
'''simple docstring'''

import argparse
import copy
import json
import multiprocessing
import os
import time

from create_dummy_models import COMPOSITE_MODELS, create_tiny_models
from huggingface_hub import ModelFilter, hf_api

import transformers
from transformers import AutoFeatureExtractor, AutoImageProcessor, AutoTokenizer
from transformers.image_processing_utils import BaseImageProcessor







def      A   (					)    ->				Union[str, Any]:







			'''simple docstring'''



			lowerCAmelCase__									=							set()
			# Each auto modeling files contains multiple mappings. Let's get them in a dynamic way.
			for module_name in ["modeling_auto", "modeling_tf_auto", "modeling_flax_auto"]:
						lowerCAmelCase__									=							getattr(transformers.models.auto  ,		lowerCamelCase_  ,		lowerCamelCase_					)
						if module is None:
									continue
						# all mappings in a single auto modeling file
						lowerCAmelCase__									=							[
						    x
						    for x in dir(lowerCamelCase_					)
						    if x.endswith("_MAPPING_NAMES"					)
						    and (x.startswith("MODEL_"					) or x.startswith("TF_MODEL_"					) or x.startswith("FLAX_MODEL_"					))
						]
						for name in mapping_names:
									lowerCAmelCase__									=							getattr(lowerCamelCase_  ,		lowerCamelCase_					)
									if mapping is not None:
												for v in mapping.values():
															if isinstance(lowerCamelCase_  ,		(list, tuple)					):
																		model_names.update(lowerCamelCase_					)
															elif isinstance(lowerCamelCase_  ,		lowerCamelCase_					):
																		model_names.add(lowerCamelCase_					)

			return sorted(lowerCamelCase_					)







def      A   (					)    ->				List[Any]:







			'''simple docstring'''



			lowerCAmelCase__									=							set(get_all_model_names()					)

			with open("tests/utils/tiny_model_summary.json"					) as fp:
						lowerCAmelCase__									=							json.load(lowerCamelCase_					)
			lowerCAmelCase__									=							set()
			for model_base_name in tiny_model_info:
						tiny_models_names.update(tiny_model_info[model_base_name]["model_classes"]					)

			# Remove a tiny model name if one of its framework implementation hasn't yet a tiny version on the Hub.
			lowerCAmelCase__									=							model_names.difference(lowerCamelCase_					)
			for model_name in copy.copy(lowerCamelCase_					):
						if not model_name.startswith("TF"					) and F"""TF{model_name}""" in not_on_hub:
									tiny_models_names.remove(lowerCamelCase_					)
						elif model_name.startswith("TF"					) and model_name[2:] in not_on_hub:
									tiny_models_names.remove(lowerCamelCase_					)

			return sorted(lowerCamelCase_					)







def      A   (					UpperCamelCase_    :   Optional[int]					)    ->				str:







			'''simple docstring'''



			lowerCAmelCase__									=							COMPOSITE_MODELS.values()

			# All tiny model base names on Hub
			lowerCAmelCase__									=							get_all_model_names()
			lowerCAmelCase__									=							hf_api.list_models(
			    filter=ModelFilter(
			        author="hf-internal-testing"  ,		)					)
			lowerCAmelCase__									=							set()
			for x in models:
						lowerCAmelCase__									=							x.modelId
						lowerCAmelCase__		,lowerCAmelCase__									=							model.split("/"					)
						if not model.startswith("tiny-random-"					):
									continue
						lowerCAmelCase__									=							model.replace("tiny-random-"  ,		""					)
						if not model[0].isupper():
									continue
						if model not in model_names and model not in special_models:
									continue
						_models.add(lowerCamelCase_					)

