

"""simple docstring"""




import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import MarianConfig, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
 import os

 # The slow tests are often failing with OOM error on GPU
 # This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
 # but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
 _UpperCamelCase			      =		"platform"

 import jax
 import jax.numpy as jnp

 from transformers import MarianTokenizer
 from transformers.models.marian.modeling_flax_marian import FlaxMarianModel, FlaxMarianMTModel, shift_tokens_right







def 		_lowercase			(					lowercase__       ,     lowercase__       ,     lowercase__=None       ,     lowercase__=None       ,     lowercase__=None       ,     lowercase__=None       ,     lowercase__=None       ,     lowercase__=None       ,     ):




      if attention_mask is None:
            __lowerCAmelCase :						str = np.where(input_ids != config.pad_token_id       ,     1       ,     0						)
      if decoder_attention_mask is None:
            __lowerCAmelCase :						int = np.where(decoder_input_ids != config.pad_token_id       ,     1       ,     0						)
      if head_mask is None:
            __lowerCAmelCase :						Optional[int] = np.ones((config.encoder_layers, config.encoder_attention_heads)						)
      if decoder_head_mask is None:
            __lowerCAmelCase :						int = np.ones((config.decoder_layers, config.decoder_attention_heads)						)
      if cross_attn_head_mask is None:
            __lowerCAmelCase :						Union[str, Any] = np.ones((config.decoder_layers, config.decoder_attention_heads)						)
      return {
          "input_ids": input_ids,
          "decoder_input_ids": decoder_input_ids,
          "attention_mask": attention_mask,
          "decoder_attention_mask": attention_mask,
      }







class   __lowercase				:
 def __init__(							self				,							A_				,							A_=13				,							A_=7				,							A_=True				,							A_=False				,							A_=99				,							A_=16				,							A_=2				,							A_=4				,							A_=4				,							A_="gelu"				,							A_=0.1				,							A_=0.1				,							A_=32				,							A_=2				,							A_=1				,							A_=0				,							A_=0.02				,							) ->Optional[Any]:



       '''simple docstring'''





       __lowerCAmelCase :						List[str] = parent
       __lowerCAmelCase :						Optional[int] = batch_size
       __lowerCAmelCase :						str = seq_length
       __lowerCAmelCase :						str = is_training
       __lowerCAmelCase :						int = use_labels
       __lowerCAmelCase :						Optional[int] = vocab_size
       __lowerCAmelCase :						List[str] = hidden_size
       __lowerCAmelCase :						Optional[Any] = num_hidden_layers
       __lowerCAmelCase :						int = num_attention_heads
       __lowerCAmelCase :						Tuple = intermediate_size
       __lowerCAmelCase :						Any = hidden_act
       __lowerCAmelCase :						Union[str, Any] = hidden_dropout_prob
       __lowerCAmelCase :						Dict = attention_probs_dropout_prob
       __lowerCAmelCase :						Union[str, Any] = max_position_embeddings
       __lowerCAmelCase :						Any = eos_token_id
       __lowerCAmelCase :						List[str] = pad_token_id
       __lowerCAmelCase :						List[Any] = bos_token_id
       __lowerCAmelCase :						List[str] = initializer_range
 def      UpperCamelCase__  (							self ) ->List[Any]:



       '''simple docstring'''





       __lowerCAmelCase :						Tuple = np.clip(ids_tensor([self.batch_size, self.seq_length - 1]				,							self.vocab_size )				,							3				,							self.vocab_size )
       __lowerCAmelCase :						Tuple = np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)				,							dtype=np.intaa ))				,							-1 )

       __lowerCAmelCase :						str = shift_tokens_right(A_				,							1				,							2 )

       __lowerCAmelCase :						str = MarianConfig(
           vocab_size=self.vocab_size				,							d_model=self.hidden_size				,							encoder_layers=self.num_hidden_layers				,							decoder_layers=self.num_hidden_layers				,							encoder_attention_heads=self.num_attention_heads				,							decoder_attention_heads=self.num_attention_heads				,							encoder_ffn_dim=self.intermediate_size				,							decoder_ffn_dim=self.intermediate_size				,							dropout=self.hidden_dropout_prob				,							attention_dropout=self.attention_probs_dropout_prob				,							max_position_embeddings=self.max_position_embeddings				,							eos_token_id=self.eos_token_id				,							bos_token_id=self.bos_token_id				,							pad_token_id=self.pad_token_id				,							initializer_range=self.initializer_range				,							use_cache=A_				,							)
       __lowerCAmelCase :						int = prepare_marian_inputs_dict(A_				,							A_				,							A_ )
       return config, inputs_dict
 def      UpperCamelCase__  (							self ) ->Optional[int]:



       '''simple docstring'''





       __lowerCAmelCase :						List[str] = self.prepare_config_and_inputs()
       return config, inputs_dict
 def      UpperCamelCase__  (							self				,							A_				,							A_				,							A_ ) ->Optional[Any]:



       '''simple docstring'''





       __lowerCAmelCase :						Optional[Any] = 20
       __lowerCAmelCase :						List[str] = model_class_name(A_ )

       __lowerCAmelCase :						List[str] = model.encode(inputs_dict['''input_ids'''] )

       __lowerCAmelCase :						List[str] = (
           inputs_dict["decoder_input_ids"],
           inputs_dict["decoder_attention_mask"],
       )

