






"""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


UpperCAmelCase_   : List[str]   			= get_tests_dir("""fixtures/dummy_feature_extractor_config.json""")
UpperCAmelCase_   : Optional[Any]   			= get_tests_dir("""fixtures/vocab.json""")
UpperCAmelCase_   : List[Any]   			= get_tests_dir("""fixtures""")




class 							lowerCAmelCase__				(			unittest.TestCase ):






      '''simple docstring'''

      __UpperCamelCase	 =  ["[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]", "bla", "blou"]
      def _SCREAMING_SNAKE_CASE (						self     :  str):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		0
      def _SCREAMING_SNAKE_CASE (						self     :  str):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  Dict    						=		AutoProcessor.from_pretrained('''facebook/wav2vec2-base-960h''')
          self.assertIsInstance(lowercase_						,	lowercase_)
      def _SCREAMING_SNAKE_CASE (						self     :  Dict):




          '''simple docstring'''


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

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

              SCREAMING_SNAKE_CASE_      :  Any    						=		AutoProcessor.from_pretrained(lowercase_)

          self.assertIsInstance(lowercase_						,	lowercase_)
      def _SCREAMING_SNAKE_CASE (						self     :  str):




          '''simple docstring'''


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

              SCREAMING_SNAKE_CASE_      :  Any    						=		AutoProcessor.from_pretrained(lowercase_)

          self.assertIsInstance(lowercase_						,	lowercase_)
      def _SCREAMING_SNAKE_CASE (						self     :  Optional[int]):




          '''simple docstring'''


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

              SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		WavaVecaProcessor(lowercase_						,	lowercase_)

              # save in new folder
              processor.save_pretrained(lowercase_)

              # drop `processor_class` in tokenizer
              with open(os.path.join(lowercase_						,	lowercase_)						,	'''r''') as f:
                  SCREAMING_SNAKE_CASE_      :  List[Any]    						=		json.load(lowercase_)
                  config_dict.pop('''processor_class''')

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

              SCREAMING_SNAKE_CASE_      :  List[Any]    						=		AutoProcessor.from_pretrained(lowercase_)

          self.assertIsInstance(lowercase_						,	lowercase_)
      def _SCREAMING_SNAKE_CASE (						self     :  Dict):




          '''simple docstring'''


          with tempfile.TemporaryDirectory() as tmpdirname:
              SCREAMING_SNAKE_CASE_      :  str    						=		WavaVecaFeatureExtractor()
              SCREAMING_SNAKE_CASE_      :  int    						=		AutoTokenizer.from_pretrained('''facebook/wav2vec2-base-960h''')

              SCREAMING_SNAKE_CASE_      :  str    						=		WavaVecaProcessor(lowercase_						,	lowercase_)

              # save in new folder
              processor.save_pretrained(lowercase_)

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

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

              SCREAMING_SNAKE_CASE_      :  Tuple    						=		AutoProcessor.from_pretrained(lowercase_)

          self.assertIsInstance(lowercase_						,	lowercase_)
      def _SCREAMING_SNAKE_CASE (						self     :  Dict):




          '''simple docstring'''


          with tempfile.TemporaryDirectory() as tmpdirname:
              SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		WavaVecaConfig(processor_class='''Wav2Vec2Processor''')
              model_config.save_pretrained(lowercase_)
              # copy relevant files
              copyfile(lowercase_						,	os.path.join(lowercase_						,	'''vocab.json'''))
              # create emtpy sample processor
              with open(os.path.join(lowercase_						,	lowercase_)						,	'''w''') as f:
                  f.write('''{}''')

              SCREAMING_SNAKE_CASE_      :  int    						=		AutoProcessor.from_pretrained(lowercase_)

          self.assertIsInstance(lowercase_						,	lowercase_)
      def _SCREAMING_SNAKE_CASE (						self     :  Any):




