





import json
import os
import shutil
import tempfile
import unittest

import numpy as np

from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES, BertTokenizer
from transformers.testing_utils import require_tokenizers, require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
     from PIL import Image

     from transformers import VisionTextDualEncoderProcessor, ViTImageProcessor

@require_tokenizers
@require_vision
class   A							(						unittest.TestCase    ):



      def 						lowerCAmelCase__     (						self:    Any							)    ->     Optional[Any]:
         '''simple docstring'''

         UpperCAmelCase_           =tempfile.mkdtemp()

         # fmt: off
         UpperCAmelCase_           =["[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]", "want", "##want", "##ed", "wa", "un", "runn", "##ing", ",", "low", "lowest"]
         # fmt: on
         UpperCAmelCase_           =os.path.join(self.tmpdirname		,	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]							)							)

         UpperCAmelCase_           ={
             "do_resize": True,
             "size": {"height": 18, "width": 18},
             "do_normalize": True,
             "image_mean": [0.5, 0.5, 0.5],
             "image_std": [0.5, 0.5, 0.5],
         }
         UpperCAmelCase_           =os.path.join(self.tmpdirname		,	_lowerCAmelCase							)
         with open(self.image_processor_file		,	"w"		,	encoding="utf-8"							) as fp:
            json.dump(_lowerCAmelCase		,	_lowerCAmelCase							)



      def 						lowerCAmelCase__     (						self:    str		,	**_lowerCAmelCase:    List[Any]							)    ->     Dict:
         '''simple docstring'''

         return BertTokenizer.from_pretrained(self.tmpdirname		,	**_lowerCAmelCase							)



      def 						lowerCAmelCase__     (						self:    Union[str, Any]		,	**_lowerCAmelCase:    Tuple							)    ->     Optional[Any]:
         '''simple docstring'''

         return ViTImageProcessor.from_pretrained(self.tmpdirname		,	**_lowerCAmelCase							)



      def 						lowerCAmelCase__     (						self:    Any							)    ->     Any:
         '''simple docstring'''

         shutil.rmtree(self.tmpdirname							)



      def 						lowerCAmelCase__     (						self:    Optional[int]							)    ->     Optional[int]:
         '''simple docstring'''

         UpperCAmelCase_           =[np.random.randint(255		,	size=(3, 30, 400)		,	dtype=np.uinta							)]

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

         return image_inputs



      def 						lowerCAmelCase__     (						self:    int							)    ->     List[str]:
         '''simple docstring'''

         UpperCAmelCase_           =self.get_tokenizer()
         UpperCAmelCase_           =self.get_image_processor()

         UpperCAmelCase_           =VisionTextDualEncoderProcessor(tokenizer=_lowerCAmelCase		,	image_processor=_lowerCAmelCase							)

         processor.save_pretrained(self.tmpdirname							)
         UpperCAmelCase_           =VisionTextDualEncoderProcessor.from_pretrained(self.tmpdirname							)

         self.assertEqual(processor.tokenizer.get_vocab()		,	tokenizer.get_vocab()							)
         self.assertIsInstance(processor.tokenizer		,	(BertTokenizer, BertTokenizerFast)							)

         self.assertEqual(processor.image_processor.to_json_string()		,	image_processor.to_json_string()							)
         self.assertIsInstance(processor.image_processor		,	_lowerCAmelCase							)



      def 						lowerCAmelCase__     (						self:    List[str]							)    ->     Tuple:
         '''simple docstring'''

         UpperCAmelCase_           =VisionTextDualEncoderProcessor(
             tokenizer=self.get_tokenizer()		,	image_processor=self.get_image_processor()							)
         processor.save_pretrained(self.tmpdirname							)

