





import math

import torch
from torch import nn

from ..configuration_utils import ConfigMixin, register_to_config
from .attention_processor import Attention
from .embeddings import get_timestep_embedding
from .modeling_utils import ModelMixin

class    _UpperCAmelCase							(		UpperCamelCase__			,       UpperCamelCase__     ):
						@register_to_config
						def __init__(		self      ,       a__ = 128      ,       a__ = 256      ,       a__ = 2000.0      ,       a__ = 768      ,       a__ = 12      ,       a__ = 12      ,       a__ = 64      ,       a__ = 2048      ,       a__ = 0.1      ,       ):
									super().__init__()

									A_     :				Optional[int]			      =	nn.Sequential(
									    nn.Linear(_a      ,       d_model * 4      ,       bias=_a       )      ,       nn.SiLU()      ,       nn.Linear(d_model * 4      ,       d_model * 4      ,       bias=_a       )      ,       nn.SiLU()      ,       )

									A_     :				Tuple			      =	nn.Embedding(_a      ,       _a       )
									A_     :				str			      =	False

									A_     :				Optional[int]			      =	nn.Linear(_a      ,       _a      ,       bias=_a       )

									A_     :				Optional[Any]			      =	nn.Dropout(p=_a       )

									A_     :				Dict			      =	nn.ModuleList()
									for lyr_num in range(_a       ):
												# FiLM conditional T5 decoder
												A_     :				int			      =	DecoderLayer(d_model=_a      ,       d_kv=_a      ,       num_heads=_a      ,       d_ff=_a      ,       dropout_rate=_a       )
												self.decoders.append(_a       )

									A_     :				List[str]			      =	TaLayerNorm(_a       )

									A_     :				Tuple			      =	nn.Dropout(p=_a       )
									A_     :				str			      =	nn.Linear(_a      ,       _a      ,       bias=_a       )
						def 		_lowerCamelCase      (		self      ,       a__      ,       a__       ):
									A_     :				Optional[int]			      =	torch.mul(query_input.unsqueeze(-1       )      ,       key_input.unsqueeze(-2       )       )
									return mask.unsqueeze(-3       )






						def 		_lowerCamelCase      (		self      ,       a__      ,       a__      ,       a__       ):
									A_     :				Any			      =	decoder_input_tokens.shape
									assert decoder_noise_time.shape == (batch,)

									# decoder_noise_time is in [0, 1), so rescale to expected timing range.
									A_     :				int			      =	get_timestep_embedding(
									    decoder_noise_time * self.config.max_decoder_noise_time      ,       embedding_dim=self.config.d_model      ,       max_period=self.config.max_decoder_noise_time      ,       ).to(dtype=self.dtype       )

									A_     :				int			      =	self.conditioning_emb(_a       ).unsqueeze(1       )

									assert conditioning_emb.shape == (batch, 1, self.config.d_model * 4)

									A_     :				List[str]			      =	decoder_input_tokens.shape[1]

									# If we want to use relative positions for audio context, we can just offset
									# this sequence by the length of encodings_and_masks.
									A_     :				int			      =	torch.broadcast_to(
									    torch.arange(_a      ,       device=decoder_input_tokens.device       )      ,       (batch, seq_length)      ,       )

									A_     :				List[Any]			      =	self.position_encoding(_a       )

									A_     :				Optional[int]			      =	self.continuous_inputs_projection(_a       )
									inputs += position_encodings
									A_     :				Dict			      =	self.dropout(_a       )

									# decoder: No padding present.
									A_     :				Union[str, Any]			      =	torch.ones(
									    decoder_input_tokens.shape[:2]      ,       device=decoder_input_tokens.device      ,       dtype=inputs.dtype       )

									# Translate encoding masks to encoder-decoder masks.
									A_     :				List[str]			      =	[(x, self.encoder_decoder_mask(_a      ,       _a       )) for x, y in encodings_and_masks]

