"""simple docstring"""






from __future__ import annotations

import random

# Maximum size of the population.  Bigger could be faster but is more memory expensive.
__lowerCAmelCase       :			str   	=2_0_0
# Number of elements selected in every generation of evolution. The selection takes
# place from best to worst of that generation and must be smaller than N_POPULATION.
__lowerCAmelCase       :			Dict   	=5_0
# Probability that an element of a generation can mutate, changing one of its genes.
# This will guarantee that all genes will be used during evolution.
__lowerCAmelCase       :			List[str]   	=0.4
# Just a seed to improve randomness required by the algorithm.
random.seed(random.randint(0, 1_0_0_0))


def      UpperCAmelCase__			(							lowerCAmelCase__	:str   ,						lowerCAmelCase__	:str						)					->       tuple[str, float]:
      '''simple docstring'''


      lowercase								=      len([g for position, g in enumerate(lowerCAmelCase__						) if g == main_target[position]]						)
      return (item, float(lowerCAmelCase__						))


def      UpperCAmelCase__			(							lowerCAmelCase__	:str   ,						lowerCAmelCase__	:str						)					->       tuple[str, str]:
      '''simple docstring'''


      lowercase								=      random.randint(0   ,						len(lowerCAmelCase__						) - 1						)
      lowercase								=      parent_a[:random_slice] + parent_a[random_slice:]
      lowercase								=      parent_a[:random_slice] + parent_a[random_slice:]
      return (child_a, child_a)


def      UpperCAmelCase__			(							lowerCAmelCase__	:str   ,						lowerCAmelCase__	:list[str]						)					->       str:
      '''simple docstring'''


      lowercase								=      list(lowerCAmelCase__						)
      if random.uniform(0   ,						1						) < MUTATION_PROBABILITY:
            lowercase								=      random.choice(lowerCAmelCase__						)
      return "".join(lowerCAmelCase__						)


def      UpperCAmelCase__			(							lowerCAmelCase__	:tuple[str, float]   ,						lowerCAmelCase__	:list[tuple[str, float]]   ,						lowerCAmelCase__	:list[str]   ,						)					->       list[str]:
      '''simple docstring'''


      lowercase								=      []
      # Generate more children proportionally to the fitness score.
      lowercase								=      int(parent_a[1] * 1_0_0						) + 1
      lowercase								=      1_0 if child_n >= 1_0 else child_n
      for _ in range(lowerCAmelCase__						):
            lowercase								=      population_score[random.randint(0   ,						lowerCAmelCase__						)][0]

            lowercase				,				lowercase								=      crossover(parent_a[0]   ,						lowerCAmelCase__						)
            # Append new string to the population list.
            pop.append(mutate(lowerCAmelCase__   ,						lowerCAmelCase__						)						)
            pop.append(mutate(lowerCAmelCase__   ,						lowerCAmelCase__						)						)
      return pop


def      UpperCAmelCase__			(							lowerCAmelCase__	:str   ,						lowerCAmelCase__	:list[str]   ,						lowerCAmelCase__	:bool = True						)					->       tuple[int, int, str]:
      '''simple docstring'''


      if N_POPULATION < N_SELECTED:
            lowercase								=      f'{N_POPULATION} must be bigger than {N_SELECTED}'
            raise ValueError(lowerCAmelCase__						)
      # Verify that the target contains no genes besides the ones inside genes variable.
      lowercase								=      sorted({c for c in target if c not in genes}						)
      if not_in_genes_list:
            lowercase								=      f'{not_in_genes_list} is not in genes list, evolution cannot converge'
            raise ValueError(lowerCAmelCase__						)

      # Generate random starting population.
      lowercase								=      []
      for _ in range(lowerCAmelCase__						):
            population.append("""""".join([random.choice(lowerCAmelCase__						) for i in range(len(lowerCAmelCase__						)						)]						)						)

      # Just some logs to know what the algorithms is doing.
      lowercase				,				lowercase								=      0, 0

      # This loop will end when we find a perfect match for our target.
      while True:
            generation += 1
            total_population += len(lowerCAmelCase__						)