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

         UpperCAmelCase_           =VisionTextDualEncoderProcessor.from_pretrained(
             self.tmpdirname		,	bos_token="(BOS)"		,	eos_token="(EOS)"		,	do_normalize=_lowerCAmelCase		,	padding_value=1.0							)

         self.assertEqual(processor.tokenizer.get_vocab()		,	tokenizer_add_kwargs.get_vocab()							)
         self.assertIsInstance(processor.tokenizer		,	(BertTokenizer, BertTokenizerFast)							)

         self.assertEqual(processor.image_processor.to_json_string()		,	image_processor_add_kwargs.to_json_string()							)
         self.assertIsInstance(processor.image_processor		,	_lowerCAmelCase							)



      def 						lowerCAmelCase__     (						self:    Union[str, Any]							)    ->     int:
         '''simple docstring'''

         UpperCAmelCase_           =self.get_image_processor()
         UpperCAmelCase_           =self.get_tokenizer()

         UpperCAmelCase_           =VisionTextDualEncoderProcessor(tokenizer=_lowerCAmelCase		,	image_processor=_lowerCAmelCase							)

         UpperCAmelCase_           =self.prepare_image_inputs()

         UpperCAmelCase_           =image_processor(_lowerCAmelCase		,	return_tensors="np"							)
         UpperCAmelCase_           =processor(images=_lowerCAmelCase		,	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 						lowerCAmelCase__     (						self:    Union[str, Any]							)    ->     Union[str, Any]:
         '''simple docstring'''

         UpperCAmelCase_           =self.get_image_processor()
         UpperCAmelCase_           =self.get_tokenizer()

         UpperCAmelCase_           =VisionTextDualEncoderProcessor(tokenizer=_lowerCAmelCase		,	image_processor=_lowerCAmelCase							)

         UpperCAmelCase_           ="lower newer"

         UpperCAmelCase_           =processor(text=_lowerCAmelCase							)

         UpperCAmelCase_           =tokenizer(_lowerCAmelCase							)

         for key in encoded_tok.keys():
            self.assertListEqual(encoded_tok[key]		,	encoded_processor[key]							)



      def 						lowerCAmelCase__     (						self:    List[str]							)    ->     Optional[int]:
         '''simple docstring'''

         UpperCAmelCase_           =self.get_image_processor()
         UpperCAmelCase_           =self.get_tokenizer()

         UpperCAmelCase_           =VisionTextDualEncoderProcessor(tokenizer=_lowerCAmelCase		,	image_processor=_lowerCAmelCase							)

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

         UpperCAmelCase_           =processor(text=_lowerCAmelCase		,	images=_lowerCAmelCase							)

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

         # test if it raises when no input is passed
         with self.assertRaises(_lowerCAmelCase							):
            processor()



      def 						lowerCAmelCase__     (						self:    List[str]							)    ->     List[Any]:
         '''simple docstring'''

         UpperCAmelCase_           =self.get_image_processor()
         UpperCAmelCase_           =self.get_tokenizer()

         UpperCAmelCase_           =VisionTextDualEncoderProcessor(tokenizer=_lowerCAmelCase		,	image_processor=_lowerCAmelCase							)

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

         UpperCAmelCase_           =processor.batch_decode(_lowerCAmelCase							)
         UpperCAmelCase_           =tokenizer.batch_decode(_lowerCAmelCase							)

         self.assertListEqual(_lowerCAmelCase		,	_lowerCAmelCase							)






      def 						lowerCAmelCase__     (						self:    Any							)    ->     Optional[int]:
         '''simple docstring'''

         UpperCAmelCase_           =self.get_image_processor()
         UpperCAmelCase_           =self.get_tokenizer()

         UpperCAmelCase_           =VisionTextDualEncoderProcessor(tokenizer=_lowerCAmelCase		,	image_processor=_lowerCAmelCase							)

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

         UpperCAmelCase_           =processor(text=_lowerCAmelCase		,	images=_lowerCAmelCase							)

         self.assertListEqual(list(inputs.keys()							)		,	processor.model_input_names							)






