


'''simple docstring'''






import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPImageProcessor, CLIPVisionConfig

from diffusers import AutoencoderKL, PaintByExamplePipeline, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.paint_by_example import PaintByExampleImageEncoder
from diffusers.utils import floats_tensor, load_image, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import IMAGE_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, IMAGE_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()




class 			a  (  __A	,					unittest.TestCase							):
 _lowerCAmelCase			       =						PaintByExamplePipeline
 _lowerCAmelCase			       =						IMAGE_GUIDED_IMAGE_INPAINTING_PARAMS
 _lowerCAmelCase			       =						IMAGE_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
 _lowerCAmelCase			       =						frozenset([]							)  # TO_DO: update the image_prams once refactored VaeImageProcessor.preprocess
 def 				__UpperCAmelCase		(			self    )       ->				Tuple:
      torch.manual_seed(0    )
      _a         =				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   ,					)
      _a         =				PNDMScheduler(skip_prk_steps=A_    )
      torch.manual_seed(0    )
      _a         =				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   ,					)
      torch.manual_seed(0    )
      _a         =				CLIPVisionConfig(
          hidden_size=32   ,					projection_dim=32   ,					intermediate_size=37   ,					layer_norm_eps=1e-05   ,					num_attention_heads=4   ,					num_hidden_layers=5   ,					image_size=32   ,					patch_size=4   ,					)
      _a         =				PaintByExampleImageEncoder(A_   ,					proj_size=32    )
      _a         =				CLIPImageProcessor(crop_size=32   ,					size=32    )

      _a         =				{
          'unet': unet,
          'scheduler': scheduler,
          'vae': vae,
          'image_encoder': image_encoder,
          'safety_checker': None,
          'feature_extractor': feature_extractor,
      }
      return components
 def 				__UpperCAmelCase		(			self   ,					__magic_name__    )       ->				Any:
      _a         =				np.array(image.convert('RGB'    )    )
      _a         =				image[None].transpose(0   ,					3   ,					1   ,					2    )
      _a         =				torch.from_numpy(A_    ).to(dtype=torch.floataa    ) / 1_2_7.5 - 1.0
      return image
 def 				__UpperCAmelCase		(			self   ,					__magic_name__="cpu"   ,					__magic_name__=0    )       ->				Union[str, Any]:
      _a         =				floats_tensor((1, 3, 32, 32)   ,					rng=random.Random(A_    )    ).to(A_    )
      _a         =				image.cpu().permute(0   ,					2   ,					3   ,					1    )[0]
      _a         =				Image.fromarray(np.uinta(A_    )    ).convert('RGB'    ).resize((64, 64)    )
      _a         =				Image.fromarray(np.uinta(image + 4    )    ).convert('RGB'    ).resize((64, 64)    )
      _a         =				Image.fromarray(np.uinta(A_    )    ).convert('RGB'    ).resize((32, 32)    )

      if str(A_    ).startswith('mps'    ):
           _a         =				torch.manual_seed(A_    )
      else:
           _a         =				torch.Generator(device=A_    ).manual_seed(A_    )
      _a         =				{
          'example_image': example_image,
          'image': init_image,
          'mask_image': mask_image,
          'generator': generator,
          'num_inference_steps': 2,
          'guidance_scale': 6.0,
          'output_type': 'numpy',
      }
      return inputs
 def 				__UpperCAmelCase		(			self    )       ->				str:
      _a         =				self.get_dummy_components()

      # make sure here that pndm scheduler skips prk
      _a         =				PaintByExamplePipeline(**A_    )
      _a         =				pipe.to('cpu'    )
      pipe.set_progress_bar_config(disable=A_    )

      _a         =				self.get_dummy_inputs()
      _a         =				pipe(**A_    )
      _a         =				output.images

      _a         =				image[0, -3:, -3:, -1]

      assert image.shape == (1, 64, 64, 3)
      _a         =				np.array([0.4_6_8_6, 0.5_6_8_7, 0.4_0_0_7, 0.5_2_1_8, 0.5_7_4_1, 0.4_4_8_2, 0.4_9_4_0, 0.4_6_2_9, 0.4_5_0_3]    )

      assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1e-2
 def 				__UpperCAmelCase		(			self    )       ->				Optional[Any]:
      _a         =				'cpu'
      _a         =				self.get_dummy_inputs()
      inputs.pop('mask_image'    )
      _a         =				self.convert_to_pt(inputs.pop('image'    )    )
      _a         =				image.clamp(0   ,					1    ) / 2

