






'''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, BertTokenizer, BlipImageProcessor, BlipProcessor, PreTrainedTokenizerFast

@require_vision
class _lowercase			(       unittest.TestCase	):




							def 	lowerCamelCase_       (							self:       Tuple  ):
														lowerCamelCase__      :			List[Any] 						= tempfile.mkdtemp()

														lowerCamelCase__      :			int 						= BlipImageProcessor()
														lowerCamelCase__      :			Tuple 						= BertTokenizer.from_pretrained("""hf-internal-testing/tiny-random-BertModel"""  )

														lowerCamelCase__      :			List[Any] 						= BlipProcessor(UpperCamelCase__    ,							UpperCamelCase__  )

														processor.save_pretrained(self.tmpdirname  )




							def 	lowerCamelCase_       (							self:       List[Any]    ,							**UpperCamelCase__:       str  ):
														return AutoProcessor.from_pretrained(self.tmpdirname    ,							**UpperCamelCase__  ).tokenizer




							def 	lowerCamelCase_       (							self:       Union[str, Any]    ,							**UpperCamelCase__:       Optional[Any]  ):
														return AutoProcessor.from_pretrained(self.tmpdirname    ,							**UpperCamelCase__  ).image_processor




							def 	lowerCamelCase_       (							self:       List[str]  ):
														shutil.rmtree(self.tmpdirname  )




							def 	lowerCamelCase_       (							self:       Dict  ):
														lowerCamelCase__      :			Dict 						= [np.random.randint(255    ,							size=(3, 30, 400)    ,							dtype=np.uinta  )]

														lowerCamelCase__      :			Optional[int] 						= [Image.fromarray(np.moveaxis(UpperCamelCase__    ,							0    ,							-1  )  ) for x in image_inputs]

														return image_inputs




							def 	lowerCamelCase_       (							self:       Tuple  ):
														lowerCamelCase__      :			Dict 						= BlipProcessor(tokenizer=self.get_tokenizer()    ,							image_processor=self.get_image_processor()  )
														processor.save_pretrained(self.tmpdirname  )

														lowerCamelCase__      :			Optional[Any] 						= self.get_tokenizer(bos_token="""(BOS)"""    ,							eos_token="""(EOS)"""  )
														lowerCamelCase__      :			int 						= self.get_image_processor(do_normalize=UpperCamelCase__    ,							padding_value=1.0  )

														lowerCamelCase__      :			Any 						= BlipProcessor.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 	lowerCamelCase_       (							self:       Any  ):
														lowerCamelCase__      :			int 						= self.get_image_processor()
														lowerCamelCase__      :			int 						= self.get_tokenizer()

														lowerCamelCase__      :			int 						= BlipProcessor(tokenizer=UpperCamelCase__    ,							image_processor=UpperCamelCase__  )

														lowerCamelCase__      :			Optional[Any] 						= self.prepare_image_inputs()

														lowerCamelCase__      :			List[Any] 						= image_processor(UpperCamelCase__    ,							return_tensors="""np"""  )
														lowerCamelCase__      :			Dict 						= 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 	lowerCamelCase_       (							self:       Tuple  ):
														lowerCamelCase__      :			Optional[Any] 						= self.get_image_processor()
														lowerCamelCase__      :			List[str] 						= self.get_tokenizer()

														lowerCamelCase__      :			Optional[Any] 						= BlipProcessor(tokenizer=UpperCamelCase__    ,							image_processor=UpperCamelCase__  )

														lowerCamelCase__      :			str 						= """lower newer"""

														lowerCamelCase__      :			List[str] 						= processor(text=UpperCamelCase__  )

														lowerCamelCase__      :			str 						= tokenizer(UpperCamelCase__    ,							return_token_type_ids=UpperCamelCase__  )

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




							def 	lowerCamelCase_       (							self:       int  ):
														lowerCamelCase__      :			List[Any] 						= self.get_image_processor()
														lowerCamelCase__      :			Optional[Any] 						= self.get_tokenizer()

														lowerCamelCase__      :			Union[str, Any] 						= BlipProcessor(tokenizer=UpperCamelCase__    ,							image_processor=UpperCamelCase__  )

														lowerCamelCase__      :			str 						= """lower newer"""
														lowerCamelCase__      :			Union[str, Any] 						= self.prepare_image_inputs()

														lowerCamelCase__      :			int 						= processor(text=UpperCamelCase__    ,							images=UpperCamelCase__  )

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

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




							def 	lowerCamelCase_       (							self:       List[Any]  ):
														lowerCamelCase__      :			Tuple 						= self.get_image_processor()
														lowerCamelCase__      :			Union[str, Any] 						= self.get_tokenizer()

														lowerCamelCase__      :			Any 						= BlipProcessor(tokenizer=UpperCamelCase__    ,							image_processor=UpperCamelCase__  )

														lowerCamelCase__      :			str 						= [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

														lowerCamelCase__      :			Optional[int] 						= processor.batch_decode(UpperCamelCase__  )
														lowerCamelCase__      :			Tuple 						= tokenizer.batch_decode(UpperCamelCase__  )

														self.assertListEqual(UpperCamelCase__    ,							UpperCamelCase__  )




							def 	lowerCamelCase_       (							self:       str  ):
														lowerCamelCase__      :			List[Any] 						= self.get_image_processor()
														lowerCamelCase__      :			Tuple 						= self.get_tokenizer()

														lowerCamelCase__      :			Tuple 						= BlipProcessor(tokenizer=UpperCamelCase__    ,							image_processor=UpperCamelCase__  )

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

														lowerCamelCase__      :			List[str] 						= processor(text=UpperCamelCase__    ,							images=UpperCamelCase__  )

														# 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


_A			:    Dict					  ={
    '''configuration_git''': ['''GIT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GitConfig''', '''GitVisionConfig'''],
    '''processing_git''': ['''GitProcessor'''],
}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_A			:    Union[str, Any]					  =[
			    '''GIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''GitForCausalLM''',
			    '''GitModel''',
			    '''GitPreTrainedModel''',
			    '''GitVisionModel''',
			]

if TYPE_CHECKING:
			from .configuration_git import GIT_PRETRAINED_CONFIG_ARCHIVE_MAP, GitConfig, GitVisionConfig
			from .processing_git import GitProcessor

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_git import (
						    GIT_PRETRAINED_MODEL_ARCHIVE_LIST,
						    GitForCausalLM,
						    GitModel,
						    GitPreTrainedModel,
						    GitVisionModel,
						)

else:
			import sys

			_A			:    Dict					  =_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)

