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 						a__							(       unittest.TestCase		):



			def __SCREAMING_SNAKE_CASE							(						self   )			->   Dict:
								__a		 =       tempfile.mkdtemp()

								__a		 =       BlipImageProcessor()
								__a		 =       BertTokenizer.from_pretrained('hf-internal-testing/tiny-random-BertModel'   )

								__a		 =       BlipProcessor(UpperCAmelCase				,						UpperCAmelCase   )

								processor.save_pretrained(self.tmpdirname   )



			def __SCREAMING_SNAKE_CASE							(						self				,						**UpperCAmelCase   )			->   Dict:
								return AutoProcessor.from_pretrained(self.tmpdirname				,						**UpperCAmelCase   ).tokenizer



			def __SCREAMING_SNAKE_CASE							(						self				,						**UpperCAmelCase   )			->   Tuple:
								return AutoProcessor.from_pretrained(self.tmpdirname				,						**UpperCAmelCase   ).image_processor



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



			def __SCREAMING_SNAKE_CASE							(						self   )			->   Union[str, Any]:
								__a		 =       [np.random.randint(2_5_5				,						size=(3, 3_0, 4_0_0)				,						dtype=np.uinta   )]

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

								return image_inputs



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

								__a		 =       BlipProcessor(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   )			->   Tuple:
								__a		 =       self.get_image_processor()
								__a		 =       self.get_tokenizer()

								__a		 =       BlipProcessor(tokenizer=UpperCAmelCase				,						image_processor=UpperCAmelCase   )

								__a		 =       'lower newer'

								__a		 =       processor(text=UpperCAmelCase   )

								__a		 =       tokenizer(UpperCAmelCase				,						return_token_type_ids=UpperCAmelCase   )

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



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

								__a		 =       BlipProcessor(tokenizer=UpperCAmelCase				,						image_processor=UpperCAmelCase   )

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

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

								__a		 =       BlipProcessor(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   )







			def __SCREAMING_SNAKE_CASE							(						self   )			->   Optional[int]:
								__a		 =       self.get_image_processor()
								__a		 =       self.get_tokenizer()

								__a		 =       BlipProcessor(tokenizer=UpperCAmelCase				,						image_processor=UpperCAmelCase   )

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

								__a		 =       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']   )





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCamelCase_    :		Optional[int]							=	{"""configuration_xlnet""": ["""XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP""", """XLNetConfig"""]}

try:
	if not is_sentencepiece_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowerCamelCase_    :		str							=	["""XLNetTokenizer"""]

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowerCamelCase_    :		Optional[Any]							=	["""XLNetTokenizerFast"""]

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowerCamelCase_    :		Union[str, Any]							=	[
	    """XLNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """XLNetForMultipleChoice""",
	    """XLNetForQuestionAnswering""",
	    """XLNetForQuestionAnsweringSimple""",
	    """XLNetForSequenceClassification""",
	    """XLNetForTokenClassification""",
	    """XLNetLMHeadModel""",
	    """XLNetModel""",
	    """XLNetPreTrainedModel""",
	    """load_tf_weights_in_xlnet""",
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowerCamelCase_    :		Optional[int]							=	[
	    """TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """TFXLNetForMultipleChoice""",
	    """TFXLNetForQuestionAnsweringSimple""",
	    """TFXLNetForSequenceClassification""",
	    """TFXLNetForTokenClassification""",
	    """TFXLNetLMHeadModel""",
	    """TFXLNetMainLayer""",
	    """TFXLNetModel""",
	    """TFXLNetPreTrainedModel""",
	]


if TYPE_CHECKING:
	from .configuration_xlnet import XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP, XLNetConfig

	try:
		if not is_sentencepiece_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_xlnet import XLNetTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_xlnet_fast import XLNetTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_xlnet import (
		    XLNET_PRETRAINED_MODEL_ARCHIVE_LIST,
		    XLNetForMultipleChoice,
		    XLNetForQuestionAnswering,
		    XLNetForQuestionAnsweringSimple,
		    XLNetForSequenceClassification,
		    XLNetForTokenClassification,
		    XLNetLMHeadModel,
		    XLNetModel,
		    XLNetPreTrainedModel,
		    load_tf_weights_in_xlnet,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_xlnet import (
		    TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFXLNetForMultipleChoice,
		    TFXLNetForQuestionAnsweringSimple,
		    TFXLNetForSequenceClassification,
		    TFXLNetForTokenClassification,
		    TFXLNetLMHeadModel,
		    TFXLNetMainLayer,
		    TFXLNetModel,
		    TFXLNetPreTrainedModel,
		)

else:
	import sys

	lowerCamelCase_    :		Tuple							=	_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




