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()
snake_case__				= logging.get_logger(__name__)

snake_case__				= {
    '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',
}
snake_case__				= [
    'lm_head',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
    'projector',
    'classifier',
]
def       __magic_name__(  __UpperCAmelCase		):



      '''simple docstring'''

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



      '''simple docstring'''

      for attribute in key.split('''.'''		):
            _lowerCamelCase			    =				getattr(snake_case__ ,						snake_case__		)

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

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

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

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



      '''simple docstring'''

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

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

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


snake_case__				= {
    '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       __magic_name__(  __UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase=None ,						__UpperCAmelCase=None		):



      '''simple docstring'''

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



      '''simple docstring'''

      _lowerCamelCase			    =				[]
      _lowerCamelCase			    =				fairseq_model.state_dict()

      _lowerCamelCase			    =				hf_model.wavaveca.feature_extractor

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

      logger.warning(F'Unused weights: {unused_weights}'		)
def       __magic_name__(  __UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase		):



      '''simple docstring'''

      _lowerCamelCase			    =				full_name.split('''conv_layers.'''		)[-1]
      _lowerCamelCase			    =				name.split('''.'''		)
      _lowerCamelCase			    =				int(items[0]		)
      _lowerCamelCase			    =				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.'		)
                  _lowerCamelCase			    =				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.'		)
                  _lowerCamelCase			    =				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.'		)
                  _lowerCamelCase			    =				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.'		)
                  _lowerCamelCase			    =				value
                  logger.info(F'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'		)
      else:
            unused_weights.append(snake_case__		)
@torch.no_grad()
def       __magic_name__(  __UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase=None ,						__UpperCAmelCase=None ,						__UpperCAmelCase=True ,						__UpperCAmelCase=False		):



      '''simple docstring'''

      if config_path is not None:
            _lowerCamelCase			    =				WavaVecaConfig.from_pretrained(snake_case__		)
      else:
            _lowerCamelCase			    =				WavaVecaConfig()

      if is_seq_class:
            _lowerCamelCase			    =				read_txt_into_dict(snake_case__		)
            _lowerCamelCase			    =				idalabel
            _lowerCamelCase			    =				WavaVecaForSequenceClassification(snake_case__		)
            _lowerCamelCase			    =				WavaVecaFeatureExtractor(
                feature_size=1 ,						sampling_rate=1_6000 ,						padding_value=0 ,						do_normalize=snake_case__ ,						return_attention_mask=snake_case__ ,						)
            feature_extractor.save_pretrained(snake_case__		)

      elif is_finetuned:
            if dict_path:
                  _lowerCamelCase			    =				Dictionary.load(snake_case__		)

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

                  # fairseq has the <pad> and <s> switched
                  _lowerCamelCase			    =				0
                  _lowerCamelCase			    =				1
                  with open(snake_case__ ,						'''w''' ,						encoding='''utf-8'''		) as vocab_handle:
                        json.dump(snake_case__ ,						snake_case__		)
                  _lowerCamelCase			    =				WavaVecaCTCTokenizer(
                      snake_case__ ,						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=snake_case__ ,						)
                  _lowerCamelCase			    =				True if config.feat_extract_norm == '''layer''' else False
                  _lowerCamelCase			    =				WavaVecaFeatureExtractor(
                      feature_size=1 ,						sampling_rate=1_6000 ,						padding_value=0 ,						do_normalize=snake_case__ ,						return_attention_mask=snake_case__ ,						)
                  _lowerCamelCase			    =				WavaVecaProcessor(feature_extractor=snake_case__ ,						tokenizer=snake_case__		)
                  processor.save_pretrained(snake_case__		)

            _lowerCamelCase			    =				WavaVecaForCTC(snake_case__		)
      else:
            _lowerCamelCase			    =				WavaVecaForPreTraining(snake_case__		)

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

            _lowerCamelCase    ,					_lowerCamelCase    ,					_lowerCamelCase			    =				fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path] ,						task=snake_case__		)

      _lowerCamelCase			    =				model[0].eval()

      recursively_load_weights(snake_case__ ,						snake_case__ ,						not is_finetuned		)

      hf_wavavec.save_pretrained(snake_case__		)


if __name__ == "__main__":
     snake_case__				= 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',
     )
     snake_case__				= parser.parse_args()

     snake_case__				= 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,
     )
from .testing import (
    are_the_same_tensors,
    execute_subprocess_async,
    require_bnb,
    require_cpu,
    require_cuda,
    require_huggingface_suite,
    require_mps,
    require_multi_gpu,
    require_multi_xpu,
    require_safetensors,
    require_single_gpu,
    require_single_xpu,
    require_torch_min_version,
    require_tpu,
    require_xpu,
    skip,
    slow,
)
from .training import RegressionDataset, RegressionModel, RegressionModelaXPU


from .scripts import test_script, test_sync, test_ops  # isort: skip