

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 (
    SEWDConfig,
    SEWDForCTC,
    SEWDModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


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

lowerCamelCase__			:					Union[str, Any]							  =					{
    '''post_extract_proj''': '''feature_projection''',
    '''encoder.pos_conv.0''': '''encoder.pos_conv_embed.conv''',
    '''attention.self.query_proj''': '''encoder.encoder.layer.*.attention.self.query_proj''',
    '''attention.self.key_proj''': '''encoder.encoder.layer.*.attention.self.key_proj''',
    '''attention.self.value_proj''': '''encoder.encoder.layer.*.attention.self.value_proj''',
    '''attention.output.dense''': '''encoder.encoder.layer.*.attention.output.dense''',
    '''attention.output.LayerNorm''': '''encoder.encoder.layer.*.attention.output.LayerNorm''',
    '''intermediate.dense''': '''encoder.encoder.layer.*.intermediate.dense''',
    '''output.dense''': '''encoder.encoder.layer.*.output.dense''',
    '''output.LayerNorm''': '''encoder.encoder.layer.*.output.LayerNorm''',
    '''encoder.encoder.rel_embeddings''': '''encoder.encoder.rel_embeddings''',
    '''encoder.encoder.LayerNorm''': '''encoder.encoder.LayerNorm''',
    '''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  UpperCamelCase   (     _lowerCAmelCase       :  Dict,					_lowerCAmelCase       :  Optional[int],					_lowerCAmelCase       :  Any,					_lowerCAmelCase       :  Any,					_lowerCAmelCase       :  Optional[int]	)			->     int:







	for attribute in key.split("""."""	):
		_UpperCAmelCase      :							Tuple       =   getattr(__UpperCamelCase,					__UpperCamelCase	)

	if weight_type is not None:
		_UpperCAmelCase      :							Union[str, Any]       =   getattr(__UpperCamelCase,					__UpperCamelCase	).shape
	else:
		_UpperCAmelCase      :							str       =   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":
		_UpperCAmelCase      :							str       =   value
	elif weight_type == "weight_g":
		_UpperCAmelCase      :							str       =   value
	elif weight_type == "weight_v":
		_UpperCAmelCase      :							Optional[Any]       =   value
	elif weight_type == "bias":
		_UpperCAmelCase      :							Tuple       =   value
	else:
		_UpperCAmelCase      :							Union[str, Any]       =   value

	logger.info(f'''{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.'''	)
def  UpperCamelCase   (     _lowerCAmelCase       :  Union[str, Any],					_lowerCAmelCase       :  Dict,					_lowerCAmelCase       :  Dict	)			->     Dict:
	_UpperCAmelCase      :							Dict       =   []
	_UpperCAmelCase      :							str       =   fairseq_model.state_dict()

	_UpperCAmelCase      :							Dict       =   hf_model.sew_d.feature_extractor if is_finetuned else hf_model.feature_extractor

	for name, value in fairseq_dict.items():
		_UpperCAmelCase      :							Optional[Any]       =   False
		if "conv_layers" in name:
			load_conv_layer(
			    __UpperCamelCase,					__UpperCamelCase,					__UpperCamelCase,					__UpperCamelCase,					hf_model.config.feat_extract_norm == """group""",					)
			_UpperCAmelCase      :							Optional[Any]       =   True
		else:
			for key, mapped_key in MAPPING.items():
				_UpperCAmelCase      :							Optional[int]       =   """sew_d.""" + 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]:
					_UpperCAmelCase      :							List[str]       =   True
					if "*" in mapped_key:
						_UpperCAmelCase      :							Union[str, Any]       =   name.split(__UpperCamelCase	)[0].split("""."""	)[-2]
						if not layer_index.isnumeric():
							continue
						_UpperCAmelCase      :							Any       =   mapped_key.replace("""*""",					__UpperCamelCase	)
					if "weight_g" in name:
						_UpperCAmelCase      :							List[Any]       =   """weight_g"""
					elif "weight_v" in name:
						_UpperCAmelCase      :							Tuple       =   """weight_v"""
					elif "weight" in name:
						_UpperCAmelCase      :							str       =   """weight"""
					elif "bias" in name:
						_UpperCAmelCase      :							List[Any]       =   """bias"""
					else:
						_UpperCAmelCase      :							int       =   None
					set_recursively(__UpperCamelCase,					__UpperCamelCase,					__UpperCamelCase,					__UpperCamelCase,					__UpperCamelCase	)
				continue
		if not is_used:
			unused_weights.append(__UpperCamelCase	)

