"""simple docstring"""
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()
SCREAMING_SNAKE_CASE__   =					logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__   =					{
    "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",
}
SCREAMING_SNAKE_CASE__   =					[
    "ctc_proj",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
]





def 				lowerCAmelCase__      (     _UpperCamelCase    :			Union[str, Any]	,     _UpperCamelCase    :			List[Any]	,     _UpperCamelCase    :			str	,     _UpperCamelCase    :			List[str]	,     _UpperCamelCase    :			Any	,     _UpperCamelCase    :			List[Any]   )   ->			Union[str, Any]:





      """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
                        snake_case        =     "lm_head"

            snake_case        =     getattr(__lowerCamelCase	,     __lowerCamelCase   )

      if weight_type is not None:
            snake_case        =     getattr(__lowerCamelCase	,     __lowerCamelCase   ).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 				lowerCAmelCase__      (     _UpperCamelCase    :			int	,     _UpperCamelCase    :			Dict	,     _UpperCamelCase    :			List[str]   )   ->			Dict:





      """simple docstring"""






      snake_case        =     []
      snake_case        =     fairseq_model.state_dict()

      snake_case        =     hf_model.unispeech.feature_extractor

      for name, value in fairseq_dict.items():
            snake_case        =     False
            if "conv_layers" in name:
                  load_conv_layer(
                      __lowerCamelCase	,     __lowerCamelCase	,     __lowerCamelCase	,     __lowerCamelCase	,     hf_model.config.feat_extract_norm == 'group'	,     )
                  snake_case        =     True
            else:
                  for key, mapped_key in MAPPING.items():
                        snake_case        =     "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]:
                              snake_case        =     True
                              if "*" in mapped_key:
                                    snake_case        =     name.split(__lowerCamelCase   )[0].split('.'   )[-2]
                                    snake_case        =     mapped_key.replace('*'	,     __lowerCamelCase   )
                              if "weight_g" in name:
                                    snake_case        =     "weight_g"
                              elif "weight_v" in name:
                                    snake_case        =     "weight_v"
                              elif "bias" in name:
                                    snake_case        =     "bias"
                              elif "weight" in name:
                                    # TODO: don't match quantizer.weight_proj
                                    snake_case        =     "weight"
                              else:
                                    snake_case        =     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__      (     _UpperCamelCase    :			str	,     _UpperCamelCase    :			Any	,     _UpperCamelCase    :			List[str]	,     _UpperCamelCase    :			Tuple	,     _UpperCamelCase    :			str   )   ->			Optional[int]:





      """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(__lowerCamelCase   )





@torch.no_grad()
def 				lowerCAmelCase__      (     _UpperCamelCase    :			Union[str, Any]	,     _UpperCamelCase    :			str	,     _UpperCamelCase    :			str=None	,     _UpperCamelCase    :			Optional[Any]=None	,     _UpperCamelCase    :			int=True   )   ->			Optional[int]:





      """simple docstring"""






      if config_path is not None:
            snake_case        =     UniSpeechConfig.from_pretrained(__lowerCamelCase   )
      else:
            snake_case        =     UniSpeechConfig()

      if is_finetuned:
            if dict_path:
                  snake_case        =     Dictionary.load_from_json(__lowerCamelCase   )

                  # 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.eos_index
                  snake_case        =     len(target_dict.symbols   )
                  snake_case        =     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   )
                  snake_case        =     target_dict.indices

                  # fairseq has the <pad> and <s> switched
                  snake_case        =     4_2
                  snake_case        =     4_3
                  with open(__lowerCamelCase	,     'w'	,     encoding='utf-8'   ) as vocab_handle:
                        json.dump(__lowerCamelCase	,     __lowerCamelCase   )
                  snake_case        =     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	,     )
                  snake_case        =     True if config.feat_extract_norm == "layer" else False
                  snake_case        =     WavaVecaFeatureExtractor(
                      feature_size=1	,     sampling_rate=1_6_0_0_0	,     padding_value=0	,     do_normalize=__lowerCamelCase	,     return_attention_mask=__lowerCamelCase	,     )
                  snake_case        =     WavaVecaProcessor(feature_extractor=__lowerCamelCase	,     tokenizer=__lowerCamelCase   )
                  processor.save_pretrained(__lowerCamelCase   )

            snake_case        =     UniSpeechForCTC(__lowerCamelCase   )
      else:
            snake_case        =     UniSpeechForPreTraining(__lowerCamelCase   )

      if is_finetuned:
            snake_case        =     fairseq.checkpoint_utils.load_model_ensemble_and_task(
                [checkpoint_path]	,     arg_overrides={'data': '/'.join(dict_path.split('/'   )[:-1]   ), 'w2v_path': checkpoint_path}   )
      else:
            snake_case        =     fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]   )

      snake_case        =     model[0].eval()

      recursively_load_weights(__lowerCamelCase	,     __lowerCamelCase	,     __lowerCamelCase   )

      hf_unispeech.save_pretrained(__lowerCamelCase   )


