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






class   UpperCAmelCase		(						unittest.TestCase ):
   A__					:		Optional[Any]						     =     ["[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]", "bla", "blou"]



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


          '''simple docstring'''


          snake_case       :   List[str]							=      0



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


          '''simple docstring'''


          snake_case       :   Optional[Any]							=      AutoProcessor.from_pretrained("facebook/wav2vec2-base-960h" )
          self.assertIsInstance(lowerCamelCase__    ,						lowerCamelCase__ )



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


          '''simple docstring'''


          with tempfile.TemporaryDirectory() as tmpdirname:
                 snake_case       :   List[str]							=      WavaVecaConfig()
                 snake_case       :   Tuple							=      AutoProcessor.from_pretrained("facebook/wav2vec2-base-960h" )

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

                 snake_case       :   List[str]							=      AutoProcessor.from_pretrained(lowerCamelCase__ )

          self.assertIsInstance(lowerCamelCase__    ,						lowerCamelCase__ )



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


          '''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" ) )

                 snake_case       :   Optional[int]							=      AutoProcessor.from_pretrained(lowerCamelCase__ )

          self.assertIsInstance(lowerCamelCase__    ,						lowerCamelCase__ )



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


          '''simple docstring'''


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

                 snake_case       :   List[str]							=      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:
                        snake_case       :   Any							=      json.load(lowerCamelCase__ )
                        config_dict.pop("processor_class" )

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

                 snake_case       :   Union[str, Any]							=      AutoProcessor.from_pretrained(lowerCamelCase__ )

          self.assertIsInstance(lowerCamelCase__    ,						lowerCamelCase__ )



   def 				_SCREAMING_SNAKE_CASE   (self		:					int )		->	Any:


          '''simple docstring'''


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

                 snake_case       :   str							=      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:
                        snake_case       :   Union[str, Any]							=      json.load(lowerCamelCase__ )
                        config_dict.pop("processor_class" )

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

                 snake_case       :   Tuple							=      AutoProcessor.from_pretrained(lowerCamelCase__ )

          self.assertIsInstance(lowerCamelCase__    ,						lowerCamelCase__ )



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


          '''simple docstring'''


          with tempfile.TemporaryDirectory() as tmpdirname:
                 snake_case       :   str							=      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("{}" )

                 snake_case       :   List[str]							=      AutoProcessor.from_pretrained(lowerCamelCase__ )

          self.assertIsInstance(lowerCamelCase__    ,						lowerCamelCase__ )



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


          '''simple docstring'''


          with self.assertRaises(lowerCamelCase__ ):
                 snake_case       :   Optional[int]							=      AutoProcessor.from_pretrained("hf-internal-testing/test_dynamic_processor" )
          # If remote code is disabled, we can't load this config.
          with self.assertRaises(lowerCamelCase__ ):
                 snake_case       :   List[Any]							=      AutoProcessor.from_pretrained(
                     "hf-internal-testing/test_dynamic_processor"    ,						trust_remote_code=lowerCamelCase__ )

          snake_case       :   Any							=      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" )

          snake_case       :   int							=      processor.feature_extractor
          self.assertTrue(feature_extractor.special_attribute_present )
          self.assertEqual(feature_extractor.__class__.__name__    ,						"NewFeatureExtractor" )

          snake_case       :   Union[str, Any]							=      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
                 snake_case       :   str							=      AutoProcessor.from_pretrained(
                     "hf-internal-testing/test_dynamic_processor"    ,						trust_remote_code=lowerCamelCase__    ,						use_fast=lowerCamelCase__ )
                 snake_case       :   str							=      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 				_SCREAMING_SNAKE_CASE   (self		:					List[str] )		->	Dict:


          '''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
                 snake_case       :   List[str]							=      CustomFeatureExtractor.from_pretrained(lowerCamelCase__ )

                 with tempfile.TemporaryDirectory() as tmp_dir:
                        snake_case       :   str							=      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] ) )
                        snake_case       :   Optional[Any]							=      CustomTokenizer(lowerCamelCase__ )

                 snake_case       :   Any							=      CustomProcessor(lowerCamelCase__    ,						lowerCamelCase__ )

                 with tempfile.TemporaryDirectory() as tmp_dir:
                        processor.save_pretrained(lowerCamelCase__ )
                        snake_case       :   str							=      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 				_SCREAMING_SNAKE_CASE   (self		:					Tuple )		->	List[str]:


