"""simple docstring"""







import os
import unittest

from transformers.models.transfo_xl.tokenization_transfo_xl import VOCAB_FILES_NAMES, TransfoXLTokenizer

from ...test_tokenization_common import TokenizerTesterMixin

class 		UpperCamelCase      (   __lowerCAmelCase				,     unittest.TestCase						):






				"""simple docstring"""




				UpperCAmelCase    :				int   =		TransfoXLTokenizer
				UpperCAmelCase    :				Any   =		False
				UpperCAmelCase    :				int   =		False



				def  _lowercase					(self    :				str)   ->     Dict:
					super().setUp()

					__snake_case      :							List[Any]						=			[
					    '<unk>',
					    '[CLS]',
					    '[SEP]',
					    'want',
					    'unwanted',
					    'wa',
					    'un',
					    'running',
					    ',',
					    'low',
					    'l',
					]
					__snake_case      :							Dict						=			os.path.join(self.tmpdirname     ,  VOCAB_FILES_NAMES['vocab_file'])
					with open(self.vocab_file     ,  'w'     ,  encoding='utf-8') as vocab_writer:
						vocab_writer.write(''.join([x + '\n' for x in vocab_tokens]))



				def  _lowercase					(self    :				Any     ,  **_A    :				Optional[int])   ->     int:
					__snake_case      :							Tuple						=			True
					return TransfoXLTokenizer.from_pretrained(self.tmpdirname     ,  **lowerCAmelCase_)



				def  _lowercase					(self    :				Optional[Any]     ,  _A    :				str)   ->     List[Any]:
					__snake_case      :							List[str]						=			'<unk> UNwanted , running'
					__snake_case      :							List[Any]						=			'<unk> unwanted, running'
					return input_text, output_text



				def  _lowercase					(self    :				str)   ->     Optional[Any]:
					__snake_case      :							Optional[Any]						=			TransfoXLTokenizer(vocab_file=self.vocab_file     ,  lower_case=lowerCAmelCase_)

					__snake_case      :							int						=			tokenizer.tokenize('<unk> UNwanted , running')
					self.assertListEqual(lowerCAmelCase_     ,  ['<unk>', 'unwanted', ',', 'running'])

					self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCAmelCase_)     ,  [0, 4, 8, 7])



				def  _lowercase					(self    :				Optional[int])   ->     List[str]:
					__snake_case      :							Union[str, Any]						=			TransfoXLTokenizer(lower_case=lowerCAmelCase_)

					self.assertListEqual(
					    tokenizer.tokenize(' \tHeLLo ! how  \n Are yoU ?  ')     ,  ['hello', '!', 'how', 'are', 'you', '?'])



				def  _lowercase					(self    :				Union[str, Any])   ->     str:
					__snake_case      :							List[str]						=			TransfoXLTokenizer(lower_case=lowerCAmelCase_)

					self.assertListEqual(
					    tokenizer.tokenize(' \tHeLLo ! how  \n Are yoU ?  ')     ,  ['HeLLo', '!', 'how', 'Are', 'yoU', '?'])



				def  _lowercase					(self    :				List[str])   ->     Dict:
					__snake_case      :							Optional[Any]						=			TransfoXLTokenizer(lower_case=lowerCAmelCase_)
					__snake_case      :							Any						=			'Hello (bracket) and side-scrolled [and] Henry\'s $5,000 with 3.34 m. What\'s up!?'
					__snake_case      :							str						=			[
					    'Hello',
					    '(',
					    'bracket',
					    ')',
					    'and',
					    'side',
					    '@-@',
					    'scrolled',
					    '[',
					    'and',
					    ']',
					    'Henry',
					    '\'s',
					    '$',
					    '5',
					    '@,@',
					    '000',
					    'with',
					    '3',
					    '@.@',
					    '34',
					    'm',
					    '.',
					    'What',
					    '\'s',
					    'up',
					    '!',
					    '?',
					]

					self.assertListEqual(tokenizer.tokenize(lowerCAmelCase_)     ,  lowerCAmelCase_)

					self.assertEqual(tokenizer.convert_tokens_to_string(lowerCAmelCase_)     ,  lowerCAmelCase_)






				def  _lowercase					(self    :				str)   ->     str:
					__snake_case      :							Union[str, Any]						=			self.get_tokenizer()
					__snake_case      :							List[Any]						=			len(lowerCAmelCase_)

					tokenizer.add_tokens(['new1', 'new2'])
					tokenizer.move_added_token('new1'     ,  1)

					# Check that moved token is not copied (duplicate)
					self.assertEqual(len(lowerCAmelCase_)     ,  original_len + 2)
					# Check that token is moved to specified id
					self.assertEqual(tokenizer.encode('new1')     ,  [1])
					self.assertEqual(tokenizer.decode([1])     ,  'new1')