"""simple docstring"""




import gc
import random
import unittest

import torch

from diffusers import (
    IFImgaImgPipeline,
    IFImgaImgSuperResolutionPipeline,
    IFInpaintingPipeline,
    IFInpaintingSuperResolutionPipeline,
    IFPipeline,
    IFSuperResolutionPipeline,
)
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import floats_tensor, load_numpy, require_torch_gpu, skip_mps, slow, torch_device

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
from . import IFPipelineTesterMixin

@skip_mps
class 						a__		( a_,     a_,     unittest.TestCase		):
   __lowerCAmelCase				=							IFPipeline
   __lowerCAmelCase				=							TEXT_TO_IMAGE_PARAMS - {"""width""", """height""", """latents"""}
   __lowerCAmelCase				=							TEXT_TO_IMAGE_BATCH_PARAMS
   __lowerCAmelCase				=							PipelineTesterMixin.required_optional_params - {"""latents"""}
   def      __magic_name__     (     self							):
          return self._get_dummy_components()
   def      __magic_name__     (     self      ,       _a      ,       _a=0							):
          if str(_a							).startswith("mps"							):
                 lowercase       :							List[str]									=				torch.manual_seed(_a							)
          else:
                 lowercase       :							Dict									=				torch.Generator(device=_a							).manual_seed(_a							)

          lowercase       :							str									=				{
              "prompt": "A painting of a squirrel eating a burger",
              "generator": generator,
              "num_inference_steps": 2,
              "output_type": "numpy",
          }

          return inputs
   def      __magic_name__     (     self							):
          self._test_save_load_optional_components()
   @unittest.skipIf(torch_device != "cuda"      ,       reason="float16 requires CUDA"							)
   def      __magic_name__     (     self							):
          # Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
          super().test_save_load_floataa(expected_max_diff=1E-1							)
   def      __magic_name__     (     self							):
          self._test_attention_slicing_forward_pass(expected_max_diff=1E-2							)
   def      __magic_name__     (     self							):
          self._test_save_load_local()
   def      __magic_name__     (     self							):
          self._test_inference_batch_single_identical(
              expected_max_diff=1E-2      ,       )

   @unittest.skipIf(
       torch_device != "cuda" or not is_xformers_available()      ,       reason="XFormers attention is only available with CUDA and `xformers` installed"      ,       )
   def      __magic_name__     (     self							):
          self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3							)


@slow
@require_torch_gpu
class 						a__		( unittest.TestCase		):
   def      __magic_name__     (     self							):
          # clean up the VRAM after each test
          super().tearDown()
          gc.collect()
          torch.cuda.empty_cache()
   def      __magic_name__     (     self							):
          # if

          lowercase       :							Tuple									=				IFPipeline.from_pretrained("DeepFloyd/IF-I-XL-v1.0"      ,       variant="fp16"      ,       torch_dtype=torch.floataa							)

          lowercase       :							List[str]									=				IFSuperResolutionPipeline.from_pretrained(
              "DeepFloyd/IF-II-L-v1.0"      ,       variant="fp16"      ,       torch_dtype=torch.floataa      ,       text_encoder=_a      ,       tokenizer=_a							)

          # pre compute text embeddings and remove T5 to save memory

          pipe_a.text_encoder.to("cuda"							)

          lowercase						,       lowercase       :							int									=				pipe_a.encode_prompt("anime turtle"      ,       device="cuda"							)

          del pipe_a.tokenizer
          del pipe_a.text_encoder
          gc.collect()

          lowercase       :							List[str]									=				None
          lowercase       :							Union[str, Any]									=				None

          pipe_a.enable_model_cpu_offload()
          pipe_a.enable_model_cpu_offload()

          pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()							)
          pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()							)

          self._test_if(_a      ,       _a      ,       _a      ,       _a							)

          pipe_a.remove_all_hooks()
          pipe_a.remove_all_hooks()

