






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     _lowerCAmelCase(	unittest.TestCase ):





       """simple docstring"""







       def       _a   ( self							):
         UpperCamelCase_: List[Any]   				=    tempfile.mkdtemp()

         UpperCamelCase_: Optional[Any]   				=    [
             '[UNK]',
             '[CLS]',
             '[SEP]',
             '[PAD]',
             '[MASK]',
             'want',
             '##want',
             '##ed',
             'wa',
             'un',
             'runn',
             '##ing',
             ',',
             'low',
             'lowest',
         ]
         UpperCamelCase_: Optional[int]   				=    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]							)							)

         UpperCamelCase_: str   				=    {
             'do_resize': True,
             'size': 2_0,
             'do_center_crop': True,
             'crop_size': 1_8,
             'do_normalize': True,
             'image_mean': [0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3],
             'image_std': [0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1],
         }
         UpperCamelCase_: List[Any]   				=    os.path.join(self.tmpdirname  ,      _lowerCamelCase							)
         with open(self.image_processor_file  ,      'w'  ,      encoding='utf-8'							) as fp:
           json.dump(_lowerCamelCase  ,      _lowerCamelCase							)





       def       _a   ( self  ,      **_lowerCamelCase							):
         return BertTokenizer.from_pretrained(self.tmpdirname  ,      **_lowerCamelCase							)





       def       _a   ( self  ,      **_lowerCamelCase							):
         return BertTokenizerFast.from_pretrained(self.tmpdirname  ,      **_lowerCamelCase							)





       def       _a   ( self  ,      **_lowerCamelCase							):
         return EfficientNetImageProcessor.from_pretrained(self.tmpdirname  ,      **_lowerCamelCase							)





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





       def       _a   ( self							):
         UpperCamelCase_: Tuple   				=    [np.random.randint(2_5_5  ,      size=(3, 3_0, 4_0_0)  ,      dtype=np.uinta							)]
         UpperCamelCase_: str   				=    [Image.fromarray(np.moveaxis(_lowerCamelCase  ,      0  ,      -1							)							) for x in image_inputs]
         return image_inputs





       def       _a   ( self							):
         UpperCamelCase_: Dict   				=    self.get_tokenizer()
         UpperCamelCase_: Optional[int]   				=    self.get_rust_tokenizer()
         UpperCamelCase_: Union[str, Any]   				=    self.get_image_processor()

         UpperCamelCase_: str   				=    AlignProcessor(tokenizer=_lowerCamelCase  ,      image_processor=_lowerCamelCase							)
         processor_slow.save_pretrained(self.tmpdirname							)
         UpperCamelCase_: str   				=    AlignProcessor.from_pretrained(self.tmpdirname  ,      use_fast=_lowerCamelCase							)

         UpperCamelCase_: int   				=    AlignProcessor(tokenizer=_lowerCamelCase  ,      image_processor=_lowerCamelCase							)
         processor_fast.save_pretrained(self.tmpdirname							)
         UpperCamelCase_: Optional[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  ,      _lowerCamelCase							)
         self.assertIsInstance(processor_fast.tokenizer  ,      _lowerCamelCase							)

         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  ,      _lowerCamelCase							)
         self.assertIsInstance(processor_fast.image_processor  ,      _lowerCamelCase							)





       def       _a   ( self							):
         UpperCamelCase_: Union[str, Any]   				=    AlignProcessor(tokenizer=self.get_tokenizer()  ,      image_processor=self.get_image_processor()							)
         processor.save_pretrained(self.tmpdirname							)

         UpperCamelCase_: str   				=    self.get_tokenizer(bos_token='(BOS)'  ,      eos_token='(EOS)'							)
         UpperCamelCase_: List[str]   				=    self.get_image_processor(do_normalize=_lowerCamelCase  ,      padding_value=1.0							)

         UpperCamelCase_: Any   				=    AlignProcessor.from_pretrained(
             self.tmpdirname  ,      bos_token='(BOS)'  ,      eos_token='(EOS)'  ,      do_normalize=_lowerCamelCase  ,      padding_value=1.0							)

         self.assertEqual(processor.tokenizer.get_vocab()  ,      tokenizer_add_kwargs.get_vocab()							)
         self.assertIsInstance(processor.tokenizer  ,      _lowerCamelCase							)

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





       def       _a   ( self							):
         UpperCamelCase_: str   				=    self.get_image_processor()
         UpperCamelCase_: List[str]   				=    self.get_tokenizer()

         UpperCamelCase_: str   				=    AlignProcessor(tokenizer=_lowerCamelCase  ,      image_processor=_lowerCamelCase							)

         UpperCamelCase_: List[str]   				=    self.prepare_image_inputs()

         UpperCamelCase_: int   				=    image_processor(_lowerCamelCase  ,      return_tensors='np'							)
         UpperCamelCase_: Dict   				=    processor(images=_lowerCamelCase  ,      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       _a   ( self							):
         UpperCamelCase_: Any   				=    self.get_image_processor()
         UpperCamelCase_: str   				=    self.get_tokenizer()

         UpperCamelCase_: Dict   				=    AlignProcessor(tokenizer=_lowerCamelCase  ,      image_processor=_lowerCamelCase							)

