
import random
import unittest

import numpy as np
import torch

from diffusers import (
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    OnnxStableDiffusionUpscalePipeline,
    PNDMScheduler,
)
from diffusers.utils import floats_tensor
from diffusers.utils.testing_utils import (
    is_onnx_available,
    load_image,
    nightly,
    require_onnxruntime,
    require_torch_gpu,
)

from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin


if is_onnx_available():
  import onnxruntime as ort

class __snake_case		(      lowerCAmelCase					,	unittest.TestCase     ):
    # TODO: is there an appropriate internal test set?
    _a		:						List[str]=				"ssube/stable-diffusion-x4-upscaler-onnx"
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case=0      ):



           '''simple docstring'''







           lowercase							:     List[Any]       	=      floats_tensor((1, 3, 128, 128)		,rng=random.Random(snake_case      )      )
           lowercase							:     str       	=      torch.manual_seed(snake_case      )
           lowercase							:     Dict       	=      {
               """prompt""": """A painting of a squirrel eating a burger""",
               """image""": image,
               """generator""": generator,
               """num_inference_steps""": 3,
               """guidance_scale""": 7.5,
               """output_type""": """numpy""",
           }
           return inputs
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     Union[str, Any]       	=      OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint		,provider="""CPUExecutionProvider"""      )
           pipe.set_progress_bar_config(disable=snake_case      )

           lowercase							:     Tuple       	=      self.get_dummy_inputs()
           lowercase							:     List[str]       	=      pipe(**snake_case      ).images
           lowercase							:     List[str]       	=      image[0, -3:, -3:, -1].flatten()

           # started as 128, should now be 512
           assert image.shape == (1, 512, 512, 3)
           lowercase							:     List[Any]       	=      np.array(
               [0.6_974_782, 0.68_902_093, 0.70_135_885, 0.7_583_618, 0.7_804_545, 0.7_854_912, 0.78_667_426, 0.78_743_863, 0.78_070_223]      )
           assert np.abs(image_slice - expected_slice      ).max() < 1e-1
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     List[Any]       	=      OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint		,provider="""CPUExecutionProvider"""      )
           lowercase							:     Optional[int]       	=      PNDMScheduler.from_config(pipe.scheduler.config		,skip_prk_steps=snake_case      )
           pipe.set_progress_bar_config(disable=snake_case      )

           lowercase							:     Union[str, Any]       	=      self.get_dummy_inputs()
           lowercase							:     Tuple       	=      pipe(**snake_case      ).images
           lowercase							:     List[Any]       	=      image[0, -3:, -3:, -1]

           assert image.shape == (1, 512, 512, 3)
           lowercase							:     List[str]       	=      np.array(
               [0.6_898_892, 0.59_240_556, 0.52_499_527, 0.58_866_215, 0.52_258_235, 0.52_572_715, 0.62_414_473, 0.6_174_387, 0.6_214_964]      )
           assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1e-1
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     int       	=      OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint		,provider="""CPUExecutionProvider"""      )
           lowercase							:     Dict       	=      DPMSolverMultistepScheduler.from_config(pipe.scheduler.config      )
           pipe.set_progress_bar_config(disable=snake_case      )

           lowercase							:     int       	=      self.get_dummy_inputs()
           lowercase							:     str       	=      pipe(**snake_case      ).images
           lowercase							:     int       	=      image[0, -3:, -3:, -1]

           assert image.shape == (1, 512, 512, 3)
           lowercase							:     Any       	=      np.array(
               [0.7_659_278, 0.76_437_664, 0.75_579_107, 0.7_691_116, 0.77_666_986, 0.7_727_672, 0.7_758_664, 0.7_812_226, 0.76_942_515]      )

           assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1e-1
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     List[Any]       	=      OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint		,provider="""CPUExecutionProvider"""      )
           lowercase							:     str       	=      EulerDiscreteScheduler.from_config(pipe.scheduler.config      )
           pipe.set_progress_bar_config(disable=snake_case      )