	logger.warning(f'''Unused weights: {unused_weights}'''	)
def  UpperCamelCase   (     _lowerCAmelCase       :  Union[str, Any],					_lowerCAmelCase       :  int,					_lowerCAmelCase       :  Dict,					_lowerCAmelCase       :  Optional[Any],					_lowerCAmelCase       :  Tuple	)			->     List[Any]:
	_UpperCAmelCase      :							List[Any]       =   full_name.split("""conv_layers."""	)[-1]
	_UpperCAmelCase      :							Any       =   name.split("""."""	)
	_UpperCAmelCase      :							Optional[int]       =   int(items[0]	)
	_UpperCAmelCase      :							Tuple       =   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.'''
			)
			_UpperCAmelCase      :							Union[str, Any]       =   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.'''
			)
			_UpperCAmelCase      :							str       =   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."
			)
			_UpperCAmelCase      :							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.'''
			)
			_UpperCAmelCase      :							List[str]       =   value
			logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''	)
	else:
		unused_weights.append(__UpperCamelCase	)
def  UpperCamelCase   (     _lowerCAmelCase       :  Optional[Any],					_lowerCAmelCase       :  Any	)			->     Tuple:
	_UpperCAmelCase      :							Tuple       =   SEWDConfig()
	if is_finetuned:
		_UpperCAmelCase      :							List[Any]       =   model.wav_encoder.wav_model.cfg
	else:
		_UpperCAmelCase      :							Union[str, Any]       =   model.cfg

	_UpperCAmelCase      :							List[str]       =   fs_config.conv_bias
	_UpperCAmelCase      :							Union[str, Any]       =   eval(fs_config.conv_feature_layers	)
	_UpperCAmelCase      :							Union[str, Any]       =   [x[0] for x in conv_layers]
	_UpperCAmelCase      :							Tuple       =   [x[1] for x in conv_layers]
	_UpperCAmelCase      :							Optional[Any]       =   [x[2] for x in conv_layers]
	_UpperCAmelCase      :							str       =   """gelu"""
	_UpperCAmelCase      :							Union[str, Any]       =   """layer""" if fs_config.extractor_mode == """layer_norm""" else """group"""
	_UpperCAmelCase      :							Dict       =   0.0
	_UpperCAmelCase      :							List[Any]       =   fs_config.activation_fn.name
	_UpperCAmelCase      :							List[str]       =   fs_config.encoder_embed_dim
	_UpperCAmelCase      :							Optional[Any]       =   0.02
	_UpperCAmelCase      :							Dict       =   fs_config.encoder_ffn_embed_dim
	_UpperCAmelCase      :							Optional[int]       =   1E-5
	_UpperCAmelCase      :							Tuple       =   fs_config.encoder_layerdrop
	_UpperCAmelCase      :							Optional[Any]       =   fs_config.encoder_attention_heads
	_UpperCAmelCase      :							List[Any]       =   fs_config.conv_pos_groups
	_UpperCAmelCase      :							Optional[Any]       =   fs_config.conv_pos
	_UpperCAmelCase      :							Any       =   len(__UpperCamelCase	)
	_UpperCAmelCase      :							Optional[int]       =   fs_config.encoder_layers
	_UpperCAmelCase      :							Dict       =   fs_config.squeeze_factor
	# DeBERTa-specific parameters:
	_UpperCAmelCase      :							int       =   fs_config.max_position_embeddings
	_UpperCAmelCase      :							Tuple       =   fs_config.position_buckets
	_UpperCAmelCase      :							str       =   fs_config.share_att_key
	_UpperCAmelCase      :							List[Any]       =   fs_config.relative_attention
	_UpperCAmelCase      :							Dict       =   fs_config.position_biased_input
	_UpperCAmelCase      :							Dict       =   tuple(fs_config.pos_att_type.split("""|"""	)	)
	_UpperCAmelCase      :							str       =   fs_config.norm_rel_ebd

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

	_UpperCAmelCase      :							Optional[int]       =   """Wav2Vec2FeatureExtractor"""
	_UpperCAmelCase      :							Tuple       =   """Wav2Vec2CTCTokenizer"""

	return config
@torch.no_grad()
def  UpperCamelCase   (     _lowerCAmelCase       :  Any,					_lowerCAmelCase       :  int,					_lowerCAmelCase       :  int=None,					_lowerCAmelCase       :  Dict=None,					_lowerCAmelCase       :  str=True	)			->     Optional[Any]:







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

	if config_path is not None:
		_UpperCAmelCase      :							str       =   SEWDConfig.from_pretrained(__UpperCamelCase	)
	else:
		_UpperCAmelCase      :							Optional[Any]       =   convert_config(model[0],					__UpperCamelCase	)
	_UpperCAmelCase      :							Union[str, Any]       =   model[0].eval()

