




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, CLIPImageProcessor, GitProcessor, PreTrainedTokenizerFast






@require_vision
class        __lowerCAmelCase					(			unittest.TestCase					):







			def UpperCamelCase     (			self       :     List[Any] ):




									"""simple docstring"""


									_UpperCAmelCase						=	tempfile.mkdtemp()

									_UpperCAmelCase						=	CLIPImageProcessor()
									_UpperCAmelCase						=	BertTokenizer.from_pretrained(
									    "hf-internal-testing/tiny-random-BertModel"       ,       model_input_names=["input_ids", "attention_mask"] )

									_UpperCAmelCase						=	GitProcessor(__snake_case       ,       __snake_case )

									processor.save_pretrained(self.tmpdirname )







			def UpperCamelCase     (			self       :     List[str]       ,       **snake_case__       :     List[Any] ):




									"""simple docstring"""


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







			def UpperCamelCase     (			self       :     Optional[Any]       ,       **snake_case__       :     str ):




									"""simple docstring"""


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







			def UpperCamelCase     (			self       :     Any ):




									"""simple docstring"""


									shutil.rmtree(self.tmpdirname )







			def UpperCamelCase     (			self       :     Tuple ):




									"""simple docstring"""


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

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

									return image_inputs







			def UpperCamelCase     (			self       :     Any ):




									"""simple docstring"""


									_UpperCAmelCase						=	GitProcessor(tokenizer=self.get_tokenizer()       ,       image_processor=self.get_image_processor() )
									processor.save_pretrained(self.tmpdirname )

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

									_UpperCAmelCase						=	GitProcessor.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 UpperCamelCase     (			self       :     List[Any] ):




									"""simple docstring"""


									_UpperCAmelCase						=	self.get_image_processor()
									_UpperCAmelCase						=	self.get_tokenizer()

									_UpperCAmelCase						=	GitProcessor(tokenizer=__snake_case       ,       image_processor=__snake_case )

									_UpperCAmelCase						=	self.prepare_image_inputs()

									_UpperCAmelCase						=	image_processor(__snake_case       ,       return_tensors="np" )
									_UpperCAmelCase						=	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 UpperCamelCase     (			self       :     Union[str, Any] ):




									"""simple docstring"""


									_UpperCAmelCase						=	self.get_image_processor()
									_UpperCAmelCase						=	self.get_tokenizer()

									_UpperCAmelCase						=	GitProcessor(tokenizer=__snake_case       ,       image_processor=__snake_case )

									_UpperCAmelCase						=	"lower newer"

									_UpperCAmelCase						=	processor(text=__snake_case )

									_UpperCAmelCase						=	tokenizer(__snake_case       ,       return_token_type_ids=__snake_case )

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







			def UpperCamelCase     (			self       :     str ):




									"""simple docstring"""


									_UpperCAmelCase						=	self.get_image_processor()
									_UpperCAmelCase						=	self.get_tokenizer()

									_UpperCAmelCase						=	GitProcessor(tokenizer=__snake_case       ,       image_processor=__snake_case )

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

									_UpperCAmelCase						=	processor(text=__snake_case       ,       images=__snake_case )

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

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







			def UpperCamelCase     (			self       :     List[str] ):




									"""simple docstring"""


									_UpperCAmelCase						=	self.get_image_processor()
									_UpperCAmelCase						=	self.get_tokenizer()

									_UpperCAmelCase						=	GitProcessor(tokenizer=__snake_case       ,       image_processor=__snake_case )

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

									_UpperCAmelCase						=	processor.batch_decode(__snake_case )
									_UpperCAmelCase						=	tokenizer.batch_decode(__snake_case )

									self.assertListEqual(__snake_case       ,       __snake_case )







			def UpperCamelCase     (			self       :     int ):




									"""simple docstring"""


									_UpperCAmelCase						=	self.get_image_processor()
									_UpperCAmelCase						=	self.get_tokenizer()

									_UpperCAmelCase						=	GitProcessor(tokenizer=__snake_case       ,       image_processor=__snake_case )

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

									_UpperCAmelCase						=	processor(text=__snake_case       ,       images=__snake_case )

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






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


snake_case_      :					Any				      =     {
    'configuration_imagegpt': ['IMAGEGPT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ImageGPTConfig', 'ImageGPTOnnxConfig']
}

try:
	if not is_vision_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	snake_case_      :					str				      =     ['ImageGPTFeatureExtractor']
	snake_case_      :					Optional[int]				      =     ['ImageGPTImageProcessor']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	snake_case_      :					List[str]				      =     [
	    'IMAGEGPT_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'ImageGPTForCausalImageModeling',
	    'ImageGPTForImageClassification',
	    'ImageGPTModel',
	    'ImageGPTPreTrainedModel',
	    'load_tf_weights_in_imagegpt',
	]


if TYPE_CHECKING:
	from .configuration_imagegpt import IMAGEGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, ImageGPTConfig, ImageGPTOnnxConfig

	try:
		if not is_vision_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .feature_extraction_imagegpt import ImageGPTFeatureExtractor
		from .image_processing_imagegpt import ImageGPTImageProcessor

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_imagegpt import (
		    IMAGEGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    ImageGPTForCausalImageModeling,
		    ImageGPTForImageClassification,
		    ImageGPTModel,
		    ImageGPTPreTrainedModel,
		    load_tf_weights_in_imagegpt,
		)

else:
	import sys

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