			lowerCAmelCase__									=							sorted(_models					)
			# All tiny model names on Hub
			lowerCAmelCase__									=							{}
			for model in models:
						lowerCAmelCase__									=							F"""hf-internal-testing/tiny-random-{model}"""
						lowerCAmelCase__									=							model.split("-"					)[0]
						try:
									lowerCAmelCase__									=							hf_api.repo_info(lowerCamelCase_					)
									lowerCAmelCase__									=							{
									    "tokenizer_classes": set(),
									    "processor_classes": set(),
									    "model_classes": set(),
									    "sha": repo_info.sha,
									}
						except Exception:
									continue
						try:
									time.sleep(1					)
									lowerCAmelCase__									=							AutoTokenizer.from_pretrained(lowerCamelCase_					)
									content["tokenizer_classes"].add(tokenizer_fast.__class__.__name__					)
						except Exception:
									pass
						try:
									time.sleep(1					)
									lowerCAmelCase__									=							AutoTokenizer.from_pretrained(lowerCamelCase_  ,		use_fast=lowerCamelCase_					)
									content["tokenizer_classes"].add(tokenizer_slow.__class__.__name__					)
						except Exception:
									pass
						try:
									time.sleep(1					)
									lowerCAmelCase__									=							AutoImageProcessor.from_pretrained(lowerCamelCase_					)
									content["processor_classes"].add(img_p.__class__.__name__					)
						except Exception:
									pass
						try:
									time.sleep(1					)
									lowerCAmelCase__									=							AutoFeatureExtractor.from_pretrained(lowerCamelCase_					)
									if not isinstance(lowerCamelCase_  ,		lowerCamelCase_					):
												content["processor_classes"].add(feat_p.__class__.__name__					)
						except Exception:
									pass
						try:
									time.sleep(1					)
									lowerCAmelCase__									=							getattr(lowerCamelCase_  ,		lowerCamelCase_					)
									lowerCAmelCase__									=							model_class.from_pretrained(lowerCamelCase_					)
									content["model_classes"].add(m.__class__.__name__					)
						except Exception:
									pass
						try:
									time.sleep(1					)
									lowerCAmelCase__									=							getattr(lowerCamelCase_  ,		F"""TF{model}"""					)
									lowerCAmelCase__									=							model_class.from_pretrained(lowerCamelCase_					)
									content["model_classes"].add(m.__class__.__name__					)
						except Exception:
									pass

						lowerCAmelCase__									=							sorted(content["tokenizer_classes"]					)
						lowerCAmelCase__									=							sorted(content["processor_classes"]					)
						lowerCAmelCase__									=							sorted(content["model_classes"]					)

						lowerCAmelCase__									=							content
						with open(os.path.join(lowerCamelCase_  ,		"hub_tiny_model_summary.json"					)  ,		"w"					) as fp:
									json.dump(lowerCamelCase_  ,		lowerCamelCase_  ,		ensure_ascii=lowerCamelCase_  ,		indent=4					)


if __name__ == "__main__":
		UpperCAmelCase__      :       Optional[int] 		=						argparse.ArgumentParser()
		parser.add_argument("--num_workers", default=1, type=int, help="The number of workers to run.")
		UpperCAmelCase__      :       Union[str, Any] 		=						parser.parse_args()

		# This has to be `spawn` to avoid hanging forever!
		multiprocessing.set_start_method("spawn")

		UpperCAmelCase__      :       List[str] 		=						"tiny_models"
		UpperCAmelCase__      :       Optional[Any] 		=						True
		UpperCAmelCase__      :       Optional[int] 		=						None
		UpperCAmelCase__      :       List[str] 		=						get_tiny_model_names_from_repo()
		UpperCAmelCase__      :       Dict 		=						True
		UpperCAmelCase__      :       List[Any] 		=						True
		UpperCAmelCase__      :       str 		=						"hf-internal-testing"

		create_tiny_models(
		    output_path,
		    all,
		    model_types,
		    models_to_skip,
		    no_check,
		    upload,
		    organization,
		    token=os.environ.get("TOKEN", None),
		    num_workers=args.num_workers,
		)












