





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[str]				=			{
    """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					:				Optional[int]				=			[
    """lm_head""",
    """quantizer.weight_proj""",
    """quantizer.codevectors""",
    """project_q""",
    """project_hid""",
    """projector""",
    """classifier""",
]


def 							__snake_case							(    lowerCAmelCase_	)				->						Tuple:
    SCREAMING_SNAKE_CASE__											=			{}
    with open(lowerCAmelCase_ ,							'''r'''	) as file:
        for line_number, line in enumerate(lowerCAmelCase_	):
            SCREAMING_SNAKE_CASE__											=			line.strip()
            if line:
                SCREAMING_SNAKE_CASE__											=			line.split()
                SCREAMING_SNAKE_CASE__											=			line_number
                SCREAMING_SNAKE_CASE__											=			words[0]
                SCREAMING_SNAKE_CASE__											=			value
    return result


def 							__snake_case							(    lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_	)				->						Optional[Any]:
    for attribute in key.split('''.'''	):
        SCREAMING_SNAKE_CASE__											=			getattr(lowerCAmelCase_ ,							lowerCAmelCase_	)

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

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

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

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


def 							__snake_case							(    lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_	)				->						List[str]:
    SCREAMING_SNAKE_CASE__											=			None
    for param_key in PARAM_MAPPING.keys():
        if full_name.endswith(lowerCAmelCase_	):
            SCREAMING_SNAKE_CASE__											=			PARAM_MAPPING[full_name.split('''.'''	)[-1]]
            SCREAMING_SNAKE_CASE__											=			'''param'''

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

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


_A					:				Union[str, 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 							__snake_case							(    lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_=None ,							lowerCAmelCase_=None	)				->						Tuple:
    SCREAMING_SNAKE_CASE__											=			False
    for key, mapped_key in MAPPING.items():
        SCREAMING_SNAKE_CASE__											=			'''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]:
            SCREAMING_SNAKE_CASE__											=			True
            if "*" in mapped_key:
                SCREAMING_SNAKE_CASE__											=			name.split(lowerCAmelCase_	)[0].split('''.'''	)[-2]
                SCREAMING_SNAKE_CASE__											=			mapped_key.replace('''*''' ,							lowerCAmelCase_	)
            if "weight_g" in name:
                SCREAMING_SNAKE_CASE__											=			'''weight_g'''
            elif "weight_v" in name:
                SCREAMING_SNAKE_CASE__											=			'''weight_v'''
            elif "bias" in name:
                SCREAMING_SNAKE_CASE__											=			'''bias'''
            elif "weight" in name:
                # TODO: don't match quantizer.weight_proj
                SCREAMING_SNAKE_CASE__											=			'''weight'''
            else:
                SCREAMING_SNAKE_CASE__											=			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 							__snake_case							(    lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_	)				->						Optional[int]:
    SCREAMING_SNAKE_CASE__											=			[]
    SCREAMING_SNAKE_CASE__											=			fairseq_model.state_dict()

    SCREAMING_SNAKE_CASE__											=			hf_model.wavaveca.feature_extractor

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

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


def 							__snake_case							(    lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_	)				->						Any:
    SCREAMING_SNAKE_CASE__											=			full_name.split('''conv_layers.'''	)[-1]
    SCREAMING_SNAKE_CASE__											=			name.split('''.'''	)
    SCREAMING_SNAKE_CASE__											=			int(items[0]	)
    SCREAMING_SNAKE_CASE__											=			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.'''	)
            SCREAMING_SNAKE_CASE__											=			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.'''	)
            SCREAMING_SNAKE_CASE__											=			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.'''	)
            SCREAMING_SNAKE_CASE__											=			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.'''	)
            SCREAMING_SNAKE_CASE__											=			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 							__snake_case							(    lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_=None ,							lowerCAmelCase_=None ,							lowerCAmelCase_=True ,							lowerCAmelCase_=False	)				->						int:
    if config_path is not None:
        SCREAMING_SNAKE_CASE__											=			WavaVecaConfig.from_pretrained(lowerCAmelCase_	)
    else:
        SCREAMING_SNAKE_CASE__											=			WavaVecaConfig()

