
"""simple docstring"""






import gc
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    EulerAncestralDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionModelEditingPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()




@skip_mps
class      _SCREAMING_SNAKE_CASE   (     A__      ,					A__      ,					A__      ,					unittest.TestCase				):
			UpperCAmelCase_    :List[Any]												=		StableDiffusionModelEditingPipeline
			UpperCAmelCase_    :int												=		TEXT_TO_IMAGE_PARAMS
			UpperCAmelCase_    :int												=		TEXT_TO_IMAGE_BATCH_PARAMS
			UpperCAmelCase_    :Optional[int]												=		TEXT_TO_IMAGE_IMAGE_PARAMS
			UpperCAmelCase_    :List[str]												=		TEXT_TO_IMAGE_IMAGE_PARAMS






			def 					__lowerCAmelCase					(   self			)     ->	Optional[Any]:
						torch.manual_seed(0			)
						lowerCAmelCase_       :Union[str, Any]     	=    UNetaDConditionModel(
						    block_out_channels=(32, 64)			,  layers_per_block=2			,  sample_size=32			,  in_channels=4			,  out_channels=4			,  down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")			,  up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""")			,  cross_attention_dim=32			,  )
						lowerCAmelCase_       :str     	=    DDIMScheduler()
						torch.manual_seed(0			)
						lowerCAmelCase_       :int     	=    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			)
						lowerCAmelCase_       :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			,  )
						lowerCAmelCase_       :Tuple     	=    CLIPTextModel(_UpperCAmelCase			)
						lowerCAmelCase_       :Any     	=    CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""			)

						lowerCAmelCase_       :Optional[int]     	=    {
						    '''unet''': unet,
						    '''scheduler''': scheduler,
						    '''vae''': vae,
						    '''text_encoder''': text_encoder,
						    '''tokenizer''': tokenizer,
						    '''safety_checker''': None,
						    '''feature_extractor''': None,
						}
						return components






			def 					__lowerCAmelCase					(   self			,  __A			,  __A=0			)     ->	Tuple:
						lowerCAmelCase_       :int     	=    torch.manual_seed(_UpperCAmelCase			)
						lowerCAmelCase_       :List[Any]     	=    {
						    '''prompt''': '''A field of roses''',
						    '''generator''': generator,
						    # Setting height and width to None to prevent OOMs on CPU.
						    '''height''': None,
						    '''width''': None,
						    '''num_inference_steps''': 2,
						    '''guidance_scale''': 6.0,
						    '''output_type''': '''numpy''',
						}
						return inputs






			def 					__lowerCAmelCase					(   self			)     ->	List[str]:
						lowerCAmelCase_       :int     	=    '''cpu'''  # ensure determinism for the device-dependent torch.Generator
						lowerCAmelCase_       :List[str]     	=    self.get_dummy_components()
						lowerCAmelCase_       :Union[str, Any]     	=    StableDiffusionModelEditingPipeline(**_UpperCAmelCase			)
						lowerCAmelCase_       :Any     	=    sd_pipe.to(_UpperCAmelCase			)
						sd_pipe.set_progress_bar_config(disable=_UpperCAmelCase			)

						lowerCAmelCase_       :List[str]     	=    self.get_dummy_inputs(_UpperCAmelCase			)
						lowerCAmelCase_       :Union[str, Any]     	=    sd_pipe(**_UpperCAmelCase			).images
						lowerCAmelCase_       :int     	=    image[0, -3:, -3:, -1]
						assert image.shape == (1, 64, 64, 3)

						lowerCAmelCase_       :Optional[int]     	=    np.array([0.4_7_5_5, 0.5_1_3_2, 0.4_9_7_6, 0.3_9_0_4, 0.3_5_5_4, 0.4_7_6_5, 0.5_1_3_9, 0.5_1_5_8, 0.4_8_8_9]			)

