import numpy as np
import torch
from torch.utils.data import Dataset, IterableDataset

from ..utils.generic import ModelOutput

class lowerCamelCase		(_SCREAMING_SNAKE_CASE      ):






					'''simple docstring'''


					def __init__(						self			:				Any   , _snake_case			:				Dict   , _snake_case			:				Tuple   , _snake_case			:				List[str]		)							->       List[Any]:
									SCREAMING_SNAKE_CASE__		  =				dataset
									SCREAMING_SNAKE_CASE__		  =				process
									SCREAMING_SNAKE_CASE__		  =				params



					def __len__(						self			:				Dict		)							->       Tuple:
									return len(self.dataset		)



					def __getitem__(						self			:				str   , _snake_case			:				List[Any]		)							->       List[str]:
									SCREAMING_SNAKE_CASE__		  =				self.dataset[i]
									SCREAMING_SNAKE_CASE__		  =				self.process(_snake_case   , **self.params		)
									return processed



class lowerCamelCase		(_SCREAMING_SNAKE_CASE      ):






					'''simple docstring'''


					def __init__(						self			:				Optional[int]   , _snake_case			:				List[str]   , _snake_case			:				int   , _snake_case			:				List[str]   , _snake_case			:				Optional[Any]=None		)							->       Union[str, Any]:
									SCREAMING_SNAKE_CASE__		  =				loader
									SCREAMING_SNAKE_CASE__		  =				infer
									SCREAMING_SNAKE_CASE__		  =				params
									if loader_batch_size == 1:
													# Let's spare some time by deactivating altogether
													SCREAMING_SNAKE_CASE__		  =				None
									SCREAMING_SNAKE_CASE__		  =				loader_batch_size

									# Internal bookkeeping
									SCREAMING_SNAKE_CASE__		  =				None
									SCREAMING_SNAKE_CASE__		  =				None



					def __len__(						self			:				List[Any]		)							->       int:
									return len(self.loader		)



					def __iter__(						self			:				Optional[int]		)							->       Optional[Any]:
									SCREAMING_SNAKE_CASE__		  =				iter(self.loader		)
									return self



					def 			lowerCAmelCase_					(						self			:				List[Any]		)							->       int:
									if isinstance(self._loader_batch_data   , torch.Tensor		):
													# Batch data is simple tensor, just fetch the slice
													SCREAMING_SNAKE_CASE__		  =				self._loader_batch_data[self._loader_batch_index]
									else:
													# Batch data is assumed to be BaseModelOutput (or dict)
													SCREAMING_SNAKE_CASE__		  =				{}
													for k, element in self._loader_batch_data.items():
																	if isinstance(_snake_case   , _snake_case		):
																					# Convert ModelOutput to tuple first
																					SCREAMING_SNAKE_CASE__		  =				element.to_tuple()
																					if isinstance(element[0]   , torch.Tensor		):
																									SCREAMING_SNAKE_CASE__		  =				tuple(el[self._loader_batch_index].unsqueeze(0		) for el in element		)
																					elif isinstance(element[0]   , np.ndarray		):
																									SCREAMING_SNAKE_CASE__		  =				tuple(np.expand_dims(el[self._loader_batch_index]   , 0		) for el in element		)
																					continue
																	if k in {"hidden_states", "past_key_values", "attentions"} and isinstance(_snake_case   , _snake_case		):
																					# Those are stored as lists of tensors so need specific unbatching.
																					if isinstance(element[0]   , torch.Tensor		):
																									SCREAMING_SNAKE_CASE__		  =				tuple(el[self._loader_batch_index].unsqueeze(0		) for el in element		)
																					elif isinstance(element[0]   , np.ndarray		):
																									SCREAMING_SNAKE_CASE__		  =				tuple(np.expand_dims(el[self._loader_batch_index]   , 0		) for el in element		)
																					continue
																	if element is None:
																					# This can happen for optional data that get passed around
																					SCREAMING_SNAKE_CASE__		  =				None
																	elif isinstance(element[self._loader_batch_index]   , torch.Tensor		):
																					# Take correct batch data, but make it looked like batch_size=1
																					# For compatibility with other methods within transformers

