

import json
import os
import shutil
import tempfile
from unittest import TestCase

from transformers import BartTokenizer, BartTokenizerFast, DPRQuestionEncoderTokenizer, DPRQuestionEncoderTokenizerFast
from transformers.models.bart.configuration_bart import BartConfig
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES as DPR_VOCAB_FILES_NAMES
from transformers.models.dpr.configuration_dpr import DPRConfig
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES as BART_VOCAB_FILES_NAMES
from transformers.testing_utils import require_faiss, require_tokenizers, require_torch, slow
from transformers.utils import is_datasets_available, is_faiss_available, is_torch_available


if is_torch_available() and is_datasets_available() and is_faiss_available():
 from transformers.models.rag.configuration_rag import RagConfig
 from transformers.models.rag.tokenization_rag import RagTokenizer







@require_faiss
@require_torch
class   __lowercase      (_UpperCAmelCase ):
 def 	UpperCamelCase__  (							self					)					->str:



       '''simple docstring'''





       __lowerCAmelCase					:						Optional[Any]					=					tempfile.mkdtemp()
       __lowerCAmelCase					:						Dict					=					8

       # DPR tok
       __lowerCAmelCase					:						Union[str, Any]					=					[
           '''[UNK]''',
           '''[CLS]''',
           '''[SEP]''',
           '''[PAD]''',
           '''[MASK]''',
           '''want''',
           '''##want''',
           '''##ed''',
           '''wa''',
           '''un''',
           '''runn''',
           '''##ing''',
           ''',''',
           '''low''',
           '''lowest''',
       ]
       __lowerCAmelCase					:						Dict					=					os.path.join(self.tmpdirname      ,							'''dpr_tokenizer'''					)
       os.makedirs(A_      ,							exist_ok=A_					)
       __lowerCAmelCase					:						List[str]					=					os.path.join(A_      ,							DPR_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]					)					)

       # BART tok
       __lowerCAmelCase					:						int					=					[
           '''l''',
           '''o''',
           '''w''',
           '''e''',
           '''r''',
           '''s''',
           '''t''',
           '''i''',
           '''d''',
           '''n''',
           '''\u0120''',
           '''\u0120l''',
           '''\u0120n''',
           '''\u0120lo''',
           '''\u0120low''',
           '''er''',
           '''\u0120lowest''',
           '''\u0120newer''',
           '''\u0120wider''',
           '''<unk>''',
       ]
       __lowerCAmelCase					:						List[str]					=					dict(zip(A_      ,							range(len(A_					)					)					)					)
       __lowerCAmelCase					:						Optional[int]					=					['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
       __lowerCAmelCase					:						Union[str, Any]					=					{'''unk_token''': '''<unk>'''}

       __lowerCAmelCase					:						int					=					os.path.join(self.tmpdirname      ,							'''bart_tokenizer'''					)
       os.makedirs(A_      ,							exist_ok=A_					)
       __lowerCAmelCase					:						Optional[Any]					=					os.path.join(A_      ,							BART_VOCAB_FILES_NAMES['''vocab_file''']					)
       __lowerCAmelCase					:						Tuple					=					os.path.join(A_      ,							BART_VOCAB_FILES_NAMES['''merges_file''']					)
       with open(self.vocab_file      ,							'''w'''      ,							encoding='''utf-8'''					) as fp:
             fp.write(json.dumps(A_					) + '''\n'''					)
       with open(self.merges_file      ,							'''w'''      ,							encoding='''utf-8'''					) as fp:
             fp.write('''\n'''.join(A_					)					)
 def 	UpperCamelCase__  (							self					)					->DPRQuestionEncoderTokenizer:



       '''simple docstring'''





       return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname      ,							'''dpr_tokenizer'''					)					)
 def 	UpperCamelCase__  (							self					)					->BartTokenizer:



       '''simple docstring'''





       return BartTokenizer.from_pretrained(os.path.join(self.tmpdirname      ,							'''bart_tokenizer'''					)					)
 def 	UpperCamelCase__  (							self					)					->Union[str, Any]:



       '''simple docstring'''





       shutil.rmtree(self.tmpdirname					)
 @require_tokenizers
 def 	UpperCamelCase__  (							self					)					->Union[str, Any]:



       '''simple docstring'''





