


'''simple docstring'''

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()
lowerCAmelCase				:int        =      logging.get_logger(__name__)

lowerCAmelCase				:List[str]        =      {
    '''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 lowerCamelCase      (      lowerCAmelCase    :     Optional[int]		,		lowerCAmelCase    :     int		,		lowerCAmelCase    :     List[str]		,		lowerCAmelCase    :     List[Any]		,		lowerCAmelCase    :     int		):





	"""simple docstring"""







	for attribute in key.split('.'		):
		__magic_name__	:   List[str]					     =					getattr(lowerCAmelCase		,		lowerCAmelCase		)

	if weight_type is not None:
		__magic_name__	:   Dict					     =					getattr(lowerCAmelCase		,		lowerCAmelCase		).shape
	else:
		__magic_name__	:   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":
		__magic_name__	:   Tuple					     =					value
	elif weight_type == "weight_g":
		__magic_name__	:   List[Any]					     =					value
	elif weight_type == "weight_v":
		__magic_name__	:   List[str]					     =					value
	elif weight_type == "bias":
		__magic_name__	:   Any					     =					value
	else:
		__magic_name__	:   List[str]					     =					value

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





def lowerCamelCase      (      lowerCAmelCase    :     str		,		lowerCAmelCase    :     int		,		lowerCAmelCase    :     List[Any]		):





	"""simple docstring"""







	__magic_name__	:   str					     =					[]
	__magic_name__	:   Optional[int]					     =					fairseq_model.state_dict()

	__magic_name__	:   List[str]					     =					hf_model.sew.feature_extractor if is_finetuned else hf_model.feature_extractor

	for name, value in fairseq_dict.items():
		__magic_name__	:   Tuple					     =					False
		if "conv_layers" in name:
			load_conv_layer(
			    lowerCAmelCase		,		lowerCAmelCase		,		lowerCAmelCase		,		lowerCAmelCase		,		hf_model.config.feat_extract_norm == 'group'		,		)
			__magic_name__	:   Tuple					     =					True
		else:
			for key, mapped_key in MAPPING.items():
				__magic_name__	:   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]:
					__magic_name__	:   int					     =					True
					if "*" in mapped_key:
						__magic_name__	:   List[Any]					     =					name.split(lowerCAmelCase		)[0].split('.'		)[-2]
						__magic_name__	:   List[str]					     =					mapped_key.replace('*'		,		lowerCAmelCase		)
					if "weight_g" in name:
						__magic_name__	:   Optional[int]					     =					'weight_g'
					elif "weight_v" in name:
						__magic_name__	:   str					     =					'weight_v'
					elif "weight" in name:
						__magic_name__	:   Dict					     =					'weight'
					elif "bias" in name:
						__magic_name__	:   Optional[Any]					     =					'bias'
					else:
						__magic_name__	:   int					     =					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    :     str		,		lowerCAmelCase    :     Optional[Any]		,		lowerCAmelCase    :     Union[str, Any]		,		lowerCAmelCase    :     Union[str, Any]		,		lowerCAmelCase    :     Tuple		):





	"""simple docstring"""







	__magic_name__	:   Dict					     =					full_name.split('conv_layers.'		)[-1]
	__magic_name__	:   Optional[int]					     =					name.split('.'		)
	__magic_name__	:   List[Any]					     =					int(items[0]		)
	__magic_name__	:   str					     =					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.'
			)
			__magic_name__	:   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.'
			)
			__magic_name__	:   Optional[int]					     =					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."
			)
			__magic_name__	:   List[Any]					     =					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.'
			)
			__magic_name__	:   Optional[int]					     =					value
			logger.info(f'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'		)
	else:
		unused_weights.append(lowerCAmelCase		)





def lowerCamelCase      (      lowerCAmelCase    :     int		,		lowerCAmelCase    :     Any		):





	"""simple docstring"""







	__magic_name__	:   Tuple					     =					SEWConfig()
	if is_finetuned:
		__magic_name__	:   Tuple					     =					model.wav_encoder.wav_model.cfg
	else:
		__magic_name__	:   List[Any]					     =					model.cfg

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

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

	__magic_name__	:   Dict					     =					'Wav2Vec2FeatureExtractor'
	__magic_name__	:   List[str]					     =					'Wav2Vec2CTCTokenizer'

	return config





@torch.no_grad()
def lowerCamelCase      (      lowerCAmelCase    :     List[str]		,		lowerCAmelCase    :     Tuple		,		lowerCAmelCase    :     List[str]=None		,		lowerCAmelCase    :     Any=None		,		lowerCAmelCase    :     str=True		):





	"""simple docstring"""







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

	if config_path is not None:
		__magic_name__	:   Optional[int]					     =					SEWConfig.from_pretrained(lowerCAmelCase		)
	else:
		__magic_name__	:   Union[str, Any]					     =					convert_config(model[0]		,		lowerCAmelCase		)
	__magic_name__	:   List[str]					     =					model[0].eval()

