"""simple docstring"""





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   UpperCAmelCase_			:


      def __init__(     self	,    a	,    a=1_3	,    a=7	,    a=False	,    a=True	,    a=False	,    a=False	,    a=1_9	,    a=3_2	,    a=5	,    a=4	,    a=3_7	,    a="gelu"	,    a=0.1	,    a=0.1	,    a=5_1_2	,    a=1_6	,    a=2	,    a=0.02	,    a=3	,    a=4	,    a=None	,    ) ->      Union[str, Any]:
             lowercase__    :	Optional[Any]     					=			parent
             lowercase__    :	Optional[Any]     					=			batch_size
             lowercase__    :	str     					=			seq_length
             lowercase__    :	Optional[Any]     					=			is_training
             lowercase__    :	int     					=			use_input_mask
             lowercase__    :	List[str]     					=			use_token_type_ids
             lowercase__    :	List[Any]     					=			use_labels
             lowercase__    :	Any     					=			vocab_size
             lowercase__    :	Optional[int]     					=			hidden_size
             lowercase__    :	Tuple     					=			num_hidden_layers
             lowercase__    :	Tuple     					=			num_attention_heads
             lowercase__    :	str     					=			intermediate_size
             lowercase__    :	Union[str, Any]     					=			hidden_act
             lowercase__    :	Optional[Any]     					=			hidden_dropout_prob
             lowercase__    :	Any     					=			attention_probs_dropout_prob
             lowercase__    :	Any     					=			max_position_embeddings
             lowercase__    :	str     					=			type_vocab_size
             lowercase__    :	Union[str, Any]     					=			type_sequence_label_size
             lowercase__    :	Dict     					=			initializer_range
             lowercase__    :	Tuple     					=			num_labels
             lowercase__    :	Any     					=			num_choices
             lowercase__    :	List[Any]     					=			scope


      def 	_UpperCAmelCase (     self							) ->      Dict:
             lowercase__    :	List[Any]     					=			ids_tensor([self.batch_size, self.seq_length]	,    self.vocab_size							)

             lowercase__    :	Optional[Any]     					=			None
             if self.use_input_mask:
                    lowercase__    :	Dict     					=			random_attention_mask([self.batch_size, self.seq_length]							)

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

             lowercase__    :	Union[str, Any]     					=			self.get_config()

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


      def 	_UpperCAmelCase (     self							) ->      Union[str, Any]:
             lowercase__    :	Tuple     					=			EsmConfig(
                 vocab_size=3_3	,    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=a	,    esmfold_config={'trunk': {'num_blocks': 2}, 'fp16_esm': False}	,    )
             return config


      def 	_UpperCAmelCase (     self	,    a	,    a	,    a	,    a	,    a	,    a							) ->      str:
             lowercase__    :	List[str]     					=			EsmForProteinFolding(config=a							).float()
             model.to(a							)
             model.eval()
             lowercase__    :	Dict     					=			model(a	,    attention_mask=a							)
             lowercase__    :	List[str]     					=			model(a							)
             lowercase__    :	str     					=			model(a							)

             self.parent.assertEqual(result.positions.shape	,    (8, self.batch_size, self.seq_length, 1_4, 3)							)
             self.parent.assertEqual(result.angles.shape	,    (8, self.batch_size, self.seq_length, 7, 2)							)


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





@require_torch
class   UpperCAmelCase_			(					_a				,							_a				,							unittest.TestCase):
      lowerCamelCase__	:					Any										=						False

      lowerCamelCase__	:					List[Any]										=						(EsmForProteinFolding,) if is_torch_available() else ()
      lowerCamelCase__	:					List[str]										=						()
      lowerCamelCase__	:					Optional[int]										=						{} if is_torch_available() else {}
      lowerCamelCase__	:					Optional[Any]										=						False


      def 	_UpperCAmelCase (     self							) ->      Dict:
             lowercase__    :	Optional[Any]     					=			EsmFoldModelTester(self							)
             lowercase__    :	Optional[int]     					=			ConfigTester(self	,    config_class=a	,    hidden_size=3_7							)


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


      def 	_UpperCAmelCase (     self							) ->      str:
             lowercase__    :	Optional[int]     					=			self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_model(*a							)