									# cross attend style: concat encodings
									A_     :				Dict			      =	torch.cat([x[0] for x in encodings_and_encdec_masks]      ,       dim=1       )
									A_     :				List[Any]			      =	torch.cat([x[1] for x in encodings_and_encdec_masks]      ,       dim=-1       )

									for lyr in self.decoders:
												A_     :				Tuple			      =	lyr(
												    _a      ,       conditioning_emb=_a      ,       encoder_hidden_states=_a      ,       encoder_attention_mask=_a      ,       )[0]

									A_     :				str			      =	self.decoder_norm(_a       )
									A_     :				Dict			      =	self.post_dropout(_a       )

									A_     :				List[str]			      =	self.spec_out(_a       )
									return spec_out






class    _UpperCAmelCase							(		nn.Module     ):
						def __init__(		self      ,       a__      ,       a__      ,       a__      ,       a__      ,       a__      ,       a__=1E-6       ):
									super().__init__()
									A_     :				Tuple			      =	nn.ModuleList()

									# cond self attention: layer 0
									self.layer.append(
									    TaLayerSelfAttentionCond(d_model=_a      ,       d_kv=_a      ,       num_heads=_a      ,       dropout_rate=_a       )       )

									# cross attention: layer 1
									self.layer.append(
									    TaLayerCrossAttention(
									        d_model=_a      ,       d_kv=_a      ,       num_heads=_a      ,       dropout_rate=_a      ,       layer_norm_epsilon=_a      ,       )       )

									# Film Cond MLP + dropout: last layer
									self.layer.append(
									    TaLayerFFCond(d_model=_a      ,       d_ff=_a      ,       dropout_rate=_a      ,       layer_norm_epsilon=_a       )       )






						def 		_lowerCamelCase      (		self      ,       a__      ,       a__=None      ,       a__=None      ,       a__=None      ,       a__=None      ,       a__=None      ,       ):
									A_     :				Any			      =	self.layer[0](
									    _a      ,       conditioning_emb=_a      ,       attention_mask=_a      ,       )

									if encoder_hidden_states is not None:
												A_     :				Any			      =	torch.where(encoder_attention_mask > 0      ,       0      ,       -1E10       ).to(
												    encoder_hidden_states.dtype       )

												A_     :				Any			      =	self.layer[1](
												    _a      ,       key_value_states=_a      ,       attention_mask=_a      ,       )

									# Apply Film Conditional Feed Forward layer
									A_     :				Optional[int]			      =	self.layer[-1](_a      ,       _a       )

									return (hidden_states,)






class    _UpperCAmelCase							(		nn.Module     ):
						def __init__(		self      ,       a__      ,       a__      ,       a__      ,       a__       ):
									super().__init__()
									A_     :				Any			      =	TaLayerNorm(_a       )
									A_     :				Optional[int]			      =	TaFiLMLayer(in_features=d_model * 4      ,       out_features=_a       )
									A_     :				Union[str, Any]			      =	Attention(query_dim=_a      ,       heads=_a      ,       dim_head=_a      ,       out_bias=_a      ,       scale_qk=_a       )
									A_     :				Any			      =	nn.Dropout(_a       )






						def 		_lowerCamelCase      (		self      ,       a__      ,       a__=None      ,       a__=None      ,       ):
									# pre_self_attention_layer_norm
									A_     :				List[str]			      =	self.layer_norm(_a       )

									if conditioning_emb is not None:
												A_     :				int			      =	self.FiLMLayer(_a      ,       _a       )

									# Self-attention block
									A_     :				List[str]			      =	self.attention(_a       )

									A_     :				Optional[Any]			      =	hidden_states + self.dropout(_a       )

									return hidden_states






class    _UpperCAmelCase							(		nn.Module     ):
						def __init__(		self      ,       a__      ,       a__      ,       a__      ,       a__      ,       a__       ):
									super().__init__()
									A_     :				Union[str, Any]			      =	Attention(query_dim=_a      ,       heads=_a      ,       dim_head=_a      ,       out_bias=_a      ,       scale_qk=_a       )
									A_     :				Any			      =	TaLayerNorm(_a      ,       eps=_a       )
									A_     :				List[Any]			      =	nn.Dropout(_a       )






