


"""simple docstring"""




import importlib
import math
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Tuple, Union

import flax
import jax.numpy as jnp

from ..utils import BaseOutput


lowercase_			     =  "scheduler_config.json"
class 					__lowerCAmelCase					(   __SCREAMING_SNAKE_CASE       ):


		'''simple docstring'''



		__UpperCAmelCase		:			str										=				1
		__UpperCAmelCase		:			Dict										=				2
		__UpperCAmelCase		:			int										=				3
		__UpperCAmelCase		:			Tuple										=				4
		__UpperCAmelCase		:			Union[str, Any]										=				5







@dataclass
class 					__lowerCAmelCase					(   __SCREAMING_SNAKE_CASE       ):


		'''simple docstring'''



		__UpperCAmelCase		:			jnp.ndarray







class 					__lowerCAmelCase					:


		'''simple docstring'''



		__UpperCAmelCase		:			int										=				SCHEDULER_CONFIG_NAME
		__UpperCAmelCase		:			str										=				['dtype']
		__UpperCAmelCase		:			List[str]										=				[]
		__UpperCAmelCase		:			Any										=				True

		@classmethod
		def 			__UpperCAmelCase       (  cls							, _a = None							, _a = None							, _a=False							, **_a							, ):
				__a      ,     __a           =			cls.load_config(
				    pretrained_model_name_or_path=_a							, subfolder=_a							, return_unused_kwargs=_a							, **_a							, )
				__a      ,     __a           =			cls.from_config(_a							, return_unused_kwargs=_a							, **_a      )

				if hasattr(_a							, '''create_state'''      ) and getattr(_a							, '''has_state'''							, _a      ):
						__a           =			scheduler.create_state()

				if return_unused_kwargs:
						return scheduler, state, unused_kwargs

				return scheduler, state

		def 			__UpperCAmelCase       (  self							, _a							, _a = False							, **_a      ):
				self.save_config(save_directory=_a							, push_to_hub=_a							, **_a      )

		@property
		def 			__UpperCAmelCase       (  self      ):
				return self._get_compatibles()





		@classmethod
		def 			__UpperCAmelCase       (  cls      ):
				__a           =			list(set([cls.__name__] + cls._compatibles      )      )
				__a           =			importlib.import_module(__name__.split('''.'''      )[0]      )
				__a           =			[
				    getattr(_a							, _a      ) for c in compatible_classes_str if hasattr(_a							, _a      )
				]
				return compatible_classes







def        lowercase							(		lowerCAmelCase__ :     jnp.ndarray    ,					lowerCAmelCase__ :     Tuple[int]			) ->      jnp.ndarray:
		assert len(lowerCAmelCase__			) >= x.ndim
		return jnp.broadcast_to(x.reshape(x.shape + (1,) * (len(lowerCAmelCase__			) - x.ndim)			)    ,					lowerCAmelCase__			)

def        lowercase							(		lowerCAmelCase__ :     int    ,					lowerCAmelCase__ :     str=0.9_99    ,					lowerCAmelCase__ :     List[str]=jnp.floataa			) ->      jnp.ndarray:

		def alpha_bar(lowerCAmelCase__ :     str			):
				return math.cos((time_step + 0.0_08) / 1.0_08 * math.pi / 2			) ** 2

		__a           =			[]
		for i in range(lowerCAmelCase__			):
				__a           =			i / num_diffusion_timesteps
				__a           =			(i + 1) / num_diffusion_timesteps
				betas.append(min(1 - alpha_bar(lowerCAmelCase__			) / alpha_bar(lowerCAmelCase__			)    ,					lowerCAmelCase__			)			)
		return jnp.array(lowerCAmelCase__    ,					dtype=lowerCAmelCase__			)
@flax.struct.dataclass
class 					__lowerCAmelCase					:


		'''simple docstring'''



