

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,
         BertTokenizerFast,
         BlipImageProcessor,
         GPTaTokenizer,
         InstructBlipProcessor,
         PreTrainedTokenizerFast,
     )







@require_vision
class 				lowercase       ( unittest.TestCase    ):




    def 					a							(      self ):
           snake_case_           =					tempfile.mkdtemp()

           snake_case_           =					BlipImageProcessor()
           snake_case_           =					GPTaTokenizer.from_pretrained('hf-internal-testing/tiny-random-GPT2Model' )
           snake_case_           =					BertTokenizerFast.from_pretrained('hf-internal-testing/tiny-random-bert' )

           snake_case_           =					InstructBlipProcessor(__lowerCamelCase   ,     __lowerCamelCase   ,     __lowerCamelCase )

           processor.save_pretrained(self.tmpdirname )




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




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




    def 					a							(      self   ,     **snake_case ):
           return AutoProcessor.from_pretrained(self.tmpdirname   ,     **__lowerCamelCase ).qformer_tokenizer




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




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

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

           return image_inputs




    def 					a							(      self ):
           snake_case_           =					InstructBlipProcessor(
               tokenizer=self.get_tokenizer()   ,     image_processor=self.get_image_processor()   ,     qformer_tokenizer=self.get_qformer_tokenizer()   ,     )
           processor.save_pretrained(self.tmpdirname )

           snake_case_           =					self.get_tokenizer(bos_token='(BOS)'   ,     eos_token='(EOS)' )
           snake_case_           =					self.get_image_processor(do_normalize=__lowerCamelCase   ,     padding_value=1.0 )

           snake_case_           =					InstructBlipProcessor.from_pretrained(
               self.tmpdirname   ,     bos_token='(BOS)'   ,     eos_token='(EOS)'   ,     do_normalize=__lowerCamelCase   ,     padding_value=1.0 )

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

           self.assertEqual(processor.image_processor.to_json_string()   ,     image_processor_add_kwargs.to_json_string() )
           self.assertIsInstance(processor.image_processor   ,     __lowerCamelCase )
           self.assertIsInstance(processor.qformer_tokenizer   ,     __lowerCamelCase )




    def 					a							(      self ):
           snake_case_           =					self.get_image_processor()
           snake_case_           =					self.get_tokenizer()
           snake_case_           =					self.get_qformer_tokenizer()

           snake_case_           =					InstructBlipProcessor(
               tokenizer=__lowerCamelCase   ,     image_processor=__lowerCamelCase   ,     qformer_tokenizer=__lowerCamelCase )

           snake_case_           =					self.prepare_image_inputs()

           snake_case_           =					image_processor(__lowerCamelCase   ,     return_tensors='np' )
           snake_case_           =					processor(images=__lowerCamelCase   ,     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 					a							(      self ):
           snake_case_           =					self.get_image_processor()
           snake_case_           =					self.get_tokenizer()
           snake_case_           =					self.get_qformer_tokenizer()

           snake_case_           =					InstructBlipProcessor(
               tokenizer=__lowerCamelCase   ,     image_processor=__lowerCamelCase   ,     qformer_tokenizer=__lowerCamelCase )

           snake_case_           =					"lower newer"

           snake_case_           =					processor(text=__lowerCamelCase )

           snake_case_           =					tokenizer(__lowerCamelCase   ,     return_token_type_ids=__lowerCamelCase )
           snake_case_           =					qformer_tokenizer(__lowerCamelCase   ,     return_token_type_ids=__lowerCamelCase )

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

           for key in encoded_tokens_qformer.keys():
                  self.assertListEqual(encoded_tokens_qformer[key]   ,     encoded_processor['qformer_' + key] )




    def 					a							(      self ):
           snake_case_           =					self.get_image_processor()
           snake_case_           =					self.get_tokenizer()
           snake_case_           =					self.get_qformer_tokenizer()

           snake_case_           =					InstructBlipProcessor(
               tokenizer=__lowerCamelCase   ,     image_processor=__lowerCamelCase   ,     qformer_tokenizer=__lowerCamelCase )

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

           snake_case_           =					processor(text=__lowerCamelCase   ,     images=__lowerCamelCase )

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

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




    def 					a							(      self ):
           snake_case_           =					self.get_image_processor()
           snake_case_           =					self.get_tokenizer()
           snake_case_           =					self.get_qformer_tokenizer()

           snake_case_           =					InstructBlipProcessor(
               tokenizer=__lowerCamelCase   ,     image_processor=__lowerCamelCase   ,     qformer_tokenizer=__lowerCamelCase )

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

           snake_case_           =					processor.batch_decode(__lowerCamelCase )
           snake_case_           =					tokenizer.batch_decode(__lowerCamelCase )

           self.assertListEqual(__lowerCamelCase   ,     __lowerCamelCase )




    def 					a							(      self ):
           snake_case_           =					self.get_image_processor()
           snake_case_           =					self.get_tokenizer()
           snake_case_           =					self.get_qformer_tokenizer()

           snake_case_           =					InstructBlipProcessor(
               tokenizer=__lowerCamelCase   ,     image_processor=__lowerCamelCase   ,     qformer_tokenizer=__lowerCamelCase )

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

           snake_case_           =					processor(text=__lowerCamelCase   ,     images=__lowerCamelCase )

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







# 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.multicontrolnet import MultiControlNetModel  # noqa: F401
from ..controlnet.pipeline_controlnet import StableDiffusionControlNetPipeline  # noqa: F401


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




