



import argparse
import json

import torch

from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel



def __lowerCAmelCase	(       SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_=1					):
			if n_shave_prefix_segments >= 0:
						return ".".join(path.split("."					)[n_shave_prefix_segments:]					)
			else:
						return ".".join(path.split("."					)[:n_shave_prefix_segments]					)



def __lowerCAmelCase	(       SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_=0					):
			lowercase__   					=			[]
			for old_item in old_list:
						lowercase__   					=			old_item.replace("in_layers.0"  ,					"norm1"					)
						lowercase__   					=			new_item.replace("in_layers.2"  ,					"conv1"					)

						lowercase__   					=			new_item.replace("out_layers.0"  ,					"norm2"					)
						lowercase__   					=			new_item.replace("out_layers.3"  ,					"conv2"					)

						lowercase__   					=			new_item.replace("emb_layers.1"  ,					"time_emb_proj"					)
						lowercase__   					=			new_item.replace("skip_connection"  ,					"conv_shortcut"					)

						lowercase__   					=			shave_segments(SCREAMING_SNAKE_CASE_  ,					n_shave_prefix_segments=SCREAMING_SNAKE_CASE_					)

						mapping.append({"old": old_item, "new": new_item}					)

			return mapping



def __lowerCAmelCase	(       SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_=0					):
			lowercase__   					=			[]
			for old_item in old_list:
						lowercase__   					=			old_item

						lowercase__   					=			new_item.replace("norm.weight"  ,					"group_norm.weight"					)
						lowercase__   					=			new_item.replace("norm.bias"  ,					"group_norm.bias"					)

						lowercase__   					=			new_item.replace("proj_out.weight"  ,					"proj_attn.weight"					)
						lowercase__   					=			new_item.replace("proj_out.bias"  ,					"proj_attn.bias"					)

						lowercase__   					=			shave_segments(SCREAMING_SNAKE_CASE_  ,					n_shave_prefix_segments=SCREAMING_SNAKE_CASE_					)

						mapping.append({"old": old_item, "new": new_item}					)

			return mapping



def __lowerCAmelCase	(       SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_=None  ,					SCREAMING_SNAKE_CASE_=None  ,					SCREAMING_SNAKE_CASE_=None					):
			assert isinstance(SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_					), "Paths should be a list of dicts containing 'old' and 'new' keys."

			# Splits the attention layers into three variables.
			if attention_paths_to_split is not None:
						for path, path_map in attention_paths_to_split.items():
									lowercase__   					=			old_checkpoint[path]
									lowercase__   					=			old_tensor.shape[0] // 3

									lowercase__   					=			(-1, channels) if len(old_tensor.shape					) == 3 else (-1)

									lowercase__   					=			old_tensor.shape[0] // config["num_head_channels"] // 3

									lowercase__   					=			old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:]					)
									lowercase__					,    lowercase__					,    lowercase__   					=			old_tensor.split(channels // num_heads  ,					dim=1					)

									lowercase__   					=			query.reshape(SCREAMING_SNAKE_CASE_					)
									lowercase__   					=			key.reshape(SCREAMING_SNAKE_CASE_					)
									lowercase__   					=			value.reshape(SCREAMING_SNAKE_CASE_					)

			for path in paths:
						lowercase__   					=			path["new"]

						# These have already been assigned
						if attention_paths_to_split is not None and new_path in attention_paths_to_split:
									continue

						# Global renaming happens here
						lowercase__   					=			new_path.replace("middle_block.0"  ,					"mid_block.resnets.0"					)
						lowercase__   					=			new_path.replace("middle_block.1"  ,					"mid_block.attentions.0"					)
						lowercase__   					=			new_path.replace("middle_block.2"  ,					"mid_block.resnets.1"					)

						if additional_replacements is not None:
									for replacement in additional_replacements:
												lowercase__   					=			new_path.replace(replacement["old"]  ,					replacement["new"]					)

