"""simple docstring"""






from typing import Optional, Tuple, Union

import torch
from einops import rearrange, reduce

from diffusers import DDIMScheduler, DDPMScheduler, DiffusionPipeline, ImagePipelineOutput, UNetaDConditionModel
from diffusers.schedulers.scheduling_ddim import DDIMSchedulerOutput
from diffusers.schedulers.scheduling_ddpm import DDPMSchedulerOutput


_A   					=    8




def 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase  ,					__UpperCAmelCase=BITS       )							->   Union[str, Any]:
				SCREAMING_SNAKE_CASE__		  =				x.device

				SCREAMING_SNAKE_CASE__		  =				(x * 255).int().clamp(0  ,					255       )

				SCREAMING_SNAKE_CASE__		  =				2 ** torch.arange(bits - 1  ,					-1  ,					-1  ,					device=__UpperCAmelCase       )
				SCREAMING_SNAKE_CASE__		  =				rearrange(__UpperCAmelCase  ,					"d -> d 1 1"       )
				SCREAMING_SNAKE_CASE__		  =				rearrange(__UpperCAmelCase  ,					"b c h w -> b c 1 h w"       )

				SCREAMING_SNAKE_CASE__		  =				((x & mask) != 0).float()
				SCREAMING_SNAKE_CASE__		  =				rearrange(__UpperCAmelCase  ,					"b c d h w -> b (c d) h w"       )
				SCREAMING_SNAKE_CASE__		  =				bits * 2 - 1
				return bits




def 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase  ,					__UpperCAmelCase=BITS       )							->   Tuple:
				SCREAMING_SNAKE_CASE__		  =				x.device

				SCREAMING_SNAKE_CASE__		  =				(x > 0).int()
				SCREAMING_SNAKE_CASE__		  =				2 ** torch.arange(bits - 1  ,					-1  ,					-1  ,					device=__UpperCAmelCase  ,					dtype=torch.intaa       )

				SCREAMING_SNAKE_CASE__		  =				rearrange(__UpperCAmelCase  ,					"d -> d 1 1"       )
				SCREAMING_SNAKE_CASE__		  =				rearrange(__UpperCAmelCase  ,					"b (c d) h w -> b c d h w"  ,					d=8       )
				SCREAMING_SNAKE_CASE__		  =				reduce(x * mask  ,					"b c d h w -> b c h w"  ,					"sum"       )
				return (dec / 255).clamp(0.0  ,					1.0       )




def 						SCREAMING_SNAKE_CASE					(  self  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase = 0.0  ,					__UpperCAmelCase = True  ,					__UpperCAmelCase=None  ,					__UpperCAmelCase = True  ,					)							->   Union[DDIMSchedulerOutput, Tuple]:
				if self.num_inference_steps is None:
								raise ValueError(
								    "Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler"       )

				# See formulas (12) and (16) of DDIM paper https://arxiv.org/pdf/2010.02502.pdf
				# Ideally, read DDIM paper in-detail understanding

				# Notation (<variable name> -> <name in paper>
				# - pred_noise_t -> e_theta(x_t, t)
				# - pred_original_sample -> f_theta(x_t, t) or x_0
				# - std_dev_t -> sigma_t
				# - eta -> η
				# - pred_sample_direction -> "direction pointing to x_t"
				# - pred_prev_sample -> "x_t-1"

				# 1. get previous step value (=t-1)
				SCREAMING_SNAKE_CASE__		  =				timestep - self.config.num_train_timesteps // self.num_inference_steps

				# 2. compute alphas, betas
				SCREAMING_SNAKE_CASE__		  =				self.alphas_cumprod[timestep]
				SCREAMING_SNAKE_CASE__		  =				self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod

				SCREAMING_SNAKE_CASE__		  =				1 - alpha_prod_t

				# 3. compute predicted original sample from predicted noise also called
				# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
				SCREAMING_SNAKE_CASE__		  =				(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5

				# 4. Clip "predicted x_0"
				SCREAMING_SNAKE_CASE__		  =				self.bit_scale
				if self.config.clip_sample:
								SCREAMING_SNAKE_CASE__		  =				torch.clamp(__UpperCAmelCase  ,					-scale  ,					__UpperCAmelCase       )

				# 5. compute variance: "sigma_t(η)" -> see formula (16)
				# σ_t = sqrt((1 − α_t−1)/(1 − α_t)) * sqrt(1 − α_t/α_t−1)
				SCREAMING_SNAKE_CASE__		  =				self._get_variance(__UpperCAmelCase  ,					__UpperCAmelCase       )
				SCREAMING_SNAKE_CASE__		  =				eta * variance ** 0.5

				if use_clipped_model_output:
								# the model_output is always re-derived from the clipped x_0 in Glide
								SCREAMING_SNAKE_CASE__		  =				(sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5

				# 6. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
				SCREAMING_SNAKE_CASE__		  =				(1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * model_output

				# 7. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
				SCREAMING_SNAKE_CASE__		  =				alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction

				if eta > 0:
								# randn_like does not support generator https://github.com/pytorch/pytorch/issues/27072
								SCREAMING_SNAKE_CASE__		  =				model_output.device if torch.is_tensor(__UpperCAmelCase       ) else "cpu"
								SCREAMING_SNAKE_CASE__		  =				torch.randn(model_output.shape  ,					dtype=model_output.dtype  ,					generator=__UpperCAmelCase       ).to(__UpperCAmelCase       )
								SCREAMING_SNAKE_CASE__		  =				self._get_variance(__UpperCAmelCase  ,					__UpperCAmelCase       ) ** 0.5 * eta * noise

