


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

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

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

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

             return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
      def  SCREAMING_SNAKE_CASE							( self       : List[str]  )	->    Optional[Any]:
             a_      :	Any							=						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=SCREAMING_SNAKE_CASE__  ,							esmfold_config={'trunk': {'num_blocks': 2}, 'fp16_esm': False}  ,							)
             return config
      def  SCREAMING_SNAKE_CASE							( self       : Any  ,							SCREAMING_SNAKE_CASE__       : Union[str, Any]  ,							SCREAMING_SNAKE_CASE__       : Optional[int]  ,							SCREAMING_SNAKE_CASE__       : str  ,							SCREAMING_SNAKE_CASE__       : Dict  ,							SCREAMING_SNAKE_CASE__       : Any  ,							SCREAMING_SNAKE_CASE__       : List[Any]  )	->    Optional[int]:
             a_      :	int							=						EsmForProteinFolding(config=SCREAMING_SNAKE_CASE__  ).float()
             model.to(SCREAMING_SNAKE_CASE__  )
             model.eval()
             a_      :	Tuple							=						model(SCREAMING_SNAKE_CASE__  ,							attention_mask=SCREAMING_SNAKE_CASE__  )
             a_      :	Any							=						model(SCREAMING_SNAKE_CASE__  )
             a_      :	Tuple							=						model(SCREAMING_SNAKE_CASE__  )

             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  SCREAMING_SNAKE_CASE							( self       : int  )	->    List[str]:
             a_      :	Tuple							=						self.prepare_config_and_inputs()
             (
                 a_	
             )      :	Tuple							=						config_and_inputs
             a_      :	Any							=						{'input_ids': input_ids, 'attention_mask': input_mask}
             return config, inputs_dict
@require_torch
class 				SCREAMING_SNAKE_CASE__						(       lowercase__		,					lowercase__		,					unittest.TestCase    ):
      snake_case__       :  Optional[int]       =		False

      snake_case__       :  Union[str, Any]       =		(EsmForProteinFolding,) if is_torch_available() else ()
      snake_case__       :  List[str]       =		()
      snake_case__       :  Optional[Any]       =		{} if is_torch_available() else {}
      snake_case__       :  List[Any]       =		False
      def  SCREAMING_SNAKE_CASE							( self       : Optional[int]  )	->    Optional[int]:
             a_      :	int							=						EsmFoldModelTester(self  )
             a_      :	List[Any]							=						ConfigTester(self  ,							config_class=SCREAMING_SNAKE_CASE__  ,							hidden_size=3_7  )
      def  SCREAMING_SNAKE_CASE							( self       : str  )	->    List[str]:
             self.config_tester.run_common_tests()
      def  SCREAMING_SNAKE_CASE							( self       : str  )	->    str:
             a_      :	Optional[int]							=						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  SCREAMING_SNAKE_CASE							( self       : Tuple  )	->    Union[str, Any]:
             pass
      @unittest.skip
      def  SCREAMING_SNAKE_CASE							( self       : Tuple  )	->    Optional[int]:
             pass
      @unittest.skip('Esm does not support embedding resizing'  )
      def  SCREAMING_SNAKE_CASE							( self       : int  )	->    Any:
             pass
      @unittest.skip('Esm does not support embedding resizing'  )
      def  SCREAMING_SNAKE_CASE							( self       : Union[str, Any]  )	->    Tuple:
             pass
      @unittest.skip('ESMFold does not support passing input embeds!'  )
      def  SCREAMING_SNAKE_CASE							( self       : Tuple  )	->    str:
             pass
      @unittest.skip('ESMFold does not support head pruning.'  )
      def  SCREAMING_SNAKE_CASE							( self       : int  )	->    Any:
             pass
      @unittest.skip('ESMFold does not support head pruning.'  )
      def  SCREAMING_SNAKE_CASE							( self       : str  )	->    List[str]:
             pass
      @unittest.skip('ESMFold does not support head pruning.'  )
      def  SCREAMING_SNAKE_CASE							( self       : Dict  )	->    str:
             pass
      @unittest.skip('ESMFold does not support head pruning.'  )
      def  SCREAMING_SNAKE_CASE							( self       : Optional[Any]  )	->    Any:
             pass
      @unittest.skip('ESMFold does not support head pruning.'  )
      def  SCREAMING_SNAKE_CASE							( self       : int  )	->    Tuple:
             pass
      @unittest.skip('ESMFold does not output hidden states in the normal way.'  )
      def  SCREAMING_SNAKE_CASE							( self       : Any  )	->    Union[str, Any]:
             pass
      @unittest.skip('ESMfold does not output hidden states in the normal way.'  )
      def  SCREAMING_SNAKE_CASE							( self       : Union[str, Any]  )	->    Optional[int]:
             pass
      @unittest.skip('ESMFold only has one output format.'  )
      def  SCREAMING_SNAKE_CASE							( self       : Any  )	->    str:
             pass
      @unittest.skip('This test doesn\'t work for ESMFold and doesn\'t test core functionality'  )
      def  SCREAMING_SNAKE_CASE							( self       : List[Any]  )	->    List[Any]:
             pass
      @unittest.skip('ESMFold does not support input chunking.'  )
      def  SCREAMING_SNAKE_CASE							( self       : Tuple  )	->    Any:
             pass
      @unittest.skip('ESMFold doesn\'t respect you and it certainly doesn\'t respect your initialization arguments.'  )
      def  SCREAMING_SNAKE_CASE							( self       : List[str]  )	->    List[Any]:
             pass
      @unittest.skip('ESMFold doesn\'t support torchscript compilation.'  )
      def  SCREAMING_SNAKE_CASE							( self       : Union[str, Any]  )	->    List[str]:
             pass
      @unittest.skip('ESMFold doesn\'t support torchscript compilation.'  )
      def  SCREAMING_SNAKE_CASE							( self       : Any  )	->    str:
             pass
      @unittest.skip('ESMFold doesn\'t support torchscript compilation.'  )
      def  SCREAMING_SNAKE_CASE							( self       : Optional[Any]  )	->    Any:
             pass
      @unittest.skip('ESMFold doesn\'t support data parallel.'  )
      def  SCREAMING_SNAKE_CASE							( self       : int  )	->    str:
             pass

