import unittest

from transformers import CamembertTokenizer, CamembertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import is_torch_available

from ...test_tokenization_common import TokenizerTesterMixin


SCREAMING_SNAKE_CASE__		:       Union[str, Any]       						=       get_tests_dir("fixtures/test_sentencepiece.model")
SCREAMING_SNAKE_CASE__		:       Optional[int]       						=       get_tests_dir("fixtures/test_sentencepiece_bpe.model")

SCREAMING_SNAKE_CASE__		:       Any       						=       "pt" if is_torch_available() else "tf"

@require_sentencepiece
@require_tokenizers
class    snake_case       (   UpperCamelCase_ ,    unittest.TestCase    ):
  lowercase_								=  CamembertTokenizer
  lowercase_								=  CamembertTokenizerFast
  lowercase_								=  True
  lowercase_								=  True




  def 						__lowercase(   self  :						Tuple					)->							str:




       """simple docstring"""
       super().setUp()

       # We have a SentencePiece fixture for testing
       SCREAMING_SNAKE_CASE__					:					Dict      =		CamembertTokenizer(a_					)
       tokenizer.save_pretrained(self.tmpdirname					)




  def 						__lowercase(   self  :						Any					)->							Dict:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					Union[str, Any]      =		'<pad>'
       SCREAMING_SNAKE_CASE__					:					int      =		1

       self.assertEqual(self.get_tokenizer()._convert_token_to_id(a_					)  ,			a_					)
       self.assertEqual(self.get_tokenizer()._convert_id_to_token(a_					)  ,			a_					)




  def 						__lowercase(   self  :						Optional[Any]					)->							Any:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					Any      =		list(self.get_tokenizer().get_vocab().keys()					)

       self.assertEqual(vocab_keys[0]  ,			'<s>NOTUSED'					)
       self.assertEqual(vocab_keys[1]  ,			'<pad>'					)
       self.assertEqual(vocab_keys[-1]  ,			'<mask>'					)
       self.assertEqual(len(a_					)  ,			1004					)




  def 						__lowercase(   self  :						Union[str, Any]					)->							Optional[Any]:




       """simple docstring"""
       self.assertEqual(self.get_tokenizer().vocab_size  ,			1005					)




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




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					Optional[int]      =		CamembertTokenizer(a_					)
       tokenizer.save_pretrained(self.tmpdirname					)
       SCREAMING_SNAKE_CASE__					:					int      =		CamembertTokenizerFast.from_pretrained(self.tmpdirname					)

       SCREAMING_SNAKE_CASE__					:					str      =		'I was born in 92000, and this is falsé.'

       SCREAMING_SNAKE_CASE__					:					Tuple      =		tokenizer.encode(a_					)
       SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		rust_tokenizer.encode(a_					)
       self.assertListEqual(a_  ,			a_					)

       SCREAMING_SNAKE_CASE__					:					str      =		tokenizer.encode(a_  ,			add_special_tokens=a_					)
       SCREAMING_SNAKE_CASE__					:					List[str]      =		rust_tokenizer.encode(a_  ,			add_special_tokens=a_					)
       self.assertListEqual(a_  ,			a_					)

       # <unk> tokens are not the same for `rust` than for `slow`.
       # Because spm gives back raw token instead of `unk` in EncodeAsPieces
       # tokens = tokenizer.tokenize(sequence)
       SCREAMING_SNAKE_CASE__					:					List[str]      =		tokenizer.convert_ids_to_tokens(a_					)
       SCREAMING_SNAKE_CASE__					:					List[Any]      =		rust_tokenizer.tokenize(a_					)
       self.assertListEqual(a_  ,			a_					)




  def 						__lowercase(   self  :						Union[str, Any]					)->							str:




       """simple docstring"""
       if not self.test_rust_tokenizer:
            return

       SCREAMING_SNAKE_CASE__					:					Optional[int]      =		self.get_tokenizer()
       SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		self.get_rust_tokenizer()

       SCREAMING_SNAKE_CASE__					:					Tuple      =		'I was born in 92000, and this is falsé.'

       SCREAMING_SNAKE_CASE__					:					str      =		tokenizer.tokenize(a_					)
       SCREAMING_SNAKE_CASE__					:					List[Any]      =		rust_tokenizer.tokenize(a_					)
       self.assertListEqual(a_  ,			a_					)

       SCREAMING_SNAKE_CASE__					:					Optional[int]      =		tokenizer.encode(a_  ,			add_special_tokens=a_					)
       SCREAMING_SNAKE_CASE__					:					Optional[int]      =		rust_tokenizer.encode(a_  ,			add_special_tokens=a_					)
       self.assertListEqual(a_  ,			a_					)

       SCREAMING_SNAKE_CASE__					:					int      =		self.get_rust_tokenizer()
       SCREAMING_SNAKE_CASE__					:					Union[str, Any]      =		tokenizer.encode(a_					)
       SCREAMING_SNAKE_CASE__					:					Tuple      =		rust_tokenizer.encode(a_					)
       self.assertListEqual(a_  ,			a_					)




  @slow
  def 						__lowercase(   self  :						List[str]					)->							Dict:




       """simple docstring"""
       # fmt: off
       SCREAMING_SNAKE_CASE__					:					Union[str, Any]      =		{'input_ids': [[5, 54, 7196, 297, 30, 23, 776, 18, 11, 3215, 3705, 8252, 22, 3164, 1181, 2116, 29, 16, 813, 25, 791, 3314, 20, 3446, 38, 2_7575, 120, 6, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [5, 468, 17, 11, 9088, 20, 1517, 8, 2_2804, 1_8818, 10, 38, 629, 607, 607, 142, 19, 7196, 867, 56, 1_0326, 24, 2267, 20, 416, 5072, 1_5612, 233, 734, 7, 2399, 27, 16, 3015, 1649, 7, 24, 20, 4338, 2399, 27, 13, 3400, 14, 13, 6189, 8, 930, 9, 6]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}  # noqa: E501
       # fmt: on