"""simple docstring"""







import json
import sys
import tempfile
import unittest
from pathlib import Path

import transformers
from transformers import (
    CONFIG_MAPPING,
    IMAGE_PROCESSOR_MAPPING,
    AutoConfig,
    AutoImageProcessor,
    CLIPConfig,
    CLIPImageProcessor,
)
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER


sys.path.append(str(Path(__file__).parent.parent.parent.parent / "utils"))

from test_module.custom_configuration import CustomConfig  # noqa E402
from test_module.custom_image_processing import CustomImageProcessor  # noqa E402

class 		UpperCamelCase      (   unittest.TestCase						):



				def  _lowercase					(self    :				Union[str, Any])   ->     Optional[int]:
					__snake_case      :							Optional[Any]						=			0



				def  _lowercase					(self    :				Tuple)   ->     int:
					__snake_case      :							Optional[Any]						=			AutoImageProcessor.from_pretrained('openai/clip-vit-base-patch32')
					self.assertIsInstance(_A     ,  _A)



				def  _lowercase					(self    :				str)   ->     List[str]:
					with tempfile.TemporaryDirectory() as tmpdirname:
						__snake_case      :							List[str]						=			Path(_A) / 'preprocessor_config.json'
						__snake_case      :							Optional[Any]						=			Path(_A) / 'config.json'
						json.dump(
						    {'image_processor_type': 'CLIPImageProcessor', 'processor_class': 'CLIPProcessor'}     ,  open(_A     ,  'w')     ,  )
						json.dump({'model_type': 'clip'}     ,  open(_A     ,  'w'))

						__snake_case      :							Optional[int]						=			AutoImageProcessor.from_pretrained(_A)
						self.assertIsInstance(_A     ,  _A)



				def  _lowercase					(self    :				Any)   ->     Optional[int]:
					# Ensure we can load the image processor from the feature extractor config
					with tempfile.TemporaryDirectory() as tmpdirname:
						__snake_case      :							Any						=			Path(_A) / 'preprocessor_config.json'
						__snake_case      :							List[Any]						=			Path(_A) / 'config.json'
						json.dump(
						    {'feature_extractor_type': 'CLIPFeatureExtractor', 'processor_class': 'CLIPProcessor'}     ,  open(_A     ,  'w')     ,  )
						json.dump({'model_type': 'clip'}     ,  open(_A     ,  'w'))

						__snake_case      :							Tuple						=			AutoImageProcessor.from_pretrained(_A)
						self.assertIsInstance(_A     ,  _A)



				def  _lowercase					(self    :				List[Any])   ->     Optional[Any]:
					with tempfile.TemporaryDirectory() as tmpdirname:
						__snake_case      :							str						=			CLIPConfig()

						# Create a dummy config file with image_proceesor_type
						__snake_case      :							List[Any]						=			Path(_A) / 'preprocessor_config.json'
						__snake_case      :							Optional[Any]						=			Path(_A) / 'config.json'
						json.dump(
						    {'image_processor_type': 'CLIPImageProcessor', 'processor_class': 'CLIPProcessor'}     ,  open(_A     ,  'w')     ,  )
						json.dump({'model_type': 'clip'}     ,  open(_A     ,  'w'))

						# remove image_processor_type to make sure config.json alone is enough to load image processor locally
						__snake_case      :							List[str]						=			AutoImageProcessor.from_pretrained(_A).to_dict()

						config_dict.pop('image_processor_type')
						__snake_case      :							Optional[int]						=			CLIPImageProcessor(**_A)

						# save in new folder
						model_config.save_pretrained(_A)
						config.save_pretrained(_A)

						__snake_case      :							Optional[int]						=			AutoImageProcessor.from_pretrained(_A)

						# make sure private variable is not incorrectly saved
						__snake_case      :							int						=			json.loads(config.to_json_string())
						self.assertTrue('_processor_class' not in dict_as_saved)

					self.assertIsInstance(_A     ,  _A)



				def  _lowercase					(self    :				Union[str, Any])   ->     Optional[Any]:
					with tempfile.TemporaryDirectory() as tmpdirname:
						__snake_case      :							int						=			Path(_A) / 'preprocessor_config.json'
						json.dump(
						    {'image_processor_type': 'CLIPImageProcessor', 'processor_class': 'CLIPProcessor'}     ,  open(_A     ,  'w')     ,  )

