


'''simple docstring'''

import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

# Register SEW's fairseq modules
from sew_asapp import tasks  # noqa: F401

from transformers import (
    SEWConfig,
    SEWForCTC,
    SEWModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
lowerCamelCase				:  Union[str, Any]    		=      logging.get_logger(__name__)

lowerCamelCase				:  Any    		=      {
    'post_extract_proj': 'feature_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.upsample.0': 'encoder.upsample.projection',
    'encoder.layer_norm': 'encoder.layer_norm',
    'w2v_model.layer_norm': 'layer_norm',
    'w2v_encoder.proj': 'lm_head',
    'mask_emb': 'masked_spec_embed',
}

def _SCREAMING_SNAKE_CASE (A    ,					A    ,					A    ,					A    ,					A			)			->			str:






       """simple docstring"""







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

       if weight_type is not None:
              lowercase__      =					getattr(A    ,					A			).shape
       else:
              lowercase__      =					hf_pointer.shape

       assert hf_shape == value.shape, (
           f"Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be"
           f" {value.shape} for {full_name}"
       )

       if weight_type == "weight":
              lowercase__      =					value
       elif weight_type == "weight_g":
              lowercase__      =					value
       elif weight_type == "weight_v":
              lowercase__      =					value
       elif weight_type == "bias":
              lowercase__      =					value
       else:
              lowercase__      =					value

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

def _SCREAMING_SNAKE_CASE (A    ,					A    ,					A			)			->			Any:






       """simple docstring"""







       lowercase__      =					[]
       lowercase__      =					fairseq_model.state_dict()

       lowercase__      =					hf_model.sew.feature_extractor if is_finetuned else hf_model.feature_extractor

       for name, value in fairseq_dict.items():
              lowercase__      =					False
              if "conv_layers" in name:
                     load_conv_layer(
                         A    ,					A    ,					A    ,					A    ,					hf_model.config.feat_extract_norm == '''group'''    ,					)
                     lowercase__      =					True
              else:
                     for key, mapped_key in MAPPING.items():
                            lowercase__      =					'''sew.''' + mapped_key if (is_finetuned and mapped_key != '''lm_head''') else mapped_key

                            if key in name or key.split('''w2v_model.'''			)[-1] == name.split('''.'''			)[0]:
                                   lowercase__      =					True
                                   if "*" in mapped_key:
                                          lowercase__      =					name.split(A			)[0].split('''.'''			)[-2]
                                          lowercase__      =					mapped_key.replace('''*'''    ,					A			)
                                   if "weight_g" in name:
                                          lowercase__      =					'''weight_g'''
                                   elif "weight_v" in name:
                                          lowercase__      =					'''weight_v'''
                                   elif "weight" in name:
                                          lowercase__      =					'''weight'''
                                   elif "bias" in name:
                                          lowercase__      =					'''bias'''
                                   else:
                                          lowercase__      =					None
                                   set_recursively(A    ,					A    ,					A    ,					A    ,					A			)
                            continue
              if not is_used:
                     unused_weights.append(A			)

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

def _SCREAMING_SNAKE_CASE (A    ,					A    ,					A    ,					A    ,					A			)			->			Tuple:






       """simple docstring"""







       lowercase__      =					full_name.split('''conv_layers.'''			)[-1]
       lowercase__      =					name.split('''.'''			)
       lowercase__      =					int(items[0]			)
       lowercase__      =					int(items[1]			)

       if type_id == 0:
              if "bias" in name:
                     assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
                         f"{full_name} has size {value.shape}, but"
                         f" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."
                     )
                     lowercase__      =					value
                     logger.info(f"Feat extract conv layer {layer_id} was initialized from {full_name}."			)
              elif "weight" in name:
                     assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
                         f"{full_name} has size {value.shape}, but"
                         f" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."
                     )
                     lowercase__      =					value
                     logger.info(f"Feat extract conv layer {layer_id} was initialized from {full_name}."			)
       elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
              if "bias" in name:
                     assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
                         f"{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was"
                         " found."
                     )
                     lowercase__      =					value
                     logger.info(f"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."			)
              elif "weight" in name:
                     assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
                         f"{full_name} has size {value.shape}, but"
                         f" {feature_extractor[layer_id].layer_norm.weight.data.shape} was found."
                     )
                     lowercase__      =					value
                     logger.info(f"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."			)
       else:
              unused_weights.append(A			)

def _SCREAMING_SNAKE_CASE (A    ,					A			)			->			Tuple:






       """simple docstring"""







       lowercase__      =					SEWConfig()
       if is_finetuned:
              lowercase__      =					model.wav_encoder.wav_model.cfg
       else:
              lowercase__      =					model.cfg

       lowercase__      =					fs_config.conv_bias
       lowercase__      =					eval(fs_config.conv_feature_layers			)
       lowercase__      =					[x[0] for x in conv_layers]
       lowercase__      =					[x[1] for x in conv_layers]
       lowercase__      =					[x[2] for x in conv_layers]
       lowercase__      =					'''gelu'''
       lowercase__      =					'''layer''' if fs_config.extractor_mode == '''layer_norm''' else '''group'''
       lowercase__      =					0.0
       lowercase__      =					fs_config.activation_fn.name
       lowercase__      =					fs_config.encoder_embed_dim
       lowercase__      =					0.02
       lowercase__      =					fs_config.encoder_ffn_embed_dim
       lowercase__      =					1E-5
       lowercase__      =					fs_config.encoder_layerdrop
       lowercase__      =					fs_config.encoder_attention_heads
       lowercase__      =					fs_config.conv_pos_groups
       lowercase__      =					fs_config.conv_pos
       lowercase__      =					len(A			)
       lowercase__      =					fs_config.encoder_layers
       lowercase__      =					fs_config.squeeze_factor

       # take care of any params that are overridden by the Wav2VecCtc model
       if is_finetuned:
              lowercase__      =					model.cfg
              lowercase__      =					fs_config.final_dropout
              lowercase__      =					fs_config.layerdrop
       lowercase__      =					fs_config.activation_dropout
       lowercase__      =					fs_config.mask_prob > 0 or fs_config.mask_channel_prob > 0
       lowercase__      =					fs_config.attention_dropout
       lowercase__      =					fs_config.dropout_input
       lowercase__      =					fs_config.dropout
       lowercase__      =					fs_config.mask_channel_length
       lowercase__      =					fs_config.mask_channel_prob
       lowercase__      =					fs_config.mask_length
       lowercase__      =					fs_config.mask_prob

       lowercase__      =					'''Wav2Vec2FeatureExtractor'''
       lowercase__      =					'''Wav2Vec2CTCTokenizer'''

       return config

@torch.no_grad()
def _SCREAMING_SNAKE_CASE (A    ,					A    ,					A=None    ,					A=None    ,					A=True			)			->			int:






       """simple docstring"""







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

       if config_path is not None:
              lowercase__      =					SEWConfig.from_pretrained(A			)
       else:
              lowercase__      =					convert_config(model[0]    ,					A			)
       lowercase__      =					model[0].eval()

       lowercase__      =					True if config.feat_extract_norm == '''layer''' else False
       lowercase__      =					WavaVecaFeatureExtractor(
           feature_size=1    ,					sampling_rate=16_000    ,					padding_value=0    ,					do_normalize=A    ,					return_attention_mask=A    ,					)

       if is_finetuned:
              if dict_path:
                     lowercase__      =					Dictionary.load(A			)

                     # important change bos & pad token id since CTC symbol is <pad> and
                     # not <s> as in fairseq
                     lowercase__      =					target_dict.pad_index
                     lowercase__      =					target_dict.bos_index
                     lowercase__      =					target_dict.pad_index
                     lowercase__      =					target_dict.bos_index
                     lowercase__      =					target_dict.eos_index
                     lowercase__      =					len(target_dict.symbols			)
                     lowercase__      =					os.path.join(A    ,					'''vocab.json'''			)
                     if not os.path.isdir(A			):
                            logger.error('''--pytorch_dump_folder_path ({}) should be a directory'''.format(A			)			)
                            return
                     os.makedirs(A    ,					exist_ok=A			)
                     with open(A    ,					'''w'''    ,					encoding='''utf-8'''			) as vocab_handle:
                            json.dump(target_dict.indices    ,					A			)
                     lowercase__      =					WavaVecaCTCTokenizer(
                         A    ,					unk_token=target_dict.unk_word    ,					pad_token=target_dict.pad_word    ,					bos_token=target_dict.bos_word    ,					eos_token=target_dict.eos_word    ,					word_delimiter_token='''|'''    ,					do_lower_case=A    ,					)
                     lowercase__      =					WavaVecaProcessor(feature_extractor=A    ,					tokenizer=A			)
                     processor.save_pretrained(A			)

              lowercase__      =					SEWForCTC(A			)
       else:
              lowercase__      =					SEWModel(A			)
              feature_extractor.save_pretrained(A			)

       recursively_load_weights(A    ,					A    ,					A			)

       hf_model.save_pretrained(A			)


if __name__ == "__main__":
 lowerCamelCase				:  int    		=      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(
     '--is_finetuned', action='store_true', help='Whether the model to convert is a fine-tuned model or not'
 )
 lowerCamelCase				:  int    		=      parser.parse_args()
 convert_sew_checkpoint(
     args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
 )








