






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 __lowerCamelCase (      unittest.TestCase):





	"""simple docstring"""






	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase									=			tempfile.mkdtemp()

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

							_UpperCAmelCase									=			BlipProcessor(UpperCAmelCase       ,    UpperCAmelCase    )

							processor.save_pretrained(self.tmpdirname    )

	def        UpperCamelCase    (							self       ,    **UpperCAmelCase    ):
							"""simple docstring"""







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

	def        UpperCamelCase    (							self       ,    **UpperCAmelCase    ):
							"""simple docstring"""







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

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							shutil.rmtree(self.tmpdirname    )

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







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

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

							return image_inputs

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase									=			BlipProcessor(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=UpperCAmelCase       ,    padding_value=1.0    )

							_UpperCAmelCase									=			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        UpperCamelCase    (							self    ):
							"""simple docstring"""







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

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

							_UpperCAmelCase									=			self.prepare_image_inputs()

							_UpperCAmelCase									=			image_processor(UpperCAmelCase       ,    return_tensors='np'    )
							_UpperCAmelCase									=			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        UpperCamelCase    (							self    ):
							"""simple docstring"""







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

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

							_UpperCAmelCase									=			'lower newer'

							_UpperCAmelCase									=			processor(text=UpperCAmelCase    )

							_UpperCAmelCase									=			tokenizer(UpperCAmelCase       ,    return_token_type_ids=UpperCAmelCase    )

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

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







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

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

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

							_UpperCAmelCase									=			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        UpperCamelCase    (							self    ):
							"""simple docstring"""







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

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

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

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

							self.assertListEqual(UpperCAmelCase       ,    UpperCAmelCase    )

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







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

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

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

							_UpperCAmelCase									=			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_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_a      	=  {'''configuration_opt''': ['''OPT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''OPTConfig''']}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_a      	=  [
		    '''OPT_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''OPTForCausalLM''',
		    '''OPTModel''',
		    '''OPTPreTrainedModel''',
		    '''OPTForSequenceClassification''',
		    '''OPTForQuestionAnswering''',
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_a      	=  ['''TFOPTForCausalLM''', '''TFOPTModel''', '''TFOPTPreTrainedModel''']

try:
		if not is_flax_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_a      	=  [
		    '''FlaxOPTForCausalLM''',
		    '''FlaxOPTModel''',
		    '''FlaxOPTPreTrainedModel''',
		]


if TYPE_CHECKING:
		from .configuration_opt import OPT_PRETRAINED_CONFIG_ARCHIVE_MAP, OPTConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_opt import (
				    OPT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    OPTForCausalLM,
				    OPTForQuestionAnswering,
				    OPTForSequenceClassification,
				    OPTModel,
				    OPTPreTrainedModel,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_opt import TFOPTForCausalLM, TFOPTModel, TFOPTPreTrainedModel

		try:
				if not is_flax_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_flax_opt import FlaxOPTForCausalLM, FlaxOPTModel, FlaxOPTPreTrainedModel

else:
		import sys

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