           lowercase							:     List[Any]       	=      self.get_dummy_inputs()
           lowercase							:     Dict       	=      pipe(**snake_case      ).images
           lowercase							:     Any       	=      image[0, -3:, -3:, -1]

           assert image.shape == (1, 512, 512, 3)
           lowercase							:     Optional[int]       	=      np.array(
               [0.6_974_782, 0.68_902_093, 0.70_135_885, 0.7_583_618, 0.7_804_545, 0.7_854_912, 0.78_667_426, 0.78_743_863, 0.78_070_223]      )
           assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1e-1
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     str       	=      OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint		,provider="""CPUExecutionProvider"""      )
           lowercase							:     List[Any]       	=      EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config      )
           pipe.set_progress_bar_config(disable=snake_case      )

           lowercase							:     Tuple       	=      self.get_dummy_inputs()
           lowercase							:     Optional[int]       	=      pipe(**snake_case      ).images
           lowercase							:     Dict       	=      image[0, -3:, -3:, -1]

           assert image.shape == (1, 512, 512, 3)
           lowercase							:     str       	=      np.array(
               [0.77_424_496, 0.773_601, 0.7_645_288, 0.7_769_598, 0.7_772_739, 0.7_738_688, 0.78_187_233, 0.77_879_584, 0.767_043]      )

           assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1e-1






@nightly
@require_onnxruntime
@require_torch_gpu
class __snake_case		(      unittest.TestCase     ):
    @property
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           return (
               "CUDAExecutionProvider",
               {
                   "gpu_mem_limit": "15000000000",  # 15GB
                   "arena_extend_strategy": "kSameAsRequested",
               },
           )
    @property
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     Dict       	=      ort.SessionOptions()
           lowercase							:     Tuple       	=      False
           return options
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     Optional[Any]       	=      load_image(
               """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
               """/img2img/sketch-mountains-input.jpg"""      )
           lowercase							:     Dict       	=      init_image.resize((128, 128)      )
           # using the PNDM scheduler by default
           lowercase							:     List[Any]       	=      OnnxStableDiffusionUpscalePipeline.from_pretrained(
               """ssube/stable-diffusion-x4-upscaler-onnx"""		,provider=self.gpu_provider		,sess_options=self.gpu_options		,)
           pipe.set_progress_bar_config(disable=snake_case      )

           lowercase							:     List[str]       	=      """A fantasy landscape, trending on artstation"""

           lowercase							:     str       	=      torch.manual_seed(0      )
           lowercase							:     Any       	=      pipe(
               prompt=snake_case		,image=snake_case		,guidance_scale=7.5		,num_inference_steps=10		,generator=snake_case		,output_type="""np"""		,)
           lowercase							:     Optional[Any]       	=      output.images
           lowercase							:     str       	=      images[0, 255:258, 383:386, -1]

           assert images.shape == (1, 512, 512, 3)
           lowercase							:     Optional[int]       	=      np.array([0.4_883, 0.4_947, 0.4_980, 0.4_975, 0.4_982, 0.4_980, 0.5_000, 0.5_006, 0.4_972]      )
           # TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

           assert np.abs(image_slice.flatten() - expected_slice      ).max() < 2e-2
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     List[Any]       	=      load_image(
               """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
               """/img2img/sketch-mountains-input.jpg"""      )
           lowercase							:     Dict       	=      init_image.resize((128, 128)      )
           lowercase							:     Union[str, Any]       	=      LMSDiscreteScheduler.from_pretrained(
               """ssube/stable-diffusion-x4-upscaler-onnx"""		,subfolder="""scheduler"""      )
           lowercase							:     List[Any]       	=      OnnxStableDiffusionUpscalePipeline.from_pretrained(
               """ssube/stable-diffusion-x4-upscaler-onnx"""		,scheduler=snake_case		,provider=self.gpu_provider		,sess_options=self.gpu_options		,)
           pipe.set_progress_bar_config(disable=snake_case      )

