




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


a_   		=     get_tests_dir("fixtures/dummy_feature_extractor_config.json")
a_   		=     get_tests_dir("fixtures/vocab.json")
a_   		=     get_tests_dir("fixtures")

class  _lowerCamelCase						(			unittest.TestCase   ):

      """simple docstring"""

      lowerCAmelCase__			: Optional[int]    =					["[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]", "bla", "blou"]







      def       snake_case	(     self					: Optional[Any]				):
          __UpperCamelCase  		=						0







      def       snake_case	(     self					: Tuple				):
          __UpperCamelCase  		=						AutoProcessor.from_pretrained('''facebook/wav2vec2-base-960h'''				)
          self.assertIsInstance(snake_case ,       snake_case				)







      def       snake_case	(     self					: List[Any]				):
          with tempfile.TemporaryDirectory() as tmpdirname:
              __UpperCamelCase  		=						WavaVecaConfig()
              __UpperCamelCase  		=						AutoProcessor.from_pretrained('''facebook/wav2vec2-base-960h'''				)

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

              __UpperCamelCase  		=						AutoProcessor.from_pretrained(snake_case				)

          self.assertIsInstance(snake_case ,       snake_case				)







      def       snake_case	(     self					: Any				):
          with tempfile.TemporaryDirectory() as tmpdirname:
              # copy relevant files
              copyfile(snake_case ,       os.path.join(snake_case ,       snake_case				)				)
              copyfile(snake_case ,       os.path.join(snake_case ,       '''vocab.json'''				)				)

              __UpperCamelCase  		=						AutoProcessor.from_pretrained(snake_case				)

          self.assertIsInstance(snake_case ,       snake_case				)







      def       snake_case	(     self					: List[Any]				):
          with tempfile.TemporaryDirectory() as tmpdirname:
              __UpperCamelCase  		=						WavaVecaFeatureExtractor()
              __UpperCamelCase  		=						AutoTokenizer.from_pretrained('''facebook/wav2vec2-base-960h'''				)

              __UpperCamelCase  		=						WavaVecaProcessor(snake_case ,       snake_case				)

              # save in new folder
              processor.save_pretrained(snake_case				)

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

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

              __UpperCamelCase  		=						AutoProcessor.from_pretrained(snake_case				)

          self.assertIsInstance(snake_case ,       snake_case				)







      def       snake_case	(     self					: Tuple				):
          with tempfile.TemporaryDirectory() as tmpdirname:
              __UpperCamelCase  		=						WavaVecaFeatureExtractor()
              __UpperCamelCase  		=						AutoTokenizer.from_pretrained('''facebook/wav2vec2-base-960h'''				)

              __UpperCamelCase  		=						WavaVecaProcessor(snake_case ,       snake_case				)

              # save in new folder
              processor.save_pretrained(snake_case				)

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

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

              __UpperCamelCase  		=						AutoProcessor.from_pretrained(snake_case				)

          self.assertIsInstance(snake_case ,       snake_case				)







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

              __UpperCamelCase  		=						AutoProcessor.from_pretrained(snake_case				)

          self.assertIsInstance(snake_case ,       snake_case				)







      def       snake_case	(     self					: int				):
          # If remote code is not set, we will time out when asking whether to load the model.
          with self.assertRaises(snake_case				):
              __UpperCamelCase  		=						AutoProcessor.from_pretrained('''hf-internal-testing/test_dynamic_processor'''				)
          # If remote code is disabled, we can't load this config.
          with self.assertRaises(snake_case				):
              __UpperCamelCase  		=						AutoProcessor.from_pretrained(
                  '''hf-internal-testing/test_dynamic_processor''' ,       trust_remote_code=snake_case				)

          __UpperCamelCase  		=						AutoProcessor.from_pretrained('''hf-internal-testing/test_dynamic_processor''' ,       trust_remote_code=snake_case				)
          self.assertTrue(processor.special_attribute_present				)
          self.assertEqual(processor.__class__.__name__ ,       '''NewProcessor'''				)

          __UpperCamelCase  		=						processor.feature_extractor
          self.assertTrue(feature_extractor.special_attribute_present				)
          self.assertEqual(feature_extractor.__class__.__name__ ,       '''NewFeatureExtractor'''				)

