






import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

# Register SEW's fairseq modules
from sew_asapp import tasks  # noqa: F401

from transformers import (
    SEWConfig,
    SEWForCTC,
    SEWModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
_UpperCamelCase			:	List[Any]								=logging.get_logger(__name__)

_UpperCamelCase			:	Tuple								={
    'post_extract_proj': 'feature_projection',
    'encoder.pos_conv.0': 'encoder.pos_conv_embed.conv',
    'self_attn.k_proj': 'encoder.layers.*.attention.k_proj',
    'self_attn.v_proj': 'encoder.layers.*.attention.v_proj',
    'self_attn.q_proj': 'encoder.layers.*.attention.q_proj',
    'self_attn.out_proj': 'encoder.layers.*.attention.out_proj',
    'self_attn_layer_norm': 'encoder.layers.*.layer_norm',
    'fc1': 'encoder.layers.*.feed_forward.intermediate_dense',
    'fc2': 'encoder.layers.*.feed_forward.output_dense',
    'final_layer_norm': 'encoder.layers.*.final_layer_norm',
    'encoder.upsample.0': 'encoder.upsample.projection',
    'encoder.layer_norm': 'encoder.layer_norm',
    'w2v_model.layer_norm': 'layer_norm',
    'w2v_encoder.proj': 'lm_head',
    'mask_emb': 'masked_spec_embed',
}



def  a__ (__lowercase							:int					,  __lowercase							:int					,  __lowercase							:Optional[Any]					,  __lowercase							:Optional[Any]					,  __lowercase							:int   )		-> List[Any]:
				for attribute in key.split('''.'''   ):
								_A	:				Any   =      getattr(snake_case__					,  snake_case__   )

				if weight_type is not None:
								_A	:				Optional[Any]   =      getattr(snake_case__					,  snake_case__   ).shape
				else:
								_A	:				Any   =      hf_pointer.shape

				assert hf_shape == value.shape, (
				    f"""Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be"""
				    f""" {value.shape} for {full_name}"""
				)

				if weight_type == "weight":
								_A	:				Dict   =      value
				elif weight_type == "weight_g":
								_A	:				Any   =      value
				elif weight_type == "weight_v":
								_A	:				List[Any]   =      value
				elif weight_type == "bias":
								_A	:				Optional[Any]   =      value
				else:
								_A	:				Dict   =      value

				logger.info(f"""{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}."""   )



def  a__ (__lowercase							:Tuple					,  __lowercase							:Dict					,  __lowercase							:List[str]   )		-> List[Any]:
				_A	:				List[Any]   =      []
				_A	:				List[Any]   =      fairseq_model.state_dict()

				_A	:				Union[str, Any]   =      hf_model.sew.feature_extractor if is_finetuned else hf_model.feature_extractor

				for name, value in fairseq_dict.items():
								_A	:				List[Any]   =      False
								if "conv_layers" in name:
												load_conv_layer(
												    snake_case__					,  snake_case__					,  snake_case__					,  snake_case__					,  hf_model.config.feat_extract_norm == '''group'''					,  )
												_A	:				Union[str, Any]   =      True
								else:
												for key, mapped_key in MAPPING.items():
																_A	:				Optional[Any]   =      """sew.""" + mapped_key if (is_finetuned and mapped_key != """lm_head""") else mapped_key

																if key in name or key.split('''w2v_model.'''   )[-1] == name.split('''.'''   )[0]:
																				_A	:				Tuple   =      True
																				if "*" in mapped_key:
																								_A	:				List[Any]   =      name.split(snake_case__   )[0].split('''.'''   )[-2]
																								_A	:				Optional[int]   =      mapped_key.replace('''*'''					,  snake_case__   )
																				if "weight_g" in name:
																								_A	:				int   =      """weight_g"""
																				elif "weight_v" in name:
																								_A	:				Union[str, Any]   =      """weight_v"""
																				elif "weight" in name:
																								_A	:				Any   =      """weight"""
																				elif "bias" in name:
																								_A	:				List[Any]   =      """bias"""
																				else:
																								_A	:				List[Any]   =      None
																				set_recursively(snake_case__					,  snake_case__					,  snake_case__					,  snake_case__					,  snake_case__   )
																continue
								if not is_used:
												unused_weights.append(snake_case__   )