																					SCREAMING_SNAKE_CASE__		  =				element[self._loader_batch_index].unsqueeze(0		)
																	elif isinstance(element[self._loader_batch_index]   , np.ndarray		):
																					# Take correct batch data, but make it looked like batch_size=1
																					# For compatibility with other methods within transformers
																					SCREAMING_SNAKE_CASE__		  =				np.expand_dims(element[self._loader_batch_index]   , 0		)
																	else:
																					# This is typically a list, so no need to `unsqueeze`.
																					SCREAMING_SNAKE_CASE__		  =				element[self._loader_batch_index]
            # Recreate the element by reusing the original class to make it look
            # batch_size=1
													SCREAMING_SNAKE_CASE__		  =				self._loader_batch_data.__class__(_snake_case		)
									self._loader_batch_index += 1
									return result



					def 			lowerCAmelCase_					(						self			:				Union[str, Any]		)							->       Optional[Any]:
									if self._loader_batch_index is not None and self._loader_batch_index < self.loader_batch_size:
													# We are currently unrolling a batch so we just need to return
													# the current item within a batch
													return self.loader_batch_item()

									# We're out of items within a batch
									SCREAMING_SNAKE_CASE__		  =				next(self.iterator		)
									SCREAMING_SNAKE_CASE__		  =				self.infer(_snake_case   , **self.params		)
									# We now have a batch of "inferred things".
									if self.loader_batch_size is not None:
													# Try to infer the size of the batch
													if isinstance(_snake_case   , torch.Tensor		):
																	SCREAMING_SNAKE_CASE__		  =				processed
													else:
																	SCREAMING_SNAKE_CASE__		  =				list(processed.keys()		)[0]
																	SCREAMING_SNAKE_CASE__		  =				processed[key]
													if isinstance(_snake_case   , _snake_case		):
																	SCREAMING_SNAKE_CASE__		  =				len(_snake_case		)
													else:
																	SCREAMING_SNAKE_CASE__		  =				first_tensor.shape[0]
													if 0 < observed_batch_size < self.loader_batch_size:
																	# could be last batch so we can't unroll as many
																	# elements.
																	SCREAMING_SNAKE_CASE__		  =				observed_batch_size
													# Setting internal index to unwrap the batch
													SCREAMING_SNAKE_CASE__		  =				processed
													SCREAMING_SNAKE_CASE__		  =				0
													return self.loader_batch_item()
									else:
													# We're not unrolling batches
													return processed



class lowerCamelCase		(_SCREAMING_SNAKE_CASE      ):






					'''simple docstring'''


					def __init__(						self			:				Dict   , _snake_case			:				Union[str, Any]   , _snake_case			:				Optional[int]   , _snake_case			:				Any   , _snake_case			:				List[str]=None		)							->       List[str]:
									super().__init__(_snake_case   , _snake_case   , _snake_case		)



					def __iter__(						self			:				List[Any]		)							->       Tuple:
									SCREAMING_SNAKE_CASE__		  =				iter(self.loader		)
									SCREAMING_SNAKE_CASE__		  =				None
									return self



					def 			lowerCAmelCase_					(						self			:				Tuple		)							->       List[Any]:
									if self.subiterator is None:
													SCREAMING_SNAKE_CASE__		  =				self.infer(next(self.iterator		)   , **self.params		)
									try:
													# Try to return next item
													SCREAMING_SNAKE_CASE__		  =				next(self.subiterator		)
									except StopIteration:
													# When a preprocess iterator ends, we can start lookig at the next item
													# ChunkIterator will keep feeding until ALL elements of iterator
													# all have created their subiterator and have been iterating against.
													#
													# Another way to look at it, is we're basically flattening lists of lists
													# into a single list, but with generators
													SCREAMING_SNAKE_CASE__		  =				self.infer(next(self.iterator		)   , **self.params		)
													SCREAMING_SNAKE_CASE__		  =				next(self.subiterator		)
									return processed



class lowerCamelCase		(_SCREAMING_SNAKE_CASE      ):






					'''simple docstring'''


					def __iter__(						self			:				str		)							->       int:
									SCREAMING_SNAKE_CASE__		  =				iter(self.loader		)
									return self



					def 			lowerCAmelCase_					(						self			:				List[Any]		)							->       Dict:
									# Extremely similar to PipelineIterator in its unpacking mechanism
									# BUT, we have an extra required item which is the presence of `is_last`
									# That is because everything is flattened by `PipelineChunkIterator` we
									# need to keep track of how to regroup here in the original `process`
									# boundaries so that `process` and `postprocess` see the same data.

