
'''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  _UpperCAmelCase :

     """simple docstring"""

     def __init__(      self							,		lowerCAmelCase_							,		lowerCAmelCase_=13							,		lowerCAmelCase_=7							,		lowerCAmelCase_=False							,		lowerCAmelCase_=True							,		lowerCAmelCase_=False							,		lowerCAmelCase_=True							,		lowerCAmelCase_=33							,		lowerCAmelCase_=32							,		lowerCAmelCase_=5							,		lowerCAmelCase_=4							,		lowerCAmelCase_=37							,		lowerCAmelCase_="gelu"							,		lowerCAmelCase_=0.1							,		lowerCAmelCase_=0.1							,		lowerCAmelCase_=5_12							,		lowerCAmelCase_=16							,		lowerCAmelCase_=2							,		lowerCAmelCase_=0.02							,		lowerCAmelCase_=3							,		lowerCAmelCase_=4							,		lowerCAmelCase_=None							,		):

       '''simple docstring'''

       a_      :	List[str]      =							parent
       a_      :	int      =							batch_size
       a_      :	Optional[int]      =							seq_length
       a_      :	Optional[Any]      =							is_training
       a_      :	Tuple      =							use_input_mask
       a_      :	str      =							use_token_type_ids
       a_      :	int      =							use_labels
       a_      :	str      =							vocab_size
       a_      :	Union[str, Any]      =							hidden_size
       a_      :	int      =							num_hidden_layers
       a_      :	Optional[int]      =							num_attention_heads
       a_      :	List[Any]      =							intermediate_size
       a_      :	Optional[Any]      =							hidden_act
       a_      :	Any      =							hidden_dropout_prob
       a_      :	List[Any]      =							attention_probs_dropout_prob
       a_      :	Tuple      =							max_position_embeddings
       a_      :	Any      =							type_vocab_size
       a_      :	List[Any]      =							type_sequence_label_size
       a_      :	List[str]      =							initializer_range
       a_      :	Optional[int]      =							num_labels
       a_      :	int      =							num_choices
       a_      :	List[Any]      =							scope


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	str      =							ids_tensor([self.batch_size, self.seq_length]							,		self.vocab_size )

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

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

       '''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 							_lowerCAmelCase  (      self							,		lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_ ):

       '''simple docstring'''

       a_      :	Any      =							EsmModel(config=lowerCAmelCase_ )
       model.to(lowerCAmelCase_ )
       model.eval()
       a_      :	List[Any]      =							model(lowerCAmelCase_							,		attention_mask=lowerCAmelCase_ )
       a_      :	Union[str, Any]      =							model(lowerCAmelCase_ )
       a_      :	int      =							model(lowerCAmelCase_ )

       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 							_lowerCAmelCase  (      self							,		lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_ ):

       '''simple docstring'''

       a_      :	Dict      =							EsmForMaskedLM(config=lowerCAmelCase_ )
       model.to(lowerCAmelCase_ )
       model.eval()
       a_      :	Optional[int]      =							model(lowerCAmelCase_							,		attention_mask=lowerCAmelCase_							,		labels=lowerCAmelCase_ )
       self.parent.assertEqual(result.logits.shape							,		(self.batch_size, self.seq_length, self.vocab_size) )


     def 							_lowerCAmelCase  (      self							,		lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_ ):

       '''simple docstring'''

       a_      :	Optional[int]      =							self.num_labels
       a_      :	Optional[int]      =							EsmForTokenClassification(config=lowerCAmelCase_ )
       model.to(lowerCAmelCase_ )
       model.eval()
       a_      :	List[str]      =							model(lowerCAmelCase_							,		attention_mask=lowerCAmelCase_							,		labels=lowerCAmelCase_ )
       self.parent.assertEqual(result.logits.shape							,		(self.batch_size, self.seq_length, self.num_labels) )






     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	int      =							self.prepare_config_and_inputs()
       (
           a_ 
       )      :	List[str]      =							config_and_inputs
       a_      :	str      =							{"""input_ids""": input_ids, """attention_mask""": input_mask}
       return config, inputs_dict







@require_torch
class  _UpperCAmelCase ( lowerCAmelCase__	,lowerCAmelCase__	,unittest.TestCase    ):

     """simple docstring"""

     a_    	=					False

     a_    	=					(
         (
             EsmForMaskedLM,
             EsmModel,
             EsmForSequenceClassification,
             EsmForTokenClassification,
         )
         if is_torch_available()
         else ()
     )
     a_    	=					()
     a_    	=					(
         {
             "feature-extraction": EsmModel,
             "fill-mask": EsmForMaskedLM,
             "text-classification": EsmForSequenceClassification,
             "token-classification": EsmForTokenClassification,
             "zero-shot": EsmForSequenceClassification,
         }
         if is_torch_available()
         else {}
     )
     a_    	=					True


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	str      =							EsmModelTester(self )
       a_      :	int      =							ConfigTester(self							,		config_class=lowerCAmelCase_							,		hidden_size=37 )


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       self.config_tester.run_common_tests()


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	Optional[int]      =							self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*lowerCAmelCase_ )


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	int      =							self.model_tester.prepare_config_and_inputs()
       for type in ["absolute", "relative_key", "relative_key_query"]:
         a_      :	str      =							type
         self.model_tester.create_and_check_model(*lowerCAmelCase_ )