            # Random population created. Now it's time to evaluate.

            # Adding a bit of concurrency can make everything faster,
            #
            # import concurrent.futures
            # population_score: list[tuple[str, float]] = []
            # with concurrent.futures.ThreadPoolExecutor(
            #                                   max_workers=NUM_WORKERS) as executor:
            #     futures = {executor.submit(evaluate, item) for item in population}
            #     concurrent.futures.wait(futures)
            #     population_score = [item.result() for item in futures]
            #
            # but with a simple algorithm like this, it will probably be slower.
            # We just need to call evaluate for every item inside the population.
            lowercase								=      [evaluate(lowerCAmelCase__   ,						lowerCAmelCase__						) for item in population]

            # Check if there is a matching evolution.
            lowercase								=      sorted(lowerCAmelCase__   ,						key=lambda lowerCAmelCase__						: x[1]   ,						reverse=lowerCAmelCase__						)
            if population_score[0][0] == target:
                  return (generation, total_population, population_score[0][0])

            # Print the best result every 10 generation.
            # Just to know that the algorithm is working.
            if debug and generation % 1_0 == 0:
                  print(
                      f'\nGeneration: {generation}'
                      f'\nTotal Population:{total_population}'
                      f'\nBest score: {population_score[0][1]}'
                      f'\nBest string: {population_score[0][0]}'						)

            # Flush the old population, keeping some of the best evolutions.
            # Keeping this avoid regression of evolution.
            lowercase								=      population[: int(N_POPULATION / 3						)]
            population.clear()
            population.extend(lowerCAmelCase__						)
            # Normalize population score to be between 0 and 1.
            lowercase								=      [
                (item, score / len(lowerCAmelCase__						)) for item, score in population_score
            ]

            # This is selection
            for i in range(lowerCAmelCase__						):
                  population.extend(select(population_score[int(lowerCAmelCase__						)]   ,						lowerCAmelCase__   ,						lowerCAmelCase__						)						)
                  # Check if the population has already reached the maximum value and if so,
                  # break the cycle.  If this check is disabled, the algorithm will take
                  # forever to compute large strings, but will also calculate small strings in
                  # a far fewer generations.
                  if len(lowerCAmelCase__						) > N_POPULATION:
                        break


if __name__ == "__main__":
       __lowerCAmelCase       :			Any   	=(
           """This is a genetic algorithm to evaluate, combine, evolve, and mutate a string!"""
       )
       __lowerCAmelCase       :			Optional[Any]   	=list(
           """ ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklm"""
           """nopqrstuvwxyz.,;!?+-*#@^'èéòà€ù=)(&%$£/\\"""
       )
       __lowerCAmelCase	,	__lowerCAmelCase	,	__lowerCAmelCase       :			Any   	=basic(target_str, genes_list)
       print(
           F"""\nGeneration: {generation}\nTotal Population: {population}\nTarget: {target}"""
       )




"""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


__lowerCAmelCase       :			str   	=get_tests_dir("""fixtures/dummy_feature_extractor_config.json""")
__lowerCAmelCase       :			Tuple   	=get_tests_dir("""fixtures/vocab.json""")
__lowerCAmelCase       :			Tuple   	=get_tests_dir("""fixtures""")





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

      def 							A__			(		self		):







            """simple docstring"""



            lowercase								=      0

      def 							A__			(		self		):







            """simple docstring"""



            lowercase								=      AutoProcessor.from_pretrained("""facebook/wav2vec2-base-960h"""		)
            self.assertIsInstance(__lowerCAmelCase						,			__lowerCAmelCase		)

      def 							A__			(		self		):







            """simple docstring"""



            with tempfile.TemporaryDirectory() as tmpdirname:
                  lowercase								=      WavaVecaConfig()
                  lowercase								=      AutoProcessor.from_pretrained("""facebook/wav2vec2-base-960h"""		)