						__snake_case      :							List[str]						=			AutoImageProcessor.from_pretrained(_A)
						self.assertIsInstance(_A     ,  _A)



				def  _lowercase					(self    :				Optional[int])   ->     Dict:
					with self.assertRaisesRegex(
					    _A     ,  'clip-base is not a local folder and is not a valid model identifier'):
						__snake_case      :							Tuple						=			AutoImageProcessor.from_pretrained('clip-base')



				def  _lowercase					(self    :				str)   ->     int:
					with self.assertRaisesRegex(
					    _A     ,  r'aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)'):
						__snake_case      :							str						=			AutoImageProcessor.from_pretrained(_A     ,  revision='aaaaaa')



				def  _lowercase					(self    :				List[Any])   ->     str:
					with self.assertRaisesRegex(
					    _A     ,  'hf-internal-testing/config-no-model does not appear to have a file named preprocessor_config.json.'     ,  ):
						__snake_case      :							List[Any]						=			AutoImageProcessor.from_pretrained('hf-internal-testing/config-no-model')



				def  _lowercase					(self    :				Optional[int])   ->     List[str]:
					# If remote code is not set, we will time out when asking whether to load the model.
					with self.assertRaises(_A):
						__snake_case      :							Any						=			AutoImageProcessor.from_pretrained('hf-internal-testing/test_dynamic_image_processor')
					# If remote code is disabled, we can't load this config.
					with self.assertRaises(_A):
						__snake_case      :							Tuple						=			AutoImageProcessor.from_pretrained(
						    'hf-internal-testing/test_dynamic_image_processor'     ,  trust_remote_code=_A)

					__snake_case      :							Union[str, Any]						=			AutoImageProcessor.from_pretrained(
					    'hf-internal-testing/test_dynamic_image_processor'     ,  trust_remote_code=_A)
					self.assertEqual(image_processor.__class__.__name__     ,  'NewImageProcessor')

					# Test image processor can be reloaded.
					with tempfile.TemporaryDirectory() as tmp_dir:
						image_processor.save_pretrained(_A)
						__snake_case      :							Optional[int]						=			AutoImageProcessor.from_pretrained(_A     ,  trust_remote_code=_A)
					self.assertEqual(reloaded_image_processor.__class__.__name__     ,  'NewImageProcessor')



				def  _lowercase					(self    :				int)   ->     Optional[int]:
					try:
						AutoConfig.register('custom'     ,  _A)
						AutoImageProcessor.register(_A     ,  _A)
						# Trying to register something existing in the Transformers library will raise an error
						with self.assertRaises(_A):
							AutoImageProcessor.register(_A     ,  _A)

						with tempfile.TemporaryDirectory() as tmpdirname:
							__snake_case      :							Tuple						=			Path(_A) / 'preprocessor_config.json'
							__snake_case      :							Dict						=			Path(_A) / 'config.json'
							json.dump(
							    {'feature_extractor_type': 'CLIPFeatureExtractor', 'processor_class': 'CLIPProcessor'}     ,  open(_A     ,  'w')     ,  )
							json.dump({'model_type': 'clip'}     ,  open(_A     ,  'w'))

							__snake_case      :							Tuple						=			CustomImageProcessor.from_pretrained(_A)

						# Now that the config is registered, it can be used as any other config with the auto-API
						with tempfile.TemporaryDirectory() as tmp_dir:
							image_processor.save_pretrained(_A)
							__snake_case      :							Tuple						=			AutoImageProcessor.from_pretrained(_A)
							self.assertIsInstance(_A     ,  _A)

					finally:
						if "custom" in CONFIG_MAPPING._extra_content:
							del CONFIG_MAPPING._extra_content["custom"]
						if CustomConfig in IMAGE_PROCESSOR_MAPPING._extra_content:
							del IMAGE_PROCESSOR_MAPPING._extra_content[CustomConfig]






				def  _lowercase					(self    :				List[Any])   ->     Tuple:

					class 		UpperCamelCase      (   lowercase						):
									UpperCAmelCase    :				str   =		True

					try:
						AutoConfig.register('custom'     ,  _A)
						AutoImageProcessor.register(_A     ,  _A)
						# If remote code is not set, the default is to use local
						__snake_case      :							Tuple						=			AutoImageProcessor.from_pretrained('hf-internal-testing/test_dynamic_image_processor')
						self.assertEqual(image_processor.__class__.__name__     ,  'NewImageProcessor')
						self.assertTrue(image_processor.is_local)

						# If remote code is disabled, we load the local one.
						__snake_case      :							Optional[int]						=			AutoImageProcessor.from_pretrained(
						    'hf-internal-testing/test_dynamic_image_processor'     ,  trust_remote_code=_A)
						self.assertEqual(image_processor.__class__.__name__     ,  'NewImageProcessor')
						self.assertTrue(image_processor.is_local)

						# If remote is enabled, we load from the Hub
						__snake_case      :							List[Any]						=			AutoImageProcessor.from_pretrained(
						    'hf-internal-testing/test_dynamic_image_processor'     ,  trust_remote_code=_A)
						self.assertEqual(image_processor.__class__.__name__     ,  'NewImageProcessor')
						self.assertTrue(not hasattr(_A     ,  'is_local'))

					finally:
						if "custom" in CONFIG_MAPPING._extra_content:
							del CONFIG_MAPPING._extra_content["custom"]
						if CustomConfig in IMAGE_PROCESSOR_MAPPING._extra_content:
							del IMAGE_PROCESSOR_MAPPING._extra_content[CustomConfig]