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

    elif is_finetuned:
        if dict_path:
            SCREAMING_SNAKE_CASE__											=			Dictionary.load(lowerCAmelCase_	)

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

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

        SCREAMING_SNAKE_CASE__											=			WavaVecaForCTC(lowerCAmelCase_	)
    else:
        SCREAMING_SNAKE_CASE__											=			WavaVecaForPreTraining(lowerCAmelCase_	)

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

        SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__											=			fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path] ,							task=lowerCAmelCase_	)

    SCREAMING_SNAKE_CASE__											=			model[0].eval()

    recursively_load_weights(lowerCAmelCase_ ,							lowerCAmelCase_ ,							not is_finetuned	)

    hf_wavavec.save_pretrained(lowerCAmelCase_	)


if __name__ == "__main__":
  _A					:				int				=			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					:				List[str]				=			parser.parse_args()

  _A					:				List[str]				=			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'''
import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class   lowercase      (				A__   ):

		"""simple docstring"""

		_a       					=							['image_processor', 'tokenizer']
		_a       					=							'ChineseCLIPImageProcessor'
		_a       					=							('BertTokenizer', 'BertTokenizerFast')




		def __init__(						self       ,  UpperCamelCase_=None       ,  UpperCamelCase_=None       ,  **UpperCamelCase_				):

							'''simple docstring'''


							UpperCamelCase__  :List[Any]				   =					None
							if "feature_extractor" in kwargs:
												warnings.warn(
												    '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
												    ''' instead.'''       ,  UpperCamelCase_       ,  )
												UpperCamelCase__  :List[Any]				   =					kwargs.pop('''feature_extractor'''				)

							UpperCamelCase__  :List[str]				   =					image_processor if image_processor is not None else feature_extractor
							if image_processor is None:
												raise ValueError('''You need to specify an `image_processor`.'''				)
							if tokenizer is None:
												raise ValueError('''You need to specify a `tokenizer`.'''				)

							super().__init__(UpperCamelCase_       ,  UpperCamelCase_				)
							UpperCamelCase__  :Any				   =					self.image_processor




		def __call__(						self       ,  UpperCamelCase_=None       ,  UpperCamelCase_=None       ,  UpperCamelCase_=None       ,  **UpperCamelCase_				):

							'''simple docstring'''


							if text is None and images is None:
												raise ValueError('''You have to specify either text or images. Both cannot be none.'''				)

							if text is not None:
												UpperCamelCase__  :Tuple				   =					self.tokenizer(UpperCamelCase_       ,  return_tensors=UpperCamelCase_       ,  **UpperCamelCase_				)

							if images is not None:
												UpperCamelCase__  :Any				   =					self.image_processor(UpperCamelCase_       ,  return_tensors=UpperCamelCase_       ,  **UpperCamelCase_				)

							if text is not None and images is not None:
												UpperCamelCase__  :Optional[int]				   =					image_features.pixel_values
												return encoding
							elif text is not None:
												return encoding
							else:
												return BatchEncoding(data=dict(**UpperCamelCase_				)       ,  tensor_type=UpperCamelCase_				)




		def        lowerCAmelCase__							(						self       ,  *UpperCamelCase_       ,  **UpperCamelCase_				):

							'''simple docstring'''


							return self.tokenizer.batch_decode(*UpperCamelCase_       ,  **UpperCamelCase_				)




		def        lowerCAmelCase__							(						self       ,  *UpperCamelCase_       ,  **UpperCamelCase_				):

							'''simple docstring'''


							return self.tokenizer.decode(*UpperCamelCase_       ,  **UpperCamelCase_				)




		@property
		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							UpperCamelCase__  :int				   =					self.tokenizer.model_input_names
							UpperCamelCase__  :Any				   =					self.image_processor.model_input_names
							return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names				)				)




		@property
		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							warnings.warn(
							    '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''       ,  UpperCamelCase_       ,  )
							return self.image_processor_class