




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 lowerCamelCase__       (      _lowercase			,						_lowercase			,						_lowercase			,						_lowercase			,						_lowercase				):

     '''simple docstring'''


     for attribute in key.split('''.'''				):
          UpperCAmelCase_      :							Optional[int]       =      getattr(_lowercase			,						_lowercase				)

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

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







def lowerCamelCase__       (      _lowercase			,						_lowercase				):

     '''simple docstring'''


     UpperCAmelCase_      :							Any       =      []
     UpperCAmelCase_      :							Optional[int]       =      fairseq_model.state_dict()

     UpperCAmelCase_      :							List[str]       =      hf_model.unispeech_sat.feature_extractor

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

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







def lowerCamelCase__       (      _lowercase			,						_lowercase			,						_lowercase			,						_lowercase			,						_lowercase				):

     '''simple docstring'''


     UpperCAmelCase_      :							int       =      full_name.split('''conv_layers.'''				)[-1]
     UpperCAmelCase_      :							Tuple       =      name.split('''.'''				)
     UpperCAmelCase_      :							Dict       =      int(items[0]				)
     UpperCAmelCase_      :							Tuple       =      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.'''				)
               UpperCAmelCase_      :							Optional[Any]       =      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.'''				)
               UpperCAmelCase_      :							Any       =      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.'''				)
               UpperCAmelCase_      :							Tuple       =      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.'''				)
               UpperCAmelCase_      :							Dict       =      value
               logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''				)
     else:
          unused_weights.append(_lowercase				)







@torch.no_grad()
def lowerCamelCase__       (      _lowercase			,						_lowercase			,						_lowercase=None			,						_lowercase=None			,						_lowercase=True				):

     '''simple docstring'''


     if config_path is not None:
          UpperCAmelCase_      :							Any       =      UniSpeechSatConfig.from_pretrained(_lowercase				)
     else:
          UpperCAmelCase_      :							Union[str, Any]       =      UniSpeechSatConfig()

     UpperCAmelCase_      :							Optional[Any]       =      ''''''

     if is_finetuned:
          UpperCAmelCase_      :							str       =      UniSpeechSatForCTC(_lowercase				)
     else:
          UpperCAmelCase_      :							int       =      UniSpeechSatForPreTraining(_lowercase				)

     UpperCAmelCase_,   UpperCAmelCase_,   UpperCAmelCase_      :							str       =      fairseq.checkpoint_utils.load_model_ensemble_and_task(
         [checkpoint_path]			,						arg_overrides={'''data''': '''/'''.join(dict_path.split('''/'''				)[:-1]				)}				)
     UpperCAmelCase_      :							Optional[Any]       =      model[0].eval()

     recursively_load_weights(_lowercase			,						_lowercase				)

     hf_wavavec.save_pretrained(_lowercase				)


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
     )





import unittest

from transformers import EsmConfig, is_torch_available
from transformers.testing_utils import TestCasePlus, require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
     import torch

     from transformers.models.esm.modeling_esmfold import EsmForProteinFolding

class   __a:






    """simple docstring"""





    def __init__(				self       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE=13       ,_SCREAMING_SNAKE_CASE=7       ,_SCREAMING_SNAKE_CASE=False       ,_SCREAMING_SNAKE_CASE=True       ,_SCREAMING_SNAKE_CASE=False       ,_SCREAMING_SNAKE_CASE=False       ,_SCREAMING_SNAKE_CASE=19       ,_SCREAMING_SNAKE_CASE=32       ,_SCREAMING_SNAKE_CASE=5       ,_SCREAMING_SNAKE_CASE=4       ,_SCREAMING_SNAKE_CASE=37       ,_SCREAMING_SNAKE_CASE="gelu"       ,_SCREAMING_SNAKE_CASE=0.1       ,_SCREAMING_SNAKE_CASE=0.1       ,_SCREAMING_SNAKE_CASE=512       ,_SCREAMING_SNAKE_CASE=16       ,_SCREAMING_SNAKE_CASE=2       ,_SCREAMING_SNAKE_CASE=0.02       ,_SCREAMING_SNAKE_CASE=3       ,_SCREAMING_SNAKE_CASE=4       ,_SCREAMING_SNAKE_CASE=None       ,)			->		Dict:
         UpperCAmelCase_      :							Optional[Any]       =      parent
         UpperCAmelCase_      :							Dict       =      batch_size
         UpperCAmelCase_      :							Optional[int]       =      seq_length
         UpperCAmelCase_      :							Union[str, Any]       =      is_training
         UpperCAmelCase_      :							Any       =      use_input_mask
         UpperCAmelCase_      :							Tuple       =      use_token_type_ids
         UpperCAmelCase_      :							Optional[int]       =      use_labels
         UpperCAmelCase_      :							List[Any]       =      vocab_size
         UpperCAmelCase_      :							List[Any]       =      hidden_size
         UpperCAmelCase_      :							str       =      num_hidden_layers
         UpperCAmelCase_      :							Dict       =      num_attention_heads
         UpperCAmelCase_      :							Optional[Any]       =      intermediate_size
         UpperCAmelCase_      :							str       =      hidden_act
         UpperCAmelCase_      :							int       =      hidden_dropout_prob
         UpperCAmelCase_      :							List[Any]       =      attention_probs_dropout_prob
         UpperCAmelCase_      :							Optional[int]       =      max_position_embeddings
         UpperCAmelCase_      :							Optional[int]       =      type_vocab_size
         UpperCAmelCase_      :							Any       =      type_sequence_label_size
         UpperCAmelCase_      :							int       =      initializer_range
         UpperCAmelCase_      :							Any       =      num_labels
         UpperCAmelCase_      :							Optional[Any]       =      num_choices
         UpperCAmelCase_      :							List[str]       =      scope





