



'''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()
snake_case_			=     logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
snake_case_			=     []
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       __lowercase  (_SCREAMING_SNAKE_CASE      :int    ,	_SCREAMING_SNAKE_CASE      :List[str]    ,	_SCREAMING_SNAKE_CASE      :Optional[Any]       ):

				'''simple docstring'''





				SCREAMING_SNAKE_CASE  :						List[str]      					= state_dict.pop(_lowerCamelCase       )
				SCREAMING_SNAKE_CASE  :						Tuple      					= val






def       __lowercase  (_SCREAMING_SNAKE_CASE      :Optional[Any]       ):

				'''simple docstring'''





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

				return new_state_dict






def       __lowercase  (_SCREAMING_SNAKE_CASE      :List[str]    ,	_SCREAMING_SNAKE_CASE      :List[str]=False       ):

				'''simple docstring'''





				SCREAMING_SNAKE_CASE  :						Optional[Any]      					= ''
				if is_panoptic:
								SCREAMING_SNAKE_CASE  :						Optional[Any]      					= '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)
								SCREAMING_SNAKE_CASE  :						Optional[int]      					= state_dict.pop(F'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight'''       )
								SCREAMING_SNAKE_CASE  :						Tuple      					= 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
								SCREAMING_SNAKE_CASE  :						Dict      					= in_proj_weight[:2_56, :]
								SCREAMING_SNAKE_CASE  :						List[Any]      					= in_proj_bias[:2_56]
								SCREAMING_SNAKE_CASE  :						Dict      					= in_proj_weight[2_56:5_12, :]
								SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= in_proj_bias[2_56:5_12]
								SCREAMING_SNAKE_CASE  :						Any      					= in_proj_weight[-2_56:, :]
								SCREAMING_SNAKE_CASE  :						Any      					= in_proj_bias[-2_56:]






def       __lowercase  ():

				'''simple docstring'''





				SCREAMING_SNAKE_CASE  :						Any      					= 'http://images.cocodataset.org/val2017/000000039769.jpg'
				SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= Image.open(requests.get(_lowerCamelCase    ,	stream=_lowerCamelCase       ).raw       )

				return im






@torch.no_grad()
def       __lowercase  (_SCREAMING_SNAKE_CASE      :Tuple    ,	_SCREAMING_SNAKE_CASE      :Optional[Any]       ):

				'''simple docstring'''





				SCREAMING_SNAKE_CASE  :						List[Any]      					= ConditionalDetrConfig()
				# set backbone and dilation attributes
				if "resnet101" in model_name:
								SCREAMING_SNAKE_CASE  :						Dict      					= 'resnet101'
				if "dc5" in model_name:
								SCREAMING_SNAKE_CASE  :						List[Any]      					= True
				SCREAMING_SNAKE_CASE  :						Dict      					= 'panoptic' in model_name
				if is_panoptic:
								SCREAMING_SNAKE_CASE  :						List[str]      					= 2_50
				else:
								SCREAMING_SNAKE_CASE  :						str      					= 91
								SCREAMING_SNAKE_CASE  :						Any      					= 'huggingface/label-files'
								SCREAMING_SNAKE_CASE  :						List[str]      					= 'coco-detection-id2label.json'
								SCREAMING_SNAKE_CASE  :						int      					= json.load(open(hf_hub_download(_lowerCamelCase    ,	_lowerCamelCase    ,	repo_type='''dataset'''       )    ,	'''r'''       )       )
								SCREAMING_SNAKE_CASE  :						str      					= {int(_lowerCamelCase       ): v for k, v in idalabel.items()}
								SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= idalabel
								SCREAMING_SNAKE_CASE  :						Any      					= {v: k for k, v in idalabel.items()}

				# load image processor
				SCREAMING_SNAKE_CASE  :						Optional[int]      					= 'coco_panoptic' if is_panoptic else 'coco_detection'
				SCREAMING_SNAKE_CASE  :						str      					= ConditionalDetrImageProcessor(format=_lowerCamelCase       )