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

                  lowercase								=      AutoProcessor.from_pretrained(__lowerCAmelCase		)

            self.assertIsInstance(__lowerCAmelCase						,			__lowerCAmelCase		)

      def 							A__			(		self		):







            """simple docstring"""



            with tempfile.TemporaryDirectory() as tmpdirname:
                  # copy relevant files
                  copyfile(__lowerCAmelCase						,			os.path.join(__lowerCAmelCase						,			__lowerCAmelCase		)		)
                  copyfile(__lowerCAmelCase						,			os.path.join(__lowerCAmelCase						,			"""vocab.json"""		)		)

                  lowercase								=      AutoProcessor.from_pretrained(__lowerCAmelCase		)

            self.assertIsInstance(__lowerCAmelCase						,			__lowerCAmelCase		)

      def 							A__			(		self		):







            """simple docstring"""



            with tempfile.TemporaryDirectory() as tmpdirname:
                  lowercase								=      WavaVecaFeatureExtractor()
                  lowercase								=      AutoTokenizer.from_pretrained("""facebook/wav2vec2-base-960h"""		)

                  lowercase								=      WavaVecaProcessor(__lowerCAmelCase						,			__lowerCAmelCase		)

                  # save in new folder
                  processor.save_pretrained(__lowerCAmelCase		)

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

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

                  lowercase								=      AutoProcessor.from_pretrained(__lowerCAmelCase		)

            self.assertIsInstance(__lowerCAmelCase						,			__lowerCAmelCase		)

      def 							A__			(		self		):







            """simple docstring"""



            with tempfile.TemporaryDirectory() as tmpdirname:
                  lowercase								=      WavaVecaFeatureExtractor()
                  lowercase								=      AutoTokenizer.from_pretrained("""facebook/wav2vec2-base-960h"""		)

                  lowercase								=      WavaVecaProcessor(__lowerCAmelCase						,			__lowerCAmelCase		)

                  # save in new folder
                  processor.save_pretrained(__lowerCAmelCase		)

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

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

                  lowercase								=      AutoProcessor.from_pretrained(__lowerCAmelCase		)

            self.assertIsInstance(__lowerCAmelCase						,			__lowerCAmelCase		)

      def 							A__			(		self		):







            """simple docstring"""



            with tempfile.TemporaryDirectory() as tmpdirname:
                  lowercase								=      WavaVecaConfig(processor_class="""Wav2Vec2Processor"""		)
                  model_config.save_pretrained(__lowerCAmelCase		)
                  # copy relevant files
                  copyfile(__lowerCAmelCase						,			os.path.join(__lowerCAmelCase						,			"""vocab.json"""		)		)
                  # create emtpy sample processor
                  with open(os.path.join(__lowerCAmelCase						,			__lowerCAmelCase		)						,			"""w"""		) as f:
                        f.write("""{}"""		)

                  lowercase								=      AutoProcessor.from_pretrained(__lowerCAmelCase		)

            self.assertIsInstance(__lowerCAmelCase						,			__lowerCAmelCase		)

      def 							A__			(		self		):







            """simple docstring"""



            with self.assertRaises(__lowerCAmelCase		):
                  lowercase								=      AutoProcessor.from_pretrained("""hf-internal-testing/test_dynamic_processor"""		)
            # If remote code is disabled, we can't load this config.
            with self.assertRaises(__lowerCAmelCase		):
                  lowercase								=      AutoProcessor.from_pretrained(
                      """hf-internal-testing/test_dynamic_processor"""						,			trust_remote_code=__lowerCAmelCase		)

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

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

            lowercase								=      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
                  lowercase								=      AutoProcessor.from_pretrained(
                      """hf-internal-testing/test_dynamic_processor"""						,			trust_remote_code=__lowerCAmelCase						,			use_fast=__lowerCAmelCase		)
                  lowercase								=      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 							A__			(		self		):







            """simple docstring"""



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

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

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

                  lowercase								=      CustomProcessor(__lowerCAmelCase						,			__lowerCAmelCase		)