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






			def 					__lowerCAmelCase					(   self			)     ->	List[str]:
						lowerCAmelCase_       :Any     	=    '''cpu'''  # ensure determinism for the device-dependent torch.Generator
						lowerCAmelCase_       :Tuple     	=    self.get_dummy_components()
						lowerCAmelCase_       :Tuple     	=    StableDiffusionModelEditingPipeline(**_UpperCAmelCase			)
						lowerCAmelCase_       :Optional[int]     	=    sd_pipe.to(_UpperCAmelCase			)
						sd_pipe.set_progress_bar_config(disable=_UpperCAmelCase			)

						lowerCAmelCase_       :List[str]     	=    self.get_dummy_inputs(_UpperCAmelCase			)
						lowerCAmelCase_       :Tuple     	=    '''french fries'''
						lowerCAmelCase_       :Tuple     	=    sd_pipe(**_UpperCAmelCase			,  negative_prompt=_UpperCAmelCase			)
						lowerCAmelCase_       :Any     	=    output.images
						lowerCAmelCase_       :List[str]     	=    image[0, -3:, -3:, -1]

						assert image.shape == (1, 64, 64, 3)

						lowerCAmelCase_       :Optional[Any]     	=    np.array([0.4_9_9_2, 0.5_1_0_1, 0.5_0_0_4, 0.3_9_4_9, 0.3_6_0_4, 0.4_7_3_5, 0.5_2_1_6, 0.5_2_0_4, 0.4_9_1_3]			)

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






			def 					__lowerCAmelCase					(   self			)     ->	Dict:
						lowerCAmelCase_       :Dict     	=    '''cpu'''  # ensure determinism for the device-dependent torch.Generator
						lowerCAmelCase_       :List[str]     	=    self.get_dummy_components()
						lowerCAmelCase_       :str     	=    EulerAncestralDiscreteScheduler(
						    beta_start=0.0_0_0_8_5			,  beta_end=0.0_1_2			,  beta_schedule="""scaled_linear"""			)
						lowerCAmelCase_       :str     	=    StableDiffusionModelEditingPipeline(**_UpperCAmelCase			)
						lowerCAmelCase_       :Any     	=    sd_pipe.to(_UpperCAmelCase			)
						sd_pipe.set_progress_bar_config(disable=_UpperCAmelCase			)

						lowerCAmelCase_       :str     	=    self.get_dummy_inputs(_UpperCAmelCase			)
						lowerCAmelCase_       :Optional[int]     	=    sd_pipe(**_UpperCAmelCase			).images
						lowerCAmelCase_       :Optional[int]     	=    image[0, -3:, -3:, -1]

						assert image.shape == (1, 64, 64, 3)

						lowerCAmelCase_       :int     	=    np.array([0.4_7_4_7, 0.5_3_7_2, 0.4_7_7_9, 0.4_9_8_2, 0.5_5_4_3, 0.4_8_1_6, 0.5_2_3_8, 0.4_9_0_4, 0.5_0_2_7]			)

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






			def 					__lowerCAmelCase					(   self			)     ->	Tuple:
						lowerCAmelCase_       :Any     	=    '''cpu'''  # ensure determinism for the device-dependent torch.Generator
						lowerCAmelCase_       :int     	=    self.get_dummy_components()
						lowerCAmelCase_       :Tuple     	=    PNDMScheduler()
						lowerCAmelCase_       :int     	=    StableDiffusionModelEditingPipeline(**_UpperCAmelCase			)
						lowerCAmelCase_       :List[Any]     	=    sd_pipe.to(_UpperCAmelCase			)
						sd_pipe.set_progress_bar_config(disable=_UpperCAmelCase			)

						lowerCAmelCase_       :int     	=    self.get_dummy_inputs(_UpperCAmelCase			)
						# the pipeline does not expect pndm so test if it raises error.
						with self.assertRaises(_UpperCAmelCase			):
									lowerCAmelCase_       :str     	=    sd_pipe(**_UpperCAmelCase			).images






			def 					__lowerCAmelCase					(   self			)     ->	Dict:
						super().test_inference_batch_single_identical(expected_max_diff=5E-3			)






			def 					__lowerCAmelCase					(   self			)     ->	Tuple:
						super().test_attention_slicing_forward_pass(expected_max_diff=5E-3			)