        # proj_attn.weight has to be converted from conv 1D to linear
						if "proj_attn.weight" in new_path:
									lowercase__   					=			old_checkpoint[path["old"]][:, :, 0]
						else:
									lowercase__   					=			old_checkpoint[path["old"]]



def __lowerCAmelCase	(       SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_					):
			lowercase__   					=			{}

			lowercase__   					=			checkpoint["time_embed.0.weight"]
			lowercase__   					=			checkpoint["time_embed.0.bias"]
			lowercase__   					=			checkpoint["time_embed.2.weight"]
			lowercase__   					=			checkpoint["time_embed.2.bias"]

			lowercase__   					=			checkpoint["input_blocks.0.0.weight"]
			lowercase__   					=			checkpoint["input_blocks.0.0.bias"]

			lowercase__   					=			checkpoint["out.0.weight"]
			lowercase__   					=			checkpoint["out.0.bias"]
			lowercase__   					=			checkpoint["out.2.weight"]
			lowercase__   					=			checkpoint["out.2.bias"]

			# Retrieves the keys for the input blocks only
			lowercase__   					=			len({".".join(layer.split("."					)[:2]					) for layer in checkpoint if "input_blocks" in layer}					)
			lowercase__   					=			{
			    layer_id: [key for key in checkpoint if f'''input_blocks.{layer_id}''' in key]
			    for layer_id in range(SCREAMING_SNAKE_CASE_					)
			}

			# Retrieves the keys for the middle blocks only
			lowercase__   					=			len({".".join(layer.split("."					)[:2]					) for layer in checkpoint if "middle_block" in layer}					)
			lowercase__   					=			{
			    layer_id: [key for key in checkpoint if f'''middle_block.{layer_id}''' in key]
			    for layer_id in range(SCREAMING_SNAKE_CASE_					)
			}

			# Retrieves the keys for the output blocks only
			lowercase__   					=			len({".".join(layer.split("."					)[:2]					) for layer in checkpoint if "output_blocks" in layer}					)
			lowercase__   					=			{
			    layer_id: [key for key in checkpoint if f'''output_blocks.{layer_id}''' in key]
			    for layer_id in range(SCREAMING_SNAKE_CASE_					)
			}

			for i in range(1  ,					SCREAMING_SNAKE_CASE_					):
						lowercase__   					=			(i - 1) // (config["num_res_blocks"] + 1)
						lowercase__   					=			(i - 1) % (config["num_res_blocks"] + 1)

						lowercase__   					=			[key for key in input_blocks[i] if f'''input_blocks.{i}.0''' in key]
						lowercase__   					=			[key for key in input_blocks[i] if f'''input_blocks.{i}.1''' in key]

						if f'''input_blocks.{i}.0.op.weight''' in checkpoint:
									lowercase__   					=			checkpoint[
									    f'''input_blocks.{i}.0.op.weight'''
									]
									lowercase__   					=			checkpoint[
									    f'''input_blocks.{i}.0.op.bias'''
									]
									continue

						lowercase__   					=			renew_resnet_paths(SCREAMING_SNAKE_CASE_					)
						lowercase__   					=			{"old": f'''input_blocks.{i}.0''', "new": f'''down_blocks.{block_id}.resnets.{layer_in_block_id}'''}
						lowercase__   					=			{"old": "resnets.2.op", "new": "downsamplers.0.op"}
						assign_to_checkpoint(
						    SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_  ,					additional_replacements=[meta_path, resnet_op]  ,					config=SCREAMING_SNAKE_CASE_					)