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



"""simple docstring"""
from typing import Optional

import numpy as np
import torch
from torch import nn
from transformers import GPTaConfig, GPTaLMHeadModel
from transformers.modeling_utils import ModuleUtilsMixin

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin



class 					lowerCAmelCase_      (     lowerCAmelCase       ,      lowerCAmelCase       ,      lowerCAmelCase   ):



				"""simple docstring"""
				_lowerCAmelCase       :      int     					=  [r"""h\.\d+\.attn\.bias""", r"""h\.\d+\.attn\.masked_bias"""]

				@register_to_config
				def __init__(			self						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase = None						,      lowerCAmelCase = 5_02_57						,      lowerCAmelCase = 10_24						,      lowerCAmelCase = 7_68						,      lowerCAmelCase = 12						,      lowerCAmelCase = 12						,      lowerCAmelCase = None						,      lowerCAmelCase = "gelu_new"						,      lowerCAmelCase = 0.1						,      lowerCAmelCase = 0.1						,      lowerCAmelCase = 0.1						,      lowerCAmelCase = 1E-5						,      lowerCAmelCase = 0.02						,      lowerCAmelCase = True						,      lowerCAmelCase = True						,      lowerCAmelCase = False						,      lowerCAmelCase = False						,      ):






										"""simple docstring"""

										super().__init__()

										snake_case        =     prefix_length

										if prefix_inner_dim != n_embd and prefix_hidden_dim is None:
																raise ValueError(
																    F"""`prefix_hidden_dim` cannot be `None` when `prefix_inner_dim`: {prefix_hidden_dim} and"""
																    F""" `n_embd`: {n_embd} are not equal."""      )

										snake_case        =     prefix_inner_dim
										snake_case        =     prefix_hidden_dim

										snake_case        =     (
										    nn.Linear(self.prefix_inner_dim						,      self.prefix_hidden_dim      )
										    if self.prefix_hidden_dim is not None
										    else nn.Identity()
										)
										snake_case        =     (
										    nn.Linear(self.prefix_hidden_dim						,      lowerCAmelCase      ) if self.prefix_hidden_dim is not None else nn.Identity()
										)

										snake_case        =     GPTaConfig(
										    vocab_size=lowerCAmelCase						,      n_positions=lowerCAmelCase						,      n_embd=lowerCAmelCase						,      n_layer=lowerCAmelCase						,      n_head=lowerCAmelCase						,      n_inner=lowerCAmelCase						,      activation_function=lowerCAmelCase						,      resid_pdrop=lowerCAmelCase						,      embd_pdrop=lowerCAmelCase						,      attn_pdrop=lowerCAmelCase						,      layer_norm_epsilon=lowerCAmelCase						,      initializer_range=lowerCAmelCase						,      scale_attn_weights=lowerCAmelCase						,      use_cache=lowerCAmelCase						,      scale_attn_by_inverse_layer_idx=lowerCAmelCase						,      reorder_and_upcast_attn=lowerCAmelCase						,      )
										snake_case        =     GPTaLMHeadModel(lowerCAmelCase      )

				def 						snake_case	(			self						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase = None						,      lowerCAmelCase = None						,      ):






										"""simple docstring"""

										snake_case        =     self.transformer.transformer.wte(lowerCAmelCase      )
										snake_case        =     self.encode_prefix(lowerCAmelCase      )
										snake_case        =     self.decode_prefix(lowerCAmelCase      )
										snake_case        =     torch.cat((prefix_embeds, embedding_text)						,      dim=1      )

										if labels is not None:
																snake_case        =     self.get_dummy_token(input_ids.shape[0]						,      input_ids.device      )
																snake_case        =     torch.cat((dummy_token, input_ids)						,      dim=1      )
										snake_case        =     self.transformer(inputs_embeds=lowerCAmelCase						,      labels=lowerCAmelCase						,      attention_mask=lowerCAmelCase      )
										if self.prefix_hidden_dim is not None:
																return out, hidden
										else:
																return out

				def 						snake_case	(			self						,      lowerCAmelCase						,      lowerCAmelCase      ):






										"""simple docstring"""

										return torch.zeros(lowerCAmelCase						,      self.prefix_length						,      dtype=torch.intaa						,      device=lowerCAmelCase      )

				def 						snake_case	(			self						,      lowerCAmelCase      ):