    def 							a__				(				self  )			->		Optional[Any]:
         UpperCAmelCase_      :							List[Any]       =      ids_tensor([self.batch_size, self.seq_length]       ,self.vocab_size  )

         UpperCAmelCase_      :							str       =      None
         if self.use_input_mask:
              UpperCAmelCase_      :							Optional[int]       =      random_attention_mask([self.batch_size, self.seq_length]  )

         UpperCAmelCase_      :							Union[str, Any]       =      None
         UpperCAmelCase_      :							List[str]       =      None
         UpperCAmelCase_      :							Optional[Any]       =      None
         if self.use_labels:
              UpperCAmelCase_      :							Optional[int]       =      ids_tensor([self.batch_size]       ,self.type_sequence_label_size  )
              UpperCAmelCase_      :							List[str]       =      ids_tensor([self.batch_size, self.seq_length]       ,self.num_labels  )
              UpperCAmelCase_      :							Optional[int]       =      ids_tensor([self.batch_size]       ,self.num_choices  )

         UpperCAmelCase_      :							Optional[Any]       =      self.get_config()

         return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels





    def 							a__				(				self  )			->		List[str]:
         UpperCAmelCase_      :							Union[str, Any]       =      EsmConfig(
             vocab_size=33       ,hidden_size=self.hidden_size       ,pad_token_id=1       ,num_hidden_layers=self.num_hidden_layers       ,num_attention_heads=self.num_attention_heads       ,intermediate_size=self.intermediate_size       ,hidden_act=self.hidden_act       ,hidden_dropout_prob=self.hidden_dropout_prob       ,attention_probs_dropout_prob=self.attention_probs_dropout_prob       ,max_position_embeddings=self.max_position_embeddings       ,type_vocab_size=self.type_vocab_size       ,initializer_range=self.initializer_range       ,is_folding_model=_SCREAMING_SNAKE_CASE       ,esmfold_config={'''trunk''': {'''num_blocks''': 2}, '''fp16_esm''': False}       ,)
         return config





    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE  )			->		int:
         UpperCAmelCase_      :							Optional[int]       =      EsmForProteinFolding(config=_SCREAMING_SNAKE_CASE  ).float()
         model.to(_SCREAMING_SNAKE_CASE  )
         model.eval()
         UpperCAmelCase_      :							Union[str, Any]       =      model(_SCREAMING_SNAKE_CASE       ,attention_mask=_SCREAMING_SNAKE_CASE  )
         UpperCAmelCase_      :							List[str]       =      model(_SCREAMING_SNAKE_CASE  )
         UpperCAmelCase_      :							Any       =      model(_SCREAMING_SNAKE_CASE  )

         self.parent.assertEqual(result.positions.shape       ,(8, self.batch_size, self.seq_length, 14, 3)  )
         self.parent.assertEqual(result.angles.shape       ,(8, self.batch_size, self.seq_length, 7, 2)  )





    def 							a__				(				self  )			->		Optional[Any]:
         UpperCAmelCase_      :							int       =      self.prepare_config_and_inputs()
         (
             (
             UpperCAmelCase_
         ),   (
             UpperCAmelCase_
         ),   (
             UpperCAmelCase_
         ),   (
             UpperCAmelCase_
         ),   (
             UpperCAmelCase_
         ),   (
             UpperCAmelCase_
         ),   
         )      :							Optional[Any]       =      config_and_inputs
         UpperCAmelCase_      :							int       =      {'''input_ids''': input_ids, '''attention_mask''': input_mask}
         return config, inputs_dict


@require_torch
class   __a(					_a    ,						_a    ,						unittest.TestCase       ):