          # img2img

          lowercase       :							Optional[int]									=				IFImgaImgPipeline(**pipe_a.components							)
          lowercase       :							Dict									=				IFImgaImgSuperResolutionPipeline(**pipe_a.components							)

          pipe_a.enable_model_cpu_offload()
          pipe_a.enable_model_cpu_offload()

          pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()							)
          pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()							)

          self._test_if_imgaimg(_a      ,       _a      ,       _a      ,       _a							)

          pipe_a.remove_all_hooks()
          pipe_a.remove_all_hooks()

          # inpainting

          lowercase       :							List[str]									=				IFInpaintingPipeline(**pipe_a.components							)
          lowercase       :							Optional[Any]									=				IFInpaintingSuperResolutionPipeline(**pipe_a.components							)

          pipe_a.enable_model_cpu_offload()
          pipe_a.enable_model_cpu_offload()

          pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()							)
          pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()							)

          self._test_if_inpainting(_a      ,       _a      ,       _a      ,       _a							)
   def      __magic_name__     (     self      ,       _a      ,       _a      ,       _a      ,       _a							):
          # pipeline 1

          _start_torch_memory_measurement()

          lowercase       :							Optional[Any]									=				torch.Generator(device="cpu"							).manual_seed(0							)
          lowercase       :							Optional[Any]									=				pipe_a(
              prompt_embeds=_a      ,       negative_prompt_embeds=_a      ,       num_inference_steps=2      ,       generator=_a      ,       output_type="np"      ,       )

          lowercase       :							Union[str, Any]									=				output.images[0]

          assert image.shape == (64, 64, 3)

          lowercase       :							Optional[Any]									=				torch.cuda.max_memory_allocated()
          assert mem_bytes < 13 * 10**9

          lowercase       :							List[Any]									=				load_numpy(
              "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if.npy"							)
          assert_mean_pixel_difference(_a      ,       _a							)

          # pipeline 2

          _start_torch_memory_measurement()

          lowercase       :							List[str]									=				torch.Generator(device="cpu"							).manual_seed(0							)

          lowercase       :							Union[str, Any]									=				floats_tensor((1, 3, 64, 64)      ,       rng=random.Random(0							)							).to(_a							)

          lowercase       :							Optional[int]									=				pipe_a(
              prompt_embeds=_a      ,       negative_prompt_embeds=_a      ,       image=_a      ,       generator=_a      ,       num_inference_steps=2      ,       output_type="np"      ,       )

          lowercase       :							Union[str, Any]									=				output.images[0]

          assert image.shape == (256, 256, 3)

          lowercase       :							Union[str, Any]									=				torch.cuda.max_memory_allocated()
          assert mem_bytes < 4 * 10**9

          lowercase       :							Tuple									=				load_numpy(
              "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_superresolution_stage_II.npy"							)
          assert_mean_pixel_difference(_a      ,       _a							)
   def      __magic_name__     (     self      ,       _a      ,       _a      ,       _a      ,       _a							):
          # pipeline 1

          _start_torch_memory_measurement()

          lowercase       :							Optional[int]									=				floats_tensor((1, 3, 64, 64)      ,       rng=random.Random(0							)							).to(_a							)

          lowercase       :							Union[str, Any]									=				torch.Generator(device="cpu"							).manual_seed(0							)

          lowercase       :							Union[str, Any]									=				pipe_a(
              prompt_embeds=_a      ,       negative_prompt_embeds=_a      ,       image=_a      ,       num_inference_steps=2      ,       generator=_a      ,       output_type="np"      ,       )

          lowercase       :							Optional[Any]									=				output.images[0]

          assert image.shape == (64, 64, 3)

          lowercase       :							Optional[Any]									=				torch.cuda.max_memory_allocated()
          assert mem_bytes < 10 * 10**9

          lowercase       :							Union[str, Any]									=				load_numpy(
              "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_img2img.npy"							)
          assert_mean_pixel_difference(_a      ,       _a							)

