:
               lowerCamelCase__						=			json.load(SCREAMING_SNAKE_CASE__		)
               config_dict.pop("processor_class"		)

            with open(os.path.join(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__		)    ,						"w"		) as f:
               f.write(json.dumps(SCREAMING_SNAKE_CASE__		)		)

            lowerCamelCase__						=			AutoProcessor.from_pretrained(SCREAMING_SNAKE_CASE__		)

         self.assertIsInstance(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__		)





      def 					_lowerCamelCase  (  self		)   ->					Dict:
         with tempfile.TemporaryDirectory() as tmpdirname:
            lowerCamelCase__						=			WavaVecaConfig(processor_class="Wav2Vec2Processor"		)
            model_config.save_pretrained(SCREAMING_SNAKE_CASE__		)
            # copy relevant files
            copyfile(SCREAMING_SNAKE_CASE__    ,						os.path.join(SCREAMING_SNAKE_CASE__    ,						"vocab.json"		)		)
            # create emtpy sample processor
            with open(os.path.join(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__		)    ,						"w"		) as f:
               f.write("{}"		)

            lowerCamelCase__						=			AutoProcessor.from_pretrained(SCREAMING_SNAKE_CASE__		)

         self.assertIsInstance(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__		)





      def 					_lowerCamelCase  (  self		)   ->					List[Any]:
         # If remote code is not set, we will time out when asking whether to load the model.
         with self.assertRaises(SCREAMING_SNAKE_CASE__		):
            lowerCamelCase__						=			AutoProcessor.from_pretrained("hf-internal-testing/test_dynamic_processor"		)
         # If remote code is disabled, we can't load this config.
         with self.assertRaises(SCREAMING_SNAKE_CASE__		):
            lowerCamelCase__						=			AutoProcessor.from_pretrained(
                "hf-internal-testing/test_dynamic_processor"    ,						trust_remote_code=SCREAMING_SNAKE_CASE__		)

         lowerCamelCase__						=			AutoProcessor.from_pretrained("hf-internal-testing/test_dynamic_processor"    ,						trust_remote_code=SCREAMING_SNAKE_CASE__		)
         self.assertTrue(processor.special_attribute_present		)
         self.assertEqual(processor.__class__.__name__    ,						"NewProcessor"		)

         lowerCamelCase__						=			processor.feature_extractor
         self.assertTrue(feature_extractor.special_attribute_present		)
         self.assertEqual(feature_extractor.__class__.__name__    ,						"NewFeatureExtractor"		)

         lowerCamelCase__						=			processor.tokenizer
         self.assertTrue(tokenizer.special_attribute_present		)
         if is_tokenizers_available():
            self.assertEqual(tokenizer.__class__.__name__    ,						"NewTokenizerFast"		)

            # Test we can also load the slow version
            lowerCamelCase__						=			AutoProcessor.from_pretrained(
                "hf-internal-testing/test_dynamic_processor"    ,						trust_remote_code=SCREAMING_SNAKE_CASE__    ,						use_fast=SCREAMING_SNAKE_CASE__		)
            lowerCamelCase__						=			new_processor.tokenizer
            self.assertTrue(new_tokenizer.special_attribute_present		)
            self.assertEqual(new_tokenizer.__class__.__name__    ,						"NewTokenizer"		)
         else:
            self.assertEqual(tokenizer.__class__.__name__    ,						"NewTokenizer"		)





      def 					_lowerCamelCase  (  self		)   ->					str:
         try:
            AutoConfig.register("custom"    ,						SCREAMING_SNAKE_CASE__		)
            AutoFeatureExtractor.register(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__		)
            AutoTokenizer.register(SCREAMING_SNAKE_CASE__    ,						slow_tokenizer_class=SCREAMING_SNAKE_CASE__		)
            AutoProcessor.register(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__		)
            # Trying to register something existing in the Transformers library will raise an error
            with self.assertRaises(SCREAMING_SNAKE_CASE__		):
               AutoProcessor.register(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__		)

