


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     :   Optional[int]            =   logging.get_logger(__name__)

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






def 							_lowercase    (		SCREAMING_SNAKE_CASE_   :		List[str]	):





			"""simple docstring"""




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






def 							_lowercase    (		SCREAMING_SNAKE_CASE_   :		List[str]       ,  SCREAMING_SNAKE_CASE_   :		Optional[Any]       ,  SCREAMING_SNAKE_CASE_   :		Any       ,  SCREAMING_SNAKE_CASE_   :		Any       ,  SCREAMING_SNAKE_CASE_   :		List[Any]	):





			"""simple docstring"""




			for attribute in key.split("""."""	):
						UpperCamelCase         =					getattr(SCREAMING_SNAKE_CASE_       ,  SCREAMING_SNAKE_CASE_	)

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

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

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

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






def 							_lowercase    (		SCREAMING_SNAKE_CASE_   :		Dict       ,  SCREAMING_SNAKE_CASE_   :		str       ,  SCREAMING_SNAKE_CASE_   :		Dict       ,  SCREAMING_SNAKE_CASE_   :		int       ,  SCREAMING_SNAKE_CASE_   :		Optional[Any]	):





			"""simple docstring"""




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

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

			UpperCamelCase         =					value if """lm_head""" in full_key else value[0]


__snake_case     :   Optional[int]            =   {
    """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    (		SCREAMING_SNAKE_CASE_   :		int       ,  SCREAMING_SNAKE_CASE_   :		List[str]       ,  SCREAMING_SNAKE_CASE_   :		str=None       ,  SCREAMING_SNAKE_CASE_   :		List[Any]=None	):





			"""simple docstring"""




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






def 							_lowercase    (		SCREAMING_SNAKE_CASE_   :		Any       ,  SCREAMING_SNAKE_CASE_   :		Tuple       ,  SCREAMING_SNAKE_CASE_   :		Any	):





			"""simple docstring"""




			UpperCamelCase         =					[]
			UpperCamelCase         =					fairseq_model.state_dict()

			UpperCamelCase         =					hf_model.wavaveca.feature_extractor

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

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






def 							_lowercase    (		SCREAMING_SNAKE_CASE_   :		int       ,  SCREAMING_SNAKE_CASE_   :		List[Any]       ,  SCREAMING_SNAKE_CASE_   :		Union[str, Any]       ,  SCREAMING_SNAKE_CASE_   :		Optional[Any]       ,  SCREAMING_SNAKE_CASE_   :		List[str]	):





			"""simple docstring"""




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






@torch.no_grad()
def 							_lowercase    (		SCREAMING_SNAKE_CASE_   :		List[Any]       ,  SCREAMING_SNAKE_CASE_   :		str       ,  SCREAMING_SNAKE_CASE_   :		Any=None       ,  SCREAMING_SNAKE_CASE_   :		int=None       ,  SCREAMING_SNAKE_CASE_   :		str=True       ,  SCREAMING_SNAKE_CASE_   :		Any=False	):





			"""simple docstring"""




			if config_path is not None:
						UpperCamelCase         =					WavaVecaConfig.from_pretrained(SCREAMING_SNAKE_CASE_	)
			else:
						UpperCamelCase         =					WavaVecaConfig()

			if is_seq_class:
						UpperCamelCase         =					read_txt_into_dict(SCREAMING_SNAKE_CASE_	)
						UpperCamelCase         =					idalabel
						UpperCamelCase         =					WavaVecaForSequenceClassification(SCREAMING_SNAKE_CASE_	)
						UpperCamelCase         =					WavaVecaFeatureExtractor(
						    feature_size=1       ,  sampling_rate=16_000       ,  padding_value=0       ,  do_normalize=SCREAMING_SNAKE_CASE_       ,  return_attention_mask=SCREAMING_SNAKE_CASE_       ,  )
						feature_extractor.save_pretrained(SCREAMING_SNAKE_CASE_	)

			elif is_finetuned:
						if dict_path:
									UpperCamelCase         =					Dictionary.load(SCREAMING_SNAKE_CASE_	)

