





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()
SCREAMING_SNAKE_CASE_   :      Any			=   logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_   :      Optional[Any]			=   {
    '''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  SCREAMING_SNAKE_CASE				(      snake_case						,   snake_case						,   snake_case						,   snake_case						,   snake_case							)		->    Dict:
		for attribute in key.split('.'							):
				__lowercase											=     getattr(snake_case						,   snake_case							)

		if weight_type is not None:
				__lowercase											=     getattr(snake_case						,   snake_case							).shape
		else:
				__lowercase											=     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":
				__lowercase											=     value
		elif weight_type == "weight_g":
				__lowercase											=     value
		elif weight_type == "weight_v":
				__lowercase											=     value
		elif weight_type == "bias":
				__lowercase											=     value
		else:
				__lowercase											=     value

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



def  SCREAMING_SNAKE_CASE				(      snake_case						,   snake_case						,   snake_case							)		->    Optional[int]:
		__lowercase											=     []
		__lowercase											=     fairseq_model.state_dict()

		__lowercase											=     hf_model.sew.feature_extractor if is_finetuned else hf_model.feature_extractor

		for name, value in fairseq_dict.items():
				__lowercase											=     False
				if "conv_layers" in name:
						load_conv_layer(
						    snake_case						,   snake_case						,   snake_case						,   snake_case						,   hf_model.config.feat_extract_norm == 'group'						,   )
						__lowercase											=     True
				else:
						for key, mapped_key in MAPPING.items():
								__lowercase											=     '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]:
										__lowercase											=     True
										if "*" in mapped_key:
												__lowercase											=     name.split(snake_case							)[0].split('.'							)[-2]
												__lowercase											=     mapped_key.replace('*'						,   snake_case							)
										if "weight_g" in name:
												__lowercase											=     'weight_g'
										elif "weight_v" in name:
												__lowercase											=     'weight_v'
										elif "weight" in name:
												__lowercase											=     'weight'
										elif "bias" in name:
												__lowercase											=     'bias'
										else:
												__lowercase											=     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  SCREAMING_SNAKE_CASE				(      snake_case						,   snake_case						,   snake_case						,   snake_case						,   snake_case							)		->    Tuple:
		__lowercase											=     full_name.split('conv_layers.'							)[-1]
		__lowercase											=     name.split('.'							)
		__lowercase											=     int(items[0]							)
		__lowercase											=     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."
						)
						__lowercase											=     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."
						)
						__lowercase											=     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."
						)
						__lowercase											=     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."
						)
						__lowercase											=     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  SCREAMING_SNAKE_CASE				(      snake_case						,   snake_case							)		->    List[str]:
		__lowercase											=     SEWConfig()
		if is_finetuned:
				__lowercase											=     model.wav_encoder.wav_model.cfg
		else:
				__lowercase											=     model.cfg

		__lowercase											=     fs_config.conv_bias
		__lowercase											=     eval(fs_config.conv_feature_layers							)
		__lowercase											=     [x[0] for x in conv_layers]
		__lowercase											=     [x[1] for x in conv_layers]
		__lowercase											=     [x[2] for x in conv_layers]
		__lowercase											=     'gelu'
		__lowercase											=     'layer' if fs_config.extractor_mode == 'layer_norm' else 'group'
		__lowercase											=     0.0
		__lowercase											=     fs_config.activation_fn.name
		__lowercase											=     fs_config.encoder_embed_dim
		__lowercase											=     0.02
		__lowercase											=     fs_config.encoder_ffn_embed_dim
		__lowercase											=     1E-5
		__lowercase											=     fs_config.encoder_layerdrop
		__lowercase											=     fs_config.encoder_attention_heads
		__lowercase											=     fs_config.conv_pos_groups
		__lowercase											=     fs_config.conv_pos
		__lowercase											=     len(snake_case							)
		__lowercase											=     fs_config.encoder_layers
		__lowercase											=     fs_config.squeeze_factor

