
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    __A(					unittest.TestCase							):

					def  lowercase__   (						self       :  str ):
												lowerCamelCase_					=       tempfile.mkdtemp()

												# fmt: off
												lowerCamelCase_					=       ["""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
												lowerCamelCase_					=       dict(zip(__UpperCamelCase ,       range(len(__UpperCamelCase ) ) ) )
												lowerCamelCase_					=       ["""#version: 0.2""", """l o""", """lo w</w>""", """e r</w>""", """"""]
												lowerCamelCase_					=       {"""unk_token""": """<unk>"""}

												lowerCamelCase_					=       os.path.join(self.tmpdirname ,       VOCAB_FILES_NAMES["""vocab_file"""] )
												lowerCamelCase_					=       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 ) )

												lowerCamelCase_					=       {
												    """do_resize""": True,
												    """size""": 2_0,
												    """do_center_crop""": True,
												    """crop_size""": 1_8,
												    """do_normalize""": True,
												    """image_mean""": [0.48145466, 0.4578275, 0.40821073],
												    """image_std""": [0.26862954, 0.26130258, 0.27577711],
												}
												lowerCamelCase_					=       os.path.join(self.tmpdirname ,       __UpperCamelCase )
												with open(self.image_processor_file ,       """w""" ,       encoding="""utf-8""" ) as fp:
																			json.dump(__UpperCamelCase ,       __UpperCamelCase )

					def  lowercase__   (						self       :  Optional[int] ,       **__UpperCamelCase       :  Optional[Any] ):
												return CLIPTokenizer.from_pretrained(self.tmpdirname ,       **__UpperCamelCase )

					def  lowercase__   (						self       :  str ,       **__UpperCamelCase       :  Union[str, Any] ):
												return CLIPTokenizerFast.from_pretrained(self.tmpdirname ,       **__UpperCamelCase )

					def  lowercase__   (						self       :  Optional[int] ,       **__UpperCamelCase       :  Any ):
												return ViTImageProcessor.from_pretrained(self.tmpdirname ,       **__UpperCamelCase )

					def  lowercase__   (						self       :  Union[str, Any] ):
												shutil.rmtree(self.tmpdirname )

					def  lowercase__   (						self       :  Dict ):
												lowerCamelCase_					=       [np.random.randint(2_5_5 ,       size=(3, 3_0, 4_0_0) ,       dtype=np.uinta )]

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

												return image_inputs

					def  lowercase__   (						self       :  Dict ):
												lowerCamelCase_					=       self.get_tokenizer()
												lowerCamelCase_					=       self.get_rust_tokenizer()
												lowerCamelCase_					=       self.get_image_processor()

												lowerCamelCase_					=       CLIPSegProcessor(tokenizer=__UpperCamelCase ,       image_processor=__UpperCamelCase )
												processor_slow.save_pretrained(self.tmpdirname )
												lowerCamelCase_					=       CLIPSegProcessor.from_pretrained(self.tmpdirname ,       use_fast=__UpperCamelCase )

												lowerCamelCase_					=       CLIPSegProcessor(tokenizer=__UpperCamelCase ,       image_processor=__UpperCamelCase )
												processor_fast.save_pretrained(self.tmpdirname )
												lowerCamelCase_					=       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  lowercase__   (						self       :  List[str] ):
												lowerCamelCase_					=       CLIPSegProcessor(tokenizer=self.get_tokenizer() ,       image_processor=self.get_image_processor() )
												processor.save_pretrained(self.tmpdirname )

