import copy
from typing import TYPE_CHECKING, Any, Mapping, Optional, OrderedDict

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig


if TYPE_CHECKING:
			from ... import PreTrainedTokenizerBase, TensorType

lowercase		:  Dict     					=       logging.get_logger(__name__)






class 					a__     ( __SCREAMING_SNAKE_CASE ):
				_A       					=							'''vision-encoder-decoder'''
				_A       					=							True






				def __init__(    self       :						str  ,							**A_       :						Union[str, Any]							)       ->      int:

										"""simple docstring"""
										super().__init__(**A__							)
										if "encoder" not in kwargs or "decoder" not in kwargs:
																raise ValueError(
																    f"""A configuraton of type {self.model_type} cannot be instantiated because """
																    f"""not both `encoder` and `decoder` sub-configurations are passed, but only {kwargs}"""							)

										lowerCamelCase_:       Optional[int]					     = kwargs.pop("""encoder"""							)
										lowerCamelCase_:       str					     = encoder_config.pop("""model_type"""							)
										lowerCamelCase_:       Optional[Any]					     = kwargs.pop("""decoder"""							)
										lowerCamelCase_:       str					     = decoder_config.pop("""model_type"""							)

										lowerCamelCase_:       Union[str, Any]					     = AutoConfig.for_model(A__  ,							**A__							)
										lowerCamelCase_:       int					     = AutoConfig.for_model(A__  ,							**A__							)
										lowerCamelCase_:       str					     = True






				@classmethod
				def   lowerCAmelCase (    cls       :						Optional[Any]  ,							A_       :						Union[str, Any]  ,							A_       :						Dict  ,							**A_       :						Optional[Any]							)       ->      Any:

										"""simple docstring"""
										logger.info("""Setting `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config"""							)
										lowerCamelCase_:       List[Any]					     = True
										lowerCamelCase_:       Union[str, Any]					     = True

										return cls(encoder=encoder_config.to_dict()  ,							decoder=decoder_config.to_dict()  ,							**A__							)






				def   lowerCAmelCase (    self       :						Union[str, Any]							)       ->      List[Any]:

										"""simple docstring"""
										lowerCamelCase_:       List[Any]					     = copy.deepcopy(self.__dict__							)
										lowerCamelCase_:       Union[str, Any]					     = self.encoder.to_dict()
										lowerCamelCase_:       Any					     = self.decoder.to_dict()
										lowerCamelCase_:       str					     = self.__class__.model_type
										return output






class 					a__     ( __SCREAMING_SNAKE_CASE ):
				_A       					=							version.parse("1.11" )






				@property
				def   lowerCAmelCase (    self       :						List[str]							)       ->      List[Any]:

										"""simple docstring"""
										return OrderedDict(
										    [
										        ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
										    ]							)






				@property
				def   lowerCAmelCase (    self       :						Union[str, Any]							)       ->      int:

										"""simple docstring"""
										return 1e-4






				@property
				def   lowerCAmelCase (    self       :						str							)       ->      Any:

										"""simple docstring"""
										return OrderedDict({"""last_hidden_state""": {0: """batch""", 1: """encoder_sequence"""}}							)






class 					a__     ( __SCREAMING_SNAKE_CASE ):






				@property
				def   lowerCAmelCase (    self       :						str							)       ->      int:

										"""simple docstring"""
										lowerCamelCase_:       Dict					     = OrderedDict()
										lowerCamelCase_:       str					     = {0: """batch""", 1: """past_decoder_sequence + sequence"""}
										lowerCamelCase_:       Optional[Any]					     = {0: """batch""", 1: """past_decoder_sequence + sequence"""}
										lowerCamelCase_:       List[str]					     = {0: """batch""", 1: """encoder_sequence"""}

										return common_inputs






				def   lowerCAmelCase (    self       :						Optional[int]  ,							A_       :						Any  ,							A_       :						List[Any] = -1  ,							A_       :						Optional[Any] = -1  ,							A_       :						Optional[Any] = False  ,							A_       :						Union[str, Any] = None  ,							)       ->      Any:

										"""simple docstring"""
										import torch

										lowerCamelCase_:       int					     = OrderedDict()

										lowerCamelCase_:       Optional[Any]					     = super().generate_dummy_inputs(
										    A__  ,							batch_size=A__  ,							seq_length=A__  ,							is_pair=A__  ,							framework=A__							)

										lowerCamelCase_:       Dict					     = dummy_input["""input_ids"""].shape
										lowerCamelCase_:       Union[str, Any]					     = (batch, encoder_sequence, self._config.encoder_hidden_size)
										lowerCamelCase_:       List[Any]					     = dummy_input.pop("""input_ids"""							)
										lowerCamelCase_:       int					     = dummy_input.pop("""attention_mask"""							)
										lowerCamelCase_:       int					     = torch.zeros(A__							)

										return common_inputs






class 					a__     ( __SCREAMING_SNAKE_CASE ):






				@property
				def   lowerCAmelCase (    self       :						Any							)       ->      str:

										"""simple docstring"""
										pass






				def   lowerCAmelCase (    self       :						Tuple  ,							A_       :						str							)       ->      Optional[Any]:

										"""simple docstring"""
										return VisionEncoderDecoderEncoderOnnxConfig(A__							)






				def   lowerCAmelCase (    self       :						List[Any]  ,							A_       :						int  ,							A_       :						Optional[Any]  ,							A_       :						Dict = "default"							)       ->      List[str]:

										"""simple docstring"""
										lowerCamelCase_:       List[Any]					     = encoder_config.hidden_size
										return VisionEncoderDecoderDecoderOnnxConfig(A__  ,							A__							)







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

A_			:					List[Any]    			=     {
    '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',
}
A_			:					str    			=     [
    'lm_head',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
    'projector',
    'classifier',
]

def 		UpperCamelCase   (lowercase_:					List[Any]   )	-> List[Any]:
	A__				:       Optional[Any]	  =						{}
	with open(lowercase_       , """r"""   ) as file:
		for line_number, line in enumerate(lowercase_   ):
			A__				:       Any	  =						line.strip()
			if line:
				A__				:       Optional[int]	  =						line.split()
				A__				:       Any	  =						line_number
				A__				:       Tuple	  =						words[0]
				A__				:       Union[str, Any]	  =						value
	return result

def 		UpperCamelCase   (lowercase_:					List[str]       , lowercase_:					Tuple       , lowercase_:					int       , lowercase_:					Optional[int]       , lowercase_:					Optional[int]   )	-> Dict:
	for attribute in key.split("""."""   ):
		A__				:       Optional[int]	  =						getattr(lowercase_       , lowercase_   )

	A__				:       Optional[int]	  =						None
	for param_key in PARAM_MAPPING.keys():
		if full_name.endswith(lowercase_   ):
			A__				:       Dict	  =						PARAM_MAPPING[full_name.split("""."""   )[-1]]
			A__				:       str	  =						"""param"""

	if weight_type is not None and weight_type != "param":
		A__				:       List[Any]	  =						getattr(lowercase_       , lowercase_   ).shape
	elif weight_type is not None and weight_type == "param":
		A__				:       Optional[int]	  =						hf_pointer
		for attribute in hf_param_name.split("""."""   ):
			A__				:       Optional[Any]	  =						getattr(lowercase_       , lowercase_   )
		A__				:       List[Any]	  =						shape_pointer.shape

		# let's reduce dimension
		A__				:       str	  =						value[0]
	else:
		A__				:       Dict	  =						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":
		A__				:       Any	  =						value
	elif weight_type == "weight_g":
		A__				:       List[str]	  =						value
	elif weight_type == "weight_v":
		A__				:       Union[str, Any]	  =						value
	elif weight_type == "bias":
		A__				:       Optional[Any]	  =						value
	elif weight_type == "param":
		for attribute in hf_param_name.split("""."""   ):
			A__				:       Dict	  =						getattr(lowercase_       , lowercase_   )
		A__				:       Tuple	  =						value
	else:
		A__				:       List[str]	  =						value

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

def 		UpperCamelCase   (lowercase_:					Any       , lowercase_:					Optional[Any]       , lowercase_:					Tuple       , lowercase_:					Any       , lowercase_:					Union[str, Any]   )	-> Tuple:
	A__				:       Any	  =						None
	for param_key in PARAM_MAPPING.keys():
		if full_name.endswith(lowercase_   ):
			A__				:       List[Any]	  =						PARAM_MAPPING[full_name.split("""."""   )[-1]]
			A__				:       List[str]	  =						"""param"""

	if weight_type is not None and weight_type != "param":
		A__				:       int	  =						""".""".join([key, weight_type]   )
	elif weight_type is not None and weight_type == "param":
		A__				:       Union[str, Any]	  =						""".""".join([key, hf_param_name]   )
	else:
		A__				:       Optional[Any]	  =						key

	A__				:       Tuple	  =						value if """lm_head""" in full_key else value[0]


A_			:					List[Any]    			=     {
    '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 		UpperCamelCase   (lowercase_:					List[str]       , lowercase_:					Tuple       , lowercase_:					Any=None       , lowercase_:					Optional[int]=None   )	-> List[Any]:
	A__				:       int	  =						False
	for key, mapped_key in MAPPING.items():
		A__				:       int	  =						"""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]:
			A__				:       Optional[Any]	  =						True
			if "*" in mapped_key:
				A__				:       List[Any]	  =						name.split(lowercase_   )[0].split("""."""   )[-2]
				A__				:       int	  =						mapped_key.replace("""*"""       , lowercase_   )
			if "weight_g" in name:
				A__				:       List[Any]	  =						"""weight_g"""
			elif "weight_v" in name:
				A__				:       int	  =						"""weight_v"""
			elif "bias" in name:
				A__				:       Any	  =						"""bias"""
			elif "weight" in name:
				# TODO: don't match quantizer.weight_proj
				A__				:       str	  =						"""weight"""
			else:
				A__				:       str	  =						None
			if hf_dict is not None:
				rename_dict(lowercase_       , lowercase_       , lowercase_       , lowercase_       , lowercase_   )
			else:
				set_recursively(lowercase_       , lowercase_       , lowercase_       , lowercase_       , lowercase_   )
			return is_used
	return is_used

def 		UpperCamelCase   (lowercase_:					Union[str, Any]       , lowercase_:					Optional[int]       , lowercase_:					Any   )	-> Tuple:
	A__				:       List[Any]	  =						[]
	A__				:       Tuple	  =						fairseq_model.state_dict()

	A__				:       Any	  =						hf_model.wavaveca.feature_extractor

	for name, value in fairseq_dict.items():
		A__				:       Any	  =						False
		if "conv_layers" in name:
			load_conv_layer(
			    lowercase_       , lowercase_       , lowercase_       , lowercase_       , hf_model.config.feat_extract_norm == """group"""       , )
			A__				:       Any	  =						True
		else:
			A__				:       Union[str, Any]	  =						load_wavaveca_layer(lowercase_       , lowercase_       , lowercase_   )
		if not is_used:
			unused_weights.append(lowercase_   )

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

def 		UpperCamelCase   (lowercase_:					Optional[Any]       , lowercase_:					List[str]       , lowercase_:					Any       , lowercase_:					Union[str, Any]       , lowercase_:					Any   )	-> Dict:
	A__				:       str	  =						full_name.split("""conv_layers."""   )[-1]
	A__				:       Optional[Any]	  =						name.split("""."""   )
	A__				:       Optional[int]	  =						int(items[0]   )
	A__				:       List[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."""   )
			A__				:       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."""   )
			A__				:       Any	  =						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."""   )
			A__				:       Union[str, Any]	  =						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."""   )
			A__				:       Dict	  =						value
			logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""   )
	else:
		unused_weights.append(lowercase_   )

@torch.no_grad()
def 		UpperCamelCase   (lowercase_:					List[Any]       , lowercase_:					int       , lowercase_:					str=None       , lowercase_:					Tuple=None       , lowercase_:					List[str]=True       , lowercase_:					Any=False   )	-> str:
	if config_path is not None:
		A__				:       Any	  =						WavaVecaConfig.from_pretrained(lowercase_   )
	else:
		A__				:       int	  =						WavaVecaConfig()

	if is_seq_class:
		A__				:       Optional[Any]	  =						read_txt_into_dict(lowercase_   )
		A__				:       List[str]	  =						idalabel
		A__				:       Any	  =						WavaVecaForSequenceClassification(lowercase_   )
		A__				:       str	  =						WavaVecaFeatureExtractor(
		    feature_size=1       , sampling_rate=16000       , padding_value=0       , do_normalize=lowercase_       , return_attention_mask=lowercase_       , )
		feature_extractor.save_pretrained(lowercase_   )

	elif is_finetuned:
		if dict_path:
			A__				:       List[Any]	  =						Dictionary.load(lowercase_   )

			# important change bos & pad token id since CTC symbol is <pad> and
			# not <s> as in fairseq
			A__				:       Tuple	  =						target_dict.pad_index
			A__				:       Union[str, Any]	  =						target_dict.bos_index
			A__				:       List[Any]	  =						target_dict.eos_index
			A__				:       List[str]	  =						len(target_dict.symbols   )
			A__				:       str	  =						os.path.join(lowercase_       , """vocab.json"""   )
			if not os.path.isdir(lowercase_   ):
				logger.error("""--pytorch_dump_folder_path ({}) should be a directory""".format(lowercase_   )   )
				return
			os.makedirs(lowercase_       , exist_ok=lowercase_   )
			A__				:       Dict	  =						target_dict.indices

			# fairseq has the <pad> and <s> switched
			A__				:       List[str]	  =						0
			A__				:       Optional[Any]	  =						1
			with open(lowercase_       , """w"""       , encoding="""utf-8"""   ) as vocab_handle:
				json.dump(lowercase_       , lowercase_   )
			A__				:       List[str]	  =						WavaVecaCTCTokenizer(
			    lowercase_       , 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=lowercase_       , )
			A__				:       List[Any]	  =						True if config.feat_extract_norm == """layer""" else False
			A__				:       int	  =						WavaVecaFeatureExtractor(
			    feature_size=1       , sampling_rate=16000       , padding_value=0       , do_normalize=lowercase_       , return_attention_mask=lowercase_       , )
			A__				:       str	  =						WavaVecaProcessor(feature_extractor=lowercase_       , tokenizer=lowercase_   )
			processor.save_pretrained(lowercase_   )

		A__				:       Optional[Any]	  =						WavaVecaForCTC(lowercase_   )
	else:
		A__				:       str	  =						WavaVecaForPreTraining(lowercase_   )

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

		A__  ,  A__  ,  A__				:       List[str]	  =						fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]       , task=lowercase_   )

	A__				:       Tuple	  =						model[0].eval()

	recursively_load_weights(lowercase_       , lowercase_       , not is_finetuned   )

	hf_wavavec.save_pretrained(lowercase_   )


if __name__ == "__main__":
	A_			:					Optional[Any]    			=     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',
	)
	A_			:					Any    			=     parser.parse_args()

	A_			:					List[Any]    			=     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,
	)