		# take care of any params that are overridden by the Wav2VecCtc model
		if is_finetuned:
				__lowercase											=     model.cfg
				__lowercase											=     fs_config.final_dropout
				__lowercase											=     fs_config.layerdrop
		__lowercase											=     fs_config.activation_dropout
		__lowercase											=     fs_config.mask_prob > 0 or fs_config.mask_channel_prob > 0
		__lowercase											=     fs_config.attention_dropout
		__lowercase											=     fs_config.dropout_input
		__lowercase											=     fs_config.dropout
		__lowercase											=     fs_config.mask_channel_length
		__lowercase											=     fs_config.mask_channel_prob
		__lowercase											=     fs_config.mask_length
		__lowercase											=     fs_config.mask_prob

		__lowercase											=     'Wav2Vec2FeatureExtractor'
		__lowercase											=     'Wav2Vec2CTCTokenizer'

		return config



@torch.no_grad()
def  SCREAMING_SNAKE_CASE				(      snake_case						,   snake_case						,   snake_case=None						,   snake_case=None						,   snake_case=True							)		->    List[Any]:

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

		if config_path is not None:
				__lowercase											=     SEWConfig.from_pretrained(snake_case							)
		else:
				__lowercase											=     convert_config(model[0]						,   snake_case							)
		__lowercase											=     model[0].eval()

		__lowercase											=     True if config.feat_extract_norm == 'layer' else False
		__lowercase											=     WavaVecaFeatureExtractor(
		    feature_size=1						,   sampling_rate=16_000						,   padding_value=0						,   do_normalize=snake_case						,   return_attention_mask=snake_case						,   )

		if is_finetuned:
				if dict_path:
						__lowercase											=     Dictionary.load(snake_case							)

						# important change bos & pad token id since CTC symbol is <pad> and
						# not <s> as in fairseq
						__lowercase											=     target_dict.pad_index
						__lowercase											=     target_dict.bos_index
						__lowercase											=     target_dict.pad_index
						__lowercase											=     target_dict.bos_index
						__lowercase											=     target_dict.eos_index
						__lowercase											=     len(target_dict.symbols							)
						__lowercase											=     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							)
						__lowercase											=     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						,   )
						__lowercase											=     WavaVecaProcessor(feature_extractor=snake_case						,   tokenizer=snake_case							)
						processor.save_pretrained(snake_case							)

				__lowercase											=     SEWForCTC(snake_case							)
		else:
				__lowercase											=     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__":
		SCREAMING_SNAKE_CASE_   :      List[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'''
		)
		SCREAMING_SNAKE_CASE_   :      int			=   parser.parse_args()
		convert_sew_checkpoint(
		    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
		)











import unittest

from diffusers.models.unet_ad_blocks import *  # noqa F403
from diffusers.utils import torch_device

from .test_unet_blocks_common import UNetBlockTesterMixin

class 		snake_case_   (							UpperCAmelCase_     ,     unittest.TestCase						):







	'''simple docstring'''
	__UpperCamelCase   =		DownBlockaD  # noqa F405
	__UpperCamelCase   =		'''down'''




	def    UpperCAmelCase			(						self							:     Optional[Any]   )       -> Tuple:

			'''simple docstring'''




			__lowercase											=     [-0.0232, -0.9869, 0.8054, -0.0637, -0.1688, -1.4264, 0.4470, -1.3394, 0.0904]
			super().test_output(__lowerCamelCase   )





class 		snake_case_   (							UpperCAmelCase_     ,     unittest.TestCase						):







	'''simple docstring'''
	__UpperCamelCase   =		ResnetDownsampleBlockaD  # noqa F405
	__UpperCamelCase   =		'''down'''




	def    UpperCAmelCase			(						self							:     str   )       -> int:

			'''simple docstring'''




			__lowercase											=     [0.0710, 0.2410, -0.7320, -1.0757, -1.1343, 0.3540, -0.0133, -0.2576, 0.0948]
			super().test_output(__lowerCamelCase   )





class 		snake_case_   (							UpperCAmelCase_     ,     unittest.TestCase						):







	'''simple docstring'''
	__UpperCamelCase   =		AttnDownBlockaD  # noqa F405
	__UpperCamelCase   =		'''down'''




	def    UpperCAmelCase			(						self							:     Any   )       -> List[Any]:

			'''simple docstring'''




