"""simple docstring"""







import json
import os
import shutil
import tempfile
import unittest

import numpy as np

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


if is_vision_available():
   from PIL import Image

   from transformers import VisionTextDualEncoderProcessor, ViTImageProcessor







@require_tokenizers
@require_vision
class  UpperCamelCase	(    unittest.TestCase						):
   def     _UpperCAmelCase     (    self    )			->   Dict:


        '''simple docstring'''


        lowercase_			:       Tuple									=			tempfile.mkdtemp()

        # fmt: off
        lowercase_			:       List[Any]									=			['[UNK]', '[CLS]', '[SEP]', '[PAD]', '[MASK]', 'want', '##want', '##ed', 'wa', 'un', 'runn', '##ing', ',', 'low', 'lowest']
        # fmt: on
        lowercase_			:       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]    )    )

        lowercase_			:       str									=			{
            'do_resize': True,
            'size': {'height': 18, 'width': 18},
            'do_normalize': True,
            'image_mean': [0.5, 0.5, 0.5],
            'image_std': [0.5, 0.5, 0.5],
        }
        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     _UpperCAmelCase     (    self  ,**__UpperCamelCase    )			->   List[str]:


        '''simple docstring'''


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


        '''simple docstring'''


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


        '''simple docstring'''


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


        '''simple docstring'''


        lowercase_			:       Any									=			[np.random.randint(255  ,size=(3, 30, 400)  ,dtype=np.uinta    )]

        lowercase_			:       List[str]									=			[Image.fromarray(np.moveaxis(_A  ,0  ,-1    )    ) for x in image_inputs]

        return image_inputs
   def     _UpperCAmelCase     (    self    )			->   int:


        '''simple docstring'''


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

        lowercase_			:       List[Any]									=			VisionTextDualEncoderProcessor(tokenizer=_A  ,image_processor=_A    )

        processor.save_pretrained(self.tmpdirname    )
        lowercase_			:       Union[str, Any]									=			VisionTextDualEncoderProcessor.from_pretrained(self.tmpdirname    )

        self.assertEqual(processor.tokenizer.get_vocab()  ,tokenizer.get_vocab()    )
        self.assertIsInstance(processor.tokenizer  ,(BertTokenizer, BertTokenizerFast)    )

        self.assertEqual(processor.image_processor.to_json_string()  ,image_processor.to_json_string()    )
        self.assertIsInstance(processor.image_processor  ,_A    )
   def     _UpperCAmelCase     (    self    )			->   int:


        '''simple docstring'''


        lowercase_			:       Tuple									=			VisionTextDualEncoderProcessor(
            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_			:       Tuple									=			self.get_image_processor(do_normalize=_A  ,padding_value=1.0    )

        lowercase_			:       List[str]									=			VisionTextDualEncoderProcessor.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  ,(BertTokenizer, BertTokenizerFast)    )

        self.assertEqual(processor.image_processor.to_json_string()  ,image_processor_add_kwargs.to_json_string()    )
        self.assertIsInstance(processor.image_processor  ,_A    )
   def     _UpperCAmelCase     (    self    )			->   Optional[Any]:


        '''simple docstring'''


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

        lowercase_			:       List[str]									=			VisionTextDualEncoderProcessor(tokenizer=_A  ,image_processor=_A    )

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

        lowercase_			:       Optional[int]									=			image_processor(_A  ,return_tensors='np'    )
        lowercase_			:       List[Any]									=			processor(images=_A  ,return_tensors='np'    )

        for key in input_feat_extract.keys():
             self.assertAlmostEqual(input_feat_extract[key].sum()  ,input_processor[key].sum()  ,delta=1e-2    )
   def     _UpperCAmelCase     (    self    )			->   Optional[int]:


        '''simple docstring'''


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

        lowercase_			:       Any									=			VisionTextDualEncoderProcessor(tokenizer=_A  ,image_processor=_A    )

        lowercase_			:       Tuple									=			'lower newer'

        lowercase_			:       Any									=			processor(text=_A    )

        lowercase_			:       str									=			tokenizer(_A    )

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


        '''simple docstring'''


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

        lowercase_			:       int									=			VisionTextDualEncoderProcessor(tokenizer=_A  ,image_processor=_A    )

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

        lowercase_			:       List[Any]									=			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 self.assertRaises(_A    ):
             processor()
   def     _UpperCAmelCase     (    self    )			->   List[Any]:


        '''simple docstring'''


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

        lowercase_			:       Tuple									=			VisionTextDualEncoderProcessor(tokenizer=_A  ,image_processor=_A    )

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

        lowercase_			:       List[str]									=			processor.batch_decode(_A    )
        lowercase_			:       Optional[Any]									=			tokenizer.batch_decode(_A    )

        self.assertListEqual(_A  ,_A    )







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


        '''simple docstring'''


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

        lowercase_			:       Tuple									=			VisionTextDualEncoderProcessor(tokenizer=_A  ,image_processor=_A    )

        lowercase_			:       Tuple									=			'lower newer'
        lowercase_			:       int									=			self.prepare_image_inputs()

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

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


"""simple docstring"""







from argparse import ArgumentParser

from .env import EnvironmentCommand



def     lowercase__( ):
     lowercase_			:       List[Any]									=			ArgumentParser('Diffusers CLI tool'  ,    usage='diffusers-cli <command> [<args>]'   )
     lowercase_			:       int									=			parser.add_subparsers(help='diffusers-cli command helpers'   )

     # Register commands
     EnvironmentCommand.register_subcommand(__SCREAMING_SNAKE_CASE   )

     # Let's go
     lowercase_			:       str									=			parser.parse_args()

     if not hasattr(__SCREAMING_SNAKE_CASE  ,    'func'   ):
          parser.print_help()
          exit(1   )

     # Run
     lowercase_			:       Optional[Any]									=			args.func(__SCREAMING_SNAKE_CASE   )
     service.run()


if __name__ == "__main__":
       main()