									# This iterator accumulates items (possibly while unbatching) until it
									# its a `is_last` and then just passes it on to the caller.
									SCREAMING_SNAKE_CASE__		  =				False
									SCREAMING_SNAKE_CASE__		  =				[]
									if self._loader_batch_index is not None and self._loader_batch_index < self.loader_batch_size:
													while self._loader_batch_index < self.loader_batch_size:
																	SCREAMING_SNAKE_CASE__		  =				self.loader_batch_item()
																	SCREAMING_SNAKE_CASE__		  =				item.pop("is_last"		)
																	accumulator.append(_snake_case		)
																	if is_last:
																					return accumulator

									while not is_last:
													SCREAMING_SNAKE_CASE__		  =				self.infer(next(self.iterator		)   , **self.params		)
													if self.loader_batch_size is not None:
																	if isinstance(_snake_case   , torch.Tensor		):
																					SCREAMING_SNAKE_CASE__		  =				processed
																	else:
																					SCREAMING_SNAKE_CASE__		  =				list(processed.keys()		)[0]
																					SCREAMING_SNAKE_CASE__		  =				processed[key]
																	if isinstance(_snake_case   , _snake_case		):
																					SCREAMING_SNAKE_CASE__		  =				len(_snake_case		)
																	else:
																					SCREAMING_SNAKE_CASE__		  =				first_tensor.shape[0]
																	if 0 < observed_batch_size < self.loader_batch_size:
																					# could be last batch so we can't unroll as many
																					# elements.
																					SCREAMING_SNAKE_CASE__		  =				observed_batch_size
																	SCREAMING_SNAKE_CASE__		  =				processed
																	SCREAMING_SNAKE_CASE__		  =				0
																	while self._loader_batch_index < self.loader_batch_size:
																					SCREAMING_SNAKE_CASE__		  =				self.loader_batch_item()
																					SCREAMING_SNAKE_CASE__		  =				item.pop("is_last"		)
																					accumulator.append(_snake_case		)
																					if is_last:
																									return accumulator
													else:
																	SCREAMING_SNAKE_CASE__		  =				processed
																	SCREAMING_SNAKE_CASE__		  =				item.pop("is_last"		)
																	accumulator.append(_snake_case		)
									return accumulator



class lowerCamelCase		(_SCREAMING_SNAKE_CASE      ):






					'''simple docstring'''


					def __init__(						self			:				str   , _snake_case			:				Dataset   , _snake_case			:				str		)							->       int:
									SCREAMING_SNAKE_CASE__		  =				dataset
									SCREAMING_SNAKE_CASE__		  =				key



					def __len__(						self			:				Optional[Any]		)							->       str:
									return len(self.dataset		)



					def __getitem__(						self			:				Optional[int]   , _snake_case			:				Dict		)							->       Tuple:
									return self.dataset[i][self.key]



class lowerCamelCase		(_SCREAMING_SNAKE_CASE      ):






					'''simple docstring'''


					def __init__(						self			:				Dict   , _snake_case			:				Dataset   , _snake_case			:				str   , _snake_case			:				str		)							->       int:
									SCREAMING_SNAKE_CASE__		  =				dataset
									SCREAMING_SNAKE_CASE__		  =				keya
									SCREAMING_SNAKE_CASE__		  =				keya



					def __len__(						self			:				List[str]		)							->       Any:
									return len(self.dataset		)



					def __getitem__(						self			:				Any   , _snake_case			:				Any		)							->       Union[str, Any]:
									return {"text": self.dataset[i][self.keya], "text_pair": self.dataset[i][self.keya]}





"""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			:				Union[str, Any]   , _snake_case			:				Optional[int]   , _snake_case			:				Dict=13   , _snake_case			:				Optional[Any]=7   , _snake_case			:				Union[str, Any]=False   , _snake_case			:				Any=True   , _snake_case			:				int=False   , _snake_case			:				int=True   , _snake_case			:				Tuple=33   , _snake_case			:				Optional[int]=32   , _snake_case			:				List[Any]=5   , _snake_case			:				Union[str, Any]=4   , _snake_case			:				int=37   , _snake_case			:				Tuple="gelu"   , _snake_case			:				Dict=0.1   , _snake_case			:				Dict=0.1   , _snake_case			:				Tuple=512   , _snake_case			:				Any=16   , _snake_case			:				Union[str, Any]=2   , _snake_case			:				List[str]=0.02   , _snake_case			:				Optional[Any]=3   , _snake_case			:				int=4   , _snake_case			:				List[str]=None   , )							->       Tuple:
									SCREAMING_SNAKE_CASE__		  =				parent
									SCREAMING_SNAKE_CASE__		  =				batch_size
									SCREAMING_SNAKE_CASE__		  =				seq_length
									SCREAMING_SNAKE_CASE__		  =				is_training
									SCREAMING_SNAKE_CASE__		  =				use_input_mask
									SCREAMING_SNAKE_CASE__		  =				use_token_type_ids
									SCREAMING_SNAKE_CASE__		  =				use_labels
									SCREAMING_SNAKE_CASE__		  =				vocab_size
									SCREAMING_SNAKE_CASE__		  =				hidden_size
									SCREAMING_SNAKE_CASE__		  =				num_hidden_layers
									SCREAMING_SNAKE_CASE__		  =				num_attention_heads
									SCREAMING_SNAKE_CASE__		  =				intermediate_size
									SCREAMING_SNAKE_CASE__		  =				hidden_act
									SCREAMING_SNAKE_CASE__		  =				hidden_dropout_prob
									SCREAMING_SNAKE_CASE__		  =				attention_probs_dropout_prob
									SCREAMING_SNAKE_CASE__		  =				max_position_embeddings
									SCREAMING_SNAKE_CASE__		  =				type_vocab_size
									SCREAMING_SNAKE_CASE__		  =				type_sequence_label_size
									SCREAMING_SNAKE_CASE__		  =				initializer_range
									SCREAMING_SNAKE_CASE__		  =				num_labels
									SCREAMING_SNAKE_CASE__		  =				num_choices
									SCREAMING_SNAKE_CASE__		  =				scope