      # make sure here that pndm scheduler skips prk
      _a         =				PaintByExamplePipeline(**self.get_dummy_components()    )
      _a         =				pipe.to(A_    )
      pipe.set_progress_bar_config(disable=A_    )

      _a         =				pipe(image=A_   ,					mask_image=mask_image[:, 0]   ,					**A_    )
      _a         =				output.images

      _a         =				image.cpu().permute(0   ,					2   ,					3   ,					1    )[0]
      _a         =				mask_image.cpu().permute(0   ,					2   ,					3   ,					1    )[0]

      _a         =				Image.fromarray(np.uinta(A_    )    ).convert('RGB'    )
      _a         =				Image.fromarray(np.uinta(A_    )    ).convert('RGB'    )

      _a         =				pipe(**self.get_dummy_inputs()    )
      _a         =				output.images

      assert out_a.shape == (1, 64, 64, 3)
      assert np.abs(out_a.flatten() - out_a.flatten()    ).max() < 5e-2




 def 				__UpperCAmelCase		(			self    )       ->				Tuple:
      super().test_inference_batch_single_identical(expected_max_diff=3e-3    )





@slow
@require_torch_gpu
class 			a  (  unittest.TestCase							):
 def 				__UpperCAmelCase		(			self    )       ->				Dict:
      super().tearDown()
      gc.collect()
      torch.cuda.empty_cache()




 def 				__UpperCAmelCase		(			self    )       ->				Optional[Any]:
      _a         =				load_image(
          'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
          '/paint_by_example/dog_in_bucket.png'    )
      _a         =				load_image(
          'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
          '/paint_by_example/mask.png'    )
      _a         =				load_image(
          'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
          '/paint_by_example/panda.jpg'    )

      _a         =				PaintByExamplePipeline.from_pretrained('Fantasy-Studio/Paint-by-Example'    )
      _a         =				pipe.to(A_    )
      pipe.set_progress_bar_config(disable=A_    )

      _a         =				torch.manual_seed(3_21    )
      _a         =				pipe(
          image=A_   ,					mask_image=A_   ,					example_image=A_   ,					generator=A_   ,					guidance_scale=5.0   ,					num_inference_steps=50   ,					output_type='np'   ,					)

      _a         =				output.images

      _a         =				image[0, -3:, -3:, -1]

      assert image.shape == (1, 5_12, 5_12, 3)
      _a         =				np.array([0.4_8_3_4, 0.4_8_1_1, 0.4_8_7_4, 0.5_1_2_2, 0.5_0_8_1, 0.5_1_4_4, 0.5_2_9_1, 0.5_2_9_0, 0.5_3_7_4]    )

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






"""simple docstring"""






import itertools
import os
import subprocess
from os.path import dirname

from parameterized import parameterized

from tests.trainer.test_trainer import TrainerIntegrationCommon  # noqa
from transformers import is_torch_available
from transformers.testing_utils import (
    TestCasePlus,
    execute_subprocess_async,
    get_gpu_count,
    get_tests_dir,
    require_deepspeed,
    require_torch_gpu,
    slow,
)
from transformers.trainer_utils import set_seed


if is_torch_available():
    from tests.trainer.test_trainer import (  # noqa
        RegressionModelConfig,
        RegressionPreTrainedModel,
        get_regression_trainer,
    )


set_seed(4_2)

lowerCamelCase				:     Union[str, Any]								=  get_tests_dir("""fixtures""")
lowerCamelCase				:     str								=  os.path.join(dirname(get_tests_dir()))
lowerCamelCase				:     Union[str, Any]								=  dirname(os.path.abspath(__file__))

# default torch.distributed port
lowerCamelCase				:     Optional[Any]								=  """10999"""

lowerCamelCase				:     Optional[Any]								=  """t5-small"""