						if len(SCREAMING_SNAKE_CASE_					):
									lowercase__   					=			renew_attention_paths(SCREAMING_SNAKE_CASE_					)
									lowercase__   					=			{
									    "old": f'''input_blocks.{i}.1''',
									    "new": f'''down_blocks.{block_id}.attentions.{layer_in_block_id}''',
									}
									lowercase__   					=			{
									    f'''input_blocks.{i}.1.qkv.bias''': {
									        "key": f'''down_blocks.{block_id}.attentions.{layer_in_block_id}.key.bias''',
									        "query": f'''down_blocks.{block_id}.attentions.{layer_in_block_id}.query.bias''',
									        "value": f'''down_blocks.{block_id}.attentions.{layer_in_block_id}.value.bias''',
									    },
									    f'''input_blocks.{i}.1.qkv.weight''': {
									        "key": f'''down_blocks.{block_id}.attentions.{layer_in_block_id}.key.weight''',
									        "query": f'''down_blocks.{block_id}.attentions.{layer_in_block_id}.query.weight''',
									        "value": f'''down_blocks.{block_id}.attentions.{layer_in_block_id}.value.weight''',
									    },
									}
									assign_to_checkpoint(
									    SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_  ,					additional_replacements=[meta_path]  ,					attention_paths_to_split=SCREAMING_SNAKE_CASE_  ,					config=SCREAMING_SNAKE_CASE_  ,					)

			lowercase__   					=			middle_blocks[0]
			lowercase__   					=			middle_blocks[1]
			lowercase__   					=			middle_blocks[2]

			lowercase__   					=			renew_resnet_paths(SCREAMING_SNAKE_CASE_					)
			assign_to_checkpoint(SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_  ,					config=SCREAMING_SNAKE_CASE_					)

			lowercase__   					=			renew_resnet_paths(SCREAMING_SNAKE_CASE_					)
			assign_to_checkpoint(SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_  ,					config=SCREAMING_SNAKE_CASE_					)

			lowercase__   					=			renew_attention_paths(SCREAMING_SNAKE_CASE_					)
			lowercase__   					=			{
			    "middle_block.1.qkv.bias": {
			        "key": "mid_block.attentions.0.key.bias",
			        "query": "mid_block.attentions.0.query.bias",
			        "value": "mid_block.attentions.0.value.bias",
			    },
			    "middle_block.1.qkv.weight": {
			        "key": "mid_block.attentions.0.key.weight",
			        "query": "mid_block.attentions.0.query.weight",
			        "value": "mid_block.attentions.0.value.weight",
			    },
			}
			assign_to_checkpoint(
			    SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_  ,					attention_paths_to_split=SCREAMING_SNAKE_CASE_  ,					config=SCREAMING_SNAKE_CASE_					)

			for i in range(SCREAMING_SNAKE_CASE_					):
						lowercase__   					=			i // (config["num_res_blocks"] + 1)
						lowercase__   					=			i % (config["num_res_blocks"] + 1)
						lowercase__   					=			[shave_segments(SCREAMING_SNAKE_CASE_  ,					2					) for name in output_blocks[i]]
						lowercase__   					=			{}

						for layer in output_block_layers:
									lowercase__					,    lowercase__   					=			layer.split("."					)[0], shave_segments(SCREAMING_SNAKE_CASE_  ,					1					)
									if layer_id in output_block_list:
												output_block_list[layer_id].append(SCREAMING_SNAKE_CASE_					)
									else:
												lowercase__   					=			[layer_name]

						if len(SCREAMING_SNAKE_CASE_					) > 1:
									lowercase__   					=			[key for key in output_blocks[i] if f'''output_blocks.{i}.0''' in key]
									lowercase__   					=			[key for key in output_blocks[i] if f'''output_blocks.{i}.1''' in key]

									lowercase__   					=			renew_resnet_paths(SCREAMING_SNAKE_CASE_					)
									lowercase__   					=			renew_resnet_paths(SCREAMING_SNAKE_CASE_					)

									lowercase__   					=			{"old": f'''output_blocks.{i}.0''', "new": f'''up_blocks.{block_id}.resnets.{layer_in_block_id}'''}
									assign_to_checkpoint(SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_  ,					additional_replacements=[meta_path]  ,					config=SCREAMING_SNAKE_CASE_					)

									if ["conv.weight", "conv.bias"] in output_block_list.values():
												lowercase__   					=			list(output_block_list.values()					).index(["conv.weight", "conv.bias"]					)
												lowercase__   					=			checkpoint[
												    f'''output_blocks.{i}.{index}.conv.weight'''
												]
												lowercase__   					=			checkpoint[
												    f'''output_blocks.{i}.{index}.conv.bias'''
												]

