import argparse

import torch

from transformers import (
    SpeechTaConfig,
    SpeechTaFeatureExtractor,
    SpeechTaForSpeechToSpeech,
    SpeechTaForSpeechToText,
    SpeechTaForTextToSpeech,
    SpeechTaProcessor,
    SpeechTaTokenizer,
    logging,
)
from transformers.tokenization_utils import AddedToken


logging.set_verbosity_info()
lowerCamelCase_    :		Optional[Any]							=	logging.get_logger("""transformers.models.speecht5""")

lowerCamelCase_    :		Optional[Any]							=	{
    """speech_encoder_prenet.layer_norm""": """speecht5.encoder.prenet.feature_projection.layer_norm""",
    """speech_encoder_prenet.post_extract_proj""": """speecht5.encoder.prenet.feature_projection.projection""",
    """speech_encoder_prenet.pos_conv.0""": """speecht5.encoder.prenet.pos_conv_embed.conv""",
    """speech_encoder_prenet.mask_emb""": """speecht5.encoder.prenet.masked_spec_embed""",
}
lowerCamelCase_    :		int							=	{
    """text_encoder_prenet.encoder_prenet.0""": """speecht5.encoder.prenet.embed_tokens""",
    """text_encoder_prenet.encoder_prenet.1.alpha""": """speecht5.encoder.prenet.encode_positions.alpha""",
}
lowerCamelCase_    :		List[str]							=	{
    """speech_decoder_prenet.decoder_prenet.0.0.prenet.0.0""": """speecht5.decoder.prenet.layers.0""",
    """speech_decoder_prenet.decoder_prenet.0.0.prenet.1.0""": """speecht5.decoder.prenet.layers.1""",
    """speech_decoder_prenet.decoder_prenet.0.1""": """speecht5.decoder.prenet.final_layer""",
    """speech_decoder_prenet.decoder_prenet.1.alpha""": """speecht5.decoder.prenet.encode_positions.alpha""",
    """speech_decoder_prenet.spkembs_layer.0""": """speecht5.decoder.prenet.speaker_embeds_layer""",
}
lowerCamelCase_    :		Optional[Any]							=	{
    """speech_decoder_postnet.feat_out""": """speech_decoder_postnet.feat_out""",
    """speech_decoder_postnet.prob_out""": """speech_decoder_postnet.prob_out""",
    """speech_decoder_postnet.postnet.postnet.0.0""": """speech_decoder_postnet.layers.0.conv""",
    """speech_decoder_postnet.postnet.postnet.0.1""": """speech_decoder_postnet.layers.0.batch_norm""",
    """speech_decoder_postnet.postnet.postnet.1.0""": """speech_decoder_postnet.layers.1.conv""",
    """speech_decoder_postnet.postnet.postnet.1.1""": """speech_decoder_postnet.layers.1.batch_norm""",
    """speech_decoder_postnet.postnet.postnet.2.0""": """speech_decoder_postnet.layers.2.conv""",
    """speech_decoder_postnet.postnet.postnet.2.1""": """speech_decoder_postnet.layers.2.batch_norm""",
    """speech_decoder_postnet.postnet.postnet.3.0""": """speech_decoder_postnet.layers.3.conv""",
    """speech_decoder_postnet.postnet.postnet.3.1""": """speech_decoder_postnet.layers.3.batch_norm""",
    """speech_decoder_postnet.postnet.postnet.4.0""": """speech_decoder_postnet.layers.4.conv""",
    """speech_decoder_postnet.postnet.postnet.4.1""": """speech_decoder_postnet.layers.4.batch_norm""",
}
lowerCamelCase_    :		int							=	{
    """text_decoder_prenet.embed_tokens""": """speecht5.decoder.prenet.embed_tokens""",
}
lowerCamelCase_    :		Tuple							=	{
    """text_decoder_postnet.output_projection""": """text_decoder_postnet.lm_head""",
}
lowerCamelCase_    :		List[Any]							=	{
    """encoder.layers.*.self_attn.k_proj""": """speecht5.encoder.wrapped_encoder.layers.*.attention.k_proj""",
    """encoder.layers.*.self_attn.v_proj""": """speecht5.encoder.wrapped_encoder.layers.*.attention.v_proj""",
    """encoder.layers.*.self_attn.q_proj""": """speecht5.encoder.wrapped_encoder.layers.*.attention.q_proj""",
    """encoder.layers.*.self_attn.out_proj""": """speecht5.encoder.wrapped_encoder.layers.*.attention.out_proj""",
    """encoder.layers.*.self_attn_layer_norm""": """speecht5.encoder.wrapped_encoder.layers.*.layer_norm""",
