






'''simple docstring'''





import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput






def 				lowerCamelCase__				(_UpperCAmelCase							,       _UpperCAmelCase=0.9_99							,       _UpperCAmelCase="cosine"							,       ):
			if alpha_transform_type == "cosine":

						def alpha_bar_fn(_UpperCAmelCase):
									return math.cos((t + 0.0_08) / 1.0_08 * math.pi / 2) ** 2

			elif alpha_transform_type == "exp":

						def alpha_bar_fn(_UpperCAmelCase):
									return math.exp(t * -12.0)

			else:
						raise ValueError(F'''Unsupported alpha_tranform_type: {alpha_transform_type}''')

			SCREAMING_SNAKE_CASE						= []
			for i in range(_UpperCAmelCase):
						SCREAMING_SNAKE_CASE						= i / num_diffusion_timesteps
						SCREAMING_SNAKE_CASE						= (i + 1) / num_diffusion_timesteps
						betas.append(min(1 - alpha_bar_fn(_UpperCAmelCase) / alpha_bar_fn(_UpperCAmelCase)							,       _UpperCAmelCase))
			return torch.tensor(_UpperCAmelCase							,       dtype=torch.floataa)







class 		_snake_case  (  A__   ,    A__					):
			_lowercase		:    Optional[Any]			     =							[e.name for e in KarrasDiffusionSchedulers]
			_lowercase		:    List[str]			     =							2






			@register_to_config
			def __init__(       self		,					a = 1000		,					a = 0.0_00_85		,					a = 0.0_12		,					a = "linear"		,					a = None		,					a = "epsilon"		,					a = "linspace"		,					a = 0		,					)      ->       Any:
						if trained_betas is not None:
									SCREAMING_SNAKE_CASE						= torch.tensor(a		,					dtype=torch.floataa)
						elif beta_schedule == "linear":
									SCREAMING_SNAKE_CASE						= torch.linspace(a		,					a		,					a		,					dtype=torch.floataa)
						elif beta_schedule == "scaled_linear":
									# this schedule is very specific to the latent diffusion model.
									SCREAMING_SNAKE_CASE						= (
									    torch.linspace(beta_start**0.5		,					beta_end**0.5		,					a		,					dtype=torch.floataa) ** 2
									)
						elif beta_schedule == "squaredcos_cap_v2":
									# Glide cosine schedule
									SCREAMING_SNAKE_CASE						= betas_for_alpha_bar(a)
						else:
									raise NotImplementedError(f'''{beta_schedule} does is not implemented for {self.__class__}''')

						SCREAMING_SNAKE_CASE						= 1.0 - self.betas
						SCREAMING_SNAKE_CASE						= torch.cumprod(self.alphas		,					dim=0)

						#  set all values
						self.set_timesteps(a		,					a		,					a)






			def    SCREAMING_SNAKE_CASE__    (       self		,					a		,					a=None)      ->       Any:
						if schedule_timesteps is None:
									SCREAMING_SNAKE_CASE						= self.timesteps

						SCREAMING_SNAKE_CASE						= (schedule_timesteps == timestep).nonzero()

						# The sigma index that is taken for the **very** first `step`
						# is always the second index (or the last index if there is only 1)
						# This way we can ensure we don't accidentally skip a sigma in
						# case we start in the middle of the denoising schedule (e.g. for image-to-image)
						if len(self._index_counter) == 0:
									SCREAMING_SNAKE_CASE						= 1 if len(a) > 1 else 0
						else:
									SCREAMING_SNAKE_CASE						= timestep.cpu().item() if torch.is_tensor(a) else timestep
									SCREAMING_SNAKE_CASE						= self._index_counter[timestep_int]

						return indices[pos].item()






			@property
			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Optional[Any]:
						# standard deviation of the initial noise distribution
						if self.config.timestep_spacing in ["linspace", "trailing"]:
									return self.sigmas.max()

						return (self.sigmas.max() ** 2 + 1) ** 0.5






			def    SCREAMING_SNAKE_CASE__    (       self		,					a		,					a		,					)      ->       torch.FloatTensor:
						SCREAMING_SNAKE_CASE						= self.index_for_timestep(a)

						if self.state_in_first_order:
									SCREAMING_SNAKE_CASE						= self.sigmas[step_index]
						else:
									SCREAMING_SNAKE_CASE						= self.sigmas_interpol[step_index]

