




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

import faiss
import numpy as np
from datasets import Dataset

from transformers import BartConfig, BartTokenizer, DPRConfig, DPRQuestionEncoderTokenizer, RagConfig
from transformers.file_utils import is_datasets_available, is_faiss_available, is_psutil_available, is_torch_available
from transformers.integrations import is_ray_available
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES as DPR_VOCAB_FILES_NAMES
from transformers.models.rag.retrieval_rag import CustomHFIndex, RagRetriever
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES as BART_VOCAB_FILES_NAMES
from transformers.testing_utils import require_ray


sys.path.append(os.path.join(os.getcwd()))  # noqa: E402 # noqa: E402 # isort:skip

if is_torch_available():
 from distributed_pytorch_retriever import RagPyTorchDistributedRetriever  # noqa: E402 # isort:skip
else:
 a_										=       None

if is_ray_available():
 import ray  # noqa: E402 # isort:skip
 from distributed_ray_retriever import RagRayDistributedRetriever, RayRetriever  # noqa: E402 # isort:skip
else:
 a_										=       None
 a_										=       None
 a_										=       None




def    __SCREAMING_SNAKE_CASE				(					lowercase_ )    ->		int:

    """simple docstring"""


    if not (is_datasets_available() and is_faiss_available() and is_psutil_available()):
        __UpperCamelCase											=  unittest.skip('''test requires Datasets, Faiss, psutil''' )(lowercase_ )
    return test_case


@require_distributed_retrieval
class 	_lowerCamelCase  (     _SCREAMING_SNAKE_CASE				):

      """simple docstring"""

      def  snake_case		(       self   :	List[Any]			):
          __UpperCamelCase											=  tempfile.mkdtemp()
          __UpperCamelCase											=  8

          # DPR tok
          __UpperCamelCase											=  [
              '''[UNK]''',
              '''[CLS]''',
              '''[SEP]''',
              '''[PAD]''',
              '''[MASK]''',
              '''want''',
              '''##want''',
              '''##ed''',
              '''wa''',
              '''un''',
              '''runn''',
              '''##ing''',
              ''',''',
              '''low''',
              '''lowest''',
          ]
          __UpperCamelCase											=  os.path.join(self.tmpdirname	,    '''dpr_tokenizer'''			)
          os.makedirs(snake_case	,    exist_ok=snake_case			)
          __UpperCamelCase											=  os.path.join(snake_case	,    DPR_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]			)			)

          # BART tok
          __UpperCamelCase											=  [
              '''l''',
              '''o''',
              '''w''',
              '''e''',
              '''r''',
              '''s''',
              '''t''',
              '''i''',
              '''d''',
              '''n''',
              '''\u0120''',
              '''\u0120l''',
              '''\u0120n''',
              '''\u0120lo''',
              '''\u0120low''',
              '''er''',
              '''\u0120lowest''',
              '''\u0120newer''',
              '''\u0120wider''',
              '''<unk>''',
          ]
          __UpperCamelCase											=  dict(zip(snake_case	,    range(len(snake_case			)			)			)			)
          __UpperCamelCase											=  ['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
          __UpperCamelCase											=  {'''unk_token''': '''<unk>'''}

          __UpperCamelCase											=  os.path.join(self.tmpdirname	,    '''bart_tokenizer'''			)
          os.makedirs(snake_case	,    exist_ok=snake_case			)
          __UpperCamelCase											=  os.path.join(snake_case	,    BART_VOCAB_FILES_NAMES['''vocab_file''']			)
          __UpperCamelCase											=  os.path.join(snake_case	,    BART_VOCAB_FILES_NAMES['''merges_file''']			)
          with open(self.vocab_file	,    '''w'''	,    encoding='''utf-8'''			) as fp:
              fp.write(json.dumps(snake_case			) + '''\n'''			)
          with open(self.merges_file	,    '''w'''	,    encoding='''utf-8'''			) as fp:
              fp.write('''\n'''.join(snake_case			)			)







      def  snake_case		(       self   :	Optional[Any]			):
          return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname	,    '''dpr_tokenizer'''			)			)







      def  snake_case		(       self   :	Tuple			):
          return BartTokenizer.from_pretrained(os.path.join(self.tmpdirname	,    '''bart_tokenizer'''			)			)







