
"""simple docstring"""


import torch

from diffusers import DDPMParallelScheduler

from .test_schedulers import SchedulerCommonTest







class 				lowerCAmelCase_				(      _lowercase    ):



						'''simple docstring'''


						_lowerCamelCase:    Any      =    (DDPMParallelScheduler,)


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Dict  ,**A_     :      int      )     ->   Dict:
							A						   =   {
							    'num_train_timesteps': 1000,
							    'beta_start': 0.00_01,
							    'beta_end': 0.02,
							    'beta_schedule': 'linear',
							    'variance_type': 'fixed_small',
							    'clip_sample': True,
							}

							config.update(**A_      )
							return config


						def 						_SCREAMING_SNAKE_CASE      ( self     :      List[str]      )     ->   List[str]:
							for timesteps in [1, 5, 100, 1000]:
								self.check_over_configs(num_train_timesteps=A_      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      List[str]      )     ->   str:
							for beta_start, beta_end in zip([0.00_01, 0.0_01, 0.01, 0.1]  ,[0.0_02, 0.02, 0.2, 2]      ):
								self.check_over_configs(beta_start=A_  ,beta_end=A_      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      List[str]      )     ->   Optional[int]:
							for schedule in ["linear", "squaredcos_cap_v2"]:
								self.check_over_configs(beta_schedule=A_      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      int      )     ->   Optional[Any]:
							for variance in ["fixed_small", "fixed_large", "other"]:
								self.check_over_configs(variance_type=A_      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Dict      )     ->   List[Any]:
							for clip_sample in [True, False]:
								self.check_over_configs(clip_sample=A_      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Any      )     ->   str:
							self.check_over_configs(thresholding=A_      )
							for threshold in [0.5, 1.0, 2.0]:
								for prediction_type in ["epsilon", "sample", "v_prediction"]:
									self.check_over_configs(
									    thresholding=A_  ,prediction_type=A_  ,sample_max_value=A_  ,)


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Tuple      )     ->   List[Any]:
							for prediction_type in ["epsilon", "sample", "v_prediction"]:
								self.check_over_configs(prediction_type=A_      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      str      )     ->   List[str]:
							for t in [0, 500, 999]:
								self.check_over_forward(time_step=A_      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Optional[Any]      )     ->   Optional[Any]:
							A						   =   self.scheduler_classes[0]
							A						   =   self.get_scheduler_config()
							A						   =   scheduler_class(**A_      )

							assert torch.sum(torch.abs(scheduler._get_variance(0      ) - 0.0      )      ) < 1e-5
							assert torch.sum(torch.abs(scheduler._get_variance(487      ) - 0.0_09_79      )      ) < 1e-5
							assert torch.sum(torch.abs(scheduler._get_variance(999      ) - 0.02      )      ) < 1e-5


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Tuple      )     ->   str:
							A						   =   self.scheduler_classes[0]
							A						   =   self.get_scheduler_config()
							A						   =   scheduler_class(**A_      )

							A						   =   len(A_      )

							A						   =   self.dummy_model()
							A						   =   self.dummy_sample_deter
							A						   =   self.dummy_sample_deter + 0.1
							A						   =   self.dummy_sample_deter - 0.1

							A						   =   samplea.shape[0]
							A						   =   torch.stack([samplea, samplea, samplea]  ,dim=0      )
							A						   =   torch.arange(A_      )[0:3, None].repeat(1  ,A_      )

							A						   =   model(samples.flatten(0  ,1      )  ,timesteps.flatten(0  ,1      )      )
							A						   =   scheduler.batch_step_no_noise(A_  ,timesteps.flatten(0  ,1      )  ,samples.flatten(0  ,1      )      )

							A						   =   torch.sum(torch.abs(A_      )      )
							A						   =   torch.mean(torch.abs(A_      )      )

							assert abs(result_sum.item() - 11_53.18_33      ) < 1e-2
							assert abs(result_mean.item() - 0.50_05      ) < 1e-3


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Union[str, Any]      )     ->   Any:
							A						   =   self.scheduler_classes[0]
							A						   =   self.get_scheduler_config()
							A						   =   scheduler_class(**A_      )

							A						   =   len(A_      )

							A						   =   self.dummy_model()
							A						   =   self.dummy_sample_deter
							A						   =   torch.manual_seed(0      )

							for t in reversed(range(A_      )      ):
								# 1. predict noise residual
								A						   =   model(A_  ,A_      )

								# 2. predict previous mean of sample x_t-1
								A						   =   scheduler.step(A_  ,A_  ,A_  ,generator=A_      ).prev_sample

								A						   =   pred_prev_sample

							A						   =   torch.sum(torch.abs(A_      )      )
							A						   =   torch.mean(torch.abs(A_      )      )

							assert abs(result_sum.item() - 2_58.96_06      ) < 1e-2
							assert abs(result_mean.item() - 0.33_72      ) < 1e-3


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Any      )     ->   Dict:
							A						   =   self.scheduler_classes[0]
							A						   =   self.get_scheduler_config(prediction_type='v_prediction'      )
							A						   =   scheduler_class(**A_      )

							A						   =   len(A_      )

							A						   =   self.dummy_model()
							A						   =   self.dummy_sample_deter
							A						   =   torch.manual_seed(0      )

							for t in reversed(range(A_      )      ):
								# 1. predict noise residual
								A						   =   model(A_  ,A_      )

								# 2. predict previous mean of sample x_t-1
								A						   =   scheduler.step(A_  ,A_  ,A_  ,generator=A_      ).prev_sample

