
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     ):
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     Union[str, Any]       	=      tempfile.mkdtemp()

           lowercase							:     Tuple       	=      BlipImageProcessor()
           lowercase							:     str       	=      GPTaTokenizer.from_pretrained("""hf-internal-testing/tiny-random-GPT2Model"""      )
           lowercase							:     Dict       	=      BertTokenizerFast.from_pretrained("""hf-internal-testing/tiny-random-bert"""      )

           lowercase							:     Tuple       	=      InstructBlipProcessor(snake_case		,snake_case		,snake_case      )

           processor.save_pretrained(self.tmpdirname      )
    def       _SCREAMING_SNAKE_CASE       (  self		,**snake_case      ):



           '''simple docstring'''







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



           '''simple docstring'''







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



           '''simple docstring'''







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



           '''simple docstring'''







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



           '''simple docstring'''







           lowercase							:     Optional[Any]       	=      [np.random.randint(255		,size=(3, 30, 400)		,dtype=np.uinta      )]

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

           return image_inputs
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     Union[str, Any]       	=      InstructBlipProcessor(
               tokenizer=self.get_tokenizer()		,image_processor=self.get_image_processor()		,qformer_tokenizer=self.get_qformer_tokenizer()		,)
           processor.save_pretrained(self.tmpdirname      )

           lowercase							:     Optional[Any]       	=      self.get_tokenizer(bos_token="""(BOS)"""		,eos_token="""(EOS)"""      )
           lowercase							:     Union[str, Any]       	=      self.get_image_processor(do_normalize=snake_case		,padding_value=1.0      )

           lowercase							:     Dict       	=      InstructBlipProcessor.from_pretrained(
               self.tmpdirname		,bos_token="""(BOS)"""		,eos_token="""(EOS)"""		,do_normalize=snake_case		,padding_value=1.0      )

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

           self.assertEqual(processor.image_processor.to_json_string()		,image_processor_add_kwargs.to_json_string()      )
           self.assertIsInstance(processor.image_processor		,snake_case      )
           self.assertIsInstance(processor.qformer_tokenizer		,snake_case      )
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     str       	=      self.get_image_processor()
           lowercase							:     Optional[Any]       	=      self.get_tokenizer()
           lowercase							:     int       	=      self.get_qformer_tokenizer()

           lowercase							:     Optional[Any]       	=      InstructBlipProcessor(
               tokenizer=snake_case		,image_processor=snake_case		,qformer_tokenizer=snake_case      )

           lowercase							:     List[str]       	=      self.prepare_image_inputs()

           lowercase							:     Optional[int]       	=      image_processor(snake_case		,return_tensors="""np"""      )
           lowercase							:     Union[str, Any]       	=      processor(images=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       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     Union[str, Any]       	=      self.get_image_processor()
           lowercase							:     Optional[Any]       	=      self.get_tokenizer()
           lowercase							:     int       	=      self.get_qformer_tokenizer()

           lowercase							:     Dict       	=      InstructBlipProcessor(
               tokenizer=snake_case		,image_processor=snake_case		,qformer_tokenizer=snake_case      )

           lowercase							:     Optional[int]       	=      """lower newer"""

           lowercase							:     Any       	=      processor(text=snake_case      )

           lowercase							:     Any       	=      tokenizer(snake_case		,return_token_type_ids=snake_case      )
           lowercase							:     List[str]       	=      qformer_tokenizer(snake_case		,return_token_type_ids=snake_case      )

           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       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     List[Any]       	=      self.get_image_processor()
           lowercase							:     List[Any]       	=      self.get_tokenizer()
           lowercase							:     Optional[int]       	=      self.get_qformer_tokenizer()

           lowercase							:     List[Any]       	=      InstructBlipProcessor(
               tokenizer=snake_case		,image_processor=snake_case		,qformer_tokenizer=snake_case      )

           lowercase							:     Any       	=      """lower newer"""
           lowercase							:     Tuple       	=      self.prepare_image_inputs()

           lowercase							:     Any       	=      processor(text=snake_case		,images=snake_case      )

           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(snake_case      ):
                  processor()
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     Any       	=      self.get_image_processor()
           lowercase							:     str       	=      self.get_tokenizer()
           lowercase							:     str       	=      self.get_qformer_tokenizer()

           lowercase							:     Tuple       	=      InstructBlipProcessor(
               tokenizer=snake_case		,image_processor=snake_case		,qformer_tokenizer=snake_case      )

           lowercase							:     Tuple       	=      [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

           lowercase							:     Optional[Any]       	=      processor.batch_decode(snake_case      )
           lowercase							:     Any       	=      tokenizer.batch_decode(snake_case      )

           self.assertListEqual(snake_case		,snake_case      )
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     Optional[int]       	=      self.get_image_processor()
           lowercase							:     List[Any]       	=      self.get_tokenizer()
           lowercase							:     Dict       	=      self.get_qformer_tokenizer()

           lowercase							:     Tuple       	=      InstructBlipProcessor(
               tokenizer=snake_case		,image_processor=snake_case		,qformer_tokenizer=snake_case      )

           lowercase							:     int       	=      """lower newer"""
           lowercase							:     Any       	=      self.prepare_image_inputs()

           lowercase							:     Dict       	=      processor(text=snake_case		,images=snake_case      )

