






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()
__UpperCamelCase      :      Dict     						=						logging.get_logger(__name__)

__UpperCamelCase      :      Optional[Any]     						=						{
    """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""",
}
__UpperCamelCase      :      Dict     						=						[
    """lm_head""",
    """quantizer.weight_proj""",
    """quantizer.codevectors""",
    """project_q""",
    """project_hid""",
    """projector""",
    """classifier""",
]





def 				snake_case  (     lowerCamelCase					):




	'''simple docstring'''







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





def 				snake_case  (     lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase					):




	'''simple docstring'''







	for attribute in key.split("""."""					):
		__lowercase             =  getattr(A__				,  A__					)

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

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

		# let's reduce dimension
		__lowercase             =  value[0]
	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 == "param":
		for attribute in hf_param_name.split("""."""					):
			__lowercase             =  getattr(A__				,  A__					)
		__lowercase             =  value
	else:
		__lowercase             =  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					):




	'''simple docstring'''







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

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

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


__UpperCamelCase      :      Dict     						=						{
    """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					):




	'''simple docstring'''







	__lowercase             =  False
	for key, mapped_key in MAPPING.items():
		__lowercase             =  """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]:
			__lowercase             =  True
			if "*" in mapped_key:
				__lowercase             =  name.split(A__					)[0].split("""."""					)[-2]
				__lowercase             =  mapped_key.replace("""*"""				,  A__					)
			if "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"""
			else:
				__lowercase             =  None
			if hf_dict is not None:
				rename_dict(A__				,  A__				,  A__				,  A__				,  A__					)
			else:
				set_recursively(A__				,  A__				,  A__				,  A__				,  A__					)
			return is_used
	return is_used





def 				snake_case  (     lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase					):




	'''simple docstring'''







	__lowercase             =  []
	__lowercase             =  fairseq_model.state_dict()

	__lowercase             =  hf_model.wavaveca.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:
			__lowercase             =  load_wavaveca_layer(A__				,  A__				,  A__					)
		if not is_used:
			unused_weights.append(A__					)

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





def 				snake_case  (     lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase					):




	'''simple docstring'''







	__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 				snake_case  (     lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase=None				,  lowerCamelCase=None				,  lowerCamelCase=True				,  lowerCamelCase=False					):




	'''simple docstring'''







	if config_path is not None:
		__lowercase             =  WavaVecaConfig.from_pretrained(A__					)
	else:
		__lowercase             =  WavaVecaConfig()

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

	elif 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             =  WavaVecaForCTC(A__					)
	else:
		__lowercase             =  WavaVecaForPreTraining(A__					)

	if is_finetuned or is_seq_class:
		__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__":
						__UpperCamelCase      :      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""",
						)
						__UpperCamelCase      :      Dict     						=						parser.parse_args()

						__UpperCamelCase      :      Union[str, Any]     						=						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 __future__ import annotations





def 				snake_case  (     lowerCamelCase					):




	'''simple docstring'''







	if len(lowerCamelCase					) < 2:
		raise ValueError("""Monogons and Digons are not polygons in the Euclidean space"""					)
	if any(i <= 0 for i in nums					):
		raise ValueError("""All values must be greater than 0"""					)
	__lowercase             =  nums.copy()
	copy_nums.sort()
	return copy_nums[-1] < sum(copy_nums[:-1]					)


if __name__ == "__main__":
						import doctest

						doctest.testmod()