       __lowerCAmelCase					:						Union[str, Any]					=					os.path.join(self.tmpdirname      ,							'''rag_tokenizer'''					)
       __lowerCAmelCase					:						int					=					RagConfig(question_encoder=DPRConfig().to_dict()      ,							generator=BartConfig().to_dict()					)
       __lowerCAmelCase					:						List[Any]					=					RagTokenizer(question_encoder=self.get_dpr_tokenizer()      ,							generator=self.get_bart_tokenizer()					)
       rag_config.save_pretrained(A_					)
       rag_tokenizer.save_pretrained(A_					)
       __lowerCAmelCase					:						Dict					=					RagTokenizer.from_pretrained(A_      ,							config=A_					)
       self.assertIsInstance(new_rag_tokenizer.question_encoder      ,							A_					)
       self.assertEqual(new_rag_tokenizer.question_encoder.get_vocab()      ,							rag_tokenizer.question_encoder.get_vocab()					)
       self.assertIsInstance(new_rag_tokenizer.generator      ,							A_					)
       self.assertEqual(new_rag_tokenizer.generator.get_vocab()      ,							rag_tokenizer.generator.get_vocab()					)
 @slow
 def 	UpperCamelCase__  (							self					)					->List[Any]:



       '''simple docstring'''





       __lowerCAmelCase					:						Optional[int]					=					RagTokenizer.from_pretrained('''facebook/rag-token-nq'''					)
       __lowerCAmelCase					:						Optional[Any]					=					[
           '''who got the first nobel prize in physics''',
           '''when is the next deadpool movie being released''',
           '''which mode is used for short wave broadcast service''',
           '''who is the owner of reading football club''',
           '''when is the next scandal episode coming out''',
           '''when is the last time the philadelphia won the superbowl''',
           '''what is the most current adobe flash player version''',
           '''how many episodes are there in dragon ball z''',
           '''what is the first step in the evolution of the eye''',
           '''where is gall bladder situated in human body''',
           '''what is the main mineral in lithium batteries''',
           '''who is the president of usa right now''',
           '''where do the greasers live in the outsiders''',
           '''panda is a national animal of which country''',
           '''what is the name of manchester united stadium''',
       ]
       __lowerCAmelCase					:						Union[str, Any]					=					tokenizer(A_					)
       self.assertIsNotNone(A_					)





 @slow
 def 	UpperCamelCase__  (							self					)					->Tuple:



       '''simple docstring'''





       __lowerCAmelCase					:						Union[str, Any]					=					RagTokenizer.from_pretrained('''facebook/rag-sequence-nq'''					)
       __lowerCAmelCase					:						Union[str, Any]					=					[
           '''who got the first nobel prize in physics''',
           '''when is the next deadpool movie being released''',
           '''which mode is used for short wave broadcast service''',
           '''who is the owner of reading football club''',
           '''when is the next scandal episode coming out''',
           '''when is the last time the philadelphia won the superbowl''',
           '''what is the most current adobe flash player version''',
           '''how many episodes are there in dragon ball z''',
           '''what is the first step in the evolution of the eye''',
           '''where is gall bladder situated in human body''',
           '''what is the main mineral in lithium batteries''',
           '''who is the president of usa right now''',
           '''where do the greasers live in the outsiders''',
           '''panda is a national animal of which country''',
           '''what is the name of manchester united stadium''',
       ]
       __lowerCAmelCase					:						List[str]					=					tokenizer(A_					)
       self.assertIsNotNone(A_					)





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   __lowercase      (unittest.TestCase ):
 _UpperCamelCase			  =  ViTImageProcessor if is_vision_available() else None
 @property
 def 	UpperCamelCase__  (							self					)					->str:



       '''simple docstring'''





       return self.image_processor_tester.prepare_image_processor_dict()
 def 	UpperCamelCase__  (							self					)					->str:



       '''simple docstring'''





       __lowerCAmelCase					:						Tuple					=					(3, 32, 128)
       __lowerCAmelCase					:						List[str]					=					tempfile.mkdtemp()

       # fmt: off
       __lowerCAmelCase					:						List[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
       __lowerCAmelCase					:						Optional[int]					=					dict(zip(A_      ,							range(len(A_					)					)					)					)

       __lowerCAmelCase					:						Tuple					=					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'''					)

       __lowerCAmelCase					:						Union[str, Any]					=					{
           '''do_normalize''': False,
           '''do_resize''': True,
           '''image_processor_type''': '''ViTImageProcessor''',
           '''resample''': 3,
           '''size''': {'''height''': 32, '''width''': 128},
       }
       __lowerCAmelCase					:						Optional[Any]					=					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      ,							**A_					)					->Tuple:



       '''simple docstring'''





       return MgpstrTokenizer.from_pretrained(self.tmpdirname      ,							**A_					)
 def 	UpperCamelCase__  (							self      ,							**A_					)					->Tuple:



       '''simple docstring'''





       return ViTImageProcessor.from_pretrained(self.tmpdirname      ,							**A_					)
 def 	UpperCamelCase__  (							self					)					->Optional[int]:



       '''simple docstring'''





       shutil.rmtree(self.tmpdirname					)
 def 	UpperCamelCase__  (							self					)					->Optional[Any]:



       '''simple docstring'''





