import argparse
import json
import pickle
from pathlib import Path

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

from transformers import MaskFormerConfig, MaskFormerForInstanceSegmentation, MaskFormerImageProcessor, SwinConfig
from transformers.utils import logging


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



def lowerCamelCase						(     SCREAMING_SNAKE_CASE			):
				'''simple docstring'''







				__UpperCamelCase  :int    						=	SwinConfig.from_pretrained(
				    '''microsoft/swin-tiny-patch4-window7-224'''						,    out_features=['''stage1''', '''stage2''', '''stage3''', '''stage4''']			)
				__UpperCamelCase  :Tuple    						=	MaskFormerConfig(backbone_config=a_			)

				__UpperCamelCase  :Tuple    						=	'''huggingface/label-files'''
				if "ade20k-full" in model_name:
								# this should be ok
								__UpperCamelCase  :Any    						=	847
								__UpperCamelCase  :List[Any]    						=	'''maskformer-ade20k-full-id2label.json'''
				elif "ade" in model_name:
								# this should be ok
								__UpperCamelCase  :Union[str, Any]    						=	150
								__UpperCamelCase  :str    						=	'''ade20k-id2label.json'''
				elif "coco-stuff" in model_name:
								# this should be ok
								__UpperCamelCase  :List[Any]    						=	171
								__UpperCamelCase  :str    						=	'''maskformer-coco-stuff-id2label.json'''
				elif "coco" in model_name:
								# TODO
								__UpperCamelCase  :Optional[Any]    						=	133
								__UpperCamelCase  :Tuple    						=	'''coco-panoptic-id2label.json'''
				elif "cityscapes" in model_name:
								# this should be ok
								__UpperCamelCase  :Optional[Any]    						=	19
								__UpperCamelCase  :List[Any]    						=	'''cityscapes-id2label.json'''
				elif "vistas" in model_name:
								# this should be ok
								__UpperCamelCase  :str    						=	65
								__UpperCamelCase  :Optional[Any]    						=	'''mapillary-vistas-id2label.json'''

				__UpperCamelCase  :Tuple    						=	json.load(open(hf_hub_download(a_						,    a_						,    repo_type='''dataset'''			)						,    '''r'''			)			)
				__UpperCamelCase  :Union[str, Any]    						=	{int(a_			): v for k, v in idalabel.items()}

				return config



def lowerCamelCase						(     SCREAMING_SNAKE_CASE			):
				'''simple docstring'''







				__UpperCamelCase  :Any    						=	[]
				# stem
				# fmt: off
				rename_keys.append(('''backbone.patch_embed.proj.weight''', '''model.pixel_level_module.encoder.model.embeddings.patch_embeddings.projection.weight''')			)
				rename_keys.append(('''backbone.patch_embed.proj.bias''', '''model.pixel_level_module.encoder.model.embeddings.patch_embeddings.projection.bias''')			)
				rename_keys.append(('''backbone.patch_embed.norm.weight''', '''model.pixel_level_module.encoder.model.embeddings.norm.weight''')			)
				rename_keys.append(('''backbone.patch_embed.norm.bias''', '''model.pixel_level_module.encoder.model.embeddings.norm.bias''')			)
				# stages
				for i in range(len(config.backbone_config.depths			)			):
								for j in range(config.backbone_config.depths[i]			):
												rename_keys.append((f"""backbone.layers.{i}.blocks.{j}.norm1.weight""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_before.weight""")			)
												rename_keys.append((f"""backbone.layers.{i}.blocks.{j}.norm1.bias""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_before.bias""")			)
												rename_keys.append((f"""backbone.layers.{i}.blocks.{j}.attn.relative_position_bias_table""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table""")			)
												rename_keys.append((f"""backbone.layers.{i}.blocks.{j}.attn.relative_position_index""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index""")			)
												rename_keys.append((f"""backbone.layers.{i}.blocks.{j}.attn.proj.weight""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight""")			)
												rename_keys.append((f"""backbone.layers.{i}.blocks.{j}.attn.proj.bias""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias""")			)
												rename_keys.append((f"""backbone.layers.{i}.blocks.{j}.norm2.weight""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_after.weight""")			)
												rename_keys.append((f"""backbone.layers.{i}.blocks.{j}.norm2.bias""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_after.bias""")			)
												rename_keys.append((f"""backbone.layers.{i}.blocks.{j}.mlp.fc1.weight""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight""")			)
												rename_keys.append((f"""backbone.layers.{i}.blocks.{j}.mlp.fc1.bias""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias""")			)
												rename_keys.append((f"""backbone.layers.{i}.blocks.{j}.mlp.fc2.weight""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.output.dense.weight""")			)
												rename_keys.append((f"""backbone.layers.{i}.blocks.{j}.mlp.fc2.bias""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.output.dense.bias""")			)