       __lowerCAmelCase :						Dict = model.init_cache(decoder_input_ids.shape[0]				,							A_				,							A_ )
       __lowerCAmelCase :						int = jnp.ones((decoder_input_ids.shape[0], max_decoder_length)				,							dtype='''i4''' )

       __lowerCAmelCase :						str = jnp.broadcast_to(
           jnp.arange(decoder_input_ids.shape[-1] - 1 )[None, :]				,							(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)				,							)
       __lowerCAmelCase :						Dict = model.decode(
           decoder_input_ids[:, :-1]				,							A_				,							decoder_attention_mask=A_				,							past_key_values=A_				,							decoder_position_ids=A_				,							)

       __lowerCAmelCase :						Optional[int] = jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]				,							dtype='''i4''' )
       __lowerCAmelCase :						int = model.decode(
           decoder_input_ids[:, -1:]				,							A_				,							decoder_attention_mask=A_				,							past_key_values=outputs_cache.past_key_values				,							decoder_position_ids=A_				,							)

       __lowerCAmelCase :						int = model.decode(A_				,							A_ )

       __lowerCAmelCase :						Union[str, Any] = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5]) ) )
       self.parent.assertTrue(diff < 1e-3				,							msg=f"""Max diff is {diff}""" )





 def      UpperCamelCase__  (							self				,							A_				,							A_				,							A_ ) ->Optional[int]:



       '''simple docstring'''





       __lowerCAmelCase :						Tuple = 20
       __lowerCAmelCase :						Optional[int] = model_class_name(A_ )

       __lowerCAmelCase :						Any = model.encode(inputs_dict['''input_ids'''] )

       __lowerCAmelCase :						Tuple = (
           inputs_dict["decoder_input_ids"],
           inputs_dict["decoder_attention_mask"],
       )

       __lowerCAmelCase :						Dict = jnp.concatenate(
           [
               decoder_attention_mask,
               jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1]) ),
           ]				,							axis=-1				,							)

       __lowerCAmelCase :						List[Any] = model.init_cache(decoder_input_ids.shape[0]				,							A_				,							A_ )
       __lowerCAmelCase :						List[Any] = jnp.broadcast_to(
           jnp.arange(decoder_input_ids.shape[-1] - 1 )[None, :]				,							(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)				,							)

       __lowerCAmelCase :						str = model.decode(
           decoder_input_ids[:, :-1]				,							A_				,							decoder_attention_mask=A_				,							past_key_values=A_				,							decoder_position_ids=A_				,							)
       __lowerCAmelCase :						Any = jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]				,							dtype='''i4''' )
       __lowerCAmelCase :						Optional[Any] = model.decode(
           decoder_input_ids[:, -1:]				,							A_				,							past_key_values=outputs_cache.past_key_values				,							decoder_attention_mask=A_				,							decoder_position_ids=A_				,							)

       __lowerCAmelCase :						Optional[Any] = model.decode(A_				,							A_				,							decoder_attention_mask=A_ )

       __lowerCAmelCase :						Dict = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5]) ) )
       self.parent.assertTrue(diff < 1e-3				,							msg=f"""Max diff is {diff}""" )







@require_flax
class   __lowercase				(_UpperCAmelCase		,       unittest.TestCase		,       _UpperCAmelCase			):
 _UpperCamelCase        =  True
 _UpperCamelCase        =  (FlaxMarianModel, FlaxMarianMTModel) if is_flax_available() else ()
 _UpperCamelCase        =  (FlaxMarianMTModel,) if is_flax_available() else ()
 def      UpperCamelCase__  (							self ) ->Union[str, Any]:



       '''simple docstring'''





       __lowerCAmelCase :						Optional[Any] = FlaxMarianModelTester(self )
 def      UpperCamelCase__  (							self ) ->int:



       '''simple docstring'''





       __lowerCAmelCase :						Optional[int] = self.model_tester.prepare_config_and_inputs()
       for model_class in self.all_model_classes:
             self.model_tester.check_use_cache_forward(A_				,							A_				,							A_ )
 def      UpperCamelCase__  (							self ) ->Tuple:



       '''simple docstring'''





       __lowerCAmelCase :						Tuple = self.model_tester.prepare_config_and_inputs()
       for model_class in self.all_model_classes:
             self.model_tester.check_use_cache_forward_with_attn_mask(A_				,							A_				,							A_ )
 def      UpperCamelCase__  (							self ) ->List[Any]:



       '''simple docstring'''





       __lowerCAmelCase :						Dict = self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
             with self.subTest(model_class.__name__ ):
                   __lowerCAmelCase :						Dict = self._prepare_for_class(A_				,							A_ )
                   __lowerCAmelCase :						Dict = model_class(A_ )

                   @jax.jit
                   def encode_jitted(A_				,							A_=None				,							**A_ ):
                         return model.encode(input_ids=A_				,							attention_mask=A_ )

                   with self.subTest('''JIT Enabled''' ):
                         __lowerCAmelCase :						Dict = encode_jitted(**A_ ).to_tuple()

                   with self.subTest('''JIT Disabled''' ):
                         with jax.disable_jit():
                               __lowerCAmelCase :						List[str] = encode_jitted(**A_ ).to_tuple()

                   self.assertEqual(len(A_ )				,							len(A_ ) )
                   for jitted_output, output in zip(A_				,							A_ ):
                         self.assertEqual(jitted_output.shape				,							output.shape )
 def      UpperCamelCase__  (							self ) ->Optional[int]:



       '''simple docstring'''





       __lowerCAmelCase :						Optional[Any] = self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
             with self.subTest(model_class.__name__ ):
                   __lowerCAmelCase :						Any = model_class(A_ )
                   __lowerCAmelCase :						Dict = model.encode(inputs_dict['''input_ids''']				,							inputs_dict['''attention_mask'''] )

                   __lowerCAmelCase :						List[Any] = {
                       "decoder_input_ids": inputs_dict["decoder_input_ids"],
                       "decoder_attention_mask": inputs_dict["decoder_attention_mask"],
                       "encoder_outputs": encoder_outputs,
                   }

                   @jax.jit
                   def decode_jitted(A_				,							A_				,							A_ ):
                         return model.decode(
                             decoder_input_ids=A_				,							decoder_attention_mask=A_				,							encoder_outputs=A_				,							)

                   with self.subTest('''JIT Enabled''' ):
                         __lowerCAmelCase :						Any = decode_jitted(**A_ ).to_tuple()

                   with self.subTest('''JIT Disabled''' ):
                         with jax.disable_jit():
                               __lowerCAmelCase :						Optional[Any] = decode_jitted(**A_ ).to_tuple()

                   self.assertEqual(len(A_ )				,							len(A_ ) )
                   for jitted_output, output in zip(A_				,							A_ ):
                         self.assertEqual(jitted_output.shape				,							output.shape )





 @slow
 def      UpperCamelCase__  (							self ) ->Optional[Any]:



       '''simple docstring'''





       for model_class_name in self.all_model_classes:
             __lowerCAmelCase :						Union[str, Any] = model_class_name.from_pretrained('''Helsinki-NLP/opus-mt-en-de''' )
             # FlaxMarianForSequenceClassification expects eos token in input_ids
             __lowerCAmelCase :						Optional[Any] = np.ones((1, 1) ) * model.config.eos_token_id
             __lowerCAmelCase :						int = model(A_ )
             self.assertIsNotNone(A_ )







@require_flax
@require_sentencepiece
@require_tokenizers
class   __lowercase				(unittest.TestCase			):
 _UpperCamelCase        =  None
 _UpperCamelCase        =  None
 @classmethod
 def      UpperCamelCase__  (							cls ) ->Any:



       '''simple docstring'''





       __lowerCAmelCase :						Optional[Any] = f"""Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}"""
       return cls
 @cached_property
 def      UpperCamelCase__  (							self ) ->Union[str, Any]:



       '''simple docstring'''





       return MarianTokenizer.from_pretrained(self.model_name )
 @property
 def      UpperCamelCase__  (							self ) ->Optional[Any]:



       '''simple docstring'''





       return self.tokenizer.eos_token_id
 @cached_property
 def      UpperCamelCase__  (							self ) ->Optional[int]:



       '''simple docstring'''





       __lowerCAmelCase :						FlaxMarianMTModel = FlaxMarianMTModel.from_pretrained(self.model_name )
       self.assertEqual(model.config.decoder_start_token_id				,							model.config.pad_token_id )
       return model
 def      UpperCamelCase__  (							self				,							**A_ ) ->List[Any]:



       '''simple docstring'''





       __lowerCAmelCase :						List[str] = self.translate_src_text(**A_ )
       self.assertListEqual(self.expected_text				,							A_ )





 def      UpperCamelCase__  (							self				,							**A_ ) ->Optional[Any]:



       '''simple docstring'''





       __lowerCAmelCase :						Dict = self.tokenizer(self.src_text				,							padding=A_				,							return_tensors='''np'''				,							**A_ )
       __lowerCAmelCase :						str = self.model.generate(
           model_inputs.input_ids				,							attention_mask=model_inputs.attention_mask				,							num_beams=2				,							max_length=128				,							).sequences
       __lowerCAmelCase :						int = self.tokenizer.batch_decode(A_				,							skip_special_tokens=A_ )
       return generated_words







@require_flax
@require_sentencepiece
@require_tokenizers
class   __lowercase				(_UpperCAmelCase			):
 _UpperCamelCase        =  'en'
 _UpperCamelCase        =  'fr'
 _UpperCamelCase        =  [
     'I am a small frog.',
     'Now I can forget the 100 words of german that I know.',
 ]
 _UpperCamelCase        =  [
     'Je suis une petite grenouille.',
     'Maintenant, je peux oublier les 100 mots d\'allemand que je connais.',
 ]
 @slow
 def      UpperCamelCase__  (							self ) ->Tuple:



       '''simple docstring'''





       self._assert_generated_batch_equal_expected()







@require_flax
@require_sentencepiece
@require_tokenizers
class   __lowercase				(_UpperCAmelCase			):
 _UpperCamelCase        =  'fr'
 _UpperCamelCase        =  'en'
 _UpperCamelCase        =  [
     'Donnez moi le micro.',
     'Tom et Mary étaient assis à une table.',  # Accents
 ]
 _UpperCamelCase        =  [
     'Give me the microphone.',
     'Tom and Mary were sitting at a table.',
 ]
 @slow
 def      UpperCamelCase__  (							self ) ->List[Any]:



       '''simple docstring'''





       self._assert_generated_batch_equal_expected()







@require_flax
@require_sentencepiece
@require_tokenizers
class   __lowercase				(_UpperCAmelCase			):
 _UpperCamelCase        =  'mt'
 _UpperCamelCase        =  'en'
 _UpperCamelCase        =  ['Billi messu b\'mod ġentili, Ġesù fejjaq raġel li kien milqut bil - marda kerha tal - ġdiem.']
 _UpperCamelCase        =  ['Touching gently, Jesus healed a man who was affected by the sad disease of leprosy.']
 @slow
 def      UpperCamelCase__  (							self ) ->Dict:



       '''simple docstring'''





       self._assert_generated_batch_equal_expected()







@require_flax
@require_sentencepiece
@require_tokenizers
class   __lowercase				(_UpperCAmelCase			):
 _UpperCamelCase        =  'en'
 _UpperCamelCase        =  'de'
 _UpperCamelCase        =  [
     'I am a small frog.',
     'Now I can forget the 100 words of german that I know.',
     'Tom asked his teacher for advice.',
     'That\'s how I would do it.',
     'Tom really admired Mary\'s courage.',
     'Turn around and close your eyes.',
 ]
 _UpperCamelCase        =  [
     'Ich bin ein kleiner Frosch.',
     'Jetzt kann ich die 100 Wörter des Deutschen vergessen, die ich kenne.',
     'Tom bat seinen Lehrer um Rat.',
     'So würde ich das machen.',
     'Tom bewunderte Marias Mut wirklich.',
     'Drehen Sie sich um und schließen Sie die Augen.',
 ]
 @slow
 def      UpperCamelCase__  (							self ) ->Any:



       '''simple docstring'''





       self._assert_generated_batch_equal_expected()







@require_flax
@require_sentencepiece
@require_tokenizers
class   __lowercase				(_UpperCAmelCase			):
 _UpperCamelCase        =  'en'
 _UpperCamelCase        =  'zh'
 _UpperCamelCase        =  ['My name is Wolfgang and I live in Berlin']
 _UpperCamelCase        =  ['我叫沃尔夫冈 我住在柏林']
 @slow
 def      UpperCamelCase__  (							self ) ->Optional[Any]:



       '''simple docstring'''





       self._assert_generated_batch_equal_expected()







@require_flax
@require_sentencepiece
@require_tokenizers
class   __lowercase				(_UpperCAmelCase			):
 _UpperCamelCase        =  'ru'
 _UpperCamelCase        =  'fr'
 _UpperCamelCase        =  ['Он показал мне рукопись своей новой пьесы.']
 _UpperCamelCase        =  ['Il m\'a montré le manuscrit de sa nouvelle pièce.']
 @slow
 def      UpperCamelCase__  (							self ) ->Optional[int]:



       '''simple docstring'''





       self._assert_generated_batch_equal_expected()







@require_flax
@require_sentencepiece
@require_tokenizers
class   __lowercase				(_UpperCAmelCase			):
 _UpperCamelCase        =  'en'
 _UpperCamelCase        =  'ROMANCE'
 _UpperCamelCase        =  [
     '>>fr<< Don\'t spend so much time watching TV.',
     '>>pt<< Your message has been sent.',
     '>>es<< He\'s two years older than me.',
 ]
 _UpperCamelCase        =  [
     'Ne passez pas autant de temps à regarder la télé.',
     'A sua mensagem foi enviada.',
     'Es dos años más viejo que yo.',
 ]
 @slow
 def      UpperCamelCase__  (							self ) ->int:



       '''simple docstring'''





       self._assert_generated_batch_equal_expected()





import argparse
import pathlib
from pathlib import Path
from tempfile import TemporaryDirectory

import esm as esm_module
import torch
from esm.esmfold.va.misc import batch_encode_sequences as esmfold_encode_sequences
from esm.esmfold.va.pretrained import esmfold_va

from transformers.models.esm.configuration_esm import EsmConfig, EsmFoldConfig
from transformers.models.esm.modeling_esm import (
    EsmForMaskedLM,
    EsmForSequenceClassification,
    EsmIntermediate,
    EsmLayer,
    EsmOutput,
    EsmSelfAttention,
    EsmSelfOutput,
)
from transformers.models.esm.modeling_esmfold import EsmForProteinFolding
from transformers.models.esm.tokenization_esm import EsmTokenizer
from transformers.utils import logging


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

_UpperCamelCase			      =		[
    (
        "protein1",
        "MNGTEGPNFYVPFSNATGVVRSPFEYPQYYLAEPWQFSMLAAYMFLLIVLGFPINFLTLYVTVQHKKLRTPLNYILLNLAVADLFMVLGGFTSTLYTSLHGYFVFGPTGCNLEGFFATLGGEIALWSLVVLAIERYVVVCKPMSNFRFGENHAIMGVAFTWVMALACAAPPLAGWSRYIPEGLQCSCGIDYYTLKPEVNNESFVIYMFVVHFTIPMIIIFFCYGQLVFTVKEAAAQQQESATTQKAEKEVTRMVIIMVIAFLICWVPYASVAFYIFTHQGSNFGPIFMTIPAFFAKSAAIYNPVIYIMMNKQFRNCMLTTICCGKNPLGDDEASATVSKTETSQVAPA",
    ),
    ("protein2", "MKTVRQERLKSIVRILERSKEPVSGAQLAEELSVSRQVIVQDIAYLRSLGYNIVATPRGYVLA"),
    ("protein3", "MKTVRQERLKSI<mask>RILERSKEPVSGAQLAEELS<mask>SRQVIVQDIAYLRSLGYN<mask>VATPRGYVLAGG"),
    ("protein4", "MKTVRQERLKSI<mask>RILERSKEPVSGAQLAEELS<mask>SRQVIVQDIAYLRSLGYN<mask>VATPRGYVLA"),
]