       # camembert is a french model. So we also use french texts.
       SCREAMING_SNAKE_CASE__					:					str      =		[
           'Le transformeur est un modèle d\'apprentissage profond introduit en 2017, '
           'utilisé principalement dans le domaine du traitement automatique des langues (TAL).',
           'À l\'instar des réseaux de neurones récurrents (RNN), les transformeurs sont conçus '
           'pour gérer des données séquentielles, telles que le langage naturel, pour des tâches '
           'telles que la traduction et la synthèse de texte.',
       ]

       self.tokenizer_integration_test_util(
           expected_encoding=a_  ,			model_name='camembert-base'  ,			revision='3a0641d9a1aeb7e848a74299e7e4c4bca216b4cf'  ,			sequences=a_  ,			)









'''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				):



					'''simple docstring'''





					_UpperCamelCase							=		ViTImageProcessor if is_vision_available() else None



					@property
					def     __snake_case						(	self     :						List[str])    ->							Optional[int]:
								return self.image_processor_tester.prepare_image_processor_dict()



					def     __snake_case						(	self     :						Optional[Any])    ->							List[str]:
								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(_lowercase      ,       range(len(_lowercase))))

								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(_lowercase) + '\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      ,       _lowercase)
								with open(self.image_processor_file      ,       'w'      ,       encoding='utf-8') as fp:
											json.dump(_lowercase      ,       _lowercase)



					def     __snake_case						(	self     :						int      ,       **_lowercase     :						Optional[int])    ->							int:
								return MgpstrTokenizer.from_pretrained(self.tmpdirname      ,       **_lowercase)



					def     __snake_case						(	self     :						Optional[int]      ,       **_lowercase     :						Optional[int])    ->							Union[str, Any]:
								return ViTImageProcessor.from_pretrained(self.tmpdirname      ,       **_lowercase)



					def     __snake_case						(	self     :						Dict)    ->							str:
								shutil.rmtree(self.tmpdirname)



					def     __snake_case						(	self     :						Union[str, Any])    ->							Any:
								A_									= np.random.randint(255      ,       size=(3, 30, 400)      ,       dtype=np.uinta)

								A_									= Image.fromarray(np.moveaxis(_lowercase      ,       0      ,       -1))

								return image_input



					def     __snake_case						(	self     :						Optional[Any])    ->							List[Any]:
								A_									= self.get_tokenizer()
								A_									= self.get_image_processor()

								A_									= MgpstrProcessor(tokenizer=_lowercase      ,       image_processor=_lowercase)
								processor.save_pretrained(self.tmpdirname)
								A_									= MgpstrProcessor.from_pretrained(self.tmpdirname      ,       use_fast=_lowercase)

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

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



					def     __snake_case						(	self     :						Union[str, Any])    ->							Optional[Any]:
								A_									= self.get_tokenizer()
								A_									= self.get_image_processor()

								A_									= MgpstrProcessor(tokenizer=_lowercase      ,       image_processor=_lowercase)
								processor.save_pretrained(self.tmpdirname)

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

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

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

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



					def     __snake_case						(	self     :						List[Any])    ->							str:
								A_									= self.get_image_processor()
								A_									= self.get_tokenizer()

								A_									= MgpstrProcessor(tokenizer=_lowercase      ,       image_processor=_lowercase)

								A_									= self.prepare_image_inputs()

								A_									= image_processor(_lowercase      ,       return_tensors='np')
								A_									= processor(images=_lowercase      ,       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     __snake_case						(	self     :						Any)    ->							str:
								A_									= self.get_image_processor()
								A_									= self.get_tokenizer()

								A_									= MgpstrProcessor(tokenizer=_lowercase      ,       image_processor=_lowercase)

								A_									= 'test'

								A_									= processor(text=_lowercase)

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



					def     __snake_case						(	self     :						str)    ->							Dict:
								A_									= self.get_image_processor()
								A_									= self.get_tokenizer()

								A_									= MgpstrProcessor(tokenizer=_lowercase      ,       image_processor=_lowercase)

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

								A_									= processor(text=_lowercase      ,       images=_lowercase)

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

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



					def     __snake_case						(	self     :						Union[str, Any])    ->							Optional[int]:
								A_									= self.get_image_processor()
								A_									= self.get_tokenizer()

								A_									= MgpstrProcessor(tokenizer=_lowercase      ,       image_processor=_lowercase)

								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(_lowercase)
								A_									= tokenizer.batch_decode(_lowercase)
								A_									= [seq.replace(' '      ,       '') for seq in decoded_tok]

								self.assertListEqual(_lowercase      ,       _lowercase)



					def     __snake_case						(	self     :						List[str])    ->							str:
								A_									= self.get_image_processor()
								A_									= self.get_tokenizer()

								A_									= MgpstrProcessor(tokenizer=_lowercase      ,       image_processor=_lowercase)

								A_									= None
								A_									= self.prepare_image_inputs()

								A_									= processor(text=_lowercase      ,       images=_lowercase)

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



					def     __snake_case						(	self     :						List[str])    ->							Any:
								A_									= self.get_image_processor()
								A_									= self.get_tokenizer()

								A_									= MgpstrProcessor(tokenizer=_lowercase      ,       image_processor=_lowercase)

								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'])