								if i < 3:
												rename_keys.append((f"""backbone.layers.{i}.downsample.reduction.weight""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.reduction.weight""")			)
												rename_keys.append((f"""backbone.layers.{i}.downsample.norm.weight""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.norm.weight""")			)
												rename_keys.append((f"""backbone.layers.{i}.downsample.norm.bias""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.norm.bias""")			)
								rename_keys.append((f"""backbone.norm{i}.weight""", f"""model.pixel_level_module.encoder.hidden_states_norms.{i}.weight""")			)
								rename_keys.append((f"""backbone.norm{i}.bias""", f"""model.pixel_level_module.encoder.hidden_states_norms.{i}.bias""")			)

				# FPN
				rename_keys.append(('''sem_seg_head.layer_4.weight''', '''model.pixel_level_module.decoder.fpn.stem.0.weight''')			)
				rename_keys.append(('''sem_seg_head.layer_4.norm.weight''', '''model.pixel_level_module.decoder.fpn.stem.1.weight''')			)
				rename_keys.append(('''sem_seg_head.layer_4.norm.bias''', '''model.pixel_level_module.decoder.fpn.stem.1.bias''')			)
				for source_index, target_index in zip(range(3						,    0						,    -1			)						,    range(0						,    3			)			):
								rename_keys.append((f"""sem_seg_head.adapter_{source_index}.weight""", f"""model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.0.weight""")			)
								rename_keys.append((f"""sem_seg_head.adapter_{source_index}.norm.weight""", f"""model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.1.weight""")			)
								rename_keys.append((f"""sem_seg_head.adapter_{source_index}.norm.bias""", f"""model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.1.bias""")			)
								rename_keys.append((f"""sem_seg_head.layer_{source_index}.weight""", f"""model.pixel_level_module.decoder.fpn.layers.{target_index}.block.0.weight""")			)
								rename_keys.append((f"""sem_seg_head.layer_{source_index}.norm.weight""", f"""model.pixel_level_module.decoder.fpn.layers.{target_index}.block.1.weight""")			)
								rename_keys.append((f"""sem_seg_head.layer_{source_index}.norm.bias""", f"""model.pixel_level_module.decoder.fpn.layers.{target_index}.block.1.bias""")			)
				rename_keys.append(('''sem_seg_head.mask_features.weight''', '''model.pixel_level_module.decoder.mask_projection.weight''')			)
				rename_keys.append(('''sem_seg_head.mask_features.bias''', '''model.pixel_level_module.decoder.mask_projection.bias''')			)

				# Transformer decoder
				for idx in range(config.decoder_config.decoder_layers			):
								# self-attention out projection
								rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.out_proj.weight""", f"""model.transformer_module.decoder.layers.{idx}.self_attn.out_proj.weight""")			)
								rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.out_proj.bias""", f"""model.transformer_module.decoder.layers.{idx}.self_attn.out_proj.bias""")			)
								# cross-attention out projection
								rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.out_proj.weight""", f"""model.transformer_module.decoder.layers.{idx}.encoder_attn.out_proj.weight""")			)
								rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.out_proj.bias""", f"""model.transformer_module.decoder.layers.{idx}.encoder_attn.out_proj.bias""")			)
								# MLP 1
								rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear1.weight""", f"""model.transformer_module.decoder.layers.{idx}.fc1.weight""")			)
								rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear1.bias""", f"""model.transformer_module.decoder.layers.{idx}.fc1.bias""")			)
								# MLP 2
								rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear2.weight""", f"""model.transformer_module.decoder.layers.{idx}.fc2.weight""")			)
								rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear2.bias""", f"""model.transformer_module.decoder.layers.{idx}.fc2.bias""")			)
								# layernorm 1 (self-attention layernorm)
								rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm1.weight""", f"""model.transformer_module.decoder.layers.{idx}.self_attn_layer_norm.weight""")			)
								rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm1.bias""", f"""model.transformer_module.decoder.layers.{idx}.self_attn_layer_norm.bias""")			)
								# layernorm 2 (cross-attention layernorm)
								rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm2.weight""", f"""model.transformer_module.decoder.layers.{idx}.encoder_attn_layer_norm.weight""")			)
								rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm2.bias""", f"""model.transformer_module.decoder.layers.{idx}.encoder_attn_layer_norm.bias""")			)
								# layernorm 3 (final layernorm)
								rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm3.weight""", f"""model.transformer_module.decoder.layers.{idx}.final_layer_norm.weight""")			)
								rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm3.bias""", f"""model.transformer_module.decoder.layers.{idx}.final_layer_norm.bias""")			)