_UpperCamelCase			      =		{
    "esm1b_t33_650M_UR50S": esm_module.pretrained.esm1b_t33_650M_UR50S,
    "esm1v_t33_650M_UR90S_1": esm_module.pretrained.esm1v_t33_650M_UR90S_1,
    "esm1v_t33_650M_UR90S_2": esm_module.pretrained.esm1v_t33_650M_UR90S_2,
    "esm1v_t33_650M_UR90S_3": esm_module.pretrained.esm1v_t33_650M_UR90S_3,
    "esm1v_t33_650M_UR90S_4": esm_module.pretrained.esm1v_t33_650M_UR90S_4,
    "esm1v_t33_650M_UR90S_5": esm_module.pretrained.esm1v_t33_650M_UR90S_5,
    "esm2_t48_15B_UR50D": esm_module.pretrained.esm2_t48_15B_UR50D,
    "esm2_t36_3B_UR50D": esm_module.pretrained.esm2_t36_3B_UR50D,
    "esm2_t33_650M_UR50D": esm_module.pretrained.esm2_t33_650M_UR50D,
    "esm2_t30_150M_UR50D": esm_module.pretrained.esm2_t30_150M_UR50D,
    "esm2_t12_35M_UR50D": esm_module.pretrained.esm2_t12_35M_UR50D,
    "esm2_t6_8M_UR50D": esm_module.pretrained.esm2_t6_8M_UR50D,
    "esmfold_v1": esmfold_va,
}

_UpperCamelCase			      =		list("ARNDCQEGHILKMFPSTWYV")

_UpperCamelCase			      =		restypes + ["X"]
_UpperCamelCase			      =		restypes_with_x + ["<pad>", "<mask>", "<cls>", "<sep>", "<eos>"]







def 		_lowercase			(					):
      with TemporaryDirectory() as tempdir:
            __lowerCAmelCase :						Optional[Any] = '''\n'''.join(lowercase__						)
            __lowerCAmelCase :						str = Path(lowercase__						) / '''vocab.txt'''
            vocab_file.write_text(lowercase__						)
            __lowerCAmelCase :						int = EsmTokenizer(vocab_file=str(lowercase__						)						)
      __lowerCAmelCase :						Union[str, Any] = 0  # Overlaps with 'A' but that seems to be what they want
      return hf_tokenizer







def 		_lowercase			(					lowercase__       ,     lowercase__						):
      __lowerCAmelCase :						int = our_module.load_state_dict(original_module.state_dict()						)
      if status.missing_keys:
            raise ValueError(f"""Missing keys: {status.missing_keys}"""						)
      if status.unexpected_keys:
            raise ValueError(f"""Unexpected keys: {status.unexpected_keys}"""						)







def 		_lowercase			(					lowercase__       ,     lowercase__       ,     lowercase__       ,     lowercase__       ,     lowercase__						):
      if model.startswith('''esmfold'''						):
            __lowerCAmelCase :						Tuple = MODEL_MAPPING[model]()
      else:
            __lowerCAmelCase,							__lowerCAmelCase :						str = MODEL_MAPPING[model]()
      esm.eval()  # disable dropout

      if model.startswith('''esmfold'''						):
            __lowerCAmelCase :						int = esm.esm.embed_dim
            __lowerCAmelCase :						Optional[Any] = esm.esm.num_layers
            __lowerCAmelCase :						Optional[Any] = esm.esm.attention_heads
            __lowerCAmelCase :						Dict = 4 * embed_dim
            __lowerCAmelCase :						Dict = esm.esm.token_dropout
            __lowerCAmelCase :						int = False  # This code path does not exist in ESM-2
            __lowerCAmelCase :						str = '''rotary'''
            __lowerCAmelCase :						List[Any] = True
            __lowerCAmelCase :						List[str] = EsmFoldConfig()
            for key, val in esm.cfg.items():
                  if hasattr(lowercase__       ,     lowercase__						) and key != "trunk":
                        setattr(lowercase__       ,     lowercase__       ,     lowercase__						)
            for key, val in esm.cfg.trunk.items():
                  if hasattr(esmfold_config.trunk       ,     lowercase__						) and key != "structure_module":
                        setattr(esmfold_config.trunk       ,     lowercase__       ,     lowercase__						)
            for key, val in esm.cfg.trunk.structure_module.items():
                  if hasattr(esmfold_config.trunk.structure_module       ,     lowercase__						):
                        setattr(esmfold_config.trunk.structure_module       ,     lowercase__       ,     lowercase__						)
      elif hasattr(lowercase__       ,     '''args'''						):
            # Indicates an ESM-1b or ESM-1v model
            __lowerCAmelCase :						Dict = esm.args.embed_dim
            __lowerCAmelCase :						List[str] = esm.args.layers
            __lowerCAmelCase :						Tuple = esm.args.attention_heads
            __lowerCAmelCase :						List[Any] = esm.args.ffn_embed_dim
            __lowerCAmelCase :						str = esm.args.token_dropout
            __lowerCAmelCase :						Any = True if esm.emb_layer_norm_before else False
            __lowerCAmelCase :						Optional[Any] = '''absolute'''
            __lowerCAmelCase :						Any = False
            __lowerCAmelCase :						Tuple = None
      else:
            # Indicates an ESM-2 model
            __lowerCAmelCase :						Optional[int] = esm.embed_dim
            __lowerCAmelCase :						int = esm.num_layers
            __lowerCAmelCase :						Any = esm.attention_heads
            __lowerCAmelCase :						Dict = 4 * embed_dim  # This is hardcoded in ESM-2
            __lowerCAmelCase :						Optional[Any] = esm.token_dropout
            __lowerCAmelCase :						Optional[Any] = False  # This code path does not exist in ESM-2
            __lowerCAmelCase :						Optional[Any] = '''rotary'''
            __lowerCAmelCase :						List[Any] = False
            __lowerCAmelCase :						str = None