						SCREAMING_SNAKE_CASE						= sample / ((sigma**2 + 1) ** 0.5)
						return sample






			def    SCREAMING_SNAKE_CASE__    (       self		,					a		,					a = None		,					a = None		,					)      ->       int:
						SCREAMING_SNAKE_CASE						= num_inference_steps

						SCREAMING_SNAKE_CASE						= num_train_timesteps or self.config.num_train_timesteps

						# "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
						if self.config.timestep_spacing == "linspace":
									SCREAMING_SNAKE_CASE						= np.linspace(0		,					num_train_timesteps - 1		,					a		,					dtype=a)[::-1].copy()
						elif self.config.timestep_spacing == "leading":
									SCREAMING_SNAKE_CASE						= num_train_timesteps // self.num_inference_steps
									# creates integer timesteps by multiplying by ratio
									# casting to int to avoid issues when num_inference_step is power of 3
									SCREAMING_SNAKE_CASE						= (np.arange(0		,					a) * step_ratio).round()[::-1].copy().astype(a)
									timesteps += self.config.steps_offset
						elif self.config.timestep_spacing == "trailing":
									SCREAMING_SNAKE_CASE						= num_train_timesteps / self.num_inference_steps
									# creates integer timesteps by multiplying by ratio
									# casting to int to avoid issues when num_inference_step is power of 3
									SCREAMING_SNAKE_CASE						= (np.arange(a		,					0		,					-step_ratio)).round().copy().astype(a)
									timesteps -= 1
						else:
									raise ValueError(
									    f'''{self.config.timestep_spacing} is not supported. Please make sure to choose one of \'linspace\', \'leading\' or \'trailing\'.''')

						SCREAMING_SNAKE_CASE						= np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5)
						SCREAMING_SNAKE_CASE						= torch.from_numpy(np.log(a)).to(a)

						SCREAMING_SNAKE_CASE						= np.interp(a		,					np.arange(0		,					len(a))		,					a)
						SCREAMING_SNAKE_CASE						= np.concatenate([sigmas, [0.0]]).astype(np.floataa)
						SCREAMING_SNAKE_CASE						= torch.from_numpy(a).to(device=a)

						# interpolate sigmas
						SCREAMING_SNAKE_CASE						= sigmas.log().lerp(sigmas.roll(1).log()		,					0.5).exp()

						SCREAMING_SNAKE_CASE						= torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2), sigmas[-1:]])
						SCREAMING_SNAKE_CASE						= torch.cat(
						    [sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2), sigmas_interpol[-1:]])

						if str(a).startswith('mps'):
									# mps does not support float64
									SCREAMING_SNAKE_CASE						= torch.from_numpy(a).to(a		,					dtype=torch.floataa)
						else:
									SCREAMING_SNAKE_CASE						= torch.from_numpy(a).to(a)

						# interpolate timesteps
						SCREAMING_SNAKE_CASE						= self.sigma_to_t(a).to(a		,					dtype=timesteps.dtype)
						SCREAMING_SNAKE_CASE						= torch.stack((timesteps_interpol[1:-1, None], timesteps[1:, None])		,					dim=-1).flatten()

						SCREAMING_SNAKE_CASE						= torch.cat([timesteps[:1], interleaved_timesteps])

						SCREAMING_SNAKE_CASE						= None

						# for exp beta schedules, such as the one for `pipeline_shap_e.py`
						# we need an index counter
						SCREAMING_SNAKE_CASE						= defaultdict(a)






			def    SCREAMING_SNAKE_CASE__    (       self		,					a)      ->       Tuple:
						# get log sigma
						SCREAMING_SNAKE_CASE						= sigma.log()

						# get distribution
						SCREAMING_SNAKE_CASE						= log_sigma - self.log_sigmas[:, None]

						# get sigmas range
						SCREAMING_SNAKE_CASE						= dists.ge(0).cumsum(dim=0).argmax(dim=0).clamp(max=self.log_sigmas.shape[0] - 2)
						SCREAMING_SNAKE_CASE						= low_idx + 1

						SCREAMING_SNAKE_CASE						= self.log_sigmas[low_idx]
						SCREAMING_SNAKE_CASE						= self.log_sigmas[high_idx]

						# interpolate sigmas
						SCREAMING_SNAKE_CASE						= (low - log_sigma) / (low - high)
						SCREAMING_SNAKE_CASE						= w.clamp(0		,					1)

						# transform interpolation to time range
						SCREAMING_SNAKE_CASE						= (1 - w) * low_idx + w * high_idx
						SCREAMING_SNAKE_CASE						= t.view(sigma.shape)
						return t