    """encoder.layers.*.fc1""": """speecht5.encoder.wrapped_encoder.layers.*.feed_forward.intermediate_dense""",
    """encoder.layers.*.fc2""": """speecht5.encoder.wrapped_encoder.layers.*.feed_forward.output_dense""",
    """encoder.layers.*.final_layer_norm""": """speecht5.encoder.wrapped_encoder.layers.*.final_layer_norm""",
    """encoder.layer_norm""": """speecht5.encoder.wrapped_encoder.layer_norm""",
    """encoder.pos_emb.pe_k""": """speecht5.encoder.wrapped_encoder.embed_positions.pe_k""",
}
lowerCamelCase_    :		Optional[Any]							=	{
    """decoder.layers.*.self_attn.k_proj""": """speecht5.decoder.wrapped_decoder.layers.*.self_attn.k_proj""",
    """decoder.layers.*.self_attn.v_proj""": """speecht5.decoder.wrapped_decoder.layers.*.self_attn.v_proj""",
    """decoder.layers.*.self_attn.q_proj""": """speecht5.decoder.wrapped_decoder.layers.*.self_attn.q_proj""",
    """decoder.layers.*.self_attn.out_proj""": """speecht5.decoder.wrapped_decoder.layers.*.self_attn.out_proj""",
    """decoder.layers.*.self_attn_layer_norm""": """speecht5.decoder.wrapped_decoder.layers.*.self_attn_layer_norm""",
    """decoder.layers.*.encoder_attn.k_proj""": """speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.k_proj""",
    """decoder.layers.*.encoder_attn.v_proj""": """speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.v_proj""",
    """decoder.layers.*.encoder_attn.q_proj""": """speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.q_proj""",
    """decoder.layers.*.encoder_attn.out_proj""": """speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.out_proj""",
    """decoder.layers.*.encoder_attn_layer_norm""": """speecht5.decoder.wrapped_decoder.layers.*.encoder_attn_layer_norm""",
    """decoder.layers.*.fc1""": """speecht5.decoder.wrapped_decoder.layers.*.feed_forward.intermediate_dense""",
    """decoder.layers.*.fc2""": """speecht5.decoder.wrapped_decoder.layers.*.feed_forward.output_dense""",
    """decoder.layers.*.final_layer_norm""": """speecht5.decoder.wrapped_decoder.layers.*.final_layer_norm""",
}
lowerCamelCase_    :		Optional[Any]							=	{
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_TEXT_DECODER_PRENET,
    **MAPPING_TEXT_DECODER_POSTNET,
}
lowerCamelCase_    :		int							=	{
    **MAPPING_TEXT_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
lowerCamelCase_    :		Union[str, Any]							=	{
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
lowerCamelCase_    :		Optional[int]							=	[]
lowerCamelCase_    :		str							=	[
    """encoder.version""",
    """encoder.layers.*.norm_k.weight""",
    """encoder.layers.*.norm_k.bias""",
    """decoder.version""",
    """decoder.layers.*.norm_k.weight""",
    """decoder.layers.*.norm_k.bias""",
    """decoder.pos_emb.pe_k""",
    """speech_encoder_prenet.embed_positions._float_tensor""",
    """text_decoder_prenet.embed_positions._float_tensor""",
]
lowerCamelCase_    :		str							=	IGNORE_KEYS + [
    """encoder.proj""",
    """text_encoder_prenet.*""",
    """speech_decoder_prenet.*""",
    """speech_decoder_postnet.*""",
]
lowerCamelCase_    :		Any							=	IGNORE_KEYS + [
    """encoder.proj""",
    """speech_encoder_prenet.*""",
    """text_decoder_prenet.*""",
    """text_decoder_postnet.*""",
]
lowerCamelCase_    :		str							=	IGNORE_KEYS + [
    """encoder.proj""",
    """text_encoder_prenet.*""",
    """text_decoder_prenet.*""",
    """text_decoder_postnet.*""",
]
def 							lowerCAmelCase(     __lowerCamelCase				,    __lowerCamelCase				,    __lowerCamelCase				,    __lowerCamelCase				,    __lowerCamelCase						):
					for attribute in key.split('.'						):
										__a		 =       getattr(__lowerCamelCase				,    __lowerCamelCase						)

