





"""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						=			get_tests_dir('''fixtures/dummy_feature_extractor_config.json''')
__UpperCAmelCase						=			get_tests_dir('''fixtures/vocab.json''')
__UpperCAmelCase						=			get_tests_dir('''fixtures''')



class 					__UpperCAmelCase							(						unittest.TestCase  ):
				__lowerCamelCase				:	Optional[int]				     =				["[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]", "bla", "blou"]
				def       UpperCAmelCase      (		self    :			int    )	->	Any:



							'''simple docstring'''



							a__  :	List[str]		 =			0
				def       UpperCAmelCase      (		self    :			List[str]    )	->	int:



							'''simple docstring'''



							a__  :	Union[str, Any]		 =			AutoProcessor.from_pretrained("facebook/wav2vec2-base-960h"    )
							self.assertIsInstance(a_	, a_    )
				def       UpperCAmelCase      (		self    :			Optional[Any]    )	->	Optional[int]:



							'''simple docstring'''



							with tempfile.TemporaryDirectory() as tmpdirname:
										a__  :	Dict		 =			WavaVecaConfig()
										a__  :	Optional[int]		 =			AutoProcessor.from_pretrained("facebook/wav2vec2-base-960h"    )

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

										a__  :	Optional[Any]		 =			AutoProcessor.from_pretrained(a_    )

							self.assertIsInstance(a_	, a_    )
				def       UpperCAmelCase      (		self    :			str    )	->	str:



							'''simple docstring'''



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

										a__  :	int		 =			AutoProcessor.from_pretrained(a_    )

							self.assertIsInstance(a_	, a_    )
				def       UpperCAmelCase      (		self    :			List[str]    )	->	List[Any]:



							'''simple docstring'''



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

										a__  :	List[Any]		 =			WavaVecaProcessor(a_	, a_    )

										# save in new folder
										processor.save_pretrained(a_    )

										# drop `processor_class` in tokenizer
										with open(os.path.join(a_	, a_    )	, "r"    ) as f:
													a__  :	Optional[Any]		 =			json.load(a_    )
													config_dict.pop("processor_class"    )

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

										a__  :	List[str]		 =			AutoProcessor.from_pretrained(a_    )

							self.assertIsInstance(a_	, a_    )
				def       UpperCAmelCase      (		self    :			Dict    )	->	Optional[int]:



							'''simple docstring'''



							with tempfile.TemporaryDirectory() as tmpdirname:
										a__  :	Optional[int]		 =			WavaVecaFeatureExtractor()
										a__  :	Optional[int]		 =			AutoTokenizer.from_pretrained("facebook/wav2vec2-base-960h"    )

										a__  :	int		 =			WavaVecaProcessor(a_	, a_    )

										# save in new folder
										processor.save_pretrained(a_    )

										# drop `processor_class` in feature extractor
										with open(os.path.join(a_	, a_    )	, "r"    ) as f:
													a__  :	List[str]		 =			json.load(a_    )
													config_dict.pop("processor_class"    )

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

										a__  :	str		 =			AutoProcessor.from_pretrained(a_    )

							self.assertIsInstance(a_	, a_    )
				def       UpperCAmelCase      (		self    :			Optional[Any]    )	->	List[str]:



							'''simple docstring'''



							with tempfile.TemporaryDirectory() as tmpdirname:
										a__  :	str		 =			WavaVecaConfig(processor_class="Wav2Vec2Processor"    )
										model_config.save_pretrained(a_    )
										# copy relevant files
										copyfile(a_	, os.path.join(a_	, "vocab.json"    )    )
										# create emtpy sample processor
										with open(os.path.join(a_	, a_    )	, "w"    ) as f:
													f.write("{}"    )

										a__  :	Optional[Any]		 =			AutoProcessor.from_pretrained(a_    )

							self.assertIsInstance(a_	, a_    )
				def       UpperCAmelCase      (		self    :			Any    )	->	Any:



							'''simple docstring'''



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

							a__  :	Dict		 =			AutoProcessor.from_pretrained("hf-internal-testing/test_dynamic_processor"	, trust_remote_code=a_    )
							self.assertTrue(processor.special_attribute_present    )
							self.assertEqual(processor.__class__.__name__	, "NewProcessor"    )

							a__  :	List[str]		 =			processor.feature_extractor
							self.assertTrue(feature_extractor.special_attribute_present    )
							self.assertEqual(feature_extractor.__class__.__name__	, "NewFeatureExtractor"    )

							a__  :	Tuple		 =			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
										a__  :	str		 =			AutoProcessor.from_pretrained(
										    "hf-internal-testing/test_dynamic_processor"	, trust_remote_code=a_	, use_fast=a_    )
										a__  :	int		 =			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       UpperCAmelCase      (		self    :			int    )	->	Dict:



