
'''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()
_lowercase          =						logging.get_logger(__name__)

_lowercase          =						{
    """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""",
}
_lowercase          =						[
    """lm_head""",
    """quantizer.weight_proj""",
    """quantizer.codevectors""",
    """project_q""",
    """project_hid""",
    """projector""",
    """classifier""",
]


def A (__lowerCamelCase :Optional[int]						):
						_lowerCAmelCase    		=					{}
						with open(__lowerCamelCase					,      """r"""						) as file:
												for line_number, line in enumerate(__lowerCamelCase						):
																		_lowerCAmelCase    		=					line.strip()
																		if line:
																								_lowerCAmelCase    		=					line.split()
																								_lowerCAmelCase    		=					line_number
																								_lowerCAmelCase    		=					words[0]
																								_lowerCAmelCase    		=					value
						return result


def A (__lowerCamelCase :Optional[Any]					,      __lowerCamelCase :Any					,      __lowerCamelCase :Tuple					,      __lowerCamelCase :List[Any]					,      __lowerCamelCase :List[str]						):
						for attribute in key.split("""."""						):
												_lowerCAmelCase    		=					getattr(__lowerCamelCase					,      __lowerCamelCase						)

						_lowerCAmelCase    		=					None
						for param_key in PARAM_MAPPING.keys():
												if full_name.endswith(__lowerCamelCase						):
																		_lowerCAmelCase    		=					PARAM_MAPPING[full_name.split("""."""						)[-1]]
																		_lowerCAmelCase    		=					"""param"""

						if weight_type is not None and weight_type != "param":
												_lowerCAmelCase    		=					getattr(__lowerCamelCase					,      __lowerCamelCase						).shape
						elif weight_type is not None and weight_type == "param":
												_lowerCAmelCase    		=					hf_pointer
												for attribute in hf_param_name.split("""."""						):
																		_lowerCAmelCase    		=					getattr(__lowerCamelCase					,      __lowerCamelCase						)
												_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(__lowerCamelCase					,      __lowerCamelCase						)
												_lowerCAmelCase    		=					value
						else:
												_lowerCAmelCase    		=					value

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


def A (__lowerCamelCase :Union[str, Any]					,      __lowerCamelCase :Tuple					,      __lowerCamelCase :Dict					,      __lowerCamelCase :List[Any]					,      __lowerCamelCase :int						):
						_lowerCAmelCase    		=					None
						for param_key in PARAM_MAPPING.keys():
												if full_name.endswith(__lowerCamelCase						):
																		_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]


_lowercase          =						{
    """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 A (__lowerCamelCase :Any					,      __lowerCamelCase :int					,      __lowerCamelCase :List[str]=None					,      __lowerCamelCase :List[Any]=None						):
						_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(__lowerCamelCase						)[0].split("""."""						)[-2]
																								_lowerCAmelCase    		=					mapped_key.replace("""*"""					,      __lowerCamelCase						)
																		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(__lowerCamelCase					,      __lowerCamelCase					,      __lowerCamelCase					,      __lowerCamelCase					,      __lowerCamelCase						)
																		else:
																								set_recursively(__lowerCamelCase					,      __lowerCamelCase					,      __lowerCamelCase					,      __lowerCamelCase					,      __lowerCamelCase						)
																		return is_used
						return is_used


def A (__lowerCamelCase :Any					,      __lowerCamelCase :Dict					,      __lowerCamelCase :Dict						):
						_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(
																		    __lowerCamelCase					,      __lowerCamelCase					,      __lowerCamelCase					,      __lowerCamelCase					,      hf_model.config.feat_extract_norm == """group"""					,      )
																		_lowerCAmelCase    		=					True
												else:
																		_lowerCAmelCase    		=					load_wavaveca_layer(__lowerCamelCase					,      __lowerCamelCase					,      __lowerCamelCase						)
												if not is_used:
																		unused_weights.append(__lowerCamelCase						)

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


def A (__lowerCamelCase :Tuple					,      __lowerCamelCase :Optional[int]					,      __lowerCamelCase :Any					,      __lowerCamelCase :List[Any]					,      __lowerCamelCase :List[Any]						):
						_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(__lowerCamelCase						)


@torch.no_grad()
def A (__lowerCamelCase :List[str]					,      __lowerCamelCase :Tuple					,      __lowerCamelCase :List[Any]=None					,      __lowerCamelCase :Union[str, Any]=None					,      __lowerCamelCase :str=True					,      __lowerCamelCase :str=False						):
						if config_path is not None:
												_lowerCAmelCase    		=					WavaVecaConfig.from_pretrained(__lowerCamelCase						)
						else:
												_lowerCAmelCase    		=					WavaVecaConfig()

						if is_seq_class:
												_lowerCAmelCase    		=					read_txt_into_dict(__lowerCamelCase						)
												_lowerCAmelCase    		=					idalabel
												_lowerCAmelCase    		=					WavaVecaForSequenceClassification(__lowerCamelCase						)
												_lowerCAmelCase    		=					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:
																		_lowerCAmelCase    		=					Dictionary.load(__lowerCamelCase						)

																		# 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(__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						)
																		_lowerCAmelCase    		=					target_dict.indices

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

												_lowerCAmelCase    		=					WavaVecaForCTC(__lowerCamelCase						)
						else:
												_lowerCAmelCase    		=					WavaVecaForPreTraining(__lowerCamelCase						)

						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(__lowerCamelCase						)

												_lowerCAmelCase		,       _lowerCAmelCase		,       _lowerCAmelCase    		=					fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]					,      task=__lowerCamelCase						)

						_lowerCAmelCase    		=					model[0].eval()

						recursively_load_weights(__lowerCamelCase					,      __lowerCamelCase					,      not is_finetuned						)

						hf_wavavec.save_pretrained(__lowerCamelCase						)


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

						_lowercase          =						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 typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__a  			=						{
    "configuration_bigbird_pegasus": [
        "BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "BigBirdPegasusConfig",
        "BigBirdPegasusOnnxConfig",
    ],
}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __a  			=						[
           "BIGBIRD_PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST",
           "BigBirdPegasusForCausalLM",
           "BigBirdPegasusForConditionalGeneration",
           "BigBirdPegasusForQuestionAnswering",
           "BigBirdPegasusForSequenceClassification",
           "BigBirdPegasusModel",
           "BigBirdPegasusPreTrainedModel",
       ]


if TYPE_CHECKING:
       from .configuration_bigbird_pegasus import (
           BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP,
           BigBirdPegasusConfig,
           BigBirdPegasusOnnxConfig,
       )

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_bigbird_pegasus import (
                  BIGBIRD_PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST,
                  BigBirdPegasusForCausalLM,
                  BigBirdPegasusForConditionalGeneration,
                  BigBirdPegasusForQuestionAnswering,
                  BigBirdPegasusForSequenceClassification,
                  BigBirdPegasusModel,
                  BigBirdPegasusPreTrainedModel,
              )


else:
       import sys

       __a  			=						_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
