


"""simple docstring"""




import copy
import tempfile
import unittest

from transformers import MaMaaaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from transformers.utils import cached_property

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
				import torch

				from transformers import MaMaaaForConditionalGeneration, MaMaaaModel, MaMaaaTokenizer
				from transformers.models.mam_aaa.modeling_mam_aaa import MaMaaaDecoder, MaMaaaEncoder

def        lowercase							(		lowerCAmelCase__ :     Tuple    ,					lowerCAmelCase__ :     Union[str, Any]    ,					lowerCAmelCase__ :     Tuple    ,					lowerCAmelCase__ :     Tuple=None    ,					lowerCAmelCase__ :     Optional[Any]=None    ,					lowerCAmelCase__ :     Union[str, Any]=None    ,					lowerCAmelCase__ :     Optional[Any]=None    ,					lowerCAmelCase__ :     str=None    ,					) ->      List[str]:
		if attention_mask is None:
				__a           =			input_ids.ne(config.pad_token_id			)
		if decoder_attention_mask is None:
				__a           =			decoder_input_ids.ne(config.pad_token_id			)
		if head_mask is None:
				__a           =			torch.ones(config.encoder_layers    ,					config.encoder_attention_heads    ,					device=lowerCAmelCase__			)
		if decoder_head_mask is None:
				__a           =			torch.ones(config.decoder_layers    ,					config.decoder_attention_heads    ,					device=lowerCAmelCase__			)
		if cross_attn_head_mask is None:
				__a           =			torch.ones(config.decoder_layers    ,					config.decoder_attention_heads    ,					device=lowerCAmelCase__			)
		return {
		    "input_ids": input_ids,
		    "decoder_input_ids": decoder_input_ids,
		    "attention_mask": attention_mask,
		    "decoder_attention_mask": attention_mask,
		    "head_mask": head_mask,
		    "decoder_head_mask": decoder_head_mask,
		    "cross_attn_head_mask": cross_attn_head_mask,
		}
class 					__lowerCAmelCase					:


		'''simple docstring'''



		def __init__(  self							, _a							, _a=13							, _a=7							, _a=True							, _a=False							, _a=99							, _a=16							, _a=2							, _a=4							, _a=4							, _a="relu"							, _a=0.1							, _a=0.1							, _a=0.0							, _a=0.0							, _a=20							, _a=2							, _a=1							, _a=0							, ):
				__a           =			parent
				__a           =			batch_size
				__a           =			seq_length
				__a           =			is_training
				__a           =			use_labels
				__a           =			vocab_size
				__a           =			hidden_size
				__a           =			num_hidden_layers
				__a           =			num_attention_heads
				__a           =			intermediate_size
				__a           =			hidden_act
				__a           =			hidden_dropout_prob
				__a           =			attention_probs_dropout_prob
				__a           =			encoder_layerdrop
				__a           =			decoder_layerdrop
				__a           =			max_position_embeddings
				__a           =			eos_token_id
				__a           =			pad_token_id
				__a           =			bos_token_id

		def 			__UpperCAmelCase       (  self      ):
				__a           =			ids_tensor([self.batch_size, self.seq_length]							, self.vocab_size      )
				__a           =			self.eos_token_id  # Eos Token
				__a           =			ids_tensor([self.batch_size, self.seq_length]							, self.vocab_size      )

				# we need to clamp the input ids here to avoid having pad token in between
				# this is because for M2M100 the position_ids are prepared such that
				# all pad tokens have pos id = 2 and rest are between 2..seq_length
				# and the seq_length here is seq_length - num_pad_tokens
				# but when using past, there is no way of knowing if the past input ids had
				# pad tokens in them, which results in incorrect seq_lenth and which in turn results in
				# position_ids being off by num_pad_tokens in past input
				__a           =			input_ids.clamp(self.pad_token_id + 1      )
				__a           =			decoder_input_ids.clamp(self.pad_token_id + 1      )