	_UpperCAmelCase      :							Tuple       =   True if config.feat_extract_norm == """layer""" else False
	_UpperCAmelCase      :							Dict       =   WavaVecaFeatureExtractor(
	    feature_size=1,					sampling_rate=16000,					padding_value=0,					do_normalize=__UpperCamelCase,					return_attention_mask=__UpperCamelCase,					)

	if is_finetuned:
		if dict_path:
			_UpperCAmelCase      :							Optional[Any]       =   Dictionary.load(__UpperCamelCase	)

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

		_UpperCAmelCase      :							Tuple       =   SEWDForCTC(__UpperCamelCase	)
	else:
		_UpperCAmelCase      :							int       =   SEWDModel(__UpperCamelCase	)
		feature_extractor.save_pretrained(__UpperCamelCase	)

	recursively_load_weights(__UpperCamelCase,					__UpperCamelCase,					__UpperCamelCase	)

	hf_model.save_pretrained(__UpperCamelCase	)


if __name__ == "__main__":
						lowerCamelCase__			:					List[str]							  =					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__			:					Tuple							  =					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 unittest

import numpy as np
import torch
from torch import nn
from transformers import (
    CLIPImageProcessor,
    CLIPTextConfig,
    CLIPTextModelWithProjection,
    CLIPTokenizer,
    CLIPVisionConfig,
    CLIPVisionModelWithProjection,
)

from diffusers import KandinskyPriorPipeline, PriorTransformer, UnCLIPScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import enable_full_determinism, skip_mps

from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()






class       _UpperCAmelCase     (						__a      ,       unittest.TestCase):
			__a	:    List[Any]			   =							KandinskyPriorPipeline
			__a	:    List[Any]			   =							["""prompt"""]
			__a	:    List[str]			   =							["""prompt""", """negative_prompt"""]
			__a	:    Tuple			   =							[
			    """num_images_per_prompt""",
			    """generator""",
			    """num_inference_steps""",
			    """latents""",
			    """negative_prompt""",
			    """guidance_scale""",
			    """output_type""",
			    """return_dict""",
			]
			__a	:    str			   =							False
			@property
			def 							__snake_case	(						self       )      ->      Any:






				'''simple docstring'''







				return 32
			@property
			def 							__snake_case	(						self       )      ->      Union[str, Any]:






				'''simple docstring'''







				return 32
			@property
			def 							__snake_case	(						self       )      ->      Dict:






				'''simple docstring'''







				return self.time_input_dim
			@property
			def 							__snake_case	(						self       )      ->      Optional[Any]:






				'''simple docstring'''







				return self.time_input_dim * 4
			@property
			def 							__snake_case	(						self       )      ->      List[Any]:






				'''simple docstring'''







				return 1_00
			@property
			def 							__snake_case	(						self       )      ->      Optional[int]:






				'''simple docstring'''







				_UpperCAmelCase      :							List[str]       =   CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""       )
				return tokenizer
			@property
			def 							__snake_case	(						self       )      ->      int:






				'''simple docstring'''







				torch.manual_seed(0       )
				_UpperCAmelCase      :							List[Any]       =   CLIPTextConfig(
				    bos_token_id=0 ,						eos_token_id=2 ,						hidden_size=self.text_embedder_hidden_size ,						projection_dim=self.text_embedder_hidden_size ,						intermediate_size=37 ,						layer_norm_eps=1e-05 ,						num_attention_heads=4 ,						num_hidden_layers=5 ,						pad_token_id=1 ,						vocab_size=10_00 ,						)
				return CLIPTextModelWithProjection(_A       )
			@property
			def 							__snake_case	(						self       )      ->      List[Any]:






				'''simple docstring'''







				torch.manual_seed(0       )

				_UpperCAmelCase      :							Optional[int]       =   {
				    """num_attention_heads""": 2,
				    """attention_head_dim""": 12,
				    """embedding_dim""": self.text_embedder_hidden_size,
				    """num_layers""": 1,
				}

				_UpperCAmelCase      :							List[Any]       =   PriorTransformer(**_A       )
				# clip_std and clip_mean is initialized to be 0 so PriorTransformer.post_process_latents will always return 0 - set clip_std to be 1 so it won't return 0
				_UpperCAmelCase      :							List[Any]       =   nn.Parameter(torch.ones(model.clip_std.shape       )       )
				return model
			@property
			def 							__snake_case	(						self       )      ->      Dict:






				'''simple docstring'''







				torch.manual_seed(0       )
				_UpperCAmelCase      :							Optional[Any]       =   CLIPVisionConfig(
				    hidden_size=self.text_embedder_hidden_size ,						image_size=2_24 ,						projection_dim=self.text_embedder_hidden_size ,						intermediate_size=37 ,						num_attention_heads=4 ,						num_channels=3 ,						num_hidden_layers=5 ,						patch_size=14 ,						)