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






			def 					__lowerCAmelCase					(   self			)     ->	List[Any]:
						super().tearDown()
						gc.collect()
						torch.cuda.empty_cache()






			def 					__lowerCAmelCase					(   self			,  __A=0			)     ->	Optional[Any]:
						lowerCAmelCase_       :str     	=    torch.manual_seed(_UpperCAmelCase			)
						lowerCAmelCase_       :Optional[Any]     	=    {
						    '''prompt''': '''A field of roses''',
						    '''generator''': generator,
						    '''num_inference_steps''': 3,
						    '''guidance_scale''': 7.5,
						    '''output_type''': '''numpy''',
						}
						return inputs






			def 					__lowerCAmelCase					(   self			)     ->	Optional[Any]:
						lowerCAmelCase_       :Any     	=    '''CompVis/stable-diffusion-v1-4'''
						lowerCAmelCase_       :Tuple     	=    StableDiffusionModelEditingPipeline.from_pretrained(_UpperCAmelCase			,  safety_checker=_UpperCAmelCase			)
						pipe.to(_UpperCAmelCase			)
						pipe.set_progress_bar_config(disable=_UpperCAmelCase			)
						pipe.enable_attention_slicing()

						lowerCAmelCase_       :Tuple     	=    self.get_inputs()
						lowerCAmelCase_       :Optional[Any]     	=    pipe(**_UpperCAmelCase			).images
						lowerCAmelCase_       :str     	=    image[0, -3:, -3:, -1].flatten()

						assert image.shape == (1, 512, 512, 3)

						lowerCAmelCase_       :Tuple     	=    np.array(
						    [0.6_7_4_9_4_9_6, 0.6_3_8_6_4_5_3, 0.5_1_4_4_3_2_6_7, 0.6_6_0_9_4_9_0_5, 0.6_1_9_2_1_2_1_5, 0.5_4_9_1_3_3_2, 0.5_7_4_4_4_1_7, 0.5_8_0_7_5_1_0_6, 0.5_1_7_4_6_5_8]			)

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

						# make sure image changes after editing
						pipe.edit_model("""A pack of roses"""			,  """A pack of blue roses"""			)

						lowerCAmelCase_       :Optional[int]     	=    pipe(**_UpperCAmelCase			).images
						lowerCAmelCase_       :List[Any]     	=    image[0, -3:, -3:, -1].flatten()

						assert image.shape == (1, 512, 512, 3)

						assert np.abs(expected_slice - image_slice			).max() > 1E-1






			def 					__lowerCAmelCase					(   self			)     ->	Optional[Any]:
						torch.cuda.empty_cache()
						torch.cuda.reset_max_memory_allocated()
						torch.cuda.reset_peak_memory_stats()

						lowerCAmelCase_       :Dict     	=    '''CompVis/stable-diffusion-v1-4'''
						lowerCAmelCase_       :Tuple     	=    DDIMScheduler.from_pretrained(_UpperCAmelCase			,  subfolder="""scheduler"""			)
						lowerCAmelCase_       :Any     	=    StableDiffusionModelEditingPipeline.from_pretrained(
						    _UpperCAmelCase			,  scheduler=_UpperCAmelCase			,  safety_checker=_UpperCAmelCase			)
						lowerCAmelCase_       :Dict     	=    pipe.to(_UpperCAmelCase			)
						pipe.set_progress_bar_config(disable=_UpperCAmelCase			)
						pipe.enable_attention_slicing(1			)
						pipe.enable_sequential_cpu_offload()

						lowerCAmelCase_       :List[str]     	=    self.get_inputs()
						lowerCAmelCase_       :List[str]     	=    pipe(**_UpperCAmelCase			)

						lowerCAmelCase_       :Union[str, Any]     	=    torch.cuda.max_memory_allocated()
						# make sure that less than 4.4 GB is allocated
						assert mem_bytes < 4.4 * 10**9













