"""simple docstring"""

import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    WavaVecaConfig,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaForCTC,
    WavaVecaForPreTraining,
    WavaVecaProcessor,
    logging,
)
from transformers.models.wavaveca.modeling_wavaveca import WavaVecaForSequenceClassification


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

UpperCAmelCase       =  {
    """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""",
    """adapter_layer""": """encoder.layers.*.adapter_layer""",
    """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""",
    """mask_emb""": """masked_spec_embed""",
    """pooling_layer.linear""": """projector""",
    """pooling_layer.projection""": """classifier""",
}
UpperCAmelCase       =  [
    """lm_head""",
    """quantizer.weight_proj""",
    """quantizer.codevectors""",
    """project_q""",
    """project_hid""",
    """projector""",
    """classifier""",
]






def      lowercase		(						a__	:		Dict     )						->   Union[str, Any]:
					_UpperCamelCase      						=	{}
					with open(lowerCAmelCase__ ,  '''r'''     ) as file:
										for line_number, line in enumerate(lowerCAmelCase__     ):
															_UpperCamelCase      						=	line.strip()
															if line:
																				_UpperCamelCase      						=	line.split()
																				_UpperCamelCase      						=	line_number
																				_UpperCamelCase      						=	words[0]
																				_UpperCamelCase      						=	value
					return result






def      lowercase		(						a__	:		int ,  a__	:		str ,  a__	:		Tuple ,  a__	:		Optional[Any] ,  a__	:		Optional[int]     )						->   Union[str, Any]:
					for attribute in key.split('''.'''     ):
										_UpperCamelCase      						=	getattr(lowerCAmelCase__ ,  lowerCAmelCase__     )

					_UpperCamelCase      						=	None
					for param_key in PARAM_MAPPING.keys():
										if full_name.endswith(lowerCAmelCase__     ):
															_UpperCamelCase      						=	PARAM_MAPPING[full_name.split('''.'''     )[-1]]
															_UpperCamelCase      						=	'''param'''

					if weight_type is not None and weight_type != "param":
										_UpperCamelCase      						=	getattr(lowerCAmelCase__ ,  lowerCAmelCase__     ).shape
					elif weight_type is not None and weight_type == "param":
										_UpperCamelCase      						=	hf_pointer
										for attribute in hf_param_name.split('''.'''     ):
															_UpperCamelCase      						=	getattr(lowerCAmelCase__ ,  lowerCAmelCase__     )
										_UpperCamelCase      						=	shape_pointer.shape

										# let's reduce dimension
										_UpperCamelCase      						=	value[0]
					else:
										_UpperCamelCase      						=	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":
										_UpperCamelCase      						=	value
					elif weight_type == "weight_g":
										_UpperCamelCase      						=	value
					elif weight_type == "weight_v":
										_UpperCamelCase      						=	value
					elif weight_type == "bias":
										_UpperCamelCase      						=	value
					elif weight_type == "param":
										for attribute in hf_param_name.split('''.'''     ):
															_UpperCamelCase      						=	getattr(lowerCAmelCase__ ,  lowerCAmelCase__     )
										_UpperCamelCase      						=	value
					else:
										_UpperCamelCase      						=	value

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






def      lowercase		(						a__	:		Any ,  a__	:		int ,  a__	:		Tuple ,  a__	:		int ,  a__	:		Any     )						->   Any:
					_UpperCamelCase      						=	None
					for param_key in PARAM_MAPPING.keys():
										if full_name.endswith(lowerCAmelCase__     ):
															_UpperCamelCase      						=	PARAM_MAPPING[full_name.split('''.'''     )[-1]]
															_UpperCamelCase      						=	'''param'''

					if weight_type is not None and weight_type != "param":
										_UpperCamelCase      						=	'''.'''.join([key, weight_type]     )
					elif weight_type is not None and weight_type == "param":
										_UpperCamelCase      						=	'''.'''.join([key, hf_param_name]     )
					else:
										_UpperCamelCase      						=	key

					_UpperCamelCase      						=	value if '''lm_head''' in full_key else value[0]


UpperCAmelCase       =  {
    """W_a""": """linear_1.weight""",
    """W_b""": """linear_2.weight""",
    """b_a""": """linear_1.bias""",
    """b_b""": """linear_2.bias""",
    """ln_W""": """norm.weight""",
    """ln_b""": """norm.bias""",
}






def      lowercase		(						a__	:		Dict ,  a__	:		Any ,  a__	:		Union[str, Any]=None ,  a__	:		Union[str, Any]=None     )						->   List[Any]:
					_UpperCamelCase      						=	False
					for key, mapped_key in MAPPING.items():
										_UpperCamelCase      						=	'''wav2vec2.''' + 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]:
															_UpperCamelCase      						=	True
															if "*" in mapped_key:
																				_UpperCamelCase      						=	name.split(lowerCAmelCase__     )[0].split('''.'''     )[-2]
																				_UpperCamelCase      						=	mapped_key.replace('''*''' ,  lowerCAmelCase__     )
															if "weight_g" in name:
																				_UpperCamelCase      						=	'''weight_g'''
															elif "weight_v" in name:
																				_UpperCamelCase      						=	'''weight_v'''
															elif "bias" in name:
																				_UpperCamelCase      						=	'''bias'''
															elif "weight" in name:
																				# TODO: don't match quantizer.weight_proj
																				_UpperCamelCase      						=	'''weight'''
															else:
																				_UpperCamelCase      						=	None
															if hf_dict is not None:
																				rename_dict(lowerCAmelCase__ ,  lowerCAmelCase__ ,  lowerCAmelCase__ ,  lowerCAmelCase__ ,  lowerCAmelCase__     )
															else:
																				set_recursively(lowerCAmelCase__ ,  lowerCAmelCase__ ,  lowerCAmelCase__ ,  lowerCAmelCase__ ,  lowerCAmelCase__     )
															return is_used
					return is_used






def      lowercase		(						a__	:		Union[str, Any] ,  a__	:		Optional[int] ,  a__	:		Union[str, Any]     )						->   Dict:
					_UpperCamelCase      						=	[]
					_UpperCamelCase      						=	fairseq_model.state_dict()

					_UpperCamelCase      						=	hf_model.wavaveca.feature_extractor

					for name, value in fairseq_dict.items():
										_UpperCamelCase      						=	False
										if "conv_layers" in name:
															load_conv_layer(
															    lowerCAmelCase__ ,  lowerCAmelCase__ ,  lowerCAmelCase__ ,  lowerCAmelCase__ ,  hf_model.config.feat_extract_norm == '''group''' ,  )
															_UpperCamelCase      						=	True
										else:
															_UpperCamelCase      						=	load_wavaveca_layer(lowerCAmelCase__ ,  lowerCAmelCase__ ,  lowerCAmelCase__     )
										if not is_used:
															unused_weights.append(lowerCAmelCase__     )

					logger.warning(F'''Unused weights: {unused_weights}'''     )






def      lowercase		(						a__	:		int ,  a__	:		Union[str, Any] ,  a__	:		Dict ,  a__	:		List[str] ,  a__	:		List[Any]     )						->   Union[str, Any]:
					_UpperCamelCase      						=	full_name.split('''conv_layers.'''     )[-1]
					_UpperCamelCase      						=	name.split('''.'''     )
					_UpperCamelCase      						=	int(items[0]     )
					_UpperCamelCase      						=	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.'''     )
															_UpperCamelCase      						=	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.'''     )
															_UpperCamelCase      						=	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.conv_layers[layer_id].layer_norm.bias.data.shape} was found.'''     )
															_UpperCamelCase      						=	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.conv_layers[layer_id].layer_norm.weight.data.shape} was found.'''     )
															_UpperCamelCase      						=	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      lowercase		(						a__	:		Optional[Any] ,  a__	:		Optional[int] ,  a__	:		int=None ,  a__	:		Optional[Any]=None ,  a__	:		Dict=True ,  a__	:		int=False     )						->   Optional[Any]:
					if config_path is not None:
										_UpperCamelCase      						=	WavaVecaConfig.from_pretrained(lowerCAmelCase__     )
					else:
										_UpperCamelCase      						=	WavaVecaConfig()

					if is_seq_class:
										_UpperCamelCase      						=	read_txt_into_dict(lowerCAmelCase__     )
										_UpperCamelCase      						=	idalabel
										_UpperCamelCase      						=	WavaVecaForSequenceClassification(lowerCAmelCase__     )
										_UpperCamelCase      						=	WavaVecaFeatureExtractor(
										    feature_size=1 ,  sampling_rate=16000 ,  padding_value=0 ,  do_normalize=lowerCAmelCase__ ,  return_attention_mask=lowerCAmelCase__ ,  )
										feature_extractor.save_pretrained(lowerCAmelCase__     )

					elif is_finetuned:
										if dict_path:
															_UpperCamelCase      						=	Dictionary.load(lowerCAmelCase__     )

															# important change bos & pad token id since CTC symbol is <pad> and
															# not <s> as in fairseq
															_UpperCamelCase      						=	target_dict.pad_index
															_UpperCamelCase      						=	target_dict.bos_index
															_UpperCamelCase      						=	target_dict.eos_index
															_UpperCamelCase      						=	len(target_dict.symbols     )
															_UpperCamelCase      						=	os.path.join(lowerCAmelCase__ ,  '''vocab.json'''     )
															if not os.path.isdir(lowerCAmelCase__     ):
																				logger.error('''--pytorch_dump_folder_path ({}) should be a directory'''.format(lowerCAmelCase__     )     )
																				return
															os.makedirs(lowerCAmelCase__ ,  exist_ok=lowerCAmelCase__     )
															_UpperCamelCase      						=	target_dict.indices

															# fairseq has the <pad> and <s> switched
															_UpperCamelCase      						=	0
															_UpperCamelCase      						=	1
															with open(lowerCAmelCase__ ,  '''w''' ,  encoding='''utf-8'''     ) as vocab_handle:
																				json.dump(lowerCAmelCase__ ,  lowerCAmelCase__     )
															_UpperCamelCase      						=	WavaVecaCTCTokenizer(
															    lowerCAmelCase__ ,  unk_token=target_dict.unk_word ,  pad_token=target_dict.pad_word ,  bos_token=target_dict.bos_word ,  eos_token=target_dict.eos_word ,  word_delimiter_token='''|''' ,  do_lower_case=lowerCAmelCase__ ,  )
															_UpperCamelCase      						=	True if config.feat_extract_norm == '''layer''' else False
															_UpperCamelCase      						=	WavaVecaFeatureExtractor(
															    feature_size=1 ,  sampling_rate=16000 ,  padding_value=0 ,  do_normalize=lowerCAmelCase__ ,  return_attention_mask=lowerCAmelCase__ ,  )
															_UpperCamelCase      						=	WavaVecaProcessor(feature_extractor=lowerCAmelCase__ ,  tokenizer=lowerCAmelCase__     )
															processor.save_pretrained(lowerCAmelCase__     )

										_UpperCamelCase      						=	WavaVecaForCTC(lowerCAmelCase__     )
					else:
										_UpperCamelCase      						=	WavaVecaForPreTraining(lowerCAmelCase__     )

					if is_finetuned or is_seq_class:
										_UpperCamelCase						,							_UpperCamelCase						,							_UpperCamelCase      						=	fairseq.checkpoint_utils.load_model_ensemble_and_task(
										    [checkpoint_path] ,  arg_overrides={'''data''': '''/'''.join(dict_path.split('''/'''     )[:-1]     )}     )
					else:
										_UpperCamelCase      						=	argparse.Namespace(task='''audio_pretraining'''     )
										_UpperCamelCase      						=	fairseq.tasks.setup_task(lowerCAmelCase__     )

										_UpperCamelCase						,							_UpperCamelCase						,							_UpperCamelCase      						=	fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path] ,  task=lowerCAmelCase__     )

					_UpperCamelCase      						=	model[0].eval()

					recursively_load_weights(lowerCAmelCase__ ,  lowerCAmelCase__ ,  not is_finetuned     )

					hf_wavavec.save_pretrained(lowerCAmelCase__     )


if __name__ == "__main__":
		UpperCAmelCase       =  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"""
		)
		parser.add_argument(
		    """--is_seq_class""",
		    action="""store_true""",
		    help="""Whether the model to convert is a fine-tuned sequence classification model or not""",
		)
		UpperCAmelCase       =  parser.parse_args()

		UpperCAmelCase       =  not args.not_finetuned and not args.is_seq_class
		convert_wavaveca_checkpoint(
		    args.checkpoint_path,
		    args.pytorch_dump_folder_path,
		    args.config_path,
		    args.dict_path,
		    is_finetuned,
		    args.is_seq_class,
		)




"""simple docstring"""




from collections import OrderedDict
from typing import Mapping

from packaging import version

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


lowercase_			     =  logging.get_logger(__name__)

lowercase_			     =  {
    "microsoft/beit-base-patch16-224-pt22k": (
        "https://huggingface.co/microsoft/beit-base-patch16-224-pt22k/resolve/main/config.json"
    ),
    # See all BEiT models at https://huggingface.co/models?filter=beit
}
class 					__lowerCAmelCase					(   __SCREAMING_SNAKE_CASE       ):


		'''simple docstring'''



		__UpperCAmelCase		:			List[str]										=				'beit'

		def __init__(  self							, _a=8_192							, _a=768							, _a=12							, _a=12							, _a=3_072							, _a="gelu"							, _a=0.0							, _a=0.0							, _a=0.02							, _a=1E-12							, _a=224							, _a=16							, _a=3							, _a=False							, _a=False							, _a=False							, _a=False							, _a=0.1							, _a=0.1							, _a=True							, _a=[3, 5, 7, 11]							, _a=[1, 2, 3, 6]							, _a=True							, _a=0.4							, _a=256							, _a=1							, _a=False							, _a=255							, **_a							, ):
				super().__init__(**_a      )

				__a           =			vocab_size
				__a           =			hidden_size
				__a           =			num_hidden_layers
				__a           =			num_attention_heads
				__a           =			intermediate_size
				__a           =			hidden_act
				__a           =			hidden_dropout_prob
				__a           =			attention_probs_dropout_prob
				__a           =			initializer_range
				__a           =			layer_norm_eps

				__a           =			image_size
				__a           =			patch_size
				__a           =			num_channels
				__a           =			use_mask_token
				__a           =			use_absolute_position_embeddings
				__a           =			use_relative_position_bias
				__a           =			use_shared_relative_position_bias
				__a           =			layer_scale_init_value
				__a           =			drop_path_rate
				__a           =			use_mean_pooling
				# decode head attributes (semantic segmentation)
				__a           =			out_indices
				__a           =			pool_scales
				# auxiliary head attributes (semantic segmentation)
				__a           =			use_auxiliary_head
				__a           =			auxiliary_loss_weight
				__a           =			auxiliary_channels
				__a           =			auxiliary_num_convs
				__a           =			auxiliary_concat_input
				__a           =			semantic_loss_ignore_index







class 					__lowerCAmelCase					(   __SCREAMING_SNAKE_CASE       ):


		'''simple docstring'''



		__UpperCAmelCase		:			List[Any]										=				version.parse('1.11'       )

		@property
		def 			__UpperCAmelCase       (  self      ):
				return OrderedDict(
				    [
				        ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
				    ]      )





		@property
		def 			__UpperCAmelCase       (  self      ):
				return 1E-4