				__a           =			self.get_config()
				__a           =			prepare_mam_aaa_inputs_dict(_a							, _a							, _a      )
				return config, inputs_dict

		def 			__UpperCAmelCase       (  self      ):
				return MaMaaaConfig(
				    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							, encoder_layerdrop=self.encoder_layerdrop							, decoder_layerdrop=self.decoder_layerdrop							, 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							, )

		def 			__UpperCAmelCase       (  self      ):
				__a      ,     __a           =			self.prepare_config_and_inputs()
				return config, inputs_dict

		def 			__UpperCAmelCase       (  self							, _a							, _a      ):
				__a           =			MaMaaaModel(config=_a      ).get_decoder().to(_a      ).eval()
				__a           =			inputs_dict['''input_ids''']
				__a           =			inputs_dict['''attention_mask''']
				__a           =			inputs_dict['''head_mask''']

				# first forward pass
				__a           =			model(_a							, attention_mask=_a							, head_mask=_a							, use_cache=_a      )

				__a      ,     __a           =			outputs.to_tuple()

				# create hypothetical multiple next token and extent to next_input_ids
				__a           =			ids_tensor((self.batch_size, 3)							, config.vocab_size      )
				__a           =			ids_tensor((self.batch_size, 3)							, 2      )

				# append to next input_ids and
				__a           =			torch.cat([input_ids, next_tokens]							, dim=-1      )
				__a           =			torch.cat([attention_mask, next_attn_mask]							, dim=-1      )

				__a           =			model(_a							, attention_mask=_a      )['''last_hidden_state''']
				__a           =			model(_a							, attention_mask=_a							, past_key_values=_a      )[
				    '''last_hidden_state'''
				]

				# select random slice
				__a           =			ids_tensor((1,)							, output_from_past.shape[-1]      ).item()
				__a           =			output_from_no_past[:, -3:, random_slice_idx].detach()
				__a           =			output_from_past[:, :, random_slice_idx].detach()

				self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]      )

				# test that outputs are equal for slice
				self.parent.assertTrue(torch.allclose(_a							, _a							, atol=1E-2      )      )





		def 			__UpperCAmelCase       (  self							, _a							, _a      ):
				__a           =			MaMaaaModel(config=_a      ).to(_a      ).eval()
				__a           =			model(**_a      )

				__a           =			outputs.encoder_last_hidden_state
				__a           =			outputs.last_hidden_state

				with tempfile.TemporaryDirectory() as tmpdirname:
						__a           =			model.get_encoder()
						encoder.save_pretrained(_a      )
						__a           =			MaMaaaEncoder.from_pretrained(_a      ).to(_a      )

				__a           =			encoder(inputs_dict['''input_ids''']							, attention_mask=inputs_dict['''attention_mask''']      )[
				    0
				]

				self.parent.assertTrue((encoder_last_hidden_state_a - encoder_last_hidden_state).abs().max().item() < 1E-3      )

				with tempfile.TemporaryDirectory() as tmpdirname:
						__a           =			model.get_decoder()
						decoder.save_pretrained(_a      )
						__a           =			MaMaaaDecoder.from_pretrained(_a      ).to(_a      )

				__a           =			decoder(
				    input_ids=inputs_dict['''decoder_input_ids''']							, attention_mask=inputs_dict['''decoder_attention_mask''']							, encoder_hidden_states=_a							, encoder_attention_mask=inputs_dict['''attention_mask''']							, )[0]

				self.parent.assertTrue((last_hidden_state_a - last_hidden_state).abs().max().item() < 1E-3      )







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


		'''simple docstring'''



		__UpperCAmelCase		:			Any										=				(
		    (
		        MaMaaaModel,
		        MaMaaaForConditionalGeneration,
		    )
		    if is_torch_available()
		    else ()
		)
		__UpperCAmelCase		:			Dict										=				(MaMaaaForConditionalGeneration,) if is_torch_available() else ()
		__UpperCAmelCase		:			Optional[int]										=				(
		    {
		        'conversational': MaMaaaForConditionalGeneration,
		        'feature-extraction': MaMaaaModel,
		        'summarization': MaMaaaForConditionalGeneration,
		        'text2text-generation': MaMaaaForConditionalGeneration,
		        'translation': MaMaaaForConditionalGeneration,
		    }
		    if is_torch_available()
		    else {}
		)
		__UpperCAmelCase		:			int										=				True
		__UpperCAmelCase		:			Optional[int]										=				True
		__UpperCAmelCase		:			Dict										=				False
		__UpperCAmelCase		:			int										=				False

