

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__						=							logging.get_logger(__name__)

A__						=							{
    "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__						=							[
    "lm_head",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
    "projector",
    "classifier",
]




def 					_lowercase     (				a_				:					Optional[Any]				)				->	Union[str, Any]:




					'''simple docstring'''







					__magic_name__							=    {}
					with open(a_					,'r'				) as file:
										for line_number, line in enumerate(a_				):
															__magic_name__							=    line.strip()
															if line:
																				__magic_name__							=    line.split()
																				__magic_name__							=    line_number
																				__magic_name__							=    words[0]
																				__magic_name__							=    value
					return result




def 					_lowercase     (				a_				:					Union[str, Any]					,a_				:					str					,a_				:					List[str]					,a_				:					str					,a_				:					Tuple				)				->	Dict:




					'''simple docstring'''







					for attribute in key.split('.'				):
										__magic_name__							=    getattr(a_					,a_				)

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

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

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

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




def 					_lowercase     (				a_				:					Any					,a_				:					Optional[Any]					,a_				:					Optional[Any]					,a_				:					Optional[Any]					,a_				:					Optional[Any]				)				->	str:




					'''simple docstring'''







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

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

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


A__						=							{
    "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_				:					List[Any]					,a_				:					int					,a_				:					str=None					,a_				:					Optional[int]=None				)				->	List[Any]:




					'''simple docstring'''







					__magic_name__							=    False
					for key, mapped_key in MAPPING.items():
										__magic_name__							=    '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]:
															__magic_name__							=    True
															if "*" in mapped_key:
																				__magic_name__							=    name.split(a_				)[0].split('.'				)[-2]
																				__magic_name__							=    mapped_key.replace('*'					,a_				)
															if "weight_g" in name:
																				__magic_name__							=    'weight_g'
															elif "weight_v" in name:
																				__magic_name__							=    'weight_v'
															elif "bias" in name:
																				__magic_name__							=    'bias'
															elif "weight" in name:
																				# TODO: don't match quantizer.weight_proj
																				__magic_name__							=    'weight'
															else:
																				__magic_name__							=    None
															if hf_dict is not None:
																				rename_dict(a_					,a_					,a_					,a_					,a_				)
															else:
																				set_recursively(a_					,a_					,a_					,a_					,a_				)
															return is_used
					return is_used




def 					_lowercase     (				a_				:					Optional[Any]					,a_				:					Optional[int]					,a_				:					List[str]				)				->	List[Any]:




					'''simple docstring'''







					__magic_name__							=    []
					__magic_name__							=    fairseq_model.state_dict()

					__magic_name__							=    hf_model.wavaveca.feature_extractor

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

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




def 					_lowercase     (				a_				:					List[str]					,a_				:					Dict					,a_				:					List[str]					,a_				:					Any					,a_				:					Optional[Any]				)				->	Tuple:




					'''simple docstring'''







					__magic_name__							=    full_name.split('conv_layers.'				)[-1]
					__magic_name__							=    name.split('.'				)
					__magic_name__							=    int(items[0]				)
					__magic_name__							=    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.'				)
															__magic_name__							=    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.'				)
															__magic_name__							=    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.'				)
															__magic_name__							=    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.'				)
															__magic_name__							=    value
															logger.info(F'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'				)
					else:
										unused_weights.append(a_				)




@torch.no_grad()
def 					_lowercase     (				a_				:					Optional[Any]					,a_				:					Optional[Any]					,a_				:					Optional[int]=None					,a_				:					Dict=None					,a_				:					List[Any]=True					,a_				:					int=False				)				->	Any:




					'''simple docstring'''







					if config_path is not None:
										__magic_name__							=    WavaVecaConfig.from_pretrained(a_				)
					else:
										__magic_name__							=    WavaVecaConfig()

					if is_seq_class:
										__magic_name__							=    read_txt_into_dict(a_				)
										__magic_name__							=    idalabel
										__magic_name__							=    WavaVecaForSequenceClassification(a_				)
										__magic_name__							=    WavaVecaFeatureExtractor(
										    feature_size=1					,sampling_rate=1_6_0_0_0					,padding_value=0					,do_normalize=a_					,return_attention_mask=a_					,)
										feature_extractor.save_pretrained(a_				)

					elif is_finetuned:
										if dict_path:
															__magic_name__							=    Dictionary.load(a_				)

