'''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


_A			:      Dict					      =				get_tests_dir('''fixtures/dummy_feature_extractor_config.json''')
_A			:      Dict					      =				get_tests_dir('''fixtures/vocab.json''')
_A			:      List[Any]					      =				get_tests_dir('''fixtures''')







class 				_lowercase     (				unittest.TestCase			):


 '''simple docstring'''






 _SCREAMING_SNAKE_CASE     :       Any      	=					["[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]", "bla", "blou"]
 def a					(      self   :	Any							)  ->    Optional[int]:
  __lowerCAmelCase											=						0
 def a					(      self   :	List[Any]							)  ->    str:
  __lowerCAmelCase											=						AutoProcessor.from_pretrained("""facebook/wav2vec2-base-960h"""							)
  self.assertIsInstance(UpperCAmelCase__      ,       UpperCAmelCase__							)
 def a					(      self   :	Optional[Any]							)  ->    Any:
  with tempfile.TemporaryDirectory() as tmpdirname:
   __lowerCAmelCase											=						WavaVecaConfig()
   __lowerCAmelCase											=						AutoProcessor.from_pretrained("""facebook/wav2vec2-base-960h"""							)

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

   __lowerCAmelCase											=						AutoProcessor.from_pretrained(UpperCAmelCase__							)

  self.assertIsInstance(UpperCAmelCase__      ,       UpperCAmelCase__							)
 def a					(      self   :	Any							)  ->    Any:
  with tempfile.TemporaryDirectory() as tmpdirname:
   # copy relevant files
   copyfile(UpperCAmelCase__      ,       os.path.join(UpperCAmelCase__      ,       UpperCAmelCase__							)							)
   copyfile(UpperCAmelCase__      ,       os.path.join(UpperCAmelCase__      ,       """vocab.json"""							)							)

   __lowerCAmelCase											=						AutoProcessor.from_pretrained(UpperCAmelCase__							)

  self.assertIsInstance(UpperCAmelCase__      ,       UpperCAmelCase__							)
 def a					(      self   :	List[Any]							)  ->    Any:
  with tempfile.TemporaryDirectory() as tmpdirname:
   __lowerCAmelCase											=						WavaVecaFeatureExtractor()
   __lowerCAmelCase											=						AutoTokenizer.from_pretrained("""facebook/wav2vec2-base-960h"""							)

   __lowerCAmelCase											=						WavaVecaProcessor(UpperCAmelCase__      ,       UpperCAmelCase__							)

   # save in new folder
   processor.save_pretrained(UpperCAmelCase__							)

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

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

   __lowerCAmelCase											=						AutoProcessor.from_pretrained(UpperCAmelCase__							)

  self.assertIsInstance(UpperCAmelCase__      ,       UpperCAmelCase__							)
 def a					(      self   :	Dict							)  ->    Tuple:
  with tempfile.TemporaryDirectory() as tmpdirname:
   __lowerCAmelCase											=						WavaVecaFeatureExtractor()
   __lowerCAmelCase											=						AutoTokenizer.from_pretrained("""facebook/wav2vec2-base-960h"""							)

   __lowerCAmelCase											=						WavaVecaProcessor(UpperCAmelCase__      ,       UpperCAmelCase__							)

   # save in new folder
   processor.save_pretrained(UpperCAmelCase__							)

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

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

   __lowerCAmelCase											=						AutoProcessor.from_pretrained(UpperCAmelCase__							)

  self.assertIsInstance(UpperCAmelCase__      ,       UpperCAmelCase__							)
 def a					(      self   :	List[str]							)  ->    List[str]:
  with tempfile.TemporaryDirectory() as tmpdirname:
   __lowerCAmelCase											=						WavaVecaConfig(processor_class="""Wav2Vec2Processor"""							)
   model_config.save_pretrained(UpperCAmelCase__							)
   # copy relevant files
   copyfile(UpperCAmelCase__      ,       os.path.join(UpperCAmelCase__      ,       """vocab.json"""							)							)
   # create emtpy sample processor
   with open(os.path.join(UpperCAmelCase__      ,       UpperCAmelCase__							)      ,       """w"""							) as f:
    f.write("""{}"""							)

   __lowerCAmelCase											=						AutoProcessor.from_pretrained(UpperCAmelCase__							)

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

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

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

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

   # Test we can also load the slow version
   __lowerCAmelCase											=						AutoProcessor.from_pretrained(
       """hf-internal-testing/test_dynamic_processor"""      ,       trust_remote_code=UpperCAmelCase__      ,       use_fast=UpperCAmelCase__							)
   __lowerCAmelCase											=						new_processor.tokenizer
   self.assertTrue(new_tokenizer.special_attribute_present							)
   self.assertEqual(new_tokenizer.__class__.__name__      ,       """NewTokenizer"""							)
  else:
   self.assertEqual(tokenizer.__class__.__name__      ,       """NewTokenizer"""							)
 def a					(      self   :	Any							)  ->    Optional[Any]:
  try:
   AutoConfig.register("""custom"""      ,       UpperCAmelCase__							)
   AutoFeatureExtractor.register(UpperCAmelCase__      ,       UpperCAmelCase__							)
   AutoTokenizer.register(UpperCAmelCase__      ,       slow_tokenizer_class=UpperCAmelCase__							)
   AutoProcessor.register(UpperCAmelCase__      ,       UpperCAmelCase__							)
   # Trying to register something existing in the Transformers library will raise an error
   with self.assertRaises(UpperCAmelCase__							):
    AutoProcessor.register(UpperCAmelCase__      ,       UpperCAmelCase__							)