				# prepare image
				SCREAMING_SNAKE_CASE  :						List[str]      					= prepare_img()
				SCREAMING_SNAKE_CASE  :						int      					= image_processor(images=_lowerCamelCase    ,	return_tensors='''pt'''       )
				SCREAMING_SNAKE_CASE  :						Dict      					= encoding['pixel_values']

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

				# load original model from torch hub
				SCREAMING_SNAKE_CASE  :						Any      					= torch.hub.load('''DeppMeng/ConditionalDETR'''    ,	_lowerCamelCase    ,	pretrained=_lowerCamelCase       ).eval()
				SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= conditional_detr.state_dict()
				# rename keys
				for src, dest in rename_keys:
								if is_panoptic:
												SCREAMING_SNAKE_CASE  :						Optional[Any]      					= 'conditional_detr.' + src
								rename_key(_lowerCamelCase    ,	_lowerCamelCase    ,	_lowerCamelCase       )
				SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= rename_backbone_keys(_lowerCamelCase       )
				# query, key and value matrices need special treatment
				read_in_q_k_v(_lowerCamelCase    ,	is_panoptic=_lowerCamelCase       )
				# important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
				SCREAMING_SNAKE_CASE  :						Dict      					= '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'''       )
												):
																SCREAMING_SNAKE_CASE  :						List[Any]      					= state_dict.pop(_lowerCamelCase       )
																SCREAMING_SNAKE_CASE  :						Tuple      					= val
												elif "class_labels_classifier" in key or "bbox_predictor" in key:
																SCREAMING_SNAKE_CASE  :						Any      					= state_dict.pop(_lowerCamelCase       )
																SCREAMING_SNAKE_CASE  :						Dict      					= val
												elif key.startswith('''bbox_attention'''       ) or key.startswith('''mask_head'''       ):
																continue
												else:
																SCREAMING_SNAKE_CASE  :						str      					= state_dict.pop(_lowerCamelCase       )
																SCREAMING_SNAKE_CASE  :						List[str]      					= val
								else:
												if not key.startswith('''class_labels_classifier'''       ) and not key.startswith('''bbox_predictor'''       ):
																SCREAMING_SNAKE_CASE  :						Dict      					= state_dict.pop(_lowerCamelCase       )
																SCREAMING_SNAKE_CASE  :						List[Any]      					= val
    # finally, create HuggingFace model and load state dict
				SCREAMING_SNAKE_CASE  :						str      					= ConditionalDetrForSegmentation(_lowerCamelCase       ) if is_panoptic else ConditionalDetrForObjectDetection(_lowerCamelCase       )
				model.load_state_dict(_lowerCamelCase       )
				model.eval()
				model.push_to_hub(repo_id=_lowerCamelCase    ,	organization='''DepuMeng'''    ,	commit_message='''Add model'''       )
				# verify our conversion
				SCREAMING_SNAKE_CASE  :						List[Any]      					= conditional_detr(_lowerCamelCase       )
				SCREAMING_SNAKE_CASE  :						Dict      					= model(_lowerCamelCase       )
				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(_lowerCamelCase       ).mkdir(exist_ok=_lowerCamelCase       )
				model.save_pretrained(_lowerCamelCase       )
				image_processor.save_pretrained(_lowerCamelCase       )


if __name__ == "__main__":
	snake_case_			=     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."""
	)
	snake_case_			=     parser.parse_args()
	convert_conditional_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)









