"""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						):
   def     _UpperCAmelCase     (    self    )			->   List[str]:


        '''simple docstring'''


        lowercase_			:       List[Any]									=			tempfile.mkdtemp()

        lowercase_			:       Tuple									=			[
            '[UNK]',
            '[CLS]',
            '[SEP]',
            '[PAD]',
            '[MASK]',
            'want',
            '##want',
            '##ed',
            'wa',
            'un',
            'runn',
            '##ing',
            ',',
            'low',
            'lowest',
        ]
        lowercase_			:       str									=			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_			:       int									=			{
            'do_resize': True,
            'size': 20,
            'do_center_crop': True,
            'crop_size': 18,
            'do_normalize': True,
            'image_mean': [0.4814_5466, 0.457_8275, 0.4082_1073],
            'image_std': [0.2686_2954, 0.2613_0258, 0.2757_7711],
        }
        lowercase_			:       Union[str, Any]									=			os.path.join(self.tmpdirname  ,__lowerCAmelCase    )
        with open(self.image_processor_file  ,'w'  ,encoding='utf-8'    ) as fp:
             json.dump(__lowerCAmelCase  ,__lowerCAmelCase    )
   def     _UpperCAmelCase     (    self  ,**__UpperCamelCase    )			->   int:


        '''simple docstring'''


        return BertTokenizer.from_pretrained(self.tmpdirname  ,**__lowerCAmelCase    )
   def     _UpperCAmelCase     (    self  ,**__UpperCamelCase    )			->   List[Any]:


        '''simple docstring'''


        return BertTokenizerFast.from_pretrained(self.tmpdirname  ,**__lowerCAmelCase    )
   def     _UpperCAmelCase     (    self  ,**__UpperCamelCase    )			->   Any:


        '''simple docstring'''


        return EfficientNetImageProcessor.from_pretrained(self.tmpdirname  ,**__lowerCAmelCase    )
   def     _UpperCAmelCase     (    self    )			->   Optional[int]:


        '''simple docstring'''


        shutil.rmtree(self.tmpdirname    )
   def     _UpperCAmelCase     (    self    )			->   Optional[Any]:


        '''simple docstring'''


        lowercase_			:       Optional[Any]									=			[np.random.randint(255  ,size=(3, 30, 400)  ,dtype=np.uinta    )]
        lowercase_			:       int									=			[Image.fromarray(np.moveaxis(__lowerCAmelCase  ,0  ,-1    )    ) for x in image_inputs]
        return image_inputs
   def     _UpperCAmelCase     (    self    )			->   Optional[Any]:


        '''simple docstring'''


        lowercase_			:       Union[str, Any]									=			self.get_tokenizer()
        lowercase_			:       int									=			self.get_rust_tokenizer()
        lowercase_			:       Optional[Any]									=			self.get_image_processor()

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

        lowercase_			:       List[str]									=			AlignProcessor(tokenizer=__lowerCAmelCase  ,image_processor=__lowerCAmelCase    )
        processor_fast.save_pretrained(self.tmpdirname    )
        lowercase_			:       Union[str, 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     _UpperCAmelCase     (    self    )			->   Any:


        '''simple docstring'''


        lowercase_			:       str									=			AlignProcessor(tokenizer=self.get_tokenizer()  ,image_processor=self.get_image_processor()    )
        processor.save_pretrained(self.tmpdirname    )

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

        lowercase_			:       str									=			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     _UpperCAmelCase     (    self    )			->   Tuple:


        '''simple docstring'''


        lowercase_			:       int									=			self.get_image_processor()
        lowercase_			:       Union[str, Any]									=			self.get_tokenizer()

        lowercase_			:       Tuple									=			AlignProcessor(tokenizer=__lowerCAmelCase  ,image_processor=__lowerCAmelCase    )

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

        lowercase_			:       Optional[Any]									=			image_processor(__lowerCAmelCase  ,return_tensors='np'    )
        lowercase_			:       Union[str, Any]									=			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     _UpperCAmelCase     (    self    )			->   Any:


        '''simple docstring'''


        lowercase_			:       int									=			self.get_image_processor()
        lowercase_			:       Optional[int]									=			self.get_tokenizer()

        lowercase_			:       int									=			AlignProcessor(tokenizer=__lowerCAmelCase  ,image_processor=__lowerCAmelCase    )

        lowercase_			:       Union[str, Any]									=			'lower newer'

        lowercase_			:       List[Any]									=			processor(text=__lowerCAmelCase    )

        lowercase_			:       Tuple									=			tokenizer(__lowerCAmelCase  ,padding='max_length'  ,max_length=64    )

        for key in encoded_tok.keys():
             self.assertListEqual(encoded_tok[key]  ,encoded_processor[key]    )
   def     _UpperCAmelCase     (    self    )			->   List[Any]:


        '''simple docstring'''


        lowercase_			:       str									=			self.get_image_processor()
        lowercase_			:       int									=			self.get_tokenizer()

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

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

        lowercase_			:       Optional[int]									=			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     _UpperCAmelCase     (    self    )			->   Any:


        '''simple docstring'''


        lowercase_			:       int									=			self.get_image_processor()
        lowercase_			:       str									=			self.get_tokenizer()

        lowercase_			:       List[Any]									=			AlignProcessor(tokenizer=__lowerCAmelCase  ,image_processor=__lowerCAmelCase    )

        lowercase_			:       Dict									=			[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

        lowercase_			:       Tuple									=			processor.batch_decode(__lowerCAmelCase    )
        lowercase_			:       Tuple									=			tokenizer.batch_decode(__lowerCAmelCase    )

        self.assertListEqual(__lowerCAmelCase  ,__lowerCAmelCase    )







   def     _UpperCAmelCase     (    self    )			->   Dict:


        '''simple docstring'''


        lowercase_			:       str									=			self.get_image_processor()
        lowercase_			:       Tuple									=			self.get_tokenizer()

        lowercase_			:       Union[str, Any]									=			AlignProcessor(tokenizer=__lowerCAmelCase  ,image_processor=__lowerCAmelCase    )

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

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

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


"""simple docstring"""







__SCREAMING_SNAKE_CASE								={
    "Pillow": "Pillow",
    "accelerate": "accelerate>=0.11.0",
    "compel": "compel==0.1.8",
    "black": "black~=23.1",
    "datasets": "datasets",
    "filelock": "filelock",
    "flax": "flax>=0.4.1",
    "hf-doc-builder": "hf-doc-builder>=0.3.0",
    "huggingface-hub": "huggingface-hub>=0.13.2",
    "requests-mock": "requests-mock==1.10.0",
    "importlib_metadata": "importlib_metadata",
    "invisible-watermark": "invisible-watermark",
    "isort": "isort>=5.5.4",
    "jax": "jax>=0.2.8,!=0.3.2",
    "jaxlib": "jaxlib>=0.1.65",
    "Jinja2": "Jinja2",
    "k-diffusion": "k-diffusion>=0.0.12",
    "torchsde": "torchsde",
    "note_seq": "note_seq",
    "librosa": "librosa",
    "numpy": "numpy",
    "omegaconf": "omegaconf",
    "parameterized": "parameterized",
    "protobuf": "protobuf>=3.20.3,<4",
    "pytest": "pytest",
    "pytest-timeout": "pytest-timeout",
    "pytest-xdist": "pytest-xdist",
    "ruff": "ruff>=0.0.241",
    "safetensors": "safetensors",
    "sentencepiece": "sentencepiece>=0.1.91,!=0.1.92",
    "scipy": "scipy",
    "onnx": "onnx",
    "regex": "regex!=2019.12.17",
    "requests": "requests",
    "tensorboard": "tensorboard",
    "torch": "torch>=1.4",
    "torchvision": "torchvision",
    "transformers": "transformers>=4.25.1",
    "urllib3": "urllib3<=2.0.0",
}