          __UpperCamelCase  		=						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
              __UpperCamelCase  		=						AutoProcessor.from_pretrained(
                  '''hf-internal-testing/test_dynamic_processor''' ,       trust_remote_code=snake_case ,       use_fast=snake_case				)
              __UpperCamelCase  		=						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       snake_case	(     self					: List[Any]				):
          try:
              AutoConfig.register('''custom''' ,       snake_case				)
              AutoFeatureExtractor.register(snake_case ,       snake_case				)
              AutoTokenizer.register(snake_case ,       slow_tokenizer_class=snake_case				)
              AutoProcessor.register(snake_case ,       snake_case				)
              # Trying to register something existing in the Transformers library will raise an error
              with self.assertRaises(snake_case				):
                  AutoProcessor.register(snake_case ,       snake_case				)

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

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

              __UpperCamelCase  		=						CustomProcessor(snake_case ,       snake_case				)

              with tempfile.TemporaryDirectory() as tmp_dir:
                  processor.save_pretrained(snake_case				)
                  __UpperCamelCase  		=						AutoProcessor.from_pretrained(snake_case				)
                  self.assertIsInstance(snake_case ,       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       snake_case	(     self					: Optional[int]				):

          class  _lowerCamelCase						(			_SCREAMING_SNAKE_CASE   ):

                """simple docstring"""

                lowerCAmelCase__			: Tuple    =					False







          class  _lowerCamelCase						(			_SCREAMING_SNAKE_CASE   ):

                """simple docstring"""

                lowerCAmelCase__			: Tuple    =					False







          class  _lowerCamelCase						(			_SCREAMING_SNAKE_CASE   ):

                """simple docstring"""

                lowerCAmelCase__			: Any    =					"AutoFeatureExtractor"
                lowerCAmelCase__			: str    =					"AutoTokenizer"
                lowerCAmelCase__			: str    =					False

          try:
              AutoConfig.register('''custom''' ,       snake_case				)
              AutoFeatureExtractor.register(snake_case ,       snake_case				)
              AutoTokenizer.register(snake_case ,       slow_tokenizer_class=snake_case				)
              AutoProcessor.register(snake_case ,       snake_case				)
              # If remote code is not set, the default is to use local classes.
              __UpperCamelCase  		=						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.
              __UpperCamelCase  		=						AutoProcessor.from_pretrained(
                  '''hf-internal-testing/test_dynamic_processor''' ,       trust_remote_code=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.
              __UpperCamelCase  		=						AutoProcessor.from_pretrained(
                  '''hf-internal-testing/test_dynamic_processor''' ,       trust_remote_code=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       snake_case	(     self					: Optional[int]				):
          __UpperCamelCase  		=						AutoProcessor.from_pretrained('''hf-internal-testing/tiny-random-bert'''				)
          self.assertEqual(processor.__class__.__name__ ,       '''BertTokenizerFast'''				)







      def       snake_case	(     self					: str				):
          __UpperCamelCase  		=						AutoProcessor.from_pretrained('''hf-internal-testing/tiny-random-convnext'''				)
          self.assertEqual(processor.__class__.__name__ ,       '''ConvNextImageProcessor'''				)







@is_staging_test
class  _lowerCamelCase						(			unittest.TestCase   ):

      """simple docstring"""

      lowerCAmelCase__			: int    =					["[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]", "bla", "blou"]







      @classmethod
      def       snake_case	(     cls					: List[str]				):
          __UpperCamelCase  		=						TOKEN
          HfFolder.save_token(snake_case				)







      @classmethod
      def       snake_case	(     cls					: Union[str, Any]				):
          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       snake_case	(     self					: int				):
          __UpperCamelCase  		=						WavaVecaProcessor.from_pretrained(snake_case				)
          with tempfile.TemporaryDirectory() as tmp_dir:
              processor.save_pretrained(
                  os.path.join(snake_case ,       '''test-processor'''				) ,       push_to_hub=snake_case ,       use_auth_token=self._token				)

              __UpperCamelCase  		=						WavaVecaProcessor.from_pretrained(F"{USER}/test-processor"				)
              for k, v in processor.feature_extractor.__dict__.items():
                  self.assertEqual(snake_case ,       getattr(new_processor.feature_extractor ,       snake_case				)				)
              self.assertDictEqual(new_processor.tokenizer.get_vocab() ,       processor.tokenizer.get_vocab()				)







      def       snake_case	(     self					: Any				):
          __UpperCamelCase  		=						WavaVecaProcessor.from_pretrained(snake_case				)

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

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







      def       snake_case	(     self					: List[str]				):
          CustomFeatureExtractor.register_for_auto_class()
          CustomTokenizer.register_for_auto_class()
          CustomProcessor.register_for_auto_class()

