

'''simple docstring'''




import argparse
import json
from collections import OrderedDict
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    ConditionalDetrConfig,
    ConditionalDetrForObjectDetection,
    ConditionalDetrForSegmentation,
    ConditionalDetrImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
_lowerCAmelCase         =						logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
_lowerCAmelCase         =						[]
for i in range(6):
						# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
						rename_keys.append(
						    (F'''transformer.encoder.layers.{i}.self_attn.out_proj.weight''', F'''encoder.layers.{i}.self_attn.out_proj.weight''')
						)
						rename_keys.append(
						    (F'''transformer.encoder.layers.{i}.self_attn.out_proj.bias''', F'''encoder.layers.{i}.self_attn.out_proj.bias''')
						)
						rename_keys.append((F'''transformer.encoder.layers.{i}.linear1.weight''', F'''encoder.layers.{i}.fc1.weight'''))
						rename_keys.append((F'''transformer.encoder.layers.{i}.linear1.bias''', F'''encoder.layers.{i}.fc1.bias'''))
						rename_keys.append((F'''transformer.encoder.layers.{i}.linear2.weight''', F'''encoder.layers.{i}.fc2.weight'''))
						rename_keys.append((F'''transformer.encoder.layers.{i}.linear2.bias''', F'''encoder.layers.{i}.fc2.bias'''))
						rename_keys.append(
						    (F'''transformer.encoder.layers.{i}.norm1.weight''', F'''encoder.layers.{i}.self_attn_layer_norm.weight''')
						)
						rename_keys.append((F'''transformer.encoder.layers.{i}.norm1.bias''', F'''encoder.layers.{i}.self_attn_layer_norm.bias'''))
						rename_keys.append((F'''transformer.encoder.layers.{i}.norm2.weight''', F'''encoder.layers.{i}.final_layer_norm.weight'''))
						rename_keys.append((F'''transformer.encoder.layers.{i}.norm2.bias''', F'''encoder.layers.{i}.final_layer_norm.bias'''))
						# decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms
						rename_keys.append(
						    (F'''transformer.decoder.layers.{i}.self_attn.out_proj.weight''', F'''decoder.layers.{i}.self_attn.out_proj.weight''')
						)
						rename_keys.append(
						    (F'''transformer.decoder.layers.{i}.self_attn.out_proj.bias''', F'''decoder.layers.{i}.self_attn.out_proj.bias''')
						)
						rename_keys.append(
						    (
						        F'''transformer.decoder.layers.{i}.cross_attn.out_proj.weight''',
						        F'''decoder.layers.{i}.encoder_attn.out_proj.weight''',
						    )
						)
						rename_keys.append(
						    (
						        F'''transformer.decoder.layers.{i}.cross_attn.out_proj.bias''',
						        F'''decoder.layers.{i}.encoder_attn.out_proj.bias''',
						    )
						)
						rename_keys.append((F'''transformer.decoder.layers.{i}.linear1.weight''', F'''decoder.layers.{i}.fc1.weight'''))
						rename_keys.append((F'''transformer.decoder.layers.{i}.linear1.bias''', F'''decoder.layers.{i}.fc1.bias'''))
						rename_keys.append((F'''transformer.decoder.layers.{i}.linear2.weight''', F'''decoder.layers.{i}.fc2.weight'''))
						rename_keys.append((F'''transformer.decoder.layers.{i}.linear2.bias''', F'''decoder.layers.{i}.fc2.bias'''))
						rename_keys.append(
						    (F'''transformer.decoder.layers.{i}.norm1.weight''', F'''decoder.layers.{i}.self_attn_layer_norm.weight''')
						)
						rename_keys.append((F'''transformer.decoder.layers.{i}.norm1.bias''', F'''decoder.layers.{i}.self_attn_layer_norm.bias'''))
						rename_keys.append(
						    (F'''transformer.decoder.layers.{i}.norm2.weight''', F'''decoder.layers.{i}.encoder_attn_layer_norm.weight''')
						)
						rename_keys.append(
						    (F'''transformer.decoder.layers.{i}.norm2.bias''', F'''decoder.layers.{i}.encoder_attn_layer_norm.bias''')
						)
						rename_keys.append((F'''transformer.decoder.layers.{i}.norm3.weight''', F'''decoder.layers.{i}.final_layer_norm.weight'''))
						rename_keys.append((F'''transformer.decoder.layers.{i}.norm3.bias''', F'''decoder.layers.{i}.final_layer_norm.bias'''))