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	Any      =							self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_masked_lm(*lowerCAmelCase_ )


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	str      =							self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_token_classification(*lowerCAmelCase_ )


     @slow
     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       for model_name in ESM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         a_      :	str      =							EsmModel.from_pretrained(lowerCAmelCase_ )
         self.assertIsNotNone(lowerCAmelCase_ )


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	str      =							self.model_tester.prepare_config_and_inputs()[0]
       a_      :	str      =							EsmEmbeddings(config=lowerCAmelCase_ )

       a_      :	Any      =							torch.as_tensor([[12, 31, 13, model.padding_idx]] )
       a_      :	Tuple      =							torch.as_tensor(
           [
               [
                   0 + model.padding_idx + 1,
                   1 + model.padding_idx + 1,
                   2 + model.padding_idx + 1,
                   model.padding_idx,
               ]
           ] )
       a_      :	List[str]      =							create_position_ids_from_input_ids(lowerCAmelCase_							,		model.padding_idx )
       self.assertEqual(position_ids.shape							,		expected_positions.shape )
       self.assertTrue(torch.all(torch.eq(lowerCAmelCase_							,		lowerCAmelCase_ ) ) )


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	Union[str, Any]      =							self.model_tester.prepare_config_and_inputs()[0]
       a_      :	Union[str, Any]      =							EsmEmbeddings(config=lowerCAmelCase_ )

       a_      :	int      =							torch.empty(2							,		4							,		30 )
       a_      :	List[Any]      =							[
           0 + embeddings.padding_idx + 1,
           1 + embeddings.padding_idx + 1,
           2 + embeddings.padding_idx + 1,
           3 + embeddings.padding_idx + 1,
       ]
       a_      :	List[Any]      =							torch.as_tensor([expected_single_positions, expected_single_positions] )
       a_      :	Any      =							embeddings.create_position_ids_from_inputs_embeds(lowerCAmelCase_ )
       self.assertEqual(position_ids.shape							,		expected_positions.shape )
       self.assertTrue(torch.all(torch.eq(lowerCAmelCase_							,		lowerCAmelCase_ ) ) )


     @unittest.skip("""Esm does not support embedding resizing""" )
     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       pass


     @unittest.skip("""Esm does not support embedding resizing""" )
     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       pass






     @unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests.""" )
     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       pass







@require_torch
class  _UpperCAmelCase ( lowerCAmelCase__    ):

     """simple docstring"""

     @slow
     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       with torch.no_grad():
         a_      :	str      =							EsmForMaskedLM.from_pretrained("""facebook/esm2_t6_8M_UR50D""" )
         model.eval()
         a_      :	Dict      =							torch.tensor([[0, 1, 2, 3, 4, 5]] )
         a_      :	int      =							model(lowerCAmelCase_ )[0]

         a_      :	Optional[Any]      =							33

         a_      :	Optional[int]      =							torch.Size((1, 6, vocab_size) )
         self.assertEqual(output.shape							,		lowerCAmelCase_ )

         a_      :	List[str]      =							torch.tensor(
             [[[8.9215, -10.5898, -6.4671], [-6.3967, -13.9114, -1.1212], [-7.7812, -13.9516, -3.7406]]] )
         self.assertTrue(torch.allclose(output[:, :3, :3]							,		lowerCAmelCase_							,		atol=1E-4 ) )






     @slow
     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       with torch.no_grad():
         a_      :	List[str]      =							EsmModel.from_pretrained("""facebook/esm2_t6_8M_UR50D""" )
         model.eval()

         a_      :	Union[str, Any]      =							torch.tensor([[0, 6, 4, 13, 5, 4, 16, 12, 11, 7, 2]] )
         a_      :	str      =							model(lowerCAmelCase_ )[0]
         # compare the actual values for a slice.
         a_      :	Any      =							torch.tensor(
             [[[0.1444, 0.5413, 0.3248], [0.3034, 0.0053, 0.3108], [0.3228, -0.2499, 0.3415]]] )
         self.assertTrue(torch.allclose(output[:, :3, :3]							,		lowerCAmelCase_							,		atol=1E-4 ) )




'''simple docstring'''

import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import Audio, Features, Value
from .base import TaskTemplate


@dataclass(frozen=lowerCAmelCase__    )
class  _UpperCAmelCase ( lowerCAmelCase__    ):

     """simple docstring"""

     a_    	=					field(default="automatic-speech-recognition"	,metadata={"include_in_asdict_even_if_is_default": True}    )
     a_    	=					Features({"audio": Audio()}    )
     a_    	=					Features({"transcription": Value("string"    )}    )
     a_    	=					"audio"
     a_    	=					"transcription"


     def 							_lowerCAmelCase  (      self							,		lowerCAmelCase_ ):

       '''simple docstring'''

       if self.audio_column not in features:
         raise ValueError(f'''Column {self.audio_column} is not present in features.''' )
       if not isinstance(features[self.audio_column]							,		lowerCAmelCase_ ):
         raise ValueError(f'''Column {self.audio_column} is not an Audio type.''' )
       a_      :	int      =							copy.deepcopy(self )
       a_      :	Optional[int]      =							self.input_schema.copy()
       a_      :	Any      =							features[self.audio_column]
       a_      :	Any      =							input_schema
       return task_template






     @property
     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       return {self.audio_column: "audio", self.transcription_column: "transcription"}


