
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       _snake_case					(     _a				):







			def 			SCREAMING_SNAKE_CASE    (				self		):
				__magic_name__		:		Any	   =     tempfile.mkdtemp()
				__magic_name__		:		str	   =     5

				# Realm tok
				__magic_name__		:		Dict	   =     [
				    "[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",
				]
				__magic_name__		:		Tuple	   =     os.path.join(self.tmpdirname						,   "realm_tokenizer"		)
				os.makedirs(__UpperCAmelCase						,   exist_ok=__UpperCAmelCase		)
				__magic_name__		:		Optional[Any]	   =     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]		)		)

				__magic_name__		:		Dict	   =     os.path.join(self.tmpdirname						,   "realm_block_records"		)
				os.makedirs(__UpperCAmelCase						,   exist_ok=__UpperCAmelCase		)







			def 			SCREAMING_SNAKE_CASE    (				self		):
				return RealmTokenizer.from_pretrained(os.path.join(self.tmpdirname						,   "realm_tokenizer"		)		)







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







			def 			SCREAMING_SNAKE_CASE    (				self		):
				__magic_name__		:		Tuple	   =     RealmConfig(num_block_records=self.num_block_records		)
				return config







			def 			SCREAMING_SNAKE_CASE    (				self		):
				__magic_name__		:		Dict	   =     Dataset.from_dict(
				    {
				        "id": ["0", "1"],
				        "question": ["foo", "bar"],
				        "answers": [["Foo", "Bar"], ["Bar"]],
				    }		)
				return dataset







			def 			SCREAMING_SNAKE_CASE    (				self		):
				__magic_name__		:		List[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		):
				__magic_name__		:		Union[str, Any]	   =     RealmRetriever(
				    block_records=self.get_dummy_block_records()						,   tokenizer=self.get_tokenizer()						,   )
				return retriever







			def 			SCREAMING_SNAKE_CASE    (				self		):
				__magic_name__		:		Union[str, Any]	   =     self.get_config()
				__magic_name__		:		List[str]	   =     self.get_dummy_retriever()
				__magic_name__		:		Optional[Any]	   =     retriever.tokenizer

				__magic_name__		:		int	   =     np.array([0, 3]						,   dtype="long"		)
				__magic_name__		:		List[str]	   =     tokenizer(["Test question"]		).input_ids
				__magic_name__		:		Union[str, Any]	   =     tokenizer(
				    ["the fourth"]						,   add_special_tokens=__UpperCAmelCase						,   return_token_type_ids=__UpperCAmelCase						,   return_attention_mask=__UpperCAmelCase						,   ).input_ids
				__magic_name__		:		List[str]	   =     config.reader_seq_len

				__magic_name__		:		Optional[Any]	   =     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		):
				__magic_name__		:		Union[str, Any]	   =     self.get_config()
				__magic_name__		:		List[str]	   =     self.get_dummy_retriever()
				__magic_name__		:		str	   =     retriever.tokenizer

				__magic_name__		:		Any	   =     np.array([0, 3, 5]						,   dtype="long"		)
				__magic_name__		:		Dict	   =     tokenizer(["Test question"]		).input_ids
				__magic_name__		:		List[Any]	   =     tokenizer(
				    ["the fourth", "longer longer"]						,   add_special_tokens=__UpperCAmelCase						,   return_token_type_ids=__UpperCAmelCase						,   return_attention_mask=__UpperCAmelCase						,   ).input_ids
				__magic_name__		:		List[Any]	   =     config.reader_seq_len

				__magic_name__		:		Tuple	   =     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		):
				__magic_name__		:		str	   =     self.get_dummy_retriever()
				retriever.save_pretrained(os.path.join(self.tmpdirname						,   "realm_block_records"		)		)

				# Test local path
				__magic_name__		:		Any	   =     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:
					__magic_name__		:		Dict	   =     os.path.join(
					    os.path.join(self.tmpdirname						,   "realm_block_records"		)						,   _REALM_BLOCK_RECORDS_FILENAME		)
					__magic_name__		:		Any	   =     RealmRetriever.from_pretrained("google/realm-cc-news-pretrained-openqa"		)

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




"""simple docstring"""







import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, PNDMScheduler, StableDiffusionInpaintPipeline, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow

from ..pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()






class 						_A  (  _a    ,_a    ,_a    ,unittest.TestCase  ):







    """simple docstring"""