			__lowercase											=     [0.0636, 0.8964, -0.6234, -1.0131, 0.0844, 0.4935, 0.3437, 0.0911, -0.2957]
			super().test_output(__lowerCamelCase   )





class 		snake_case_   (							UpperCAmelCase_     ,     unittest.TestCase						):







	'''simple docstring'''
	__UpperCamelCase   =		CrossAttnDownBlockaD  # noqa F405
	__UpperCamelCase   =		'''down'''




	def    UpperCAmelCase			(						self							:     Optional[Any]   )       -> List[Any]:

			'''simple docstring'''




			__lowercase						,      __lowercase											=     super().prepare_init_args_and_inputs_for_common()
			__lowercase											=     32
			return init_dict, inputs_dict






	def    UpperCAmelCase			(						self							:     Any   )       -> int:

			'''simple docstring'''




			__lowercase											=     [0.2238, -0.7396, -0.2255, -0.3829, 0.1925, 1.1665, 0.0603, -0.7295, 0.1983]
			super().test_output(__lowerCamelCase   )





class 		snake_case_   (							UpperCAmelCase_     ,     unittest.TestCase						):







	'''simple docstring'''
	__UpperCamelCase   =		SimpleCrossAttnDownBlockaD  # noqa F405
	__UpperCamelCase   =		'''down'''




	@property
	def    UpperCAmelCase			(						self							:     List[Any]   )       -> Union[str, Any]:

			'''simple docstring'''




			return super().get_dummy_input(include_encoder_hidden_states=__lowerCamelCase   )




	def    UpperCAmelCase			(						self							:     List[Any]   )       -> List[str]:

			'''simple docstring'''




			__lowercase						,      __lowercase											=     super().prepare_init_args_and_inputs_for_common()
			__lowercase											=     32
			return init_dict, inputs_dict






	@unittest.skipIf(torch_device == 'mps'					,      'MPS result is not consistent'   )
	def    UpperCAmelCase			(						self							:     Dict   )       -> Optional[int]:

			'''simple docstring'''




			__lowercase											=     [0.7921, -0.0992, -0.1962, -0.7695, -0.4242, 0.7804, 0.4737, 0.2765, 0.3338]
			super().test_output(__lowerCamelCase   )





class 		snake_case_   (							UpperCAmelCase_     ,     unittest.TestCase						):







	'''simple docstring'''
	__UpperCamelCase   =		SkipDownBlockaD  # noqa F405
	__UpperCamelCase   =		'''down'''




	@property
	def    UpperCAmelCase			(						self							:     Optional[Any]   )       -> Any:

			'''simple docstring'''




			return super().get_dummy_input(include_skip_sample=__lowerCamelCase   )






	def    UpperCAmelCase			(						self							:     List[Any]   )       -> int:

			'''simple docstring'''




			__lowercase											=     [-0.0845, -0.2087, -0.2465, 0.0971, 0.1900, -0.0484, 0.2664, 0.4179, 0.5069]
			super().test_output(__lowerCamelCase   )





class 		snake_case_   (							UpperCAmelCase_     ,     unittest.TestCase						):







	'''simple docstring'''
	__UpperCamelCase   =		AttnSkipDownBlockaD  # noqa F405
	__UpperCamelCase   =		'''down'''




	@property
	def    UpperCAmelCase			(						self							:     Tuple   )       -> List[str]:

			'''simple docstring'''




			return super().get_dummy_input(include_skip_sample=__lowerCamelCase   )






	def    UpperCAmelCase			(						self							:     Tuple   )       -> Optional[Any]:

			'''simple docstring'''




			__lowercase											=     [0.5539, 0.1609, 0.4924, 0.0537, -0.1995, 0.4050, 0.0979, -0.2721, -0.0642]
			super().test_output(__lowerCamelCase   )





class 		snake_case_   (							UpperCAmelCase_     ,     unittest.TestCase						):







	'''simple docstring'''
	__UpperCamelCase   =		DownEncoderBlockaD  # noqa F405
	__UpperCamelCase   =		'''down'''




	@property
	def    UpperCAmelCase			(						self							:     Optional[Any]   )       -> Any:

			'''simple docstring'''




			return super().get_dummy_input(include_temb=__lowerCamelCase   )




	def    UpperCAmelCase			(						self							:     Union[str, Any]   )       -> int:

			'''simple docstring'''




			__lowercase											=     {
			    'in_channels': 32,
			    'out_channels': 32,
			}
			__lowercase											=     self.dummy_input
			return init_dict, inputs_dict






	def    UpperCAmelCase			(						self							:     str   )       -> Optional[int]:

			'''simple docstring'''




			__lowercase											=     [1.1102, 0.5302, 0.4872, -0.0023, -0.8042, 0.0483, -0.3489, -0.5632, 0.7626]
			super().test_output(__lowerCamelCase   )





class 		snake_case_   (							UpperCAmelCase_     ,     unittest.TestCase						):







	'''simple docstring'''
	__UpperCamelCase   =		AttnDownEncoderBlockaD  # noqa F405
	__UpperCamelCase   =		'''down'''




	@property
	def    UpperCAmelCase			(						self							:     int   )       -> str:

			'''simple docstring'''




			return super().get_dummy_input(include_temb=__lowerCamelCase   )




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

			'''simple docstring'''




			__lowercase											=     {
			    'in_channels': 32,
			    'out_channels': 32,
			}
			__lowercase											=     self.dummy_input
			return init_dict, inputs_dict






	def    UpperCAmelCase			(						self							:     Tuple   )       -> Optional[int]:

			'''simple docstring'''




			__lowercase											=     [0.8966, -0.1486, 0.8568, 0.8141, -0.9046, -0.1342, -0.0972, -0.7417, 0.1538]
			super().test_output(__lowerCamelCase   )





class 		snake_case_   (							UpperCAmelCase_     ,     unittest.TestCase						):







	'''simple docstring'''
	__UpperCamelCase   =		UNetMidBlockaD  # noqa F405
	__UpperCamelCase   =		'''mid'''




	def    UpperCAmelCase			(						self							:     int   )       -> List[str]:

			'''simple docstring'''




			__lowercase											=     {
			    'in_channels': 32,
			    'temb_channels': 128,
			}
			__lowercase											=     self.dummy_input
			return init_dict, inputs_dict






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

			'''simple docstring'''




			__lowercase											=     [-0.1062, 1.7248, 0.3494, 1.4569, -0.0910, -1.2421, -0.9984, 0.6736, 1.0028]
			super().test_output(__lowerCamelCase   )





class 		snake_case_   (							UpperCAmelCase_     ,     unittest.TestCase						):







	'''simple docstring'''
	__UpperCamelCase   =		UNetMidBlockaDCrossAttn  # noqa F405
	__UpperCamelCase   =		'''mid'''




	def    UpperCAmelCase			(						self							:     int   )       -> Tuple:

			'''simple docstring'''




			__lowercase						,      __lowercase											=     super().prepare_init_args_and_inputs_for_common()
			__lowercase											=     32
			return init_dict, inputs_dict






	def    UpperCAmelCase			(						self							:     Dict   )       -> List[Any]:

			'''simple docstring'''




			__lowercase											=     [0.0187, 2.4220, 0.4484, 1.1203, -0.6121, -1.5122, -0.8270, 0.7851, 1.8335]
			super().test_output(__lowerCamelCase   )





class 		snake_case_   (							UpperCAmelCase_     ,     unittest.TestCase						):







	'''simple docstring'''
	__UpperCamelCase   =		UNetMidBlockaDSimpleCrossAttn  # noqa F405
	__UpperCamelCase   =		'''mid'''




	@property
	def    UpperCAmelCase			(						self							:     str   )       -> Tuple:

			'''simple docstring'''




			return super().get_dummy_input(include_encoder_hidden_states=__lowerCamelCase   )




	def    UpperCAmelCase			(						self							:     Optional[int]   )       -> List[str]:

			'''simple docstring'''




			__lowercase						,      __lowercase											=     super().prepare_init_args_and_inputs_for_common()
			__lowercase											=     32
			return init_dict, inputs_dict






	def    UpperCAmelCase			(						self							:     Optional[int]   )       -> int:

			'''simple docstring'''