'''simple docstring'''






import os
import sys
from pathlib import Path

import torch

from transformers.testing_utils import TestCasePlus, execute_subprocess_async, require_torch_multi_gpu
from utils import load_json


snake_case_     			=						torch.cuda.is_available()
snake_case_     			=						[""" Sam ate lunch today.""", """Sams lunch ingredients."""]
snake_case_     			=						["""A very interesting story about what I ate for lunch.""", """Avocado, celery, turkey, coffee"""]
snake_case_     			=						{
    """max_tokens_per_batch""": None,
    """supervise_forward""": True,
    """normalize_hidden""": True,
    """label_smoothing""": 0.2,
    """eval_max_gen_length""": None,
    """eval_beams""": 1,
    """val_metric""": """loss""",
    """save_top_k""": 1,
    """adafactor""": True,
    """early_stopping_patience""": 2,
    """logger_name""": """default""",
    """length_penalty""": 0.5,
    """cache_dir""": """""",
    """task""": """summarization""",
    """num_workers""": 2,
    """alpha_hid""": 0,
    """freeze_embeds""": True,
    """enc_only""": False,
    """tgt_suffix""": """""",
    """resume_from_checkpoint""": None,
    """sortish_sampler""": True,
    """student_decoder_layers""": 1,
    """val_check_interval""": 1.0,
    """output_dir""": """""",
    """fp16""": False,  # TODO(SS): set this to CUDA_AVAILABLE if ci installs apex or start using native amp
    """no_teacher""": False,
    """fp16_opt_level""": """O1""",
    """gpus""": 1 if CUDA_AVAILABLE else 0,
    """n_tpu_cores""": 0,
    """max_grad_norm""": 1.0,
    """do_train""": True,
    """do_predict""": True,
    """accumulate_grad_batches""": 1,
    """server_ip""": """""",
    """server_port""": """""",
    """seed""": 42,
    """model_name_or_path""": """sshleifer/bart-tiny-random""",
    """config_name""": """""",
    """tokenizer_name""": """facebook/bart-large""",
    """do_lower_case""": False,
    """learning_rate""": 0.3,
    """lr_scheduler""": """linear""",
    """weight_decay""": 0.0,
    """adam_epsilon""": 1E-08,
    """warmup_steps""": 0,
    """max_epochs""": 1,
    """train_batch_size""": 2,
    """eval_batch_size""": 2,
    """max_source_length""": 12,
    """max_target_length""": 12,
    """val_max_target_length""": 12,
    """test_max_target_length""": 12,
    """fast_dev_run""": False,
    """no_cache""": False,
    """n_train""": -1,
    """n_val""": -1,
    """n_test""": -1,
    """student_encoder_layers""": 1,
    """freeze_encoder""": False,
    """auto_scale_batch_size""": False,
    """overwrite_output_dir""": False,
    """student""": None,
}
def 				_lowerCamelCase(	UpperCamelCase__     :			Path     ,							UpperCamelCase__     :			list		)		->	str:
    A							:							List[Any]				=    '''\n'''.join(UpperCamelCase__		)
    Path(UpperCamelCase__		).open('''w'''		).writelines(UpperCamelCase__		)
def 				_lowerCamelCase(	UpperCamelCase__     :			List[Any]		)		->	Dict:
    for split in ["train", "val", "test"]:
        _dump_articles(os.path.join(UpperCamelCase__     ,							F'''{split}.source'''		)     ,							UpperCamelCase__		)
        _dump_articles(os.path.join(UpperCamelCase__     ,							F'''{split}.target'''		)     ,							UpperCamelCase__		)
    return tmp_dir





class 	_lowercase    (				a					):


