






import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

# Register SEW's fairseq modules
from sew_asapp import tasks  # noqa: F401

from transformers import (
    SEWConfig,
    SEWForCTC,
    SEWModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
__UpperCamelCase					:		Union[str, Any]	  =						logging.get_logger(__name__)

__UpperCamelCase					:		Optional[Any]	  =						{
    """post_extract_proj""": """feature_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.upsample.0""": """encoder.upsample.projection""",
    """encoder.layer_norm""": """encoder.layer_norm""",
    """w2v_model.layer_norm""": """layer_norm""",
    """w2v_encoder.proj""": """lm_head""",
    """mask_emb""": """masked_spec_embed""",
}
def      a_ (      _A      ,   _A      ,   _A      ,   _A      ,   _A  )			->			Any:







				"""simple docstring"""
				for attribute in key.split('.'  ):
								snake_case__									=     getattr(_A      ,   _A  )

				if weight_type is not None:
								snake_case__									=     getattr(_A      ,   _A  ).shape
				else:
								snake_case__									=     hf_pointer.shape

				assert hf_shape == value.shape, (
				    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":
								snake_case__									=     value
				elif weight_type == "weight_g":
								snake_case__									=     value
				elif weight_type == "weight_v":
								snake_case__									=     value
				elif weight_type == "bias":
								snake_case__									=     value
				else:
								snake_case__									=     value

				logger.info(f'''{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}.'''  )
def      a_ (      _A      ,   _A      ,   _A  )			->			int:







				"""simple docstring"""
				snake_case__									=     []
				snake_case__									=     fairseq_model.state_dict()

				snake_case__									=     hf_model.sew.feature_extractor if is_finetuned else hf_model.feature_extractor

				for name, value in fairseq_dict.items():
								snake_case__									=     False
								if "conv_layers" in name:
												load_conv_layer(
												    _A      ,   _A      ,   _A      ,   _A      ,   hf_model.config.feat_extract_norm == 'group'      ,   )
												snake_case__									=     True
								else:
												for key, mapped_key in MAPPING.items():
																snake_case__									=     'sew.' + mapped_key if (is_finetuned and mapped_key != 'lm_head') else mapped_key

																if key in name or key.split('w2v_model.'  )[-1] == name.split('.'  )[0]:
																				snake_case__									=     True
																				if "*" in mapped_key:
																								snake_case__									=     name.split(_A  )[0].split('.'  )[-2]
																								snake_case__									=     mapped_key.replace('*'      ,   _A  )
																				if "weight_g" in name:
																								snake_case__									=     'weight_g'
																				elif "weight_v" in name:
																								snake_case__									=     'weight_v'
																				elif "weight" in name:
																								snake_case__									=     'weight'
																				elif "bias" in name:
																								snake_case__									=     'bias'
																				else:
																								snake_case__									=     None
																				set_recursively(_A      ,   _A      ,   _A      ,   _A      ,   _A  )
																continue
								if not is_used:
												unused_weights.append(_A  )

				logger.warning(f'''Unused weights: {unused_weights}'''  )
def      a_ (      _A      ,   _A      ,   _A      ,   _A      ,   _A  )			->			Dict:







				"""simple docstring"""
				snake_case__									=     full_name.split('conv_layers.'  )[-1]
				snake_case__									=     name.split('.'  )
				snake_case__									=     int(items[0]  )
				snake_case__									=     int(items[1]  )

				if type_id == 0:
								if "bias" in name:
												assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
												    f'''{full_name} has size {value.shape}, but'''
												    f''' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'''
												)
												snake_case__									=     value
												logger.info(f'''Feat extract conv layer {layer_id} was initialized from {full_name}.'''  )
								elif "weight" in name:
												assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
												    f'''{full_name} has size {value.shape}, but'''
												    f''' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'''
												)
												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:
												assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
												    f'''{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was'''
												    " found."
												)
												snake_case__									=     value
												logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''  )
								elif "weight" in name:
												assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
												    f'''{full_name} has size {value.shape}, but'''
												    f''' {feature_extractor[layer_id].layer_norm.weight.data.shape} was found.'''
												)
												snake_case__									=     value
												logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''  )
				else:
								unused_weights.append(_A  )
def      a_ (      _A      ,   _A  )			->			Optional[Any]:







				"""simple docstring"""
				snake_case__									=     SEWConfig()
				if is_finetuned:
								snake_case__									=     model.wav_encoder.wav_model.cfg
				else:
								snake_case__									=     model.cfg

				snake_case__									=     fs_config.conv_bias
				snake_case__									=     eval(fs_config.conv_feature_layers  )
				snake_case__									=     [x[0] for x in conv_layers]
				snake_case__									=     [x[1] for x in conv_layers]
				snake_case__									=     [x[2] for x in conv_layers]
				snake_case__									=     'gelu'
				snake_case__									=     'layer' if fs_config.extractor_mode == 'layer_norm' else 'group'
				snake_case__									=     0.0
				snake_case__									=     fs_config.activation_fn.name
				snake_case__									=     fs_config.encoder_embed_dim
				snake_case__									=     0.02
				snake_case__									=     fs_config.encoder_ffn_embed_dim
				snake_case__									=     1e-5
				snake_case__									=     fs_config.encoder_layerdrop
				snake_case__									=     fs_config.encoder_attention_heads
				snake_case__									=     fs_config.conv_pos_groups
				snake_case__									=     fs_config.conv_pos
				snake_case__									=     len(_A  )
				snake_case__									=     fs_config.encoder_layers
				snake_case__									=     fs_config.squeeze_factor

				# take care of any params that are overridden by the Wav2VecCtc model
				if is_finetuned:
								snake_case__									=     model.cfg
								snake_case__									=     fs_config.final_dropout
								snake_case__									=     fs_config.layerdrop
				snake_case__									=     fs_config.activation_dropout
				snake_case__									=     fs_config.mask_prob > 0 or fs_config.mask_channel_prob > 0
				snake_case__									=     fs_config.attention_dropout
				snake_case__									=     fs_config.dropout_input
				snake_case__									=     fs_config.dropout
				snake_case__									=     fs_config.mask_channel_length
				snake_case__									=     fs_config.mask_channel_prob
				snake_case__									=     fs_config.mask_length
				snake_case__									=     fs_config.mask_prob

				snake_case__									=     'Wav2Vec2FeatureExtractor'
				snake_case__									=     'Wav2Vec2CTCTokenizer'

				return config
@torch.no_grad()
def      a_ (      _A      ,   _A      ,   _A=None      ,   _A=None      ,   _A=True  )			->			List[Any]:







				"""simple docstring"""

				if is_finetuned:
								snake_case__									=     fairseq.checkpoint_utils.load_model_ensemble_and_task(
								    [checkpoint_path]      ,   arg_overrides={'data': '/'.join(dict_path.split('/'  )[:-1]  )}  )
				else:
								snake_case__									=     fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]  )

				if config_path is not None:
								snake_case__									=     SEWConfig.from_pretrained(_A  )
				else:
								snake_case__									=     convert_config(model[0]      ,   _A  )
				snake_case__									=     model[0].eval()

				snake_case__									=     True if config.feat_extract_norm == 'layer' else False
				snake_case__									=     WavaVecaFeatureExtractor(
				    feature_size=1      ,   sampling_rate=16000      ,   padding_value=0      ,   do_normalize=_A      ,   return_attention_mask=_A      ,   )

				if is_finetuned:
								if dict_path:
												snake_case__									=     Dictionary.load(_A  )

												# important change bos & pad token id since CTC symbol is <pad> and
												# not <s> as in fairseq
												snake_case__									=     target_dict.pad_index
												snake_case__									=     target_dict.bos_index
												snake_case__									=     target_dict.pad_index
												snake_case__									=     target_dict.bos_index
												snake_case__									=     target_dict.eos_index
												snake_case__									=     len(target_dict.symbols  )
												snake_case__									=     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  )
												with open(_A      ,   'w'      ,   encoding='utf-8'  ) as vocab_handle:
																json.dump(target_dict.indices      ,   _A  )
												snake_case__									=     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      ,   )
												snake_case__									=     WavaVecaProcessor(feature_extractor=_A      ,   tokenizer=_A  )
												processor.save_pretrained(_A  )

								snake_case__									=     SEWForCTC(_A  )
				else:
								snake_case__									=     SEWModel(_A  )
								feature_extractor.save_pretrained(_A  )

				recursively_load_weights(_A      ,   _A      ,   _A  )

				hf_model.save_pretrained(_A  )


if __name__ == "__main__":
							__UpperCamelCase					:		Optional[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(
							    """--is_finetuned""", action="""store_true""", help="""Whether the model to convert is a fine-tuned model or not"""
							)
							__UpperCamelCase					:		List[Any]	  =						parser.parse_args()
							convert_sew_checkpoint(
							    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
							)












from collections.abc import Sequence
def      a_ (      _A      ,   _A  )			->			float:







				"""simple docstring"""
				return sum(c * (x**i) for i, c in enumerate(_A  )  )
def      a_ (      _A      ,   _A  )			->			float:







				"""simple docstring"""
				snake_case__									=     0.0
				for coeff in reversed(_A  ):
								snake_case__									=     result * x + coeff
				return result


if __name__ == "__main__":
							__UpperCamelCase					:		str	  =						(0.0, 0.0, 5.0, 9.3, 7.0)
							__UpperCamelCase					:		Dict	  =						1_0.0
							print(evaluate_poly(poly, x))
							print(horner(poly, x))