    """simple docstring"""





    lowerCAmelCase        =				False

    lowerCAmelCase        =				(EsmForProteinFolding,) if is_torch_available() else ()
    lowerCAmelCase        =				()
    lowerCAmelCase        =				{} if is_torch_available() else {}
    lowerCAmelCase        =				False





    def 							a__				(				self  )			->		List[str]:
         UpperCAmelCase_      :							Optional[int]       =      EsmFoldModelTester(self  )
         UpperCAmelCase_      :							int       =      ConfigTester(self       ,config_class=_SCREAMING_SNAKE_CASE       ,hidden_size=37  )





    def 							a__				(				self  )			->		Tuple:
         self.config_tester.run_common_tests()





    def 							a__				(				self  )			->		Dict:
         UpperCAmelCase_      :							Optional[Any]       =      self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*_SCREAMING_SNAKE_CASE  )





    @unittest.skip('''Does not support attention outputs'''  )
    def 							a__				(				self  )			->		Optional[int]:
         pass





    @unittest.skip
    def 							a__				(				self  )			->		Dict:
         pass





    @unittest.skip('''Esm does not support embedding resizing'''  )
    def 							a__				(				self  )			->		Union[str, Any]:
         pass





    @unittest.skip('''Esm does not support embedding resizing'''  )
    def 							a__				(				self  )			->		List[Any]:
         pass





    @unittest.skip('''ESMFold does not support passing input embeds!'''  )
    def 							a__				(				self  )			->		Any:
         pass





    @unittest.skip('''ESMFold does not support head pruning.'''  )
    def 							a__				(				self  )			->		Optional[Any]:
         pass





    @unittest.skip('''ESMFold does not support head pruning.'''  )
    def 							a__				(				self  )			->		Optional[Any]:
         pass





    @unittest.skip('''ESMFold does not support head pruning.'''  )
    def 							a__				(				self  )			->		Optional[int]:
         pass





    @unittest.skip('''ESMFold does not support head pruning.'''  )
    def 							a__				(				self  )			->		Optional[int]:
         pass





    @unittest.skip('''ESMFold does not support head pruning.'''  )
    def 							a__				(				self  )			->		Dict:
         pass





    @unittest.skip('''ESMFold does not output hidden states in the normal way.'''  )
    def 							a__				(				self  )			->		str:
         pass





    @unittest.skip('''ESMfold does not output hidden states in the normal way.'''  )
    def 							a__				(				self  )			->		Optional[Any]:
         pass





    @unittest.skip('''ESMFold only has one output format.'''  )
    def 							a__				(				self  )			->		Optional[int]:
         pass





    @unittest.skip('''This test doesn\'t work for ESMFold and doesn\'t test core functionality'''  )
    def 							a__				(				self  )			->		int:
         pass





    @unittest.skip('''ESMFold does not support input chunking.'''  )
    def 							a__				(				self  )			->		List[Any]:
         pass





    @unittest.skip('''ESMFold doesn\'t respect you and it certainly doesn\'t respect your initialization arguments.'''  )
    def 							a__				(				self  )			->		Tuple:
         pass





    @unittest.skip('''ESMFold doesn\'t support torchscript compilation.'''  )
    def 							a__				(				self  )			->		Optional[int]:
         pass





    @unittest.skip('''ESMFold doesn\'t support torchscript compilation.'''  )
    def 							a__				(				self  )			->		List[str]:
         pass





    @unittest.skip('''ESMFold doesn\'t support torchscript compilation.'''  )
    def 							a__				(				self  )			->		Tuple:
         pass





    @unittest.skip('''ESMFold doesn\'t support data parallel.'''  )
    def 							a__				(				self  )			->		Dict:
         pass





    @unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.'''  )
    def 							a__				(				self  )			->		List[Any]:
         pass


@require_torch
class   __a(					_a       ):






    """simple docstring"""





    @slow
    def 							a__				(				self  )			->		List[Any]:
         UpperCAmelCase_      :							List[str]       =      EsmForProteinFolding.from_pretrained('''facebook/esmfold_v1'''  ).float()
         model.eval()
         UpperCAmelCase_      :							str       =      torch.tensor([[0, 6, 4, 13, 5, 4, 16, 12, 11, 7, 2]]  )
         UpperCAmelCase_      :							Optional[int]       =      model(_SCREAMING_SNAKE_CASE  )['''positions''']
         UpperCAmelCase_      :							List[str]       =      torch.tensor([2.58_28, 0.79_93, -10.93_34]       ,dtype=torch.floataa  )
         self.assertTrue(torch.allclose(position_outputs[0, 0, 0, 0]       ,_SCREAMING_SNAKE_CASE       ,atol=1e-4  )  )