		def 			__UpperCAmelCase       (  self							, _a							, _a							, _a							, _a							, _a      ):
				if pipeline_test_casse_name == "TranslationPipelineTests":
						# Get `ValueError: Translation requires a `src_lang` and a `tgt_lang` for this model`.
						# `M2M100Config` was never used in pipeline tests: cannot create a simple tokenizer.
						return True

				return False

		def 			__UpperCAmelCase       (  self      ):
				__a           =			MaMaaaModelTester(self      )
				__a           =			ConfigTester(self							, config_class=_a      )

		def 			__UpperCAmelCase       (  self      ):
				self.config_tester.run_common_tests()

		def 			__UpperCAmelCase       (  self      ):
				__a      ,     __a           =			self.model_tester.prepare_config_and_inputs()
				for model_class in self.all_model_classes:
						__a           =			model_class(_a      )

						with tempfile.TemporaryDirectory() as tmpdirname:
								model.save_pretrained(_a      )
								__a      ,     __a           =			model_class.from_pretrained(_a							, output_loading_info=_a      )
						self.assertEqual(info['''missing_keys''']							, []      )

		def 			__UpperCAmelCase       (  self      ):
				__a           =			self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_decoder_model_past_large_inputs(*_a      )

		def 			__UpperCAmelCase       (  self      ):
				__a           =			self.model_tester.prepare_config_and_inputs_for_common()
				self.model_tester.check_encoder_decoder_model_standalone(*_a      )

		def 			__UpperCAmelCase       (  self      ):
				__a      ,     __a           =			self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in (MaMaaaModel, MaMaaaForConditionalGeneration):
						__a           =			model_class(_a      )
						model.to(_a      )
						model.eval()

						__a           =			copy.deepcopy(self._prepare_for_class(_a							, _a      )      )

						if not self.is_encoder_decoder:
								__a           =			inputs['''input_ids''']
								del inputs["input_ids"]
						else:
								__a           =			inputs['''input_ids''']
								__a           =			inputs.get('''decoder_input_ids'''							, _a      )
								del inputs["input_ids"]
								inputs.pop('''decoder_input_ids'''							, _a      )

						__a           =			model.get_input_embeddings()
						if not self.is_encoder_decoder:
								__a           =			wte(_a      )
						else:
								__a           =			wte(_a      )
								__a           =			wte(_a      )

						with torch.no_grad():
								model(**_a      )[0]





		def 			__UpperCAmelCase       (  self      ):
				__a      ,     __a           =			self.model_tester.prepare_config_and_inputs()
				__a           =			input_dict['''input_ids''']
				__a           =			input_ids.ne(1      ).to(_a      )
				__a           =			MaMaaaForConditionalGeneration(_a      ).eval().to(_a      )
				if torch_device == "cuda":
						model.half()
				model.generate(_a							, attention_mask=_a      )
				model.generate(num_beams=4							, do_sample=_a							, early_stopping=_a							, num_return_sequences=3      )







def        lowercase							(		lowerCAmelCase__ :     List[str]			) ->      Tuple:
		return torch.tensor(lowerCAmelCase__    ,					dtype=torch.long    ,					device=lowerCAmelCase__			)


lowercase_			     =  1e-4




@require_torch
@require_sentencepiece
@require_tokenizers
@slow
class 					__lowerCAmelCase					(   unittest.TestCase       ):


		'''simple docstring'''



		@cached_property
		def 			__UpperCAmelCase       (  self      ):
				return MaMaaaTokenizer.from_pretrained('''facebook/m2m100_418M'''      )