# *** Working Models ***
lowerCamelCase				:     Union[str, Any]								=  """hf-internal-testing/tiny-albert"""
lowerCamelCase				:     int								=  """sshleifer/bart-tiny-random"""
lowerCamelCase				:     List[Any]								=  """hf-internal-testing/tiny-bert"""
lowerCamelCase				:     int								=  """hf-internal-testing/tiny-random-bigbird_pegasus"""
lowerCamelCase				:     List[Any]								=  """hf-internal-testing/tiny-random-big_bird"""
lowerCamelCase				:     Tuple								=  """hf-internal-testing/tiny-random-blenderbot"""
lowerCamelCase				:     str								=  """bigscience/bigscience-small-testing"""
lowerCamelCase				:     int								=  """hf-internal-testing/tiny-random-deberta"""
lowerCamelCase				:     Tuple								=  """hf-internal-testing/tiny-random-deberta-v2"""
lowerCamelCase				:     List[str]								=  """sshleifer/tiny-distilbert-base-cased"""
lowerCamelCase				:     int								=  """hf-internal-testing/tiny-electra"""
lowerCamelCase				:     Optional[Any]								=  """hf-internal-testing/tiny-random-flaubert"""
lowerCamelCase				:     Union[str, Any]								=  """stas/tiny-wmt19-en-de"""
lowerCamelCase				:     Optional[Any]								=  """hf-internal-testing/tiny-random-funnel"""
lowerCamelCase				:     List[str]								=  """sshleifer/tiny-gpt2"""
lowerCamelCase				:     int								=  """hf-internal-testing/tiny-random-gptj"""
lowerCamelCase				:     List[Any]								=  """hf-internal-testing/tiny-random-gpt_neo"""
lowerCamelCase				:     Optional[Any]								=  """hf-internal-testing/tiny-layoutlm"""
lowerCamelCase				:     int								=  """hf-internal-testing/tiny-random-led"""
lowerCamelCase				:     List[str]								=  """hf-internal-testing/tiny-random-longformer"""
lowerCamelCase				:     Tuple								=  """stas/tiny-m2m_100"""  # hf tiny model is unsuitable
lowerCamelCase				:     Optional[int]								=  """sshleifer/tiny-marian-en-de"""
lowerCamelCase				:     Optional[int]								=  """sshleifer/tiny-mbart"""
lowerCamelCase				:     Dict								=  """hf-internal-testing/tiny-random-mobilebert"""
lowerCamelCase				:     Tuple								=  """hf-internal-testing/tiny-random-mpnet"""
lowerCamelCase				:     Dict								=  """stas/pegasus-cnn_dailymail-tiny-random"""
lowerCamelCase				:     Optional[Any]								=  """hf-internal-testing/tiny-random-prophetnet"""
lowerCamelCase				:     Any								=  """sshleifer/tiny-distilroberta-base"""
lowerCamelCase				:     Dict								=  """hf-internal-testing/tiny-random-squeezebert"""
lowerCamelCase				:     Tuple								=  """patrickvonplaten/t5-tiny-random"""
lowerCamelCase				:     Optional[Any]								=  """hf-internal-testing/tiny-random-t5-v1.1"""
lowerCamelCase				:     Optional[int]								=  """hf-internal-testing/tiny-random-vit"""
lowerCamelCase				:     Dict								=  """hf-internal-testing/tiny-xlm-roberta"""
lowerCamelCase				:     Dict								=  """sshleifer/tiny-xlnet-base-cased"""


# *** To Fix ***


# *** tiny model issues ***
# missing model files:
lowerCamelCase				:     Optional[int]								=  """hf-internal-testing/tiny-random-mt5"""
lowerCamelCase				:     Optional[int]								=  """hf-internal-testing/tiny-random-camembert"""
lowerCamelCase				:     Any								=  """hf-internal-testing/tiny-random-openai-gpt"""

# missing tokenizer files
lowerCamelCase				:     Optional[int]								=  """hf-internal-testing/tiny-random-convbert"""
lowerCamelCase				:     str								=  """hf-internal-testing/tiny-random-layoutlmv2"""
lowerCamelCase				:     List[Any]								=  """hf-internal-testing/tiny-random-hubert"""

# issues with tokenizer
lowerCamelCase				:     List[str]								=  """hf-internal-testing/tiny-random-ctrl"""
lowerCamelCase				:     Optional[int]								=  """hf-internal-testing/tiny-random-transfo-xl"""  # same as ctrl

# other issues with tiny models
lowerCamelCase				:     Tuple								=  """hf-internal-testing/tiny-random-ibert"""  # multiple issues with either mlm/qa/clas
lowerCamelCase				:     Dict								=  """hf-internal-testing/tiny-random-reformer"""  # multiple issues with either mlm/qa/clas