         UpperCamelCase_: List[Any]   				=    'lower newer'

         UpperCamelCase_: int   				=    processor(text=_lowerCamelCase							)

         UpperCamelCase_: str   				=    tokenizer(_lowerCamelCase  ,      padding='max_length'  ,      max_length=6_4							)

         for key in encoded_tok.keys():
           self.assertListEqual(encoded_tok[key]  ,      encoded_processor[key]							)





       def       _a   ( self							):
         UpperCamelCase_: List[str]   				=    self.get_image_processor()
         UpperCamelCase_: Dict   				=    self.get_tokenizer()

         UpperCamelCase_: Optional[Any]   				=    AlignProcessor(tokenizer=_lowerCamelCase  ,      image_processor=_lowerCamelCase							)

         UpperCamelCase_: Any   				=    'lower newer'
         UpperCamelCase_: str   				=    self.prepare_image_inputs()

         UpperCamelCase_: Optional[Any]   				=    processor(text=_lowerCamelCase  ,      images=_lowerCamelCase							)

         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(_lowerCamelCase							):
           processor()





       def       _a   ( self							):
         UpperCamelCase_: str   				=    self.get_image_processor()
         UpperCamelCase_: Any   				=    self.get_tokenizer()

         UpperCamelCase_: Union[str, Any]   				=    AlignProcessor(tokenizer=_lowerCamelCase  ,      image_processor=_lowerCamelCase							)

         UpperCamelCase_: Any   				=    [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

         UpperCamelCase_: Optional[Any]   				=    processor.batch_decode(_lowerCamelCase							)
         UpperCamelCase_: List[str]   				=    tokenizer.batch_decode(_lowerCamelCase							)

         self.assertListEqual(_lowerCamelCase  ,      _lowerCamelCase							)





       def       _a   ( self							):
         UpperCamelCase_: Optional[int]   				=    self.get_image_processor()
         UpperCamelCase_: str   				=    self.get_tokenizer()

         UpperCamelCase_: Union[str, Any]   				=    AlignProcessor(tokenizer=_lowerCamelCase  ,      image_processor=_lowerCamelCase							)

         UpperCamelCase_: Dict   				=    'lower newer'
         UpperCamelCase_: Any   				=    self.prepare_image_inputs()

         UpperCamelCase_: Optional[Any]   				=    processor(text=_lowerCamelCase  ,      images=_lowerCamelCase							)

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







import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch


if is_torch_available():
   import torch

   from transformers.activations import gelu_new, gelu_python, get_activation




@require_torch
class     _lowerCAmelCase(	unittest.TestCase ):





       """simple docstring"""







       def       _a   ( self							):
         UpperCamelCase_: Any   				=    torch.tensor([-1_0_0, -1, -0.1, 0, 0.1, 1.0, 1_0_0]							)
         UpperCamelCase_: List[str]   				=    get_activation('gelu'							)
         self.assertTrue(torch.allclose(gelu_python(_lowerCamelCase							)  ,      torch_builtin(_lowerCamelCase							)							)							)
         self.assertFalse(torch.allclose(gelu_python(_lowerCamelCase							)  ,      gelu_new(_lowerCamelCase							)							)							)





       def       _a   ( self							):
         UpperCamelCase_: Optional[Any]   				=    torch.tensor([-1_0_0, -1, -0.1, 0, 0.1, 1.0, 1_0_0]							)
         UpperCamelCase_: Union[str, Any]   				=    get_activation('gelu'							)
         UpperCamelCase_: int   				=    get_activation('gelu_10'							)

         UpperCamelCase_: Union[str, Any]   				=    torch_builtin(_lowerCamelCase							)
         UpperCamelCase_: List[str]   				=    geluaa(_lowerCamelCase							)

         UpperCamelCase_: Dict   				=    torch.where(y_gelu_aa < 1_0.0  ,      1  ,      0							)

         self.assertTrue(torch.max(_lowerCamelCase							).item() == 1_0.0							)
         self.assertTrue(torch.allclose(y_gelu * clipped_mask  ,      y_gelu_aa * clipped_mask							)							)





       def       _a   ( self							):
         get_activation('gelu'							)
         get_activation('gelu_10'							)
         get_activation('gelu_fast'							)
         get_activation('gelu_new'							)
         get_activation('gelu_python'							)
         get_activation('gelu_pytorch_tanh'							)
         get_activation('linear'							)
         get_activation('mish'							)
         get_activation('quick_gelu'							)
         get_activation('relu'							)
         get_activation('sigmoid'							)
         get_activation('silu'							)
         get_activation('swish'							)
         get_activation('tanh'							)
         with self.assertRaises(_lowerCamelCase							):
           get_activation('bogus'							)
         with self.assertRaises(_lowerCamelCase							):
           get_activation(_lowerCamelCase							)





       def       _a   ( self							):
         UpperCamelCase_: str   				=    get_activation('gelu'							)
         UpperCamelCase_: str   				=    1
         UpperCamelCase_: int   				=    get_activation('gelu'							)
         self.assertEqual(acta.a  ,      1							)
         with self.assertRaises(_lowerCamelCase							):
           UpperCamelCase_: Tuple   				=    acta.a