		__UpperCAmelCase		:			jnp.ndarray
		__UpperCAmelCase		:			jnp.ndarray
		__UpperCAmelCase		:			jnp.ndarray

		@classmethod
		def 			__UpperCAmelCase       (  cls							, _a      ):
				__a           =			scheduler.config

				if config.trained_betas is not None:
						__a           =			jnp.asarray(config.trained_betas							, dtype=scheduler.dtype      )
				elif config.beta_schedule == "linear":
						__a           =			jnp.linspace(config.beta_start							, config.beta_end							, config.num_train_timesteps							, dtype=scheduler.dtype      )
				elif config.beta_schedule == "scaled_linear":
						# this schedule is very specific to the latent diffusion model.
						__a           =			(
						    jnp.linspace(
						        config.beta_start**0.5							, config.beta_end**0.5							, config.num_train_timesteps							, dtype=scheduler.dtype      )
						    ** 2
						)
				elif config.beta_schedule == "squaredcos_cap_v2":
						# Glide cosine schedule
						__a           =			betas_for_alpha_bar(config.num_train_timesteps							, dtype=scheduler.dtype      )
				else:
						raise NotImplementedError(
						    f'''beta_schedule {config.beta_schedule} is not implemented for scheduler {scheduler.__class__.__name__}'''      )

				__a           =			1.0 - betas

				__a           =			jnp.cumprod(_a							, axis=0      )

				return cls(
				    alphas=_a							, betas=_a							, alphas_cumprod=_a							, )







def        lowercase							(		lowerCAmelCase__ :     CommonSchedulerState    ,					lowerCAmelCase__ :     jnp.ndarray    ,					lowerCAmelCase__ :     jnp.ndarray    ,					lowerCAmelCase__ :     jnp.ndarray			) ->      Optional[int]:
		__a           =			state.alphas_cumprod

		__a           =			alphas_cumprod[timesteps] ** 0.5
		__a           =			sqrt_alpha_prod.flatten()
		__a           =			broadcast_to_shape_from_left(lowerCAmelCase__    ,					original_samples.shape			)

		__a           =			(1 - alphas_cumprod[timesteps]) ** 0.5
		__a           =			sqrt_one_minus_alpha_prod.flatten()
		__a           =			broadcast_to_shape_from_left(lowerCAmelCase__    ,					original_samples.shape			)

		return sqrt_alpha_prod, sqrt_one_minus_alpha_prod

def        lowercase							(		lowerCAmelCase__ :     CommonSchedulerState    ,					lowerCAmelCase__ :     jnp.ndarray    ,					lowerCAmelCase__ :     jnp.ndarray    ,					lowerCAmelCase__ :     jnp.ndarray			) ->      Dict:
		__a      ,     __a           =			get_sqrt_alpha_prod(lowerCAmelCase__    ,					lowerCAmelCase__    ,					lowerCAmelCase__    ,					lowerCAmelCase__			)
		__a           =			sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
		return noisy_samples




def        lowercase							(		lowerCAmelCase__ :     CommonSchedulerState    ,					lowerCAmelCase__ :     jnp.ndarray    ,					lowerCAmelCase__ :     jnp.ndarray    ,					lowerCAmelCase__ :     jnp.ndarray			) ->      List[Any]:
		__a      ,     __a           =			get_sqrt_alpha_prod(lowerCAmelCase__    ,					lowerCAmelCase__    ,					lowerCAmelCase__    ,					lowerCAmelCase__			)
		__a           =			sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample
		return velocity









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.models.esm.modeling_esmfold import EsmForProteinFolding

class 				UpperCAmelCase_		:
							"""simple docstring"""







