"""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()
__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   UpperCAmelCase					(	UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__				):






       """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__	     =							"lm_head"

              A__	     =							getattr(SCREAMING_SNAKE_CASE_       ,    SCREAMING_SNAKE_CASE_				)

       if weight_type is not None:
              A__	     =							getattr(SCREAMING_SNAKE_CASE_       ,    SCREAMING_SNAKE_CASE_				).shape
       else:
              A__	     =							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__	     =							value
       elif weight_type == "weight_g":
              A__	     =							value
       elif weight_type == "weight_v":
              A__	     =							value
       elif weight_type == "bias":
              A__	     =							value
       else:
              A__	     =							value

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






def   UpperCAmelCase					(	UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__				):






       """simple docstring"""





       A__	     =							[]
       A__	     =							fairseq_model.state_dict()

       A__	     =							hf_model.unispeech.feature_extractor

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

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






def   UpperCAmelCase					(	UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__				):






       """simple docstring"""





       A__	     =							full_name.split('conv_layers.'				)[-1]
       A__	     =							name.split('.'				)
       A__	     =							int(items[0]				)
       A__	     =							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__	     =							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__	     =							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__	     =							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__	     =							value
                     logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''				)
       else:
              unused_weights.append(SCREAMING_SNAKE_CASE_				)






@torch.no_grad()
def   UpperCAmelCase					(	UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__=None       ,    UpperCamelCase__=None       ,    UpperCamelCase__=True				):






       """simple docstring"""





       if config_path is not None:
              A__	     =							UniSpeechConfig.from_pretrained(SCREAMING_SNAKE_CASE_				)
       else:
              A__	     =							UniSpeechConfig()

       if is_finetuned:
              if dict_path:
                     A__	     =							Dictionary.load_from_json(SCREAMING_SNAKE_CASE_				)

                     # important change bos & pad token id since CTC symbol is <pad> and
                     # not <s> as in fairseq
                     A__	     =							target_dict.pad_index
                     A__	     =							target_dict.bos_index
                     A__	     =							target_dict.eos_index
                     A__	     =							len(target_dict.symbols				)
                     A__	     =							os.path.join(SCREAMING_SNAKE_CASE_       ,    'vocab.json'				)
                     if not os.path.isdir(SCREAMING_SNAKE_CASE_				):
                            logger.error('--pytorch_dump_folder_path ({}) should be a directory'.format(SCREAMING_SNAKE_CASE_				)				)
                            return
                     os.makedirs(SCREAMING_SNAKE_CASE_       ,    exist_ok=SCREAMING_SNAKE_CASE_				)
                     A__	     =							target_dict.indices

                     # fairseq has the <pad> and <s> switched
                     A__	     =							42
                     A__	     =							43
                     with open(SCREAMING_SNAKE_CASE_       ,    'w'       ,    encoding='utf-8'				) as vocab_handle:
                            json.dump(SCREAMING_SNAKE_CASE_       ,    SCREAMING_SNAKE_CASE_				)
                     A__	     =							WavaVecaPhonemeCTCTokenizer(
                         SCREAMING_SNAKE_CASE_       ,    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=SCREAMING_SNAKE_CASE_       ,    )
                     A__	     =							True if config.feat_extract_norm == "layer" else False
                     A__	     =							WavaVecaFeatureExtractor(
                         feature_size=1       ,    sampling_rate=16_000       ,    padding_value=0       ,    do_normalize=SCREAMING_SNAKE_CASE_       ,    return_attention_mask=SCREAMING_SNAKE_CASE_       ,    )
                     A__	     =							WavaVecaProcessor(feature_extractor=SCREAMING_SNAKE_CASE_       ,    tokenizer=SCREAMING_SNAKE_CASE_				)
                     processor.save_pretrained(SCREAMING_SNAKE_CASE_				)

              A__	     =							UniSpeechForCTC(SCREAMING_SNAKE_CASE_				)
       else:
              A__	     =							UniSpeechForPreTraining(SCREAMING_SNAKE_CASE_				)

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

       A__	     =							model[0].eval()

       recursively_load_weights(SCREAMING_SNAKE_CASE_       ,    SCREAMING_SNAKE_CASE_       ,    SCREAMING_SNAKE_CASE_				)

       hf_unispeech.save_pretrained(SCREAMING_SNAKE_CASE_				)


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
    )






"""simple docstring"""




import unittest

import torch
from torch import nn

from diffusers.models.activations import get_activation
class     UpperCamelCase__(			unittest.TestCase ):





						def 				snake_case__	(       self     )					->				str:
													A__	     =							get_activation('swish'     )

													self.assertIsInstance(__UpperCAmelCase		,nn.SiLU     )

													self.assertEqual(act(torch.tensor(-1_00		,dtype=torch.floataa     )     ).item()		,0     )
													self.assertNotEqual(act(torch.tensor(-1		,dtype=torch.floataa     )     ).item()		,0     )
													self.assertEqual(act(torch.tensor(0		,dtype=torch.floataa     )     ).item()		,0     )
													self.assertEqual(act(torch.tensor(20		,dtype=torch.floataa     )     ).item()		,20     )





						def 				snake_case__	(       self     )					->				Optional[Any]:
													A__	     =							get_activation('silu'     )

													self.assertIsInstance(__UpperCAmelCase		,nn.SiLU     )

													self.assertEqual(act(torch.tensor(-1_00		,dtype=torch.floataa     )     ).item()		,0     )
													self.assertNotEqual(act(torch.tensor(-1		,dtype=torch.floataa     )     ).item()		,0     )
													self.assertEqual(act(torch.tensor(0		,dtype=torch.floataa     )     ).item()		,0     )
													self.assertEqual(act(torch.tensor(20		,dtype=torch.floataa     )     ).item()		,20     )





						def 				snake_case__	(       self     )					->				List[str]:
													A__	     =							get_activation('mish'     )

													self.assertIsInstance(__UpperCAmelCase		,nn.Mish     )

													self.assertEqual(act(torch.tensor(-2_00		,dtype=torch.floataa     )     ).item()		,0     )
													self.assertNotEqual(act(torch.tensor(-1		,dtype=torch.floataa     )     ).item()		,0     )
													self.assertEqual(act(torch.tensor(0		,dtype=torch.floataa     )     ).item()		,0     )
													self.assertEqual(act(torch.tensor(20		,dtype=torch.floataa     )     ).item()		,20     )





						def 				snake_case__	(       self     )					->				List[str]:
													A__	     =							get_activation('gelu'     )

													self.assertIsInstance(__UpperCAmelCase		,nn.GELU     )

													self.assertEqual(act(torch.tensor(-1_00		,dtype=torch.floataa     )     ).item()		,0     )
													self.assertNotEqual(act(torch.tensor(-1		,dtype=torch.floataa     )     ).item()		,0     )
													self.assertEqual(act(torch.tensor(0		,dtype=torch.floataa     )     ).item()		,0     )
													self.assertEqual(act(torch.tensor(20		,dtype=torch.floataa     )     ).item()		,20     )