							'''simple docstring'''



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

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

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

										a__  :	Optional[int]		 =			CustomProcessor(a_	, a_    )

										with tempfile.TemporaryDirectory() as tmp_dir:
													processor.save_pretrained(a_    )
													a__  :	Any		 =			AutoProcessor.from_pretrained(a_    )
													self.assertIsInstance(a_	, a_    )

							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       UpperCAmelCase      (		self    :			List[str]    )	->	Any:



							'''simple docstring'''



							class 					__UpperCAmelCase							(						_UpperCamelCase  ):
											__lowerCamelCase				:	Union[str, Any]				     =				False



							class 					__UpperCAmelCase							(						_UpperCamelCase  ):
											__lowerCamelCase				:	Union[str, Any]				     =				False



							class 					__UpperCAmelCase							(						_UpperCamelCase  ):
											__lowerCamelCase				:	int				     =				"AutoFeatureExtractor"
											__lowerCamelCase				:	List[Any]				     =				"AutoTokenizer"
											__lowerCamelCase				:	Optional[Any]				     =				False

							try:
										AutoConfig.register("custom"	, a_    )
										AutoFeatureExtractor.register(a_	, a_    )
										AutoTokenizer.register(a_	, slow_tokenizer_class=a_    )
										AutoProcessor.register(a_	, a_    )
										# If remote code is not set, the default is to use local classes.
										a__  :	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.
										a__  :	Dict		 =			AutoProcessor.from_pretrained(
										    "hf-internal-testing/test_dynamic_processor"	, trust_remote_code=a_    )
										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.
										a__  :	str		 =			AutoProcessor.from_pretrained(
										    "hf-internal-testing/test_dynamic_processor"	, trust_remote_code=a_    )
										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       UpperCAmelCase      (		self    :			Optional[int]    )	->	int:



							'''simple docstring'''



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






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



							'''simple docstring'''



							a__  :	Optional[Any]		 =			AutoProcessor.from_pretrained("hf-internal-testing/tiny-random-convnext"    )
							self.assertEqual(processor.__class__.__name__	, "ConvNextImageProcessor"    )



@is_staging_test
class 					__UpperCAmelCase							(						unittest.TestCase  ):
				__lowerCamelCase				:	Dict				     =				["[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]", "bla", "blou"]
				@classmethod
				def       UpperCAmelCase      (		cls    :			Union[str, Any]    )	->	Union[str, Any]:



							'''simple docstring'''



							a__  :	Union[str, Any]		 =			TOKEN
							HfFolder.save_token(a_    )
				@classmethod
				def       UpperCAmelCase      (		cls    :			List[Any]    )	->	Optional[Any]:



							'''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       UpperCAmelCase      (		self    :			List[str]    )	->	Any:



							'''simple docstring'''



							a__  :	int		 =			WavaVecaProcessor.from_pretrained(a_    )
							with tempfile.TemporaryDirectory() as tmp_dir:
										processor.save_pretrained(
										    os.path.join(a_	, "test-processor"    )	, push_to_hub=a_	, use_auth_token=self._token    )

										a__  :	Optional[Any]		 =			WavaVecaProcessor.from_pretrained(F"{USER}/test-processor"    )
										for k, v in processor.feature_extractor.__dict__.items():
													self.assertEqual(a_	, getattr(new_processor.feature_extractor	, a_    )    )
										self.assertDictEqual(new_processor.tokenizer.get_vocab()	, processor.tokenizer.get_vocab()    )
				def       UpperCAmelCase      (		self    :			Dict    )	->	Optional[Any]:



							'''simple docstring'''



							a__  :	int		 =			WavaVecaProcessor.from_pretrained(a_    )

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

										a__  :	Optional[Any]		 =			WavaVecaProcessor.from_pretrained("valid_org/test-processor-org"    )
										for k, v in processor.feature_extractor.__dict__.items():
													self.assertEqual(a_	, getattr(new_processor.feature_extractor	, a_    )    )
										self.assertDictEqual(new_processor.tokenizer.get_vocab()	, processor.tokenizer.get_vocab()    )






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



							'''simple docstring'''



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

							a__  :	Optional[Any]		 =			CustomFeatureExtractor.from_pretrained(a_    )

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

							a__  :	List[Any]		 =			CustomProcessor(a_	, a_    )

							with tempfile.TemporaryDirectory() as tmp_dir:
										create_repo(F"{USER}/test-dynamic-processor"	, token=self._token    )
										a__  :	str		 =			Repository(a_	, clone_from=F"{USER}/test-dynamic-processor"	, token=self._token    )
										processor.save_pretrained(a_    )