      @unittest.skip('Does not support attention outputs'							)
      def 	_UpperCAmelCase (     self							) ->      Tuple:
             pass


      @unittest.skip
      def 	_UpperCAmelCase (     self							) ->      Optional[int]:
             pass


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


      @unittest.skip('Esm does not support embedding resizing'							)
      def 	_UpperCAmelCase (     self							) ->      int:
             pass


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


      @unittest.skip('ESMFold does not support head pruning.'							)
      def 	_UpperCAmelCase (     self							) ->      List[str]:
             pass


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


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


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


      @unittest.skip('ESMFold does not support head pruning.'							)
      def 	_UpperCAmelCase (     self							) ->      List[str]:
             pass


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


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


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


      @unittest.skip('This test doesn\'t work for ESMFold and doesn\'t test core functionality'							)
      def 	_UpperCAmelCase (     self							) ->      Optional[Any]:
             pass


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


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


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


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


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


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


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





@require_torch
class   UpperCAmelCase_			(					_a):


      @slow
      def 	_UpperCAmelCase (     self							) ->      Union[str, Any]:
             lowercase__    :	Optional[Any]     					=			EsmForProteinFolding.from_pretrained('facebook/esmfold_v1'							).float()
             model.eval()
             lowercase__    :	str     					=			torch.tensor([[0, 6, 4, 1_3, 5, 4, 1_6, 1_2, 1_1, 7, 2]]							)
             lowercase__    :	Union[str, Any]     					=			model(a							)['positions']
             lowercase__    :	Any     					=			torch.tensor([2.5_828, 0.7_993, -10.9_334]	,    dtype=torch.floataa							)
             self.assertTrue(torch.allclose(position_outputs[0, 0, 0, 0]	,    a	,    atol=1e-4							)							)


"""simple docstring"""





import argparse

import fairseq
import torch

from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging


logging.set_verbosity_info()
_UpperCamelCase     :		Dict											=					logging.get_logger(__name__)

_UpperCamelCase     :		List[Any]											=					{
    "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",
}
_UpperCamelCase     :		List[str]											=					[
    "lm_head",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
    "label_embeddings_concat",
    "speaker_proj",
    "layer_norm_for_extract",
]





def        a_  (   _lowerCAmelCase						:				Any     ,				_lowerCAmelCase						:				int     ,				_lowerCAmelCase						:				Any     ,				_lowerCAmelCase						:				Optional[Any]     ,				_lowerCAmelCase						:				Tuple  ):





       '''simple docstring'''

       for attribute in key.split('.'  ):
              lowercase__    :	Dict     					=			getattr(_lowerCAmelCase     ,				_lowerCAmelCase  )

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

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





def        a_  (   _lowerCAmelCase						:				Any     ,				_lowerCAmelCase						:				Optional[Any]  ):





       '''simple docstring'''

       lowercase__    :	Tuple     					=			[]
       lowercase__    :	List[str]     					=			fairseq_model.state_dict()

       lowercase__    :	Union[str, Any]     					=			hf_model.unispeech_sat.feature_extractor

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

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





def        a_  (   _lowerCAmelCase						:				List[Any]     ,				_lowerCAmelCase						:				Union[str, Any]     ,				_lowerCAmelCase						:				Optional[int]     ,				_lowerCAmelCase						:				int     ,				_lowerCAmelCase						:				Dict  ):





       '''simple docstring'''

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





       '''simple docstring'''

       if config_path is not None:
              lowercase__    :	Any     					=			UniSpeechSatConfig.from_pretrained(_lowerCAmelCase  )
       else:
              lowercase__    :	Any     					=			UniSpeechSatConfig()

       lowercase__    :	Union[str, Any]     					=			''

       if is_finetuned:
              lowercase__    :	Optional[Any]     					=			UniSpeechSatForCTC(_lowerCAmelCase  )
       else:
              lowercase__    :	List[Any]     					=			UniSpeechSatForPreTraining(_lowerCAmelCase  )

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

       recursively_load_weights(_lowerCAmelCase     ,				_lowerCAmelCase  )

       hf_wavavec.save_pretrained(_lowerCAmelCase  )


if __name__ == "__main__":
       _UpperCamelCase     :		Tuple											=					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"
       )
       _UpperCamelCase     :		str											=					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
       )