	__magic_name__	:   Tuple					     =					True if config.feat_extract_norm == 'layer' else False
	__magic_name__	:   Tuple					     =					WavaVecaFeatureExtractor(
	    feature_size=1		,		sampling_rate=1_6000		,		padding_value=0		,		do_normalize=lowerCAmelCase		,		return_attention_mask=lowerCAmelCase		,		)

	if is_finetuned:
		if dict_path:
			__magic_name__	:   Union[str, Any]					     =					Dictionary.load(lowerCAmelCase		)

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

		__magic_name__	:   Tuple					     =					SEWForCTC(lowerCAmelCase		)
	else:
		__magic_name__	:   int					     =					SEWModel(lowerCAmelCase		)
		feature_extractor.save_pretrained(lowerCAmelCase		)

	recursively_load_weights(lowerCAmelCase		,		lowerCAmelCase		,		lowerCAmelCase		)

	hf_model.save_pretrained(lowerCAmelCase		)


if __name__ == "__main__":
							lowerCAmelCase				:Union[str, Any]        =      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'''
							)
							lowerCAmelCase				:str        =      parser.parse_args()
							convert_sew_checkpoint(
							    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
							)



'''simple docstring'''

import gc
import random
import unittest

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

from diffusers import (
    AutoencoderKL,
    DiffusionPipeline,
    EulerDiscreteScheduler,
    StableDiffusionXLImgaImgPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import floats_tensor, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()
class _lowerCamelCase	(						lowercase__		,					lowercase__		,					unittest.TestCase							):



		'''simple docstring'''







		A_     :      int				   =    StableDiffusionXLImgaImgPipeline
		A_     :      str				   =    TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"""height""", """width"""}
		A_     :      Optional[Any]				   =    PipelineTesterMixin.required_optional_params - {"""latents"""}
		A_     :      Any				   =    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
		A_     :      Any				   =    IMAGE_TO_IMAGE_IMAGE_PARAMS
		A_     :      Optional[int]				   =    IMAGE_TO_IMAGE_IMAGE_PARAMS







		def        __lowerCAmelCase		( self		:						Dict      )  ->			Optional[int]:
			torch.manual_seed(0      )
			__magic_name__	:   int					     =					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')  ,					attention_head_dim=(2, 4)  ,					use_linear_projection=_A  ,					addition_embed_type='text_time'  ,					addition_time_embed_dim=8  ,					transformer_layers_per_block=(1, 2)  ,					projection_class_embeddings_input_dim=80  ,					cross_attention_dim=64  ,					)
			__magic_name__	:   str					     =					EulerDiscreteScheduler(
			    beta_start=0.0_0085  ,					beta_end=0.012  ,					steps_offset=1  ,					beta_schedule='scaled_linear'  ,					timestep_spacing='leading'  ,					)
			torch.manual_seed(0      )
			__magic_name__	:   str					     =					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  ,					sample_size=128  ,					)
			torch.manual_seed(0      )
			__magic_name__	:   Any					     =					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  ,					hidden_act='gelu'  ,					projection_dim=32  ,					)
			__magic_name__	:   Dict					     =					CLIPTextModel(_A      )
			__magic_name__	:   List[str]					     =					CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'  ,					local_files_only=_A      )

			__magic_name__	:   Optional[Any]					     =					CLIPTextModelWithProjection(_A      )
			__magic_name__	:   Dict					     =					CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'  ,					local_files_only=_A      )

			__magic_name__	:   List[Any]					     =					{
			    'unet': unet,
			    'scheduler': scheduler,
			    'vae': vae,
			    'text_encoder': text_encoder,
			    'tokenizer': tokenizer,
			    'text_encoder_2': text_encoder_a,
			    'tokenizer_2': tokenizer_a,
			    # "safety_checker": None,
			    # "feature_extractor": None,
			}
			return components







		def        __lowerCAmelCase		( self		:						List[Any]  ,					_A		:						List[str]  ,					_A		:						Any=0      )  ->			Union[str, Any]:
			__magic_name__	:   Any					     =					floats_tensor((1, 3, 32, 32)  ,					rng=random.Random(_A      )      ).to(_A      )
			__magic_name__	:   Dict					     =					image / 2 + 0.5
			if str(_A      ).startswith('mps'      ):
				__magic_name__	:   Any					     =					torch.manual_seed(_A      )
			else:
				__magic_name__	:   int					     =					torch.Generator(device=_A      ).manual_seed(_A      )
			__magic_name__	:   List[Any]					     =					{
			    'prompt': 'A painting of a squirrel eating a burger',
			    'image': image,
			    'generator': generator,
			    'num_inference_steps': 2,
			    'guidance_scale': 5.0,
			    'output_type': 'numpy',
			    'strength': 0.75,
			}
			return inputs