          '''simple docstring'''


          with self.assertRaises(lowercase_):
              SCREAMING_SNAKE_CASE_      :  str    						=		AutoProcessor.from_pretrained('''hf-internal-testing/test_dynamic_processor''')
          # If remote code is disabled, we can't load this config.
          with self.assertRaises(lowercase_):
              SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		AutoProcessor.from_pretrained(
                  '''hf-internal-testing/test_dynamic_processor'''						,	trust_remote_code=lowercase_)

          SCREAMING_SNAKE_CASE_      :  Tuple    						=		AutoProcessor.from_pretrained('''hf-internal-testing/test_dynamic_processor'''						,	trust_remote_code=lowercase_)
          self.assertTrue(processor.special_attribute_present)
          self.assertEqual(processor.__class__.__name__						,	'''NewProcessor''')

          SCREAMING_SNAKE_CASE_      :  Tuple    						=		processor.feature_extractor
          self.assertTrue(feature_extractor.special_attribute_present)
          self.assertEqual(feature_extractor.__class__.__name__						,	'''NewFeatureExtractor''')

          SCREAMING_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
              SCREAMING_SNAKE_CASE_      :  Any    						=		AutoProcessor.from_pretrained(
                  '''hf-internal-testing/test_dynamic_processor'''						,	trust_remote_code=lowercase_						,	use_fast=lowercase_)
              SCREAMING_SNAKE_CASE_      :  List[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]):




          '''simple docstring'''


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

              # Now that the config is registered, it can be used as any other config with the auto-API
              SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		CustomFeatureExtractor.from_pretrained(lowercase_)

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

              SCREAMING_SNAKE_CASE_      :  Dict    						=		CustomProcessor(lowercase_						,	lowercase_)

              with tempfile.TemporaryDirectory() as tmp_dir:
                  processor.save_pretrained(lowercase_)
                  SCREAMING_SNAKE_CASE_      :  str    						=		AutoProcessor.from_pretrained(lowercase_)
                  self.assertIsInstance(lowercase_						,	lowercase_)

          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     :  int):




          '''simple docstring'''


          class 							lowerCAmelCase__				(			UpperCAmelCase__ ):






                '''simple docstring'''

                __UpperCamelCase	 =  False




          class 							lowerCAmelCase__				(			UpperCAmelCase__ ):






                '''simple docstring'''

                __UpperCamelCase	 =  False




          class 							lowerCAmelCase__				(			UpperCAmelCase__ ):






                '''simple docstring'''

                __UpperCamelCase	 =  "AutoFeatureExtractor"
                __UpperCamelCase	 =  "AutoTokenizer"
                __UpperCamelCase	 =  False

          try:
              AutoConfig.register('''custom'''						,	lowercase_)
              AutoFeatureExtractor.register(lowercase_						,	lowercase_)
              AutoTokenizer.register(lowercase_						,	slow_tokenizer_class=lowercase_)
              AutoProcessor.register(lowercase_						,	lowercase_)
              # If remote code is not set, the default is to use local classes.
              SCREAMING_SNAKE_CASE_      :  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.
              SCREAMING_SNAKE_CASE_      :  Any    						=		AutoProcessor.from_pretrained(
                  '''hf-internal-testing/test_dynamic_processor'''						,	trust_remote_code=lowercase_)
              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.
              SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		AutoProcessor.from_pretrained(
                  '''hf-internal-testing/test_dynamic_processor'''						,	trust_remote_code=lowercase_)
              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     :  List[Any]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  Tuple    						=		AutoProcessor.from_pretrained('''hf-internal-testing/tiny-random-bert''')
          self.assertEqual(processor.__class__.__name__						,	'''BertTokenizerFast''')



      def _SCREAMING_SNAKE_CASE (						self     :  Any):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		AutoProcessor.from_pretrained('''hf-internal-testing/tiny-random-convnext''')
          self.assertEqual(processor.__class__.__name__						,	'''ConvNextImageProcessor''')




@is_staging_test
class 							lowerCAmelCase__				(			unittest.TestCase ):






      '''simple docstring'''

