
"""simple docstring"""






import unittest

from transformers import SPIECE_UNDERLINE, ReformerTokenizer, ReformerTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


__SCREAMING_SNAKE_CASE							=     get_tests_dir('fixtures/test_sentencepiece.model')





@require_sentencepiece
@require_tokenizers
class 				a__					(						A__   ,							unittest.TestCase       ):
     UpperCAmelCase__         =      ReformerTokenizer
     UpperCAmelCase__         =      ReformerTokenizerFast
     UpperCAmelCase__         =      True
     UpperCAmelCase__         =      False
     UpperCAmelCase__         =      True



     def  lowerCamelCase_						(							self :Optional[int]   ):







      '''simple docstring'''



      super().setUp()

      UpperCamelCase_     :					Optional[Any]   	=ReformerTokenizer(_lowercase      ,  keep_accents=_lowercase   )
      tokenizer.save_pretrained(self.tmpdirname   )



     def  lowerCamelCase_						(							self :List[Any]   ):







      '''simple docstring'''



      UpperCamelCase_     :					Any   	='<s>'
      UpperCamelCase_     :					Union[str, Any]   	=1

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



     def  lowerCamelCase_						(							self :Any   ):







      '''simple docstring'''



      UpperCamelCase_     :					Tuple   	=list(self.get_tokenizer().get_vocab().keys()   )

      self.assertEqual(vocab_keys[0]      ,  '<unk>'   )
      self.assertEqual(vocab_keys[1]      ,  '<s>'   )
      self.assertEqual(vocab_keys[-1]      ,  'j'   )
      self.assertEqual(len(_lowercase   )      ,  1_000   )



     def  lowerCamelCase_						(							self :int   ):







      '''simple docstring'''



      self.assertEqual(self.get_tokenizer().vocab_size      ,  1_000   )



     def  lowerCamelCase_						(							self :List[Any]   ):







      '''simple docstring'''



      if not self.test_rust_tokenizer:
       return

      UpperCamelCase_     :					int   	=self.get_tokenizer()
      UpperCamelCase_     :					Union[str, Any]   	=self.get_rust_tokenizer()

      UpperCamelCase_     :					Dict   	='I was born in 92000, and this is falsé.'

      UpperCamelCase_     :					List[str]   	=tokenizer.tokenize(_lowercase   )
      UpperCamelCase_     :					str   	=rust_tokenizer.tokenize(_lowercase   )
      self.assertListEqual(_lowercase      ,  _lowercase   )

      UpperCamelCase_     :					Optional[int]   	=tokenizer.encode(_lowercase      ,  add_special_tokens=_lowercase   )
      UpperCamelCase_     :					Union[str, Any]   	=rust_tokenizer.encode(_lowercase      ,  add_special_tokens=_lowercase   )
      self.assertListEqual(_lowercase      ,  _lowercase   )

      UpperCamelCase_     :					List[Any]   	=self.get_rust_tokenizer()
      UpperCamelCase_     :					int   	=tokenizer.encode(_lowercase   )
      UpperCamelCase_     :					str   	=rust_tokenizer.encode(_lowercase   )
      self.assertListEqual(_lowercase      ,  _lowercase   )



     def  lowerCamelCase_						(							self :Optional[int]      ,  _lowerCamelCase :Optional[int]=15   ):







      '''simple docstring'''



      for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
       with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''   ):
        UpperCamelCase_     :					Dict   	=self.rust_tokenizer_class.from_pretrained(_lowercase      ,  **_lowercase   )

        # Simple input
        UpperCamelCase_     :					int   	='This is a simple input'
        UpperCamelCase_     :					List[Any]   	=['This is a simple input 1', 'This is a simple input 2']
        UpperCamelCase_     :					Tuple   	=('This is a simple input', 'This is a pair')
        UpperCamelCase_     :					Any   	=[
            ('This is a simple input 1', 'This is a simple input 2'),
            ('This is a simple pair 1', 'This is a simple pair 2'),
        ]

        # Simple input tests
        self.assertRaises(_lowercase      ,  tokenizer_r.encode      ,  _lowercase      ,  max_length=_lowercase      ,  padding='max_length'   )