			@property
			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Optional[int]:
						return self.sample is None






			def    SCREAMING_SNAKE_CASE__    (       self		,					a		,					a		,					a		,					a = True		,					)      ->       Union[SchedulerOutput, Tuple]:
						SCREAMING_SNAKE_CASE						= self.index_for_timestep(a)

						# advance index counter by 1
						SCREAMING_SNAKE_CASE						= timestep.cpu().item() if torch.is_tensor(a) else timestep
						self._index_counter[timestep_int] += 1

						if self.state_in_first_order:
									SCREAMING_SNAKE_CASE						= self.sigmas[step_index]
									SCREAMING_SNAKE_CASE						= self.sigmas_interpol[step_index + 1]
									SCREAMING_SNAKE_CASE						= self.sigmas[step_index + 1]
						else:
									# 2nd order / KDPM2's method
									SCREAMING_SNAKE_CASE						= self.sigmas[step_index - 1]
									SCREAMING_SNAKE_CASE						= self.sigmas_interpol[step_index]
									SCREAMING_SNAKE_CASE						= self.sigmas[step_index]

						# currently only gamma=0 is supported. This usually works best anyways.
						# We can support gamma in the future but then need to scale the timestep before
						# passing it to the model which requires a change in API
						SCREAMING_SNAKE_CASE						= 0
						SCREAMING_SNAKE_CASE						= sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

						# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
						if self.config.prediction_type == "epsilon":
									SCREAMING_SNAKE_CASE						= sigma_hat if self.state_in_first_order else sigma_interpol
									SCREAMING_SNAKE_CASE						= sample - sigma_input * model_output
						elif self.config.prediction_type == "v_prediction":
									SCREAMING_SNAKE_CASE						= sigma_hat if self.state_in_first_order else sigma_interpol
									SCREAMING_SNAKE_CASE						= model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
									    sample / (sigma_input**2 + 1)
									)
						elif self.config.prediction_type == "sample":
									raise NotImplementedError('prediction_type not implemented yet: sample')
						else:
									raise ValueError(
									    f'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`''')

						if self.state_in_first_order:
									# 2. Convert to an ODE derivative for 1st order
									SCREAMING_SNAKE_CASE						= (sample - pred_original_sample) / sigma_hat
									# 3. delta timestep
									SCREAMING_SNAKE_CASE						= sigma_interpol - sigma_hat

									# store for 2nd order step
									SCREAMING_SNAKE_CASE						= sample
						else:
									# DPM-Solver-2
									# 2. Convert to an ODE derivative for 2nd order
									SCREAMING_SNAKE_CASE						= (sample - pred_original_sample) / sigma_interpol

									# 3. delta timestep
									SCREAMING_SNAKE_CASE						= sigma_next - sigma_hat

									SCREAMING_SNAKE_CASE						= self.sample
									SCREAMING_SNAKE_CASE						= None

						SCREAMING_SNAKE_CASE						= sample + derivative * dt

						if not return_dict:
									return (prev_sample,)

						return SchedulerOutput(prev_sample=a)






			def    SCREAMING_SNAKE_CASE__    (       self		,					a		,					a		,					a		,					)      ->       torch.FloatTensor:
						# Make sure sigmas and timesteps have the same device and dtype as original_samples
						SCREAMING_SNAKE_CASE						= self.sigmas.to(device=original_samples.device		,					dtype=original_samples.dtype)
						if original_samples.device.type == "mps" and torch.is_floating_point(a):
									# mps does not support float64
									SCREAMING_SNAKE_CASE						= self.timesteps.to(original_samples.device		,					dtype=torch.floataa)
									SCREAMING_SNAKE_CASE						= timesteps.to(original_samples.device		,					dtype=torch.floataa)
						else:
									SCREAMING_SNAKE_CASE						= self.timesteps.to(original_samples.device)
									SCREAMING_SNAKE_CASE						= timesteps.to(original_samples.device)

						SCREAMING_SNAKE_CASE						= [self.index_for_timestep(a		,					a) for t in timesteps]

						SCREAMING_SNAKE_CASE						= sigmas[step_indices].flatten()
						while len(sigma.shape) < len(original_samples.shape):
									SCREAMING_SNAKE_CASE						= sigma.unsqueeze(-1)

						SCREAMING_SNAKE_CASE						= original_samples + noise * sigma
						return noisy_samples







			def __len__(       self)      ->       Optional[int]:
						return self.config.num_train_timesteps