					if weight_type is not None:
										__a		 =       getattr(__lowerCamelCase				,    __lowerCamelCase						).shape
					else:
										__a		 =       hf_pointer.shape

					if hf_shape != value.shape:
										raise ValueError(
										    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		 =       value
					elif weight_type == "weight_g":
										__a		 =       value
					elif weight_type == "weight_v":
										__a		 =       value
					elif weight_type == "bias":
										__a		 =       value
					elif weight_type == "running_mean":
										__a		 =       value
					elif weight_type == "running_var":
										__a		 =       value
					elif weight_type == "num_batches_tracked":
										__a		 =       value
					else:
										__a		 =       value

					logger.info(f'''{key + ("." + weight_type if weight_type is not None else "")} was initialized from {full_name}.'''						)
def 							lowerCAmelCase(     __lowerCamelCase				,    __lowerCamelCase						):
					for key in ignore_keys:
										if key.endswith('.*'						):
															if name.startswith(key[:-1]						):
																				return True
										elif ".*." in key:
															__a ,				__a		 =       key.split('.*.'						)
															if prefix in name and suffix in name:
																				return True
										elif key in name:
															return True
					return False
def 							lowerCAmelCase(     __lowerCamelCase				,    __lowerCamelCase				,    __lowerCamelCase						):
					__a		 =       []

					if task == "s2t":
										__a		 =       hf_model.speechta.encoder.prenet.feature_encoder
										__a		 =       MAPPING_S2T
										__a		 =       IGNORE_KEYS_S2T
					elif task == "t2s":
										__a		 =       None
										__a		 =       MAPPING_T2S
										__a		 =       IGNORE_KEYS_T2S
					elif task == "s2s":
										__a		 =       hf_model.speechta.encoder.prenet.feature_encoder
										__a		 =       MAPPING_S2S
										__a		 =       IGNORE_KEYS_S2S
					else:
										raise ValueError(f'''Unsupported task: {task}'''						)

					for name, value in fairseq_dict.items():
										if should_ignore(__lowerCamelCase				,    __lowerCamelCase						):
															logger.info(f'''{name} was ignored'''						)
															continue

										__a		 =       False
										if "conv_layers" in name:
															load_conv_layer(
															    __lowerCamelCase				,    __lowerCamelCase				,    __lowerCamelCase				,    __lowerCamelCase				,    hf_model.config.feat_extract_norm == 'group'				,    )
															__a		 =       True
										else:
															for key, mapped_key in MAPPING.items():
																				# mapped_key = "speecht5." + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key

																				if "*" in key:
																									__a ,				__a		 =       key.split('.*.'						)
																									if prefix in name and suffix in name:
																														__a		 =       suffix

                # if key in name or key.split("w2v_model.")[-1] == name.split(".")[0]:
																				if key in name:
																									__a		 =       True
																									if "*" in mapped_key:
																														__a		 =       name.split(__lowerCamelCase						)[0].split('.'						)[-2]
																														__a		 =       mapped_key.replace('*'				,    __lowerCamelCase						)
																									if "weight_g" in name:
																														__a		 =       'weight_g'
																									elif "weight_v" in name:
																														__a		 =       'weight_v'
																									elif "bias" in name:
																														__a		 =       'bias'
																									elif "weight" in name:
																														__a		 =       'weight'
																									elif "running_mean" in name:
																														__a		 =       'running_mean'
																									elif "running_var" in name:
																														__a		 =       'running_var'
																									elif "num_batches_tracked" in name:
																														__a		 =       'num_batches_tracked'
																									else:
																														__a		 =       None
																									set_recursively(__lowerCamelCase				,    __lowerCamelCase				,    __lowerCamelCase				,    __lowerCamelCase				,    __lowerCamelCase						)
																				continue
										if not is_used:
															unused_weights.append(__lowerCamelCase						)

					logger.warning(f'''Unused weights: {unused_weights}'''						)
def 							lowerCAmelCase(     __lowerCamelCase				,    __lowerCamelCase				,    __lowerCamelCase				,    __lowerCamelCase				,    __lowerCamelCase						):
					__a		 =       full_name.split('conv_layers.'						)[-1]
					__a		 =       name.split('.'						)
					__a		 =       int(items[0]						)
					__a		 =       int(items[1]						)