      @unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.'  )
      def  SCREAMING_SNAKE_CASE							( self       : List[Any]  )	->    int:
             pass
@require_torch
class 				SCREAMING_SNAKE_CASE__						(       lowercase__    ):
      @slow
      def  SCREAMING_SNAKE_CASE							( self       : Tuple  )	->    List[str]:
             a_      :	Union[str, Any]							=						EsmForProteinFolding.from_pretrained('facebook/esmfold_v1'  ).float()
             model.eval()
             a_      :	Tuple							=						torch.tensor([[0, 6, 4, 1_3, 5, 4, 1_6, 1_2, 1_1, 7, 2]]  )
             a_      :	int							=						model(SCREAMING_SNAKE_CASE__  )['positions']
             a_      :	Dict							=						torch.tensor([2.5828, 0.7993, -10.9334]  ,							dtype=torch.floataa  )
             self.assertTrue(torch.allclose(position_outputs[0, 0, 0, 0]  ,							SCREAMING_SNAKE_CASE__  ,							atol=1E-4  )  )






import argparse
import os
from pathlib import Path
from typing import Dict

import tensorflow as tf
import torch
from tqdm import tqdm

from transformers import PegasusConfig, PegasusForConditionalGeneration, PegasusTokenizer
from transformers.models.pegasus.configuration_pegasus import DEFAULTS, task_specific_params


UpperCAmelCase_   :				Optional[int]						      = [
    # replace left string with right string to get the relevant state_dict key (identical state dict to bart)
    ['memory_attention', 'encoder_attn'],
    ['attention', 'attn'],
    ['/', '.'],
    ['.LayerNorm.gamma', '_layer_norm.weight'],
    ['.LayerNorm.beta', '_layer_norm.bias'],
    ['r.layer_', 'r.layers.'],
    ['output_proj', 'out_proj'],
    ['ffn.dense_1.', 'fc2.'],
    ['ffn.dense.', 'fc1.'],
    ['ffn_layer_norm', 'final_layer_norm'],
    ['kernel', 'weight'],
    ['encoder_layer_norm.', 'encoder.layer_norm.'],
    ['decoder_layer_norm.', 'decoder.layer_norm.'],
    ['embeddings.weights', 'shared.weight'],
]







def  SCREAMING_SNAKE_CASE_		(    __A				:    Any       )			->	Union[str, Any]:

       """simple docstring"""

       for pegasus_name, hf_name in PATTERNS:
              a_      :	Union[str, Any]							=						k.replace(__A						,					__A       )
       return k







def  SCREAMING_SNAKE_CASE_		(    __A				:    dict						,					__A				:    dict       )			->	PegasusForConditionalGeneration:

       """simple docstring"""

       a_      :	int							=						DEFAULTS.copy()
       cfg_kwargs.update(__A       )
       a_      :	List[str]							=						PegasusConfig(**__A       )
       a_      :	List[Any]							=						PegasusForConditionalGeneration(__A       )
       a_      :	List[str]							=						torch_model.model.state_dict()
       a_      :	Optional[int]							=						{}
       for k, v in tf_weights.items():
              a_      :	str							=						rename_state_dict_key(__A       )
              if new_k not in sd:
                     raise ValueError(F"""could not find new key {new_k} in state dict. (converted from {k})"""       )

              if "dense" in k or "proj" in new_k:
                     a_      :	Union[str, Any]							=						v.T
              a_      :	List[str]							=						torch.tensor(__A						,					dtype=sd[new_k].dtype       )
              assert v.shape == sd[new_k].shape, F"""{new_k}, {k}, {v.shape}, {sd[new_k].shape}"""
       # make sure embedding.padding_idx is respected
       a_      :	int							=						torch.zeros_like(mapping['shared.weight'][cfg.pad_token_id + 1]       )
       a_      :	Optional[Any]							=						mapping['shared.weight']
       a_      :	Any							=						mapping['shared.weight']
       a_      :	Optional[int]							=						{k: torch.zeros_like(__A       ) for k, v in sd.items() if k.endswith('bias'       ) and k not in mapping}
       mapping.update(**__A       )
       a_							,	a_      :	Optional[Any]							=						torch_model.model.load_state_dict(__A						,					strict=__A       )
       a_      :	Optional[Any]							=						[
           k for k in missing if k not in ['encoder.embed_positions.weight', 'decoder.embed_positions.weight']
       ]
       assert unexpected_missing == [], F"""no matches found for the following torch keys {unexpected_missing}"""
       assert extra == [], F"""no matches found for the following tf keys {extra}"""
       return torch_model







def  SCREAMING_SNAKE_CASE_		(    __A				:    Dict="./ckpt/aeslc/model.ckpt-32000"       )			->	Dict:

       """simple docstring"""

       a_      :	List[Any]							=						tf.train.list_variables(__A       )
       a_      :	Optional[Any]							=						{}
       a_      :	int							=						['Adafactor', 'global_step']
       for name, shape in tqdm(__A						,					desc='converting tf checkpoint to dict'       ):
              a_      :	Optional[int]							=						any(pat in name for pat in ignore_name       )
              if skip_key:
                     continue
              a_      :	List[str]							=						tf.train.load_variable(__A						,					__A       )
              a_      :	Optional[Any]							=						array
       return tf_weights







def  SCREAMING_SNAKE_CASE_		(    __A				:    str						,					__A				:    str       )			->	Union[str, Any]:

       """simple docstring"""

       a_      :	Any							=						Path(__A       ).parent.name
       a_      :	Dict							=						task_specific_params[F"""summarization_{dataset}"""]['max_position_embeddings']
       a_      :	Union[str, Any]							=						PegasusTokenizer.from_pretrained('sshleifer/pegasus'						,					model_max_length=__A       )
       assert tok.model_max_length == desired_max_model_length
       tok.save_pretrained(__A       )

       # convert model
       a_      :	List[Any]							=						get_tf_weights_as_numpy(__A       )
       a_      :	List[str]							=						task_specific_params[F"""summarization_{dataset}"""]
       if dataset == "large":
              a_      :	Union[str, Any]							=						task_specific_params
       a_      :	Optional[int]							=						convert_pegasus(__A						,					__A       )
       torch_model.save_pretrained(__A       )
       a_      :	Dict							=						torch_model.state_dict()
       sd.pop('model.decoder.embed_positions.weight'       )
       sd.pop('model.encoder.embed_positions.weight'       )
       torch.save(__A						,					Path(__A       ) / 'pytorch_model.bin'       )


if __name__ == "__main__":
   UpperCAmelCase_   :				Dict						      = argparse.ArgumentParser()
   # Required parameters
   parser.add_argument('tf_ckpt_path', type=str, help='passed to tf.train.list_variables')
   parser.add_argument('save_dir', default=None, type=str, help='Path to the output PyTorch model.')
   UpperCAmelCase_   :				str						      = parser.parse_args()
   if args.save_dir is None:
      UpperCAmelCase_   :				str						      = Path(args.tf_ckpt_path).parent.name
      UpperCAmelCase_   :				Optional[int]						      = os.path.join('pegasus', dataset)
   convert_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir)