            # Now that the config is registered, it can be used as any other config with the auto-API
            lowerCamelCase__						=			CustomFeatureExtractor.from_pretrained(SCREAMING_SNAKE_CASE__		)

            with tempfile.TemporaryDirectory() as tmp_dir:
               lowerCamelCase__						=			os.path.join(SCREAMING_SNAKE_CASE__    ,						"vocab.txt"		)
               with open(SCREAMING_SNAKE_CASE__    ,						"w"    ,						encoding="utf-8"		) as vocab_writer:
                  vocab_writer.write("".join([x + "\n" for x in self.vocab_tokens]		)		)
               lowerCamelCase__						=			CustomTokenizer(SCREAMING_SNAKE_CASE__		)

            lowerCamelCase__						=			CustomProcessor(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__		)

            with tempfile.TemporaryDirectory() as tmp_dir:
               processor.save_pretrained(SCREAMING_SNAKE_CASE__		)
               lowerCamelCase__						=			AutoProcessor.from_pretrained(SCREAMING_SNAKE_CASE__		)
               self.assertIsInstance(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__		)

         finally:
            if "custom" in CONFIG_MAPPING._extra_content:
               del CONFIG_MAPPING._extra_content["custom"]
            if CustomConfig in FEATURE_EXTRACTOR_MAPPING._extra_content:
               del FEATURE_EXTRACTOR_MAPPING._extra_content[CustomConfig]
            if CustomConfig in TOKENIZER_MAPPING._extra_content:
               del TOKENIZER_MAPPING._extra_content[CustomConfig]
            if CustomConfig in PROCESSOR_MAPPING._extra_content:
               del PROCESSOR_MAPPING._extra_content[CustomConfig]





      def 					_lowerCamelCase  (  self		)   ->					Optional[int]:


         class 		_A       (			__a					):
               __a								=	False






         class 		_A       (			__a					):
               __a								=	False






         class 		_A       (			__a					):
               __a								=	'AutoFeatureExtractor'
               __a								=	'AutoTokenizer'
               __a								=	False

         try:
            AutoConfig.register("custom"    ,						SCREAMING_SNAKE_CASE__		)
            AutoFeatureExtractor.register(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__		)
            AutoTokenizer.register(SCREAMING_SNAKE_CASE__    ,						slow_tokenizer_class=SCREAMING_SNAKE_CASE__		)
            AutoProcessor.register(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__		)
            # If remote code is not set, the default is to use local classes.
            lowerCamelCase__						=			AutoProcessor.from_pretrained("hf-internal-testing/test_dynamic_processor"		)
            self.assertEqual(processor.__class__.__name__    ,						"NewProcessor"		)
            self.assertFalse(processor.special_attribute_present		)
            self.assertFalse(processor.feature_extractor.special_attribute_present		)
            self.assertFalse(processor.tokenizer.special_attribute_present		)

            # If remote code is disabled, we load the local ones.
            lowerCamelCase__						=			AutoProcessor.from_pretrained(
                "hf-internal-testing/test_dynamic_processor"    ,						trust_remote_code=SCREAMING_SNAKE_CASE__		)
            self.assertEqual(processor.__class__.__name__    ,						"NewProcessor"		)
            self.assertFalse(processor.special_attribute_present		)
            self.assertFalse(processor.feature_extractor.special_attribute_present		)
            self.assertFalse(processor.tokenizer.special_attribute_present		)

            # If remote is enabled, we load from the Hub.
            lowerCamelCase__						=			AutoProcessor.from_pretrained(
                "hf-internal-testing/test_dynamic_processor"    ,						trust_remote_code=SCREAMING_SNAKE_CASE__		)
            self.assertEqual(processor.__class__.__name__    ,						"NewProcessor"		)
            self.assertTrue(processor.special_attribute_present		)
            self.assertTrue(processor.feature_extractor.special_attribute_present		)
            self.assertTrue(processor.tokenizer.special_attribute_present		)

         finally:
            if "custom" in CONFIG_MAPPING._extra_content:
               del CONFIG_MAPPING._extra_content["custom"]
            if CustomConfig in FEATURE_EXTRACTOR_MAPPING._extra_content:
               del FEATURE_EXTRACTOR_MAPPING._extra_content[CustomConfig]
            if CustomConfig in TOKENIZER_MAPPING._extra_content:
               del TOKENIZER_MAPPING._extra_content[CustomConfig]
            if CustomConfig in PROCESSOR_MAPPING._extra_content:
               del PROCESSOR_MAPPING._extra_content[CustomConfig]





      def 					_lowerCamelCase  (  self		)   ->					Tuple:
         lowerCamelCase__						=			AutoProcessor.from_pretrained("hf-internal-testing/tiny-random-bert"		)
         self.assertEqual(processor.__class__.__name__    ,						"BertTokenizerFast"		)





      def 					_lowerCamelCase  (  self		)   ->					Any:
         lowerCamelCase__						=			AutoProcessor.from_pretrained("hf-internal-testing/tiny-random-convnext"		)
         self.assertEqual(processor.__class__.__name__    ,						"ConvNextImageProcessor"		)






@is_staging_test
class 		_A       (			unittest.TestCase					):
      __a								=	['[UNK]', '[CLS]', '[SEP]', '[PAD]', '[MASK]', 'bla', 'blou']





      @classmethod
      def 					_lowerCamelCase  (  cls		)   ->					List[str]:
         lowerCamelCase__						=			TOKEN
         HfFolder.save_token(SCREAMING_SNAKE_CASE__		)





      @classmethod
      def 					_lowerCamelCase  (  cls		)   ->					List[str]:
         try:
            delete_repo(token=cls._token    ,						repo_id="test-processor"		)
         except HTTPError:
            pass

         try:
            delete_repo(token=cls._token    ,						repo_id="valid_org/test-processor-org"		)
         except HTTPError:
            pass

         try:
            delete_repo(token=cls._token    ,						repo_id="test-dynamic-processor"		)
         except HTTPError:
            pass





      def 					_lowerCamelCase  (  self		)   ->					Any:
         lowerCamelCase__						=			WavaVecaProcessor.from_pretrained(SCREAMING_SNAKE_CASE__		)
         with tempfile.TemporaryDirectory() as tmp_dir:
            processor.save_pretrained(
                os.path.join(SCREAMING_SNAKE_CASE__    ,						"test-processor"		)    ,						push_to_hub=SCREAMING_SNAKE_CASE__    ,						use_auth_token=self._token		)

            lowerCamelCase__						=			WavaVecaProcessor.from_pretrained(f'{USER}/test-processor'		)
            for k, v in processor.feature_extractor.__dict__.items():
               self.assertEqual(SCREAMING_SNAKE_CASE__    ,						getattr(new_processor.feature_extractor    ,						SCREAMING_SNAKE_CASE__		)		)
            self.assertDictEqual(new_processor.tokenizer.get_vocab()    ,						processor.tokenizer.get_vocab()		)





      def 					_lowerCamelCase  (  self		)   ->					Optional[Any]:
         lowerCamelCase__						=			WavaVecaProcessor.from_pretrained(SCREAMING_SNAKE_CASE__		)

         with tempfile.TemporaryDirectory() as tmp_dir:
            processor.save_pretrained(
                os.path.join(SCREAMING_SNAKE_CASE__    ,						"test-processor-org"		)    ,						push_to_hub=SCREAMING_SNAKE_CASE__    ,						use_auth_token=self._token    ,						organization="valid_org"    ,						)

            lowerCamelCase__						=			WavaVecaProcessor.from_pretrained("valid_org/test-processor-org"		)
            for k, v in processor.feature_extractor.__dict__.items():
               self.assertEqual(SCREAMING_SNAKE_CASE__    ,						getattr(new_processor.feature_extractor    ,						SCREAMING_SNAKE_CASE__		)		)
            self.assertDictEqual(new_processor.tokenizer.get_vocab()    ,						processor.tokenizer.get_vocab()		)





      def 					_lowerCamelCase  (  self		)   ->					Dict:
         CustomFeatureExtractor.register_for_auto_class()
         CustomTokenizer.register_for_auto_class()
         CustomProcessor.register_for_auto_class()

         lowerCamelCase__						=			CustomFeatureExtractor.from_pretrained(SCREAMING_SNAKE_CASE__		)

         with tempfile.TemporaryDirectory() as tmp_dir:
            lowerCamelCase__						=			os.path.join(SCREAMING_SNAKE_CASE__    ,						"vocab.txt"		)
            with open(SCREAMING_SNAKE_CASE__    ,						"w"    ,						encoding="utf-8"		) as vocab_writer:
               vocab_writer.write("".join([x + "\n" for x in self.vocab_tokens]		)		)
            lowerCamelCase__						=			CustomTokenizer(SCREAMING_SNAKE_CASE__		)

         lowerCamelCase__						=			CustomProcessor(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__		)

         with tempfile.TemporaryDirectory() as tmp_dir:
            create_repo(f'{USER}/test-dynamic-processor'    ,						token=self._token		)
            lowerCamelCase__						=			Repository(SCREAMING_SNAKE_CASE__    ,						clone_from=f'{USER}/test-dynamic-processor'    ,						token=self._token		)
            processor.save_pretrained(SCREAMING_SNAKE_CASE__		)