					if type_id == 0:
										if "bias" in name:
															if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
																				raise ValueError(
																				    f'''{full_name} has size {value.shape}, but'''
																				    f''' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'''						)
															__a		 =       value
															logger.info(f'''Feat extract conv layer {layer_id} was initialized from {full_name}.'''						)
										elif "weight" in name:
															if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
																				raise ValueError(
																				    f'''{full_name} has size {value.shape}, but'''
																				    f''' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'''						)
															__a		 =       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:
															if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
																				raise ValueError(
																				    f'''{full_name} has size {value.shape}, but'''
																				    f''' {feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape} was found.'''						)
															__a		 =       value
															logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''						)
										elif "weight" in name:
															if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
																				raise ValueError(
																				    f'''{full_name} has size {value.shape}, but'''
																				    f''' {feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape} was found.'''						)
															__a		 =       value
															logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''						)
					else:
										unused_weights.append(__lowerCamelCase						)
@torch.no_grad()
def 							lowerCAmelCase(     __lowerCamelCase				,    __lowerCamelCase				,    __lowerCamelCase				,    __lowerCamelCase=None				,    __lowerCamelCase=None				,    __lowerCamelCase=None				,    ):
					if config_path is not None:
										__a		 =       SpeechTaConfig.from_pretrained(__lowerCamelCase						)
					else:
										__a		 =       SpeechTaConfig()

					if task == "s2t":
										__a		 =       config.max_text_positions
										__a		 =       SpeechTaForSpeechToText(__lowerCamelCase						)
					elif task == "t2s":
										__a		 =       1876
										__a		 =       600
										__a		 =       config.max_speech_positions
										__a		 =       SpeechTaForTextToSpeech(__lowerCamelCase						)
					elif task == "s2s":
										__a		 =       1876
										__a		 =       config.max_speech_positions
										__a		 =       SpeechTaForSpeechToSpeech(__lowerCamelCase						)
					else:
										raise ValueError(f'''Unknown task name: {task}'''						)

					if vocab_path:
										__a		 =       SpeechTaTokenizer(__lowerCamelCase				,    model_max_length=config.max_text_positions						)

										# Mask token behaves like a normal word, i.e. include the space before it
										__a		 =       AddedToken('<mask>'				,    lstrip=__lowerCamelCase				,    rstrip=__lowerCamelCase						)
										__a		 =       mask_token
										tokenizer.add_special_tokens({'mask_token': mask_token}						)
										tokenizer.add_tokens(['<ctc_blank>']						)

					__a		 =       SpeechTaFeatureExtractor()
					__a		 =       SpeechTaProcessor(tokenizer=__lowerCamelCase				,    feature_extractor=__lowerCamelCase						)
					processor.save_pretrained(__lowerCamelCase						)

					__a		 =       torch.load(__lowerCamelCase						)
					recursively_load_weights(fairseq_checkpoint['model']				,    __lowerCamelCase				,    __lowerCamelCase						)

					model.save_pretrained(__lowerCamelCase						)

					if repo_id:
										print('Pushing to the hub...'						)
										processor.push_to_hub(__lowerCamelCase						)
										model.push_to_hub(__lowerCamelCase						)


if __name__ == "__main__":
	lowerCamelCase_    :		List[Any]							=	argparse.ArgumentParser()
	parser.add_argument(
	    """--task""",
	    default="""s2t""",
	    type=str,
	    help="""Type of the SpeechT5 model you'd like to convert. Should be one of 's2t', 't2s', 's2s'.""",
	)
	parser.add_argument("""--checkpoint_path""", required=True, default=None, type=str, help="""Path to fairseq checkpoint""")
	parser.add_argument("""--vocab_path""", default=None, type=str, help="""Path to SentencePiece model""")
	parser.add_argument("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
	parser.add_argument(
	    """--pytorch_dump_folder_path""", required=True, default=None, type=str, help="""Path to the output PyTorch model."""
	)
	parser.add_argument(
	    """--push_to_hub""", default=None, type=str, help="""Where to upload the converted model on the 🤗 hub."""
	)

	lowerCamelCase_    :		Any							=	parser.parse_args()
	convert_speechta_checkpoint(
	    args.task,
	    args.checkpoint_path,
	    args.pytorch_dump_folder_path,
	    args.config_path,
	    args.vocab_path,
	    args.push_to_hub,
	)





import tempfile

import numpy as np
import torch
from transformers import AutoTokenizer, TaEncoderModel

from diffusers import DDPMScheduler, UNetaDConditionModel
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.pipelines.deepfloyd_if import IFWatermarker
from diffusers.utils.testing_utils import torch_device

from ..test_pipelines_common import to_np


class 						a__							:



			def __SCREAMING_SNAKE_CASE							(						self   )			->   Optional[Any]:
								torch.manual_seed(0   )
								__a		 =       TaEncoderModel.from_pretrained('hf-internal-testing/tiny-random-t5'   )

								torch.manual_seed(0   )
								__a		 =       AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-t5'   )

								torch.manual_seed(0   )
								__a		 =       UNetaDConditionModel(
								    sample_size=3_2				,						layers_per_block=1				,						block_out_channels=[3_2, 6_4]				,						down_block_types=[
								        'ResnetDownsampleBlock2D',
								        'SimpleCrossAttnDownBlock2D',
								    ]				,						mid_block_type='UNetMidBlock2DSimpleCrossAttn'				,						up_block_types=['SimpleCrossAttnUpBlock2D', 'ResnetUpsampleBlock2D']				,						in_channels=3				,						out_channels=6				,						cross_attention_dim=3_2				,						encoder_hid_dim=3_2				,						attention_head_dim=8				,						addition_embed_type='text'				,						addition_embed_type_num_heads=2				,						cross_attention_norm='group_norm'				,						resnet_time_scale_shift='scale_shift'				,						act_fn='gelu'				,						)
								unet.set_attn_processor(AttnAddedKVProcessor()   )  # For reproducibility tests

								torch.manual_seed(0   )
								__a		 =       DDPMScheduler(
								    num_train_timesteps=1_0_0_0				,						beta_schedule='squaredcos_cap_v2'				,						beta_start=0.0_001				,						beta_end=0.02				,						thresholding=UpperCAmelCase				,						dynamic_thresholding_ratio=0.95				,						sample_max_value=1.0				,						prediction_type='epsilon'				,						variance_type='learned_range'				,						)

								torch.manual_seed(0   )
								__a		 =       IFWatermarker()

								return {
								    "text_encoder": text_encoder,
								    "tokenizer": tokenizer,
								    "unet": unet,
								    "scheduler": scheduler,
								    "watermarker": watermarker,
								    "safety_checker": None,
								    "feature_extractor": None,
								}



			def __SCREAMING_SNAKE_CASE							(						self   )			->   Optional[int]:
								torch.manual_seed(0   )
								__a		 =       TaEncoderModel.from_pretrained('hf-internal-testing/tiny-random-t5'   )

								torch.manual_seed(0   )
								__a		 =       AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-t5'   )

								torch.manual_seed(0   )
								__a		 =       UNetaDConditionModel(
								    sample_size=3_2				,						layers_per_block=[1, 2]				,						block_out_channels=[3_2, 6_4]				,						down_block_types=[
								        'ResnetDownsampleBlock2D',
								        'SimpleCrossAttnDownBlock2D',
								    ]				,						mid_block_type='UNetMidBlock2DSimpleCrossAttn'				,						up_block_types=['SimpleCrossAttnUpBlock2D', 'ResnetUpsampleBlock2D']				,						in_channels=6				,						out_channels=6				,						cross_attention_dim=3_2				,						encoder_hid_dim=3_2				,						attention_head_dim=8				,						addition_embed_type='text'				,						addition_embed_type_num_heads=2				,						cross_attention_norm='group_norm'				,						resnet_time_scale_shift='scale_shift'				,						act_fn='gelu'				,						class_embed_type='timestep'				,						mid_block_scale_factor=1.414				,						time_embedding_act_fn='gelu'				,						time_embedding_dim=3_2				,						)
								unet.set_attn_processor(AttnAddedKVProcessor()   )  # For reproducibility tests

								torch.manual_seed(0   )
								__a		 =       DDPMScheduler(
								    num_train_timesteps=1_0_0_0				,						beta_schedule='squaredcos_cap_v2'				,						beta_start=0.0_001				,						beta_end=0.02				,						thresholding=UpperCAmelCase				,						dynamic_thresholding_ratio=0.95				,						sample_max_value=1.0				,						prediction_type='epsilon'				,						variance_type='learned_range'				,						)

								torch.manual_seed(0   )
								__a		 =       DDPMScheduler(
								    num_train_timesteps=1_0_0_0				,						beta_schedule='squaredcos_cap_v2'				,						beta_start=0.0_001				,						beta_end=0.02				,						)

								torch.manual_seed(0   )
								__a		 =       IFWatermarker()

								return {
								    "text_encoder": text_encoder,
								    "tokenizer": tokenizer,
								    "unet": unet,
								    "scheduler": scheduler,
								    "image_noising_scheduler": image_noising_scheduler,
								    "watermarker": watermarker,
								    "safety_checker": None,
								    "feature_extractor": None,
								}



			def __SCREAMING_SNAKE_CASE							(						self   )			->   str:
								__a		 =       self.get_dummy_components()
								__a		 =       self.pipeline_class(**UpperCAmelCase   )
								pipe.to(UpperCAmelCase   )
								pipe.set_progress_bar_config(disable=UpperCAmelCase   )

