



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     _A       (	unittest.TestCase    ):
       UpperCamelCase__  :    str											=						ViTImageProcessor if is_vision_available() else None



       @property
       def 		_lowerCamelCase			(		self							:     List[Any]):


           '''simple docstring'''

           return self.image_processor_tester.prepare_image_processor_dict()



       def 		_lowerCamelCase			(		self							:     List[Any]):


           '''simple docstring'''

           __a						       =				(3, 32, 128)
           __a						       =				tempfile.mkdtemp()

           # fmt: off
           __a						       =				['''[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						       =				dict(zip(__SCREAMING_SNAKE_CASE     ,		range(len(__SCREAMING_SNAKE_CASE))))

           __a						       =				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(__SCREAMING_SNAKE_CASE) + '''\n''')

           __a						       =				{
               '''do_normalize''': False,
               '''do_resize''': True,
               '''image_processor_type''': '''ViTImageProcessor''',
               '''resample''': 3,
               '''size''': {'''height''': 32, '''width''': 128},
           }
           __a						       =				os.path.join(self.tmpdirname     ,		__SCREAMING_SNAKE_CASE)
           with open(self.image_processor_file     ,		'''w'''     ,		encoding='''utf-8''') as fp:
               json.dump(__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE)



       def 		_lowerCamelCase			(		self							:     Optional[Any]     ,		**__SCREAMING_SNAKE_CASE							:     str):


           '''simple docstring'''

           return MgpstrTokenizer.from_pretrained(self.tmpdirname     ,		**__SCREAMING_SNAKE_CASE)



       def 		_lowerCamelCase			(		self							:     str     ,		**__SCREAMING_SNAKE_CASE							:     Any):


           '''simple docstring'''

           return ViTImageProcessor.from_pretrained(self.tmpdirname     ,		**__SCREAMING_SNAKE_CASE)



       def 		_lowerCamelCase			(		self							:     str):


           '''simple docstring'''

           shutil.rmtree(self.tmpdirname)



       def 		_lowerCamelCase			(		self							:     List[Any]):


           '''simple docstring'''

           __a						       =				np.random.randint(255     ,		size=(3, 30, 400)     ,		dtype=np.uinta)

           __a						       =				Image.fromarray(np.moveaxis(__SCREAMING_SNAKE_CASE     ,		0     ,		-1))

           return image_input



       def 		_lowerCamelCase			(		self							:     Union[str, Any]):


           '''simple docstring'''

           __a						       =				self.get_tokenizer()
           __a						       =				self.get_image_processor()

           __a						       =				MgpstrProcessor(tokenizer=__SCREAMING_SNAKE_CASE     ,		image_processor=__SCREAMING_SNAKE_CASE)
           processor.save_pretrained(self.tmpdirname)
           __a						       =				MgpstrProcessor.from_pretrained(self.tmpdirname     ,		use_fast=__SCREAMING_SNAKE_CASE)

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

           self.assertEqual(processor.image_processor.to_json_string()     ,		image_processor.to_json_string())
           self.assertIsInstance(processor.image_processor     ,		__SCREAMING_SNAKE_CASE)



       def 		_lowerCamelCase			(		self							:     Union[str, Any]):


           '''simple docstring'''

           __a						       =				self.get_tokenizer()
           __a						       =				self.get_image_processor()

           __a						       =				MgpstrProcessor(tokenizer=__SCREAMING_SNAKE_CASE     ,		image_processor=__SCREAMING_SNAKE_CASE)
           processor.save_pretrained(self.tmpdirname)

           __a						       =				self.get_tokenizer(bos_token='''(BOS)'''     ,		eos_token='''(EOS)''')
           __a						       =				self.get_image_processor(do_normalize=__SCREAMING_SNAKE_CASE     ,		padding_value=1.0)

           __a						       =				MgpstrProcessor.from_pretrained(
               self.tmpdirname     ,		bos_token='''(BOS)'''     ,		eos_token='''(EOS)'''     ,		do_normalize=__SCREAMING_SNAKE_CASE     ,		padding_value=1.0)

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

           self.assertEqual(processor.image_processor.to_json_string()     ,		image_processor_add_kwargs.to_json_string())
           self.assertIsInstance(processor.image_processor     ,		__SCREAMING_SNAKE_CASE)



       def 		_lowerCamelCase			(		self							:     int):


           '''simple docstring'''

           __a						       =				self.get_image_processor()
           __a						       =				self.get_tokenizer()

           __a						       =				MgpstrProcessor(tokenizer=__SCREAMING_SNAKE_CASE     ,		image_processor=__SCREAMING_SNAKE_CASE)

           __a						       =				self.prepare_image_inputs()

           __a						       =				image_processor(__SCREAMING_SNAKE_CASE     ,		return_tensors='''np''')
           __a						       =				processor(images=__SCREAMING_SNAKE_CASE     ,		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 		_lowerCamelCase			(		self							:     Dict):


           '''simple docstring'''