'''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 							__lowercase				(					lowerCAmelCase__       ,					lowerCAmelCase__       ,					lowerCAmelCase__       ,					unittest.TestCase  ):







	'''simple docstring'''

	a			: str      =				StableDiffusionModelEditingPipeline
	a			: int      =				TEXT_TO_IMAGE_PARAMS
	a			: List[Any]      =				TEXT_TO_IMAGE_BATCH_PARAMS
	a			: Tuple      =				TEXT_TO_IMAGE_IMAGE_PARAMS
	a			: Any      =				TEXT_TO_IMAGE_IMAGE_PARAMS


	def   _UpperCAmelCase   (self       )							->							Dict:







				'''simple docstring'''





				torch.manual_seed(0       )
				__lowercase   =							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   ,)
				__lowercase   =							DDIMScheduler()
				torch.manual_seed(0       )
				__lowercase   =							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       )
				__lowercase   =							CLIPTextConfig(
				    bos_token_id=0   ,eos_token_id=2   ,hidden_size=32   ,intermediate_size=37   ,layer_norm_eps=1E-0_5   ,num_attention_heads=4   ,num_hidden_layers=5   ,pad_token_id=1   ,vocab_size=1000   ,)
				__lowercase   =							CLIPTextModel(_lowerCamelCase       )
				__lowercase   =							CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''       )

				__lowercase   =							{
				    '''unet''': unet,
				    '''scheduler''': scheduler,
				    '''vae''': vae,
				    '''text_encoder''': text_encoder,
				    '''tokenizer''': tokenizer,
				    '''safety_checker''': None,
				    '''feature_extractor''': None,
				}
				return components


	def   _UpperCAmelCase   (self   ,_lowerCamelCase   ,_lowerCamelCase=0       )							->							Any:







				'''simple docstring'''





				__lowercase   =							torch.manual_seed(_lowerCamelCase       )
				__lowercase   =							{
				    '''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   _UpperCAmelCase   (self       )							->							Tuple:







				'''simple docstring'''





				__lowercase   =							'''cpu'''  # ensure determinism for the device-dependent torch.Generator
				__lowercase   =							self.get_dummy_components()
				__lowercase   =							StableDiffusionModelEditingPipeline(**_lowerCamelCase       )
				__lowercase   =							sd_pipe.to(_lowerCamelCase       )
				sd_pipe.set_progress_bar_config(disable=_lowerCamelCase       )

				__lowercase   =							self.get_dummy_inputs(_lowerCamelCase       )
				__lowercase   =							sd_pipe(**_lowerCamelCase       ).images
				__lowercase   =							image[0, -3:, -3:, -1]
				assert image.shape == (1, 64, 64, 3)

				__lowercase   =							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   _UpperCAmelCase   (self       )							->							Optional[int]:







				'''simple docstring'''





				__lowercase   =							'''cpu'''  # ensure determinism for the device-dependent torch.Generator
				__lowercase   =							self.get_dummy_components()
				__lowercase   =							StableDiffusionModelEditingPipeline(**_lowerCamelCase       )
				__lowercase   =							sd_pipe.to(_lowerCamelCase       )
				sd_pipe.set_progress_bar_config(disable=_lowerCamelCase       )

				__lowercase   =							self.get_dummy_inputs(_lowerCamelCase       )
				__lowercase   =							'''french fries'''
				__lowercase   =							sd_pipe(**_lowerCamelCase   ,negative_prompt=_lowerCamelCase       )
				__lowercase   =							output.images
				__lowercase   =							image[0, -3:, -3:, -1]

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

				__lowercase   =							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   _UpperCAmelCase   (self       )							->							Tuple:







				'''simple docstring'''





				__lowercase   =							'''cpu'''  # ensure determinism for the device-dependent torch.Generator
				__lowercase   =							self.get_dummy_components()
				__lowercase   =							EulerAncestralDiscreteScheduler(
				    beta_start=0.0_0_0_8_5   ,beta_end=0.0_1_2   ,beta_schedule='''scaled_linear'''       )
				__lowercase   =							StableDiffusionModelEditingPipeline(**_lowerCamelCase       )
				__lowercase   =							sd_pipe.to(_lowerCamelCase       )
				sd_pipe.set_progress_bar_config(disable=_lowerCamelCase       )