       __lowerCAmelCase					:						Tuple					=					np.random.randint(255      ,							size=(3, 30, 400)      ,							dtype=np.uinta					)

       __lowerCAmelCase					:						str					=					Image.fromarray(np.moveaxis(A_      ,							0      ,							-1					)					)

       return image_input
 def 	UpperCamelCase__  (							self					)					->Any:



       '''simple docstring'''





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

       __lowerCAmelCase					:						List[Any]					=					MgpstrProcessor(tokenizer=A_      ,							image_processor=A_					)
       processor.save_pretrained(self.tmpdirname					)
       __lowerCAmelCase					:						Union[str, 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					)					->Optional[int]:



       '''simple docstring'''





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

       __lowerCAmelCase					:						List[Any]					=					MgpstrProcessor(tokenizer=A_      ,							image_processor=A_					)
       processor.save_pretrained(self.tmpdirname					)

       __lowerCAmelCase					:						List[Any]					=					self.get_tokenizer(bos_token='''(BOS)'''      ,							eos_token='''(EOS)'''					)
       __lowerCAmelCase					:						int					=					self.get_image_processor(do_normalize=A_      ,							padding_value=1.0					)

       __lowerCAmelCase					:						int					=					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					)					->List[str]:



       '''simple docstring'''





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

       __lowerCAmelCase					:						int					=					MgpstrProcessor(tokenizer=A_      ,							image_processor=A_					)

       __lowerCAmelCase					:						Optional[int]					=					self.prepare_image_inputs()

       __lowerCAmelCase					:						Optional[Any]					=					image_processor(A_      ,							return_tensors='''np'''					)
       __lowerCAmelCase					:						Tuple					=					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					)					->str:



       '''simple docstring'''





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

       __lowerCAmelCase					:						Optional[Any]					=					MgpstrProcessor(tokenizer=A_      ,							image_processor=A_					)

       __lowerCAmelCase					:						Any					=					'''test'''

       __lowerCAmelCase					:						Dict					=					processor(text=A_					)

       __lowerCAmelCase					:						str					=					tokenizer(A_					)
       for key in encoded_tok.keys():
             self.assertListEqual(encoded_tok[key]      ,							encoded_processor[key]					)
 def 	UpperCamelCase__  (							self					)					->Optional[int]:



       '''simple docstring'''





       __lowerCAmelCase					:						Dict					=					self.get_image_processor()
       __lowerCAmelCase					:						Any					=					self.get_tokenizer()

       __lowerCAmelCase					:						str					=					MgpstrProcessor(tokenizer=A_      ,							image_processor=A_					)

       __lowerCAmelCase					:						List[Any]					=					'''test'''
       __lowerCAmelCase					:						int					=					self.prepare_image_inputs()

       __lowerCAmelCase					:						int					=					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					)					->Union[str, Any]:



       '''simple docstring'''





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

       __lowerCAmelCase					:						Any					=					MgpstrProcessor(tokenizer=A_      ,							image_processor=A_					)

       __lowerCAmelCase					:						List[str]					=					[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9], [3, 4, 3, 1, 1, 8, 9]]

       __lowerCAmelCase					:						Optional[int]					=					processor.char_decode(A_					)
       __lowerCAmelCase					:						Tuple					=					tokenizer.batch_decode(A_					)
       __lowerCAmelCase					:						Any					=					[seq.replace(''' '''      ,							''''''					) for seq in decoded_tok]

       self.assertListEqual(A_      ,							A_					)
 def 	UpperCamelCase__  (							self					)					->Any:



       '''simple docstring'''





       __lowerCAmelCase					:						str					=					self.get_image_processor()
       __lowerCAmelCase					:						Any					=					self.get_tokenizer()

       __lowerCAmelCase					:						int					=					MgpstrProcessor(tokenizer=A_      ,							image_processor=A_					)

       __lowerCAmelCase					:						Union[str, Any]					=					None
       __lowerCAmelCase					:						Optional[Any]					=					self.prepare_image_inputs()

       __lowerCAmelCase					:						List[Any]					=					processor(text=A_      ,							images=A_					)

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





 def 	UpperCamelCase__  (							self					)					->List[str]:



       '''simple docstring'''





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

       __lowerCAmelCase					:						Any					=					MgpstrProcessor(tokenizer=A_      ,							image_processor=A_					)

       __lowerCAmelCase					:						List[Any]					=					torch.randn(1      ,							27      ,							38					)
       __lowerCAmelCase					:						Optional[int]					=					torch.randn(1      ,							27      ,							5_0257					)
       __lowerCAmelCase					:						Optional[Any]					=					torch.randn(1      ,							27      ,							3_0522					)

       __lowerCAmelCase					:						List[str]					=					processor.batch_decode([char_input, bpe_input, wp_input]					)

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