           __a						       =				self.get_image_processor()
           __a						       =				self.get_tokenizer()

           __a						       =				MgpstrProcessor(tokenizer=__SCREAMING_SNAKE_CASE     ,		image_processor=__SCREAMING_SNAKE_CASE)

           __a						       =				'''test'''

           __a						       =				processor(text=__SCREAMING_SNAKE_CASE)

           __a						       =				tokenizer(__SCREAMING_SNAKE_CASE)
           for key in encoded_tok.keys():
               self.assertListEqual(encoded_tok[key]     ,		encoded_processor[key])



       def 		_lowerCamelCase			(		self							:     Union[str, Any]):


           '''simple docstring'''

           __a						       =				self.get_image_processor()
           __a						       =				self.get_tokenizer()

           __a						       =				MgpstrProcessor(tokenizer=__SCREAMING_SNAKE_CASE     ,		image_processor=__SCREAMING_SNAKE_CASE)

           __a						       =				'''test'''
           __a						       =				self.prepare_image_inputs()

           __a						       =				processor(text=__SCREAMING_SNAKE_CASE     ,		images=__SCREAMING_SNAKE_CASE)

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

           # test if it raises when no input is passed
           with pytest.raises(__SCREAMING_SNAKE_CASE):
               processor()



       def 		_lowerCamelCase			(		self							:     Optional[Any]):


           '''simple docstring'''

           __a						       =				self.get_image_processor()
           __a						       =				self.get_tokenizer()

           __a						       =				MgpstrProcessor(tokenizer=__SCREAMING_SNAKE_CASE     ,		image_processor=__SCREAMING_SNAKE_CASE)

           __a						       =				[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9], [3, 4, 3, 1, 1, 8, 9]]

           __a						       =				processor.char_decode(__SCREAMING_SNAKE_CASE)
           __a						       =				tokenizer.batch_decode(__SCREAMING_SNAKE_CASE)
           __a						       =				[seq.replace(''' '''     ,		'''''') for seq in decoded_tok]

           self.assertListEqual(__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE)



       def 		_lowerCamelCase			(		self							:     Optional[Any]):


           '''simple docstring'''

           __a						       =				self.get_image_processor()
           __a						       =				self.get_tokenizer()

           __a						       =				MgpstrProcessor(tokenizer=__SCREAMING_SNAKE_CASE     ,		image_processor=__SCREAMING_SNAKE_CASE)

           __a						       =				None
           __a						       =				self.prepare_image_inputs()

           __a						       =				processor(text=__SCREAMING_SNAKE_CASE     ,		images=__SCREAMING_SNAKE_CASE)

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





       def 		_lowerCamelCase			(		self							:     Union[str, Any]):


           '''simple docstring'''

           __a						       =				self.get_image_processor()
           __a						       =				self.get_tokenizer()

           __a						       =				MgpstrProcessor(tokenizer=__SCREAMING_SNAKE_CASE     ,		image_processor=__SCREAMING_SNAKE_CASE)

           __a						       =				torch.randn(1     ,		27     ,		38)
           __a						       =				torch.randn(1     ,		27     ,		50_257)
           __a						       =				torch.randn(1     ,		27     ,		30_522)

           __a						       =				processor.batch_decode([char_input, bpe_input, wp_input])

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






'''simple docstring'''





import os
import shutil
import tempfile
from unittest import TestCase
from unittest.mock import patch

import numpy as np
from datasets import Dataset

from transformers.models.realm.configuration_realm import RealmConfig
from transformers.models.realm.retrieval_realm import _REALM_BLOCK_RECORDS_FILENAME, RealmRetriever
from transformers.models.realm.tokenization_realm import VOCAB_FILES_NAMES, RealmTokenizer




class        __A      ( UpperCamelCase__	):



    def 					_lowercase						(self    :    Optional[Any] ):
         UpperCAmelCase_           =   tempfile.mkdtemp()
         UpperCAmelCase_           =   5

         # Realm tok
         UpperCAmelCase_           =   [
             "[UNK]",
             "[CLS]",
             "[SEP]",
             "[PAD]",
             "[MASK]",
             "test",
             "question",
             "this",
             "is",
             "the",
             "first",
             "second",
             "third",
             "fourth",
             "fifth",
             "record",
             "want",
             "##want",
             "##ed",
             "wa",
             "un",
             "runn",
             "##ing",
             ",",
             "low",
             "lowest",
         ]
         UpperCAmelCase_           =   os.path.join(self.tmpdirname   ,			"realm_tokenizer" )
         os.makedirs(__a   ,			exist_ok=__a )
         UpperCAmelCase_           =   os.path.join(__a   ,			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] ) )