				rename_keys.append(('''sem_seg_head.predictor.transformer.decoder.norm.weight''', '''model.transformer_module.decoder.layernorm.weight''')			)
				rename_keys.append(('''sem_seg_head.predictor.transformer.decoder.norm.bias''', '''model.transformer_module.decoder.layernorm.bias''')			)

				# heads on top
				rename_keys.append(('''sem_seg_head.predictor.query_embed.weight''', '''model.transformer_module.queries_embedder.weight''')			)

				rename_keys.append(('''sem_seg_head.predictor.input_proj.weight''', '''model.transformer_module.input_projection.weight''')			)
				rename_keys.append(('''sem_seg_head.predictor.input_proj.bias''', '''model.transformer_module.input_projection.bias''')			)

				rename_keys.append(('''sem_seg_head.predictor.class_embed.weight''', '''class_predictor.weight''')			)
				rename_keys.append(('''sem_seg_head.predictor.class_embed.bias''', '''class_predictor.bias''')			)

				for i in range(3			):
								rename_keys.append((f"""sem_seg_head.predictor.mask_embed.layers.{i}.weight""", f"""mask_embedder.{i}.0.weight""")			)
								rename_keys.append((f"""sem_seg_head.predictor.mask_embed.layers.{i}.bias""", f"""mask_embedder.{i}.0.bias""")			)
				# fmt: on

				return rename_keys



def lowerCamelCase						(     SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE			):
				'''simple docstring'''







				__UpperCamelCase  :Optional[int]    						=	dct.pop(a_			)
				__UpperCamelCase  :int    						=	val



def lowerCamelCase						(     SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE			):
				'''simple docstring'''







				__UpperCamelCase  :Optional[Any]    						=	[int(backbone_config.embed_dim * 2**i			) for i in range(len(backbone_config.depths			)			)]
				for i in range(len(backbone_config.depths			)			):
								__UpperCamelCase  :str    						=	num_features[i]
								for j in range(backbone_config.depths[i]			):
												# fmt: off
												# read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias)
												__UpperCamelCase  :Union[str, Any]    						=	state_dict.pop(f"""backbone.layers.{i}.blocks.{j}.attn.qkv.weight"""			)
												__UpperCamelCase  :Tuple    						=	state_dict.pop(f"""backbone.layers.{i}.blocks.{j}.attn.qkv.bias"""			)
												# next, add query, keys and values (in that order) to the state dict
												__UpperCamelCase  :Dict    						=	in_proj_weight[:dim, :]
												__UpperCamelCase  :str    						=	in_proj_bias[: dim]
												__UpperCamelCase  :Any    						=	in_proj_weight[
												    dim : dim * 2, :
												]
												__UpperCamelCase  :List[Any]    						=	in_proj_bias[
												    dim : dim * 2
												]
												__UpperCamelCase  :Union[str, Any]    						=	in_proj_weight[
												    -dim :, :
												]
												__UpperCamelCase  :Optional[int]    						=	in_proj_bias[-dim :]
												# fmt: on



def lowerCamelCase						(     SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE			):
				'''simple docstring'''