							def __init__(			self       ,							_a       ,							_a=1_3       ,							_a=7       ,							_a=False       ,							_a=True       ,							_a=False       ,							_a=False       ,							_a=1_9       ,							_a=3_2       ,							_a=5       ,							_a=4       ,							_a=3_7       ,							_a="gelu"       ,							_a=0.1       ,							_a=0.1       ,							_a=5_1_2       ,							_a=1_6       ,							_a=2       ,							_a=0.02       ,							_a=3       ,							_a=4       ,							_a=None       ,							)    ->    Union[str, Any]:
												_a	:    Optional[Any]         =		parent
												_a	:    Union[str, Any]         =		batch_size
												_a	:    List[Any]         =		seq_length
												_a	:    Dict         =		is_training
												_a	:    int         =		use_input_mask
												_a	:    str         =		use_token_type_ids
												_a	:    Any         =		use_labels
												_a	:    List[Any]         =		vocab_size
												_a	:    Any         =		hidden_size
												_a	:    int         =		num_hidden_layers
												_a	:    str         =		num_attention_heads
												_a	:    Dict         =		intermediate_size
												_a	:    List[str]         =		hidden_act
												_a	:    Optional[Any]         =		hidden_dropout_prob
												_a	:    Optional[Any]         =		attention_probs_dropout_prob
												_a	:    int         =		max_position_embeddings
												_a	:    Tuple         =		type_vocab_size
												_a	:    str         =		type_sequence_label_size
												_a	:    Any         =		initializer_range
												_a	:    Union[str, Any]         =		num_labels
												_a	:    Dict         =		num_choices
												_a	:    Union[str, Any]         =		scope


							def 				__lowercase				(			self       )    ->    List[Any]:
												_a	:    str         =		ids_tensor([self.batch_size, self.seq_length]       ,							self.vocab_size       )

												_a	:    Dict         =		None
												if self.use_input_mask:
																	_a	:    int         =		random_attention_mask([self.batch_size, self.seq_length]       )

												_a	:    List[Any]         =		None
												_a	:    Tuple         =		None
												_a	:    Any         =		None
												if self.use_labels:
																	_a	:    Tuple         =		ids_tensor([self.batch_size]       ,							self.type_sequence_label_size       )
																	_a	:    Union[str, Any]         =		ids_tensor([self.batch_size, self.seq_length]       ,							self.num_labels       )
																	_a	:    List[Any]         =		ids_tensor([self.batch_size]       ,							self.num_choices       )

												_a	:    str         =		self.get_config()

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


							def 				__lowercase				(			self       )    ->    str:
												_a	:    Optional[int]         =		EsmConfig(
												    vocab_size=3_3       ,							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       ,							is_folding_model=_a       ,							esmfold_config={'''trunk''': {'''num_blocks''': 2}, '''fp16_esm''': False}       ,							)
												return config


							def 				__lowercase				(			self       ,							_a       ,							_a       ,							_a       ,							_a       ,							_a       ,							_a       )    ->    str:
												_a	:    Union[str, Any]         =		EsmForProteinFolding(config=_a       ).float()
												model.to(_a       )
												model.eval()
												_a	:    str         =		model(_a       ,							attention_mask=_a       )
												_a	:    Union[str, Any]         =		model(_a       )
												_a	:    Optional[int]         =		model(_a       )

												self.parent.assertEqual(result.positions.shape       ,							(8, self.batch_size, self.seq_length, 1_4, 3)       )
												self.parent.assertEqual(result.angles.shape       ,							(8, self.batch_size, self.seq_length, 7, 2)       )




							def 				__lowercase				(			self       )    ->    str:
												_a	:    List[str]         =		self.prepare_config_and_inputs()
												(
												    (
												    _a
												)      ,					(
												    _a
												)      ,					(
												    _a
												)      ,					(
												    _a
												)      ,					(
												    _a
												)      ,					(
												    _a
												)      ,					
												)	:    Optional[Any]         =		config_and_inputs
												_a	:    List[Any]         =		{'''input_ids''': input_ids, '''attention_mask''': input_mask}
												return config, inputs_dict



@require_torch
class 				UpperCAmelCase_		(			__lowercase				,       __lowercase				,       unittest.TestCase   ):
							"""simple docstring"""







							UpperCAmelCase__       :						Any	    =    False

							UpperCAmelCase__       :						Any	    =    (EsmForProteinFolding,) if is_torch_available() else ()
							UpperCAmelCase__       :						Union[str, Any]	    =    ()
							UpperCAmelCase__       :						int	    =    {} if is_torch_available() else {}
							UpperCAmelCase__       :						Optional[int]	    =    False


							def 				__lowercase				(			self       )    ->    List[Any]:
												_a	:    Optional[int]         =		EsmFoldModelTester(self       )
												_a	:    Dict         =		ConfigTester(self       ,							config_class=_a       ,							hidden_size=3_7       )


