'''simple docstring'''




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tensorflow_text_available, is_torch_available


_A								=   {
    'configuration_ernie': ['ERNIE_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ErnieConfig', 'ErnieOnnxConfig'],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _A								=   [
     'ERNIE_PRETRAINED_MODEL_ARCHIVE_LIST',
     'ErnieForCausalLM',
     'ErnieForMaskedLM',
     'ErnieForMultipleChoice',
     'ErnieForNextSentencePrediction',
     'ErnieForPreTraining',
     'ErnieForQuestionAnswering',
     'ErnieForSequenceClassification',
     'ErnieForTokenClassification',
     'ErnieModel',
     'ErniePreTrainedModel',
 ]

if TYPE_CHECKING:
 from .configuration_ernie import ERNIE_PRETRAINED_CONFIG_ARCHIVE_MAP, ErnieConfig, ErnieOnnxConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_ernie import (
      ERNIE_PRETRAINED_MODEL_ARCHIVE_LIST,
      ErnieForCausalLM,
      ErnieForMaskedLM,
      ErnieForMultipleChoice,
      ErnieForNextSentencePrediction,
      ErnieForPreTraining,
      ErnieForQuestionAnswering,
      ErnieForSequenceClassification,
      ErnieForTokenClassification,
      ErnieModel,
      ErniePreTrainedModel,
  )

else:
 import sys

 _A								=   _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





'''simple docstring'''




import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
 from PIL import Image

 from transformers import AlignProcessor, EfficientNetImageProcessor


@require_vision
class       UpperCAmelCase__  (       unittest.TestCase			):






   """simple docstring"""
   def 		_lowerCamelCase (self				)    ->     List[Any]:
        lowercase_					:       Any     				=					tempfile.mkdtemp()

        lowercase_					:       int     				=					[
            '[UNK]',
            '[CLS]',
            '[SEP]',
            '[PAD]',
            '[MASK]',
            'want',
            '##want',
            '##ed',
            'wa',
            'un',
            'runn',
            '##ing',
            ',',
            'low',
            'lowest',
        ]
        lowercase_					:       Union[str, Any]     				=					os.path.join(self.tmpdirname       ,				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]				)				)

        lowercase_					:       Union[str, Any]     				=					{
            'do_resize': True,
            'size': 20,
            'do_center_crop': True,
            'crop_size': 18,
            'do_normalize': True,
            'image_mean': [0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73],
            'image_std': [0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11],
        }
        lowercase_					:       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       ,				**_a				)    ->     Tuple:
        return BertTokenizer.from_pretrained(self.tmpdirname       ,				**_a				)
   def 		_lowerCamelCase (self       ,				**_a				)    ->     Tuple:
        return BertTokenizerFast.from_pretrained(self.tmpdirname       ,				**_a				)
   def 		_lowerCamelCase (self       ,				**_a				)    ->     int:
        return EfficientNetImageProcessor.from_pretrained(self.tmpdirname       ,				**_a				)
   def 		_lowerCamelCase (self				)    ->     Dict:
        shutil.rmtree(self.tmpdirname				)
   def 		_lowerCamelCase (self				)    ->     str:
        lowercase_					:       List[str]     				=					[np.random.randint(255       ,				size=(3, 30, 400)       ,				dtype=np.uinta				)]
        lowercase_					:       Optional[Any]     				=					[Image.fromarray(np.moveaxis(_a       ,				0       ,				-1				)				) for x in image_inputs]
        return image_inputs
   def 		_lowerCamelCase (self				)    ->     Optional[Any]:
        lowercase_					:       str     				=					self.get_tokenizer()
        lowercase_					:       Tuple     				=					self.get_rust_tokenizer()
        lowercase_					:       int     				=					self.get_image_processor()

        lowercase_					:       int     				=					AlignProcessor(tokenizer=_a       ,				image_processor=_a				)
        processor_slow.save_pretrained(self.tmpdirname				)
        lowercase_					:       List[str]     				=					AlignProcessor.from_pretrained(self.tmpdirname       ,				use_fast=_a				)

        lowercase_					:       Optional[Any]     				=					AlignProcessor(tokenizer=_a       ,				image_processor=_a				)
        processor_fast.save_pretrained(self.tmpdirname				)
        lowercase_					:       Any     				=					AlignProcessor.from_pretrained(self.tmpdirname				)

        self.assertEqual(processor_slow.tokenizer.get_vocab()       ,				tokenizer_slow.get_vocab()				)
        self.assertEqual(processor_fast.tokenizer.get_vocab()       ,				tokenizer_fast.get_vocab()				)
        self.assertEqual(tokenizer_slow.get_vocab()       ,				tokenizer_fast.get_vocab()				)
        self.assertIsInstance(processor_slow.tokenizer       ,				_a				)
        self.assertIsInstance(processor_fast.tokenizer       ,				_a				)