      if is_folding_model:
            __lowerCAmelCase :						Dict = esm.esm.alphabet
      __lowerCAmelCase :						str = tuple(alphabet.all_toks						)
      __lowerCAmelCase :						Tuple = alphabet.mask_idx
      __lowerCAmelCase :						Optional[int] = alphabet.padding_idx

      if is_folding_model:
            __lowerCAmelCase :						List[Any] = esm.esm
      else:
            __lowerCAmelCase :						Tuple = esm

      __lowerCAmelCase :						Optional[int] = EsmConfig(
          vocab_size=original_esm_model.embed_tokens.num_embeddings       ,     mask_token_id=lowercase__       ,     hidden_size=lowercase__       ,     num_hidden_layers=lowercase__       ,     num_attention_heads=lowercase__       ,     intermediate_size=lowercase__       ,     max_position_embeddings=1_0_2_6       ,     layer_norm_eps=1E-5       ,     attention_probs_dropout_prob=0.0       ,     hidden_dropout_prob=0.0       ,     pad_token_id=lowercase__       ,     emb_layer_norm_before=lowercase__       ,     token_dropout=lowercase__       ,     position_embedding_type=lowercase__       ,     is_folding_model=lowercase__       ,     esmfold_config=lowercase__       ,     vocab_list=lowercase__       ,     )
      if classification_head:
            __lowerCAmelCase :						str = esm.classification_heads['''mnli'''].out_proj.weight.shape[0]
      print('''Our ESM config:'''       ,     lowercase__						)

      if model.startswith('''esmfold'''						):
            __lowerCAmelCase :						Any = EsmForProteinFolding
      elif classification_head:
            __lowerCAmelCase :						Union[str, Any] = EsmForSequenceClassification
      else:
            __lowerCAmelCase :						Any = EsmForMaskedLM
      __lowerCAmelCase :						str = model_class(lowercase__						)
      model.eval()

      # Now let's copy all the weights.
      # Embeddings
      __lowerCAmelCase :						Union[str, Any] = original_esm_model.embed_tokens.weight
      if position_embedding_type == "absolute":
            __lowerCAmelCase :						Union[str, Any] = original_esm_model.embed_positions.weight

      if config.emb_layer_norm_before:
            __lowerCAmelCase :						Optional[int] = original_esm_model.emb_layer_norm_before.weight
            __lowerCAmelCase :						int = original_esm_model.emb_layer_norm_before.bias

      __lowerCAmelCase :						Union[str, Any] = original_esm_model.emb_layer_norm_after.weight
      __lowerCAmelCase :						Any = original_esm_model.emb_layer_norm_after.bias

      for i in range(config.num_hidden_layers						):
            # Encoder: start of layer
            __lowerCAmelCase :						EsmLayer = model.esm.encoder.layer[i]
            # esm_layer: TransformerSentenceEncoderLayer = original_esm_model.layers[i]
            __lowerCAmelCase :						Union[str, Any] = original_esm_model.layers[i]

            # self attention
            __lowerCAmelCase :						EsmSelfAttention = layer.attention.self
            assert (
                esm_layer.self_attn.k_proj.weight.data.shape
                == esm_layer.self_attn.q_proj.weight.data.shape
                == esm_layer.self_attn.v_proj.weight.data.shape
                == torch.Size((config.hidden_size, config.hidden_size)						)
            )

            __lowerCAmelCase :						str = esm_layer.self_attn.q_proj.weight
            __lowerCAmelCase :						Dict = esm_layer.self_attn.q_proj.bias
            __lowerCAmelCase :						Dict = esm_layer.self_attn.k_proj.weight
            __lowerCAmelCase :						Optional[Any] = esm_layer.self_attn.k_proj.bias
            __lowerCAmelCase :						Tuple = esm_layer.self_attn.v_proj.weight
            __lowerCAmelCase :						str = esm_layer.self_attn.v_proj.bias

            if getattr(esm_layer.self_attn       ,     '''rot_emb'''       ,     lowercase__						) is not None:
                  # Matt: Although inv_freq is not a trainable weight, it is computed at model init and cached.
                  # During the training of ESM-2 the model was converted to float16 precision, which also converts
                  # the inv_freq tensor, and the loss of precision remains even if the model is loaded later as float32.
                  # If we recompute inv_freq without this loss of precision then we will get subtly different rotary
                  # embeddings, which are enough to cause significant discrepancies in model outputs. To avoid this,
                  # we make sure the new model copies the data from the old inv_freq.
                  __lowerCAmelCase :						str = esm_layer.self_attn.rot_emb.inv_freq