      def  snake_case		(       self   :	List[str]			):
          shutil.rmtree(self.tmpdirname			)







      def  snake_case		(       self   :	int			):
          __UpperCamelCase											=  Dataset.from_dict(
              {
                  '''id''': ['''0''', '''1'''],
                  '''text''': ['''foo''', '''bar'''],
                  '''title''': ['''Foo''', '''Bar'''],
                  '''embeddings''': [np.ones(self.retrieval_vector_size			), 2 * np.ones(self.retrieval_vector_size			)],
              }			)
          dataset.add_faiss_index('''embeddings'''	,    string_factory='''Flat'''	,    metric_type=faiss.METRIC_INNER_PRODUCT			)
          return dataset







      def  snake_case		(       self   :	List[Any]	,    snake_case   :	bool	,    snake_case   :	str=12345			):
          __UpperCamelCase											=  self.get_dummy_dataset()
          __UpperCamelCase											=  RagConfig(
              retrieval_vector_size=self.retrieval_vector_size	,    question_encoder=DPRConfig().to_dict()	,    generator=BartConfig().to_dict()	,    )
          with patch('''transformers.models.rag.retrieval_rag.load_dataset'''			) as mock_load_dataset:
              __UpperCamelCase											=  dataset
              __UpperCamelCase											=  RagPyTorchDistributedRetriever(
                  snake_case	,    question_encoder_tokenizer=self.get_dpr_tokenizer()	,    generator_tokenizer=self.get_bart_tokenizer()	,    )
              if init_retrieval:
                  retriever.init_retrieval(snake_case			)
          return retriever







      def  snake_case		(       self   :	Optional[int]	,    snake_case   :	bool			):
          # Have to run in local mode because sys.path modifications at top of
          # file are not propogated to remote workers.
          # https://stackoverflow.com/questions/54338013/parallel-import-a-python-file-from-sibling-folder
          ray.init(local_mode=snake_case			)
          __UpperCamelCase											=  RagConfig(
              retrieval_vector_size=self.retrieval_vector_size	,    question_encoder=DPRConfig().to_dict()	,    generator=BartConfig().to_dict()	,    )
          __UpperCamelCase											=  ray.remote(snake_case			)
          __UpperCamelCase											=  [remote_cls.remote() for _ in range(1			)]
          with patch('''transformers.models.rag.retrieval_rag.load_dataset'''			) as mock_load_dataset:
              __UpperCamelCase											=  self.get_dummy_dataset()
              __UpperCamelCase											=  RagRayDistributedRetriever(
                  snake_case	,    question_encoder_tokenizer=self.get_dpr_tokenizer()	,    generator_tokenizer=self.get_bart_tokenizer()	,    retrieval_workers=snake_case	,    )
              if init_retrieval:
                  retriever.init_retrieval()
          return retriever







      def  snake_case		(       self   :	int	,    snake_case   :	bool	,    snake_case   :	bool	,    snake_case   :	List[Any]=12345			):
          __UpperCamelCase											=  self.get_dummy_dataset()
          __UpperCamelCase											=  RagConfig(
              retrieval_vector_size=self.retrieval_vector_size	,    question_encoder=DPRConfig().to_dict()	,    generator=BartConfig().to_dict()	,    index_name='''custom'''	,    )
          if from_disk:
              __UpperCamelCase											=  os.path.join(self.tmpdirname	,    '''dataset'''			)
              __UpperCamelCase											=  os.path.join(self.tmpdirname	,    '''index.faiss'''			)
              dataset.get_index('''embeddings'''			).save(os.path.join(self.tmpdirname	,    '''index.faiss'''			)			)
              dataset.drop_index('''embeddings'''			)
              dataset.save_to_disk(os.path.join(self.tmpdirname	,    '''dataset'''			)			)
              del dataset
              __UpperCamelCase											=  RagPyTorchDistributedRetriever(
                  snake_case	,    question_encoder_tokenizer=self.get_dpr_tokenizer()	,    generator_tokenizer=self.get_bart_tokenizer()	,    )
          else:
              __UpperCamelCase											=  RagPyTorchDistributedRetriever(
                  snake_case	,    question_encoder_tokenizer=self.get_dpr_tokenizer()	,    generator_tokenizer=self.get_bart_tokenizer()	,    index=CustomHFIndex(config.retrieval_vector_size	,    snake_case			)	,    )
          if init_retrieval:
              retriever.init_retrieval(snake_case			)
          return retriever







