



import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    WavaVecaConformerConfig,
    WavaVecaConformerForCTC,
    WavaVecaConformerForPreTraining,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
a__   :					List[Any] 						=      logging.get_logger(__name__)

a__   :					List[Any] 						=      {
    "post_extract_proj": "feature_projection.projection",
    "encoder.pos_conv.0": "encoder.pos_conv_embed.conv",
    "self_attn.linear_k": "encoder.layers.*.self_attn.linear_k",
    "self_attn.linear_v": "encoder.layers.*.self_attn.linear_v",
    "self_attn.linear_q": "encoder.layers.*.self_attn.linear_q",
    "self_attn.pos_bias_u": "encoder.layers.*.self_attn.pos_bias_u",
    "self_attn.pos_bias_v": "encoder.layers.*.self_attn.pos_bias_v",
    "self_attn.linear_out": "encoder.layers.*.self_attn.linear_out",
    "self_attn.linear_pos": "encoder.layers.*.self_attn.linear_pos",
    "self_attn.rotary_emb": "encoder.embed_positions",
    "self_attn_layer_norm": "encoder.layers.*.self_attn_layer_norm",
    "conv_module.pointwise_conv1": "encoder.layers.*.conv_module.pointwise_conv1",
    "conv_module.pointwise_conv2": "encoder.layers.*.conv_module.pointwise_conv2",
    "conv_module.depthwise_conv": "encoder.layers.*.conv_module.depthwise_conv",
    "conv_module.batch_norm": "encoder.layers.*.conv_module.batch_norm",
    "conv_module.layer_norm": "encoder.layers.*.conv_module.layer_norm",
    "ffn1.w_1": "encoder.layers.*.ffn1.intermediate_dense",
    "ffn1.w_2": "encoder.layers.*.ffn1.output_dense",
    "ffn1.layer_norm": "encoder.layers.*.ffn1_layer_norm",
    "ffn2.w_1": "encoder.layers.*.ffn2.intermediate_dense",
    "ffn2.w_2": "encoder.layers.*.ffn2.output_dense",
    "ffn2.layer_norm": "encoder.layers.*.ffn2_layer_norm",
    "final_layer_norm": "encoder.layers.*.final_layer_norm",
    "encoder.layer_norm": "encoder.layer_norm",
    "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",
}
a__   :					Union[str, Any] 						=      [
    "lm_head",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
]

def 	_lowerCAmelCase (    A__    ,	A__    ,	A__    ,	A__    ,	A__						):
      for attribute in key.split('.'						):
            lowercase__  =	getattr(A__    ,	A__						)

      if weight_type is not None:
            lowercase__  =	getattr(A__    ,	A__						).shape
      else:
            lowercase__  =	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":
            lowercase__  =	value
      elif weight_type == "weight_g":
            lowercase__  =	value
      elif weight_type == "weight_v":
            lowercase__  =	value
      elif weight_type == "bias":
            lowercase__  =	value
      elif weight_type == "running_mean":
            lowercase__  =	value
      elif weight_type == "running_var":
            lowercase__  =	value
      elif weight_type == "num_batches_tracked":
            lowercase__  =	value
      elif weight_type == "inv_freq":
            lowercase__  =	value
      else:
            lowercase__  =	value

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

def 	_lowerCAmelCase (    A__    ,	A__    ,	A__						):
      lowercase__  =	[]
      lowercase__  =	fairseq_model.state_dict()

      lowercase__  =	hf_model.wavaveca_conformer.feature_extractor

      for name, value in fairseq_dict.items():
            lowercase__  =	False
            if "conv_layers" in name:
                  load_conv_layer(
                      A__    ,	A__    ,	A__    ,	A__    ,	hf_model.config.feat_extract_norm == 'group'    ,	)
                  lowercase__  =	True
            else:
                  for key, mapped_key in MAPPING.items():
                        lowercase__  =	'wav2vec2_conformer.' + 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]:
                              lowercase__  =	True
                              if "*" in mapped_key:
                                    lowercase__  =	name.split(A__						)[0].split('.'						)[-2]
                                    lowercase__  =	mapped_key.replace('*'    ,	A__						)
                              if "pos_bias_u" in name:
                                    lowercase__  =	None
                              elif "pos_bias_v" in name:
                                    lowercase__  =	None
                              elif "weight_g" in name:
                                    lowercase__  =	'weight_g'
                              elif "weight_v" in name:
                                    lowercase__  =	'weight_v'
                              elif "bias" in name:
                                    lowercase__  =	'bias'
                              elif "weight" in name:
                                    # TODO: don't match quantizer.weight_proj
                                    lowercase__  =	'weight'
                              elif "running_mean" in name:
                                    lowercase__  =	'running_mean'
                              elif "inv_freq" in name:
                                    lowercase__  =	'inv_freq'
                              elif "running_var" in name:
                                    lowercase__  =	'running_var'
                              elif "num_batches_tracked" in name:
                                    lowercase__  =	'num_batches_tracked'
                              else:
                                    lowercase__  =	None
                              set_recursively(A__    ,	A__    ,	A__    ,	A__    ,	A__						)
                        continue
            if not is_used:
                  unused_weights.append(A__						)