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 		_snake_case  :






			def __init__(       self		,					a		,					)      ->       Tuple:
						SCREAMING_SNAKE_CASE						= parent
						SCREAMING_SNAKE_CASE						= 13
						SCREAMING_SNAKE_CASE						= 7
						SCREAMING_SNAKE_CASE						= True
						SCREAMING_SNAKE_CASE						= True
						SCREAMING_SNAKE_CASE						= True
						SCREAMING_SNAKE_CASE						= 99
						SCREAMING_SNAKE_CASE						= 32
						SCREAMING_SNAKE_CASE						= 2
						SCREAMING_SNAKE_CASE						= 4
						SCREAMING_SNAKE_CASE						= 37
						SCREAMING_SNAKE_CASE						= 'gelu'
						SCREAMING_SNAKE_CASE						= 0.1
						SCREAMING_SNAKE_CASE						= 0.1
						SCREAMING_SNAKE_CASE						= 512
						SCREAMING_SNAKE_CASE						= 16
						SCREAMING_SNAKE_CASE						= 2
						SCREAMING_SNAKE_CASE						= 0.02
						SCREAMING_SNAKE_CASE						= 3
						SCREAMING_SNAKE_CASE						= 4
						SCREAMING_SNAKE_CASE						= None






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       int:
						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						= 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    SCREAMING_SNAKE_CASE__    (       self)      ->       Tuple:
						(
						    (
						    SCREAMING_SNAKE_CASE
						)				,						(
						    SCREAMING_SNAKE_CASE
						)				,						(
						    SCREAMING_SNAKE_CASE
						)				,						(
						    SCREAMING_SNAKE_CASE
						)				,						(
						    SCREAMING_SNAKE_CASE
						)				,						(
						    SCREAMING_SNAKE_CASE
						)				,						
						)						= self.prepare_config_and_inputs()

						SCREAMING_SNAKE_CASE						= True
						SCREAMING_SNAKE_CASE						= floats_tensor([self.batch_size, self.seq_length, self.hidden_size])
						SCREAMING_SNAKE_CASE						= 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    SCREAMING_SNAKE_CASE__    (       self		,					a		,					a		,					a		,					a		,					a		,					a)      ->       List[str]:
						SCREAMING_SNAKE_CASE						= TFEsmModel(config=a)
						SCREAMING_SNAKE_CASE						= {'input_ids': input_ids, 'attention_mask': input_mask}
						SCREAMING_SNAKE_CASE						= model(a)

						SCREAMING_SNAKE_CASE						= [input_ids, input_mask]
						SCREAMING_SNAKE_CASE						= model(a)

						SCREAMING_SNAKE_CASE						= model(a)

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






			def    SCREAMING_SNAKE_CASE__    (       self		,					a		,					a		,					a		,					a		,					a		,					a		,					a		,					a		,					)      ->       str:
						SCREAMING_SNAKE_CASE						= True

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

						SCREAMING_SNAKE_CASE						= [input_ids, input_mask]
						SCREAMING_SNAKE_CASE						= model(a		,					encoder_hidden_states=a)

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

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






			def    SCREAMING_SNAKE_CASE__    (       self		,					a		,					a		,					a		,					a		,					a		,					a)      ->       Optional[int]:
						SCREAMING_SNAKE_CASE						= TFEsmForMaskedLM(config=a)
						SCREAMING_SNAKE_CASE						= model([input_ids, input_mask])
						self.parent.assertEqual(result.logits.shape		,					(self.batch_size, self.seq_length, self.vocab_size))






			def    SCREAMING_SNAKE_CASE__    (       self		,					a		,					a		,					a		,					a		,					a		,					a)      ->       Dict:
						SCREAMING_SNAKE_CASE						= self.num_labels
						SCREAMING_SNAKE_CASE						= TFEsmForTokenClassification(config=a)
						SCREAMING_SNAKE_CASE						= {'input_ids': input_ids, 'attention_mask': input_mask}
						SCREAMING_SNAKE_CASE						= model(a)
						self.parent.assertEqual(result.logits.shape		,					(self.batch_size, self.seq_length, self.num_labels))