            # LayerNorm changes for pre-activation
            __lowerCAmelCase :						Union[str, Any] = esm_layer.self_attn_layer_norm.weight
            __lowerCAmelCase :						List[Any] = esm_layer.self_attn_layer_norm.bias
            __lowerCAmelCase :						List[Any] = esm_layer.final_layer_norm.weight
            __lowerCAmelCase :						Union[str, Any] = esm_layer.final_layer_norm.bias

            # self-attention output
            __lowerCAmelCase :						EsmSelfOutput = layer.attention.output
            assert self_output.dense.weight.shape == esm_layer.self_attn.out_proj.weight.shape
            __lowerCAmelCase :						str = esm_layer.self_attn.out_proj.weight
            __lowerCAmelCase :						List[str] = esm_layer.self_attn.out_proj.bias

            # intermediate
            __lowerCAmelCase :						EsmIntermediate = layer.intermediate
            assert intermediate.dense.weight.shape == esm_layer.fca.weight.shape
            __lowerCAmelCase :						str = esm_layer.fca.weight
            __lowerCAmelCase :						int = esm_layer.fca.bias

            # output
            __lowerCAmelCase :						EsmOutput = layer.output
            assert bert_output.dense.weight.shape == esm_layer.fca.weight.shape
            __lowerCAmelCase :						Tuple = esm_layer.fca.weight
            __lowerCAmelCase :						List[Any] = esm_layer.fca.bias
            # end of layer

      if is_folding_model:
            __lowerCAmelCase :						Union[str, Any] = esm.esm_s_combine.data
            __lowerCAmelCase :						Optional[int] = esm.afa_to_esm.data
            transfer_and_check_weights(esm.embedding       ,     model.embedding						)
            transfer_and_check_weights(esm.esm_s_mlp       ,     model.esm_s_mlp						)
            transfer_and_check_weights(esm.trunk       ,     model.trunk						)
            transfer_and_check_weights(esm.distogram_head       ,     model.distogram_head						)
            transfer_and_check_weights(esm.ptm_head       ,     model.ptm_head						)
            transfer_and_check_weights(esm.lm_head       ,     model.lm_head						)
            transfer_and_check_weights(esm.lddt_head       ,     model.lddt_head						)

      elif classification_head:
            __lowerCAmelCase :						List[str] = esm.esm.classification_heads['''mnli'''].dense.weight
            __lowerCAmelCase :						List[str] = esm.classification_heads['''mnli'''].dense.bias
            __lowerCAmelCase :						Optional[int] = esm.classification_heads['''mnli'''].out_proj.weight
            __lowerCAmelCase :						List[str] = esm.classification_heads['''mnli'''].out_proj.bias
      else:
            # LM Head
            __lowerCAmelCase :						int = esm.lm_head.dense.weight
            __lowerCAmelCase :						Optional[int] = esm.lm_head.dense.bias
            __lowerCAmelCase :						Optional[int] = esm.lm_head.layer_norm.weight
            __lowerCAmelCase :						Union[str, Any] = esm.lm_head.layer_norm.bias
            __lowerCAmelCase :						int = esm.lm_head.weight
            __lowerCAmelCase :						str = esm.lm_head.bias

      # Contact prediction head
      transfer_and_check_weights(esm.contact_head       ,     model.esm.contact_head						)

      # Prepare data (first 2 sequences from ESMStructuralSplitDataset superfamily / 4)
      if is_folding_model:
            # Folding models aren't trained on masked inputs and don't like mask tokens.
            __lowerCAmelCase :						str = SAMPLE_DATA[:2]
      else:
            __lowerCAmelCase :						int = SAMPLE_DATA

      if is_folding_model:
            __lowerCAmelCase :						str = get_esmfold_tokenizer()
            __lowerCAmelCase :						str = hf_tokenizer(
                [row[1] for row in sample_data]       ,     return_tensors='''pt'''       ,     padding=lowercase__       ,     add_special_tokens=lowercase__						)
            __lowerCAmelCase,							__lowerCAmelCase,							__lowerCAmelCase,							__lowerCAmelCase,							__lowerCAmelCase :						str = esmfold_encode_sequences([row[1] for row in sample_data]						)
            __lowerCAmelCase :						Optional[int] = torch.all(hf_tokens['''input_ids'''] == esmfold_aas						) and torch.all(
                hf_tokens['''attention_mask'''] == esmfold_mask						)
      else:
            # Let's check that we get the same results.
            __lowerCAmelCase :						int = alphabet.get_batch_converter()
            __lowerCAmelCase,							__lowerCAmelCase,							__lowerCAmelCase :						int = batch_converter(lowercase__						)
            # Prepare tokenizer and make sure it matches
            with TemporaryDirectory() as tempdir:
                  __lowerCAmelCase :						Union[str, Any] = '''\n'''.join(alphabet.all_toks						)
                  __lowerCAmelCase :						Dict = Path(lowercase__						) / '''vocab.txt'''
                  vocab_file.write_text(lowercase__						)
                  __lowerCAmelCase :						str = EsmTokenizer(vocab_file=str(lowercase__						)						)