					def 			lowerCAmelCase_					(						self			:				Union[str, Any]		)							->       str:
									SCREAMING_SNAKE_CASE__		  =				ids_tensor([self.batch_size, self.seq_length]   , self.vocab_size		)

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

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

									SCREAMING_SNAKE_CASE__		  =				self.get_config()

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



					def 			lowerCAmelCase_					(						self			:				Tuple		)							->       Any:
									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			:				str   , _snake_case			:				int   , _snake_case			:				Dict   , _snake_case			:				Union[str, Any]   , _snake_case			:				List[str]   , _snake_case			:				Any   , _snake_case			:				List[str]		)							->       Optional[int]:
									SCREAMING_SNAKE_CASE__		  =				EsmModel(config=_snake_case		)
									model.to(_snake_case		)
									model.eval()
									SCREAMING_SNAKE_CASE__		  =				model(_snake_case   , attention_mask=_snake_case		)
									SCREAMING_SNAKE_CASE__		  =				model(_snake_case		)
									SCREAMING_SNAKE_CASE__		  =				model(_snake_case		)

									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			:				Tuple   , _snake_case			:				Union[str, Any]   , _snake_case			:				Optional[Any]   , _snake_case			:				Dict   , _snake_case			:				List[Any]   , _snake_case			:				Optional[int]   , _snake_case			:				int		)							->       int:
									SCREAMING_SNAKE_CASE__		  =				EsmForMaskedLM(config=_snake_case		)
									model.to(_snake_case		)
									model.eval()
									SCREAMING_SNAKE_CASE__		  =				model(_snake_case   , attention_mask=_snake_case   , labels=_snake_case		)
									self.parent.assertEqual(result.logits.shape   , (self.batch_size, self.seq_length, self.vocab_size)		)



					def 			lowerCAmelCase_					(						self			:				int   , _snake_case			:				List[Any]   , _snake_case			:				Union[str, Any]   , _snake_case			:				Tuple   , _snake_case			:				List[str]   , _snake_case			:				Dict   , _snake_case			:				Tuple		)							->       Dict:
									SCREAMING_SNAKE_CASE__		  =				self.num_labels
									SCREAMING_SNAKE_CASE__		  =				EsmForTokenClassification(config=_snake_case		)
									model.to(_snake_case		)
									model.eval()
									SCREAMING_SNAKE_CASE__		  =				model(_snake_case   , attention_mask=_snake_case   , labels=_snake_case		)
									self.parent.assertEqual(result.logits.shape   , (self.batch_size, self.seq_length, self.num_labels)		)



					def 			lowerCAmelCase_					(						self			:				Dict		)							->       List[Any]:
									SCREAMING_SNAKE_CASE__		  =				self.prepare_config_and_inputs()
									(
									    (
									    SCREAMING_SNAKE_CASE__
									)  ,       (
									    SCREAMING_SNAKE_CASE__
									)  ,       (
									    SCREAMING_SNAKE_CASE__
									)  ,       (
									    SCREAMING_SNAKE_CASE__
									)  ,       (
									    SCREAMING_SNAKE_CASE__
									)  ,       (
									    SCREAMING_SNAKE_CASE__
									)  ,       
									)		  =				config_and_inputs
									SCREAMING_SNAKE_CASE__		  =				{"input_ids": input_ids, "attention_mask": input_mask}
									return config, inputs_dict