						def 		_lowerCamelCase      (		self      ,       a__      ,       a__=None      ,       a__=None      ,       ):
									A_     :				Optional[Any]			      =	self.layer_norm(_a       )
									A_     :				str			      =	self.attention(
									    _a      ,       encoder_hidden_states=_a      ,       attention_mask=attention_mask.squeeze(1       )      ,       )
									A_     :				Any			      =	hidden_states + self.dropout(_a       )
									return layer_output






class    _UpperCAmelCase							(		nn.Module     ):
						def __init__(		self      ,       a__      ,       a__      ,       a__      ,       a__       ):
									super().__init__()
									A_     :				List[Any]			      =	TaDenseGatedActDense(d_model=_a      ,       d_ff=_a      ,       dropout_rate=_a       )
									A_     :				str			      =	TaFiLMLayer(in_features=d_model * 4      ,       out_features=_a       )
									A_     :				List[Any]			      =	TaLayerNorm(_a      ,       eps=_a       )
									A_     :				Optional[Any]			      =	nn.Dropout(_a       )






						def 		_lowerCamelCase      (		self      ,       a__      ,       a__=None       ):
									A_     :				Optional[int]			      =	self.layer_norm(_a       )
									if conditioning_emb is not None:
												A_     :				str			      =	self.film(_a      ,       _a       )

									A_     :				Tuple			      =	self.DenseReluDense(_a       )
									A_     :				Tuple			      =	hidden_states + self.dropout(_a       )
									return hidden_states






class    _UpperCAmelCase							(		nn.Module     ):
						def __init__(		self      ,       a__      ,       a__      ,       a__       ):
									super().__init__()
									A_     :				str			      =	nn.Linear(_a      ,       _a      ,       bias=_a       )
									A_     :				Optional[int]			      =	nn.Linear(_a      ,       _a      ,       bias=_a       )
									A_     :				int			      =	nn.Linear(_a      ,       _a      ,       bias=_a       )
									A_     :				Dict			      =	nn.Dropout(_a       )
									A_     :				Optional[Any]			      =	NewGELUActivation()






						def 		_lowerCamelCase      (		self      ,       a__       ):
									A_     :				Union[str, Any]			      =	self.act(self.wi_a(_a       )       )
									A_     :				Tuple			      =	self.wi_a(_a       )
									A_     :				Optional[Any]			      =	hidden_gelu * hidden_linear
									A_     :				Tuple			      =	self.dropout(_a       )

									A_     :				str			      =	self.wo(_a       )
									return hidden_states






class    _UpperCAmelCase							(		nn.Module     ):
						def __init__(		self      ,       a__      ,       a__=1E-6       ):
									super().__init__()
									A_     :				Dict			      =	nn.Parameter(torch.ones(_a       )       )
									A_     :				List[Any]			      =	eps






						def 		_lowerCamelCase      (		self      ,       a__       ):
									# T5 uses a layer_norm which only scales and doesn't shift, which is also known as Root Mean
									# Square Layer Normalization https://arxiv.org/abs/1910.07467 thus variance is calculated
									# w/o mean and there is no bias. Additionally we want to make sure that the accumulation for
									# half-precision inputs is done in fp32

									A_     :				int			      =	hidden_states.to(torch.floataa       ).pow(2       ).mean(-1      ,       keepdim=_a       )
									A_     :				Any			      =	hidden_states * torch.rsqrt(variance + self.variance_epsilon       )

