

import argparse

import fairseq
import torch

from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging


logging.set_verbosity_info()
lowerCAmelCase							:  Tuple   	=						logging.get_logger(__name__)

lowerCAmelCase							:  List[str]   	=						{
    "post_extract_proj": "feature_projection.projection",
    "encoder.pos_conv.0": "encoder.pos_conv_embed.conv",
    "self_attn.k_proj": "encoder.layers.*.attention.k_proj",
    "self_attn.v_proj": "encoder.layers.*.attention.v_proj",
    "self_attn.q_proj": "encoder.layers.*.attention.q_proj",
    "self_attn.out_proj": "encoder.layers.*.attention.out_proj",
    "self_attn_layer_norm": "encoder.layers.*.layer_norm",
    "fc1": "encoder.layers.*.feed_forward.intermediate_dense",
    "fc2": "encoder.layers.*.feed_forward.output_dense",
    "final_layer_norm": "encoder.layers.*.final_layer_norm",
    "encoder.layer_norm": "encoder.layer_norm",
    "encoder.layer_norm_for_extract": "layer_norm_for_extract",
    "w2v_model.layer_norm": "feature_projection.layer_norm",
    "quantizer.weight_proj": "quantizer.weight_proj",
    "quantizer.vars": "quantizer.codevectors",
    "project_q": "project_q",
    "final_proj": "project_hid",
    "w2v_encoder.proj": "lm_head",
    "label_embs_concat": "label_embeddings_concat",
    "mask_emb": "masked_spec_embed",
    "spk_proj": "speaker_proj",
}
lowerCAmelCase							:  List[Any]   	=						[
    "lm_head",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
    "label_embeddings_concat",
    "speaker_proj",
    "layer_norm_for_extract",
]







def 			A_      (    _UpperCAmelCase				,	_UpperCAmelCase				,	_UpperCAmelCase				,	_UpperCAmelCase				,	_UpperCAmelCase    ):
						for attribute in key.split("."    ):
												SCREAMING_SNAKE_CASE_: Dict  		=       getattr(__lowerCamelCase				,	__lowerCamelCase    )

						if weight_type is not None:
												SCREAMING_SNAKE_CASE_: List[Any]  		=       getattr(__lowerCamelCase				,	__lowerCamelCase    ).shape
						else:
												SCREAMING_SNAKE_CASE_: Optional[int]  		=       hf_pointer.shape

						if hf_shape != value.shape:
												raise ValueError(
												    f"Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be"
												    f" {value.shape} for {full_name}"    )

						if weight_type == "weight":
												SCREAMING_SNAKE_CASE_: Optional[int]  		=       value
						elif weight_type == "weight_g":
												SCREAMING_SNAKE_CASE_: Dict  		=       value
						elif weight_type == "weight_v":
												SCREAMING_SNAKE_CASE_: str  		=       value
						elif weight_type == "bias":
												SCREAMING_SNAKE_CASE_: Tuple  		=       value
						else:
												SCREAMING_SNAKE_CASE_: Optional[int]  		=       value

						logger.info(f"{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}."    )







def 			A_      (    _UpperCAmelCase				,	_UpperCAmelCase    ):
						SCREAMING_SNAKE_CASE_: Dict  		=       []
						SCREAMING_SNAKE_CASE_: Dict  		=       fairseq_model.state_dict()

						SCREAMING_SNAKE_CASE_: Dict  		=       hf_model.unispeech_sat.feature_extractor

						for name, value in fairseq_dict.items():
												SCREAMING_SNAKE_CASE_: int  		=       False
												if "conv_layers" in name:
																		load_conv_layer(
																		    __lowerCamelCase				,	__lowerCamelCase				,	__lowerCamelCase				,	__lowerCamelCase				,	hf_model.config.feat_extract_norm == "group"				,	)
																		SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       True
												else:
																		for key, mapped_key in MAPPING.items():
																								SCREAMING_SNAKE_CASE_: Tuple  		=       '''unispeech_sat.''' + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
																								if key in name or key.split("w2v_model."    )[-1] == name.split("."    )[0]:
																														if "layer_norm_for_extract" in name and (".".join(name.split("."    )[:-1]    ) != key):
																																				# special case since naming is very similar
																																				continue
																														SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       True
																														if "*" in mapped_key:
																																				SCREAMING_SNAKE_CASE_: Optional[int]  		=       name.split(__lowerCamelCase    )[0].split("."    )[-2]
																																				SCREAMING_SNAKE_CASE_: Dict  		=       mapped_key.replace("*"				,	__lowerCamelCase    )
																														if "weight_g" in name:
																																				SCREAMING_SNAKE_CASE_: List[str]  		=       '''weight_g'''
																														elif "weight_v" in name:
																																				SCREAMING_SNAKE_CASE_: Optional[Any]  		=       '''weight_v'''
																														elif "bias" in name:
																																				SCREAMING_SNAKE_CASE_: List[str]  		=       '''bias'''
																														elif "weight" in name:
																																				# TODO: don't match quantizer.weight_proj
																																				SCREAMING_SNAKE_CASE_: int  		=       '''weight'''
																														else:
																																				SCREAMING_SNAKE_CASE_: int  		=       None
																														set_recursively(__lowerCamelCase				,	__lowerCamelCase				,	__lowerCamelCase				,	__lowerCamelCase				,	__lowerCamelCase    )
																								continue
												if not is_used:
																		unused_weights.append(__lowerCamelCase    )

						logger.warning(f"Unused weights: {unused_weights}"    )







def 			A_      (    _UpperCAmelCase				,	_UpperCAmelCase				,	_UpperCAmelCase				,	_UpperCAmelCase				,	_UpperCAmelCase    ):
						SCREAMING_SNAKE_CASE_: Optional[Any]  		=       full_name.split("conv_layers."    )[-1]
						SCREAMING_SNAKE_CASE_: str  		=       name.split("."    )
						SCREAMING_SNAKE_CASE_: Optional[Any]  		=       int(items[0]    )
						SCREAMING_SNAKE_CASE_: str  		=       int(items[1]    )

						if type_id == 0:
												if "bias" in name:
																		if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
																								raise ValueError(
																								    f"{full_name} has size {value.shape}, but"
																								    f" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."    )
																		SCREAMING_SNAKE_CASE_: Optional[int]  		=       value
																		logger.info(f"Feat extract conv layer {layer_id} was initialized from {full_name}."    )
												elif "weight" in name:
																		if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
																								raise ValueError(
																								    f"{full_name} has size {value.shape}, but"
																								    f" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."    )
																		SCREAMING_SNAKE_CASE_: Optional[int]  		=       value
																		logger.info(f"Feat extract conv layer {layer_id} was initialized from {full_name}."    )
						elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
												if "bias" in name:
																		if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
																								raise ValueError(
																								    f"{full_name} has size {value.shape}, but"
																								    f" {feature_extractor[layer_id].layer_norm.bias.data.shape} was found."    )
																		SCREAMING_SNAKE_CASE_: Tuple  		=       value
																		logger.info(f"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."    )
												elif "weight" in name:
																		if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
																								raise ValueError(
																								    f"{full_name} has size {value.shape}, but"
																								    f" {feature_extractor[layer_id].layer_norm.weight.data.shape} was found."    )
																		SCREAMING_SNAKE_CASE_: Dict  		=       value
																		logger.info(f"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."    )
						else:
												unused_weights.append(__lowerCamelCase    )







@torch.no_grad()
def 			A_      (    _UpperCAmelCase				,	_UpperCAmelCase				,	_UpperCAmelCase=None				,	_UpperCAmelCase=None				,	_UpperCAmelCase=True    ):
						if config_path is not None:
												SCREAMING_SNAKE_CASE_: Optional[Any]  		=       UniSpeechSatConfig.from_pretrained(__lowerCamelCase    )
						else:
												SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       UniSpeechSatConfig()

						SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       ''''''

						if is_finetuned:
												SCREAMING_SNAKE_CASE_: Any  		=       UniSpeechSatForCTC(__lowerCamelCase    )
						else:
												SCREAMING_SNAKE_CASE_: Any  		=       UniSpeechSatForPreTraining(__lowerCamelCase    )

						SCREAMING_SNAKE_CASE_: int  		=       fairseq.checkpoint_utils.load_model_ensemble_and_task(
						    [checkpoint_path]				,	arg_overrides={"data": "/".join(dict_path.split("/"    )[:-1]    )}    )
						SCREAMING_SNAKE_CASE_: Optional[Any]  		=       model[0].eval()

						recursively_load_weights(__lowerCamelCase				,	__lowerCamelCase    )

						hf_wavavec.save_pretrained(__lowerCamelCase    )


if __name__ == "__main__":
						lowerCAmelCase							:  str   	=						argparse.ArgumentParser()
						parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
						parser.add_argument("""--checkpoint_path""", default=None, type=str, help="""Path to fairseq checkpoint""")
						parser.add_argument("""--dict_path""", default=None, type=str, help="""Path to dict of fine-tuned model""")
						parser.add_argument("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
						parser.add_argument(
						    """--not_finetuned""", action="""store_true""", help="""Whether the model to convert is a fine-tuned model or not"""
						)
						lowerCAmelCase							:  Union[str, Any]   	=						parser.parse_args()
						convert_unispeech_sat_checkpoint(
						    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
						)











"""simple docstring"""






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

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


if TYPE_CHECKING:
		from ... import FeatureExtractionMixin, PreTrainedTokenizerBase, TensorType


_lowercase : List[str]      						=						logging.get_logger(__name__)

_lowercase : int      						=						{
    "microsoft/deberta-v2-xlarge": "https://huggingface.co/microsoft/deberta-v2-xlarge/resolve/main/config.json",
    "microsoft/deberta-v2-xxlarge": "https://huggingface.co/microsoft/deberta-v2-xxlarge/resolve/main/config.json",
    "microsoft/deberta-v2-xlarge-mnli": (
        "https://huggingface.co/microsoft/deberta-v2-xlarge-mnli/resolve/main/config.json"
    ),
    "microsoft/deberta-v2-xxlarge-mnli": (
        "https://huggingface.co/microsoft/deberta-v2-xxlarge-mnli/resolve/main/config.json"
    ),
}

class        __SCREAMING_SNAKE_CASE	(      lowerCAmelCase_   ):


