


'''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 _lowerCamelCase	(						unittest.TestCase							):



  '''simple docstring'''







  A_     :      List[str]				   =    ViTImageProcessor if is_vision_available() else None







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







  def        __lowerCAmelCase		( self		:						int      )  ->			Dict:
   __magic_name__	:   str					     =					(3, 32, 128)
   __magic_name__	:   Tuple					     =					tempfile.mkdtemp()

   # fmt: off
   __magic_name__	:   Dict					     =					['[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
   __magic_name__	:   Union[str, Any]					     =					dict(zip(_A  ,					range(len(_A      )      )      )      )

   __magic_name__	:   Optional[int]					     =					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'      )

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







  def        __lowerCAmelCase		( self		:						Any  ,					**_A		:						List[str]      )  ->			Tuple:
   return MgpstrTokenizer.from_pretrained(self.tmpdirname  ,					**_A      )







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







  def        __lowerCAmelCase		( self		:						List[Any]      )  ->			List[Any]:
   shutil.rmtree(self.tmpdirname      )







  def        __lowerCAmelCase		( self		:						List[Any]      )  ->			int:
   __magic_name__	:   Dict					     =					np.random.randint(255  ,					size=(3, 30, 400)  ,					dtype=np.uinta      )

   __magic_name__	:   Union[str, Any]					     =					Image.fromarray(np.moveaxis(_A  ,					0  ,					-1      )      )

   return image_input







  def        __lowerCAmelCase		( self		:						str      )  ->			Optional[Any]:
   __magic_name__	:   Optional[int]					     =					self.get_tokenizer()
   __magic_name__	:   Dict					     =					self.get_image_processor()

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

   __magic_name__	:   Dict					     =					MgpstrProcessor(tokenizer=_A  ,					image_processor=_A      )
   processor.save_pretrained(self.tmpdirname      )

   __magic_name__	:   Tuple					     =					self.get_tokenizer(bos_token='(BOS)'  ,					eos_token='(EOS)'      )
   __magic_name__	:   Optional[int]					     =					self.get_image_processor(do_normalize=_A  ,					padding_value=1.0      )

   __magic_name__	:   List[str]					     =					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        __lowerCAmelCase		( self		:						Optional[int]      )  ->			Optional[int]:
   __magic_name__	:   Optional[Any]					     =					self.get_image_processor()
   __magic_name__	:   Optional[Any]					     =					self.get_tokenizer()

   __magic_name__	:   Optional[int]					     =					MgpstrProcessor(tokenizer=_A  ,					image_processor=_A      )

   __magic_name__	:   Dict					     =					self.prepare_image_inputs()

   __magic_name__	:   List[str]					     =					image_processor(_A  ,					return_tensors='np'      )
   __magic_name__	:   Dict					     =					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        __lowerCAmelCase		( self		:						Optional[Any]      )  ->			int:
   __magic_name__	:   Optional[int]					     =					self.get_image_processor()
   __magic_name__	:   Union[str, Any]					     =					self.get_tokenizer()

   __magic_name__	:   Union[str, Any]					     =					MgpstrProcessor(tokenizer=_A  ,					image_processor=_A      )

   __magic_name__	:   Optional[Any]					     =					'test'

   __magic_name__	:   str					     =					processor(text=_A      )

   __magic_name__	:   List[Any]					     =					tokenizer(_A      )
   for key in encoded_tok.keys():
    self.assertListEqual(encoded_tok[key]  ,					encoded_processor[key]      )







  def        __lowerCAmelCase		( self		:						Tuple      )  ->			Dict:
   __magic_name__	:   Dict					     =					self.get_image_processor()
   __magic_name__	:   Optional[Any]					     =					self.get_tokenizer()

   __magic_name__	:   Union[str, Any]					     =					MgpstrProcessor(tokenizer=_A  ,					image_processor=_A      )

   __magic_name__	:   str					     =					'test'
   __magic_name__	:   int					     =					self.prepare_image_inputs()

   __magic_name__	:   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        __lowerCAmelCase		( self		:						Any      )  ->			Optional[int]:
   __magic_name__	:   Optional[int]					     =					self.get_image_processor()
   __magic_name__	:   Union[str, Any]					     =					self.get_tokenizer()

   __magic_name__	:   Tuple					     =					MgpstrProcessor(tokenizer=_A  ,					image_processor=_A      )

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

   __magic_name__	:   Optional[int]					     =					processor.char_decode(_A      )
   __magic_name__	:   Tuple					     =					tokenizer.batch_decode(_A      )
   __magic_name__	:   List[str]					     =					[seq.replace(' '  ,					''      ) for seq in decoded_tok]

   self.assertListEqual(_A  ,					_A      )







  def        __lowerCAmelCase		( self		:						Any      )  ->			Union[str, Any]:
   __magic_name__	:   int					     =					self.get_image_processor()
   __magic_name__	:   str					     =					self.get_tokenizer()

   __magic_name__	:   Tuple					     =					MgpstrProcessor(tokenizer=_A  ,					image_processor=_A      )

   __magic_name__	:   Tuple					     =					None
   __magic_name__	:   Any					     =					self.prepare_image_inputs()

   __magic_name__	:   Optional[Any]					     =					processor(text=_A  ,					images=_A      )

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







  def        __lowerCAmelCase		( self		:						int      )  ->			str:
   __magic_name__	:   Optional[int]					     =					self.get_image_processor()
   __magic_name__	:   str					     =					self.get_tokenizer()

   __magic_name__	:   List[str]					     =					MgpstrProcessor(tokenizer=_A  ,					image_processor=_A      )

   __magic_name__	:   List[str]					     =					torch.randn(1  ,					27  ,					38      )
   __magic_name__	:   Optional[Any]					     =					torch.randn(1  ,					27  ,					50257      )
   __magic_name__	:   Dict					     =					torch.randn(1  ,					27  ,					30522      )

   __magic_name__	:   Any					     =					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'''

lowerCAmelCase				:Union[str, Any]        =      '''
# Transformers installation
! pip install transformers datasets
# To install from source instead of the last release, comment the command above and uncomment the following one.
# ! pip install git+https://github.com/huggingface/transformers.git
'''

lowerCAmelCase				:Union[str, Any]        =      [{'''type''': '''code''', '''content''': INSTALL_CONTENT}]
lowerCAmelCase				:Tuple        =      {
    '''{processor_class}''': '''FakeProcessorClass''',
    '''{model_class}''': '''FakeModelClass''',
    '''{object_class}''': '''FakeObjectClass''',
}