				logger.warning(f"""Unused weights: {unused_weights}"""   )



def  a__ (__lowercase							:List[Any]					,  __lowercase							:Tuple					,  __lowercase							:List[Any]					,  __lowercase							:Optional[int]					,  __lowercase							:Union[str, Any]   )		-> Optional[int]:
				_A	:				Union[str, Any]   =      full_name.split('''conv_layers.'''   )[-1]
				_A	:				List[str]   =      name.split('''.'''   )
				_A	:				Optional[Any]   =      int(items[0]   )
				_A	:				Optional[int]   =      int(items[1]   )

				if type_id == 0:
								if "bias" in name:
												assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
												    f"""{full_name} has size {value.shape}, but"""
												    f""" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."""
												)
												_A	:				List[str]   =      value
												logger.info(f"""Feat extract conv layer {layer_id} was initialized from {full_name}."""   )
								elif "weight" in name:
												assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
												    f"""{full_name} has size {value.shape}, but"""
												    f""" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."""
												)
												_A	:				Any   =      value
												logger.info(f"""Feat extract conv layer {layer_id} was initialized from {full_name}."""   )
				elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
								if "bias" in name:
												assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
												    f"""{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was"""
												    " found."
												)
												_A	:				List[str]   =      value
												logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""   )
								elif "weight" in name:
												assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
												    f"""{full_name} has size {value.shape}, but"""
												    f""" {feature_extractor[layer_id].layer_norm.weight.data.shape} was found."""
												)
												_A	:				str   =      value
												logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""   )
				else:
								unused_weights.append(snake_case__   )



def  a__ (__lowercase							:Union[str, Any]					,  __lowercase							:Optional[int]   )		-> Tuple:
				_A	:				Union[str, Any]   =      SEWConfig()
				if is_finetuned:
								_A	:				Any   =      model.wav_encoder.wav_model.cfg
				else:
								_A	:				Dict   =      model.cfg

				_A	:				Optional[int]   =      fs_config.conv_bias
				_A	:				Any   =      eval(fs_config.conv_feature_layers   )
				_A	:				Any   =      [x[0] for x in conv_layers]
				_A	:				Tuple   =      [x[1] for x in conv_layers]
				_A	:				Union[str, Any]   =      [x[2] for x in conv_layers]
				_A	:				Optional[Any]   =      """gelu"""
				_A	:				Optional[Any]   =      """layer""" if fs_config.extractor_mode == """layer_norm""" else """group"""
				_A	:				Optional[int]   =      0.0
				_A	:				Optional[int]   =      fs_config.activation_fn.name
				_A	:				Union[str, Any]   =      fs_config.encoder_embed_dim
				_A	:				Tuple   =      0.02
				_A	:				Optional[Any]   =      fs_config.encoder_ffn_embed_dim
				_A	:				Dict   =      1e-5
				_A	:				str   =      fs_config.encoder_layerdrop
				_A	:				Tuple   =      fs_config.encoder_attention_heads
				_A	:				Tuple   =      fs_config.conv_pos_groups
				_A	:				str   =      fs_config.conv_pos
				_A	:				Dict   =      len(snake_case__   )
				_A	:				Optional[Any]   =      fs_config.encoder_layers
				_A	:				Dict   =      fs_config.squeeze_factor