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

												lowerCamelCase_					=       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  lowercase__   (						self       :  Tuple ):
												lowerCamelCase_					=       self.get_image_processor()
												lowerCamelCase_					=       self.get_tokenizer()

												lowerCamelCase_					=       CLIPSegProcessor(tokenizer=__UpperCamelCase ,       image_processor=__UpperCamelCase )

												lowerCamelCase_					=       self.prepare_image_inputs()

												lowerCamelCase_					=       image_processor(__UpperCamelCase ,       return_tensors="""np""" )
												lowerCamelCase_					=       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  lowercase__   (						self       :  Any ):
												lowerCamelCase_					=       self.get_image_processor()
												lowerCamelCase_					=       self.get_tokenizer()

												lowerCamelCase_					=       CLIPSegProcessor(tokenizer=__UpperCamelCase ,       image_processor=__UpperCamelCase )

												lowerCamelCase_					=       """lower newer"""

												lowerCamelCase_					=       processor(text=__UpperCamelCase )

												lowerCamelCase_					=       tokenizer(__UpperCamelCase )

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

					def  lowercase__   (						self       :  Tuple ):
												lowerCamelCase_					=       self.get_image_processor()
												lowerCamelCase_					=       self.get_tokenizer()

												lowerCamelCase_					=       CLIPSegProcessor(tokenizer=__UpperCamelCase ,       image_processor=__UpperCamelCase )

												lowerCamelCase_					=       """lower newer"""
												lowerCamelCase_					=       self.prepare_image_inputs()

												lowerCamelCase_					=       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  lowercase__   (						self       :  Dict ):
												lowerCamelCase_					=       self.get_image_processor()
												lowerCamelCase_					=       self.get_tokenizer()

												lowerCamelCase_					=       CLIPSegProcessor(tokenizer=__UpperCamelCase ,       image_processor=__UpperCamelCase )

												lowerCamelCase_					=       self.prepare_image_inputs()
												lowerCamelCase_					=       self.prepare_image_inputs()

												lowerCamelCase_					=       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  lowercase__   (						self       :  Dict ):
												lowerCamelCase_					=       self.get_image_processor()
												lowerCamelCase_					=       self.get_tokenizer()

												lowerCamelCase_					=       CLIPSegProcessor(tokenizer=__UpperCamelCase ,       image_processor=__UpperCamelCase )

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

												lowerCamelCase_					=       processor.batch_decode(__UpperCamelCase )
												lowerCamelCase_					=       tokenizer.batch_decode(__UpperCamelCase )

												self.assertListEqual(__UpperCamelCase ,       __UpperCamelCase )







from typing import TYPE_CHECKING

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


lowercase   =	{
    '''configuration_layoutlmv2''': ['''LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''LayoutLMv2Config'''],
    '''processing_layoutlmv2''': ['''LayoutLMv2Processor'''],
    '''tokenization_layoutlmv2''': ['''LayoutLMv2Tokenizer'''],
}

try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase   =	['''LayoutLMv2TokenizerFast''']

try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase   =	['''LayoutLMv2FeatureExtractor''']
		lowercase   =	['''LayoutLMv2ImageProcessor''']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase   =	[
		    '''LAYOUTLMV2_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''LayoutLMv2ForQuestionAnswering''',
		    '''LayoutLMv2ForSequenceClassification''',
		    '''LayoutLMv2ForTokenClassification''',
		    '''LayoutLMv2Layer''',
		    '''LayoutLMv2Model''',
		    '''LayoutLMv2PreTrainedModel''',
		]

if TYPE_CHECKING:
		from .configuration_layoutlmva import LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP, LayoutLMvaConfig
		from .processing_layoutlmva import LayoutLMvaProcessor
		from .tokenization_layoutlmva import LayoutLMvaTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_layoutlmva_fast import LayoutLMvaTokenizerFast

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .feature_extraction_layoutlmva import LayoutLMvaFeatureExtractor, LayoutLMvaImageProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_layoutlmva import (
				    LAYOUTLMV2_PRETRAINED_MODEL_ARCHIVE_LIST,
				    LayoutLMvaForQuestionAnswering,
				    LayoutLMvaForSequenceClassification,
				    LayoutLMvaForTokenClassification,
				    LayoutLMvaLayer,
				    LayoutLMvaModel,
				    LayoutLMvaPreTrainedModel,
				)
else:
		import sys

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