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

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

   __lowerCAmelCase											=						CustomProcessor(UpperCAmelCase__      ,       UpperCAmelCase__							)

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

  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   :	List[Any]							)  ->    List[str]:







  class 				_lowercase     (				UpperCAmelCase__			):


   '''simple docstring'''






   _SCREAMING_SNAKE_CASE     :       Optional[Any]      	=					False







  class 				_lowercase     (				UpperCAmelCase__			):


   '''simple docstring'''






   _SCREAMING_SNAKE_CASE     :       int      	=					False







  class 				_lowercase     (				UpperCAmelCase__			):


   '''simple docstring'''






   _SCREAMING_SNAKE_CASE     :       Any      	=					"AutoFeatureExtractor"
   _SCREAMING_SNAKE_CASE     :       Tuple      	=					"AutoTokenizer"
   _SCREAMING_SNAKE_CASE     :       List[Any]      	=					False

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

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

   # If remote is enabled, we load from the Hub.
   __lowerCAmelCase											=						AutoProcessor.from_pretrained(
       """hf-internal-testing/test_dynamic_processor"""      ,       trust_remote_code=UpperCAmelCase__							)
   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   :	str							)  ->    Optional[Any]:
  __lowerCAmelCase											=						AutoProcessor.from_pretrained("""hf-internal-testing/tiny-random-bert"""							)
  self.assertEqual(processor.__class__.__name__      ,       """BertTokenizerFast"""							)





 def a					(      self   :	Optional[Any]							)  ->    Union[str, Any]:
  __lowerCAmelCase											=						AutoProcessor.from_pretrained("""hf-internal-testing/tiny-random-convnext"""							)
  self.assertEqual(processor.__class__.__name__      ,       """ConvNextImageProcessor"""							)







@is_staging_test
class 				_lowercase     (				unittest.TestCase			):


 '''simple docstring'''






 _SCREAMING_SNAKE_CASE     :       Optional[Any]      	=					["[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]", "bla", "blou"]
 @classmethod
 def a					(      cls   :	Tuple							)  ->    int:
  __lowerCAmelCase											=						TOKEN
  HfFolder.save_token(UpperCAmelCase__							)
 @classmethod
 def a					(      cls   :	int							)  ->    Tuple:
  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   :	Dict							)  ->    Any:
  __lowerCAmelCase											=						WavaVecaProcessor.from_pretrained(UpperCAmelCase__							)
  with tempfile.TemporaryDirectory() as tmp_dir:
   processor.save_pretrained(
       os.path.join(UpperCAmelCase__      ,       """test-processor"""							)      ,       push_to_hub=UpperCAmelCase__      ,       use_auth_token=self._token							)

   __lowerCAmelCase											=						WavaVecaProcessor.from_pretrained(f"""{USER}/test-processor"""							)
   for k, v in processor.feature_extractor.__dict__.items():
    self.assertEqual(UpperCAmelCase__      ,       getattr(new_processor.feature_extractor      ,       UpperCAmelCase__							)							)
   self.assertDictEqual(new_processor.tokenizer.get_vocab()      ,       processor.tokenizer.get_vocab()							)
 def a					(      self   :	List[str]							)  ->    Tuple:
  __lowerCAmelCase											=						WavaVecaProcessor.from_pretrained(UpperCAmelCase__							)

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

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





 def a					(      self   :	Optional[int]							)  ->    int:
  CustomFeatureExtractor.register_for_auto_class()
  CustomTokenizer.register_for_auto_class()
  CustomProcessor.register_for_auto_class()

  __lowerCAmelCase											=						CustomFeatureExtractor.from_pretrained(UpperCAmelCase__							)

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

  __lowerCAmelCase											=						CustomProcessor(UpperCAmelCase__      ,       UpperCAmelCase__							)

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

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

   repo.push_to_hub()

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










"""simple docstring"""







import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionImageVariationPipeline
from diffusers.utils.testing_utils import load_image, require_torch_gpu, slow, torch_device


a__				:							Tuple						=     False




class  UpperCamelCase_	( unittest.TestCase):

	"""simple docstring"""





	pass




@slow
@require_torch_gpu
class  UpperCamelCase_	( unittest.TestCase):

	"""simple docstring"""





	def   UpperCAmelCase_      ( self			:				int   )       ->	int:
						__SCREAMING_SNAKE_CASE						   =				VersatileDiffusionImageVariationPipeline.from_pretrained("shi-labs/versatile-diffusion"   )
						pipe.to(UpperCAmelCase__   )
						pipe.set_progress_bar_config(disable=UpperCAmelCase__   )

						__SCREAMING_SNAKE_CASE						   =				load_image(
						    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg"   )
						__SCREAMING_SNAKE_CASE						   =				torch.manual_seed(0   )
						__SCREAMING_SNAKE_CASE						   =				pipe(
						    image=UpperCAmelCase__			,			generator=UpperCAmelCase__			,			guidance_scale=7.5			,			num_inference_steps=5_0			,			output_type="numpy"			,			).images

						__SCREAMING_SNAKE_CASE						   =				image[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

						assert image.shape == (1, 5_1_2, 5_1_2, 3)
						__SCREAMING_SNAKE_CASE						   =				np.array([0.0_441, 0.0_469, 0.0_507, 0.0_575, 0.0_632, 0.0_650, 0.0_865, 0.0_909, 0.0_945]   )

						assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1E-2