# *** Lacking official examples to test with ***
# or not working with examples
lowerCamelCase				:     List[str]								=  """hf-internal-testing/tiny-random-dpr"""
# - "dpr"  examples/research_projects/rag-end2end-retriever/
lowerCamelCase				:     str								=  """hf-internal-testing/tiny-random-rag"""
# - "rag" research_projects
lowerCamelCase				:     List[Any]								=  """"""
# - "luke" Entities classes - no plan to make such example
lowerCamelCase				:     Optional[int]								=  """hf-internal-testing/tiny-random-lxmert"""
# - "lxmert" doesn't work with run_qa.py
lowerCamelCase				:     Dict								=  """hf-internal-testing/tiny-random-clip"""
# - "clip" nothing under pytorch examples - XXX: Suraj is working on adding some - check by end of Sep
lowerCamelCase				:     Optional[Any]								=  """hf-internal-testing/tiny-random-speech_to_text"""
# - "speech_to_text", nothing under pytorch examples


# *** Reactive mode ***
# models with low usage, unstable API, things about to change - do nothing about the following until someone runs into a problem
lowerCamelCase				:     Union[str, Any]								=  """hf-internal-testing/tiny-random-tapas"""


def 							A__ (     UpperCamelCase__=False    ):


  '''simple docstring'''


  _SCREAMING_SNAKE_CASE        =			min(2       ,							get_gpu_count()    ) if distributed else 1
  _SCREAMING_SNAKE_CASE        =			os.environ.get('''DS_TEST_PORT'''       ,							UpperCamelCase__    )
  return F'''deepspeed --num_nodes 1 --num_gpus {num_gpus} --master_port {master_port}'''.split()


def 							A__ (     ):


  '''simple docstring'''


  _SCREAMING_SNAKE_CASE        =			F'''{FIXTURE_DIRECTORY}/tests_samples'''
  _SCREAMING_SNAKE_CASE        =			F'''{data_dir_samples}/wmt_en_ro'''
  _SCREAMING_SNAKE_CASE        =			F'''{data_dir_samples}/xsum'''
  _SCREAMING_SNAKE_CASE        =			'''
        --do_train
        --max_train_samples 4
        --per_device_train_batch_size 2
        --num_train_epochs 1
        --fp16
        --report_to none
        --overwrite_output_dir
        '''.split()

  # try to cover as many models as possible once (it's enough to run on one task per model)
  # but need a tiny model for each
  #
  # should have "{model_type.upper()}_TINY" corresponding vars defined, e.g., T5_TINY, etc.
  _SCREAMING_SNAKE_CASE        =			{
      '''trans''': [
          '''bart''',
          '''fsmt''',
          '''m2m_100''',
          '''marian''',
          '''mbart''',
          '''t5''',
          '''t5_v1''',
          # "mt5", missing model files
      ],
      '''sum''': [
          '''pegasus''',
      ],
      '''clm''': [
          '''big_bird''',
          '''bigbird_pegasus''',
          '''blenderbot''',
          '''bloom''',
          '''gpt2''',
          '''gpt_neo''',
          '''gptj''',
          '''xlm-roberta''',
          '''prophetnet''',
          # "camembert", missing model files
      ],
      '''mlm''': [
          '''albert''',
          '''deberta''',
          '''deberta-v2''',
          '''distilbert''',
          '''electra''',
          '''flaubert''',
          '''funnel''',
          '''layoutlm''',
          # "reformer", # multiple issues with either mlm/qa/clas
      ],
      '''qa''': [
          '''led''',
          '''longformer''',
          '''mobilebert''',
          '''mpnet''',
          '''roberta''',
          '''squeezebert''',
          # "convbert", # missing tokenizer files
          # "layoutlmv2", missing model files
      ],
      '''clas''': [
          '''bert''',
          '''xlnet''',
          # "hubert", # missing tokenizer files
          # "ibert", # multiple issues with either mlm/qa/clas
          # "transfo-xl", # tokenizer issues as ctrl
          # "ctrl", # tokenizer issues
          # "openai-gpt", missing model files
          # "tapas", multiple issues
      ],
      '''img_clas''': [
          '''vit''',
      ],
  }

  _SCREAMING_SNAKE_CASE        =			F'''{ROOT_DIRECTORY}/examples/pytorch'''