          # pipeline 2

          _start_torch_memory_measurement()

          lowercase       :							Dict									=				torch.Generator(device="cpu"							).manual_seed(0							)

          lowercase       :							Dict									=				floats_tensor((1, 3, 256, 256)      ,       rng=random.Random(0							)							).to(_a							)
          lowercase       :							str									=				floats_tensor((1, 3, 64, 64)      ,       rng=random.Random(0							)							).to(_a							)

          lowercase       :							Dict									=				pipe_a(
              prompt_embeds=_a      ,       negative_prompt_embeds=_a      ,       image=_a      ,       original_image=_a      ,       generator=_a      ,       num_inference_steps=2      ,       output_type="np"      ,       )

          lowercase       :							int									=				output.images[0]

          assert image.shape == (256, 256, 3)

          lowercase       :							List[str]									=				torch.cuda.max_memory_allocated()
          assert mem_bytes < 4 * 10**9

          lowercase       :							int									=				load_numpy(
              "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_img2img_superresolution_stage_II.npy"							)
          assert_mean_pixel_difference(_a      ,       _a							)

   def      __magic_name__     (     self      ,       _a      ,       _a      ,       _a      ,       _a							):
          # pipeline 1

          _start_torch_memory_measurement()

          lowercase       :							Dict									=				floats_tensor((1, 3, 64, 64)      ,       rng=random.Random(0							)							).to(_a							)
          lowercase       :							Dict									=				floats_tensor((1, 3, 64, 64)      ,       rng=random.Random(1							)							).to(_a							)

          lowercase       :							Union[str, Any]									=				torch.Generator(device="cpu"							).manual_seed(0							)
          lowercase       :							Dict									=				pipe_a(
              prompt_embeds=_a      ,       negative_prompt_embeds=_a      ,       image=_a      ,       mask_image=_a      ,       num_inference_steps=2      ,       generator=_a      ,       output_type="np"      ,       )

          lowercase       :							Dict									=				output.images[0]

          assert image.shape == (64, 64, 3)

          lowercase       :							int									=				torch.cuda.max_memory_allocated()
          assert mem_bytes < 10 * 10**9

          lowercase       :							str									=				load_numpy(
              "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_inpainting.npy"							)
          assert_mean_pixel_difference(_a      ,       _a							)

          # pipeline 2

          _start_torch_memory_measurement()

          lowercase       :							Union[str, Any]									=				torch.Generator(device="cpu"							).manual_seed(0							)

          lowercase       :							Optional[Any]									=				floats_tensor((1, 3, 64, 64)      ,       rng=random.Random(0							)							).to(_a							)
          lowercase       :							Union[str, Any]									=				floats_tensor((1, 3, 256, 256)      ,       rng=random.Random(0							)							).to(_a							)
          lowercase       :							str									=				floats_tensor((1, 3, 256, 256)      ,       rng=random.Random(1							)							).to(_a							)

          lowercase       :							Dict									=				pipe_a(
              prompt_embeds=_a      ,       negative_prompt_embeds=_a      ,       image=_a      ,       mask_image=_a      ,       original_image=_a      ,       generator=_a      ,       num_inference_steps=2      ,       output_type="np"      ,       )

          lowercase       :							Union[str, Any]									=				output.images[0]

          assert image.shape == (256, 256, 3)

          lowercase       :							Tuple									=				torch.cuda.max_memory_allocated()
          assert mem_bytes < 4 * 10**9

          lowercase       :							Tuple									=				load_numpy(
              "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_inpainting_superresolution_stage_II.npy"							)
          assert_mean_pixel_difference(_a      ,       _a							)


def     __magic_name__      ( )					->							Tuple:
       torch.cuda.empty_cache()
       torch.cuda.reset_max_memory_allocated()
       torch.cuda.reset_peak_memory_stats()