		def 			__UpperCAmelCase       (  self      ):
				__a           =			MaMaaaModel.from_pretrained('''facebook/m2m100_418M'''      ).to(_a      )
				__a           =			_long_tensor([[128_028, 98, 12, 30_527, 2_732, 159, 7_755, 61_904, 39_144, 38, 2]]      )
				__a           =			_long_tensor([[2, 128_028, 98, 12, 30_527, 2_732, 159, 7_755, 61_904, 39_144, 38]]      )
				__a           =			prepare_mam_aaa_inputs_dict(model.config							, _a							, _a      )
				with torch.no_grad():
						__a           =			model(**_a      )[0]
				__a           =			torch.Size((1, 11, 1_024)      )
				self.assertEqual(output.shape							, _a      )
				# change to expected output here
				__a           =			torch.tensor(
				    [[-0.7780, -0.1676, 0.1038], [-6.7556, -1.3992, 0.0567], [-7.5383, -0.5920, -0.2779]]							, device=_a      )
				self.assertTrue(torch.allclose(output[:, :3, :3]							, _a							, atol=_a      )      )

		def 			__UpperCAmelCase       (  self      ):
				__a           =			MaMaaaForConditionalGeneration.from_pretrained('''facebook/m2m100_418M'''      ).to(_a      )

				# change to intended input
				__a           =			_long_tensor([[128_028, 98, 12, 30_527, 2_732, 159, 7_755, 61_904, 39_144, 38, 2]]      )
				__a           =			_long_tensor([[2, 128_028, 98, 12, 30_527, 2_732, 159, 7_755, 61_904, 39_144, 38]]      )
				__a           =			prepare_mam_aaa_inputs_dict(model.config							, _a							, _a      )
				with torch.no_grad():
						__a           =			model(**_a      )[0]
				__a           =			torch.Size((1, 11, model.config.vocab_size)      )
				self.assertEqual(output.shape							, _a      )
				# change to expected output here
				__a           =			torch.tensor(
				    [[-1.0448, -1.0411, 3.7992], [-3.2191, -3.2386, -1.3451], [-3.6210, -3.5993, 0.4925]]							, device=_a      )
				self.assertTrue(torch.allclose(output[:, :3, :3]							, _a							, atol=_a      )      )





		def 			__UpperCAmelCase       (  self      ):
				__a           =			MaMaaaForConditionalGeneration.from_pretrained('''facebook/m2m100_418M'''      ).to(_a      )
				__a           =			MaMaaaTokenizer.from_pretrained('''facebook/m2m100_418M'''							, src_lang='''fr'''							, tgt_lang='''en'''      )

				__a           =			[
				    '''L\'affaire NSA souligne l\'absence totale de débat sur le renseignement''',
				    '''Selon moi, il y a deux niveaux de réponse de la part du gouvernement français.''',
				    '''Lorsque François Hollande téléphone à Barack Obama ou quand le ministre des affaires étrangères Laurent'''
				    ''' Fabius convoque l\'ambassadeur des Etats-Unis, ils réagissent à une vraie découverte, qui est celle de'''
				    ''' l\'ampleur de la surveillance américaine sur l\'ensemble des communications en France.''',
				]

				# The below article tests that we don't add any hypotheses outside of the top n_beams
				__a           =			tokenizer(_a							, padding=_a							, return_tensors='''pt'''      )

				__a           =			model.generate(
				    input_ids=dct['''input_ids'''].to(_a      )							, attention_mask=dct['''attention_mask'''].to(_a      )							, num_beams=5							, forced_bos_token_id=tokenizer.get_lang_id('''en'''      )							, )

				__a           =			[
				    '''The NSA case highlights the total absence of intelligence debate''',
				    '''I think there are two levels of response from the French government.''',
				    '''When François Hollande calls Barack Obama or when Foreign Minister Laurent Fabius calls the U.S.'''
				    ''' Ambassador, they respond to a real discovery, which is that of the scale of U.S. surveillance on all'''
				    ''' communications in France.''',
				]

				__a           =			tokenizer.batch_decode(
				    hypotheses_batch.tolist()							, clean_up_tokenization_spaces=_a							, skip_special_tokens=_a      )
				assert generated == expected_en










"""simple docstring"""




from __future__ import annotations

import unittest

from transformers import EsmConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
				import numpy
				import tensorflow as tf

				from transformers.models.esm.modeling_tf_esm import (
				    TF_ESM_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFEsmForMaskedLM,
				    TFEsmForSequenceClassification,
				    TFEsmForTokenClassification,
				    TFEsmModel,
				)
class 					__lowerCAmelCase					:


		'''simple docstring'''



		def __init__(  self							, _a							, ):
				__a           =			parent
				__a           =			13
				__a           =			7
				__a           =			True
				__a           =			True
				__a           =			True
				__a           =			99
				__a           =			32
				__a           =			2
				__a           =			4
				__a           =			37
				__a           =			'''gelu'''
				__a           =			0.1
				__a           =			0.1
				__a           =			512
				__a           =			16
				__a           =			2
				__a           =			0.02
				__a           =			3
				__a           =			4
				__a           =			None

		def 			__UpperCAmelCase       (  self      ):
				__a           =			ids_tensor([self.batch_size, self.seq_length]							, self.vocab_size      )

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

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

				__a           =			EsmConfig(
				    vocab_size=self.vocab_size							, hidden_size=self.hidden_size							, num_hidden_layers=self.num_hidden_layers							, pad_token_id=1							, 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							, )

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

		def 			__UpperCAmelCase       (  self      ):
				(
				    (
				    __a
				)      ,     (
				    __a
				)      ,     (
				    __a
				)      ,     (
				    __a
				)      ,     (
				    __a
				)      ,     (
				    __a
				)      ,     
				)           =			self.prepare_config_and_inputs()

				__a           =			True
				__a           =			floats_tensor([self.batch_size, self.seq_length, self.hidden_size]      )
				__a           =			ids_tensor([self.batch_size, self.seq_length]							, vocab_size=2      )

				return (
				    config,
				    input_ids,
				    input_mask,
				    sequence_labels,
				    token_labels,
				    choice_labels,
				    encoder_hidden_states,
				    encoder_attention_mask,
				)

		def 			__UpperCAmelCase       (  self							, _a							, _a							, _a							, _a							, _a							, _a      ):
				__a           =			TFEsmModel(config=_a      )
				__a           =			{'''input_ids''': input_ids, '''attention_mask''': input_mask}
				__a           =			model(_a      )

				__a           =			[input_ids, input_mask]
				__a           =			model(_a      )

				__a           =			model(_a      )

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

		def 			__UpperCAmelCase       (  self							, _a							, _a							, _a							, _a							, _a							, _a							, _a							, _a							, ):
				__a           =			True

				__a           =			TFEsmModel(config=_a      )
				__a           =			{
				    '''input_ids''': input_ids,
				    '''attention_mask''': input_mask,
				    '''encoder_hidden_states''': encoder_hidden_states,
				    '''encoder_attention_mask''': encoder_attention_mask,
				}
				__a           =			model(_a      )

				__a           =			[input_ids, input_mask]
				__a           =			model(_a							, encoder_hidden_states=_a      )

				# Also check the case where encoder outputs are not passed
				__a           =			model(_a							, attention_mask=_a      )

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

		def 			__UpperCAmelCase       (  self							, _a							, _a							, _a							, _a							, _a							, _a      ):
				__a           =			TFEsmForMaskedLM(config=_a      )
				__a           =			model([input_ids, input_mask]      )
				self.parent.assertEqual(result.logits.shape							, (self.batch_size, self.seq_length, self.vocab_size)      )

		def 			__UpperCAmelCase       (  self							, _a							, _a							, _a							, _a							, _a							, _a      ):
				__a           =			self.num_labels
				__a           =			TFEsmForTokenClassification(config=_a      )
				__a           =			{'''input_ids''': input_ids, '''attention_mask''': input_mask}
				__a           =			model(_a      )
				self.parent.assertEqual(result.logits.shape							, (self.batch_size, self.seq_length, self.num_labels)      )





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







@require_tf
class 					__lowerCAmelCase					(   __SCREAMING_SNAKE_CASE     ,			__SCREAMING_SNAKE_CASE     ,			unittest.TestCase       ):


		'''simple docstring'''