									# convert into half-precision if necessary
									if self.weight.dtype in [torch.floataa, torch.bfloataa]:
												A_     :				str			      =	hidden_states.to(self.weight.dtype       )

									return self.weight * hidden_states






class    _UpperCAmelCase							(		nn.Module     ):
						def 		_lowerCamelCase      (		self      ,       a__       ):
									return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi       ) * (input + 0.04_4715 * torch.pow(_a      ,       3.0       ))       ))






class    _UpperCAmelCase							(		nn.Module     ):
						def __init__(		self      ,       a__      ,       a__       ):
									super().__init__()
									A_     :				Any			      =	nn.Linear(_a      ,       out_features * 2      ,       bias=_a       )






						def 		_lowerCamelCase      (		self      ,       a__      ,       a__       ):
									A_     :				Dict			      =	self.scale_bias(_a       )
									A_     :				Union[str, Any]			      =	torch.chunk(_a      ,       2      ,       -1       )
									A_     :				Dict			      =	x * (1 + scale) + shift
									return x













import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    UniSpeechConfig,
    UniSpeechForCTC,
    UniSpeechForPreTraining,
    WavaVecaFeatureExtractor,
    WavaVecaPhonemeCTCTokenizer,
    WavaVecaProcessor,
    logging,
)


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

_lowerCAmelCase 				=     {
    """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""",
    """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""": """ctc_proj""",
    """mask_emb""": """masked_spec_embed""",
}
_lowerCAmelCase 				=     [
    """ctc_proj""",
    """quantizer.weight_proj""",
    """quantizer.codevectors""",
    """project_q""",
    """project_hid""",
]



def     _lowerCAmelCase		(  _lowerCAmelCase  ,_lowerCAmelCase  ,_lowerCAmelCase  ,_lowerCAmelCase  ,_lowerCAmelCase  ,_lowerCAmelCase     ):
   '''simple docstring'''


   for attribute in key.split("""."""     ):
      if is_finetuned:
         if attribute in ["quantizer", "project_q", "project_hid"]:
            # those layers are only relevant for pretraining and should be dropped
            return

         if attribute == "ctc_proj":
            # we should rename `ctc_proj` to `lm_head` for fine-tuned phoneme models
            A_     :				Tuple			      =	"""lm_head"""

      A_     :				int			      =	getattr(_lowerCAmelCase  ,_lowerCAmelCase     )

   if weight_type is not None:
      A_     :				List[str]			      =	getattr(_lowerCAmelCase  ,_lowerCAmelCase     ).shape
   else:
      A_     :				Optional[Any]			      =	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":
      A_     :				Union[str, Any]			      =	value
   elif weight_type == "weight_g":
      A_     :				Any			      =	value
   elif weight_type == "weight_v":
      A_     :				Tuple			      =	value
   elif weight_type == "bias":
      A_     :				List[Any]			      =	value
   else:
      A_     :				int			      =	value

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



def     _lowerCAmelCase		(  _lowerCAmelCase  ,_lowerCAmelCase  ,_lowerCAmelCase     ):
   '''simple docstring'''


   A_     :				Dict			      =	[]
   A_     :				List[Any]			      =	fairseq_model.state_dict()

   A_     :				Any			      =	hf_model.unispeech.feature_extractor

   for name, value in fairseq_dict.items():
      A_     :				Any			      =	False
      if "conv_layers" in name:
         load_conv_layer(
             _lowerCAmelCase  ,_lowerCAmelCase  ,_lowerCAmelCase  ,_lowerCAmelCase  ,hf_model.config.feat_extract_norm == """group"""  ,)
         A_     :				int			      =	True
      else:
         for key, mapped_key in MAPPING.items():
            A_     :				Any			      =	"""unispeech.""" + 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]:
               A_     :				Dict			      =	True
               if "*" in mapped_key:
                  A_     :				Any			      =	name.split(_lowerCAmelCase     )[0].split("""."""     )[-2]
                  A_     :				Optional[int]			      =	mapped_key.replace("""*"""  ,_lowerCAmelCase     )
               if "weight_g" in name:
                  A_     :				Tuple			      =	"""weight_g"""
               elif "weight_v" in name:
                  A_     :				Optional[int]			      =	"""weight_v"""
               elif "bias" in name:
                  A_     :				List[Any]			      =	"""bias"""
               elif "weight" in name:
                  # TODO: don't match quantizer.weight_proj
                  A_     :				Optional[int]			      =	"""weight"""
               else:
                  A_     :				Dict			      =	None
               set_recursively(_lowerCAmelCase  ,_lowerCAmelCase  ,_lowerCAmelCase  ,_lowerCAmelCase  ,_lowerCAmelCase  ,_lowerCAmelCase     )
            continue
      if not is_used:
         unused_weights.append(_lowerCAmelCase     )