        # Simple input
        self.assertRaises(_lowercase      ,  tokenizer_r.encode_plus      ,  _lowercase      ,  max_length=_lowercase      ,  padding='max_length'   )

        # Simple input
        self.assertRaises(
            _lowercase      ,  tokenizer_r.batch_encode_plus      ,  _lowercase      ,  max_length=_lowercase      ,  padding='max_length'      ,  )

        # Pair input
        self.assertRaises(_lowercase      ,  tokenizer_r.encode      ,  _lowercase      ,  max_length=_lowercase      ,  padding='max_length'   )

        # Pair input
        self.assertRaises(_lowercase      ,  tokenizer_r.encode_plus      ,  _lowercase      ,  max_length=_lowercase      ,  padding='max_length'   )

        # Pair input
        self.assertRaises(
            _lowercase      ,  tokenizer_r.batch_encode_plus      ,  _lowercase      ,  max_length=_lowercase      ,  padding='max_length'      ,  )



     def  lowerCamelCase_						(							self :Optional[int]   ):







      '''simple docstring'''



      pass



     def  lowerCamelCase_						(							self :Tuple   ):







      '''simple docstring'''



      UpperCamelCase_     :					Dict   	=ReformerTokenizer(_lowercase      ,  keep_accents=_lowercase   )

      UpperCamelCase_     :					Dict   	=tokenizer.tokenize('This is a test'   )
      self.assertListEqual(_lowercase      ,  ['▁This', '▁is', '▁a', '▁t', 'est']   )

      self.assertListEqual(
          tokenizer.convert_tokens_to_ids(_lowercase   )      ,  [285, 46, 10, 170, 382]      ,  )

      UpperCamelCase_     :					int   	=tokenizer.tokenize('I was born in 92000, and this is falsé.'   )
      self.assertListEqual(
          _lowercase      ,  [
              SPIECE_UNDERLINE + 'I',
              SPIECE_UNDERLINE + 'was',
              SPIECE_UNDERLINE + 'b',
              'or',
              'n',
              SPIECE_UNDERLINE + 'in',
              SPIECE_UNDERLINE + '',
              '9',
              '2',
              '0',
              '0',
              '0',
              ',',
              SPIECE_UNDERLINE + 'and',
              SPIECE_UNDERLINE + 'this',
              SPIECE_UNDERLINE + 'is',
              SPIECE_UNDERLINE + 'f',
              'al',
              's',
              'é',
              '.',
          ]      ,  )
      UpperCamelCase_     :					Tuple   	=tokenizer.convert_tokens_to_ids(_lowercase   )
      self.assertListEqual(
          _lowercase      ,  [8, 21, 84, 55, 24, 19, 7, 0, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 0, 4]      ,  )

      UpperCamelCase_     :					int   	=tokenizer.convert_ids_to_tokens(_lowercase   )
      self.assertListEqual(
          _lowercase      ,  [
              SPIECE_UNDERLINE + 'I',
              SPIECE_UNDERLINE + 'was',
              SPIECE_UNDERLINE + 'b',
              'or',
              'n',
              SPIECE_UNDERLINE + 'in',
              SPIECE_UNDERLINE + '',
              '<unk>',
              '2',
              '0',
              '0',
              '0',
              ',',
              SPIECE_UNDERLINE + 'and',
              SPIECE_UNDERLINE + 'this',
              SPIECE_UNDERLINE + 'is',
              SPIECE_UNDERLINE + 'f',
              'al',
              's',
              '<unk>',
              '.',
          ]      ,  )



     @cached_property
     def  lowerCamelCase_						(							self :str   ):







      '''simple docstring'''



      return ReformerTokenizer.from_pretrained('google/reformer-crime-and-punishment'   )



     @slow
     def  lowerCamelCase_						(							self :Tuple   ):







      '''simple docstring'''



      UpperCamelCase_     :					List[Any]   	='Hello World!'
      UpperCamelCase_     :					int   	=[126, 32, 262, 152, 38, 72, 287]

      self.assertListEqual(_lowercase      ,  self.big_tokenizer.encode(_lowercase   )   )