				# take care of any params that are overridden by the Wav2VecCtc model
				if is_finetuned:
								_A	:				Tuple   =      model.cfg
								_A	:				List[str]   =      fs_config.final_dropout
								_A	:				str   =      fs_config.layerdrop
				_A	:				str   =      fs_config.activation_dropout
				_A	:				List[Any]   =      fs_config.mask_prob > 0 or fs_config.mask_channel_prob > 0
				_A	:				int   =      fs_config.attention_dropout
				_A	:				Optional[int]   =      fs_config.dropout_input
				_A	:				int   =      fs_config.dropout
				_A	:				Union[str, Any]   =      fs_config.mask_channel_length
				_A	:				Tuple   =      fs_config.mask_channel_prob
				_A	:				str   =      fs_config.mask_length
				_A	:				str   =      fs_config.mask_prob

				_A	:				Any   =      """Wav2Vec2FeatureExtractor"""
				_A	:				Tuple   =      """Wav2Vec2CTCTokenizer"""

				return config



@torch.no_grad()
def  a__ (__lowercase							:Dict					,  __lowercase							:Union[str, Any]					,  __lowercase							:List[str]=None					,  __lowercase							:Any=None					,  __lowercase							:Dict=True   )		-> Dict:

				if is_finetuned:
								_A	:				Any   =      fairseq.checkpoint_utils.load_model_ensemble_and_task(
								    [checkpoint_path]					,  arg_overrides={'''data''': '''/'''.join(dict_path.split('''/'''   )[:-1]   )}   )
				else:
								_A	:				List[str]   =      fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]   )

				if config_path is not None:
								_A	:				Any   =      SEWConfig.from_pretrained(snake_case__   )
				else:
								_A	:				List[Any]   =      convert_config(model[0]					,  snake_case__   )
				_A	:				str   =      model[0].eval()

				_A	:				str   =      True if config.feat_extract_norm == """layer""" else False
				_A	:				str   =      WavaVecaFeatureExtractor(
				    feature_size=1					,  sampling_rate=1_6000					,  padding_value=0					,  do_normalize=snake_case__					,  return_attention_mask=snake_case__					,  )

				if is_finetuned:
								if dict_path:
												_A	:				List[str]   =      Dictionary.load(snake_case__   )

												# important change bos & pad token id since CTC symbol is <pad> and
												# not <s> as in fairseq
												_A	:				str   =      target_dict.pad_index
												_A	:				Tuple   =      target_dict.bos_index
												_A	:				Optional[Any]   =      target_dict.pad_index
												_A	:				int   =      target_dict.bos_index
												_A	:				Optional[int]   =      target_dict.eos_index
												_A	:				List[Any]   =      len(target_dict.symbols   )
												_A	:				str   =      os.path.join(snake_case__					,  '''vocab.json'''   )
												if not os.path.isdir(snake_case__   ):
																logger.error('''--pytorch_dump_folder_path ({}) should be a directory'''.format(snake_case__   )   )
																return
												os.makedirs(snake_case__					,  exist_ok=snake_case__   )
												with open(snake_case__					,  '''w'''					,  encoding='''utf-8'''   ) as vocab_handle:
																json.dump(target_dict.indices					,  snake_case__   )
												_A	:				Union[str, Any]   =      WavaVecaCTCTokenizer(
												    snake_case__					,  unk_token=target_dict.unk_word					,  pad_token=target_dict.pad_word					,  bos_token=target_dict.bos_word					,  eos_token=target_dict.eos_word					,  word_delimiter_token='''|'''					,  do_lower_case=snake_case__					,  )
												_A	:				List[str]   =      WavaVecaProcessor(feature_extractor=snake_case__					,  tokenizer=snake_case__   )
												processor.save_pretrained(snake_case__   )

								_A	:				str   =      SEWForCTC(snake_case__   )
				else:
								_A	:				List[str]   =      SEWModel(snake_case__   )
								feature_extractor.save_pretrained(snake_case__   )

				recursively_load_weights(snake_case__					,  snake_case__					,  snake_case__   )

				hf_model.save_pretrained(snake_case__   )


if __name__ == "__main__":
	_UpperCamelCase			:	int								=argparse.ArgumentParser()
	parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
	parser.add_argument('--checkpoint_path', default=None, type=str, help='Path to fairseq checkpoint')
	parser.add_argument('--dict_path', default=None, type=str, help='Path to dict of fine-tuned model')
	parser.add_argument('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')
	parser.add_argument(
	    '--is_finetuned', action='store_true', help='Whether the model to convert is a fine-tuned model or not'
	)
	_UpperCamelCase			:	List[str]								=parser.parse_args()
	convert_sew_checkpoint(
	    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
	)