      def  snake_case		(       self   :	str	,    snake_case   :	bool	,    snake_case   :	bool			):
          # Have to run in local mode because sys.path modifications at top of
          # file are not propogated to remote workers.
          # https://stackoverflow.com/questions/54338013/parallel-import-a-python-file-from-sibling-folder
          ray.init(local_mode=snake_case			)
          __UpperCamelCase											=  self.get_dummy_dataset()
          __UpperCamelCase											=  RagConfig(
              retrieval_vector_size=self.retrieval_vector_size	,    question_encoder=DPRConfig().to_dict()	,    generator=BartConfig().to_dict()	,    index_name='''custom'''	,    )
          __UpperCamelCase											=  ray.remote(snake_case			)
          __UpperCamelCase											=  [remote_cls.remote() for _ in range(1			)]
          if from_disk:
              __UpperCamelCase											=  os.path.join(self.tmpdirname	,    '''dataset'''			)
              __UpperCamelCase											=  os.path.join(self.tmpdirname	,    '''index.faiss'''			)
              dataset.get_index('''embeddings'''			).save(os.path.join(self.tmpdirname	,    '''index.faiss'''			)			)
              dataset.drop_index('''embeddings'''			)
              dataset.save_to_disk(os.path.join(self.tmpdirname	,    '''dataset'''			)			)
              del dataset
              __UpperCamelCase											=  RagRayDistributedRetriever(
                  snake_case	,    question_encoder_tokenizer=self.get_dpr_tokenizer()	,    generator_tokenizer=self.get_bart_tokenizer()	,    retrieval_workers=snake_case	,    index=CustomHFIndex.load_from_disk(
                      vector_size=config.retrieval_vector_size	,    dataset_path=config.passages_path	,    index_path=config.index_path	,    )	,    )
          else:
              __UpperCamelCase											=  RagRayDistributedRetriever(
                  snake_case	,    question_encoder_tokenizer=self.get_dpr_tokenizer()	,    generator_tokenizer=self.get_bart_tokenizer()	,    retrieval_workers=snake_case	,    index=CustomHFIndex(config.retrieval_vector_size	,    snake_case			)	,    )
          if init_retrieval:
              retriever.init_retrieval()
          return retriever







      def  snake_case		(       self   :	Dict	,    snake_case   :	RagRetriever	,    snake_case   :	np.array	,    snake_case   :	int			):
          __UpperCamelCase						, __UpperCamelCase						, __UpperCamelCase											=  retriever.retrieve(snake_case	,    n_docs=snake_case			)
          self.assertEqual(retrieved_doc_embeds.shape	,    (2, n_docs, self.retrieval_vector_size)			)
          self.assertEqual(len(snake_case			)	,    2			)
          self.assertEqual(sorted(doc_dicts[0]			)	,    ['''embeddings''', '''id''', '''text''', '''title''']			)
          self.assertEqual(len(doc_dicts[0]['''id''']			)	,    snake_case			)
          self.assertEqual(doc_dicts[0]['''id'''][0]	,    '''1'''			)  # max inner product is reached with second doc
          self.assertEqual(doc_dicts[1]['''id'''][0]	,    '''0'''			)  # max inner product is reached with first doc
          self.assertListEqual(doc_ids.tolist()	,    [[1], [0]]			)







      def  snake_case		(       self   :	Tuple			):
          __UpperCamelCase											=  1
          __UpperCamelCase											=  np.array(
              [np.ones(self.retrieval_vector_size			), -np.ones(self.retrieval_vector_size			)]	,    dtype=np.floataa			)

          self.distributed_retriever_check(
              self.get_dummy_pytorch_distributed_retriever(init_retrieval=snake_case			)	,    snake_case	,    snake_case			)







      def  snake_case		(       self   :	Union[str, Any]			):
          __UpperCamelCase											=  1
          __UpperCamelCase											=  np.array(
              [np.ones(self.retrieval_vector_size			), -np.ones(self.retrieval_vector_size			)]	,    dtype=np.floataa			)