     @slow
     def  lowerCamelCase_						(							self :Optional[int]   ):







      '''simple docstring'''



      UpperCamelCase_     :					Tuple   	=(
          'This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) " [ ] ! : - . Also we will'
          ' add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth'
      )
      UpperCamelCase_     :					str   	=[
          108,
          265,
          24,
          111,
          4,
          258,
          156,
          35,
          28,
          275,
          3,
          259,
          297,
          260,
          84,
          4,
          35,
          110,
          44,
          8,
          259,
          91,
          268,
          21,
          11,
          209,
          274,
          109,
          266,
          277,
          117,
          86,
          93,
          315,
          258,
          278,
          258,
          277,
          258,
          0,
          258,
          288,
          258,
          319,
          258,
          0,
          258,
          0,
          258,
          0,
          258,
          0,
          258,
          287,
          258,
          315,
          258,
          289,
          258,
          278,
          99,
          269,
          266,
          262,
          8,
          259,
          241,
          4,
          217,
          230,
          268,
          266,
          55,
          168,
          106,
          75,
          193,
          266,
          223,
          27,
          49,
          26,
          282,
          25,
          264,
          299,
          19,
          26,
          0,
          258,
          277,
          117,
          86,
          93,
          176,
          183,
          270,
          11,
          262,
          42,
          61,
          265,
      ]

      self.assertListEqual(_lowercase      ,  self.big_tokenizer.encode(_lowercase   )   )



     @require_torch
     @slow
     def  lowerCamelCase_						(							self :List[str]   ):







      '''simple docstring'''



      import torch

      from transformers import ReformerConfig, ReformerModel

      # Build sequence
      UpperCamelCase_     :					Optional[Any]   	=list(self.big_tokenizer.get_vocab().keys()   )[:10]
      UpperCamelCase_     :					Tuple   	=' '.join(_lowercase   )
      UpperCamelCase_     :					Union[str, Any]   	=self.big_tokenizer.encode_plus(_lowercase      ,  return_tensors='pt'   )
      UpperCamelCase_     :					Dict   	=self.big_tokenizer.batch_encode_plus([sequence, sequence]      ,  return_tensors='pt'   )

      UpperCamelCase_     :					List[str]   	=ReformerConfig()
      # The input gets padded during training so adjust the axial position encodings from the pretrained model value of (512, 1024)
      UpperCamelCase_     :					Dict   	=encoded_sequence['input_ids'].shape
      UpperCamelCase_     :					Optional[Any]   	=ReformerModel(_lowercase   )

      # Reformer has config.vocab_size == tokenizer.vocab_size == len(tokenizer) - 1 = 320; len(tokenizer) is 321 (including a pad token with id 320)
      assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size

      with torch.no_grad():
       model(**_lowercase   )
       model(**_lowercase   )




     @slow
     def  lowerCamelCase_						(							self :str   ):







      '''simple docstring'''



      UpperCamelCase_     :					Tuple   	={'input_ids': [[108, 265, 24, 111, 4, 258, 156, 7, 51, 279, 58, 7, 76, 25, 69, 278], [140, 243, 264, 134, 17, 267, 77, 263, 22, 262, 297, 258, 304, 177, 279, 266, 14, 89, 13, 35, 261, 299, 272, 137, 275, 278]], '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, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}  # noqa: E501
      # fmt: on

      # This tokenizer does not know some characters like ")".
      # That is the reason why we use very simple texts here.
      # Also see https://github.com/huggingface/transformers/pull/11737#issuecomment-850769064
      UpperCamelCase_     :					Optional[int]   	=[
          'This is a very simple sentence.',
          'The quick brown fox jumps over the lazy dog.',
      ]

      self.tokenizer_integration_test_util(
          expected_encoding=_lowercase      ,  model_name='google/reformer-crime-and-punishment'      ,  revision='0e6c3decb8211d49bf881013425dc8b0448b3f5a'      ,  padding=_lowercase      ,  sequences=_lowercase      ,  )






'''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'])