'''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 import EsmForMaskedLM, EsmForSequenceClassification, EsmForTokenClassification, EsmModel
 from transformers.models.esm.modeling_esm import (
     ESM_PRETRAINED_MODEL_ARCHIVE_LIST,
     EsmEmbeddings,
     create_position_ids_from_input_ids,
 )
class     __lowerCAmelCase		:





      '''simple docstring'''






      def __init__(self       :       Optional[int]  ,		UpperCamelCase       :       Tuple  ,		UpperCamelCase       :       str=13  ,		UpperCamelCase       :       Optional[int]=7  ,		UpperCamelCase       :       Union[str, Any]=False  ,		UpperCamelCase       :       Dict=True  ,		UpperCamelCase       :       Optional[int]=False  ,		UpperCamelCase       :       Optional[Any]=True  ,		UpperCamelCase       :       Union[str, Any]=33  ,		UpperCamelCase       :       List[Any]=32  ,		UpperCamelCase       :       Dict=5  ,		UpperCamelCase       :       str=4  ,		UpperCamelCase       :       str=37  ,		UpperCamelCase       :       List[Any]="gelu"  ,		UpperCamelCase       :       List[str]=0.1  ,		UpperCamelCase       :       Dict=0.1  ,		UpperCamelCase       :       Dict=512  ,		UpperCamelCase       :       Any=16  ,		UpperCamelCase       :       List[Any]=2  ,		UpperCamelCase       :       List[Any]=0.02  ,		UpperCamelCase       :       Tuple=3  ,		UpperCamelCase       :       Optional[Any]=4  ,		UpperCamelCase       :       int=None  ,		):

             '''simple docstring'''







             lowercase__      =					parent
             lowercase__      =					batch_size
             lowercase__      =					seq_length
             lowercase__      =					is_training
             lowercase__      =					use_input_mask
             lowercase__      =					use_token_type_ids
             lowercase__      =					use_labels
             lowercase__      =					vocab_size
             lowercase__      =					hidden_size
             lowercase__      =					num_hidden_layers
             lowercase__      =					num_attention_heads
             lowercase__      =					intermediate_size
             lowercase__      =					hidden_act
             lowercase__      =					hidden_dropout_prob
             lowercase__      =					attention_probs_dropout_prob
             lowercase__      =					max_position_embeddings
             lowercase__      =					type_vocab_size
             lowercase__      =					type_sequence_label_size
             lowercase__      =					initializer_range
             lowercase__      =					num_labels
             lowercase__      =					num_choices
             lowercase__      =					scope



      def  UpperCamelCase__						(self       :       str					):

             '''simple docstring'''







             lowercase__      =					ids_tensor([self.batch_size, self.seq_length]  ,		self.vocab_size					)

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

             lowercase__      =					None
             lowercase__      =					None
             lowercase__      =					None
             if self.use_labels:
                    lowercase__      =					ids_tensor([self.batch_size]  ,		self.type_sequence_label_size					)
                    lowercase__      =					ids_tensor([self.batch_size, self.seq_length]  ,		self.num_labels					)
                    lowercase__      =					ids_tensor([self.batch_size]  ,		self.num_choices					)

             lowercase__      =					self.get_config()

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



      def  UpperCamelCase__						(self       :       Optional[Any]					):

             '''simple docstring'''







             return EsmConfig(
                 vocab_size=self.vocab_size  ,		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  ,		)



      def  UpperCamelCase__						(self       :       Optional[int]  ,		UpperCamelCase       :       Union[str, Any]  ,		UpperCamelCase       :       int  ,		UpperCamelCase       :       List[Any]  ,		UpperCamelCase       :       str  ,		UpperCamelCase       :       str  ,		UpperCamelCase       :       Dict					):