												# Clear attentions as they have been attributed above.
												if len(SCREAMING_SNAKE_CASE_					) == 2:
															lowercase__   					=			[]

									if len(SCREAMING_SNAKE_CASE_					):
												lowercase__   					=			renew_attention_paths(SCREAMING_SNAKE_CASE_					)
												lowercase__   					=			{
												    "old": f'''output_blocks.{i}.1''',
												    "new": f'''up_blocks.{block_id}.attentions.{layer_in_block_id}''',
												}
												lowercase__   					=			{
												    f'''output_blocks.{i}.1.qkv.bias''': {
												        "key": f'''up_blocks.{block_id}.attentions.{layer_in_block_id}.key.bias''',
												        "query": f'''up_blocks.{block_id}.attentions.{layer_in_block_id}.query.bias''',
												        "value": f'''up_blocks.{block_id}.attentions.{layer_in_block_id}.value.bias''',
												    },
												    f'''output_blocks.{i}.1.qkv.weight''': {
												        "key": f'''up_blocks.{block_id}.attentions.{layer_in_block_id}.key.weight''',
												        "query": f'''up_blocks.{block_id}.attentions.{layer_in_block_id}.query.weight''',
												        "value": f'''up_blocks.{block_id}.attentions.{layer_in_block_id}.value.weight''',
												    },
												}
												assign_to_checkpoint(
												    SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_  ,					additional_replacements=[meta_path]  ,					attention_paths_to_split=to_split if any("qkv" in key for key in attentions					) else None  ,					config=SCREAMING_SNAKE_CASE_  ,					)
						else:
									lowercase__   					=			renew_resnet_paths(SCREAMING_SNAKE_CASE_  ,					n_shave_prefix_segments=1					)
									for path in resnet_0_paths:
												lowercase__   					=			".".join(["output_blocks", str(SCREAMING_SNAKE_CASE_					), path["old"]]					)
												lowercase__   					=			".".join(["up_blocks", str(SCREAMING_SNAKE_CASE_					), "resnets", str(SCREAMING_SNAKE_CASE_					), path["new"]]					)

												lowercase__   					=			checkpoint[old_path]

			return new_checkpoint


if __name__ == "__main__":
							lowercase_						=       argparse.ArgumentParser()

							parser.add_argument(
							    """--checkpoint_path""", default=None, type=str, required=True, help="""Path to the checkpoint to convert."""
							)

							parser.add_argument(
							    """--config_file""",
							    default=None,
							    type=str,
							    required=True,
							    help="""The config json file corresponding to the architecture.""",
							)

							parser.add_argument("""--dump_path""", default=None, type=str, required=True, help="""Path to the output model.""")

							lowercase_						=       parser.parse_args()

							lowercase_						=       torch.load(args.checkpoint_path)

							with open(args.config_file) as f:
														lowercase_						=       json.loads(f.read())

							lowercase_						=       convert_ldm_checkpoint(checkpoint, config)

							if "ldm" in config:
														del config["ldm"]

							lowercase_						=       UNetaDModel(**config)
							model.load_state_dict(converted_checkpoint)

							try:
														lowercase_						=       DDPMScheduler.from_config("""/""".join(args.checkpoint_path.split("""/""")[:-1]))
														lowercase_						=       VQModel.from_pretrained("""/""".join(args.checkpoint_path.split("""/""")[:-1]))

														lowercase_						=       LDMPipeline(unet=model, scheduler=scheduler, vae=vqvae)
														pipe.save_pretrained(args.dump_path)
							except:  # noqa: E722
														model.save_pretrained(args.dump_path)









from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxSeqaSeqConfigWithPast
from ...utils import logging


if TYPE_CHECKING:
							from ...feature_extraction_utils import FeatureExtractionMixin
							from ...tokenization_utils_base import PreTrainedTokenizerBase
							from ...utils import TensorType

lowercase_						=       logging.get_logger(__name__)

lowercase_						=       {
    """openai/whisper-base""": """https://huggingface.co/openai/whisper-base/resolve/main/config.json""",
}