    UpperCAmelCase       :							str	=       StableDiffusionInpaintPipeline
    UpperCAmelCase       :							Optional[Any]	=       TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
    UpperCAmelCase       :							Any	=       TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
    UpperCAmelCase       :							Union[str, Any]	=       frozenset(
        []  )  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
    UpperCAmelCase       :							int	=       frozenset([]  )






    def 	__snake_case							(					self				:    Dict):
      torch.manual_seed(0)
      a						:		List[Any]      							=			UNetaDConditionModel(
          block_out_channels=(32, 64)				, layers_per_block=2				, sample_size=32				, in_channels=9				, out_channels=4				, down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")				, up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")				, cross_attention_dim=32				, attention_head_dim=(2, 4)				, use_linear_projection=__UpperCAmelCase				, )
      a						:		Tuple      							=			PNDMScheduler(skip_prk_steps=__UpperCAmelCase)
      torch.manual_seed(0)
      a						:		str      							=			AutoencoderKL(
          block_out_channels=[32, 64]				, in_channels=3				, out_channels=3				, down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]				, up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]				, latent_channels=4				, sample_size=128				, )
      torch.manual_seed(0)
      a						:		List[str]      							=			CLIPTextConfig(
          bos_token_id=0				, eos_token_id=2				, hidden_size=32				, intermediate_size=37				, layer_norm_eps=1e-05				, num_attention_heads=4				, num_hidden_layers=5				, pad_token_id=1				, vocab_size=1000				, hidden_act="gelu"				, projection_dim=512				, )
      a						:		Any      							=			CLIPTextModel(__UpperCAmelCase)
      a						:		List[str]      							=			CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")

      a						:		Optional[Any]      							=			{
          "unet": unet,
          "scheduler": scheduler,
          "vae": vae,
          "text_encoder": text_encoder,
          "tokenizer": tokenizer,
          "safety_checker": None,
          "feature_extractor": None,
      }
      return components






    def 	__snake_case							(					self				:    Union[str, Any]				, __UpperCAmelCase				:    Any				, __UpperCAmelCase				:    Union[str, Any]=0):
      # TODO: use tensor inputs instead of PIL, this is here just to leave the old expected_slices untouched
      a						:		Dict      							=			floats_tensor((1, 3, 32, 32)				, rng=random.Random(__UpperCAmelCase)).to(__UpperCAmelCase)
      a						:		List[str]      							=			image.cpu().permute(0				, 2				, 3				, 1)[0]
      a						:		Union[str, Any]      							=			Image.fromarray(np.uinta(__UpperCAmelCase)).convert("RGB").resize((64, 64))
      a						:		Dict      							=			Image.fromarray(np.uinta(image + 4)).convert("RGB").resize((64, 64))
      if str(__UpperCAmelCase).startswith("mps"):
        a						:		Tuple      							=			torch.manual_seed(__UpperCAmelCase)
      else:
        a						:		Tuple      							=			torch.Generator(device=__UpperCAmelCase).manual_seed(__UpperCAmelCase)
      a						:		Optional[Any]      							=			{
          "prompt": "A painting of a squirrel eating a burger",
          "image": init_image,
          "mask_image": mask_image,
          "generator": generator,
          "num_inference_steps": 2,
          "guidance_scale": 6.0,
          "output_type": "numpy",
      }
      return inputs






    def 	__snake_case							(					self				:    List[str]):
      a						:		Union[str, Any]      							=			"cpu"  # ensure determinism for the device-dependent torch.Generator
      a						:		Tuple      							=			self.get_dummy_components()
      a						:		Optional[int]      							=			StableDiffusionInpaintPipeline(**__UpperCAmelCase)
      a						:		int      							=			sd_pipe.to(__UpperCAmelCase)
      sd_pipe.set_progress_bar_config(disable=__UpperCAmelCase)

      a						:		Any      							=			self.get_dummy_inputs(__UpperCAmelCase)
      a						:		Optional[int]      							=			sd_pipe(**__UpperCAmelCase).images
      a						:		Optional[int]      							=			image[0, -3:, -3:, -1]

      assert image.shape == (1, 64, 64, 3)
      a						:		int      							=			np.array([0.4_727, 0.5_735, 0.3_941, 0.5_446, 0.5_926, 0.4_394, 0.5_062, 0.4_654, 0.4_476])

      assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2






    def 	__snake_case							(					self				:    str):
      super().test_inference_batch_single_identical(expected_max_diff=3e-3)






@slow
@require_torch_gpu
class 						_A  (  unittest.TestCase  ):