								A						   =   pred_prev_sample

							A						   =   torch.sum(torch.abs(A_      )      )
							A						   =   torch.mean(torch.abs(A_      )      )

							assert abs(result_sum.item() - 2_02.02_96      ) < 1e-2
							assert abs(result_mean.item() - 0.26_31      ) < 1e-3


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Any      )     ->   Union[str, Any]:
							A						   =   self.scheduler_classes[0]
							A						   =   self.get_scheduler_config()
							A						   =   scheduler_class(**A_      )

							A						   =   [100, 87, 50, 1, 0]

							scheduler.set_timesteps(timesteps=A_      )

							A						   =   scheduler.timesteps

							for i, timestep in enumerate(A_      ):
								if i == len(A_      ) - 1:
									A						   =   -1
								else:
									A						   =   timesteps[i + 1]

								A						   =   scheduler.previous_timestep(A_      )
								A						   =   prev_t.item()

								self.assertEqual(A_  ,A_      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Dict      )     ->   Union[str, Any]:
							A						   =   self.scheduler_classes[0]
							A						   =   self.get_scheduler_config()
							A						   =   scheduler_class(**A_      )

							A						   =   [100, 87, 50, 51, 0]

							with self.assertRaises(A_  ,msg='`custom_timesteps` must be in descending order.'      ):
								scheduler.set_timesteps(timesteps=A_      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Dict      )     ->   Optional[Any]:
							A						   =   self.scheduler_classes[0]
							A						   =   self.get_scheduler_config()
							A						   =   scheduler_class(**A_      )

							A						   =   [100, 87, 50, 1, 0]
							A						   =   len(A_      )

							with self.assertRaises(A_  ,msg='Can only pass one of `num_inference_steps` or `custom_timesteps`.'      ):
								scheduler.set_timesteps(num_inference_steps=A_  ,timesteps=A_      )




						def 						_SCREAMING_SNAKE_CASE      ( self     :      Dict      )     ->   Any:
							A						   =   self.scheduler_classes[0]
							A						   =   self.get_scheduler_config()
							A						   =   scheduler_class(**A_      )

							A						   =   [scheduler.config.num_train_timesteps]

							with self.assertRaises(
							    A_  ,msg='`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}'  ,):
								scheduler.set_timesteps(timesteps=A_      )

"""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     :      List[Any]  ,A_     :      Optional[Any]  ,)     ->   Optional[int]:
							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 						_SCREAMING_SNAKE_CASE      ( self     :      Union[str, Any]      )     ->   Union[str, Any]:
							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 						_SCREAMING_SNAKE_CASE      ( self     :      Union[str, Any]      )     ->   Dict:
							(
							    (
							    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 						_SCREAMING_SNAKE_CASE      ( self     :      Optional[int]  ,A_     :      int  ,A_     :      List[str]  ,A_     :      Optional[int]  ,A_     :      List[Any]  ,A_     :      Any  ,A_     :      Any      )     ->   Dict:
							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 						_SCREAMING_SNAKE_CASE      ( self     :      Union[str, Any]  ,A_     :      Any  ,A_     :      Union[str, Any]  ,A_     :      Tuple  ,A_     :      int  ,A_     :      List[Any]  ,A_     :      Optional[int]  ,A_     :      Optional[Any]  ,A_     :      List[str]  ,)     ->   Optional[int]:
							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 						_SCREAMING_SNAKE_CASE      ( self     :      str  ,A_     :      List[Any]  ,A_     :      List[Any]  ,A_     :      Optional[Any]  ,A_     :      Optional[int]  ,A_     :      Optional[Any]  ,A_     :      List[Any]      )     ->   Dict:
							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 						_SCREAMING_SNAKE_CASE      ( self     :      Any  ,A_     :      Optional[Any]  ,A_     :      str  ,A_     :      List[Any]  ,A_     :      int  ,A_     :      Tuple  ,A_     :      Optional[int]      )     ->   Union[str, Any]:
							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 						_SCREAMING_SNAKE_CASE      ( self     :      Any      )     ->   List[Any]:
							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_				(      _lowercase		,					_lowercase		,					unittest.TestCase    ):



						'''simple docstring'''


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


						def 						_SCREAMING_SNAKE_CASE      ( self     :      List[Any]      )     ->   int:
							A						   =   TFEsmModelTester(self      )
							A						   =   ConfigTester(self  ,config_class=A_  ,hidden_size=37      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Any      )     ->   Optional[int]:
							self.config_tester.run_common_tests()


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Union[str, Any]      )     ->   List[str]:
							A						   =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*A_      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      List[Any]      )     ->   List[Any]:
							A						   =   self.model_tester.prepare_config_and_inputs_for_decoder()
							self.model_tester.create_and_check_model_as_decoder(*A_      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      str      )     ->   Dict:
							A						   =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_masked_lm(*A_      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      List[Any]      )     ->   List[Any]:
							A						   =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_token_classification(*A_      )


						@slow
						def 						_SCREAMING_SNAKE_CASE      ( self     :      Tuple      )     ->   Tuple:
							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 						_SCREAMING_SNAKE_CASE      ( self     :      Optional[int]      )     ->   Optional[int]:
							pass


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




						def 						_SCREAMING_SNAKE_CASE      ( self     :      str      )     ->   Dict:
							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 						_SCREAMING_SNAKE_CASE      ( self     :      Optional[Any]      )     ->   Tuple:
							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_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     :      Optional[Any]      )     ->   int:
							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(
							    [
							        [
							            [0.14_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      )      )