        self.assertEqual(processor_slow.image_processor.to_json_string()       ,				image_processor.to_json_string()				)
        self.assertEqual(processor_fast.image_processor.to_json_string()       ,				image_processor.to_json_string()				)
        self.assertIsInstance(processor_slow.image_processor       ,				_a				)
        self.assertIsInstance(processor_fast.image_processor       ,				_a				)
   def 		_lowerCamelCase (self				)    ->     Tuple:
        lowercase_					:       str     				=					AlignProcessor(tokenizer=self.get_tokenizer()       ,				image_processor=self.get_image_processor()				)
        processor.save_pretrained(self.tmpdirname				)

        lowercase_					:       Tuple     				=					self.get_tokenizer(bos_token='(BOS)'       ,				eos_token='(EOS)'				)
        lowercase_					:       str     				=					self.get_image_processor(do_normalize=_a       ,				padding_value=1.0				)

        lowercase_					:       Optional[Any]     				=					AlignProcessor.from_pretrained(
            self.tmpdirname       ,				bos_token='(BOS)'       ,				eos_token='(EOS)'       ,				do_normalize=_a       ,				padding_value=1.0				)

        self.assertEqual(processor.tokenizer.get_vocab()       ,				tokenizer_add_kwargs.get_vocab()				)
        self.assertIsInstance(processor.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				)    ->     List[str]:
        lowercase_					:       Union[str, Any]     				=					self.get_image_processor()
        lowercase_					:       Union[str, Any]     				=					self.get_tokenizer()

        lowercase_					:       Optional[int]     				=					AlignProcessor(tokenizer=_a       ,				image_processor=_a				)

        lowercase_					:       Union[str, Any]     				=					self.prepare_image_inputs()

        lowercase_					:       Union[str, Any]     				=					image_processor(_a       ,				return_tensors='np'				)
        lowercase_					:       Union[str, Any]     				=					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				)    ->     Dict:
        lowercase_					:       List[Any]     				=					self.get_image_processor()
        lowercase_					:       int     				=					self.get_tokenizer()

        lowercase_					:       Any     				=					AlignProcessor(tokenizer=_a       ,				image_processor=_a				)

        lowercase_					:       Tuple     				=					'lower newer'

        lowercase_					:       Optional[Any]     				=					processor(text=_a				)

        lowercase_					:       List[Any]     				=					tokenizer(_a       ,				padding='max_length'       ,				max_length=64				)

        for key in encoded_tok.keys():
             self.assertListEqual(encoded_tok[key]       ,				encoded_processor[key]				)
   def 		_lowerCamelCase (self				)    ->     List[Any]:
        lowercase_					:       Any     				=					self.get_image_processor()
        lowercase_					:       List[Any]     				=					self.get_tokenizer()

        lowercase_					:       List[str]     				=					AlignProcessor(tokenizer=_a       ,				image_processor=_a				)

        lowercase_					:       Optional[Any]     				=					'lower newer'
        lowercase_					:       List[Any]     				=					self.prepare_image_inputs()

        lowercase_					:       List[str]     				=					processor(text=_a       ,				images=_a				)

        self.assertListEqual(list(inputs.keys()				)       ,				['input_ids', 'token_type_ids', 'attention_mask', 'pixel_values']				)

        # test if it raises when no input is passed
        with pytest.raises(_a				):
             processor()
   def 		_lowerCamelCase (self				)    ->     Tuple:
        lowercase_					:       Dict     				=					self.get_image_processor()
        lowercase_					:       Optional[Any]     				=					self.get_tokenizer()

        lowercase_					:       Optional[Any]     				=					AlignProcessor(tokenizer=_a       ,				image_processor=_a				)

        lowercase_					:       Union[str, Any]     				=					[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

        lowercase_					:       str     				=					processor.batch_decode(_a				)
        lowercase_					:       Dict     				=					tokenizer.batch_decode(_a				)

        self.assertListEqual(_a       ,				_a				)





   def 		_lowerCamelCase (self				)    ->     List[str]:
        lowercase_					:       str     				=					self.get_image_processor()
        lowercase_					:       str     				=					self.get_tokenizer()

        lowercase_					:       str     				=					AlignProcessor(tokenizer=_a       ,				image_processor=_a				)

        lowercase_					:       Optional[int]     				=					'lower newer'
        lowercase_					:       Union[str, Any]     				=					self.prepare_image_inputs()

        lowercase_					:       Union[str, Any]     				=					processor(text=_a       ,				images=_a				)

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