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

def 	_lowerCAmelCase (    A__    ,	A__    ,	A__    ,	A__    ,	A__						):
      lowercase__  =	full_name.split('conv_layers.'						)[-1]
      lowercase__  =	name.split('.'						)
      lowercase__  =	int(items[0]						)
      lowercase__  =	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.'''						)
                  lowercase__  =	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.'''						)
                  lowercase__  =	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.'''						)
                  lowercase__  =	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.'''						)
                  lowercase__  =	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 	_lowerCAmelCase (    A__    ,	A__    ,	A__=None    ,	A__=None    ,	A__=True						):
      if config_path is not None:
            lowercase__  =	WavaVecaConformerConfig.from_pretrained(A__    ,	hidden_act='swish'						)
      else:
            lowercase__  =	WavaVecaConformerConfig()

      if "rope" in checkpoint_path:
            lowercase__  =	'rotary'

      if is_finetuned:
            if dict_path:
                  lowercase__  =	Dictionary.load(A__						)

                  # important change bos & pad token id since CTC symbol is <pad> and
                  # not <s> as in fairseq
                  lowercase__  =	target_dict.pad_index
                  lowercase__  =	target_dict.bos_index
                  lowercase__  =	target_dict.eos_index
                  lowercase__  =	len(target_dict.symbols						)
                  lowercase__  =	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__						)
                  lowercase__  =	target_dict.indices

                  # fairseq has the <pad> and <s> switched
                  lowercase__  =	0
                  lowercase__  =	1
                  with open(A__    ,	'w'    ,	encoding='utf-8'						) as vocab_handle:
                        json.dump(A__    ,	A__						)
                  lowercase__  =	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__    ,	)
                  lowercase__  =	True if config.feat_extract_norm == 'layer' else False
                  lowercase__  =	WavaVecaFeatureExtractor(
                      feature_size=1    ,	sampling_rate=16_000    ,	padding_value=0    ,	do_normalize=A__    ,	return_attention_mask=A__    ,	)
                  lowercase__  =	WavaVecaProcessor(feature_extractor=A__    ,	tokenizer=A__						)
                  processor.save_pretrained(A__						)

            lowercase__  =	WavaVecaConformerForCTC(A__						)
      else:
            lowercase__  =	WavaVecaConformerForPreTraining(A__						)

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

            lowercase__,						lowercase__,						lowercase__  =	fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]    ,	task=A__						)

      lowercase__  =	model[0].eval()

      recursively_load_weights(A__    ,	A__    ,	not is_finetuned						)

      hf_wavavec.save_pretrained(A__						)


if __name__ == "__main__":
       a__   :					Dict 						=      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"
       )
       a__   :					Optional[Any] 						=      parser.parse_args()
       convert_wavaveca_conformer_checkpoint(
           args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
       )





import pytest
import requests

from datasets.utils.file_utils import http_head

from .utils import OfflineSimulationMode, RequestWouldHangIndefinitelyError, offline

@pytest.mark.integration
def 	_lowerCAmelCase (    ):
      with offline(OfflineSimulationMode.CONNECTION_TIMES_OUT						):
            with pytest.raises(A__						):
                  requests.request('GET'    ,	'https://huggingface.co'						)
            with pytest.raises(requests.exceptions.ConnectTimeout						):
                  requests.request('GET'    ,	'https://huggingface.co'    ,	timeout=1.0						)

@pytest.mark.integration
def 	_lowerCAmelCase (    ):
      with offline(OfflineSimulationMode.CONNECTION_FAILS						):
            with pytest.raises(requests.exceptions.ConnectionError						):
                  requests.request('GET'    ,	'https://huggingface.co'						)

def 	_lowerCAmelCase (    ):
      with offline(OfflineSimulationMode.HF_DATASETS_OFFLINE_SET_TO_1						):
            with pytest.raises(A__						):
                  http_head('https://huggingface.co'						)