			__lowercase											=     [0.7143, 1.9974, 0.5448, 1.3977, 0.1282, -1.1237, -1.4238, 0.5530, 0.8880]
			super().test_output(__lowerCamelCase   )





class 		snake_case_   (							UpperCAmelCase_     ,     unittest.TestCase						):







	'''simple docstring'''
	__UpperCamelCase   =		UpBlockaD  # noqa F405
	__UpperCamelCase   =		'''up'''




	@property
	def    UpperCAmelCase			(						self							:     str   )       -> List[Any]:

			'''simple docstring'''




			return super().get_dummy_input(include_res_hidden_states_tuple=__lowerCamelCase   )






	def    UpperCAmelCase			(						self							:     Optional[Any]   )       -> str:

			'''simple docstring'''




			__lowercase											=     [-0.2041, -0.4165, -0.3022, 0.0041, -0.6628, -0.7053, 0.1928, -0.0325, 0.0523]
			super().test_output(__lowerCamelCase   )





class 		snake_case_   (							UpperCAmelCase_     ,     unittest.TestCase						):







	'''simple docstring'''
	__UpperCamelCase   =		ResnetUpsampleBlockaD  # noqa F405
	__UpperCamelCase   =		'''up'''




	@property
	def    UpperCAmelCase			(						self							:     int   )       -> Optional[Any]:

			'''simple docstring'''




			return super().get_dummy_input(include_res_hidden_states_tuple=__lowerCamelCase   )






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

			'''simple docstring'''




			__lowercase											=     [0.2287, 0.3549, -0.1346, 0.4797, -0.1715, -0.9649, 0.7305, -0.5864, -0.6244]
			super().test_output(__lowerCamelCase   )





class 		snake_case_   (							UpperCAmelCase_     ,     unittest.TestCase						):







	'''simple docstring'''
	__UpperCamelCase   =		CrossAttnUpBlockaD  # noqa F405
	__UpperCamelCase   =		'''up'''




	@property
	def    UpperCAmelCase			(						self							:     List[Any]   )       -> Tuple:

			'''simple docstring'''




			return super().get_dummy_input(include_res_hidden_states_tuple=__lowerCamelCase   )




	def    UpperCAmelCase			(						self							:     Any   )       -> Tuple:

			'''simple docstring'''




			__lowercase						,      __lowercase											=     super().prepare_init_args_and_inputs_for_common()
			__lowercase											=     32
			return init_dict, inputs_dict






	def    UpperCAmelCase			(						self							:     Dict   )       -> int:

			'''simple docstring'''




			__lowercase											=     [-0.1403, -0.3515, -0.0420, -0.1425, 0.3167, 0.5094, -0.2181, 0.5931, 0.5582]
			super().test_output(__lowerCamelCase   )





class 		snake_case_   (							UpperCAmelCase_     ,     unittest.TestCase						):







	'''simple docstring'''
	__UpperCamelCase   =		SimpleCrossAttnUpBlockaD  # noqa F405
	__UpperCamelCase   =		'''up'''




	@property
	def    UpperCAmelCase			(						self							:     Any   )       -> Optional[Any]:

			'''simple docstring'''




			return super().get_dummy_input(include_res_hidden_states_tuple=__lowerCamelCase					,      include_encoder_hidden_states=__lowerCamelCase   )




	def    UpperCAmelCase			(						self							:     Dict   )       -> Optional[Any]:

			'''simple docstring'''




			__lowercase						,      __lowercase											=     super().prepare_init_args_and_inputs_for_common()
			__lowercase											=     32
			return init_dict, inputs_dict






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

			'''simple docstring'''




			__lowercase											=     [0.2645, 0.1480, 0.0909, 0.8044, -0.9758, -0.9083, 0.0994, -1.1453, -0.7402]
			super().test_output(__lowerCamelCase   )





class 		snake_case_   (							UpperCAmelCase_     ,     unittest.TestCase						):







	'''simple docstring'''
	__UpperCamelCase   =		AttnUpBlockaD  # noqa F405
	__UpperCamelCase   =		'''up'''




	@property
	def    UpperCAmelCase			(						self							:     Any   )       -> str:

			'''simple docstring'''




			return super().get_dummy_input(include_res_hidden_states_tuple=__lowerCamelCase   )






	@unittest.skipIf(torch_device == 'mps'					,      'MPS result is not consistent'   )
	def    UpperCAmelCase			(						self							:     int   )       -> str:

			'''simple docstring'''




			__lowercase											=     [0.0979, 0.1326, 0.0021, 0.0659, 0.2249, 0.0059, 0.1132, 0.5952, 0.1033]
			super().test_output(__lowerCamelCase   )





class 		snake_case_   (							UpperCAmelCase_     ,     unittest.TestCase						):







	'''simple docstring'''
	__UpperCamelCase   =		SkipUpBlockaD  # noqa F405
	__UpperCamelCase   =		'''up'''




	@property
	def    UpperCAmelCase			(						self							:     int   )       -> str:

			'''simple docstring'''




			return super().get_dummy_input(include_res_hidden_states_tuple=__lowerCamelCase   )






	def    UpperCAmelCase			(						self							:     List[Any]   )       -> Optional[int]:

			'''simple docstring'''




			__lowercase											=     [-0.0893, -0.1234, -0.1506, -0.0332, 0.0123, -0.0211, 0.0566, 0.0143, 0.0362]
			super().test_output(__lowerCamelCase   )





class 		snake_case_   (							UpperCAmelCase_     ,     unittest.TestCase						):







	'''simple docstring'''
	__UpperCamelCase   =		AttnSkipUpBlockaD  # noqa F405
	__UpperCamelCase   =		'''up'''




	@property
	def    UpperCAmelCase			(						self							:     int   )       -> Any:

			'''simple docstring'''




			return super().get_dummy_input(include_res_hidden_states_tuple=__lowerCamelCase   )






	def    UpperCAmelCase			(						self							:     str   )       -> List[Any]:

			'''simple docstring'''




			__lowercase											=     [0.0361, 0.0617, 0.2787, -0.0350, 0.0342, 0.3421, -0.0843, 0.0913, 0.3015]
			super().test_output(__lowerCamelCase   )





class 		snake_case_   (							UpperCAmelCase_     ,     unittest.TestCase						):







	'''simple docstring'''
	__UpperCamelCase   =		UpDecoderBlockaD  # noqa F405
	__UpperCamelCase   =		'''up'''




	@property
	def    UpperCAmelCase			(						self							:     Tuple   )       -> Any:

			'''simple docstring'''




			return super().get_dummy_input(include_temb=__lowerCamelCase   )




	def    UpperCAmelCase			(						self							:     Optional[int]   )       -> Optional[int]:

			'''simple docstring'''




			__lowercase											=     {'in_channels': 32, 'out_channels': 32}

			__lowercase											=     self.dummy_input
			return init_dict, inputs_dict






	def    UpperCAmelCase			(						self							:     Optional[Any]   )       -> Optional[Any]:

			'''simple docstring'''




			__lowercase											=     [0.4404, 0.1998, -0.9886, -0.3320, -0.3128, -0.7034, -0.6955, -0.2338, -0.3137]
			super().test_output(__lowerCamelCase   )





class 		snake_case_   (							UpperCAmelCase_     ,     unittest.TestCase						):







	'''simple docstring'''
	__UpperCamelCase   =		AttnUpDecoderBlockaD  # noqa F405
	__UpperCamelCase   =		'''up'''




	@property
	def    UpperCAmelCase			(						self							:     Any   )       -> Union[str, Any]:

			'''simple docstring'''




			return super().get_dummy_input(include_temb=__lowerCamelCase   )




	def    UpperCAmelCase			(						self							:     Dict   )       -> Dict:

			'''simple docstring'''




			__lowercase											=     {'in_channels': 32, 'out_channels': 32}

			__lowercase											=     self.dummy_input
			return init_dict, inputs_dict






	def    UpperCAmelCase			(						self							:     int   )       -> Optional[Any]:

			'''simple docstring'''




			__lowercase											=     [0.6738, 0.4491, 0.1055, 1.0710, 0.7316, 0.3339, 0.3352, 0.1023, 0.3568]
			super().test_output(__lowerCamelCase   )