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


							def 				__lowercase				(			self       )    ->    str:
												_a	:    str         =		self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_model(*_a       )


							@unittest.skip('''Does not support attention outputs'''       )
							def 				__lowercase				(			self       )    ->    int:
												pass


							@unittest.skip
							def 				__lowercase				(			self       )    ->    List[str]:
												pass


							@unittest.skip('''Esm does not support embedding resizing'''       )
							def 				__lowercase				(			self       )    ->    int:
												pass


							@unittest.skip('''Esm does not support embedding resizing'''       )
							def 				__lowercase				(			self       )    ->    Optional[Any]:
												pass


							@unittest.skip('''ESMFold does not support passing input embeds!'''       )
							def 				__lowercase				(			self       )    ->    int:
												pass


							@unittest.skip('''ESMFold does not support head pruning.'''       )
							def 				__lowercase				(			self       )    ->    str:
												pass


							@unittest.skip('''ESMFold does not support head pruning.'''       )
							def 				__lowercase				(			self       )    ->    Optional[Any]:
												pass


							@unittest.skip('''ESMFold does not support head pruning.'''       )
							def 				__lowercase				(			self       )    ->    Optional[Any]:
												pass


							@unittest.skip('''ESMFold does not support head pruning.'''       )
							def 				__lowercase				(			self       )    ->    Any:
												pass


							@unittest.skip('''ESMFold does not support head pruning.'''       )
							def 				__lowercase				(			self       )    ->    Tuple:
												pass


							@unittest.skip('''ESMFold does not output hidden states in the normal way.'''       )
							def 				__lowercase				(			self       )    ->    Tuple:
												pass


							@unittest.skip('''ESMfold does not output hidden states in the normal way.'''       )
							def 				__lowercase				(			self       )    ->    Tuple:
												pass


							@unittest.skip('''ESMFold only has one output format.'''       )
							def 				__lowercase				(			self       )    ->    Tuple:
												pass


							@unittest.skip('''This test doesn\'t work for ESMFold and doesn\'t test core functionality'''       )
							def 				__lowercase				(			self       )    ->    Dict:
												pass


							@unittest.skip('''ESMFold does not support input chunking.'''       )
							def 				__lowercase				(			self       )    ->    Tuple:
												pass


							@unittest.skip('''ESMFold doesn\'t respect you and it certainly doesn\'t respect your initialization arguments.'''       )
							def 				__lowercase				(			self       )    ->    Optional[Any]:
												pass


							@unittest.skip('''ESMFold doesn\'t support torchscript compilation.'''       )
							def 				__lowercase				(			self       )    ->    List[str]:
												pass


							@unittest.skip('''ESMFold doesn\'t support torchscript compilation.'''       )
							def 				__lowercase				(			self       )    ->    List[str]:
												pass


							@unittest.skip('''ESMFold doesn\'t support torchscript compilation.'''       )
							def 				__lowercase				(			self       )    ->    List[Any]:
												pass


							@unittest.skip('''ESMFold doesn\'t support data parallel.'''       )
							def 				__lowercase				(			self       )    ->    Union[str, Any]:
												pass




							@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.'''       )
							def 				__lowercase				(			self       )    ->    Optional[Any]:
												pass



@require_torch
class 				UpperCAmelCase_		(			__lowercase   ):
							"""simple docstring"""







							@slow
							def 				__lowercase				(			self       )    ->    Optional[int]:
												_a	:    Dict         =		EsmForProteinFolding.from_pretrained('''facebook/esmfold_v1'''       ).float()
												model.eval()
												_a	:    Tuple         =		torch.tensor([[0, 6, 4, 1_3, 5, 4, 1_6, 1_2, 1_1, 7, 2]]       )
												_a	:    Optional[Any]         =		model(_a       )['''positions''']
												_a	:    Union[str, Any]         =		torch.tensor([2.5828, 0.7993, -10.9334]       ,							dtype=torch.floataa       )
												self.assertTrue(torch.allclose(position_outputs[0, 0, 0, 0]       ,							_a       ,							atol=1e-4       )       )