										"""simple docstring"""

										return self.encode_prefix(lowerCAmelCase      )

				@torch.no_grad()
				def 						snake_case	(			self						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase      ):






										"""simple docstring"""

										snake_case        =     torch.split(lowerCAmelCase						,      1						,      dim=0      )
										snake_case        =     []
										snake_case        =     []
										for feature in features:
																snake_case        =     self.decode_prefix(feature.to(lowerCAmelCase      )      )  # back to the clip feature
																# Only support beam search for now
																snake_case   ,snake_case        =     self.generate_beam(
																    input_embeds=lowerCAmelCase						,      device=lowerCAmelCase						,      eos_token_id=lowerCAmelCase      )
																generated_tokens.append(output_tokens[0]      )
																generated_seq_lengths.append(seq_lengths[0]      )
										snake_case        =     torch.stack(lowerCAmelCase      )
										snake_case        =     torch.stack(lowerCAmelCase      )
										return generated_tokens, generated_seq_lengths

				@torch.no_grad()
				def 						snake_case	(			self						,      lowerCAmelCase=None						,      lowerCAmelCase=None						,      lowerCAmelCase=None						,      lowerCAmelCase = 5						,      lowerCAmelCase = 67						,      lowerCAmelCase = 1.0						,      lowerCAmelCase = None						,      ):






										"""simple docstring"""

										snake_case        =     eos_token_id
										snake_case        =     None
										snake_case        =     None
										snake_case        =     torch.ones(lowerCAmelCase						,      device=lowerCAmelCase						,      dtype=torch.int      )
										snake_case        =     torch.zeros(lowerCAmelCase						,      device=lowerCAmelCase						,      dtype=torch.bool      )

										if input_embeds is not None:
																snake_case        =     input_embeds
										else:
																snake_case        =     self.transformer.transformer.wte(lowerCAmelCase      )

										for i in range(lowerCAmelCase      ):
																snake_case        =     self.transformer(inputs_embeds=lowerCAmelCase      )
																snake_case        =     outputs.logits
																snake_case        =     logits[:, -1, :] / (temperature if temperature > 0 else 1.0)
																snake_case        =     logits.softmax(-1      ).log()

																if scores is None:
																						snake_case   ,snake_case        =     logits.topk(lowerCAmelCase						,      -1      )
																						snake_case        =     generated.expand(lowerCAmelCase						,      *generated.shape[1:]      )
																						snake_case   ,snake_case        =     next_tokens.permute(1						,      0      ), scores.squeeze(0      )
																						if tokens is None:
																												snake_case        =     next_tokens
																						else:
																												snake_case        =     tokens.expand(lowerCAmelCase						,      *tokens.shape[1:]      )
																												snake_case        =     torch.cat((tokens, next_tokens)						,      dim=1      )
																else:
																						snake_case        =     -float(np.inf      )
																						snake_case        =     0
																						snake_case        =     scores[:, None] + logits
																						seq_lengths[~is_stopped] += 1
																						snake_case        =     scores_sum / seq_lengths[:, None]
																						snake_case   ,snake_case        =     scores_sum_average.view(-1      ).topk(lowerCAmelCase						,      -1      )
																						snake_case        =     next_tokens // scores_sum.shape[1]
																						snake_case        =     seq_lengths[next_tokens_source]
																						snake_case        =     next_tokens % scores_sum.shape[1]
																						snake_case        =     next_tokens.unsqueeze(1      )
																						snake_case        =     tokens[next_tokens_source]
																						snake_case        =     torch.cat((tokens, next_tokens)						,      dim=1      )
																						snake_case        =     generated[next_tokens_source]
																						snake_case        =     scores_sum_average * seq_lengths
																						snake_case        =     is_stopped[next_tokens_source]

																snake_case        =     self.transformer.transformer.wte(next_tokens.squeeze()      ).view(generated.shape[0]						,      1						,      -1      )
																snake_case        =     torch.cat((generated, next_token_embed)						,      dim=1      )
																snake_case        =     is_stopped + next_tokens.eq(lowerCAmelCase      ).squeeze()
																if is_stopped.all():
																						break

										snake_case        =     scores / seq_lengths
										snake_case        =     scores.argsort(descending=lowerCAmelCase      )
										# tokens tensors are already padded to max_seq_length
										snake_case        =     [tokens[i] for i in order]
										snake_case        =     torch.stack(lowerCAmelCase						,      dim=0      )
										snake_case        =     torch.tensor([seq_lengths[i] for i in order]						,      dtype=seq_lengths.dtype      )
										return output_texts, seq_lengths