'''simple docstring'''

import functools
import operator

from ...configuration_utils import PretrainedConfig
from ...utils import logging


snake_case_			=     logging.get_logger(__name__)

snake_case_			=     {
    """facebook/wav2vec2-base-960h""": """https://huggingface.co/facebook/wav2vec2-base-960h/resolve/main/config.json""",
    # See all Wav2Vec2 models at https://huggingface.co/models?filter=wav2vec2
}


class 	a__     (				_lowercase	):
	__magic_name__		:    Dict	    =     "wav2vec2"


	def __init__(self : Tuple,					__UpperCAmelCase : Tuple=32,					__UpperCAmelCase : int=768,					__UpperCAmelCase : Optional[Any]=12,					__UpperCAmelCase : Any=12,					__UpperCAmelCase : Any=3072,					__UpperCAmelCase : Optional[int]="gelu",					__UpperCAmelCase : Dict=0.1,					__UpperCAmelCase : List[str]=0.1,					__UpperCAmelCase : List[Any]=0.1,					__UpperCAmelCase : Union[str, Any]=0.0,					__UpperCAmelCase : str=0.0,					__UpperCAmelCase : Optional[Any]=0.1,					__UpperCAmelCase : Union[str, Any]=0.1,					__UpperCAmelCase : Union[str, Any]=0.02,					__UpperCAmelCase : Dict=1e-5,					__UpperCAmelCase : List[str]="group",					__UpperCAmelCase : str="gelu",					__UpperCAmelCase : str=(512, 512, 512, 512, 512, 512, 512),					__UpperCAmelCase : str=(5, 2, 2, 2, 2, 2, 2),					__UpperCAmelCase : Union[str, Any]=(10, 3, 3, 3, 3, 2, 2),					__UpperCAmelCase : Any=False,					__UpperCAmelCase : Optional[Any]=128,					__UpperCAmelCase : Tuple=16,					__UpperCAmelCase : str=False,					__UpperCAmelCase : int=True,					__UpperCAmelCase : Tuple=0.05,					__UpperCAmelCase : List[Any]=10,					__UpperCAmelCase : Union[str, Any]=2,					__UpperCAmelCase : Optional[Any]=0.0,					__UpperCAmelCase : List[Any]=10,					__UpperCAmelCase : Dict=0,					__UpperCAmelCase : Any=320,					__UpperCAmelCase : int=2,					__UpperCAmelCase : Dict=0.1,					__UpperCAmelCase : Tuple=100,					__UpperCAmelCase : Any=256,					__UpperCAmelCase : Tuple=256,					__UpperCAmelCase : str=0.1,					__UpperCAmelCase : Any="sum",					__UpperCAmelCase : Union[str, Any]=False,					__UpperCAmelCase : str=False,					__UpperCAmelCase : str=256,					__UpperCAmelCase : Union[str, Any]=(512, 512, 512, 512, 1500),					__UpperCAmelCase : Optional[int]=(5, 3, 3, 1, 1),					__UpperCAmelCase : Dict=(1, 2, 3, 1, 1),					__UpperCAmelCase : List[str]=512,					__UpperCAmelCase : Union[str, Any]=0,					__UpperCAmelCase : List[Any]=1,					__UpperCAmelCase : List[str]=2,					__UpperCAmelCase : List[Any]=False,					__UpperCAmelCase : str=3,					__UpperCAmelCase : Tuple=2,					__UpperCAmelCase : str=3,					__UpperCAmelCase : int=None,					__UpperCAmelCase : List[Any]=None,					**__UpperCAmelCase : int,					)     ->			Tuple:

					"""simple docstring"""