             '''simple docstring'''







             lowercase__      =					EsmModel(config=UpperCamelCase					)
             model.to(UpperCamelCase					)
             model.eval()
             lowercase__      =					model(UpperCamelCase  ,		attention_mask=UpperCamelCase					)
             lowercase__      =					model(UpperCamelCase					)
             lowercase__      =					model(UpperCamelCase					)

             self.parent.assertEqual(result.last_hidden_state.shape  ,		(self.batch_size, self.seq_length, self.hidden_size)					)
             self.parent.assertEqual(result.pooler_output.shape  ,		(self.batch_size, self.hidden_size)					)



      def  UpperCamelCase__						(self       :       str  ,		UpperCamelCase       :       Any  ,		UpperCamelCase       :       Tuple  ,		UpperCamelCase       :       Any  ,		UpperCamelCase       :       List[Any]  ,		UpperCamelCase       :       Optional[Any]  ,		UpperCamelCase       :       Optional[int]					):

             '''simple docstring'''







             lowercase__      =					EsmForMaskedLM(config=UpperCamelCase					)
             model.to(UpperCamelCase					)
             model.eval()
             lowercase__      =					model(UpperCamelCase  ,		attention_mask=UpperCamelCase  ,		labels=UpperCamelCase					)
             self.parent.assertEqual(result.logits.shape  ,		(self.batch_size, self.seq_length, self.vocab_size)					)



      def  UpperCamelCase__						(self       :       Any  ,		UpperCamelCase       :       str  ,		UpperCamelCase       :       List[Any]  ,		UpperCamelCase       :       Tuple  ,		UpperCamelCase       :       int  ,		UpperCamelCase       :       Union[str, Any]  ,		UpperCamelCase       :       List[Any]					):

             '''simple docstring'''







             lowercase__      =					self.num_labels
             lowercase__      =					EsmForTokenClassification(config=UpperCamelCase					)
             model.to(UpperCamelCase					)
             model.eval()
             lowercase__      =					model(UpperCamelCase  ,		attention_mask=UpperCamelCase  ,		labels=UpperCamelCase					)
             self.parent.assertEqual(result.logits.shape  ,		(self.batch_size, self.seq_length, self.num_labels)					)






      def  UpperCamelCase__						(self       :       Optional[Any]					):

             '''simple docstring'''







             lowercase__      =					self.prepare_config_and_inputs()
             (
                 (
                 lowercase__
             )     ,(
                 lowercase__
             )     ,(
                 lowercase__
             )     ,(
                 lowercase__
             )     ,(
                 lowercase__
             )     ,(
                 lowercase__
             )     ,
             )      =					config_and_inputs
             lowercase__      =					{'''input_ids''': input_ids, '''attention_mask''': input_mask}
             return config, inputs_dict






@require_torch
class     __lowerCAmelCase		(lowercase_    ,	lowercase_    ,	unittest.TestCase	):





      '''simple docstring'''






      lowerCAmelCase__   :	Dict       =				False

      lowerCAmelCase__   :	Optional[Any]       =				(
          (
              EsmForMaskedLM,
              EsmModel,
              EsmForSequenceClassification,
              EsmForTokenClassification,
          )
          if is_torch_available()
          else ()
      )
      lowerCAmelCase__   :	Optional[int]       =				()
      lowerCAmelCase__   :	Any       =				(
          {
              """feature-extraction""": EsmModel,
              """fill-mask""": EsmForMaskedLM,
              """text-classification""": EsmForSequenceClassification,
              """token-classification""": EsmForTokenClassification,
              """zero-shot""": EsmForSequenceClassification,
          }
          if is_torch_available()
          else {}
      )
      lowerCAmelCase__   :	Any       =				True



      def  UpperCamelCase__						(self       :       List[Any]					):

             '''simple docstring'''







             lowercase__      =					EsmModelTester(self					)
             lowercase__      =					ConfigTester(self  ,		config_class=UpperCamelCase  ,		hidden_size=37					)



      def  UpperCamelCase__						(self       :       Union[str, Any]					):

             '''simple docstring'''







             self.config_tester.run_common_tests()



      def  UpperCamelCase__						(self       :       Optional[Any]					):

             '''simple docstring'''







             lowercase__      =					self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_model(*UpperCamelCase					)



      def  UpperCamelCase__						(self       :       Any					):

             '''simple docstring'''







             lowercase__      =					self.model_tester.prepare_config_and_inputs()
             for type in ["absolute", "relative_key", "relative_key_query"]:
                    lowercase__      =					type
                    self.model_tester.create_and_check_model(*UpperCamelCase					)



      def  UpperCamelCase__						(self       :       Optional[int]					):

             '''simple docstring'''







             lowercase__      =					self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_masked_lm(*UpperCamelCase					)



      def  UpperCamelCase__						(self       :       List[Any]					):