						# q, k, v projections in self/cross-attention in decoder for conditional DETR
						rename_keys.append(
						    (F'''transformer.decoder.layers.{i}.sa_qcontent_proj.weight''', F'''decoder.layers.{i}.sa_qcontent_proj.weight''')
						)
						rename_keys.append(
						    (F'''transformer.decoder.layers.{i}.sa_kcontent_proj.weight''', F'''decoder.layers.{i}.sa_kcontent_proj.weight''')
						)
						rename_keys.append(
						    (F'''transformer.decoder.layers.{i}.sa_qpos_proj.weight''', F'''decoder.layers.{i}.sa_qpos_proj.weight''')
						)
						rename_keys.append(
						    (F'''transformer.decoder.layers.{i}.sa_kpos_proj.weight''', F'''decoder.layers.{i}.sa_kpos_proj.weight''')
						)
						rename_keys.append((F'''transformer.decoder.layers.{i}.sa_v_proj.weight''', F'''decoder.layers.{i}.sa_v_proj.weight'''))
						rename_keys.append(
						    (F'''transformer.decoder.layers.{i}.ca_qcontent_proj.weight''', F'''decoder.layers.{i}.ca_qcontent_proj.weight''')
						)
						# rename_keys.append((f"transformer.decoder.layers.{i}.ca_qpos_proj.weight", f"decoder.layers.{i}.ca_qpos_proj.weight"))
						rename_keys.append(
						    (F'''transformer.decoder.layers.{i}.ca_kcontent_proj.weight''', F'''decoder.layers.{i}.ca_kcontent_proj.weight''')
						)
						rename_keys.append(
						    (F'''transformer.decoder.layers.{i}.ca_kpos_proj.weight''', F'''decoder.layers.{i}.ca_kpos_proj.weight''')
						)
						rename_keys.append((F'''transformer.decoder.layers.{i}.ca_v_proj.weight''', F'''decoder.layers.{i}.ca_v_proj.weight'''))
						rename_keys.append(
						    (F'''transformer.decoder.layers.{i}.ca_qpos_sine_proj.weight''', F'''decoder.layers.{i}.ca_qpos_sine_proj.weight''')
						)

						rename_keys.append(
						    (F'''transformer.decoder.layers.{i}.sa_qcontent_proj.bias''', F'''decoder.layers.{i}.sa_qcontent_proj.bias''')
						)
						rename_keys.append(
						    (F'''transformer.decoder.layers.{i}.sa_kcontent_proj.bias''', F'''decoder.layers.{i}.sa_kcontent_proj.bias''')
						)
						rename_keys.append((F'''transformer.decoder.layers.{i}.sa_qpos_proj.bias''', F'''decoder.layers.{i}.sa_qpos_proj.bias'''))
						rename_keys.append((F'''transformer.decoder.layers.{i}.sa_kpos_proj.bias''', F'''decoder.layers.{i}.sa_kpos_proj.bias'''))
						rename_keys.append((F'''transformer.decoder.layers.{i}.sa_v_proj.bias''', F'''decoder.layers.{i}.sa_v_proj.bias'''))
						rename_keys.append(
						    (F'''transformer.decoder.layers.{i}.ca_qcontent_proj.bias''', F'''decoder.layers.{i}.ca_qcontent_proj.bias''')
						)
						# rename_keys.append((f"transformer.decoder.layers.{i}.ca_qpos_proj.bias", f"decoder.layers.{i}.ca_qpos_proj.bias"))
						rename_keys.append(
						    (F'''transformer.decoder.layers.{i}.ca_kcontent_proj.bias''', F'''decoder.layers.{i}.ca_kcontent_proj.bias''')
						)
						rename_keys.append((F'''transformer.decoder.layers.{i}.ca_kpos_proj.bias''', F'''decoder.layers.{i}.ca_kpos_proj.bias'''))
						rename_keys.append((F'''transformer.decoder.layers.{i}.ca_v_proj.bias''', F'''decoder.layers.{i}.ca_v_proj.bias'''))
						rename_keys.append(
						    (F'''transformer.decoder.layers.{i}.ca_qpos_sine_proj.bias''', F'''decoder.layers.{i}.ca_qpos_sine_proj.bias''')
						)

# convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads
# for conditional DETR, also convert reference point head and query scale MLP
rename_keys.extend(
    [
        ("input_proj.weight", "input_projection.weight"),
        ("input_proj.bias", "input_projection.bias"),
        ("query_embed.weight", "query_position_embeddings.weight"),
        ("transformer.decoder.norm.weight", "decoder.layernorm.weight"),
        ("transformer.decoder.norm.bias", "decoder.layernorm.bias"),
        ("class_embed.weight", "class_labels_classifier.weight"),
        ("class_embed.bias", "class_labels_classifier.bias"),
        ("bbox_embed.layers.0.weight", "bbox_predictor.layers.0.weight"),
        ("bbox_embed.layers.0.bias", "bbox_predictor.layers.0.bias"),
        ("bbox_embed.layers.1.weight", "bbox_predictor.layers.1.weight"),
        ("bbox_embed.layers.1.bias", "bbox_predictor.layers.1.bias"),
        ("bbox_embed.layers.2.weight", "bbox_predictor.layers.2.weight"),
        ("bbox_embed.layers.2.bias", "bbox_predictor.layers.2.bias"),
        ("transformer.decoder.ref_point_head.layers.0.weight", "decoder.ref_point_head.layers.0.weight"),
        ("transformer.decoder.ref_point_head.layers.0.bias", "decoder.ref_point_head.layers.0.bias"),
        ("transformer.decoder.ref_point_head.layers.1.weight", "decoder.ref_point_head.layers.1.weight"),
        ("transformer.decoder.ref_point_head.layers.1.bias", "decoder.ref_point_head.layers.1.bias"),
        ("transformer.decoder.query_scale.layers.0.weight", "decoder.query_scale.layers.0.weight"),
        ("transformer.decoder.query_scale.layers.0.bias", "decoder.query_scale.layers.0.bias"),
        ("transformer.decoder.query_scale.layers.1.weight", "decoder.query_scale.layers.1.weight"),
        ("transformer.decoder.query_scale.layers.1.bias", "decoder.query_scale.layers.1.bias"),
        ("transformer.decoder.layers.0.ca_qpos_proj.weight", "decoder.layers.0.ca_qpos_proj.weight"),
        ("transformer.decoder.layers.0.ca_qpos_proj.bias", "decoder.layers.0.ca_qpos_proj.bias"),
    ]
)




def 						UpperCamelCase	(			a		,						a		,						a			)				->     Dict:






					'''simple docstring'''
					__magic_name__						       =    state_dict.pop(_a			)
					__magic_name__						       =    val




def 						UpperCamelCase	(			a			)				->     int:






					'''simple docstring'''
					__magic_name__						       =    OrderedDict()
					for key, value in state_dict.items():
										if "backbone.0.body" in key:
															__magic_name__						       =    key.replace('''backbone.0.body'''		,						'''backbone.conv_encoder.model'''			)
															__magic_name__						       =    value
										else:
															__magic_name__						       =    value

					return new_state_dict




def 						UpperCamelCase	(			a		,						a=False			)				->     List[str]:






					'''simple docstring'''
					__magic_name__						       =    """"""
					if is_panoptic:
										__magic_name__						       =    """conditional_detr."""