          self.distributed_retriever_check(
              self.get_dummy_custom_hf_index_pytorch_retriever(init_retrieval=snake_case	,    from_disk=snake_case			)	,    snake_case	,    snake_case	,    )







      def  snake_case		(       self   :	Union[str, Any]			):
          __UpperCamelCase											=  1
          __UpperCamelCase											=  np.array(
              [np.ones(self.retrieval_vector_size			), -np.ones(self.retrieval_vector_size			)]	,    dtype=np.floataa			)

          self.distributed_retriever_check(
              self.get_dummy_custom_hf_index_pytorch_retriever(init_retrieval=snake_case	,    from_disk=snake_case			)	,    snake_case	,    snake_case	,    )







      @require_ray
      def  snake_case		(       self   :	Tuple			):
          __UpperCamelCase											=  1
          __UpperCamelCase											=  np.array(
              [np.ones(self.retrieval_vector_size			), -np.ones(self.retrieval_vector_size			)]	,    dtype=np.floataa			)

          self.distributed_retriever_check(
              self.get_dummy_ray_distributed_retriever(init_retrieval=snake_case			)	,    snake_case	,    snake_case			)
          ray.shutdown()







      @require_ray
      def  snake_case		(       self   :	List[Any]			):
          __UpperCamelCase											=  1
          __UpperCamelCase											=  np.array(
              [np.ones(self.retrieval_vector_size			), -np.ones(self.retrieval_vector_size			)]	,    dtype=np.floataa			)
          with self.assertRaises(snake_case			):
              self.distributed_retriever_check(
                  self.get_dummy_custom_hf_index_ray_retriever(init_retrieval=snake_case	,    from_disk=snake_case			)	,    snake_case	,    snake_case	,    )
          ray.shutdown()







      @require_ray
      def  snake_case		(       self   :	int			):
          __UpperCamelCase											=  1
          __UpperCamelCase											=  np.array(
              [np.ones(self.retrieval_vector_size			), -np.ones(self.retrieval_vector_size			)]	,    dtype=np.floataa			)

          self.distributed_retriever_check(
              self.get_dummy_custom_hf_index_ray_retriever(init_retrieval=snake_case	,    from_disk=snake_case			)	,    snake_case	,    snake_case			)
          ray.shutdown()












import gc
import unittest

import numpy as np
import torch

from diffusers import RePaintPipeline, RePaintScheduler, UNetaDModel
from diffusers.utils.testing_utils import (
    enable_full_determinism,
    load_image,
    load_numpy,
    nightly,
    require_torch_gpu,
    skip_mps,
    torch_device,
)

from ..pipeline_params import IMAGE_INPAINTING_BATCH_PARAMS, IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()

class 	_lowerCamelCase  (     _SCREAMING_SNAKE_CASE							,     unittest.TestCase				):

      """simple docstring"""

      lowerCAmelCase__     :	Union[str, Any]      =   RePaintPipeline
      lowerCAmelCase__     :	Optional[int]      =   IMAGE_INPAINTING_PARAMS - {"width", "height", "guidance_scale"}
      lowerCAmelCase__     :	Tuple      =   PipelineTesterMixin.required_optional_params - {
          "latents",
          "num_images_per_prompt",
          "callback",
          "callback_steps",
      }
      lowerCAmelCase__     :	List[str]      =   IMAGE_INPAINTING_BATCH_PARAMS
      lowerCAmelCase__     :	Dict      =   False







      def  snake_case		(       self   :	Optional[Any]			):
          torch.manual_seed(0			)
          torch.manual_seed(0			)
          __UpperCamelCase											=  UNetaDModel(
              block_out_channels=(32, 64)	,    layers_per_block=2	,    sample_size=32	,    in_channels=3	,    out_channels=3	,    down_block_types=('''DownBlock2D''', '''AttnDownBlock2D''')	,    up_block_types=('''AttnUpBlock2D''', '''UpBlock2D''')	,    )
          __UpperCamelCase											=  RePaintScheduler()
          __UpperCamelCase											=  {'''unet''': unet, '''scheduler''': scheduler}
          return components