    """simple docstring"""




    def 	__snake_case							(					self				:    Union[str, Any]):
      # clean up the VRAM after each test
      super().tearDown()
      gc.collect()
      torch.cuda.empty_cache()






    def 	__snake_case							(					self				:    Dict):
      a						:		str      							=			load_image(
          "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
          "/sd2-inpaint/init_image.png")
      a						:		str      							=			load_image(
          "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png")
      a						:		List[Any]      							=			load_numpy(
          "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint"
          "/yellow_cat_sitting_on_a_park_bench.npy")

      a						:		Tuple      							=			"stabilityai/stable-diffusion-2-inpainting"
      a						:		Optional[Any]      							=			StableDiffusionInpaintPipeline.from_pretrained(__UpperCAmelCase				, safety_checker=__UpperCAmelCase)
      pipe.to(__UpperCAmelCase)
      pipe.set_progress_bar_config(disable=__UpperCAmelCase)
      pipe.enable_attention_slicing()

      a						:		Any      							=			"Face of a yellow cat, high resolution, sitting on a park bench"

      a						:		str      							=			torch.manual_seed(0)
      a						:		Union[str, Any]      							=			pipe(
          prompt=__UpperCAmelCase				, image=__UpperCAmelCase				, mask_image=__UpperCAmelCase				, generator=__UpperCAmelCase				, output_type="np"				, )
      a						:		List[str]      							=			output.images[0]

      assert image.shape == (512, 512, 3)
      assert np.abs(expected_image - image).max() < 9e-3






    def 	__snake_case							(					self				:    Any):
      a						:		str      							=			load_image(
          "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
          "/sd2-inpaint/init_image.png")
      a						:		Optional[Any]      							=			load_image(
          "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png")
      a						:		str      							=			load_numpy(
          "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint"
          "/yellow_cat_sitting_on_a_park_bench_fp16.npy")

      a						:		Optional[Any]      							=			"stabilityai/stable-diffusion-2-inpainting"
      a						:		Any      							=			StableDiffusionInpaintPipeline.from_pretrained(
          __UpperCAmelCase				, torch_dtype=torch.floataa				, safety_checker=__UpperCAmelCase				, )
      pipe.to(__UpperCAmelCase)
      pipe.set_progress_bar_config(disable=__UpperCAmelCase)
      pipe.enable_attention_slicing()

      a						:		Optional[int]      							=			"Face of a yellow cat, high resolution, sitting on a park bench"

      a						:		Dict      							=			torch.manual_seed(0)
      a						:		List[Any]      							=			pipe(
          prompt=__UpperCAmelCase				, image=__UpperCAmelCase				, mask_image=__UpperCAmelCase				, generator=__UpperCAmelCase				, output_type="np"				, )
      a						:		Optional[Any]      							=			output.images[0]

      assert image.shape == (512, 512, 3)
      assert np.abs(expected_image - image).max() < 5e-1






    def 	__snake_case							(					self				:    int):
      torch.cuda.empty_cache()
      torch.cuda.reset_max_memory_allocated()
      torch.cuda.reset_peak_memory_stats()

      a						:		Tuple      							=			load_image(
          "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
          "/sd2-inpaint/init_image.png")
      a						:		Optional[int]      							=			load_image(
          "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png")

      a						:		Optional[Any]      							=			"stabilityai/stable-diffusion-2-inpainting"
      a						:		Optional[int]      							=			PNDMScheduler.from_pretrained(__UpperCAmelCase				, subfolder="scheduler")
      a						:		int      							=			StableDiffusionInpaintPipeline.from_pretrained(
          __UpperCAmelCase				, safety_checker=__UpperCAmelCase				, scheduler=__UpperCAmelCase				, torch_dtype=torch.floataa				, )
      pipe.to(__UpperCAmelCase)
      pipe.set_progress_bar_config(disable=__UpperCAmelCase)
      pipe.enable_attention_slicing(1)
      pipe.enable_sequential_cpu_offload()

      a						:		Optional[int]      							=			"Face of a yellow cat, high resolution, sitting on a park bench"

      a						:		Optional[int]      							=			torch.manual_seed(0)
      a						:		str      							=			pipe(
          prompt=__UpperCAmelCase				, image=__UpperCAmelCase				, mask_image=__UpperCAmelCase				, generator=__UpperCAmelCase				, num_inference_steps=2				, output_type="np"				, )

      a						:		int      							=			torch.cuda.max_memory_allocated()
      # make sure that less than 2.65 GB is allocated
      assert mem_bytes < 2.65 * 10**9