            __lowerCAmelCase :						Any = hf_tokenizer([row[1] for row in sample_data]       ,     return_tensors='''pt'''       ,     padding=lowercase__						)
            __lowerCAmelCase :						Any = torch.all(hf_tokens['''input_ids'''] == batch_tokens						)

      print('''Do both models tokenizers output the same tokens?'''       ,     '''🔥''' if success else '''💩'''						)
      if not success:
            raise Exception('''Tokenization does not match!'''						)

      with torch.no_grad():
            if is_folding_model:
                  # Let's test the model in parts
                  # ESMFold always converts the ESM stem to float16, which requires float16 ops
                  # that don't exist on CPU. Therefore, to test it we need to run it on GPU. However,
                  # ESMFold is what we in the community call a "big boy" and so we desperately avoid putting both the
                  # original and the converted model on the GPU at the same time.
                  __lowerCAmelCase :						int = esm.cuda().infer([row[1] for row in sample_data]						)
                  __lowerCAmelCase :						Union[str, Any] = model.cuda()(
                      input_ids=hf_tokens['''input_ids'''].cuda()       ,     attention_mask=hf_tokens['''attention_mask'''].cuda()						)
            else:
                  __lowerCAmelCase :						List[Any] = model(**lowercase__       ,     output_hidden_states=lowercase__						)
                  __lowerCAmelCase :						Optional[int] = our_output['''logits''']
                  if classification_head:
                        __lowerCAmelCase :						Optional[int] = esm.model.classification_heads['''mnli'''](esm.extract_features(lowercase__						)						)
                  else:
                        __lowerCAmelCase :						Any = esm(hf_tokens['''input_ids''']       ,     repr_layers=list(range(9_9_9						)						)						)
                        __lowerCAmelCase :						Optional[Any] = their_output['''logits''']

            if is_folding_model:
                  __lowerCAmelCase :						Dict = torch.max(torch.abs(our_output['''positions'''] - their_output['''positions''']						)						).item()
                  __lowerCAmelCase :						Dict = torch.allclose(our_output['''positions''']       ,     their_output['''positions''']       ,     atol=1E-5						)
            else:
                  __lowerCAmelCase :						Optional[Any] = torch.max(torch.abs(our_output - their_output						)						).item()
                  __lowerCAmelCase :						Union[str, Any] = torch.allclose(lowercase__       ,     lowercase__       ,     atol=1E-5						)

            print(f"""max_absolute_diff = {max_absolute_diff}"""						)  # ~ 1e-5
            print('''Do both models output the same tensors?'''       ,     '''🔥''' if success else '''💩'''						)

            if not success:
                  raise Exception('''Something went wRoNg'''						)

            if not is_folding_model:
                  # Let's check contact prediction too
                  __lowerCAmelCase :						Tuple = model.predict_contacts(hf_tokens['''input_ids''']       ,     hf_tokens['''attention_mask''']						)
                  __lowerCAmelCase :						Optional[int] = esm.predict_contacts(hf_tokens['''input_ids''']						)
                  __lowerCAmelCase :						str = torch.max(torch.abs(our_output - their_output						)						).item()
                  __lowerCAmelCase :						Union[str, Any] = torch.allclose(lowercase__       ,     lowercase__       ,     atol=1E-5						)

                  print('''Contact prediction testing:'''						)
                  print(f"""max_absolute_diff = {max_absolute_diff}"""						)  # ~ 1e-5
                  print('''Do both models output the same tensors?'''       ,     '''🔥''' if success else '''💩'''						)

                  if not success:
                        raise Exception('''Something went wRoNg'''						)

            pathlib.Path(lowercase__						).mkdir(parents=lowercase__       ,     exist_ok=lowercase__						)
            print(f"""Saving model to {pytorch_dump_folder_path}"""						)
            model.save_pretrained(lowercase__						)

            del esm  # Free up some memory before continuing

      print(f"""Saving tokenizer to {pytorch_dump_folder_path}"""						)
      hf_tokenizer.save_pretrained(lowercase__						)

      if push_to_repo:
            model.push_to_hub(repo_id=lowercase__       ,     use_auth_token=lowercase__						)
            hf_tokenizer.push_to_hub(repo_id=lowercase__       ,     use_auth_token=lowercase__						)


if __name__ == "__main__":
 _UpperCamelCase			      =		argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     "--pytorch_dump_folder_path", type=str, required=True, help="Path to the output PyTorch model."
 )
 parser.add_argument(
     "--classification_head", action="store_true", help="Whether to convert a final classification head."
 )
 parser.add_argument("--model", default=None, type=str, required=True, help="Name of model to convert.")
 parser.add_argument("--push_to_repo", type=str, help="Repo to upload to (including username!).")
 parser.add_argument("--auth_token", type=str, help="HuggingFace auth token.")
 _UpperCamelCase			      =		parser.parse_args()
 convert_esm_checkpoint_to_pytorch(
     args.model, args.pytorch_dump_folder_path, args.classification_head, args.push_to_repo, args.auth_token
 )