		__UpperCAmelCase		:			int										=				(
		    (
		        TFEsmModel,
		        TFEsmForMaskedLM,
		        TFEsmForSequenceClassification,
		        TFEsmForTokenClassification,
		    )
		    if is_tf_available()
		    else ()
		)
		__UpperCAmelCase		:			Tuple										=				(
		    {
		        'feature-extraction': TFEsmModel,
		        'fill-mask': TFEsmForMaskedLM,
		        'text-classification': TFEsmForSequenceClassification,
		        'token-classification': TFEsmForTokenClassification,
		        'zero-shot': TFEsmForSequenceClassification,
		    }
		    if is_tf_available()
		    else {}
		)
		__UpperCAmelCase		:			Tuple										=				False
		__UpperCAmelCase		:			Union[str, Any]										=				False

		def 			__UpperCAmelCase       (  self      ):
				__a           =			TFEsmModelTester(self      )
				__a           =			ConfigTester(self							, config_class=_a							, hidden_size=37      )

		def 			__UpperCAmelCase       (  self      ):
				self.config_tester.run_common_tests()

		def 			__UpperCAmelCase       (  self      ):
				__a           =			self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_model(*_a      )

		def 			__UpperCAmelCase       (  self      ):
				__a           =			self.model_tester.prepare_config_and_inputs_for_decoder()
				self.model_tester.create_and_check_model_as_decoder(*_a      )

		def 			__UpperCAmelCase       (  self      ):
				__a           =			self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_masked_lm(*_a      )

		def 			__UpperCAmelCase       (  self      ):
				__a           =			self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_token_classification(*_a      )

		@slow
		def 			__UpperCAmelCase       (  self      ):
				for model_name in TF_ESM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
						__a           =			TFEsmModel.from_pretrained(_a      )
						self.assertIsNotNone(_a      )

		@unittest.skip('''Protein models do not support embedding resizing.'''      )
		def 			__UpperCAmelCase       (  self      ):
				pass

		@unittest.skip('''Protein models do not support embedding resizing.'''      )
		def 			__UpperCAmelCase       (  self      ):
				pass





		def 			__UpperCAmelCase       (  self      ):
				__a      ,     __a           =			self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
						__a           =			model_class(_a      )
						assert isinstance(model.get_input_embeddings()							, tf.keras.layers.Layer      )
						if model_class is TFEsmForMaskedLM:
								# Output embedding test differs from the main test because they're a matrix, not a layer
								__a           =			model.get_bias()
								assert isinstance(_a							, _a      )
								for k, v in name.items():
										assert isinstance(_a							, tf.Variable      )
						else:
								__a           =			model.get_output_embeddings()
								assert x is None
								__a           =			model.get_bias()
								assert name is None







@require_tf
class 					__lowerCAmelCase					(   unittest.TestCase       ):


		'''simple docstring'''



		@slow
		def 			__UpperCAmelCase       (  self      ):
				__a           =			TFEsmForMaskedLM.from_pretrained('''facebook/esm2_t6_8M_UR50D'''      )

				__a           =			tf.constant([[0, 1, 2, 3, 4, 5]]      )
				__a           =			model(_a      )[0]
				__a           =			[1, 6, 33]
				self.assertEqual(list(output.numpy().shape      )							, _a      )
				# compare the actual values for a slice.
				__a           =			tf.constant(
				    [
				        [
				            [8.92_1518, -10.58_9814, -6.467_1307],
				            [-6.396_7156, -13.91_1377, -1.121_1915],
				            [-7.78_1247, -13.95_1557, -3.74_0592],
				        ]
				    ]      )
				self.assertTrue(numpy.allclose(output[:, :3, :3].numpy()							, expected_slice.numpy()							, atol=1E-2      )      )





		@slow
		def 			__UpperCAmelCase       (  self      ):
				__a           =			TFEsmModel.from_pretrained('''facebook/esm2_t6_8M_UR50D'''      )

				__a           =			tf.constant([[0, 6, 4, 13, 5, 4, 16, 12, 11, 7, 2]]      )
				__a           =			model(_a      )[0]
				# compare the actual values for a slice.
				__a           =			tf.constant(
				    [
				        [
				            [0.1444_3092, 0.5412_5327, 0.324_7739],
				            [0.3034_0484, 0.0052_6676, 0.3107_7722],
				            [0.3227_8043, -0.2498_7096, 0.341_4628],
				        ]
				    ]      )
				self.assertTrue(numpy.allclose(output[:, :3, :3].numpy()							, expected_slice.numpy()							, atol=1E-4      )      )