# fmt: off
lowercase_						=       [
    1, 2, 7, 8, 9, 10, 14, 25,
    26, 27, 28, 29, 31, 58, 59, 60, 61, 62,
    63, 90, 91, 92, 93, 357, 366, 438, 532, 685,
    705, 796, 930, 1058, 1220, 1267, 1279, 1303, 1343, 1377,
    1391, 1635, 1782, 1875, 2162, 2361, 2488, 3467, 4008, 4211,
    4600, 4808, 5299, 5855, 6329, 7203, 9609, 9959, 1_0563, 1_0786,
    1_1420, 1_1709, 1_1907, 1_3163, 1_3697, 1_3700, 1_4808, 1_5306, 1_6410, 1_6791,
    1_7992, 1_9203, 1_9510, 2_0724, 2_2305, 2_2935, 2_7007, 3_0109, 3_0420, 3_3409,
    3_4949, 4_0283, 4_0493, 4_0549, 4_7282, 4_9146, 5_0257, 5_0359, 5_0360, 5_0361
]
lowercase_						=       [
    1, 2, 7, 8, 9, 10, 14, 25,
    26, 27, 28, 29, 31, 58, 59, 60, 61, 62,
    63, 90, 91, 92, 93, 359, 503, 522, 542, 873,
    893, 902, 918, 922, 931, 1350, 1853, 1982, 2460, 2627,
    3246, 3253, 3268, 3536, 3846, 3961, 4183, 4667, 6585, 6647,
    7273, 9061, 9383, 1_0428, 1_0929, 1_1938, 1_2033, 1_2331, 1_2562, 1_3793,
    1_4157, 1_4635, 1_5265, 1_5618, 1_6553, 1_6604, 1_8362, 1_8956, 2_0075, 2_1675,
    2_2520, 2_6130, 2_6161, 2_6435, 2_8279, 2_9464, 3_1650, 3_2302, 3_2470, 3_6865,
    4_2863, 4_7425, 4_9870, 5_0254, 5_0258, 5_0360, 5_0361, 5_0362
]
class 			_snake_case     (   lowercase__):
				UpperCamelCase__			:   Optional[int]       						="""whisper"""
				UpperCamelCase__			:   Optional[int]       						=["""past_key_values"""]
				UpperCamelCase__			:   Optional[Any]       						={"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}




				def __init__(  self   :  List[Any],   __lowercase   :  List[str]=5_1865,   __lowercase   :  Dict=80,   __lowercase   :  List[Any]=6,   __lowercase   :  Union[str, Any]=4,   __lowercase   :  Tuple=6,   __lowercase   :  Dict=4,   __lowercase   :  List[Any]=1536,   __lowercase   :  Tuple=1536,   __lowercase   :  Any=0.0,   __lowercase   :  List[str]=0.0,   __lowercase   :  List[Any]=5_0257,   __lowercase   :  List[str]=True,   __lowercase   :  str=True,   __lowercase   :  int="gelu",   __lowercase   :  Tuple=256,   __lowercase   :  Tuple=0.0,   __lowercase   :  List[Any]=0.0,   __lowercase   :  Optional[int]=0.0,   __lowercase   :  List[Any]=0.02,   __lowercase   :  Union[str, Any]=False,   __lowercase   :  str=1500,   __lowercase   :  Optional[int]=448,   __lowercase   :  Optional[Any]=5_0256,   __lowercase   :  Tuple=5_0256,   __lowercase   :  Any=5_0256,   __lowercase   :  Union[str, Any]=None,   __lowercase   :  Any=[220, 5_0256],   __lowercase   :  List[Any]=False,   __lowercase   :  int=256,   __lowercase   :  int=False,   __lowercase   :  Tuple=0.05,   __lowercase   :  int=10,   __lowercase   :  Dict=2,   __lowercase   :  List[Any]=0.0,   __lowercase   :  Optional[int]=10,   __lowercase   :  Union[str, Any]=0,   __lowercase   :  Tuple=7,   **__lowercase   :  Union[str, Any],   ):
							lowercase__   					=			vocab_size
							lowercase__   					=			num_mel_bins
							lowercase__   					=			d_model
							lowercase__   					=			encoder_layers
							lowercase__   					=			encoder_attention_heads
							lowercase__   					=			decoder_layers
							lowercase__   					=			decoder_attention_heads
							lowercase__   					=			decoder_ffn_dim
							lowercase__   					=			encoder_ffn_dim
							lowercase__   					=			dropout
							lowercase__   					=			attention_dropout
							lowercase__   					=			activation_dropout
							lowercase__   					=			activation_function
							lowercase__   					=			init_std
							lowercase__   					=			encoder_layerdrop
							lowercase__   					=			decoder_layerdrop
							lowercase__   					=			use_cache
							lowercase__   					=			encoder_layers
							lowercase__   					=			scale_embedding  # scale factor will be sqrt(d_model) if True
							lowercase__   					=			max_source_positions
							lowercase__   					=			max_target_positions