           lowercase							:     int       	=      """A fantasy landscape, trending on artstation"""

           lowercase							:     Tuple       	=      torch.manual_seed(0      )
           lowercase							:     Any       	=      pipe(
               prompt=snake_case		,image=snake_case		,guidance_scale=7.5		,num_inference_steps=20		,generator=snake_case		,output_type="""np"""		,)
           lowercase							:     str       	=      output.images
           lowercase							:     int       	=      images[0, 255:258, 383:386, -1]

           assert images.shape == (1, 512, 512, 3)
           lowercase							:     Tuple       	=      np.array(
               [0.50_173_753, 0.50_223_356, 0.502_039, 0.50_233_036, 0.5_023_725, 0.5_022_601, 0.5_018_758, 0.50_234_085, 0.50_241_566]      )
           # TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

           assert np.abs(image_slice.flatten() - expected_slice      ).max() < 2e-2







'''simple docstring'''




import os
import shutil
import tempfile
from unittest import TestCase
from unittest.mock import patch

import numpy as np
from datasets import Dataset

from transformers.models.realm.configuration_realm import RealmConfig
from transformers.models.realm.retrieval_realm import _REALM_BLOCK_RECORDS_FILENAME, RealmRetriever
from transformers.models.realm.tokenization_realm import VOCAB_FILES_NAMES, RealmTokenizer


class  SCREAMING_SNAKE_CASE    (a__							):


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						: Any      =       tempfile.mkdtemp()
          __A						: str      =       5

          # Realm tok
          __A						: Union[str, Any]      =       [
              '[UNK]',
              '[CLS]',
              '[SEP]',
              '[PAD]',
              '[MASK]',
              'test',
              'question',
              'this',
              'is',
              'the',
              'first',
              'second',
              'third',
              'fourth',
              'fifth',
              'record',
              'want',
              '##want',
              '##ed',
              'wa',
              'un',
              'runn',
              '##ing',
              ',',
              'low',
              'lowest',
          ]
          __A						: Union[str, Any]      =       os.path.join(self.tmpdirname						,	'realm_tokenizer')
          os.makedirs(_UpperCAmelCase						,	exist_ok=_UpperCAmelCase)
          __A						: Tuple      =       os.path.join(_UpperCAmelCase						,	VOCAB_FILES_NAMES['vocab_file'])
          with open(self.vocab_file						,	'w'						,	encoding='utf-8') as vocab_writer:
               vocab_writer.write(''.join([x + '\n' for x in vocab_tokens]))

          __A						: int      =       os.path.join(self.tmpdirname						,	'realm_block_records')
          os.makedirs(_UpperCAmelCase						,	exist_ok=_UpperCAmelCase)


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          return RealmTokenizer.from_pretrained(os.path.join(self.tmpdirname						,	'realm_tokenizer'))


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          shutil.rmtree(self.tmpdirname)


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						: Union[str, Any]      =       RealmConfig(num_block_records=self.num_block_records)
          return config


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						: Dict      =       Dataset.from_dict(
              {
                  'id': ['0', '1'],
                  'question': ['foo', 'bar'],
                  'answers': [['Foo', 'Bar'], ['Bar']],
              })
          return dataset


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						: Any      =       np.array(
              [
                  B'This is the first record',
                  B'This is the second record',
                  B'This is the third record',
                  B'This is the fourth record',
                  B'This is the fifth record',
                  B'This is a longer longer longer record',
              ]						,	dtype=_UpperCAmelCase						,	)
          return block_records


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						: Optional[Any]      =       RealmRetriever(
              block_records=self.get_dummy_block_records()						,	tokenizer=self.get_tokenizer()						,	)
          return retriever


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						: List[Any]      =       self.get_config()
          __A						: str      =       self.get_dummy_retriever()
          __A						: List[str]      =       retriever.tokenizer