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

															# fairseq has the <pad> and <s> switched
															__magic_name__							=    0
															__magic_name__							=    1
															with open(a_					,'w'					,encoding='utf-8'				) as vocab_handle:
																				json.dump(a_					,a_				)
															__magic_name__							=    WavaVecaCTCTokenizer(
															    a_					,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=a_					,)
															__magic_name__							=    True if config.feat_extract_norm == 'layer' else False
															__magic_name__							=    WavaVecaFeatureExtractor(
															    feature_size=1					,sampling_rate=1_6_0_0_0					,padding_value=0					,do_normalize=a_					,return_attention_mask=a_					,)
															__magic_name__							=    WavaVecaProcessor(feature_extractor=a_					,tokenizer=a_				)
															processor.save_pretrained(a_				)

										__magic_name__							=    WavaVecaForCTC(a_				)
					else:
										__magic_name__							=    WavaVecaForPreTraining(a_				)

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

										__magic_name__,	__magic_name__,	__magic_name__							=    fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]					,task=a_				)

					__magic_name__							=    model[0].eval()

					recursively_load_weights(a_					,a_					,not is_finetuned				)

					hf_wavavec.save_pretrained(a_				)


if __name__ == "__main__":
						A__						=							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__						=							parser.parse_args()

						A__						=							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,
						)







import argparse
from pathlib import Path

from transformers import AutoConfig, AutoTokenizer, RagConfig, RagSequenceForGeneration, RagTokenForGeneration




def 					_lowercase     (				a_				:					str					,a_				:					str					,a_				:					str					,a_				:					Path					,a_				:					str = None					,a_				:					str = None					,a_				:					str = None					,)				->	Tuple:




					'''simple docstring'''







					if config_name_or_path is None:
										__magic_name__							=    'facebook/rag-token-base' if model_type == 'rag_token' else 'facebook/rag-sequence-base'

					if generator_tokenizer_name_or_path is None:
										__magic_name__							=    generator_name_or_path

					if question_encoder_tokenizer_name_or_path is None:
										__magic_name__							=    question_encoder_name_or_path

					__magic_name__							=    RagTokenForGeneration if model_type == 'rag_token' else RagSequenceForGeneration

					# Save model.
					__magic_name__							=    RagConfig.from_pretrained(a_				)
					__magic_name__							=    AutoConfig.from_pretrained(a_				)
					__magic_name__							=    AutoConfig.from_pretrained(a_				)

					__magic_name__							=    gen_config
					__magic_name__							=    question_encoder_config

					__magic_name__							=    model_class.from_pretrained_question_encoder_generator(
					    a_					,a_					,config=a_				)
					rag_model.save_pretrained(a_				)

					# Sanity check.
					model_class.from_pretrained(a_				)

					# Save tokenizers.
					__magic_name__							=    AutoTokenizer.from_pretrained(a_				)
					gen_tokenizer.save_pretrained(dest_dir / 'generator_tokenizer/'				)
					__magic_name__							=    AutoTokenizer.from_pretrained(a_				)
					question_encoder_tokenizer.save_pretrained(dest_dir / 'question_encoder_tokenizer/'				)


if __name__ == "__main__":
						A__						=							argparse.ArgumentParser()
						parser.add_argument(
						    "--model_type",
						    choices=["rag_sequence", "rag_token"],
						    required=True,
						    type=str,
						    help="RAG model type: rag_sequence, rag_token",
						)
						parser.add_argument("--dest", type=str, required=True, help="Path to the output checkpoint directory.")
						parser.add_argument("--generator_name_or_path", type=str, required=True, help="Generator model identifier")
						parser.add_argument(
						    "--question_encoder_name_or_path", type=str, required=True, help="Question encoder model identifier"
						)

						parser.add_argument(
						    "--generator_tokenizer_name_or_path",
						    type=str,
						    help="Generator tokenizer identifier, if not specified, resolves to ``generator_name_or_path``",
						)
						parser.add_argument(
						    "--question_encoder_tokenizer_name_or_path",
						    type=str,
						    help="Question encoder tokenizer identifier, if not specified, resolves to ``question_encoder_name_or_path``",
						)
						parser.add_argument(
						    "--config_name_or_path",
						    type=str,
						    help=(
						        "Identifier of the model config to use, if not provided, resolves to a base config for a given"
						        " ``model_type``"
						    ),
						)

						A__						=							parser.parse_args()

						A__						=							Path(args.dest)
						dest_dir.mkdir(exist_ok=True)

						consolidate(
						    args.model_type,
						    args.generator_name_or_path,
						    args.question_encoder_name_or_path,
						    dest_dir,
						    args.config_name_or_path,
						    args.generator_tokenizer_name_or_path,
						    args.question_encoder_tokenizer_name_or_path,
						)