      def  snake_case		(       self   :	int	,    snake_case   :	str	,    snake_case   :	List[Any]=0			):
          if str(snake_case			).startswith('''mps'''			):
              __UpperCamelCase											=  torch.manual_seed(snake_case			)
          else:
              __UpperCamelCase											=  torch.Generator(device=snake_case			).manual_seed(snake_case			)
          __UpperCamelCase											=  np.random.RandomState(snake_case			).standard_normal((1, 3, 32, 32)			)
          __UpperCamelCase											=  torch.from_numpy(snake_case			).to(device=snake_case	,    dtype=torch.floataa			)
          __UpperCamelCase											=  (image > 0).to(device=snake_case	,    dtype=torch.floataa			)
          __UpperCamelCase											=  {
              '''image''': image,
              '''mask_image''': mask,
              '''generator''': generator,
              '''num_inference_steps''': 5,
              '''eta''': 0.0,
              '''jump_length''': 2,
              '''jump_n_sample''': 2,
              '''output_type''': '''numpy''',
          }
          return inputs







      def  snake_case		(       self   :	str			):
          __UpperCamelCase											=  '''cpu'''  # ensure determinism for the device-dependent torch.Generator
          __UpperCamelCase											=  self.get_dummy_components()
          __UpperCamelCase											=  RePaintPipeline(**snake_case			)
          __UpperCamelCase											=  sd_pipe.to(snake_case			)
          sd_pipe.set_progress_bar_config(disable=snake_case			)

          __UpperCamelCase											=  self.get_dummy_inputs(snake_case			)
          __UpperCamelCase											=  sd_pipe(**snake_case			).images
          __UpperCamelCase											=  image[0, -3:, -3:, -1]

          assert image.shape == (1, 32, 32, 3)
          __UpperCamelCase											=  np.array([1.00_00, 0.54_26, 0.54_97, 0.22_00, 1.00_00, 1.00_00, 0.56_23, 1.00_00, 0.62_74]			)

          assert np.abs(image_slice.flatten() - expected_slice			).max() < 1E-3







      @skip_mps
      def  snake_case		(       self   :	str			):
          return super().test_save_load_local()







      @unittest.skip('''non-deterministic pipeline'''			)
      def  snake_case		(       self   :	Any			):
          return super().test_inference_batch_single_identical()







      @skip_mps
      def  snake_case		(       self   :	List[Any]			):
          return super().test_dict_tuple_outputs_equivalent()







      @skip_mps
      def  snake_case		(       self   :	List[str]			):
          return super().test_save_load_optional_components()







      @skip_mps
      def  snake_case		(       self   :	List[str]			):
          return super().test_attention_slicing_forward_pass()







@nightly
@require_torch_gpu
class 	_lowerCamelCase  (     unittest.TestCase				):

      """simple docstring"""

      def  snake_case		(       self   :	Dict			):
          super().tearDown()
          gc.collect()
          torch.cuda.empty_cache()







      def  snake_case		(       self   :	Any			):
          __UpperCamelCase											=  load_image(
              '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/'''
              '''repaint/celeba_hq_256.png'''			)
          __UpperCamelCase											=  load_image(
              '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/repaint/mask_256.png'''			)
          __UpperCamelCase											=  load_numpy(
              '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/'''
              '''repaint/celeba_hq_256_result.npy'''			)

          __UpperCamelCase											=  '''google/ddpm-ema-celebahq-256'''
          __UpperCamelCase											=  UNetaDModel.from_pretrained(snake_case			)
          __UpperCamelCase											=  RePaintScheduler.from_pretrained(snake_case			)

          __UpperCamelCase											=  RePaintPipeline(unet=snake_case	,    scheduler=snake_case			).to(snake_case			)
          repaint.set_progress_bar_config(disable=snake_case			)
          repaint.enable_attention_slicing()

          __UpperCamelCase											=  torch.manual_seed(0			)
          __UpperCamelCase											=  repaint(
              snake_case	,    snake_case	,    num_inference_steps=250	,    eta=0.0	,    jump_length=10	,    jump_n_sample=10	,    generator=snake_case	,    output_type='''np'''	,    )
          __UpperCamelCase											=  output.images[0]

          assert image.shape == (256, 256, 3)
          assert np.abs(expected_image - image			).mean() < 1E-2