          __A						: Dict      =       np.array([0, 3]						,	dtype='long')
          __A						: Dict      =       tokenizer(['Test question']).input_ids
          __A						: Optional[Any]      =       tokenizer(
              ['the fourth']						,	add_special_tokens=_UpperCAmelCase						,	return_token_type_ids=_UpperCAmelCase						,	return_attention_mask=_UpperCAmelCase						,	).input_ids
          __A						: str      =       config.reader_seq_len

          __A ,__A ,__A ,__A						: List[str]      =       retriever(
              _UpperCAmelCase						,	_UpperCAmelCase						,	answer_ids=_UpperCAmelCase						,	max_length=_UpperCAmelCase						,	return_tensors='np')

          self.assertEqual(len(_UpperCAmelCase)						,	2)
          self.assertEqual(len(_UpperCAmelCase)						,	2)
          self.assertEqual(len(_UpperCAmelCase)						,	2)
          self.assertEqual(concat_inputs.input_ids.shape						,	(2, 10))
          self.assertEqual(concat_inputs.attention_mask.shape						,	(2, 10))
          self.assertEqual(concat_inputs.token_type_ids.shape						,	(2, 10))
          self.assertEqual(concat_inputs.special_tokens_mask.shape						,	(2, 10))
          self.assertEqual(
              tokenizer.convert_ids_to_tokens(concat_inputs.input_ids[0])						,	['[CLS]', 'test', 'question', '[SEP]', 'this', 'is', 'the', 'first', 'record', '[SEP]']						,	)
          self.assertEqual(
              tokenizer.convert_ids_to_tokens(concat_inputs.input_ids[1])						,	['[CLS]', 'test', 'question', '[SEP]', 'this', 'is', 'the', 'fourth', 'record', '[SEP]']						,	)


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						: int      =       self.get_config()
          __A						: Any      =       self.get_dummy_retriever()
          __A						: str      =       retriever.tokenizer

          __A						: Dict      =       np.array([0, 3, 5]						,	dtype='long')
          __A						: Tuple      =       tokenizer(['Test question']).input_ids
          __A						: Union[str, Any]      =       tokenizer(
              ['the fourth', 'longer longer']						,	add_special_tokens=_UpperCAmelCase						,	return_token_type_ids=_UpperCAmelCase						,	return_attention_mask=_UpperCAmelCase						,	).input_ids
          __A						: Dict      =       config.reader_seq_len

          __A ,__A ,__A ,__A						: str      =       retriever(
              _UpperCAmelCase						,	_UpperCAmelCase						,	answer_ids=_UpperCAmelCase						,	max_length=_UpperCAmelCase						,	return_tensors='np')

          self.assertEqual([False, True, True]						,	_UpperCAmelCase)
          self.assertEqual([[-1, -1, -1], [6, -1, -1], [6, 7, 8]]						,	_UpperCAmelCase)
          self.assertEqual([[-1, -1, -1], [7, -1, -1], [7, 8, 9]]						,	_UpperCAmelCase)


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						: Optional[Any]      =       self.get_dummy_retriever()
          retriever.save_pretrained(os.path.join(self.tmpdirname						,	'realm_block_records'))

          # Test local path
          __A						: str      =       retriever.from_pretrained(os.path.join(self.tmpdirname						,	'realm_block_records'))
          self.assertEqual(retriever.block_records[0]						,	B'This is the first record')

          # Test mocked remote path
          with patch('transformers.models.realm.retrieval_realm.hf_hub_download') as mock_hf_hub_download:
               __A						: int      =       os.path.join(
                   os.path.join(self.tmpdirname						,	'realm_block_records')						,	_REALM_BLOCK_RECORDS_FILENAME)
               __A						: Tuple      =       RealmRetriever.from_pretrained('google/realm-cc-news-pretrained-openqa')

          self.assertEqual(retriever.block_records[0]						,	B'This is the first record')