            # This has added the proper auto_map field to the feature extractor config
            self.assertDictEqual(
                processor.feature_extractor.auto_map    ,						{
                    "AutoFeatureExtractor": "custom_feature_extraction.CustomFeatureExtractor",
                    "AutoProcessor": "custom_processing.CustomProcessor",
                }    ,						)

            # This has added the proper auto_map field to the tokenizer config
            with open(os.path.join(SCREAMING_SNAKE_CASE__    ,						"tokenizer_config.json"		)		) as f:
               lowerCamelCase__						=			json.load(SCREAMING_SNAKE_CASE__		)
            self.assertDictEqual(
                tokenizer_config["auto_map"]    ,						{
                    "AutoTokenizer": ["custom_tokenization.CustomTokenizer", None],
                    "AutoProcessor": "custom_processing.CustomProcessor",
                }    ,						)

            # The code has been copied from fixtures
            self.assertTrue(os.path.isfile(os.path.join(SCREAMING_SNAKE_CASE__    ,						"custom_feature_extraction.py"		)		)		)
            self.assertTrue(os.path.isfile(os.path.join(SCREAMING_SNAKE_CASE__    ,						"custom_tokenization.py"		)		)		)
            self.assertTrue(os.path.isfile(os.path.join(SCREAMING_SNAKE_CASE__    ,						"custom_processing.py"		)		)		)

            repo.push_to_hub()

         lowerCamelCase__						=			AutoProcessor.from_pretrained(f'{USER}/test-dynamic-processor'    ,						trust_remote_code=SCREAMING_SNAKE_CASE__		)
         # Can't make an isinstance check because the new_processor is from the CustomProcessor class of a dynamic module
         self.assertEqual(new_processor.__class__.__name__    ,						"CustomProcessor"		)







"""simple docstring"""




import operator as op



def 	UpperCAmelCase__   (				A__   )      ->      Dict:




   """simple docstring"""
   lowerCamelCase__						=			[]
   lowerCamelCase__						=			lambda A__	,    A__   : int(x / y   )  # noqa: E731 integer division operation
   lowerCamelCase__						=			{
       "^": op.pow,
       "*": op.mul,
       "/": div,
       "+": op.add,
       "-": op.sub,
   }  # operators & their respective operation

   # print table header
   print("Symbol".center(8   )	,    "Action".center(12   )	,    "Stack"	,    sep=" | "   )
   print("-" * (30 + len(A__   ))   )

   for x in post_fix:
      if x.isdigit():  # if x in digit
         stack.append(A__   )  # append x to stack
         # output in tabular format
         print(x.rjust(8   )	,    ("push(" + x + ")").ljust(12   )	,    ",".join(A__   )	,    sep=" | "   )
      else:
         lowerCamelCase__						=			stack.pop()  # pop stack
         # output in tabular format
         print("".rjust(8   )	,    ("pop(" + b + ")").ljust(12   )	,    ",".join(A__   )	,    sep=" | "   )

         lowerCamelCase__						=			stack.pop()  # pop stack
         # output in tabular format
         print("".rjust(8   )	,    ("pop(" + a + ")").ljust(12   )	,    ",".join(A__   )	,    sep=" | "   )

         stack.append(
             str(opr[x](int(A__   )	,    int(A__   )   )   )   )  # evaluate the 2 values popped from stack & push result to stack
         # output in tabular format
         print(
             x.rjust(8   )	,    ("push(" + a + x + b + ")").ljust(12   )	,    ",".join(A__   )	,    sep=" | "	,    )

   return int(stack[0]   )


if __name__ == "__main__":
     SCREAMING_SNAKE_CASE_     :			Tuple	  =			input('''\n\nEnter a Postfix Equation (space separated) = ''').split(''' ''')
     print('''\n\tResult = ''', solve(Postfix))