				_UpperCAmelCase      :							Tuple       =   CLIPVisionModelWithProjection(_A       )
				return model
			@property
			def 							__snake_case	(						self       )      ->      List[str]:






				'''simple docstring'''







				_UpperCAmelCase      :							str       =   CLIPImageProcessor(
				    crop_size=2_24 ,						do_center_crop=_A ,						do_normalize=_A ,						do_resize=_A ,						image_mean=[0.48145466, 0.4578275, 0.40821073] ,						image_std=[0.26862954, 0.26130258, 0.27577711] ,						resample=3 ,						size=2_24 ,						)

				return image_processor
			def 							__snake_case	(						self       )      ->      Dict:






				'''simple docstring'''







				_UpperCAmelCase      :							Optional[int]       =   self.dummy_prior
				_UpperCAmelCase      :							int       =   self.dummy_image_encoder
				_UpperCAmelCase      :							int       =   self.dummy_text_encoder
				_UpperCAmelCase      :							str       =   self.dummy_tokenizer
				_UpperCAmelCase      :							Any       =   self.dummy_image_processor

				_UpperCAmelCase      :							Any       =   UnCLIPScheduler(
				    variance_type="""fixed_small_log""" ,						prediction_type="""sample""" ,						num_train_timesteps=10_00 ,						clip_sample=_A ,						clip_sample_range=10.0 ,						)

				_UpperCAmelCase      :							Any       =   {
				    """prior""": prior,
				    """image_encoder""": image_encoder,
				    """text_encoder""": text_encoder,
				    """tokenizer""": tokenizer,
				    """scheduler""": scheduler,
				    """image_processor""": image_processor,
				}

				return components
			def 							__snake_case	(						self ,						_A ,						_A=0       )      ->      Optional[Any]:






				'''simple docstring'''







				if str(_A       ).startswith("""mps"""       ):
					_UpperCAmelCase      :							Any       =   torch.manual_seed(_A       )
				else:
					_UpperCAmelCase      :							Optional[int]       =   torch.Generator(device=_A       ).manual_seed(_A       )
				_UpperCAmelCase      :							Union[str, Any]       =   {
				    """prompt""": """horse""",
				    """generator""": generator,
				    """guidance_scale""": 4.0,
				    """num_inference_steps""": 2,
				    """output_type""": """np""",
				}
				return inputs
			def 							__snake_case	(						self       )      ->      Union[str, Any]:






				'''simple docstring'''







				_UpperCAmelCase      :							List[str]       =   """cpu"""

				_UpperCAmelCase      :							int       =   self.get_dummy_components()

				_UpperCAmelCase      :							Optional[Any]       =   self.pipeline_class(**_A       )
				_UpperCAmelCase      :							List[Any]       =   pipe.to(_A       )

				pipe.set_progress_bar_config(disable=_A       )

				_UpperCAmelCase      :							str       =   pipe(**self.get_dummy_inputs(_A       )       )
				_UpperCAmelCase      :							Any       =   output.image_embeds

				_UpperCAmelCase      :							Optional[int]       =   pipe(
				    **self.get_dummy_inputs(_A       ) ,						return_dict=_A ,						)[0]

				_UpperCAmelCase      :							int       =   image[0, -10:]
				_UpperCAmelCase      :							Optional[Any]       =   image_from_tuple[0, -10:]

				assert image.shape == (1, 32)

				_UpperCAmelCase      :							Optional[Any]       =   np.array(
				    [-0.0532, 1.7120, 0.3656, -1.0852, -0.8946, -1.1756, 0.4348, 0.2482, 0.5146, -0.1156]       )

				assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1e-2
				assert np.abs(image_from_tuple_slice.flatten() - expected_slice       ).max() < 1e-2
			@skip_mps
			def 							__snake_case	(						self       )      ->      Tuple:






				'''simple docstring'''







				_UpperCAmelCase      :							str       =   torch_device == """cpu"""
				_UpperCAmelCase      :							Optional[Any]       =   True
				_UpperCAmelCase      :							List[Any]       =   False

				self._test_inference_batch_single_identical(
				    test_max_difference=_A ,						relax_max_difference=_A ,						test_mean_pixel_difference=_A ,						)
			@skip_mps
			def 							__snake_case	(						self       )      ->      Any:






				'''simple docstring'''







				_UpperCAmelCase      :							Any       =   torch_device == """cpu"""
				_UpperCAmelCase      :							List[str]       =   False

				self._test_attention_slicing_forward_pass(
				    test_max_difference=_A ,						test_mean_pixel_difference=_A ,						)