          '''simple docstring'''


          class   UpperCAmelCase		(						SCREAMING_SNAKE_CASE__ ):
             A__					:		Optional[int]						     =     False






          class   UpperCAmelCase		(						SCREAMING_SNAKE_CASE__ ):
             A__					:		str						     =     False






          class   UpperCAmelCase		(						SCREAMING_SNAKE_CASE__ ):
             A__					:		int						     =     "AutoFeatureExtractor"
             A__					:		str						     =     "AutoTokenizer"
             A__					:		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.
                 snake_case       :   Optional[int]							=      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.
                 snake_case       :   Union[str, Any]							=      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.
                 snake_case       :   List[str]							=      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 				_SCREAMING_SNAKE_CASE   (self		:					Any )		->	Tuple:


          '''simple docstring'''


          snake_case       :   List[str]							=      AutoProcessor.from_pretrained("hf-internal-testing/tiny-random-bert" )
          self.assertEqual(processor.__class__.__name__    ,						"BertTokenizerFast" )



   def 				_SCREAMING_SNAKE_CASE   (self		:					int )		->	Any:


          '''simple docstring'''


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






@is_staging_test
class   UpperCAmelCase		(						unittest.TestCase ):
   A__					:		Tuple						     =     ["[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]", "bla", "blou"]



   @classmethod
   def 				_SCREAMING_SNAKE_CASE   (cls		:					Any )		->	str:


          '''simple docstring'''


          snake_case       :   List[Any]							=      TOKEN
          HfFolder.save_token(lowerCamelCase__ )



   @classmethod
   def 				_SCREAMING_SNAKE_CASE   (cls		:					List[Any] )		->	Tuple:


          '''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 				_SCREAMING_SNAKE_CASE   (self		:					Dict )		->	Dict:


          '''simple docstring'''


          snake_case       :   Optional[Any]							=      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 )

                 snake_case       :   List[str]							=      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 				_SCREAMING_SNAKE_CASE   (self		:					Optional[Any] )		->	Optional[int]:


          '''simple docstring'''


          snake_case       :   Dict							=      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"    ,						)

                 snake_case       :   Optional[int]							=      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 				_SCREAMING_SNAKE_CASE   (self		:					List[str] )		->	Any:


          '''simple docstring'''


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

          snake_case       :   Tuple							=      CustomFeatureExtractor.from_pretrained(lowerCamelCase__ )

          with tempfile.TemporaryDirectory() as tmp_dir:
                 snake_case       :   Optional[int]							=      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] ) )
                 snake_case       :   int							=      CustomTokenizer(lowerCamelCase__ )

          snake_case       :   Optional[Any]							=      CustomProcessor(lowerCamelCase__    ,						lowerCamelCase__ )

          with tempfile.TemporaryDirectory() as tmp_dir:
                 create_repo(f"""{USER}/test-dynamic-processor"""    ,						token=self._token )
                 snake_case       :   List[Any]							=      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:
                        snake_case       :   Tuple							=      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()

          snake_case       :   List[Any]							=      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" )










from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow


if is_tf_available():
     import tensorflow as tf

     from transformers import AutoTokenizer, TFAutoModelForSeqaSeqLM






@require_tf
@require_sentencepiece
@require_tokenizers
class SCREAMING_SNAKE_CASE__     (		unittest.TestCase    ):




    '''simple docstring'''






    @slow
    def 			_lowerCAmelCase					(       self		):
        A :       Any          =   TFAutoModelForSeqaSeqLM.from_pretrained("""google/mt5-small"""		)
        A :       Dict          =   AutoTokenizer.from_pretrained("""google/mt5-small"""		)

        A :       Tuple          =   tokenizer("""Hello there""",		return_tensors="""tf"""		).input_ids
        A :       Optional[int]          =   tokenizer("""Hi I am""",		return_tensors="""tf"""		).input_ids

        A :       List[Any]          =   model(lowerCamelCase__,		labels=lowerCamelCase__		).loss
        A :       str          =   -tf.math.reduce_mean(lowerCamelCase__		).numpy()

        A :       Tuple          =   -21.22_8168
        self.assertTrue(abs(mtf_score - EXPECTED_SCORE		) < 2e-4		)