								__a		 =       self.get_dummy_inputs(UpperCAmelCase   )

								__a		 =       inputs['prompt']
								__a		 =       inputs['generator']
								__a		 =       inputs['num_inference_steps']
								__a		 =       inputs['output_type']

								if "image" in inputs:
													__a		 =       inputs['image']
								else:
													__a		 =       None

								if "mask_image" in inputs:
													__a		 =       inputs['mask_image']
								else:
													__a		 =       None

								if "original_image" in inputs:
													__a		 =       inputs['original_image']
								else:
													__a		 =       None

								__a ,				__a		 =       pipe.encode_prompt(UpperCAmelCase   )

								# inputs with prompt converted to embeddings
								__a		 =       {
								    'prompt_embeds': prompt_embeds,
								    'negative_prompt_embeds': negative_prompt_embeds,
								    'generator': generator,
								    'num_inference_steps': num_inference_steps,
								    'output_type': output_type,
								}

								if image is not None:
													__a		 =       image

								if mask_image is not None:
													__a		 =       mask_image

								if original_image is not None:
													__a		 =       original_image

								# set all optional components to None
								for optional_component in pipe._optional_components:
													setattr(UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase   )

								__a		 =       pipe(**UpperCAmelCase   )[0]

								with tempfile.TemporaryDirectory() as tmpdir:
													pipe.save_pretrained(UpperCAmelCase   )
													__a		 =       self.pipeline_class.from_pretrained(UpperCAmelCase   )
													pipe_loaded.to(UpperCAmelCase   )
													pipe_loaded.set_progress_bar_config(disable=UpperCAmelCase   )

								pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor()   )  # For reproducibility tests

								for optional_component in pipe._optional_components:
													self.assertTrue(
													    getattr(UpperCAmelCase				,						UpperCAmelCase   ) is None				,						f'''`{optional_component}` did not stay set to None after loading.'''				,						)

								__a		 =       self.get_dummy_inputs(UpperCAmelCase   )

								__a		 =       inputs['generator']
								__a		 =       inputs['num_inference_steps']
								__a		 =       inputs['output_type']

								# inputs with prompt converted to embeddings
								__a		 =       {
								    'prompt_embeds': prompt_embeds,
								    'negative_prompt_embeds': negative_prompt_embeds,
								    'generator': generator,
								    'num_inference_steps': num_inference_steps,
								    'output_type': output_type,
								}

								if image is not None:
													__a		 =       image

								if mask_image is not None:
													__a		 =       mask_image

								if original_image is not None:
													__a		 =       original_image

								__a		 =       pipe_loaded(**UpperCAmelCase   )[0]

								__a		 =       np.abs(to_np(UpperCAmelCase   ) - to_np(UpperCAmelCase   )   ).max()
								self.assertLess(UpperCAmelCase				,						1e-4   )







			def __SCREAMING_SNAKE_CASE							(						self   )			->   int:
								__a		 =       self.get_dummy_components()
								__a		 =       self.pipeline_class(**UpperCAmelCase   )
								pipe.to(UpperCAmelCase   )
								pipe.set_progress_bar_config(disable=UpperCAmelCase   )

								__a		 =       self.get_dummy_inputs(UpperCAmelCase   )
								__a		 =       pipe(**UpperCAmelCase   )[0]

								with tempfile.TemporaryDirectory() as tmpdir:
													pipe.save_pretrained(UpperCAmelCase   )
													__a		 =       self.pipeline_class.from_pretrained(UpperCAmelCase   )
													pipe_loaded.to(UpperCAmelCase   )
													pipe_loaded.set_progress_bar_config(disable=UpperCAmelCase   )

								pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor()   )  # For reproducibility tests

								__a		 =       self.get_dummy_inputs(UpperCAmelCase   )
								__a		 =       pipe_loaded(**UpperCAmelCase   )[0]

								__a		 =       np.abs(to_np(UpperCAmelCase   ) - to_np(UpperCAmelCase   )   ).max()
								self.assertLess(UpperCAmelCase				,						1e-4   )