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

            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 							A__			(		self		):







            """simple docstring"""



            class 					_A		(			lowerCAmelCase      ):
                  snake_case__     :  Optional[Any]				    =      False






            class 					_A		(			lowerCAmelCase      ):
                  snake_case__     :  int				    =      False






            class 					_A		(			lowerCAmelCase      ):
                  snake_case__     :  List[Any]				    =      'AutoFeatureExtractor'
                  snake_case__     :  List[str]				    =      'AutoTokenizer'
                  snake_case__     :  List[str]				    =      False

            try:
                  AutoConfig.register("""custom"""						,			__lowerCAmelCase		)
                  AutoFeatureExtractor.register(__lowerCAmelCase						,			__lowerCAmelCase		)
                  AutoTokenizer.register(__lowerCAmelCase						,			slow_tokenizer_class=__lowerCAmelCase		)
                  AutoProcessor.register(__lowerCAmelCase						,			__lowerCAmelCase		)
                  # If remote code is not set, the default is to use local classes.
                  lowercase								=      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.
                  lowercase								=      AutoProcessor.from_pretrained(
                      """hf-internal-testing/test_dynamic_processor"""						,			trust_remote_code=__lowerCAmelCase		)
                  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.
                  lowercase								=      AutoProcessor.from_pretrained(
                      """hf-internal-testing/test_dynamic_processor"""						,			trust_remote_code=__lowerCAmelCase		)
                  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 							A__			(		self		):







            """simple docstring"""



            lowercase								=      AutoProcessor.from_pretrained("""hf-internal-testing/tiny-random-bert"""		)
            self.assertEqual(processor.__class__.__name__						,			"""BertTokenizerFast"""		)







      def 							A__			(		self		):







            """simple docstring"""



            lowercase								=      AutoProcessor.from_pretrained("""hf-internal-testing/tiny-random-convnext"""		)
            self.assertEqual(processor.__class__.__name__						,			"""ConvNextImageProcessor"""		)






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

      @classmethod
      def 							A__			(		cls		):







            """simple docstring"""



            lowercase								=      TOKEN
            HfFolder.save_token(__lowerCAmelCase		)

      @classmethod
      def 							A__			(		cls		):







            """simple docstring"""



            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 							A__			(		self		):







            """simple docstring"""



            lowercase								=      WavaVecaProcessor.from_pretrained(__lowerCAmelCase		)
            with tempfile.TemporaryDirectory() as tmp_dir:
                  processor.save_pretrained(
                      os.path.join(__lowerCAmelCase						,			"""test-processor"""		)						,			push_to_hub=__lowerCAmelCase						,			use_auth_token=self._token		)

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

      def 							A__			(		self		):







            """simple docstring"""



            lowercase								=      WavaVecaProcessor.from_pretrained(__lowerCAmelCase		)

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

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







      def 							A__			(		self		):







            """simple docstring"""



            CustomFeatureExtractor.register_for_auto_class()
            CustomTokenizer.register_for_auto_class()
            CustomProcessor.register_for_auto_class()

            lowercase								=      CustomFeatureExtractor.from_pretrained(__lowerCAmelCase		)

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

            lowercase								=      CustomProcessor(__lowerCAmelCase						,			__lowerCAmelCase		)

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

                  # 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(__lowerCAmelCase						,			"""tokenizer_config.json"""		)		) as f:
                        lowercase								=      json.load(__lowerCAmelCase		)
                  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(__lowerCAmelCase						,			"""custom_feature_extraction.py"""		)		)		)
                  self.assertTrue(os.path.isfile(os.path.join(__lowerCAmelCase						,			"""custom_tokenization.py"""		)		)		)
                  self.assertTrue(os.path.isfile(os.path.join(__lowerCAmelCase						,			"""custom_processing.py"""		)		)		)

                  repo.push_to_hub()

            lowercase								=      AutoProcessor.from_pretrained(f'{USER}/test-dynamic-processor'						,			trust_remote_code=__lowerCAmelCase		)
            # 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"""		)



