
"""simple docstring"""


import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip 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 CLIPSegProcessor, ViTImageProcessor







@require_vision
class 				lowerCAmelCase_				(      unittest.TestCase    ):



      '''simple docstring'''


      def 						_SCREAMING_SNAKE_CASE      ( self     :      List[Any]      )     ->   int:
       A						   =   tempfile.mkdtemp()

       # fmt: off
       A						   =   ['l', 'o', 'w', 'e', 'r', 's', 't', 'i', 'd', 'n', 'lo', 'l</w>', 'w</w>', 'r</w>', 't</w>', 'low</w>', 'er</w>', 'lowest</w>', 'newer</w>', 'wider', '<unk>', '<|startoftext|>', '<|endoftext|>']
       # fmt: on
       A						   =   dict(zip(UpperCamelCase__  ,range(len(UpperCamelCase__      )      )      )      )
       A						   =   ['#version: 0.2', 'l o', 'lo w</w>', 'e r</w>', '']
       A						   =   {'unk_token': '<unk>'}

       A						   =   os.path.join(self.tmpdirname  ,VOCAB_FILES_NAMES['vocab_file']      )
       A						   =   os.path.join(self.tmpdirname  ,VOCAB_FILES_NAMES['merges_file']      )
       with open(self.vocab_file  ,'w'  ,encoding='utf-8'      ) as fp:
        fp.write(json.dumps(UpperCamelCase__      ) + '\n'      )
       with open(self.merges_file  ,'w'  ,encoding='utf-8'      ) as fp:
        fp.write('\n'.join(UpperCamelCase__      )      )

       A						   =   {
           '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],
       }
       A						   =   os.path.join(self.tmpdirname  ,UpperCamelCase__      )
       with open(self.image_processor_file  ,'w'  ,encoding='utf-8'      ) as fp:
        json.dump(UpperCamelCase__  ,UpperCamelCase__      )


      def 						_SCREAMING_SNAKE_CASE      ( self     :      str  ,**A_     :      List[Any]      )     ->   Optional[Any]:
       return CLIPTokenizer.from_pretrained(self.tmpdirname  ,**UpperCamelCase__      )


      def 						_SCREAMING_SNAKE_CASE      ( self     :      Dict  ,**A_     :      Optional[Any]      )     ->   Optional[int]:
       return CLIPTokenizerFast.from_pretrained(self.tmpdirname  ,**UpperCamelCase__      )


      def 						_SCREAMING_SNAKE_CASE      ( self     :      Optional[Any]  ,**A_     :      Optional[Any]      )     ->   Any:
       return ViTImageProcessor.from_pretrained(self.tmpdirname  ,**UpperCamelCase__      )


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


      def 						_SCREAMING_SNAKE_CASE      ( self     :      int      )     ->   Optional[int]:
       A						   =   [np.random.randint(255  ,size=(3, 30, 400)  ,dtype=np.uinta      )]

       A						   =   [Image.fromarray(np.moveaxis(UpperCamelCase__  ,0  ,-1      )      ) for x in image_inputs]

       return image_inputs


      def 						_SCREAMING_SNAKE_CASE      ( self     :      str      )     ->   str:
       A						   =   self.get_tokenizer()
       A						   =   self.get_rust_tokenizer()
       A						   =   self.get_image_processor()

       A						   =   CLIPSegProcessor(tokenizer=UpperCamelCase__  ,image_processor=UpperCamelCase__      )
       processor_slow.save_pretrained(self.tmpdirname      )
       A						   =   CLIPSegProcessor.from_pretrained(self.tmpdirname  ,use_fast=UpperCamelCase__      )

       A						   =   CLIPSegProcessor(tokenizer=UpperCamelCase__  ,image_processor=UpperCamelCase__      )
       processor_fast.save_pretrained(self.tmpdirname      )
       A						   =   CLIPSegProcessor.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  ,UpperCamelCase__      )
       self.assertIsInstance(processor_fast.tokenizer  ,UpperCamelCase__      )

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


      def 						_SCREAMING_SNAKE_CASE      ( self     :      List[str]      )     ->   Optional[int]:
       A						   =   CLIPSegProcessor(tokenizer=self.get_tokenizer()  ,image_processor=self.get_image_processor()      )
       processor.save_pretrained(self.tmpdirname      )

       A						   =   self.get_tokenizer(bos_token='(BOS)'  ,eos_token='(EOS)'      )
       A						   =   self.get_image_processor(do_normalize=UpperCamelCase__  ,padding_value=1.0      )

       A						   =   CLIPSegProcessor.from_pretrained(
           self.tmpdirname  ,bos_token='(BOS)'  ,eos_token='(EOS)'  ,do_normalize=UpperCamelCase__  ,padding_value=1.0      )