  _SCREAMING_SNAKE_CASE        =			{
      '''trans''': F'''
        {scripts_dir}/translation/run_translation.py
        --train_file {data_dir_wmt}/train.json
        --source_lang en
        --target_lang ro
        ''',
      '''sum''': F'''
        {scripts_dir}/summarization/run_summarization.py
        --train_file {data_dir_xsum}/sample.json
        --max_source_length 12
        --max_target_length 12
        --lang en
        ''',
      '''clm''': F'''
        {scripts_dir}/language-modeling/run_clm.py
        --train_file {FIXTURE_DIRECTORY}/sample_text.txt
        --block_size 8
        ''',
      '''mlm''': F'''
        {scripts_dir}/language-modeling/run_mlm.py
        --train_file {FIXTURE_DIRECTORY}/sample_text.txt
        ''',
      '''qa''': F'''
        {scripts_dir}/question-answering/run_qa.py
        --train_file {data_dir_samples}/SQUAD/sample.json
        ''',
      '''clas''': F'''
        {scripts_dir}/text-classification/run_glue.py
        --train_file {data_dir_samples}/MRPC/train.csv
        --max_seq_length 12
        --task_name MRPC
        ''',
      '''img_clas''': F'''
        {scripts_dir}/image-classification/run_image_classification.py
            --dataset_name hf-internal-testing/cats_vs_dogs_sample
            --remove_unused_columns False
            --max_steps 10
            --image_processor_name {DS_TESTS_DIRECTORY}/vit_feature_extractor.json
        ''',
  }

  _SCREAMING_SNAKE_CASE        =			get_launcher(distributed=UpperCamelCase__    )

  _SCREAMING_SNAKE_CASE        =			{}
  for task, args in tasks.items():
    _SCREAMING_SNAKE_CASE        =			args.split()
    for model in tasksamodels[task]:
      _SCREAMING_SNAKE_CASE        =			globals()[F'''{model.upper().replace("-"       ,							"_"    )}_TINY''']
      _SCREAMING_SNAKE_CASE        =			F'''--model_name_or_path {model_name}'''.split()
      _SCREAMING_SNAKE_CASE        =			launcher + args + args_model + args_main

      # # generation special case
      # if task == "gen":
      #     launcher = f"deepspeed --num_nodes 1 --num_gpus 1".split()
      #     args_model += f"--model_type {model}".split()
      #     cmds[f"{task}_{model}"] = launcher + args + args_model
      # else:

  return cmds


lowerCamelCase				:     List[str]								=  make_task_cmds()

lowerCamelCase				:     Any								=  """zero2"""
lowerCamelCase				:     List[Any]								=  """zero3"""

lowerCamelCase				:     int								=  [ZEROa, ZEROa]


def 							A__ (     UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__    ):


  '''simple docstring'''


  _SCREAMING_SNAKE_CASE        =			parameterized.to_safe_name('''_'''.join(str(UpperCamelCase__    ) for x in param.args    )    )
  return F'''{func.__name__}_{param_based_name}'''


# Cartesian-product of zero stages with models to test
lowerCamelCase				:     Optional[Any]								=  list(itertools.product(stages, task_cmds.keys()))







@slow
@require_deepspeed
@require_torch_gpu
class    __snake_case(					__A       ):




    def   A   (   self    ,							A_    ,							A_			):





      '''simple docstring'''



      if task not in task_cmds:
        raise ValueError(F'''don\'t know of task {task}, have {task_cmds.keys()}'''			)

      _SCREAMING_SNAKE_CASE        =			task_cmds[task]
      _SCREAMING_SNAKE_CASE        =			F'''--deepspeed {self.test_file_dir_str}/ds_config_{stage}.json'''.split()

      _SCREAMING_SNAKE_CASE        =			self.get_auto_remove_tmp_dir()
      _SCREAMING_SNAKE_CASE        =			F'''--output_dir {output_dir}'''.split()

      cmd += args_ds + args_out

      return cmd, output_dir




    @parameterized.expand(A_    ,							name_func=A_			)
    def   A   (   self    ,							A_    ,							A_			):





      '''simple docstring'''



      _SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE        =			self.get_task_cmd(A_    ,							A_			)

      # 1. generate the checkpoint
      cmd += "--save_steps 1".split()
      # keep for quick debug
      # print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] + cmd)); die
      execute_subprocess_async(A_    ,							env=self.get_env()			)

      # 2. test that the fp32 weights get reconsolidated
      _SCREAMING_SNAKE_CASE        =			F'''{output_dir}/checkpoint-1'''
      _SCREAMING_SNAKE_CASE        =			F'''{chkpt_dir}/out.bin'''
      _SCREAMING_SNAKE_CASE        =			F'''{chkpt_dir}/zero_to_fp32.py {chkpt_dir} {recovered_model_path}'''
      # keep for quick debug
      # print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
      subprocess.check_call(A_    ,							shell=A_			)
      assert os.path.exists(A_			), F'''{recovered_model_path} was not found'''

      # possibly could also test that the resulting saved model is usable but given that we use
      # random models we won't know if it's any good