					# first: transformer encoder
					for i in range(6			):
										# read in weights + bias of input projection layer (in PyTorch's MultiHeadAttention, this is a single matrix + bias)
										__magic_name__						       =    state_dict.pop(F'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight'''			)
										__magic_name__						       =    state_dict.pop(F'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_bias'''			)
										# next, add query, keys and values (in that order) to the state dict
										__magic_name__						       =    in_proj_weight[:256, :]
										__magic_name__						       =    in_proj_bias[:256]
										__magic_name__						       =    in_proj_weight[256:512, :]
										__magic_name__						       =    in_proj_bias[256:512]
										__magic_name__						       =    in_proj_weight[-256:, :]
										__magic_name__						       =    in_proj_bias[-256:]




def 						UpperCamelCase	(			)				->     Any:






					'''simple docstring'''
					__magic_name__						       =    """http://images.cocodataset.org/val2017/000000039769.jpg"""
					__magic_name__						       =    Image.open(requests.get(_a		,						stream=_a			).raw			)

					return im




@torch.no_grad()
def 						UpperCamelCase	(			a		,						a			)				->     Union[str, Any]:






					'''simple docstring'''
					__magic_name__						       =    ConditionalDetrConfig()
					# set backbone and dilation attributes
					if "resnet101" in model_name:
										__magic_name__						       =    """resnet101"""
					if "dc5" in model_name:
										__magic_name__						       =    True
					__magic_name__						       =    """panoptic""" in model_name
					if is_panoptic:
										__magic_name__						       =    250
					else:
										__magic_name__						       =    91
										__magic_name__						       =    """huggingface/label-files"""
										__magic_name__						       =    """coco-detection-id2label.json"""
										__magic_name__						       =    json.load(open(hf_hub_download(_a		,						_a		,						repo_type='''dataset'''			)		,						'''r'''			)			)
										__magic_name__						       =    {int(_a			): v for k, v in idalabel.items()}
										__magic_name__						       =    idalabel
										__magic_name__						       =    {v: k for k, v in idalabel.items()}

					# load image processor
					__magic_name__						       =    """coco_panoptic""" if is_panoptic else """coco_detection"""
					__magic_name__						       =    ConditionalDetrImageProcessor(format=_a			)

					# prepare image
					__magic_name__						       =    prepare_img()
					__magic_name__						       =    image_processor(images=_a		,						return_tensors='''pt'''			)
					__magic_name__						       =    encoding["""pixel_values"""]

					logger.info(F'''Converting model {model_name}...'''			)

					# load original model from torch hub
					__magic_name__						       =    torch.hub.load('''DeppMeng/ConditionalDETR'''		,						_a		,						pretrained=_a			).eval()
					__magic_name__						       =    conditional_detr.state_dict()
					# rename keys
					for src, dest in rename_keys:
										if is_panoptic:
															__magic_name__						       =    """conditional_detr.""" + src
										rename_key(_a		,						_a		,						_a			)
					__magic_name__						       =    rename_backbone_keys(_a			)
					# query, key and value matrices need special treatment
					read_in_q_k_v(_a		,						is_panoptic=_a			)
					# important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
					__magic_name__						       =    """conditional_detr.model.""" if is_panoptic else """model."""
					for key in state_dict.copy().keys():
										if is_panoptic:
															if (
															    key.startswith('''conditional_detr'''			)
															    and not key.startswith('''class_labels_classifier'''			)
															    and not key.startswith('''bbox_predictor'''			)
															):
																				__magic_name__						       =    state_dict.pop(_a			)
																				__magic_name__						       =    val
															elif "class_labels_classifier" in key or "bbox_predictor" in key:
																				__magic_name__						       =    state_dict.pop(_a			)
																				__magic_name__						       =    val
															elif key.startswith('''bbox_attention'''			) or key.startswith('''mask_head'''			):
																				continue
															else:
																				__magic_name__						       =    state_dict.pop(_a			)
																				__magic_name__						       =    val
										else:
															if not key.startswith('''class_labels_classifier'''			) and not key.startswith('''bbox_predictor'''			):
																				__magic_name__						       =    state_dict.pop(_a			)
																				__magic_name__						       =    val
    # finally, create HuggingFace model and load state dict
					__magic_name__						       =    ConditionalDetrForSegmentation(_a			) if is_panoptic else ConditionalDetrForObjectDetection(_a			)
					model.load_state_dict(_a			)
					model.eval()
					model.push_to_hub(repo_id=_a		,						organization='''DepuMeng'''		,						commit_message='''Add model'''			)
					# verify our conversion
					__magic_name__						       =    conditional_detr(_a			)
					__magic_name__						       =    model(_a			)
					assert torch.allclose(outputs.logits		,						original_outputs['''pred_logits''']		,						atol=1e-4			)
					assert torch.allclose(outputs.pred_boxes		,						original_outputs['''pred_boxes''']		,						atol=1e-4			)
					if is_panoptic:
										assert torch.allclose(outputs.pred_masks		,						original_outputs['''pred_masks''']		,						atol=1e-4			)

					# Save model and image processor
					logger.info(F'''Saving PyTorch model and image processor to {pytorch_dump_folder_path}...'''			)
					Path(_a			).mkdir(exist_ok=_a			)
					model.save_pretrained(_a			)
					image_processor.save_pretrained(_a			)


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

						parser.add_argument(
						    "--model_name",
						    default="conditional_detr_resnet50",
						    type=str,
						    help="Name of the CONDITIONAL_DETR model you\'d like to convert.",
						)
						parser.add_argument(
						    "--pytorch_dump_folder_path", default=None, type=str, help="Path to the folder to output PyTorch model."
						)
						_lowerCAmelCase         =						parser.parse_args()
						convert_conditional_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)









'''simple docstring'''




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

_lowerCAmelCase         =						logging.get_logger(__name__)

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

# fmt: off
_lowerCAmelCase         =						[
    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, 10563, 10786,
    11420, 11709, 11907, 13163, 13697, 13700, 14808, 15306, 16410, 16791,
    17992, 19203, 19510, 20724, 22305, 22935, 27007, 30109, 30420, 33409,
    34949, 40283, 40493, 40549, 47282, 49146, 50257, 50359, 50360, 50361
]
_lowerCAmelCase         =						[
    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, 10428, 10929, 11938, 12033, 12331, 12562, 13793,
    14157, 14635, 15265, 15618, 16553, 16604, 18362, 18956, 20075, 21675,
    22520, 26130, 26161, 26435, 28279, 29464, 31650, 32302, 32470, 36865,
    42863, 47425, 49870, 50254, 50258, 50360, 50361, 50362
]






class   _SCREAMING_SNAKE_CASE   (   __a     ):
 __SCREAMING_SNAKE_CASE					:str      	=					"""whisper"""
 __SCREAMING_SNAKE_CASE					:str      	=					["""past_key_values"""]
 __SCREAMING_SNAKE_CASE					:Tuple      	=					{"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}



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

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

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

      __magic_name__						       =    median_filter_width

      super().__init__(
          pad_token_id=a__			,				bos_token_id=a__			,				eos_token_id=a__			,				is_encoder_decoder=a__			,				decoder_start_token_id=a__			,				suppress_tokens=a__			,				begin_suppress_tokens=a__			,				**a__			,				)






class   _SCREAMING_SNAKE_CASE   (   __a     ):



 @property
 def 			snake_case__  (   self		:   List[str]      ):
      __magic_name__						       =    OrderedDict(
          [
              ('''input_features''', {0: '''batch''', 1: '''feature_size''', 2: '''encoder_sequence'''}),
          ]      )
      if self.use_past:
           __magic_name__						       =    {0: '''batch'''}
      else:
           __magic_name__						       =    {0: '''batch''', 1: '''decoder_sequence'''}

      if self.use_past:
           self.fill_with_past_key_values_(a__			,				direction='''inputs'''      )

      return common_inputs



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

      __magic_name__						       =    super().generate_dummy_inputs(
          preprocessor.tokenizer			,				a__			,				a__			,				a__			,				a__      )

      __magic_name__						       =    encoder_inputs.pop('''input_features'''      )
      __magic_name__						       =    decoder_inputs.pop('''decoder_input_ids'''      )

      if "past_key_values" in decoder_inputs:
           __magic_name__						       =    decoder_inputs.pop('''past_key_values'''      )

      return dummy_inputs



 @property
 def 			snake_case__  (   self		:   Dict      ):
      return 1E-3