         UpperCAmelCase_           =   os.path.join(self.tmpdirname   ,			"realm_block_records" )
         os.makedirs(__a   ,			exist_ok=__a )



    def 					_lowercase						(self    :    Optional[Any] ):
         return RealmTokenizer.from_pretrained(os.path.join(self.tmpdirname   ,			"realm_tokenizer" ) )



    def 					_lowercase						(self    :    Any ):
         shutil.rmtree(self.tmpdirname )



    def 					_lowercase						(self    :    List[Any] ):
         UpperCAmelCase_           =   RealmConfig(num_block_records=self.num_block_records )
         return config



    def 					_lowercase						(self    :    List[str] ):
         UpperCAmelCase_           =   Dataset.from_dict(
             {
                 "id": ["0", "1"],
                 "question": ["foo", "bar"],
                 "answers": [["Foo", "Bar"], ["Bar"]],
             } )
         return dataset



    def 					_lowercase						(self    :    Any ):
         UpperCAmelCase_           =   np.array(
             [
                 B"This is the first record",
                 B"This is the second record",
                 B"This is the third record",
                 B"This is the fourth record",
                 B"This is the fifth record",
                 B"This is a longer longer longer record",
             ]   ,			dtype=__a   ,			)
         return block_records



    def 					_lowercase						(self    :    Union[str, Any] ):
         UpperCAmelCase_           =   RealmRetriever(
             block_records=self.get_dummy_block_records()   ,			tokenizer=self.get_tokenizer()   ,			)
         return retriever



    def 					_lowercase						(self    :    int ):
         UpperCAmelCase_           =   self.get_config()
         UpperCAmelCase_           =   self.get_dummy_retriever()
         UpperCAmelCase_           =   retriever.tokenizer

         UpperCAmelCase_           =   np.array([0, 3]   ,			dtype="long" )
         UpperCAmelCase_           =   tokenizer(["Test question"] ).input_ids
         UpperCAmelCase_           =   tokenizer(
             ["the fourth"]   ,			add_special_tokens=__a   ,			return_token_type_ids=__a   ,			return_attention_mask=__a   ,			).input_ids
         UpperCAmelCase_           =   config.reader_seq_len

         UpperCAmelCase_    , UpperCAmelCase_    , UpperCAmelCase_    , UpperCAmelCase_           =   retriever(
             __a   ,			__a   ,			answer_ids=__a   ,			max_length=__a   ,			return_tensors="np" )

         self.assertEqual(len(__a )   ,			2 )
         self.assertEqual(len(__a )   ,			2 )
         self.assertEqual(len(__a )   ,			2 )
         self.assertEqual(concat_inputs.input_ids.shape   ,			(2, 10) )
         self.assertEqual(concat_inputs.attention_mask.shape   ,			(2, 10) )
         self.assertEqual(concat_inputs.token_type_ids.shape   ,			(2, 10) )
         self.assertEqual(concat_inputs.special_tokens_mask.shape   ,			(2, 10) )
         self.assertEqual(
             tokenizer.convert_ids_to_tokens(concat_inputs.input_ids[0] )   ,			["[CLS]", "test", "question", "[SEP]", "this", "is", "the", "first", "record", "[SEP]"]   ,			)
         self.assertEqual(
             tokenizer.convert_ids_to_tokens(concat_inputs.input_ids[1] )   ,			["[CLS]", "test", "question", "[SEP]", "this", "is", "the", "fourth", "record", "[SEP]"]   ,			)



    def 					_lowercase						(self    :    List[Any] ):
         UpperCAmelCase_           =   self.get_config()
         UpperCAmelCase_           =   self.get_dummy_retriever()
         UpperCAmelCase_           =   retriever.tokenizer

         UpperCAmelCase_           =   np.array([0, 3, 5]   ,			dtype="long" )
         UpperCAmelCase_           =   tokenizer(["Test question"] ).input_ids
         UpperCAmelCase_           =   tokenizer(
             ["the fourth", "longer longer"]   ,			add_special_tokens=__a   ,			return_token_type_ids=__a   ,			return_attention_mask=__a   ,			).input_ids
         UpperCAmelCase_           =   config.reader_seq_len

         UpperCAmelCase_    , UpperCAmelCase_    , UpperCAmelCase_    , UpperCAmelCase_           =   retriever(
             __a   ,			__a   ,			answer_ids=__a   ,			max_length=__a   ,			return_tensors="np" )

         self.assertEqual([False, True, True]   ,			__a )
         self.assertEqual([[-1, -1, -1], [6, -1, -1], [6, 7, 8]]   ,			__a )
         self.assertEqual([[-1, -1, -1], [7, -1, -1], [7, 8, 9]]   ,			__a )






    def 					_lowercase						(self    :    Optional[Any] ):
         UpperCAmelCase_           =   self.get_dummy_retriever()
         retriever.save_pretrained(os.path.join(self.tmpdirname   ,			"realm_block_records" ) )

         # Test local path
         UpperCAmelCase_           =   retriever.from_pretrained(os.path.join(self.tmpdirname   ,			"realm_block_records" ) )
         self.assertEqual(retriever.block_records[0]   ,			B"This is the first record" )

         # Test mocked remote path
         with patch("transformers.models.realm.retrieval_realm.hf_hub_download" ) as mock_hf_hub_download:
              UpperCAmelCase_           =   os.path.join(
                  os.path.join(self.tmpdirname   ,			"realm_block_records" )   ,			_REALM_BLOCK_RECORDS_FILENAME )
              UpperCAmelCase_           =   RealmRetriever.from_pretrained("google/realm-cc-news-pretrained-openqa" )

         self.assertEqual(retriever.block_records[0]   ,			B"This is the first record" )