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

										repo.push_to_hub()

							a__  :	Optional[int]		 =			AutoProcessor.from_pretrained(F"{USER}/test-dynamic-processor"	, trust_remote_code=a_    )
							# 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 json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_torch_available, is_vision_available


if is_torch_available():
		import torch


if is_vision_available():
		from PIL import Image

		from transformers import MgpstrProcessor, ViTImageProcessor



@require_torch
@require_vision
class 					__UpperCAmelCase							(						unittest.TestCase  ):
				__lowerCamelCase				:	int				     =				ViTImageProcessor if is_vision_available() else None
				@property
				def       UpperCAmelCase      (		self    :			List[str]    )	->	List[str]:



							'''simple docstring'''



							return self.image_processor_tester.prepare_image_processor_dict()
				def       UpperCAmelCase      (		self    :			Union[str, Any]    )	->	Optional[Any]:



							'''simple docstring'''



							a__  :	Optional[Any]		 =			(3, 32, 1_28)
							a__  :	Any		 =			tempfile.mkdtemp()

							# fmt: off
							a__  :	str		 =			["[GO]", "[s]", "0", "1", "2", "3", "4", "5", "6", "7", "8", "9", "a", "b", "c", "d", "e", "f", "g", "h", "i", "j", "k", "l", "m", "n", "o", "p", "q", "r", "s", "t", "u", "v", "w", "x", "y", "z"]
							# fmt: on
							a__  :	List[Any]		 =			dict(zip(a_	, range(len(a_    )    )    )    )

							a__  :	int		 =			os.path.join(self.tmpdirname	, VOCAB_FILES_NAMES["vocab_file"]    )
							with open(self.vocab_file	, "w"	, encoding="utf-8"    ) as fp:
										fp.write(json.dumps(a_    ) + "\n"    )

							a__  :	Any		 =			{
							    "do_normalize": False,
							    "do_resize": True,
							    "image_processor_type": "ViTImageProcessor",
							    "resample": 3,
							    "size": {"height": 32, "width": 1_28},
							}
							a__  :	Tuple		 =			os.path.join(self.tmpdirname	, a_    )
							with open(self.image_processor_file	, "w"	, encoding="utf-8"    ) as fp:
										json.dump(a_	, a_    )
				def       UpperCAmelCase      (		self    :			List[str]	, **a_    :			List[str]    )	->	Union[str, Any]:



							'''simple docstring'''



							return MgpstrTokenizer.from_pretrained(self.tmpdirname	, **a_    )
				def       UpperCAmelCase      (		self    :			List[Any]	, **a_    :			Union[str, Any]    )	->	Dict:



							'''simple docstring'''



							return ViTImageProcessor.from_pretrained(self.tmpdirname	, **a_    )
				def       UpperCAmelCase      (		self    :			Dict    )	->	int:



							'''simple docstring'''



							shutil.rmtree(self.tmpdirname    )
				def       UpperCAmelCase      (		self    :			List[str]    )	->	Any:



							'''simple docstring'''



							a__  :	Any		 =			np.random.randint(2_55	, size=(3, 30, 4_00)	, dtype=np.uinta    )

							a__  :	List[str]		 =			Image.fromarray(np.moveaxis(a_	, 0	, -1    )    )

							return image_input
				def       UpperCAmelCase      (		self    :			Union[str, Any]    )	->	Optional[Any]:



							'''simple docstring'''



							a__  :	List[Any]		 =			self.get_tokenizer()
							a__  :	Any		 =			self.get_image_processor()

							a__  :	Optional[int]		 =			MgpstrProcessor(tokenizer=a_	, image_processor=a_    )
							processor.save_pretrained(self.tmpdirname    )
							a__  :	Optional[Any]		 =			MgpstrProcessor.from_pretrained(self.tmpdirname	, use_fast=a_    )

							self.assertEqual(processor.char_tokenizer.get_vocab()	, tokenizer.get_vocab()    )
							self.assertIsInstance(processor.char_tokenizer	, a_    )

							self.assertEqual(processor.image_processor.to_json_string()	, image_processor.to_json_string()    )
							self.assertIsInstance(processor.image_processor	, a_    )
				def       UpperCAmelCase      (		self    :			Union[str, Any]    )	->	str:



							'''simple docstring'''



							a__  :	Optional[Any]		 =			self.get_tokenizer()
							a__  :	Tuple		 =			self.get_image_processor()

							a__  :	Optional[int]		 =			MgpstrProcessor(tokenizer=a_	, image_processor=a_    )
							processor.save_pretrained(self.tmpdirname    )

							a__  :	Union[str, Any]		 =			self.get_tokenizer(bos_token="(BOS)"	, eos_token="(EOS)"    )
							a__  :	Any		 =			self.get_image_processor(do_normalize=a_	, padding_value=1.0    )

							a__  :	Dict		 =			MgpstrProcessor.from_pretrained(
							    self.tmpdirname	, bos_token="(BOS)"	, eos_token="(EOS)"	, do_normalize=a_	, padding_value=1.0    )

							self.assertEqual(processor.char_tokenizer.get_vocab()	, tokenizer_add_kwargs.get_vocab()    )
							self.assertIsInstance(processor.char_tokenizer	, a_    )

							self.assertEqual(processor.image_processor.to_json_string()	, image_processor_add_kwargs.to_json_string()    )
							self.assertIsInstance(processor.image_processor	, a_    )
				def       UpperCAmelCase      (		self    :			Optional[int]    )	->	Dict:



							'''simple docstring'''



							a__  :	str		 =			self.get_image_processor()
							a__  :	Union[str, Any]		 =			self.get_tokenizer()

							a__  :	Any		 =			MgpstrProcessor(tokenizer=a_	, image_processor=a_    )

							a__  :	List[str]		 =			self.prepare_image_inputs()

							a__  :	List[Any]		 =			image_processor(a_	, return_tensors="np"    )
							a__  :	Optional[Any]		 =			processor(images=a_	, return_tensors="np"    )

							for key in input_image_proc.keys():
										self.assertAlmostEqual(input_image_proc[key].sum()	, input_processor[key].sum()	, delta=1E-2    )
				def       UpperCAmelCase      (		self    :			Tuple    )	->	int:



							'''simple docstring'''



							a__  :	Optional[Any]		 =			self.get_image_processor()
							a__  :	List[Any]		 =			self.get_tokenizer()

							a__  :	int		 =			MgpstrProcessor(tokenizer=a_	, image_processor=a_    )

							a__  :	List[str]		 =			"test"

							a__  :	Any		 =			processor(text=a_    )

							a__  :	Tuple		 =			tokenizer(a_    )
							for key in encoded_tok.keys():
										self.assertListEqual(encoded_tok[key]	, encoded_processor[key]    )
				def       UpperCAmelCase      (		self    :			Optional[int]    )	->	Optional[int]:



							'''simple docstring'''



							a__  :	Tuple		 =			self.get_image_processor()
							a__  :	List[str]		 =			self.get_tokenizer()

							a__  :	List[Any]		 =			MgpstrProcessor(tokenizer=a_	, image_processor=a_    )

							a__  :	str		 =			"test"
							a__  :	str		 =			self.prepare_image_inputs()

							a__  :	Any		 =			processor(text=a_	, images=a_    )

							self.assertListEqual(list(inputs.keys()    )	, ["pixel_values", "labels"]    )

							# test if it raises when no input is passed
							with pytest.raises(a_    ):
										processor()
				def       UpperCAmelCase      (		self    :			List[str]    )	->	int:



							'''simple docstring'''



							a__  :	Any		 =			self.get_image_processor()
							a__  :	Tuple		 =			self.get_tokenizer()

							a__  :	Dict		 =			MgpstrProcessor(tokenizer=a_	, image_processor=a_    )

							a__  :	Optional[Any]		 =			[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9], [3, 4, 3, 1, 1, 8, 9]]

							a__  :	Any		 =			processor.char_decode(a_    )
							a__  :	str		 =			tokenizer.batch_decode(a_    )
							a__  :	Union[str, Any]		 =			[seq.replace(" "	, ""    ) for seq in decoded_tok]

							self.assertListEqual(a_	, a_    )
				def       UpperCAmelCase      (		self    :			List[Any]    )	->	Dict:



							'''simple docstring'''



							a__  :	List[str]		 =			self.get_image_processor()
							a__  :	Any		 =			self.get_tokenizer()

							a__  :	Any		 =			MgpstrProcessor(tokenizer=a_	, image_processor=a_    )

							a__  :	str		 =			None
							a__  :	Optional[Any]		 =			self.prepare_image_inputs()

							a__  :	Optional[int]		 =			processor(text=a_	, images=a_    )

							self.assertListEqual(list(inputs.keys()    )	, processor.model_input_names    )






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



							'''simple docstring'''



							a__  :	List[Any]		 =			self.get_image_processor()
							a__  :	List[str]		 =			self.get_tokenizer()

							a__  :	Optional[Any]		 =			MgpstrProcessor(tokenizer=a_	, image_processor=a_    )

							a__  :	List[str]		 =			torch.randn(1	, 27	, 38    )
							a__  :	Tuple		 =			torch.randn(1	, 27	, 5_02_57    )
							a__  :	List[Any]		 =			torch.randn(1	, 27	, 3_05_22    )

							a__  :	Optional[Any]		 =			processor.batch_decode([char_input, bpe_input, wp_input]    )

							self.assertListEqual(list(results.keys()    )	, ["generated_text", "scores", "char_preds", "bpe_preds", "wp_preds"]    )