


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





@require_vision
class      snake_case    (    unittest.TestCase			):





  """simple docstring"""




  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  tempfile.mkdtemp()

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

   lowerCamelCase_      =  InstructBlipProcessor(UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase     )

   processor.save_pretrained(self.tmpdirname     )






  def   snake_case		(    self			,  **UpperCamelCase     ):







   """simple docstring"""
   return AutoProcessor.from_pretrained(self.tmpdirname			,  **UpperCamelCase     ).tokenizer






  def   snake_case		(    self			,  **UpperCamelCase     ):







   """simple docstring"""
   return AutoProcessor.from_pretrained(self.tmpdirname			,  **UpperCamelCase     ).image_processor






  def   snake_case		(    self			,  **UpperCamelCase     ):







   """simple docstring"""
   return AutoProcessor.from_pretrained(self.tmpdirname			,  **UpperCamelCase     ).qformer_tokenizer






  def   snake_case		(    self     ):







   """simple docstring"""
   shutil.rmtree(self.tmpdirname     )






  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  [np.random.randint(255			,  size=(3, 30, 400)			,  dtype=np.uinta     )]

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

   return image_inputs






  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  InstructBlipProcessor(
       tokenizer=self.get_tokenizer()			,  image_processor=self.get_image_processor()			,  qformer_tokenizer=self.get_qformer_tokenizer()			,  )
   processor.save_pretrained(self.tmpdirname     )

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

   lowerCamelCase_      =  InstructBlipProcessor.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     )
   self.assertIsInstance(processor.qformer_tokenizer			,  UpperCamelCase     )






  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  self.get_image_processor()
   lowerCamelCase_      =  self.get_tokenizer()
   lowerCamelCase_      =  self.get_qformer_tokenizer()

   lowerCamelCase_      =  InstructBlipProcessor(
       tokenizer=UpperCamelCase			,  image_processor=UpperCamelCase			,  qformer_tokenizer=UpperCamelCase     )

   lowerCamelCase_      =  self.prepare_image_inputs()

   lowerCamelCase_      =  image_processor(UpperCamelCase			,  return_tensors="np"     )
   lowerCamelCase_      =  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   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  self.get_image_processor()
   lowerCamelCase_      =  self.get_tokenizer()
   lowerCamelCase_      =  self.get_qformer_tokenizer()

   lowerCamelCase_      =  InstructBlipProcessor(
       tokenizer=UpperCamelCase			,  image_processor=UpperCamelCase			,  qformer_tokenizer=UpperCamelCase     )

   lowerCamelCase_      =  "lower newer"

   lowerCamelCase_      =  processor(text=UpperCamelCase     )

   lowerCamelCase_      =  tokenizer(UpperCamelCase			,  return_token_type_ids=UpperCamelCase     )
   lowerCamelCase_      =  qformer_tokenizer(UpperCamelCase			,  return_token_type_ids=UpperCamelCase     )

   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   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  self.get_image_processor()
   lowerCamelCase_      =  self.get_tokenizer()
   lowerCamelCase_      =  self.get_qformer_tokenizer()

   lowerCamelCase_      =  InstructBlipProcessor(
       tokenizer=UpperCamelCase			,  image_processor=UpperCamelCase			,  qformer_tokenizer=UpperCamelCase     )

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

   lowerCamelCase_      =  processor(text=UpperCamelCase			,  images=UpperCamelCase     )

   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(UpperCamelCase     ):
    processor()






  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  self.get_image_processor()
   lowerCamelCase_      =  self.get_tokenizer()
   lowerCamelCase_      =  self.get_qformer_tokenizer()

   lowerCamelCase_      =  InstructBlipProcessor(
       tokenizer=UpperCamelCase			,  image_processor=UpperCamelCase			,  qformer_tokenizer=UpperCamelCase     )

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

   lowerCamelCase_      =  processor.batch_decode(UpperCamelCase     )
   lowerCamelCase_      =  tokenizer.batch_decode(UpperCamelCase     )

   self.assertListEqual(UpperCamelCase			,  UpperCamelCase     )







  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  self.get_image_processor()
   lowerCamelCase_      =  self.get_tokenizer()
   lowerCamelCase_      =  self.get_qformer_tokenizer()

   lowerCamelCase_      =  InstructBlipProcessor(
       tokenizer=UpperCamelCase			,  image_processor=UpperCamelCase			,  qformer_tokenizer=UpperCamelCase     )

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

   lowerCamelCase_      =  processor(text=UpperCamelCase			,  images=UpperCamelCase     )

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





'''simple docstring'''







from ...utils import is_torch_available, is_transformers_available


if is_transformers_available() and is_torch_available():
					from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline

