


'''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				:  Optional[int]    		=      logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
lowerCamelCase				:  Optional[int]    		=      []
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 _SCREAMING_SNAKE_CASE (A    ,					A    ,					A			)			->			Union[str, Any]:






       """simple docstring"""







       lowercase__      =					state_dict.pop(A			)
       lowercase__      =					val

def _SCREAMING_SNAKE_CASE (A			)			->			int:






       """simple docstring"""







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

       return new_state_dict

def _SCREAMING_SNAKE_CASE (A    ,					A=False			)			->			Union[str, Any]:






       """simple docstring"""







       lowercase__      =					''''''
       if is_panoptic:
              lowercase__      =					'''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)
              lowercase__      =					state_dict.pop(f"{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"			)
              lowercase__      =					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
              lowercase__      =					in_proj_weight[:256, :]
              lowercase__      =					in_proj_bias[:256]
              lowercase__      =					in_proj_weight[256:512, :]
              lowercase__      =					in_proj_bias[256:512]
              lowercase__      =					in_proj_weight[-256:, :]
              lowercase__      =					in_proj_bias[-256:]

def _SCREAMING_SNAKE_CASE ()			->			List[Any]:






       """simple docstring"""







       lowercase__      =					'''http://images.cocodataset.org/val2017/000000039769.jpg'''
       lowercase__      =					Image.open(requests.get(A    ,					stream=A			).raw			)

       return im

@torch.no_grad()
def _SCREAMING_SNAKE_CASE (A    ,					A			)			->			int:






       """simple docstring"""







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

       # load image processor
       lowercase__      =					'''coco_panoptic''' if is_panoptic else '''coco_detection'''
       lowercase__      =					ConditionalDetrImageProcessor(format=A			)

       # prepare image
       lowercase__      =					prepare_img()
       lowercase__      =					image_processor(images=A    ,					return_tensors='''pt'''			)
       lowercase__      =					encoding['''pixel_values''']

       logger.info(f"Converting model {model_name}..."			)

       # load original model from torch hub
       lowercase__      =					torch.hub.load('''DeppMeng/ConditionalDETR'''    ,					A    ,					pretrained=A			).eval()
       lowercase__      =					conditional_detr.state_dict()
       # rename keys
       for src, dest in rename_keys:
              if is_panoptic:
                     lowercase__      =					'''conditional_detr.''' + src
              rename_key(A    ,					A    ,					A			)
       lowercase__      =					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
       lowercase__      =					'''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'''			)
                     ):
                            lowercase__      =					state_dict.pop(A			)
                            lowercase__      =					val
                     elif "class_labels_classifier" in key or "bbox_predictor" in key:
                            lowercase__      =					state_dict.pop(A			)
                            lowercase__      =					val
                     elif key.startswith('''bbox_attention'''			) or key.startswith('''mask_head'''			):
                            continue
                     else:
                            lowercase__      =					state_dict.pop(A			)
                            lowercase__      =					val
              else:
                     if not key.startswith('''class_labels_classifier'''			) and not key.startswith('''bbox_predictor'''			):
                            lowercase__      =					state_dict.pop(A			)
                            lowercase__      =					val
    # finally, create HuggingFace model and load state dict
       lowercase__      =					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
       lowercase__      =					conditional_detr(A			)
       lowercase__      =					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				:  Optional[int]    		=      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				:  Any    		=      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				:  Dict    		=      logging.get_logger(__name__)

lowerCamelCase				:  List[Any]    		=      {
    'openai/whisper-base': 'https://huggingface.co/openai/whisper-base/resolve/main/config.json',
}

# fmt: off
lowerCamelCase				:  Dict    		=      [
    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, 1_058, 1_220, 1_267, 1_279, 1_303, 1_343, 1_377,
    1_391, 1_635, 1_782, 1_875, 2_162, 2_361, 2_488, 3_467, 4_008, 4_211,
    4_600, 4_808, 5_299, 5_855, 6_329, 7_203, 9_609, 9_959, 10_563, 10_786,
    11_420, 11_709, 11_907, 13_163, 13_697, 13_700, 14_808, 15_306, 16_410, 16_791,
    17_992, 19_203, 19_510, 20_724, 22_305, 22_935, 27_007, 30_109, 30_420, 33_409,
    34_949, 40_283, 40_493, 40_549, 47_282, 49_146, 50_257, 50_359, 50_360, 50_361
]
lowerCamelCase				:  str    		=      [
    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, 1_350, 1_853, 1_982, 2_460, 2_627,
    3_246, 3_253, 3_268, 3_536, 3_846, 3_961, 4_183, 4_667, 6_585, 6_647,
    7_273, 9_061, 9_383, 10_428, 10_929, 11_938, 12_033, 12_331, 12_562, 13_793,
    14_157, 14_635, 15_265, 15_618, 16_553, 16_604, 18_362, 18_956, 20_075, 21_675,
    22_520, 26_130, 26_161, 26_435, 28_279, 29_464, 31_650, 32_302, 32_470, 36_865,
    42_863, 47_425, 49_870, 50_254, 50_258, 50_360, 50_361, 50_362
]
class     __lowerCAmelCase		(lowercase_	):





      '''simple docstring'''






      lowerCAmelCase__   :	List[str]       =				"""whisper"""
      lowerCAmelCase__   :	Dict       =				["""past_key_values"""]
      lowerCAmelCase__   :	Optional[int]       =				{"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}



      def __init__(self       :       int  ,		UpperCamelCase       :       Optional[int]=51865  ,		UpperCamelCase       :       Any=80  ,		UpperCamelCase       :       Dict=6  ,		UpperCamelCase       :       str=4  ,		UpperCamelCase       :       Optional[Any]=6  ,		UpperCamelCase       :       List[Any]=4  ,		UpperCamelCase       :       Tuple=1536  ,		UpperCamelCase       :       Dict=1536  ,		UpperCamelCase       :       Any=0.0  ,		UpperCamelCase       :       Any=0.0  ,		UpperCamelCase       :       int=50257  ,		UpperCamelCase       :       List[str]=True  ,		UpperCamelCase       :       List[Any]=True  ,		UpperCamelCase       :       List[str]="gelu"  ,		UpperCamelCase       :       Dict=256  ,		UpperCamelCase       :       Optional[int]=0.0  ,		UpperCamelCase       :       str=0.0  ,		UpperCamelCase       :       str=0.0  ,		UpperCamelCase       :       Dict=0.02  ,		UpperCamelCase       :       List[Any]=False  ,		UpperCamelCase       :       int=1500  ,		UpperCamelCase       :       List[str]=448  ,		UpperCamelCase       :       int=50256  ,		UpperCamelCase       :       Optional[int]=50256  ,		UpperCamelCase       :       Optional[Any]=50256  ,		UpperCamelCase       :       Any=None  ,		UpperCamelCase       :       Tuple=[220, 50256]  ,		UpperCamelCase       :       Optional[Any]=False  ,		UpperCamelCase       :       int=256  ,		UpperCamelCase       :       Optional[int]=False  ,		UpperCamelCase       :       int=0.05  ,		UpperCamelCase       :       List[Any]=10  ,		UpperCamelCase       :       Optional[int]=2  ,		UpperCamelCase       :       Optional[int]=0.0  ,		UpperCamelCase       :       Dict=10  ,		UpperCamelCase       :       Union[str, Any]=0  ,		UpperCamelCase       :       Union[str, Any]=7  ,		**UpperCamelCase       :       Union[str, Any]  ,		):

             '''simple docstring'''







             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=UpperCamelCase  ,		bos_token_id=UpperCamelCase  ,		eos_token_id=UpperCamelCase  ,		is_encoder_decoder=UpperCamelCase  ,		decoder_start_token_id=UpperCamelCase  ,		suppress_tokens=UpperCamelCase  ,		begin_suppress_tokens=UpperCamelCase  ,		**UpperCamelCase  ,		)






class     __lowerCAmelCase		(lowercase_	):





      '''simple docstring'''






      @property
      def  UpperCamelCase__						(self       :       str					):

             '''simple docstring'''







             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_(UpperCamelCase  ,		direction='''inputs'''					)

             return common_inputs



      def  UpperCamelCase__						(self       :       Union[str, Any]  ,		UpperCamelCase       :       Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"]  ,		UpperCamelCase       :       int = -1  ,		UpperCamelCase       :       int = -1  ,		UpperCamelCase       :       bool = False  ,		UpperCamelCase       :       Optional["TensorType"] = None  ,		UpperCamelCase       :       int = 22050  ,		UpperCamelCase       :       float = 5.0  ,		UpperCamelCase       :       int = 220  ,		):

             '''simple docstring'''







             lowercase__      =					OrderedDict()
             lowercase__      =					OnnxConfig.generate_dummy_inputs(
                 self  ,		preprocessor=preprocessor.feature_extractor  ,		batch_size=UpperCamelCase  ,		framework=UpperCamelCase  ,		sampling_rate=UpperCamelCase  ,		time_duration=UpperCamelCase  ,		frequency=UpperCamelCase  ,		)
             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  ,		UpperCamelCase  ,		UpperCamelCase  ,		UpperCamelCase  ,		UpperCamelCase					)

             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  UpperCamelCase__						(self       :       Union[str, Any]					):

             '''simple docstring'''







             return 1E-3