      __UpperCamelCase	 =  ["[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]", "bla", "blou"]
      @classmethod
      def _SCREAMING_SNAKE_CASE (						cls     :  List[Any]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		TOKEN
          HfFolder.save_token(lowercase_)
      @classmethod
      def _SCREAMING_SNAKE_CASE (						cls     :  str):




          '''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     :  Any):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		WavaVecaProcessor.from_pretrained(lowercase_)
          with tempfile.TemporaryDirectory() as tmp_dir:
              processor.save_pretrained(
                  os.path.join(lowercase_						,	'''test-processor''')						,	push_to_hub=lowercase_						,	use_auth_token=self._token)

              SCREAMING_SNAKE_CASE_      :  List[str]    						=		WavaVecaProcessor.from_pretrained(F'{USER}/test-processor')
              for k, v in processor.feature_extractor.__dict__.items():
                  self.assertEqual(lowercase_						,	getattr(new_processor.feature_extractor						,	lowercase_))
              self.assertDictEqual(new_processor.tokenizer.get_vocab()						,	processor.tokenizer.get_vocab())
      def _SCREAMING_SNAKE_CASE (						self     :  Union[str, Any]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  Tuple    						=		WavaVecaProcessor.from_pretrained(lowercase_)

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

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



      def _SCREAMING_SNAKE_CASE (						self     :  Optional[int]):




          '''simple docstring'''


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

          SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		CustomFeatureExtractor.from_pretrained(lowercase_)

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

          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		CustomProcessor(lowercase_						,	lowercase_)

          with tempfile.TemporaryDirectory() as tmp_dir:
              create_repo(F'{USER}/test-dynamic-processor'						,	token=self._token)
              SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		Repository(lowercase_						,	clone_from=F'{USER}/test-dynamic-processor'						,	token=self._token)
              processor.save_pretrained(lowercase_)

              # 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(lowercase_						,	'''tokenizer_config.json''')) as f:
                  SCREAMING_SNAKE_CASE_      :  Dict    						=		json.load(lowercase_)
              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(lowercase_						,	'''custom_feature_extraction.py''')))
              self.assertTrue(os.path.isfile(os.path.join(lowercase_						,	'''custom_tokenization.py''')))
              self.assertTrue(os.path.isfile(os.path.join(lowercase_						,	'''custom_processing.py''')))

              repo.push_to_hub()

          SCREAMING_SNAKE_CASE_      :  Tuple    						=		AutoProcessor.from_pretrained(F'{USER}/test-dynamic-processor'						,	trust_remote_code=lowercase_)
          # 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 unittest

from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device


if is_torch_available():
	import torch

	from transformers import AutoModelForImageClassification

if is_vision_available():
	from transformers import AutoImageProcessor




@require_torch
@require_vision
class  UpperCamelCase_      (			unittest.TestCase				):



	'''simple docstring'''




	@slow
	def 							_UpperCamelCase			(       self					)       ->    int:
								snake_case_				=							AutoImageProcessor.from_pretrained('microsoft/dit-base-finetuned-rvlcdip'					)
								snake_case_				=							AutoModelForImageClassification.from_pretrained('microsoft/dit-base-finetuned-rvlcdip'					)
								model.to(a					)

								from datasets import load_dataset

								snake_case_				=							load_dataset('nielsr/rvlcdip-demo'					)

								snake_case_				=							dataset['train'][0]['image'].convert('RGB'					)

								snake_case_				=							image_processor(a       ,			return_tensors='pt'					).to(a					)

								# forward pass
								with torch.no_grad():
															snake_case_				=							model(**a					)
															snake_case_				=							outputs.logits

								snake_case_				=							torch.Size((1, 16)					)
								self.assertEqual(logits.shape       ,			a					)

								snake_case_				=							torch.tensor(
								    [-0.4_158, -0.4_092, -0.4_347]       ,			device=a       ,			dtype=torch.float       ,			)
								self.assertTrue(torch.allclose(logits[0, :3]       ,			a       ,			atol=1E-4					)					)





