
"""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 			A__	(     unittest.TestCase):

		def 							__lowerCamelCase							(				self			):
				__lowerCAmelCase      :						int						      =				tempfile.mkdtemp()

				__lowerCAmelCase      :						Any						      =				BlipImageProcessor()
				__lowerCAmelCase      :						List[str]						      =				BertTokenizer.from_pretrained('hf-internal-testing/tiny-random-BertModel'			)

				__lowerCAmelCase      :						Tuple						      =				BlipProcessor(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE			)

				processor.save_pretrained(self.tmpdirname			)

		def 							__lowerCamelCase							(				self	,		**_SCREAMING_SNAKE_CASE			):
				return AutoProcessor.from_pretrained(self.tmpdirname	,		**_SCREAMING_SNAKE_CASE			).tokenizer

		def 							__lowerCamelCase							(				self	,		**_SCREAMING_SNAKE_CASE			):
				return AutoProcessor.from_pretrained(self.tmpdirname	,		**_SCREAMING_SNAKE_CASE			).image_processor

		def 							__lowerCamelCase							(				self			):
				shutil.rmtree(self.tmpdirname			)

		def 							__lowerCamelCase							(				self			):
				__lowerCAmelCase      :						Union[str, Any]						      =				[np.random.randint(2_55	,		size=(3, 30, 4_00)	,		dtype=np.uinta			)]

				__lowerCAmelCase      :						List[str]						      =				[Image.fromarray(np.moveaxis(_SCREAMING_SNAKE_CASE	,		0	,		-1			)			) for x in image_inputs]

				return image_inputs

		def 							__lowerCamelCase							(				self			):
				__lowerCAmelCase      :						Dict						      =				BlipProcessor(tokenizer=self.get_tokenizer()	,		image_processor=self.get_image_processor()			)
				processor.save_pretrained(self.tmpdirname			)

				__lowerCAmelCase      :						Union[str, Any]						      =				self.get_tokenizer(bos_token='(BOS)'	,		eos_token='(EOS)'			)
				__lowerCAmelCase      :						int						      =				self.get_image_processor(do_normalize=_SCREAMING_SNAKE_CASE	,		padding_value=1.0			)

				__lowerCAmelCase      :						Dict						      =				BlipProcessor.from_pretrained(
				    self.tmpdirname	,		bos_token='(BOS)'	,		eos_token='(EOS)'	,		do_normalize=_SCREAMING_SNAKE_CASE	,		padding_value=1.0			)

				self.assertEqual(processor.tokenizer.get_vocab()	,		tokenizer_add_kwargs.get_vocab()			)
				self.assertIsInstance(processor.tokenizer	,		_SCREAMING_SNAKE_CASE			)

				self.assertEqual(processor.image_processor.to_json_string()	,		image_processor_add_kwargs.to_json_string()			)
				self.assertIsInstance(processor.image_processor	,		_SCREAMING_SNAKE_CASE			)

		def 							__lowerCamelCase							(				self			):
				__lowerCAmelCase      :						Optional[int]						      =				self.get_image_processor()
				__lowerCAmelCase      :						List[str]						      =				self.get_tokenizer()

				__lowerCAmelCase      :						Optional[Any]						      =				BlipProcessor(tokenizer=_SCREAMING_SNAKE_CASE	,		image_processor=_SCREAMING_SNAKE_CASE			)

				__lowerCAmelCase      :						Dict						      =				self.prepare_image_inputs()

				__lowerCAmelCase      :						List[str]						      =				image_processor(_SCREAMING_SNAKE_CASE	,		return_tensors='np'			)
				__lowerCAmelCase      :						Optional[int]						      =				processor(images=_SCREAMING_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 							__lowerCamelCase							(				self			):
				__lowerCAmelCase      :						List[str]						      =				self.get_image_processor()
				__lowerCAmelCase      :						int						      =				self.get_tokenizer()

				__lowerCAmelCase      :						Tuple						      =				BlipProcessor(tokenizer=_SCREAMING_SNAKE_CASE	,		image_processor=_SCREAMING_SNAKE_CASE			)

				__lowerCAmelCase      :						List[str]						      =				'lower newer'

				__lowerCAmelCase      :						List[str]						      =				processor(text=_SCREAMING_SNAKE_CASE			)

				__lowerCAmelCase      :						Union[str, Any]						      =				tokenizer(_SCREAMING_SNAKE_CASE	,		return_token_type_ids=_SCREAMING_SNAKE_CASE			)

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

		def 							__lowerCamelCase							(				self			):
				__lowerCAmelCase      :						Tuple						      =				self.get_image_processor()
				__lowerCAmelCase      :						Any						      =				self.get_tokenizer()

				__lowerCAmelCase      :						Tuple						      =				BlipProcessor(tokenizer=_SCREAMING_SNAKE_CASE	,		image_processor=_SCREAMING_SNAKE_CASE			)

				__lowerCAmelCase      :						Optional[Any]						      =				'lower newer'
				__lowerCAmelCase      :						Tuple						      =				self.prepare_image_inputs()

				__lowerCAmelCase      :						List[str]						      =				processor(text=_SCREAMING_SNAKE_CASE	,		images=_SCREAMING_SNAKE_CASE			)

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

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

		def 							__lowerCamelCase							(				self			):
				__lowerCAmelCase      :						Any						      =				self.get_image_processor()
				__lowerCAmelCase      :						Dict						      =				self.get_tokenizer()

				__lowerCAmelCase      :						int						      =				BlipProcessor(tokenizer=_SCREAMING_SNAKE_CASE	,		image_processor=_SCREAMING_SNAKE_CASE			)

				__lowerCAmelCase      :						List[Any]						      =				[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

				__lowerCAmelCase      :						Tuple						      =				processor.batch_decode(_SCREAMING_SNAKE_CASE			)
				__lowerCAmelCase      :						Tuple						      =				tokenizer.batch_decode(_SCREAMING_SNAKE_CASE			)

				self.assertListEqual(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE			)







		def 							__lowerCamelCase							(				self			):
				__lowerCAmelCase      :						List[Any]						      =				self.get_image_processor()
				__lowerCAmelCase      :						List[Any]						      =				self.get_tokenizer()

				__lowerCAmelCase      :						List[Any]						      =				BlipProcessor(tokenizer=_SCREAMING_SNAKE_CASE	,		image_processor=_SCREAMING_SNAKE_CASE			)

				__lowerCAmelCase      :						int						      =				'lower newer'
				__lowerCAmelCase      :						List[str]						      =				self.prepare_image_inputs()

				__lowerCAmelCase      :						int						      =				processor(text=_SCREAMING_SNAKE_CASE	,		images=_SCREAMING_SNAKE_CASE			)

				# 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"""







# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works

from ...utils import deprecate
from ..controlnet.pipeline_flax_controlnet import FlaxStableDiffusionControlNetPipeline  # noqa: F401


deprecate(
    """stable diffusion controlnet""",
    """0.22.0""",
    """Importing `FlaxStableDiffusionControlNetPipeline` from diffusers.pipelines.stable_diffusion.flax_pipeline_stable_diffusion_controlnet is deprecated. Please import `from diffusers import FlaxStableDiffusionControlNetPipeline` instead.""",
    standard_warn=False,
    stacklevel=3,
)