'''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		):
  __A :				Union[str, Any]											= ViTImageProcessor if is_vision_available() else None





  @property
  def 						UpperCAmelCase_	(	self ):
     return self.image_processor_tester.prepare_image_processor_dict()





  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      (3, 32, 128)
     lowerCAmelCase_			=      tempfile.mkdtemp()

     # fmt: off
     lowerCAmelCase_			=      ['''[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_			=      dict(zip(_lowercase    ,     range(len(_lowercase ) ) ) )

     lowerCAmelCase_			=      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''' )

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





  def 						UpperCAmelCase_	(	self    ,     **_lowerCamelCase ):
     return MgpstrTokenizer.from_pretrained(self.tmpdirname    ,     **_lowercase )





  def 						UpperCAmelCase_	(	self    ,     **_lowerCamelCase ):
     return ViTImageProcessor.from_pretrained(self.tmpdirname    ,     **_lowercase )





  def 						UpperCAmelCase_	(	self ):
     shutil.rmtree(self.tmpdirname )





  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      np.random.randint(255    ,     size=(3, 30, 400)    ,     dtype=np.uinta )

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

     return image_input





  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      self.get_tokenizer()
     lowerCAmelCase_			=      self.get_image_processor()

     lowerCAmelCase_			=      MgpstrProcessor(tokenizer=_lowercase    ,     image_processor=_lowercase )
     processor.save_pretrained(self.tmpdirname )
     lowerCAmelCase_			=      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 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      self.get_tokenizer()
     lowerCAmelCase_			=      self.get_image_processor()

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

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

     lowerCAmelCase_			=      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 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      self.get_image_processor()
     lowerCAmelCase_			=      self.get_tokenizer()

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

     lowerCAmelCase_			=      self.prepare_image_inputs()

     lowerCAmelCase_			=      image_processor(_lowercase    ,     return_tensors='''np''' )
     lowerCAmelCase_			=      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 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      self.get_image_processor()
     lowerCAmelCase_			=      self.get_tokenizer()

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

     lowerCAmelCase_			=      '''test'''

     lowerCAmelCase_			=      processor(text=_lowercase )

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





  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      self.get_image_processor()
     lowerCAmelCase_			=      self.get_tokenizer()

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

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

     lowerCAmelCase_			=      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 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      self.get_image_processor()
     lowerCAmelCase_			=      self.get_tokenizer()

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

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

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

     self.assertListEqual(_lowercase    ,     _lowercase )





  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      self.get_image_processor()
     lowerCAmelCase_			=      self.get_tokenizer()

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

     lowerCAmelCase_			=      None
     lowerCAmelCase_			=      self.prepare_image_inputs()

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

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







  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      self.get_image_processor()
     lowerCAmelCase_			=      self.get_tokenizer()

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

     lowerCAmelCase_			=      torch.randn(1    ,     27    ,     38 )
     lowerCAmelCase_			=      torch.randn(1    ,     27    ,     5_0257 )
     lowerCAmelCase_			=      torch.randn(1    ,     27    ,     3_0522 )

     lowerCAmelCase_			=      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 pytest

from datasets.splits import SplitDict, SplitInfo
from datasets.utils.py_utils import asdict





@pytest.mark.parametrize(
    'split_dict'       ,[
        SplitDict(),
        SplitDict({'train': SplitInfo(name='train'       ,num_bytes=1337       ,num_examples=42       ,dataset_name='my_dataset'     )}     ),
        SplitDict({'train': SplitInfo(name='train'       ,num_bytes=1337       ,num_examples=42     )}     ),
        SplitDict({'train': SplitInfo()}     ),
    ]       ,)
def 			lowerCamelCase(							SCREAMING_SNAKE_CASE_     )		->		List[str]:
			A_									= split_dict._to_yaml_list()
			assert len(SCREAMING_SNAKE_CASE_     ) == len(SCREAMING_SNAKE_CASE_     )
			A_									= SplitDict._from_yaml_list(SCREAMING_SNAKE_CASE_     )
			for split_name, split_info in split_dict.items():
						# dataset_name field is deprecated, and is therefore not part of the YAML dump
						A_									= None
						# the split name of split_dict takes over the name of the split info object
						A_									= split_name
			assert split_dict == reloaded






@pytest.mark.parametrize(
    'split_info'       ,[SplitInfo(), SplitInfo(dataset_name=SCREAMING_SNAKE_CASE_     ), SplitInfo(dataset_name='my_dataset'     )]     )
def 			lowerCamelCase(							SCREAMING_SNAKE_CASE_     )		->		Optional[Any]:
			# For backward compatibility, we need asdict(split_dict) to return split info dictrionaries with the "dataset_name"
			# field even if it's deprecated. This way old versionso of `datasets` can still reload dataset_infos.json files
			A_									= asdict(SplitDict({'train': split_info}     )     )
			assert "dataset_name" in split_dict_asdict["train"]
			assert split_dict_asdict["train"]["dataset_name"] == split_info.dataset_name