             '''simple docstring'''







             lowercase__      =					self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_token_classification(*UpperCamelCase					)



      @slow
      def  UpperCamelCase__						(self       :       Union[str, Any]					):

             '''simple docstring'''







             for model_name in ESM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                    lowercase__      =					EsmModel.from_pretrained(UpperCamelCase					)
                    self.assertIsNotNone(UpperCamelCase					)



      def  UpperCamelCase__						(self       :       Optional[int]					):

             '''simple docstring'''







             lowercase__      =					self.model_tester.prepare_config_and_inputs()[0]
             lowercase__      =					EsmEmbeddings(config=UpperCamelCase					)

             lowercase__      =					torch.as_tensor([[12, 31, 13, model.padding_idx]]					)
             lowercase__      =					torch.as_tensor(
                 [
                     [
                         0 + model.padding_idx + 1,
                         1 + model.padding_idx + 1,
                         2 + model.padding_idx + 1,
                         model.padding_idx,
                     ]
                 ]					)
             lowercase__      =					create_position_ids_from_input_ids(UpperCamelCase  ,		model.padding_idx					)
             self.assertEqual(position_ids.shape  ,		expected_positions.shape					)
             self.assertTrue(torch.all(torch.eq(UpperCamelCase  ,		UpperCamelCase					)					)					)



      def  UpperCamelCase__						(self       :       Union[str, Any]					):

             '''simple docstring'''







             lowercase__      =					self.model_tester.prepare_config_and_inputs()[0]
             lowercase__      =					EsmEmbeddings(config=UpperCamelCase					)

             lowercase__      =					torch.empty(2  ,		4  ,		30					)
             lowercase__      =					[
                 0 + embeddings.padding_idx + 1,
                 1 + embeddings.padding_idx + 1,
                 2 + embeddings.padding_idx + 1,
                 3 + embeddings.padding_idx + 1,
             ]
             lowercase__      =					torch.as_tensor([expected_single_positions, expected_single_positions]					)
             lowercase__      =					embeddings.create_position_ids_from_inputs_embeds(UpperCamelCase					)
             self.assertEqual(position_ids.shape  ,		expected_positions.shape					)
             self.assertTrue(torch.all(torch.eq(UpperCamelCase  ,		UpperCamelCase					)					)					)



      @unittest.skip('''Esm does not support embedding resizing'''					)
      def  UpperCamelCase__						(self       :       Optional[int]					):

             '''simple docstring'''







             pass



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

             '''simple docstring'''







             pass






      @unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.'''					)
      def  UpperCamelCase__						(self       :       Optional[int]					):

             '''simple docstring'''







             pass






@require_torch
class     __lowerCAmelCase		(lowercase_	):





      '''simple docstring'''






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

             '''simple docstring'''







             with torch.no_grad():
                    lowercase__      =					EsmForMaskedLM.from_pretrained('''facebook/esm2_t6_8M_UR50D'''					)
                    model.eval()
                    lowercase__      =					torch.tensor([[0, 1, 2, 3, 4, 5]]					)
                    lowercase__      =					model(UpperCamelCase					)[0]

                    lowercase__      =					33

                    lowercase__      =					torch.Size((1, 6, vocab_size)					)
                    self.assertEqual(output.shape  ,		UpperCamelCase					)

                    lowercase__      =					torch.tensor(
                        [[[8.92_15, -10.58_98, -6.46_71], [-6.39_67, -13.91_14, -1.12_12], [-7.78_12, -13.95_16, -3.74_06]]]					)
                    self.assertTrue(torch.allclose(output[:, :3, :3]  ,		UpperCamelCase  ,		atol=1E-4					)					)






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

             '''simple docstring'''







             with torch.no_grad():
                    lowercase__      =					EsmModel.from_pretrained('''facebook/esm2_t6_8M_UR50D'''					)
                    model.eval()

                    lowercase__      =					torch.tensor([[0, 6, 4, 13, 5, 4, 16, 12, 11, 7, 2]]					)
                    lowercase__      =					model(UpperCamelCase					)[0]
                    # compare the actual values for a slice.
                    lowercase__      =					torch.tensor(
                        [[[0.14_44, 0.54_13, 0.32_48], [0.30_34, 0.00_53, 0.31_08], [0.32_28, -0.24_99, 0.34_15]]]					)
                    self.assertTrue(torch.allclose(output[:, :3, :3]  ,		UpperCamelCase  ,		atol=1E-4					)					)