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 		_SCREAMING_SNAKE_CASE     (						A							,	A							,	unittest.TestCase     ):
     __SCREAMING_SNAKE_CASE     					=    IFPipeline
     __SCREAMING_SNAKE_CASE     					=    TEXT_TO_IMAGE_PARAMS - {'''width''', '''height''', '''latents'''}
     __SCREAMING_SNAKE_CASE     					=    TEXT_TO_IMAGE_BATCH_PARAMS
     __SCREAMING_SNAKE_CASE     					=    PipelineTesterMixin.required_optional_params - {'''latents'''}


     def        __snake_case(  self    ):
       return self._get_dummy_components()


     def        __snake_case(  self							,					A_							,					A_=0    ):
       if str(A_    ).startswith("""mps"""    ):
         _UpperCAmelCase		:							Tuple							=     torch.manual_seed(A_    )
       else:
         _UpperCAmelCase		:							Optional[Any]							=     torch.Generator(device=A_    ).manual_seed(A_    )

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

       return inputs


     def        __snake_case(  self    ):
       self._test_save_load_optional_components()


     @unittest.skipIf(torch_device != """cuda"""							,					reason="""float16 requires CUDA"""    )
     def        __snake_case(  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        __snake_case(  self    ):
       self._test_attention_slicing_forward_pass(expected_max_diff=1e-2    )


     def        __snake_case(  self    ):
       self._test_save_load_local()


     def        __snake_case(  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        __snake_case(  self    ):
       self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3    )


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


     def        __snake_case(  self    ):
       # clean up the VRAM after each test
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()


     def        __snake_case(  self    ):
       # if

       _UpperCAmelCase		:							List[str]							=     IFPipeline.from_pretrained("""DeepFloyd/IF-I-XL-v1.0"""							,					variant="""fp16"""							,					torch_dtype=torch.floataa    )

       _UpperCAmelCase		:							Dict							=     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"""    )

       _UpperCAmelCase,_UpperCAmelCase		:							Dict							=     pipe_a.encode_prompt("""anime turtle"""							,					device="""cuda"""    )

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

       _UpperCAmelCase		:							List[Any]							=     None
       _UpperCAmelCase		:							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

       _UpperCAmelCase		:							Any							=     IFImgaImgPipeline(**pipe_a.components    )
       _UpperCAmelCase		:							Union[str, Any]							=     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

       _UpperCAmelCase		:							Optional[int]							=     IFInpaintingPipeline(**pipe_a.components    )
       _UpperCAmelCase		:							Union[str, 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        __snake_case(  self							,					A_							,					A_							,					A_							,					A_    ):
       # pipeline 1

       _start_torch_memory_measurement()

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

       _UpperCAmelCase		:							int							=     output.images[0]

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

       _UpperCAmelCase		:							Dict							=     torch.cuda.max_memory_allocated()
       assert mem_bytes < 13 * 10**9

       _UpperCAmelCase		:							str							=     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()

       _UpperCAmelCase		:							List[Any]							=     torch.Generator(device="""cpu"""    ).manual_seed(0    )

       _UpperCAmelCase		:							str							=     floats_tensor((1, 3, 64, 64)							,					rng=random.Random(0    )    ).to(A_    )

       _UpperCAmelCase		:							str							=     pipe_a(
           prompt_embeds=A_							,					negative_prompt_embeds=A_							,					image=A_							,					generator=A_							,					num_inference_steps=2							,					output_type="""np"""							,					)