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

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

						UpperCamelCase         =					WavaVecaForCTC(SCREAMING_SNAKE_CASE_	)
			else:
						UpperCamelCase         =					WavaVecaForPreTraining(SCREAMING_SNAKE_CASE_	)

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

						UpperCamelCase  ,  UpperCamelCase  ,  UpperCamelCase         =					fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]       ,  task=SCREAMING_SNAKE_CASE_	)

			UpperCamelCase         =					model[0].eval()

			recursively_load_weights(SCREAMING_SNAKE_CASE_       ,  SCREAMING_SNAKE_CASE_       ,  not is_finetuned	)

			hf_wavavec.save_pretrained(SCREAMING_SNAKE_CASE_	)


if __name__ == "__main__":
			__snake_case     :   Union[str, Any]            =   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     :   Tuple            =   parser.parse_args()

			__snake_case     :   Optional[int]            =   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 ...configuration_utils import PretrainedConfig
from ...utils import logging


__snake_case            =   logging.get_logger(__name__)

__snake_case            =   {
    "microsoft/markuplm-base": "https://huggingface.co/microsoft/markuplm-base/resolve/main/config.json",
    "microsoft/markuplm-large": "https://huggingface.co/microsoft/markuplm-large/resolve/main/config.json",
}




class    UpperCAmelCase					(       __snake_case    ):
			lowercase						= """markuplm"""




			def __init__(						self							:		Optional[Any]       ,       __magic_name__							:		List[Any]=3_0_5_2_2       ,       __magic_name__							:		int=7_6_8       ,       __magic_name__							:		List[Any]=1_2       ,       __magic_name__							:		List[Any]=1_2       ,       __magic_name__							:		str=3_0_7_2       ,       __magic_name__							:		Optional[int]="gelu"       ,       __magic_name__							:		Dict=0.1       ,       __magic_name__							:		Optional[int]=0.1       ,       __magic_name__							:		Any=5_1_2       ,       __magic_name__							:		List[str]=2       ,       __magic_name__							:		Dict=0.02       ,       __magic_name__							:		List[str]=1e-12       ,       __magic_name__							:		str=0       ,       __magic_name__							:		List[str]=0       ,       __magic_name__							:		Optional[Any]=2       ,       __magic_name__							:		Dict=2_5_6       ,       __magic_name__							:		Tuple=1_0_2_4       ,       __magic_name__							:		Any=2_1_6       ,       __magic_name__							:		str=1_0_0_1       ,       __magic_name__							:		Dict=3_2       ,       __magic_name__							:		Optional[int]=5_0       ,       __magic_name__							:		List[Any]="absolute"       ,       __magic_name__							:		Any=True       ,       __magic_name__							:		Optional[Any]=None       ,       **__magic_name__							:		List[str]       ,       ):







						"""simple docstring"""







						super().__init__(
						    pad_token_id=__magic_name__       ,       bos_token_id=__magic_name__       ,       eos_token_id=__magic_name__       ,       **__magic_name__       ,       )
						UpperCamelCase         =					vocab_size
						UpperCamelCase         =					hidden_size
						UpperCamelCase         =					num_hidden_layers
						UpperCamelCase         =					num_attention_heads
						UpperCamelCase         =					hidden_act
						UpperCamelCase         =					intermediate_size
						UpperCamelCase         =					hidden_dropout_prob
						UpperCamelCase         =					attention_probs_dropout_prob
						UpperCamelCase         =					max_position_embeddings
						UpperCamelCase         =					type_vocab_size
						UpperCamelCase         =					initializer_range
						UpperCamelCase         =					layer_norm_eps
						UpperCamelCase         =					position_embedding_type
						UpperCamelCase         =					use_cache
						UpperCamelCase         =					classifier_dropout
						# additional properties
						UpperCamelCase         =					max_depth
						UpperCamelCase         =					max_xpath_tag_unit_embeddings
						UpperCamelCase         =					max_xpath_subs_unit_embeddings
						UpperCamelCase         =					tag_pad_id
						UpperCamelCase         =					subs_pad_id
						UpperCamelCase         =					xpath_unit_hidden_size