   @classmethod
   def        snake_case					(			cls       ):
       return cls


   @require_torch_multi_gpu
   def        snake_case					(			self       ):
       A							:							Optional[int]				=    {
           '''no_teacher''': True,
           '''freeze_encoder''': True,
           '''gpus''': 2,
           '''overwrite_output_dir''': True,
           '''sortish_sampler''': True,
       }
       self._test_distiller_cli_fork(_UpperCAmelCase     , check_contents=_UpperCAmelCase       )


   def        snake_case					(			self     , _UpperCAmelCase     , _UpperCAmelCase=True       ):
       A							:							List[Any]				=    {
           '''label_smoothing''': 0.0,
           '''early_stopping_patience''': -1,
           '''train_batch_size''': 1,
           '''eval_batch_size''': 2,
           '''max_epochs''': 2,
           '''alpha_mlm''': 0.2,
           '''alpha_ce''': 0.8,
           '''do_predict''': True,
           '''model_name_or_path''': '''sshleifer/tinier_bart''',
           '''teacher''': CHEAP_ARGS['''model_name_or_path'''],
           '''val_check_interval''': 0.5,
       }
       default_updates.update(_UpperCAmelCase       )
       A							:							dict				=    CHEAP_ARGS.copy()
       A							:							Union[str, Any]				=    make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()       )
       A							:							Optional[int]				=    self.get_auto_remove_tmp_dir()
       args_d.update(data_dir=_UpperCAmelCase     , output_dir=_UpperCAmelCase     , **_UpperCAmelCase       )

       def convert(_UpperCAmelCase     , _UpperCAmelCase       ):
           if k in ["tgt_suffix", "server_ip", "server_port", "out", "n_tpu_cores"]:
               return ""
           if v is False or v is None:
               return ""
           if v is True:  # or len(str(v))==0:
               return f'''--{k}'''
           return f'''--{k}={v}'''

       A							:							int				=    [x for x in (convert(_UpperCAmelCase     , _UpperCAmelCase       ) for k, v in args_d.items()) if len(_UpperCAmelCase       )]
       A							:							Union[str, Any]				=    [sys.executable, f'''{self.test_file_dir}/distillation.py'''] + cli_args
       execute_subprocess_async(_UpperCAmelCase     , env=self.get_env()       )

       A							:							Dict				=    os.listdir(_UpperCAmelCase       )
       A							:							Optional[int]				=    {os.path.basename(_UpperCAmelCase       ) for p in contents}
       A							:							Union[str, Any]				=    [p for p in contents if p.endswith('''ckpt'''       )]
       assert len(_UpperCAmelCase       ) > 0

       self.assertIn('''test_generations.txt'''     , _UpperCAmelCase       )
       self.assertIn('''test_results.txt'''     , _UpperCAmelCase       )

       # get the following from the module, (we don't have access to `model` here)
       A							:							Optional[Any]				=    os.path.join(_UpperCAmelCase     , '''metrics.json'''       )
       A							:							List[Any]				=    '''rouge2'''

       A							:							Union[str, Any]				=    load_json(_UpperCAmelCase       )
       # {'test': [{'test_avg_loss': 10.63731575012207, 'test_avg_rouge1': 0.0, 'test_avg_rouge2': 0.0, 'test_avg_rougeL': 0.0, 'test_avg_gen_time': 0.1822289228439331, 'test_avg_gen_len': 142.0, 'step_count': 1}]}
       print(_UpperCAmelCase       )
       A							:							Optional[Any]				=    metrics['''val'''][-1]
       self.assertGreaterEqual(last_step_stats['''val_avg_gen_time''']     , 0.01       )
       self.assertIsInstance(last_step_stats[f'''val_avg_{val_metric}''']     , _UpperCAmelCase       )
       self.assertEqual(len(metrics['''test''']       )     , 1       )
       A							:							Tuple				=    int(args_d['''max_epochs'''] * (1 / args_d['''val_check_interval''']) / 2 + 1       )
       self.assertEqual(len(metrics['''val''']       )     , _UpperCAmelCase       )