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

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

				__lowercase   =							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   _UpperCAmelCase   (self       )							->							Optional[int]:







				'''simple docstring'''





				__lowercase   =							'''cpu'''  # ensure determinism for the device-dependent torch.Generator
				__lowercase   =							self.get_dummy_components()
				__lowercase   =							PNDMScheduler()
				__lowercase   =							StableDiffusionModelEditingPipeline(**_lowerCamelCase       )
				__lowercase   =							sd_pipe.to(_lowerCamelCase       )
				sd_pipe.set_progress_bar_config(disable=_lowerCamelCase       )

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


	def   _UpperCAmelCase   (self       )							->							int:







				'''simple docstring'''





				super().test_inference_batch_single_identical(expected_max_diff=5E-3       )


	def   _UpperCAmelCase   (self       )							->							str:







				'''simple docstring'''





				super().test_attention_slicing_forward_pass(expected_max_diff=5E-3       )






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







	'''simple docstring'''

	def   _UpperCAmelCase   (self       )							->							List[str]:







				'''simple docstring'''





				super().tearDown()
				gc.collect()
				torch.cuda.empty_cache()


	def   _UpperCAmelCase   (self   ,_lowerCamelCase=0       )							->							Any:







				'''simple docstring'''





				__lowercase   =							torch.manual_seed(_lowerCamelCase       )
				__lowercase   =							{
				    '''prompt''': '''A field of roses''',
				    '''generator''': generator,
				    '''num_inference_steps''': 3,
				    '''guidance_scale''': 7.5,
				    '''output_type''': '''numpy''',
				}
				return inputs


	def   _UpperCAmelCase   (self       )							->							str:







				'''simple docstring'''





				__lowercase   =							'''CompVis/stable-diffusion-v1-4'''
				__lowercase   =							StableDiffusionModelEditingPipeline.from_pretrained(_lowerCamelCase   ,safety_checker=_lowerCamelCase       )
				pipe.to(_lowerCamelCase       )
				pipe.set_progress_bar_config(disable=_lowerCamelCase       )
				pipe.enable_attention_slicing()

				__lowercase   =							self.get_inputs()
				__lowercase   =							pipe(**_lowerCamelCase       ).images
				__lowercase   =							image[0, -3:, -3:, -1].flatten()

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

				__lowercase   =							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'''       )

				__lowercase   =							pipe(**_lowerCamelCase       ).images
				__lowercase   =							image[0, -3:, -3:, -1].flatten()

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

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


	def   _UpperCAmelCase   (self       )							->							List[str]:







				'''simple docstring'''





				torch.cuda.empty_cache()
				torch.cuda.reset_max_memory_allocated()
				torch.cuda.reset_peak_memory_stats()

				__lowercase   =							'''CompVis/stable-diffusion-v1-4'''
				__lowercase   =							DDIMScheduler.from_pretrained(_lowerCamelCase   ,subfolder='''scheduler'''       )
				__lowercase   =							StableDiffusionModelEditingPipeline.from_pretrained(
				    _lowerCamelCase   ,scheduler=_lowerCamelCase   ,safety_checker=_lowerCamelCase       )
				__lowercase   =							pipe.to(_lowerCamelCase       )
				pipe.set_progress_bar_config(disable=_lowerCamelCase       )
				pipe.enable_attention_slicing(1       )
				pipe.enable_sequential_cpu_offload()

				__lowercase   =							self.get_inputs()
				__lowercase   =							pipe(**_lowerCamelCase       )

				__lowercase   =							torch.cuda.max_memory_allocated()
				# make sure that less than 4.4 GB is allocated
				assert mem_bytes < 4.4 * 10**9





