

"""simple docstring"""





import argparse

import fairseq
import torch

from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging


logging.set_verbosity_info()
_a       =							logging.get_logger(__name__)

_a       =							{
    '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',
    'encoder.layer_norm_for_extract': 'layer_norm_for_extract',
    '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',
    'label_embs_concat': 'label_embeddings_concat',
    'mask_emb': 'masked_spec_embed',
    'spk_proj': 'speaker_proj',
}
_a       =							[
    'lm_head',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
    'label_embeddings_concat',
    'speaker_proj',
    'layer_norm_for_extract',
]




def   _A  (  UpperCamelCase_     :       int,			UpperCamelCase_     :       Any,			UpperCamelCase_     :       int,			UpperCamelCase_     :       Union[str, Any],			UpperCamelCase_     :       Any)	->						List[Any]:
   '''simple docstring'''


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

   if weight_type is not None:
      __lowercase								=						getattr(__a,			__a).shape
   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
   else:
      __lowercase								=						value

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




def   _A  (  UpperCamelCase_     :       int,			UpperCamelCase_     :       Tuple)	->						Union[str, Any]:
   '''simple docstring'''


   __lowercase								=						[]
   __lowercase								=						fairseq_model.state_dict()

   __lowercase								=						hf_model.unispeech_sat.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:
         for key, mapped_key in MAPPING.items():
            __lowercase								=						'''unispeech_sat.''' + 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]:
               if "layer_norm_for_extract" in name and (".".join(name.split(".")[:-1]) != key):
                  # special case since naming is very similar
                  continue
               __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
               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  (  UpperCamelCase_     :       Any,			UpperCamelCase_     :       str,			UpperCamelCase_     :       List[Any],			UpperCamelCase_     :       List[str],			UpperCamelCase_     :       str)	->						List[Any]:
   '''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[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[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   _A  (  UpperCamelCase_     :       Optional[int],			UpperCamelCase_     :       List[str],			UpperCamelCase_     :       Dict=None,			UpperCamelCase_     :       Any=None,			UpperCamelCase_     :       Union[str, Any]=True)	->						str:
   '''simple docstring'''


   if config_path is not None:
      __lowercase								=						UniSpeechSatConfig.from_pretrained(__a)
   else:
      __lowercase								=						UniSpeechSatConfig()

   __lowercase								=						''''''

   if is_finetuned:
      __lowercase								=						UniSpeechSatForCTC(__a)
   else:
      __lowercase								=						UniSpeechSatForPreTraining(__a)

   __lowercase								=						fairseq.checkpoint_utils.load_model_ensemble_and_task(
       [checkpoint_path],			arg_overrides={"data": "/".join(dict_path.split("/")[:-1])})
   __lowercase								=						model[0].eval()

   recursively_load_weights(__a,			__a)

   hf_wavavec.save_pretrained(__a)


if __name__ == "__main__":
   _a       =							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'
   )
   _a       =							parser.parse_args()
   convert_unispeech_sat_checkpoint(
       args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
   )







from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


a__				=		logging.get_logger(__name__)

a__				=		{
    '''junnyu/roformer_chinese_small''': '''https://huggingface.co/junnyu/roformer_chinese_small/resolve/main/config.json''',
    '''junnyu/roformer_chinese_base''': '''https://huggingface.co/junnyu/roformer_chinese_base/resolve/main/config.json''',
    '''junnyu/roformer_chinese_char_small''': (
        '''https://huggingface.co/junnyu/roformer_chinese_char_small/resolve/main/config.json'''
    ),
    '''junnyu/roformer_chinese_char_base''': (
        '''https://huggingface.co/junnyu/roformer_chinese_char_base/resolve/main/config.json'''
    ),
    '''junnyu/roformer_small_discriminator''': (
        '''https://huggingface.co/junnyu/roformer_small_discriminator/resolve/main/config.json'''
    ),
    '''junnyu/roformer_small_generator''': (
        '''https://huggingface.co/junnyu/roformer_small_generator/resolve/main/config.json'''
    ),
    # See all RoFormer models at https://huggingface.co/models?filter=roformer
}

class 				UpperCAmelCase_		(			__lowercase   ):
							"""simple docstring"""







							UpperCAmelCase__       :						Optional[Any]	    =    "roformer"


							def __init__(			self       ,							_a=5_0_0_0_0       ,							_a=None       ,							_a=7_6_8       ,							_a=1_2       ,							_a=1_2       ,							_a=3_0_7_2       ,							_a="gelu"       ,							_a=0.1       ,							_a=0.1       ,							_a=1_5_3_6       ,							_a=2       ,							_a=0.02       ,							_a=1e-1_2       ,							_a=0       ,							_a=False       ,							_a=True       ,							**_a       ,							)    ->    List[str]:
												super().__init__(pad_token_id=_a       ,							**_a       )

												_a	:    Tuple         =		vocab_size
												_a	:    List[Any]         =		hidden_size if embedding_size is None else embedding_size
												_a	:    Any         =		hidden_size
												_a	:    Any         =		num_hidden_layers
												_a	:    List[Any]         =		num_attention_heads
												_a	:    str         =		hidden_act
												_a	:    Any         =		intermediate_size
												_a	:    Dict         =		hidden_dropout_prob
												_a	:    Optional[Any]         =		attention_probs_dropout_prob
												_a	:    str         =		max_position_embeddings
												_a	:    Dict         =		type_vocab_size
												_a	:    List[Any]         =		initializer_range
												_a	:    Dict         =		layer_norm_eps
												_a	:    Dict         =		rotary_value
												_a	:    Dict         =		use_cache



class 				UpperCAmelCase_		(			__lowercase   ):
							"""simple docstring"""







							@property
							def 				__lowercase				(			self       )    ->    Mapping[str, Mapping[int, str]]:
												if self.task == "multiple-choice":
																	_a	:    Dict         =		{0: '''batch''', 1: '''choice''', 2: '''sequence'''}
												else:
																	_a	:    List[Any]         =		{0: '''batch''', 1: '''sequence'''}
												_a	:    Union[str, Any]         =		{0: '''batch''', 1: '''sequence'''}
												return OrderedDict(
												    [
												        ('''input_ids''', dynamic_axis),
												        ('''attention_mask''', dynamic_axis),
												        ('''token_type_ids''', dynamic_axis),
												    ]       )



