


'''simple docstring'''







import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available


if is_vision_available():
       from PIL import Image

       from transformers import AutoProcessor, BlipaProcessor, BlipImageProcessor, GPTaTokenizer, PreTrainedTokenizerFast
@require_vision
class 	UpperCamelCase__					( unittest.TestCase	):



     """simple docstring"""






     def a	( self   ):

            '''simple docstring'''

            _lowerCAmelCase    :							Optional[int]   			=       tempfile.mkdtemp()

            _lowerCAmelCase    :							Any   			=       BlipImageProcessor()
            _lowerCAmelCase    :							Optional[Any]   			=       GPTaTokenizer.from_pretrained('hf-internal-testing/tiny-random-GPT2Model'   )

            _lowerCAmelCase    :							Optional[int]   			=       BlipaProcessor(snake_case__      ,	snake_case__   )

            processor.save_pretrained(self.tmpdirname   )







     def a	( self      ,	**snake_case__   ):

            '''simple docstring'''

            return AutoProcessor.from_pretrained(self.tmpdirname      ,	**snake_case__   ).tokenizer







     def a	( self      ,	**snake_case__   ):

            '''simple docstring'''

            return AutoProcessor.from_pretrained(self.tmpdirname      ,	**snake_case__   ).image_processor







     def a	( self   ):

            '''simple docstring'''

            shutil.rmtree(self.tmpdirname   )







     def a	( self   ):

            '''simple docstring'''

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

            _lowerCAmelCase    :							Optional[Any]   			=       [Image.fromarray(np.moveaxis(snake_case__      ,	0      ,	-1   )   ) for x in image_inputs]

            return image_inputs







     def a	( self   ):

            '''simple docstring'''

            _lowerCAmelCase    :							Dict   			=       BlipaProcessor(tokenizer=self.get_tokenizer()      ,	image_processor=self.get_image_processor()   )
            processor.save_pretrained(self.tmpdirname   )

            _lowerCAmelCase    :							int   			=       self.get_tokenizer(bos_token='(BOS)'      ,	eos_token='(EOS)'   )
            _lowerCAmelCase    :							Union[str, Any]   			=       self.get_image_processor(do_normalize=snake_case__      ,	padding_value=1.0   )

            _lowerCAmelCase    :							Any   			=       BlipaProcessor.from_pretrained(
                self.tmpdirname      ,	bos_token='(BOS)'      ,	eos_token='(EOS)'      ,	do_normalize=snake_case__      ,	padding_value=1.0   )

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

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







     def a	( self   ):

            '''simple docstring'''

            _lowerCAmelCase    :							Any   			=       self.get_image_processor()
            _lowerCAmelCase    :							Any   			=       self.get_tokenizer()

            _lowerCAmelCase    :							Optional[Any]   			=       BlipaProcessor(tokenizer=snake_case__      ,	image_processor=snake_case__   )

            _lowerCAmelCase    :							Any   			=       self.prepare_image_inputs()

            _lowerCAmelCase    :							Optional[int]   			=       image_processor(snake_case__      ,	return_tensors='np'   )
            _lowerCAmelCase    :							Union[str, Any]   			=       processor(images=snake_case__      ,	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 a	( self   ):

            '''simple docstring'''

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

            _lowerCAmelCase    :							List[Any]   			=       BlipaProcessor(tokenizer=snake_case__      ,	image_processor=snake_case__   )

            _lowerCAmelCase    :							List[str]   			=       "lower newer"

            _lowerCAmelCase    :							Dict   			=       processor(text=snake_case__   )

            _lowerCAmelCase    :							Any   			=       tokenizer(snake_case__      ,	return_token_type_ids=snake_case__   )

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







     def a	( self   ):

            '''simple docstring'''

            _lowerCAmelCase    :							Tuple   			=       self.get_image_processor()
            _lowerCAmelCase    :							Any   			=       self.get_tokenizer()

            _lowerCAmelCase    :							Tuple   			=       BlipaProcessor(tokenizer=snake_case__      ,	image_processor=snake_case__   )

            _lowerCAmelCase    :							Tuple   			=       "lower newer"
            _lowerCAmelCase    :							str   			=       self.prepare_image_inputs()

            _lowerCAmelCase    :							Any   			=       processor(text=snake_case__      ,	images=snake_case__   )

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

            # test if it raises when no input is passed
            with pytest.raises(snake_case__   ):
                   processor()







     def a	( self   ):

            '''simple docstring'''

            _lowerCAmelCase    :							int   			=       self.get_image_processor()
            _lowerCAmelCase    :							Tuple   			=       self.get_tokenizer()

            _lowerCAmelCase    :							Optional[Any]   			=       BlipaProcessor(tokenizer=snake_case__      ,	image_processor=snake_case__   )

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

            _lowerCAmelCase    :							Optional[Any]   			=       processor.batch_decode(snake_case__   )
            _lowerCAmelCase    :							Union[str, Any]   			=       tokenizer.batch_decode(snake_case__   )

            self.assertListEqual(snake_case__      ,	snake_case__   )







     def a	( self   ):

            '''simple docstring'''

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

            _lowerCAmelCase    :							Optional[Any]   			=       BlipaProcessor(tokenizer=snake_case__      ,	image_processor=snake_case__   )

            _lowerCAmelCase    :							List[Any]   			=       "lower newer"
            _lowerCAmelCase    :							Tuple   			=       self.prepare_image_inputs()

            _lowerCAmelCase    :							Tuple   			=       processor(text=snake_case__      ,	images=snake_case__   )

            # For now the processor supports only ['pixel_values', 'input_ids', 'attention_mask']
            self.assertListEqual(list(inputs.keys()   )      ,	['pixel_values', 'input_ids', 'attention_mask']   )






'''simple docstring'''







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCAmelCase    :     Any					=						{
    """configuration_x_clip""": [
        """XCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """XCLIPConfig""",
        """XCLIPTextConfig""",
        """XCLIPVisionConfig""",
    ],
    """processing_x_clip""": ["""XCLIPProcessor"""],
}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCAmelCase    :     Optional[Any]					=						[
           """XCLIP_PRETRAINED_MODEL_ARCHIVE_LIST""",
           """XCLIPModel""",
           """XCLIPPreTrainedModel""",
           """XCLIPTextModel""",
           """XCLIPVisionModel""",
       ]

if TYPE_CHECKING:
       from .configuration_x_clip import (
           XCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
           XCLIPConfig,
           XCLIPTextConfig,
           XCLIPVisionConfig,
       )
       from .processing_x_clip import XCLIPProcessor

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_x_clip import (
                  XCLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
                  XCLIPModel,
                  XCLIPPreTrainedModel,
                  XCLIPTextModel,
                  XCLIPVisionModel,
              )

else:
       import sys

       lowerCAmelCase    :     Dict					=						_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

