


'''simple docstring'''






import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_torch_available, is_vision_available


if is_torch_available():
		import torch


if is_vision_available():
		from PIL import Image

		from transformers import MgpstrProcessor, ViTImageProcessor




@require_torch
@require_vision
class 						a__							(				unittest.TestCase    ):





							'''simple docstring'''






							lowercase__     :  List[Any]      		= ViTImageProcessor if is_vision_available() else None







							@property
							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Optional[Any]:
														return self.image_processor_tester.prepare_image_processor_dict()







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				List[Any]:
														lowerCAmelCase__			  =      (3, 32, 1_28)
														lowerCAmelCase__			  =      tempfile.mkdtemp()

														# fmt: off
														lowerCAmelCase__			  =      ['''[GO]''', '''[s]''', '''0''', '''1''', '''2''', '''3''', '''4''', '''5''', '''6''', '''7''', '''8''', '''9''', '''a''', '''b''', '''c''', '''d''', '''e''', '''f''', '''g''', '''h''', '''i''', '''j''', '''k''', '''l''', '''m''', '''n''', '''o''', '''p''', '''q''', '''r''', '''s''', '''t''', '''u''', '''v''', '''w''', '''x''', '''y''', '''z''']
														# fmt: on
														lowerCAmelCase__			  =      dict(zip(lowerCamelCase_   ,       range(len(lowerCamelCase_  )  )  )  )

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

														lowerCAmelCase__			  =      {
														    '''do_normalize''': False,
														    '''do_resize''': True,
														    '''image_processor_type''': '''ViTImageProcessor''',
														    '''resample''': 3,
														    '''size''': {'''height''': 32, '''width''': 1_28},
														}
														lowerCAmelCase__			  =      os.path.join(self.tmpdirname   ,       lowerCamelCase_  )
														with open(self.image_processor_file   ,       '''w'''   ,       encoding='''utf-8'''  ) as fp:
																					json.dump(lowerCamelCase_   ,       lowerCamelCase_  )







							def     __SCREAMING_SNAKE_CASE				(						self   ,       **lowerCamelCase_  )			->				Optional[int]:
														return MgpstrTokenizer.from_pretrained(self.tmpdirname   ,       **lowerCamelCase_  )







							def     __SCREAMING_SNAKE_CASE				(						self   ,       **lowerCamelCase_  )			->				Tuple:
														return ViTImageProcessor.from_pretrained(self.tmpdirname   ,       **lowerCamelCase_  )







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Any:
														shutil.rmtree(self.tmpdirname  )







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Tuple:
														lowerCAmelCase__			  =      np.random.randint(2_55   ,       size=(3, 30, 4_00)   ,       dtype=np.uinta  )

														lowerCAmelCase__			  =      Image.fromarray(np.moveaxis(lowerCamelCase_   ,       0   ,       -1  )  )

														return image_input







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Union[str, Any]:
														lowerCAmelCase__			  =      self.get_tokenizer()
														lowerCAmelCase__			  =      self.get_image_processor()

														lowerCAmelCase__			  =      MgpstrProcessor(tokenizer=lowerCamelCase_   ,       image_processor=lowerCamelCase_  )
														processor.save_pretrained(self.tmpdirname  )
														lowerCAmelCase__			  =      MgpstrProcessor.from_pretrained(self.tmpdirname   ,       use_fast=lowerCamelCase_  )

														self.assertEqual(processor.char_tokenizer.get_vocab()   ,       tokenizer.get_vocab()  )
														self.assertIsInstance(processor.char_tokenizer   ,       lowerCamelCase_  )

														self.assertEqual(processor.image_processor.to_json_string()   ,       image_processor.to_json_string()  )
														self.assertIsInstance(processor.image_processor   ,       lowerCamelCase_  )







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				int:
														lowerCAmelCase__			  =      self.get_tokenizer()
														lowerCAmelCase__			  =      self.get_image_processor()

														lowerCAmelCase__			  =      MgpstrProcessor(tokenizer=lowerCamelCase_   ,       image_processor=lowerCamelCase_  )
														processor.save_pretrained(self.tmpdirname  )

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

														lowerCAmelCase__			  =      MgpstrProcessor.from_pretrained(
														    self.tmpdirname   ,       bos_token='''(BOS)'''   ,       eos_token='''(EOS)'''   ,       do_normalize=lowerCamelCase_   ,       padding_value=1.0  )

														self.assertEqual(processor.char_tokenizer.get_vocab()   ,       tokenizer_add_kwargs.get_vocab()  )
														self.assertIsInstance(processor.char_tokenizer   ,       lowerCamelCase_  )