@require_torch
class lowerCamelCase		(_SCREAMING_SNAKE_CASE    ,    _SCREAMING_SNAKE_CASE    ,    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			:				List[str]		)							->       Tuple:
									SCREAMING_SNAKE_CASE__		  =				EsmModelTester(self		)
									SCREAMING_SNAKE_CASE__		  =				ConfigTester(self   , config_class=_snake_case   , hidden_size=37		)



					def 			lowerCAmelCase_					(						self			:				str		)							->       List[str]:
									self.config_tester.run_common_tests()



					def 			lowerCAmelCase_					(						self			:				str		)							->       Dict:
									SCREAMING_SNAKE_CASE__		  =				self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*_snake_case		)



					def 			lowerCAmelCase_					(						self			:				Any		)							->       List[str]:
									SCREAMING_SNAKE_CASE__		  =				self.model_tester.prepare_config_and_inputs()
									for type in ["absolute", "relative_key", "relative_key_query"]:
													SCREAMING_SNAKE_CASE__		  =				type
													self.model_tester.create_and_check_model(*_snake_case		)



					def 			lowerCAmelCase_					(						self			:				str		)							->       Optional[Any]:
									SCREAMING_SNAKE_CASE__		  =				self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_masked_lm(*_snake_case		)



					def 			lowerCAmelCase_					(						self			:				Tuple		)							->       str:
									SCREAMING_SNAKE_CASE__		  =				self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_token_classification(*_snake_case		)



					@slow
					def 			lowerCAmelCase_					(						self			:				Tuple		)							->       List[Any]:
									for model_name in ESM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
													SCREAMING_SNAKE_CASE__		  =				EsmModel.from_pretrained(_snake_case		)
													self.assertIsNotNone(_snake_case		)



					def 			lowerCAmelCase_					(						self			:				Union[str, Any]		)							->       str:
									SCREAMING_SNAKE_CASE__		  =				self.model_tester.prepare_config_and_inputs()[0]
									SCREAMING_SNAKE_CASE__		  =				EsmEmbeddings(config=_snake_case		)

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



					def 			lowerCAmelCase_					(						self			:				Dict		)							->       List[str]:
									SCREAMING_SNAKE_CASE__		  =				self.model_tester.prepare_config_and_inputs()[0]
									SCREAMING_SNAKE_CASE__		  =				EsmEmbeddings(config=_snake_case		)

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



					@unittest.skip("Esm does not support embedding resizing"		)
					def 			lowerCAmelCase_					(						self			:				List[str]		)							->       int:
									pass



					@unittest.skip("Esm does not support embedding resizing"		)
					def 			lowerCAmelCase_					(						self			:				Any		)							->       Tuple:
									pass



					@unittest.skip("Will be fixed soon by reducing the size of the model used for common tests."		)
					def 			lowerCAmelCase_					(						self			:				Union[str, Any]		)							->       Optional[int]:
									pass



@require_torch
class lowerCamelCase		(_SCREAMING_SNAKE_CASE      ):






					'''simple docstring'''


					@slow
					def 			lowerCAmelCase_					(						self			:				Dict		)							->       str:
									with torch.no_grad():
													SCREAMING_SNAKE_CASE__		  =				EsmForMaskedLM.from_pretrained("facebook/esm2_t6_8M_UR50D"		)
													model.eval()
													SCREAMING_SNAKE_CASE__		  =				torch.tensor([[0, 1, 2, 3, 4, 5]]		)
													SCREAMING_SNAKE_CASE__		  =				model(_snake_case		)[0]

													SCREAMING_SNAKE_CASE__		  =				33

													SCREAMING_SNAKE_CASE__		  =				torch.Size((1, 6, vocab_size)		)
													self.assertEqual(output.shape   , _snake_case		)

													SCREAMING_SNAKE_CASE__		  =				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]   , _snake_case   , atol=1e-4		)		)



					@slow
					def 			lowerCAmelCase_					(						self			:				Union[str, Any]		)							->       Any:
									with torch.no_grad():
													SCREAMING_SNAKE_CASE__		  =				EsmModel.from_pretrained("facebook/esm2_t6_8M_UR50D"		)
													model.eval()

													SCREAMING_SNAKE_CASE__		  =				torch.tensor([[0, 6, 4, 13, 5, 4, 16, 12, 11, 7, 2]]		)
													SCREAMING_SNAKE_CASE__		  =				model(_snake_case		)[0]
													# compare the actual values for a slice.
													SCREAMING_SNAKE_CASE__		  =				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]   , _snake_case   , atol=1e-4		)		)