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



def     _lowerCAmelCase		(  _lowerCAmelCase  ,_lowerCAmelCase  ,_lowerCAmelCase  ,_lowerCAmelCase  ,_lowerCAmelCase     ):
   '''simple docstring'''


   A_     :				Union[str, Any]			      =	full_name.split("""conv_layers."""     )[-1]
   A_     :				Union[str, Any]			      =	name.split("""."""     )
   A_     :				List[str]			      =	int(items[0]     )
   A_     :				Optional[Any]			      =	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."""
         )
         A_     :				List[Any]			      =	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."""
         )
         A_     :				int			      =	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."
         )
         A_     :				List[str]			      =	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."""
         )
         A_     :				Tuple			      =	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     _lowerCAmelCase		(  _lowerCAmelCase  ,_lowerCAmelCase  ,_lowerCAmelCase=None  ,_lowerCAmelCase=None  ,_lowerCAmelCase=True     ):
   '''simple docstring'''


   if config_path is not None:
      A_     :				Optional[int]			      =	UniSpeechConfig.from_pretrained(_lowerCAmelCase     )
   else:
      A_     :				List[str]			      =	UniSpeechConfig()

   if is_finetuned:
      if dict_path:
         A_     :				str			      =	Dictionary.load_from_json(_lowerCAmelCase     )

         # important change bos & pad token id since CTC symbol is <pad> and
         # not <s> as in fairseq
         A_     :				List[Any]			      =	target_dict.pad_index
         A_     :				str			      =	target_dict.bos_index
         A_     :				Optional[Any]			      =	target_dict.eos_index
         A_     :				Optional[int]			      =	len(target_dict.symbols     )
         A_     :				str			      =	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     )
         A_     :				Optional[int]			      =	target_dict.indices

         # fairseq has the <pad> and <s> switched
         A_     :				Dict			      =	4_2
         A_     :				Optional[Any]			      =	4_3
         with open(_lowerCAmelCase  ,"""w"""  ,encoding="""utf-8"""     ) as vocab_handle:
            json.dump(_lowerCAmelCase  ,_lowerCAmelCase     )
         A_     :				Tuple			      =	WavaVecaPhonemeCTCTokenizer(
             _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  ,)
         A_     :				Any			      =	True if config.feat_extract_norm == """layer""" else False
         A_     :				List[str]			      =	WavaVecaFeatureExtractor(
             feature_size=1  ,sampling_rate=1_6_0_0_0  ,padding_value=0  ,do_normalize=_lowerCAmelCase  ,return_attention_mask=_lowerCAmelCase  ,)
         A_     :				Optional[int]			      =	WavaVecaProcessor(feature_extractor=_lowerCAmelCase  ,tokenizer=_lowerCAmelCase     )
         processor.save_pretrained(_lowerCAmelCase     )

      A_     :				int			      =	UniSpeechForCTC(_lowerCAmelCase     )
   else:
      A_     :				str			      =	UniSpeechForPreTraining(_lowerCAmelCase     )

   if is_finetuned:
      A_      ,				A_      ,				A_     :				Union[str, Any]			      =	fairseq.checkpoint_utils.load_model_ensemble_and_task(
          [checkpoint_path]  ,arg_overrides={"""data""": """/""".join(dict_path.split("""/"""     )[:-1]     ), """w2v_path""": checkpoint_path}     )
   else:
      A_      ,				A_      ,				A_     :				Any			      =	fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]     )

   A_     :				List[str]			      =	model[0].eval()

   recursively_load_weights(_lowerCAmelCase  ,_lowerCAmelCase  ,_lowerCAmelCase     )

   hf_unispeech.save_pretrained(_lowerCAmelCase     )


if __name__ == "__main__":
      _lowerCAmelCase 				=     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"""
      )
      _lowerCAmelCase 				=     parser.parse_args()
      convert_unispeech_checkpoint(
          args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
      )