					super().__init__(**__UpperCAmelCase,					pad_token_id=__UpperCAmelCase,					bos_token_id=__UpperCAmelCase,					eos_token_id=__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						Any      					= hidden_size
					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= feat_extract_norm
					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= feat_extract_activation
					SCREAMING_SNAKE_CASE  :						Dict      					= list(__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						List[str]      					= list(__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						Tuple      					= list(__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						Dict      					= conv_bias
					SCREAMING_SNAKE_CASE  :						Dict      					= num_conv_pos_embeddings
					SCREAMING_SNAKE_CASE  :						List[Any]      					= num_conv_pos_embedding_groups
					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= len(self.conv_dim      )
					SCREAMING_SNAKE_CASE  :						Tuple      					= num_hidden_layers
					SCREAMING_SNAKE_CASE  :						str      					= intermediate_size
					SCREAMING_SNAKE_CASE  :						Any      					= hidden_act
					SCREAMING_SNAKE_CASE  :						str      					= num_attention_heads
					SCREAMING_SNAKE_CASE  :						int      					= hidden_dropout
					SCREAMING_SNAKE_CASE  :						Any      					= attention_dropout
					SCREAMING_SNAKE_CASE  :						int      					= activation_dropout
					SCREAMING_SNAKE_CASE  :						int      					= feat_proj_dropout
					SCREAMING_SNAKE_CASE  :						List[str]      					= final_dropout
					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= layerdrop
					SCREAMING_SNAKE_CASE  :						Tuple      					= layer_norm_eps
					SCREAMING_SNAKE_CASE  :						Dict      					= initializer_range
					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= vocab_size
					SCREAMING_SNAKE_CASE  :						Tuple      					= do_stable_layer_norm
					SCREAMING_SNAKE_CASE  :						Dict      					= use_weighted_layer_sum

					if (
					    (len(self.conv_stride      ) != self.num_feat_extract_layers)
					    or (len(self.conv_kernel      ) != self.num_feat_extract_layers)
					    or (len(self.conv_dim      ) != self.num_feat_extract_layers)
					):
									raise ValueError(
									    '''Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =='''
									    ''' `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ='''
									    F''' {len(self.conv_dim      )}`, `len(config.conv_stride) = {len(self.conv_stride      )}`,'''
									    F''' `len(config.conv_kernel) = {len(self.conv_kernel      )}`.'''      )

					# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
					SCREAMING_SNAKE_CASE  :						str      					= apply_spec_augment
					SCREAMING_SNAKE_CASE  :						Dict      					= mask_time_prob
					SCREAMING_SNAKE_CASE  :						Optional[int]      					= mask_time_length
					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= mask_time_min_masks
					SCREAMING_SNAKE_CASE  :						Dict      					= mask_feature_prob
					SCREAMING_SNAKE_CASE  :						Tuple      					= mask_feature_length
					SCREAMING_SNAKE_CASE  :						Tuple      					= mask_feature_min_masks

					# parameters for pretraining with codevector quantized representations
					SCREAMING_SNAKE_CASE  :						Any      					= num_codevectors_per_group
					SCREAMING_SNAKE_CASE  :						int      					= num_codevector_groups
					SCREAMING_SNAKE_CASE  :						Optional[int]      					= contrastive_logits_temperature
					SCREAMING_SNAKE_CASE  :						List[Any]      					= feat_quantizer_dropout
					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= num_negatives
					SCREAMING_SNAKE_CASE  :						Any      					= codevector_dim
					SCREAMING_SNAKE_CASE  :						str      					= proj_codevector_dim
					SCREAMING_SNAKE_CASE  :						Optional[int]      					= diversity_loss_weight

					# ctc loss
					SCREAMING_SNAKE_CASE  :						Tuple      					= ctc_loss_reduction
					SCREAMING_SNAKE_CASE  :						Any      					= ctc_zero_infinity

					# adapter
					SCREAMING_SNAKE_CASE  :						int      					= add_adapter
					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= adapter_kernel_size
					SCREAMING_SNAKE_CASE  :						int      					= adapter_stride
					SCREAMING_SNAKE_CASE  :						List[str]      					= num_adapter_layers
					SCREAMING_SNAKE_CASE  :						Tuple      					= output_hidden_size or hidden_size
					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= adapter_attn_dim

					# SequenceClassification-specific parameter. Feel free to ignore for other classes.
					SCREAMING_SNAKE_CASE  :						Dict      					= classifier_proj_size

					# XVector-specific parameters. Feel free to ignore for other classes.
					SCREAMING_SNAKE_CASE  :						int      					= list(__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= list(__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						int      					= list(__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						Dict      					= xvector_output_dim






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

					"""simple docstring"""

					return functools.reduce(operator.mul,					self.conv_stride,					1      )