				__UpperCamelCase  :Dict    						=	config.decoder_config.hidden_size
				for idx in range(config.decoder_config.decoder_layers			):
								# read in weights + bias of self-attention input projection layer (in the original implementation, this is a single matrix + bias)
								__UpperCamelCase  :str    						=	state_dict.pop(f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_weight"""			)
								__UpperCamelCase  :List[str]    						=	state_dict.pop(f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_bias"""			)
								# next, add query, keys and values (in that order) to the state dict
								__UpperCamelCase  :Any    						=	in_proj_weight[: hidden_size, :]
								__UpperCamelCase  :Optional[Any]    						=	in_proj_bias[:config.hidden_size]
								__UpperCamelCase  :Any    						=	in_proj_weight[hidden_size : hidden_size * 2, :]
								__UpperCamelCase  :Tuple    						=	in_proj_bias[hidden_size : hidden_size * 2]
								__UpperCamelCase  :Union[str, Any]    						=	in_proj_weight[-hidden_size :, :]
								__UpperCamelCase  :int    						=	in_proj_bias[-hidden_size :]
								# read in weights + bias of cross-attention input projection layer (in the original implementation, this is a single matrix + bias)
								__UpperCamelCase  :Optional[Any]    						=	state_dict.pop(f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_weight"""			)
								__UpperCamelCase  :List[str]    						=	state_dict.pop(f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_bias"""			)
								# next, add query, keys and values (in that order) to the state dict
								__UpperCamelCase  :List[Any]    						=	in_proj_weight[: hidden_size, :]
								__UpperCamelCase  :List[str]    						=	in_proj_bias[:config.hidden_size]
								__UpperCamelCase  :Optional[int]    						=	in_proj_weight[hidden_size : hidden_size * 2, :]
								__UpperCamelCase  :Any    						=	in_proj_bias[hidden_size : hidden_size * 2]
								__UpperCamelCase  :List[str]    						=	in_proj_weight[-hidden_size :, :]
								__UpperCamelCase  :str    						=	in_proj_bias[-hidden_size :]
				# fmt: on



def lowerCamelCase						(     ):
				'''simple docstring'''







				__UpperCamelCase  :Optional[Any]    						=	'''http://images.cocodataset.org/val2017/000000039769.jpg'''
				__UpperCamelCase  :Union[str, Any]    						=	Image.open(requests.get(a_						,    stream=a_			).raw			)
				return im



@torch.no_grad()
def lowerCamelCase						(     SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE = False			):
				'''simple docstring'''







				__UpperCamelCase  :Dict    						=	get_maskformer_config(a_			)

				# load original state_dict
				with open(a_						,    '''rb'''			) as f:
								__UpperCamelCase  :str    						=	pickle.load(a_			)
				__UpperCamelCase  :int    						=	data['''model''']

				# for name, param in state_dict.items():
				#     print(name, param.shape)

				# rename keys
				__UpperCamelCase  :Union[str, Any]    						=	create_rename_keys(a_			)
				for src, dest in rename_keys:
								rename_key(a_						,    a_						,    a_			)
				read_in_swin_q_k_v(a_						,    config.backbone_config			)
				read_in_decoder_q_k_v(a_						,    a_			)

				# update to torch tensors
				for key, value in state_dict.items():
								__UpperCamelCase  :Any    						=	torch.from_numpy(a_			)

				# load 🤗 model
				__UpperCamelCase  :str    						=	MaskFormerForInstanceSegmentation(a_			)
				model.eval()

				for name, param in model.named_parameters():
								print(a_						,    param.shape			)

				__UpperCamelCase  :str    						=	model.load_state_dict(a_						,    strict=a_			)
				assert missing_keys == [
				    "model.pixel_level_module.encoder.model.layernorm.weight",
				    "model.pixel_level_module.encoder.model.layernorm.bias",
				]
				assert len(a_			) == 0, f"""Unexpected keys: {unexpected_keys}"""

				# verify results
				__UpperCamelCase  :Dict    						=	prepare_img()
				if "vistas" in model_name:
								__UpperCamelCase  :Dict    						=	65
				elif "cityscapes" in model_name:
								__UpperCamelCase  :List[str]    						=	65_535
				else:
								__UpperCamelCase  :Optional[Any]    						=	255
				__UpperCamelCase  :str    						=	True if '''ade''' in model_name else False
				__UpperCamelCase  :Tuple    						=	MaskFormerImageProcessor(ignore_index=a_						,    reduce_labels=a_			)