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

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


      def 						_SCREAMING_SNAKE_CASE      ( self     :      int      )     ->   str:
       A						   =   self.get_image_processor()
       A						   =   self.get_tokenizer()

       A						   =   CLIPSegProcessor(tokenizer=UpperCamelCase__  ,image_processor=UpperCamelCase__      )

       A						   =   self.prepare_image_inputs()

       A						   =   image_processor(UpperCamelCase__  ,return_tensors='np'      )
       A						   =   processor(images=UpperCamelCase__  ,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 						_SCREAMING_SNAKE_CASE      ( self     :      Optional[Any]      )     ->   Optional[int]:
       A						   =   self.get_image_processor()
       A						   =   self.get_tokenizer()

       A						   =   CLIPSegProcessor(tokenizer=UpperCamelCase__  ,image_processor=UpperCamelCase__      )

       A						   =   'lower newer'

       A						   =   processor(text=UpperCamelCase__      )

       A						   =   tokenizer(UpperCamelCase__      )

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


      def 						_SCREAMING_SNAKE_CASE      ( self     :      Any      )     ->   Any:
       A						   =   self.get_image_processor()
       A						   =   self.get_tokenizer()

       A						   =   CLIPSegProcessor(tokenizer=UpperCamelCase__  ,image_processor=UpperCamelCase__      )

       A						   =   'lower newer'
       A						   =   self.prepare_image_inputs()

       A						   =   processor(text=UpperCamelCase__  ,images=UpperCamelCase__      )

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

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


      def 						_SCREAMING_SNAKE_CASE      ( self     :      Any      )     ->   List[str]:
       A						   =   self.get_image_processor()
       A						   =   self.get_tokenizer()

       A						   =   CLIPSegProcessor(tokenizer=UpperCamelCase__  ,image_processor=UpperCamelCase__      )

       A						   =   self.prepare_image_inputs()
       A						   =   self.prepare_image_inputs()

       A						   =   processor(images=UpperCamelCase__  ,visual_prompt=UpperCamelCase__      )

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

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




      def 						_SCREAMING_SNAKE_CASE      ( self     :      Union[str, Any]      )     ->   List[Any]:
       A						   =   self.get_image_processor()
       A						   =   self.get_tokenizer()

       A						   =   CLIPSegProcessor(tokenizer=UpperCamelCase__  ,image_processor=UpperCamelCase__      )

       A						   =   [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

       A						   =   processor.batch_decode(UpperCamelCase__      )
       A						   =   tokenizer.batch_decode(UpperCamelCase__      )

       self.assertListEqual(UpperCamelCase__  ,UpperCamelCase__      )

"""simple docstring"""


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


_lowercase							   =				{
    '''configuration_perceiver''': ['''PERCEIVER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''PerceiverConfig''', '''PerceiverOnnxConfig'''],
    '''tokenization_perceiver''': ['''PerceiverTokenizer'''],
}

try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowercase							   =				['''PerceiverFeatureExtractor''']
		_lowercase							   =				['''PerceiverImageProcessor''']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowercase							   =				[
		    '''PERCEIVER_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''PerceiverForImageClassificationConvProcessing''',
		    '''PerceiverForImageClassificationFourier''',
		    '''PerceiverForImageClassificationLearned''',
		    '''PerceiverForMaskedLM''',
		    '''PerceiverForMultimodalAutoencoding''',
		    '''PerceiverForOpticalFlow''',
		    '''PerceiverForSequenceClassification''',
		    '''PerceiverLayer''',
		    '''PerceiverModel''',
		    '''PerceiverPreTrainedModel''',
		]


if TYPE_CHECKING:
		from .configuration_perceiver import PERCEIVER_PRETRAINED_CONFIG_ARCHIVE_MAP, PerceiverConfig, PerceiverOnnxConfig
		from .tokenization_perceiver import PerceiverTokenizer

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .feature_extraction_perceiver import PerceiverFeatureExtractor
				from .image_processing_perceiver import PerceiverImageProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_perceiver import (
				    PERCEIVER_PRETRAINED_MODEL_ARCHIVE_LIST,
				    PerceiverForImageClassificationConvProcessing,
				    PerceiverForImageClassificationFourier,
				    PerceiverForImageClassificationLearned,
				    PerceiverForMaskedLM,
				    PerceiverForMultimodalAutoencoding,
				    PerceiverForOpticalFlow,
				    PerceiverForSequenceClassification,
				    PerceiverLayer,
				    PerceiverModel,
				    PerceiverPreTrainedModel,
				)

else:
		import sys

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