					'''simple docstring'''





					_a   			=							'deberta-v2'



					def __init__(       self							:  Optional[Any],  lowerCamelCase							:  Optional[int]=12_8100,  lowerCamelCase							:  List[Any]=1536,  lowerCamelCase							:  Dict=24,  lowerCamelCase							:  Any=24,  lowerCamelCase							:  Union[str, Any]=6144,  lowerCamelCase							:  List[Any]="gelu",  lowerCamelCase							:  int=0.1,  lowerCamelCase							:  Union[str, Any]=0.1,  lowerCamelCase							:  Union[str, Any]=512,  lowerCamelCase							:  Optional[Any]=0,  lowerCamelCase							:  Any=0.02,  lowerCamelCase							:  int=1E-7,  lowerCamelCase							:  Union[str, Any]=False,  lowerCamelCase							:  Union[str, Any]=-1,  lowerCamelCase							:  Tuple=0,  lowerCamelCase							:  Union[str, Any]=True,  lowerCamelCase							:  int=None,  lowerCamelCase							:  Dict=0,  lowerCamelCase							:  Tuple="gelu",  **lowerCamelCase							:  Optional[int],  )->					Union[str, Any]:
								super().__init__(**lowerCamelCase     )

								lowerCamelCase__		:		str			=hidden_size
								lowerCamelCase__		:		Optional[int]			=num_hidden_layers
								lowerCamelCase__		:		Optional[Any]			=num_attention_heads
								lowerCamelCase__		:		List[Any]			=intermediate_size
								lowerCamelCase__		:		int			=hidden_act
								lowerCamelCase__		:		Tuple			=hidden_dropout_prob
								lowerCamelCase__		:		Union[str, Any]			=attention_probs_dropout_prob
								lowerCamelCase__		:		Optional[Any]			=max_position_embeddings
								lowerCamelCase__		:		int			=type_vocab_size
								lowerCamelCase__		:		Tuple			=initializer_range
								lowerCamelCase__		:		Tuple			=relative_attention
								lowerCamelCase__		:		Optional[Any]			=max_relative_positions
								lowerCamelCase__		:		List[Any]			=pad_token_id
								lowerCamelCase__		:		int			=position_biased_input

								# Backwards compatibility
								if type(lowerCamelCase     ) == str:
											lowerCamelCase__		:		Union[str, Any]			=[x.strip() for x in pos_att_type.lower().split('''|'''     )]

								lowerCamelCase__		:		Tuple			=pos_att_type
								lowerCamelCase__		:		Union[str, Any]			=vocab_size
								lowerCamelCase__		:		Optional[int]			=layer_norm_eps

								lowerCamelCase__		:		Dict			=kwargs.get('''pooler_hidden_size''',  lowerCamelCase     )
								lowerCamelCase__		:		Tuple			=pooler_dropout
								lowerCamelCase__		:		List[Any]			=pooler_hidden_act





class        __SCREAMING_SNAKE_CASE	(      lowerCAmelCase_   ):


					'''simple docstring'''





					@property
					def 		snake_case       (       self							:  List[str]     )->					Mapping[str, Mapping[int, str]]:
								if self.task == "multiple-choice":
											lowerCamelCase__		:		Union[str, Any]			={0: '''batch''', 1: '''choice''', 2: '''sequence'''}
								else:
											lowerCamelCase__		:		Any			={0: '''batch''', 1: '''sequence'''}
								if self._config.type_vocab_size > 0:
											return OrderedDict(
											    [('''input_ids''', dynamic_axis), ('''attention_mask''', dynamic_axis), ('''token_type_ids''', dynamic_axis)]     )
								else:
											return OrderedDict([('''input_ids''', dynamic_axis), ('''attention_mask''', dynamic_axis)]     )



					@property
					def 		snake_case       (       self							:  List[str]     )->					int:
								return 12




					def 		snake_case       (       self							:  str,  lowerCamelCase							:  Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"],  lowerCamelCase							:  int = -1,  lowerCamelCase							:  int = -1,  lowerCamelCase							:  int = -1,  lowerCamelCase							:  bool = False,  lowerCamelCase							:  Optional["TensorType"] = None,  lowerCamelCase							:  int = 3,  lowerCamelCase							:  int = 40,  lowerCamelCase							:  int = 40,  lowerCamelCase							:  "PreTrainedTokenizerBase" = None,  )->					Mapping[str, Any]:
								lowerCamelCase__		:		List[Any]			=super().generate_dummy_inputs(preprocessor=lowerCamelCase,  framework=lowerCamelCase     )
								if self._config.type_vocab_size == 0 and "token_type_ids" in dummy_inputs:
											del dummy_inputs["token_type_ids"]
								return dummy_inputs