          __UpperCamelCase  		=						CustomFeatureExtractor.from_pretrained(snake_case				)

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

          __UpperCamelCase  		=						CustomProcessor(snake_case ,       snake_case				)

          with tempfile.TemporaryDirectory() as tmp_dir:
              create_repo(F"{USER}/test-dynamic-processor" ,       token=self._token				)
              __UpperCamelCase  		=						Repository(snake_case ,       clone_from=F"{USER}/test-dynamic-processor" ,       token=self._token				)
              processor.save_pretrained(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(snake_case ,       '''tokenizer_config.json'''				)				) as f:
                  __UpperCamelCase  		=						json.load(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(snake_case ,       '''custom_feature_extraction.py'''				)				)				)
              self.assertTrue(os.path.isfile(os.path.join(snake_case ,       '''custom_tokenization.py'''				)				)				)
              self.assertTrue(os.path.isfile(os.path.join(snake_case ,       '''custom_processing.py'''				)				)				)

              repo.push_to_hub()

          __UpperCamelCase  		=						AutoProcessor.from_pretrained(F"{USER}/test-dynamic-processor" ,       trust_remote_code=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'''				)












import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding

class  _lowerCamelCase						(			_SCREAMING_SNAKE_CASE   ):

      """simple docstring"""

      lowerCAmelCase__			: Dict    =					["image_processor", "tokenizer"]
      lowerCAmelCase__			: List[Any]    =					"ViTImageProcessor"
      lowerCAmelCase__			: Optional[int]    =					("CLIPTokenizer", "CLIPTokenizerFast")







      def __init__(     self					: Any ,       snake_case					: Any=None ,       snake_case					: Dict=None ,       **snake_case					: Tuple				):
          __UpperCamelCase  		=						None
          if "feature_extractor" in kwargs:
              warnings.warn(
                  '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
                  ''' instead.''' ,       snake_case ,       )
              __UpperCamelCase  		=						kwargs.pop('''feature_extractor'''				)

          __UpperCamelCase  		=						image_processor if image_processor is not None else feature_extractor
          if image_processor is None:
              raise ValueError('''You need to specify an `image_processor`.'''				)
          if tokenizer is None:
              raise ValueError('''You need to specify a `tokenizer`.'''				)

          super().__init__(snake_case ,       snake_case				)







      def __call__(     self					: Dict ,       snake_case					: Union[str, Any]=None ,       snake_case					: Tuple=None ,       snake_case					: Any=None ,       snake_case					: Any=None ,       **snake_case					: Optional[int]				):
          if text is None and visual_prompt is None and images is None:
              raise ValueError('''You have to specify either text, visual prompt or images.'''				)

          if text is not None and visual_prompt is not None:
              raise ValueError('''You have to specify exactly one type of prompt. Either text or visual prompt.'''				)

          if text is not None:
              __UpperCamelCase  		=						self.tokenizer(snake_case ,       return_tensors=snake_case ,       **snake_case				)

          if visual_prompt is not None:
              __UpperCamelCase  		=						self.image_processor(snake_case ,       return_tensors=snake_case ,       **snake_case				)

          if images is not None:
              __UpperCamelCase  		=						self.image_processor(snake_case ,       return_tensors=snake_case ,       **snake_case				)

          if visual_prompt is not None and images is not None:
              __UpperCamelCase  		=						{
                  '''pixel_values''': image_features.pixel_values,
                  '''conditional_pixel_values''': prompt_features.pixel_values,
              }
              return encoding
          elif text is not None and images is not None:
              __UpperCamelCase  		=						image_features.pixel_values
              return encoding
          elif text is not None:
              return encoding
          elif visual_prompt is not None:
              __UpperCamelCase  		=						{
                  '''conditional_pixel_values''': prompt_features.pixel_values,
              }
              return encoding
          else:
              return BatchEncoding(data=dict(**snake_case				) ,       tensor_type=snake_case				)







      def       snake_case	(     self					: Optional[int] ,       *snake_case					: Tuple ,       **snake_case					: Any				):
          return self.tokenizer.batch_decode(*snake_case ,       **snake_case				)







      def       snake_case	(     self					: Optional[int] ,       *snake_case					: Dict ,       **snake_case					: List[str]				):
          return self.tokenizer.decode(*snake_case ,       **snake_case				)







      @property
      def       snake_case	(     self					: Optional[Any]				):
          warnings.warn(
              '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.''' ,       snake_case ,       )
          return self.image_processor_class







      @property
      def       snake_case	(     self					: Optional[Any]				):
          warnings.warn(
              '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.''' ,       snake_case ,       )
          return self.image_processor