       _UpperCAmelCase		:							int							=     output.images[0]

       assert image.shape == (2_56, 2_56, 3)

       _UpperCAmelCase		:							Any							=     torch.cuda.max_memory_allocated()
       assert mem_bytes < 4 * 10**9

       _UpperCAmelCase		:							List[Any]							=     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        __snake_case(  self							,					A_							,					A_							,					A_							,					A_    ):
       # pipeline 1

       _start_torch_memory_measurement()

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

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

       _UpperCAmelCase		:							List[str]							=     pipe_a(
           prompt_embeds=A_							,					negative_prompt_embeds=A_							,					image=A_							,					num_inference_steps=2							,					generator=A_							,					output_type="""np"""							,					)

       _UpperCAmelCase		:							int							=     output.images[0]

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

       _UpperCAmelCase		:							str							=     torch.cuda.max_memory_allocated()
       assert mem_bytes < 10 * 10**9

       _UpperCAmelCase		:							str							=     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()

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

       _UpperCAmelCase		:							Union[str, Any]							=     floats_tensor((1, 3, 2_56, 2_56)							,					rng=random.Random(0    )    ).to(A_    )
       _UpperCAmelCase		:							Optional[int]							=     floats_tensor((1, 3, 64, 64)							,					rng=random.Random(0    )    ).to(A_    )

       _UpperCAmelCase		:							Optional[int]							=     pipe_a(
           prompt_embeds=A_							,					negative_prompt_embeds=A_							,					image=A_							,					original_image=A_							,					generator=A_							,					num_inference_steps=2							,					output_type="""np"""							,					)

       _UpperCAmelCase		:							Dict							=     output.images[0]

       assert image.shape == (2_56, 2_56, 3)

       _UpperCAmelCase		:							Any							=     torch.cuda.max_memory_allocated()
       assert mem_bytes < 4 * 10**9

       _UpperCAmelCase		:							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        __snake_case(  self							,					A_							,					A_							,					A_							,					A_    ):
       # pipeline 1

       _start_torch_memory_measurement()

       _UpperCAmelCase		:							List[Any]							=     floats_tensor((1, 3, 64, 64)							,					rng=random.Random(0    )    ).to(A_    )
       _UpperCAmelCase		:							Dict							=     floats_tensor((1, 3, 64, 64)							,					rng=random.Random(1    )    ).to(A_    )

       _UpperCAmelCase		:							Optional[Any]							=     torch.Generator(device="""cpu"""    ).manual_seed(0    )
       _UpperCAmelCase		:							str							=     pipe_a(
           prompt_embeds=A_							,					negative_prompt_embeds=A_							,					image=A_							,					mask_image=A_							,					num_inference_steps=2							,					generator=A_							,					output_type="""np"""							,					)

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

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

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

       _UpperCAmelCase		:							Dict							=     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()

       _UpperCAmelCase		:							str							=     torch.Generator(device="""cpu"""    ).manual_seed(0    )

       _UpperCAmelCase		:							Optional[int]							=     floats_tensor((1, 3, 64, 64)							,					rng=random.Random(0    )    ).to(A_    )
       _UpperCAmelCase		:							Tuple							=     floats_tensor((1, 3, 2_56, 2_56)							,					rng=random.Random(0    )    ).to(A_    )
       _UpperCAmelCase		:							str							=     floats_tensor((1, 3, 2_56, 2_56)							,					rng=random.Random(1    )    ).to(A_    )

       _UpperCAmelCase		:							Union[str, Any]							=     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"""							,					)

       _UpperCAmelCase		:							Any							=     output.images[0]

       assert image.shape == (2_56, 2_56, 3)

       _UpperCAmelCase		:							Optional[int]							=     torch.cuda.max_memory_allocated()
       assert mem_bytes < 4 * 10**9

       _UpperCAmelCase		:							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    a__    (		):
  torch.cuda.empty_cache()
  torch.cuda.reset_max_memory_allocated()
  torch.cuda.reset_peak_memory_stats()