								SCREAMING_SNAKE_CASE__		  =				prev_sample + variance

				if not return_dict:
								return (prev_sample,)

				return DDIMSchedulerOutput(prev_sample=__UpperCAmelCase  ,					pred_original_sample=__UpperCAmelCase       )




def 						SCREAMING_SNAKE_CASE					(  self  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase="epsilon"  ,					__UpperCAmelCase=None  ,					__UpperCAmelCase = True  ,					)							->   Union[DDPMSchedulerOutput, Tuple]:
				SCREAMING_SNAKE_CASE__		  =				timestep

				if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type in ["learned", "learned_range"]:
								SCREAMING_SNAKE_CASE__  ,       SCREAMING_SNAKE_CASE__		  =				torch.split(__UpperCAmelCase  ,					sample.shape[1]  ,					dim=1       )
				else:
								SCREAMING_SNAKE_CASE__		  =				None

				# 1. compute alphas, betas
				SCREAMING_SNAKE_CASE__		  =				self.alphas_cumprod[t]
				SCREAMING_SNAKE_CASE__		  =				self.alphas_cumprod[t - 1] if t > 0 else self.one
				SCREAMING_SNAKE_CASE__		  =				1 - alpha_prod_t
				SCREAMING_SNAKE_CASE__		  =				1 - alpha_prod_t_prev

				# 2. compute predicted original sample from predicted noise also called
				# "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
				if prediction_type == "epsilon":
								SCREAMING_SNAKE_CASE__		  =				(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
				elif prediction_type == "sample":
								SCREAMING_SNAKE_CASE__		  =				model_output
				else:
								raise ValueError(F"""Unsupported prediction_type {prediction_type}."""       )

				# 3. Clip "predicted x_0"
				SCREAMING_SNAKE_CASE__		  =				self.bit_scale
				if self.config.clip_sample:
								SCREAMING_SNAKE_CASE__		  =				torch.clamp(__UpperCAmelCase  ,					-scale  ,					__UpperCAmelCase       )

				# 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
				# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
				SCREAMING_SNAKE_CASE__		  =				(alpha_prod_t_prev ** 0.5 * self.betas[t]) / beta_prod_t
				SCREAMING_SNAKE_CASE__		  =				self.alphas[t] ** 0.5 * beta_prod_t_prev / beta_prod_t

				# 5. Compute predicted previous sample µ_t
				# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
				SCREAMING_SNAKE_CASE__		  =				pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

				# 6. Add noise
				SCREAMING_SNAKE_CASE__		  =				0
				if t > 0:
								SCREAMING_SNAKE_CASE__		  =				torch.randn(
								    model_output.size()  ,					dtype=model_output.dtype  ,					layout=model_output.layout  ,					generator=__UpperCAmelCase       ).to(model_output.device       )
								SCREAMING_SNAKE_CASE__		  =				(self._get_variance(__UpperCAmelCase  ,					predicted_variance=__UpperCAmelCase       ) ** 0.5) * noise

				SCREAMING_SNAKE_CASE__		  =				pred_prev_sample + variance

				if not return_dict:
								return (pred_prev_sample,)

				return DDPMSchedulerOutput(prev_sample=__UpperCAmelCase  ,					pred_original_sample=__UpperCAmelCase       )

class lowerCamelCase		(_SCREAMING_SNAKE_CASE      ):






					'''simple docstring'''


					def __init__(						self			:				Optional[int]   , _snake_case			:				UNetaDConditionModel   , _snake_case			:				Union[DDIMScheduler, DDPMScheduler]   , _snake_case			:				Optional[float] = 1.0   , )							->       Optional[int]:
									super().__init__()
									SCREAMING_SNAKE_CASE__		  =				bit_scale
									SCREAMING_SNAKE_CASE__		  =				(
									    ddim_bit_scheduler_step if isinstance(_snake_case   , _snake_case		) else ddpm_bit_scheduler_step
									)

									self.register_modules(unet=_snake_case   , scheduler=_snake_case		)



					@torch.no_grad()
					def __call__(						self			:				Any   , _snake_case			:				Optional[int] = 256   , _snake_case			:				Optional[int] = 256   , _snake_case			:				Optional[int] = 50   , _snake_case			:				Optional[torch.Generator] = None   , _snake_case			:				Optional[int] = 1   , _snake_case			:				Optional[str] = "pil"   , _snake_case			:				bool = True   , **_snake_case			:				Tuple   , )							->       Union[Tuple, ImagePipelineOutput]:
									SCREAMING_SNAKE_CASE__		  =				torch.randn(
									    (batch_size, self.unet.config.in_channels, height, width)   , generator=_snake_case   , )
									SCREAMING_SNAKE_CASE__		  =				decimal_to_bits(_snake_case		) * self.bit_scale
									SCREAMING_SNAKE_CASE__		  =				latents.to(self.device		)

									self.scheduler.set_timesteps(_snake_case		)

									for t in self.progress_bar(self.scheduler.timesteps		):
													# predict the noise residual
													SCREAMING_SNAKE_CASE__		  =				self.unet(_snake_case   , _snake_case		).sample

													# compute the previous noisy sample x_t -> x_t-1
													SCREAMING_SNAKE_CASE__		  =				self.scheduler.step(_snake_case   , _snake_case   , _snake_case		).prev_sample

									SCREAMING_SNAKE_CASE__		  =				bits_to_decimal(_snake_case		)

									if output_type == "pil":
													SCREAMING_SNAKE_CASE__		  =				self.numpy_to_pil(_snake_case		)

									if not return_dict:
													return (image,)

									return ImagePipelineOutput(images=_snake_case		)





"""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		)		)