				__UpperCamelCase  :int    						=	image_processor(a_						,    return_tensors='''pt'''			)

				__UpperCamelCase  :List[str]    						=	model(**a_			)

				print('''Logits:'''						,    outputs.class_queries_logits[0, :3, :3]			)

				if model_name == "maskformer-swin-tiny-ade":
								__UpperCamelCase  :List[Any]    						=	torch.tensor(
								    [[3.6_353, -4.4_770, -2.6_065], [0.5_081, -4.2_394, -3.5_343], [2.1_909, -5.0_353, -1.9_323]]			)
				assert torch.allclose(outputs.class_queries_logits[0, :3, :3]						,    a_						,    atol=1e-4			)
				print('''Looks ok!'''			)

				if pytorch_dump_folder_path is not None:
								print(f"""Saving 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 push_to_hub:
								print('''Pushing model and image processor to the hub...'''			)
								model.push_to_hub(f"""nielsr/{model_name}"""			)
								image_processor.push_to_hub(f"""nielsr/{model_name}"""			)


if __name__ == "__main__":
				__lowercase											=      argparse.ArgumentParser()
				# Required parameters
				parser.add_argument(
				    '''--model_name''',
				    default='''maskformer-swin-tiny-ade''',
				    type=str,
				    help=('''Name of the MaskFormer model you\'d like to convert''',),
				)
				parser.add_argument(
				    '''--checkpoint_path''',
				    default='''/Users/nielsrogge/Documents/MaskFormer_checkpoints/MaskFormer-Swin-tiny-ADE20k/model.pkl''',
				    type=str,
				    help='''Path to the original state dict (.pth file).''',
				)
				parser.add_argument(
				    '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
				)
				parser.add_argument(
				    '''--push_to_hub''', action='''store_true''', help='''Whether or not to push the converted model to the 🤗 hub.'''
				)

				__lowercase											=      parser.parse_args()
				convert_maskformer_checkpoint(
				    args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
				)






import functools
import operator

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


__lowercase											=      logging.get_logger(__name__)

__lowercase											=      {
    '''microsoft/wavlm-base''': '''https://huggingface.co/microsoft/wavlm-base/resolve/main/config.json''',
    # See all WavLM models at https://huggingface.co/models?filter=wavlm
}


class 			lowerCamelCase_  (     UpperCAmelCase_				):






    '''simple docstring'''






    a__			:		List[Any]      			=   """wavlm"""