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







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Any:
														lowerCAmelCase__			  =      self.get_image_processor()
														lowerCAmelCase__			  =      self.get_tokenizer()

														lowerCAmelCase__			  =      MgpstrProcessor(tokenizer=lowerCamelCase_   ,       image_processor=lowerCamelCase_  )

														lowerCAmelCase__			  =      self.prepare_image_inputs()

														lowerCAmelCase__			  =      image_processor(lowerCamelCase_   ,       return_tensors='''np'''  )
														lowerCAmelCase__			  =      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     __SCREAMING_SNAKE_CASE				(						self  )			->				Union[str, Any]:
														lowerCAmelCase__			  =      self.get_image_processor()
														lowerCAmelCase__			  =      self.get_tokenizer()

														lowerCAmelCase__			  =      MgpstrProcessor(tokenizer=lowerCamelCase_   ,       image_processor=lowerCamelCase_  )

														lowerCAmelCase__			  =      '''test'''

														lowerCAmelCase__			  =      processor(text=lowerCamelCase_  )

														lowerCAmelCase__			  =      tokenizer(lowerCamelCase_  )
														for key in encoded_tok.keys():
																					self.assertListEqual(encoded_tok[key]   ,       encoded_processor[key]  )







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				int:
														lowerCAmelCase__			  =      self.get_image_processor()
														lowerCAmelCase__			  =      self.get_tokenizer()

														lowerCAmelCase__			  =      MgpstrProcessor(tokenizer=lowerCamelCase_   ,       image_processor=lowerCamelCase_  )

														lowerCAmelCase__			  =      '''test'''
														lowerCAmelCase__			  =      self.prepare_image_inputs()

														lowerCAmelCase__			  =      processor(text=lowerCamelCase_   ,       images=lowerCamelCase_  )

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

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







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				List[str]:
														lowerCAmelCase__			  =      self.get_image_processor()
														lowerCAmelCase__			  =      self.get_tokenizer()

														lowerCAmelCase__			  =      MgpstrProcessor(tokenizer=lowerCamelCase_   ,       image_processor=lowerCamelCase_  )

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

														lowerCAmelCase__			  =      processor.char_decode(lowerCamelCase_  )
														lowerCAmelCase__			  =      tokenizer.batch_decode(lowerCamelCase_  )
														lowerCAmelCase__			  =      [seq.replace(''' '''   ,       ''''''  ) for seq in decoded_tok]

														self.assertListEqual(lowerCamelCase_   ,       lowerCamelCase_  )







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Union[str, Any]:
														lowerCAmelCase__			  =      self.get_image_processor()
														lowerCAmelCase__			  =      self.get_tokenizer()

														lowerCAmelCase__			  =      MgpstrProcessor(tokenizer=lowerCamelCase_   ,       image_processor=lowerCamelCase_  )

														lowerCAmelCase__			  =      None
														lowerCAmelCase__			  =      self.prepare_image_inputs()

														lowerCAmelCase__			  =      processor(text=lowerCamelCase_   ,       images=lowerCamelCase_  )

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







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Optional[Any]:
														lowerCAmelCase__			  =      self.get_image_processor()
														lowerCAmelCase__			  =      self.get_tokenizer()

														lowerCAmelCase__			  =      MgpstrProcessor(tokenizer=lowerCamelCase_   ,       image_processor=lowerCamelCase_  )

														lowerCAmelCase__			  =      torch.randn(1   ,       27   ,       38  )
														lowerCAmelCase__			  =      torch.randn(1   ,       27   ,       5_02_57  )
														lowerCAmelCase__			  =      torch.randn(1   ,       27   ,       3_05_22  )

														lowerCAmelCase__			  =      processor.batch_decode([char_input, bpe_input, wp_input]  )

														self.assertListEqual(list(results.keys()  )   ,       ['''generated_text''', '''scores''', '''char_preds''', '''bpe_preds''', '''wp_preds''']  )



'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__UpperCAmelCase       							=				{
    '''configuration_clipseg''': [
        '''CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''CLIPSegConfig''',
        '''CLIPSegTextConfig''',
        '''CLIPSegVisionConfig''',
    ],
    '''processing_clipseg''': ['''CLIPSegProcessor'''],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__UpperCAmelCase       							=				[
		    '''CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''CLIPSegModel''',
		    '''CLIPSegPreTrainedModel''',
		    '''CLIPSegTextModel''',
		    '''CLIPSegVisionModel''',
		    '''CLIPSegForImageSegmentation''',
		]

if TYPE_CHECKING:
		from .configuration_clipseg import (
		    CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    CLIPSegConfig,
		    CLIPSegTextConfig,
		    CLIPSegVisionConfig,
		)
		from .processing_clipseg import CLIPSegProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_clipseg import (
				    CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST,
				    CLIPSegForImageSegmentation,
				    CLIPSegModel,
				    CLIPSegPreTrainedModel,
				    CLIPSegTextModel,
				    CLIPSegVisionModel,
				)

else:
		import sys

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