							# Audio Classification-specific parameters. Feel free to ignore for other classes.
							lowercase__   					=			classifier_proj_size
							lowercase__   					=			use_weighted_layer_sum

							# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
							lowercase__   					=			apply_spec_augment
							lowercase__   					=			mask_time_prob
							lowercase__   					=			mask_time_length
							lowercase__   					=			mask_time_min_masks
							lowercase__   					=			mask_feature_prob
							lowercase__   					=			mask_feature_length
							lowercase__   					=			mask_feature_min_masks

							lowercase__   					=			median_filter_width

							super().__init__(
							    pad_token_id=__lowercase,   bos_token_id=__lowercase,   eos_token_id=__lowercase,   is_encoder_decoder=__lowercase,   decoder_start_token_id=__lowercase,   suppress_tokens=__lowercase,   begin_suppress_tokens=__lowercase,   **__lowercase,   )
class 			_snake_case     (   lowercase__):




				@property
				def 							A__ (  self   :  str  ):
							lowercase__   					=			OrderedDict(
							    [
							        ("input_features", {0: "batch", 1: "feature_size", 2: "encoder_sequence"}),
							    ]  )
							if self.use_past:
										lowercase__   					=			{0: "batch"}
							else:
										lowercase__   					=			{0: "batch", 1: "decoder_sequence"}

							if self.use_past:
										self.fill_with_past_key_values_(__lowercase,   direction="inputs"  )

							return common_inputs




				def 							A__ (  self   :  int,   __lowercase   :  Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"],   __lowercase   :  int = -1,   __lowercase   :  int = -1,   __lowercase   :  bool = False,   __lowercase   :  Optional["TensorType"] = None,   __lowercase   :  int = 2_2050,   __lowercase   :  float = 5.0,   __lowercase   :  int = 220,   ):
							lowercase__   					=			OrderedDict()
							lowercase__   					=			OnnxConfig.generate_dummy_inputs(
							    self,   preprocessor=preprocessor.feature_extractor,   batch_size=__lowercase,   framework=__lowercase,   sampling_rate=__lowercase,   time_duration=__lowercase,   frequency=__lowercase,   )
							lowercase__   					=			encoder_inputs["input_features"].shape[2]
							lowercase__   					=			encoder_sequence_length // 2 if self.use_past else seq_length

							lowercase__   					=			super().generate_dummy_inputs(
							    preprocessor.tokenizer,   __lowercase,   __lowercase,   __lowercase,   __lowercase  )

							lowercase__   					=			encoder_inputs.pop("input_features"  )
							lowercase__   					=			decoder_inputs.pop("decoder_input_ids"  )

							if "past_key_values" in decoder_inputs:
										lowercase__   					=			decoder_inputs.pop("past_key_values"  )

							return dummy_inputs




				@property
				def 							A__ (  self   :  int  ):
							return 1e-3