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

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

			assert image.shape == (1, 32, 32, 3)

			__magic_name__	:   Any					     =					np.array([0.4656, 0.4840, 0.4439, 0.6698, 0.5574, 0.4524, 0.5799, 0.5943, 0.5165]      )

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







		def        __lowerCAmelCase		( self		:						Optional[int]      )  ->			Optional[Any]:
			super().test_attention_slicing_forward_pass(expected_max_diff=3E-3      )







		def        __lowerCAmelCase		( self		:						List[Any]      )  ->			int:
			super().test_inference_batch_single_identical(expected_max_diff=3E-3      )







		def        __lowerCAmelCase		( self		:						Any      )  ->			Union[str, Any]:
			pass







		def        __lowerCAmelCase		( self		:						List[Any]      )  ->			Optional[int]:
			__magic_name__	:   Dict					     =					self.get_dummy_components()
			__magic_name__	:   Optional[Any]					     =					StableDiffusionXLImgaImgPipeline(**_A      )
			__magic_name__	:   List[Any]					     =					sd_pipe.to(_A      )
			__magic_name__	:   str					     =					sd_pipe.to(_A      )
			sd_pipe.set_progress_bar_config(disable=_A      )

			# forward without prompt embeds
			__magic_name__	:   Union[str, Any]					     =					self.get_dummy_inputs(_A      )
			__magic_name__	:   Union[str, Any]					     =					3 * ['this is a negative prompt']
			__magic_name__	:   List[str]					     =					negative_prompt
			__magic_name__	:   int					     =					3 * [inputs['prompt']]

			__magic_name__	:   Tuple					     =					sd_pipe(**_A      )
			__magic_name__	:   str					     =					output.images[0, -3:, -3:, -1]

			# forward with prompt embeds
			__magic_name__	:   Optional[Any]					     =					self.get_dummy_inputs(_A      )
			__magic_name__	:   Tuple					     =					3 * ['this is a negative prompt']
			__magic_name__	:   List[str]					     =					3 * [inputs.pop('prompt'      )]

			(
			    (
			    __magic_name__
			)     ,    (
			    __magic_name__
			)     ,    (
			    __magic_name__
			)     ,    (
			    __magic_name__
			)     ,    
			)	:   List[Any]					     =					sd_pipe.encode_prompt(_A  ,					negative_prompt=_A      )

			__magic_name__	:   Tuple					     =					sd_pipe(
			    **_A  ,					prompt_embeds=_A  ,					negative_prompt_embeds=_A  ,					pooled_prompt_embeds=_A  ,					negative_pooled_prompt_embeds=_A  ,					)
			__magic_name__	:   int					     =					output.images[0, -3:, -3:, -1]

			# make sure that it's equal
			assert np.abs(image_slice_a.flatten() - image_slice_a.flatten()      ).max() < 1E-4







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



		'''simple docstring'''







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







		def        __lowerCAmelCase		( self		:						str  ,					_A		:						Optional[int]  ,					_A		:						Optional[Any]="cpu"  ,					_A		:						List[str]=torch.floataa  ,					_A		:						Any=0      )  ->			str:
			__magic_name__	:   List[str]					     =					torch.Generator(device=_A      ).manual_seed(_A      )
			__magic_name__	:   Optional[Any]					     =					np.random.RandomState(_A      ).standard_normal((1, 4, 64, 64)      )
			__magic_name__	:   Union[str, Any]					     =					torch.from_numpy(_A      ).to(device=_A  ,					dtype=_A      )
			__magic_name__	:   Optional[int]					     =					{
			    'prompt': 'a photograph of an astronaut riding a horse',
			    'latents': latents,
			    'generator': generator,
			    'num_inference_steps': 3,
			    'guidance_scale': 7.5,
			    'output_type': 'numpy',
			}
			return inputs







		def        __lowerCAmelCase		( self		:						Union[str, Any]      )  ->			Dict:
			__magic_name__	:   str					     =					DiffusionPipeline.from_pretrained('stabilityai/stable-diffusion-2-base'      )
			pipe.to(_A      )
			pipe.set_progress_bar_config(disable=_A      )

			__magic_name__	:   Optional[int]					     =					self.get_inputs(_A      )
			__magic_name__	:   Union[str, Any]					     =					pipe(**_A      ).images
			__magic_name__	:   Any					     =					image[0, -3:, -3:, -1].flatten()

			assert image.shape == (1, 512, 512, 3)
			__magic_name__	:   List[Any]					     =					np.array([0.4_9493, 0.4_7896, 0.4_0798, 0.5_4214, 0.5_3212, 0.4_8202, 0.4_7656, 0.4_6329, 0.4_8506]      )
			assert np.abs(image_slice - expected_slice      ).max() < 7E-3