    def __init__(			self				,				__lowercase=32				,				__lowercase=768				,				__lowercase=12				,				__lowercase=12				,				__lowercase=3_072				,				__lowercase="gelu"				,				__lowercase=0.1				,				__lowercase=0.1				,				__lowercase=0.1				,				__lowercase=0.0				,				__lowercase=0.1				,				__lowercase=0.1				,				__lowercase=0.02				,				__lowercase=1E-5				,				__lowercase="group"				,				__lowercase="gelu"				,				__lowercase=(512, 512, 512, 512, 512, 512, 512)				,				__lowercase=(5, 2, 2, 2, 2, 2, 2)				,				__lowercase=(10, 3, 3, 3, 3, 2, 2)				,				__lowercase=False				,				__lowercase=128				,				__lowercase=16				,				__lowercase=320				,				__lowercase=800				,				__lowercase=False				,				__lowercase=True				,				__lowercase=0.05				,				__lowercase=10				,				__lowercase=2				,				__lowercase=0.0				,				__lowercase=10				,				__lowercase=320				,				__lowercase=2				,				__lowercase=0.1				,				__lowercase=100				,				__lowercase=256				,				__lowercase=256				,				__lowercase=0.1				,				__lowercase="mean"				,				__lowercase=False				,				__lowercase=False				,				__lowercase=256				,				__lowercase=(512, 512, 512, 512, 1_500)				,				__lowercase=(5, 3, 3, 1, 1)				,				__lowercase=(1, 2, 3, 1, 1)				,				__lowercase=512				,				__lowercase=80				,				__lowercase=0				,				__lowercase=1				,				__lowercase=2				,				__lowercase=False				,				__lowercase=3				,				__lowercase=2				,				__lowercase=3				,				__lowercase=None				,				**__lowercase				,				) ->			Dict:
        super().__init__(**__lowercase				,				pad_token_id=__lowercase				,				bos_token_id=__lowercase				,				eos_token_id=__lowercase)
        __UpperCamelCase  :Any    						=	hidden_size
        __UpperCamelCase  :Tuple    						=	feat_extract_norm
        __UpperCamelCase  :List[str]    						=	feat_extract_activation
        __UpperCamelCase  :int    						=	list(__lowercase)
        __UpperCamelCase  :List[Any]    						=	list(__lowercase)
        __UpperCamelCase  :Union[str, Any]    						=	list(__lowercase)
        __UpperCamelCase  :Optional[Any]    						=	conv_bias
        __UpperCamelCase  :Tuple    						=	num_buckets
        __UpperCamelCase  :Optional[int]    						=	max_bucket_distance
        __UpperCamelCase  :Union[str, Any]    						=	num_conv_pos_embeddings
        __UpperCamelCase  :Optional[Any]    						=	num_conv_pos_embedding_groups
        __UpperCamelCase  :List[Any]    						=	len(self.conv_dim)
        __UpperCamelCase  :Tuple    						=	num_hidden_layers
        __UpperCamelCase  :str    						=	intermediate_size
        __UpperCamelCase  :Union[str, Any]    						=	hidden_act
        __UpperCamelCase  :Optional[int]    						=	num_attention_heads
        __UpperCamelCase  :str    						=	hidden_dropout
        __UpperCamelCase  :int    						=	attention_dropout
        __UpperCamelCase  :Optional[int]    						=	activation_dropout
        __UpperCamelCase  :str    						=	feat_proj_dropout
        __UpperCamelCase  :List[Any]    						=	final_dropout
        __UpperCamelCase  :int    						=	layerdrop
        __UpperCamelCase  :List[Any]    						=	layer_norm_eps
        __UpperCamelCase  :Optional[int]    						=	initializer_range
        __UpperCamelCase  :Any    						=	num_ctc_classes
        __UpperCamelCase  :Optional[int]    						=	vocab_size
        __UpperCamelCase  :List[Any]    						=	do_stable_layer_norm
        __UpperCamelCase  :str    						=	use_weighted_layer_sum
        __UpperCamelCase  :Any    						=	classifier_proj_size

        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
        __UpperCamelCase  :Union[str, Any]    						=	apply_spec_augment
        __UpperCamelCase  :Optional[Any]    						=	mask_time_prob
        __UpperCamelCase  :Union[str, Any]    						=	mask_time_length
        __UpperCamelCase  :Optional[int]    						=	mask_time_min_masks
        __UpperCamelCase  :str    						=	mask_feature_prob
        __UpperCamelCase  :Tuple    						=	mask_feature_length

        # parameters for pretraining with codevector quantized representations
        __UpperCamelCase  :Optional[Any]    						=	num_codevectors_per_group
        __UpperCamelCase  :List[Any]    						=	num_codevector_groups
        __UpperCamelCase  :str    						=	contrastive_logits_temperature
        __UpperCamelCase  :Tuple    						=	num_negatives
        __UpperCamelCase  :Any    						=	codevector_dim
        __UpperCamelCase  :Union[str, Any]    						=	proj_codevector_dim
        __UpperCamelCase  :Tuple    						=	diversity_loss_weight

        # ctc loss
        __UpperCamelCase  :int    						=	ctc_loss_reduction
        __UpperCamelCase  :Any    						=	ctc_zero_infinity

        # adapter
        __UpperCamelCase  :List[Any]    						=	add_adapter
        __UpperCamelCase  :Dict    						=	adapter_kernel_size
        __UpperCamelCase  :Any    						=	adapter_stride
        __UpperCamelCase  :Optional[int]    						=	num_adapter_layers
        __UpperCamelCase  :Union[str, Any]    						=	output_hidden_size or hidden_size

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

        # XVector-specific parameters. Feel free to ignore for other classes.
        __UpperCamelCase  :Optional[Any]    						=	list(__lowercase)
        __UpperCamelCase  :Optional[Any]    						=	list(__lowercase)
        __UpperCamelCase  :List[str]    						=	list(__lowercase)
        __UpperCamelCase  :List[Any]    						=	xvector_output_dim







    @property
    def     UpperCamelCase__     (			self) ->			Any:
        return functools.reduce(operator.mul				,				self.conv_stride				,				1)