			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Tuple:
						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_tf
class 		_snake_case  (  A__   ,    A__   ,    unittest.TestCase					):
			_lowercase		:    str			     =							(
			    (
			        TFEsmModel,
			        TFEsmForMaskedLM,
			        TFEsmForSequenceClassification,
			        TFEsmForTokenClassification,
			    )
			    if is_tf_available()
			    else ()
			)
			_lowercase		:    int			     =							(
			    {
			        '''feature-extraction''': TFEsmModel,
			        '''fill-mask''': TFEsmForMaskedLM,
			        '''text-classification''': TFEsmForSequenceClassification,
			        '''token-classification''': TFEsmForTokenClassification,
			        '''zero-shot''': TFEsmForSequenceClassification,
			    }
			    if is_tf_available()
			    else {}
			)
			_lowercase		:    str			     =							False
			_lowercase		:    Tuple			     =							False






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Tuple:
						SCREAMING_SNAKE_CASE						= TFEsmModelTester(self)
						SCREAMING_SNAKE_CASE						= ConfigTester(self		,					config_class=a		,					hidden_size=37)






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       str:
						self.config_tester.run_common_tests()






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Optional[int]:
						SCREAMING_SNAKE_CASE						= self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*a)






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Tuple:
						SCREAMING_SNAKE_CASE						= self.model_tester.prepare_config_and_inputs_for_decoder()
						self.model_tester.create_and_check_model_as_decoder(*a)






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Optional[Any]:
						SCREAMING_SNAKE_CASE						= self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_masked_lm(*a)






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Optional[int]:
						SCREAMING_SNAKE_CASE						= self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_token_classification(*a)






			@slow
			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Optional[Any]:
						for model_name in TF_ESM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									SCREAMING_SNAKE_CASE						= TFEsmModel.from_pretrained(a)
									self.assertIsNotNone(a)






			@unittest.skip('Protein models do not support embedding resizing.')
			def    SCREAMING_SNAKE_CASE__    (       self)      ->       List[Any]:
						pass






			@unittest.skip('Protein models do not support embedding resizing.')
			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Dict:
						pass







			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Any:
						SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						= self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
									SCREAMING_SNAKE_CASE						= 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
												SCREAMING_SNAKE_CASE						= model.get_bias()
												assert isinstance(a		,					a)
												for k, v in name.items():
															assert isinstance(a		,					tf.Variable)
									else:
												SCREAMING_SNAKE_CASE						= model.get_output_embeddings()
												assert x is None
												SCREAMING_SNAKE_CASE						= model.get_bias()
												assert name is None



@require_tf
class 		_snake_case  (  unittest.TestCase					):






			@slow
			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Optional[Any]:
						SCREAMING_SNAKE_CASE						= TFEsmForMaskedLM.from_pretrained('facebook/esm2_t6_8M_UR50D')

						SCREAMING_SNAKE_CASE						= tf.constant([[0, 1, 2, 3, 4, 5]])
						SCREAMING_SNAKE_CASE						= model(a)[0]
						SCREAMING_SNAKE_CASE						= [1, 6, 33]
						self.assertEqual(list(output.numpy().shape)		,					a)
						# compare the actual values for a slice.
						SCREAMING_SNAKE_CASE						= tf.constant(
						    [
						        [
						            [8.92_15_18, -10.58_98_14, -6.4_67_13_07],
						            [-6.3_96_71_56, -13.91_13_77, -1.1_21_19_15],
						            [-7.78_12_47, -13.95_15_57, -3.74_05_92],
						        ]
						    ])
						self.assertTrue(numpy.allclose(output[:, :3, :3].numpy()		,					expected_slice.numpy()		,					atol=1E-2))







			@slow
			def    SCREAMING_SNAKE_CASE__    (       self)      ->       str:
						SCREAMING_SNAKE_CASE						= TFEsmModel.from_pretrained('facebook/esm2_t6_8M_UR50D')

						SCREAMING_SNAKE_CASE						= tf.constant([[0, 6, 4, 13, 5, 4, 16, 12, 11, 7, 2]])
						SCREAMING_SNAKE_CASE						= model(a)[0]
						# compare the actual values for a slice.
						SCREAMING_SNAKE_CASE						= tf.constant(
						    [
						        [
						            [0.14_44_30_92, 0.54_12_53_27, 0.3_24_77_39],
						            [0.30_34_04_84, 0.00_52_66_76, 0.31_07_77_22],
						            [0.32_27_80_43, -0.24_98_70_96, 0.3_41_46_28],
						        ]
						    ])
						self.assertTrue(numpy.allclose(output[:, :3, :3].numpy()		,					expected_slice.numpy()		,					atol=1E-4))