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




"""simple docstring"""





import gc
import unittest

from diffusers import FlaxDPMSolverMultistepScheduler, FlaxStableDiffusionPipeline
from diffusers.utils import is_flax_available, slow
from diffusers.utils.testing_utils import require_flax


if is_flax_available():
 import jax
 import jax.numpy as jnp
 from flax.jax_utils import replicate
 from flax.training.common_utils import shard



@slow
@require_flax
class 						snake_case_(				unittest.TestCase	):






     def    lowerCamelCase__   (		self     :    int ):
           # clean up the VRAM after each test
           super().tearDown()
           gc.collect()






     def    lowerCamelCase__   (		self     :    Optional[Any] ):
           lowerCAmelCase,					lowerCAmelCase    :		Optional[int]				=      FlaxStableDiffusionPipeline.from_pretrained(
               '''stabilityai/stable-diffusion-2'''    , revision='''bf16'''    , dtype=jnp.bfloataa    , )

           lowerCAmelCase    :		Optional[int]				=      '''A painting of a squirrel eating a burger'''
           lowerCAmelCase    :		List[str]				=      jax.device_count()
           lowerCAmelCase    :		Optional[int]				=      num_samples * [prompt]
           lowerCAmelCase    :		Any				=      sd_pipe.prepare_inputs(UpperCamelCase_ )

           lowerCAmelCase    :		Optional[int]				=      replicate(UpperCamelCase_ )
           lowerCAmelCase    :		Union[str, Any]				=      shard(UpperCamelCase_ )

           lowerCAmelCase    :		Optional[int]				=      jax.random.PRNGKey(0 )
           lowerCAmelCase    :		Optional[Any]				=      jax.random.split(UpperCamelCase_    , jax.device_count() )

           lowerCAmelCase    :		str				=      sd_pipe(UpperCamelCase_    , UpperCamelCase_    , UpperCamelCase_    , num_inference_steps=2_5    , jit=UpperCamelCase_ )[0]
           assert images.shape == (jax.device_count(), 1, 7_6_8, 7_6_8, 3)

           lowerCAmelCase    :		str				=      images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:] )
           lowerCAmelCase    :		List[str]				=      images[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

           lowerCAmelCase    :		Dict				=      jnp.asarray(jax.device_get(image_slice.flatten() ) )
           lowerCAmelCase    :		List[str]				=      jnp.array([0.4_238, 0.4_414, 0.4_395, 0.4_453, 0.4_629, 0.4_590, 0.4_531, 0.45_508, 0.4_512] )
           print(F'''output_slice: {output_slice}''' )
           assert jnp.abs(output_slice - expected_slice ).max() < 1E-2






     def    lowerCamelCase__   (		self     :    Union[str, Any] ):
           lowerCAmelCase    :		Union[str, Any]				=      '''stabilityai/stable-diffusion-2'''
           lowerCAmelCase,					lowerCAmelCase    :		Dict				=      FlaxDPMSolverMultistepScheduler.from_pretrained(UpperCamelCase_    , subfolder='''scheduler''' )
           lowerCAmelCase,					lowerCAmelCase    :		int				=      FlaxStableDiffusionPipeline.from_pretrained(
               UpperCamelCase_    , scheduler=UpperCamelCase_    , revision='''bf16'''    , dtype=jnp.bfloataa    , )
           lowerCAmelCase    :		List[Any]				=      scheduler_params

           lowerCAmelCase    :		List[Any]				=      '''A painting of a squirrel eating a burger'''
           lowerCAmelCase    :		Any				=      jax.device_count()
           lowerCAmelCase    :		int				=      num_samples * [prompt]
           lowerCAmelCase    :		int				=      sd_pipe.prepare_inputs(UpperCamelCase_ )

           lowerCAmelCase    :		Dict				=      replicate(UpperCamelCase_ )
           lowerCAmelCase    :		Tuple				=      shard(UpperCamelCase_ )

           lowerCAmelCase    :		int				=      jax.random.PRNGKey(0 )
           lowerCAmelCase    :		Optional[int]				=      jax.random.split(UpperCamelCase_    , jax.device_count() )

           lowerCAmelCase    :		Tuple				=      sd_pipe(UpperCamelCase_    , UpperCamelCase_    , UpperCamelCase_    , num_inference_steps=2_5    , jit=UpperCamelCase_ )[0]
           assert images.shape == (jax.device_count(), 1, 7_6_8, 7_6_8, 3)

           lowerCAmelCase    :		Any				=      images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:] )
           lowerCAmelCase    :		str				=      images[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

           lowerCAmelCase    :		Optional[int]				=      jnp.asarray(jax.device_get(image_slice.flatten() ) )
           lowerCAmelCase    :		Tuple				=      jnp.array([0.4_336, 0.42_969, 0.4_453, 0.4_199, 0.4_297, 0.4_531, 0.4_434, 0.4_434, 0.4_297] )
           print(F'''output_slice: {output_slice}''' )
           assert jnp.abs(output_slice - expected_slice ).max() < 1E-2


