



import os
import sys
from pathlib import Path

import torch

from transformers.testing_utils import TestCasePlus, execute_subprocess_async, require_torch_multi_gpu
from utils import load_json


_lowercase	:   Optional[Any]    	=torch.cuda.is_available()
_lowercase	:   Dict    	=[""" Sam ate lunch today.""", """Sams lunch ingredients."""]
_lowercase	:   str    	=["""A very interesting story about what I ate for lunch.""", """Avocado, celery, turkey, coffee"""]
_lowercase	:   Optional[Any]    	={
    """max_tokens_per_batch""": None,
    """supervise_forward""": True,
    """normalize_hidden""": True,
    """label_smoothing""": 0.2,
    """eval_max_gen_length""": None,
    """eval_beams""": 1,
    """val_metric""": """loss""",
    """save_top_k""": 1,
    """adafactor""": True,
    """early_stopping_patience""": 2,
    """logger_name""": """default""",
    """length_penalty""": 0.5,
    """cache_dir""": """""",
    """task""": """summarization""",
    """num_workers""": 2,
    """alpha_hid""": 0,
    """freeze_embeds""": True,
    """enc_only""": False,
    """tgt_suffix""": """""",
    """resume_from_checkpoint""": None,
    """sortish_sampler""": True,
    """student_decoder_layers""": 1,
    """val_check_interval""": 1.0,
    """output_dir""": """""",
    """fp16""": False,  # TODO(SS): set this to CUDA_AVAILABLE if ci installs apex or start using native amp
    """no_teacher""": False,
    """fp16_opt_level""": """O1""",
    """gpus""": 1 if CUDA_AVAILABLE else 0,
    """n_tpu_cores""": 0,
    """max_grad_norm""": 1.0,
    """do_train""": True,
    """do_predict""": True,
    """accumulate_grad_batches""": 1,
    """server_ip""": """""",
    """server_port""": """""",
    """seed""": 42,
    """model_name_or_path""": """sshleifer/bart-tiny-random""",
    """config_name""": """""",
    """tokenizer_name""": """facebook/bart-large""",
    """do_lower_case""": False,
    """learning_rate""": 0.3,
    """lr_scheduler""": """linear""",
    """weight_decay""": 0.0,
    """adam_epsilon""": 1E-08,
    """warmup_steps""": 0,
    """max_epochs""": 1,
    """train_batch_size""": 2,
    """eval_batch_size""": 2,
    """max_source_length""": 12,
    """max_target_length""": 12,
    """val_max_target_length""": 12,
    """test_max_target_length""": 12,
    """fast_dev_run""": False,
    """no_cache""": False,
    """n_train""": -1,
    """n_val""": -1,
    """n_test""": -1,
    """student_encoder_layers""": 1,
    """freeze_encoder""": False,
    """auto_scale_batch_size""": False,
    """overwrite_output_dir""": False,
    """student""": None,
}

def 			lowerCAmelCase_  (						_lowercase       :     Optional[Any]		,     _lowercase       :     Union[str, Any])							-> Any:







			"""simple docstring"""
			a__     :		str		 =						"""\n""".join(_lowercase)
			Path(_lowercase).open("""w""").writelines(_lowercase)

def 			lowerCAmelCase_  (						_lowercase       :     List[Any])							-> Optional[int]:







			"""simple docstring"""
			for split in ["train", "val", "test"]:
						_dump_articles(os.path.join(_lowercase		,     F'''{split}.source''')		,     _lowercase)
						_dump_articles(os.path.join(_lowercase		,     F'''{split}.target''')		,     _lowercase)
			return tmp_dir





class      snake_case__       (lowerCAmelCase_	):







						"""simple docstring"""




						@classmethod
						def 							SCREAMING_SNAKE_CASE__(     cls						)      ->   Union[str, Any]:
									"""simple docstring"""




									return cls

						@require_torch_multi_gpu
						def 							SCREAMING_SNAKE_CASE__(     self						)      ->   Union[str, Any]:
									"""simple docstring"""




									a__     :		Union[str, Any]		 =						{
									    """no_teacher""": True,
									    """freeze_encoder""": True,
									    """gpus""": 2,
									    """overwrite_output_dir""": True,
									    """sortish_sampler""": True,
									}
									self._test_distiller_cli_fork(__SCREAMING_SNAKE_CASE      ,     check_contents=__SCREAMING_SNAKE_CASE						)

						def 							SCREAMING_SNAKE_CASE__(     self      ,     __lowercase      ,     __lowercase=True						)      ->   List[str]:
									"""simple docstring"""




									a__     :		Optional[Any]		 =						{
									    """label_smoothing""": 0.0,
									    """early_stopping_patience""": -1,
									    """train_batch_size""": 1,
									    """eval_batch_size""": 2,
									    """max_epochs""": 2,
									    """alpha_mlm""": 0.2,
									    """alpha_ce""": 0.8,
									    """do_predict""": True,
									    """model_name_or_path""": """sshleifer/tinier_bart""",
									    """teacher""": CHEAP_ARGS["""model_name_or_path"""],
									    """val_check_interval""": 0.5,
									}
									default_updates.update(__SCREAMING_SNAKE_CASE						)
									a__     :		Union[str, Any]		 =						CHEAP_ARGS.copy()
									a__     :		str		 =						make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()						)
									a__     :		str		 =						self.get_auto_remove_tmp_dir()
									args_d.update(data_dir=__SCREAMING_SNAKE_CASE      ,     output_dir=__SCREAMING_SNAKE_CASE      ,     **__SCREAMING_SNAKE_CASE						)

									def convert(__lowercase      ,     __lowercase						):
												if k in ["tgt_suffix", "server_ip", "server_port", "out", "n_tpu_cores"]:
															return ""
												if v is False or v is None:
															return ""
												if v is True:  # or len(str(v))==0:
															return F'''--{k}'''
												return F'''--{k}={v}'''

									a__     :		List[str]		 =						[x for x in (convert(__SCREAMING_SNAKE_CASE      ,     __SCREAMING_SNAKE_CASE						) for k, v in args_d.items()) if len(__SCREAMING_SNAKE_CASE						)]
									a__     :		Optional[Any]		 =						[sys.executable, F'''{self.test_file_dir}/distillation.py'''] + cli_args
									execute_subprocess_async(__SCREAMING_SNAKE_CASE      ,     env=self.get_env()						)

									a__     :		Optional[int]		 =						os.listdir(__SCREAMING_SNAKE_CASE						)
									a__     :		Union[str, Any]		 =						{os.path.basename(__SCREAMING_SNAKE_CASE						) for p in contents}
									a__     :		List[str]		 =						[p for p in contents if p.endswith("""ckpt"""						)]
									assert len(__SCREAMING_SNAKE_CASE						) > 0

									self.assertIn("""test_generations.txt"""      ,     __SCREAMING_SNAKE_CASE						)
									self.assertIn("""test_results.txt"""      ,     __SCREAMING_SNAKE_CASE						)

									# get the following from the module, (we don't have access to `model` here)
									a__     :		List[Any]		 =						os.path.join(__SCREAMING_SNAKE_CASE      ,     """metrics.json"""						)
									a__     :		Any		 =						"""rouge2"""

									a__     :		List[Any]		 =						load_json(__SCREAMING_SNAKE_CASE						)
									# {'test': [{'test_avg_loss': 10.63731575012207, 'test_avg_rouge1': 0.0, 'test_avg_rouge2': 0.0, 'test_avg_rougeL': 0.0, 'test_avg_gen_time': 0.1822289228439331, 'test_avg_gen_len': 142.0, 'step_count': 1}]}
									print(__SCREAMING_SNAKE_CASE						)
									a__     :		Optional[Any]		 =						metrics["""val"""][-1]
									self.assertGreaterEqual(last_step_stats["""val_avg_gen_time"""]      ,     0.0_1						)
									self.assertIsInstance(last_step_stats[F'''val_avg_{val_metric}''']      ,     __SCREAMING_SNAKE_CASE						)
									self.assertEqual(len(metrics["""test"""]						)      ,     1						)
									a__     :		Union[str, Any]		 =						int(args_d["""max_epochs"""] * (1 / args_d["""val_check_interval"""]) / 2 + 1						)
									self.assertEqual(len(metrics["""val"""]						)      ,     __SCREAMING_SNAKE_CASE						)









from typing import Dict, Tuple, overload

import torch
import torch.types
from torch import nn

from . import residue_constants as rc
from .rigid_utils import Rigid, Rotation
from .tensor_utils import batched_gather

@overload
def 			lowerCAmelCase_  (						_lowercase       :     torch.Tensor		,     _lowercase       :     torch.Tensor		,     _lowercase       :     None)							-> torch.Tensor:







			"""simple docstring"""
			...

@overload
def 			lowerCAmelCase_  (						_lowercase       :     torch.Tensor		,     _lowercase       :     torch.Tensor		,     _lowercase       :     torch.Tensor)							-> Tuple[torch.Tensor, torch.Tensor]:







			"""simple docstring"""
			...

def 			lowerCAmelCase_  (						_lowercase       :     Union[str, Any]		,     _lowercase       :     Tuple		,     _lowercase       :     Any)							-> Tuple:







			"""simple docstring"""
			a__     :		List[Any]		 =						aatype == rc.restype_order["""G"""]
			a__     :		Optional[int]		 =						rc.atom_order["""CA"""]
			a__     :		List[str]		 =						rc.atom_order["""CB"""]
			a__     :		int		 =						torch.where(
			    is_gly[..., None].expand(*((-1,) * len(is_gly.shape))		,     3)		,     all_atom_positions[..., ca_idx, :]		,     all_atom_positions[..., cb_idx, :]		,     )

			if all_atom_masks is not None:
						a__     :		Union[str, Any]		 =						torch.where(
						    _lowercase		,     all_atom_masks[..., ca_idx]		,     all_atom_masks[..., cb_idx]		,     )
						return pseudo_beta, pseudo_beta_mask
			else:
						return pseudo_beta

def 			lowerCAmelCase_  (						_lowercase       :     torch.Tensor		,     _lowercase       :     Dict[str, torch.Tensor])							-> torch.Tensor:







			"""simple docstring"""
			a__     :		int		 =						batched_gather(
			    _lowercase		,     batch["""residx_atom37_to_atom14"""]		,     dim=-2		,     no_batch_dims=len(atomaa.shape[:-2])		,     )

			a__     :		Tuple		 =						atomaa_data * batch["""atom37_atom_exists"""][..., None]

			return atomaa_data

def 			lowerCAmelCase_  (						_lowercase       :     Dict[str, torch.Tensor])							-> torch.Tensor:







			"""simple docstring"""
			a__     :		List[str]		 =						template_feats["""template_aatype"""]
			a__     :		str		 =						template_feats["""template_torsion_angles_sin_cos"""]
			a__     :		Optional[int]		 =						template_feats["""template_alt_torsion_angles_sin_cos"""]
			a__     :		Any		 =						template_feats["""template_torsion_angles_mask"""]
			a__     :		Optional[Any]		 =						torch.cat(
			    [
			        nn.functional.one_hot(_lowercase		,     22),
			        torsion_angles_sin_cos.reshape(*torsion_angles_sin_cos.shape[:-2]		,     14),
			        alt_torsion_angles_sin_cos.reshape(*alt_torsion_angles_sin_cos.shape[:-2]		,     14),
			        torsion_angles_mask,
			    ]		,     dim=-1		,     )

			return template_angle_feat

def 			lowerCAmelCase_  (						_lowercase       :     Dict[str, torch.Tensor]		,     _lowercase       :     torch.types.Number		,     _lowercase       :     torch.types.Number		,     _lowercase       :     int		,     _lowercase       :     bool = False		,     _lowercase       :     float = 1e-2_0		,     _lowercase       :     float = 1e8		,     )							-> torch.Tensor:







			"""simple docstring"""
			a__     :		List[Any]		 =						batch["""template_pseudo_beta_mask"""]
			a__     :		Union[str, Any]		 =						template_mask[..., None] * template_mask[..., None, :]

			# Compute distogram (this seems to differ slightly from Alg. 5)
			a__     :		Dict		 =						batch["""template_pseudo_beta"""]
			a__     :		Tuple		 =						torch.sum((tpb[..., None, :] - tpb[..., None, :, :]) ** 2		,     dim=-1		,     keepdim=_lowercase)
			a__     :		Tuple		 =						torch.linspace(_lowercase		,     _lowercase		,     _lowercase		,     device=tpb.device) ** 2
			a__     :		List[str]		 =						torch.cat([lower[1:], lower.new_tensor([inf])]		,     dim=-1)
			a__     :		List[Any]		 =						((dgram > lower) * (dgram < upper)).type(dgram.dtype)

			a__     :		Union[str, Any]		 =						[dgram, template_mask_ad[..., None]]

			a__     :		torch.LongTensor		 =						nn.functional.one_hot(
			    batch["""template_aatype"""]		,     rc.restype_num + 2		,     )

			a__     :		Any		 =						batch["""template_aatype"""].shape[-1]
			to_concat.append(aatype_one_hot[..., None, :, :].expand(*aatype_one_hot.shape[:-2]		,     _lowercase		,     -1		,     -1))
			to_concat.append(aatype_one_hot[..., None, :].expand(*aatype_one_hot.shape[:-2]		,     -1		,     _lowercase		,     -1))

			a__ ,		a__ ,		a__     :		Optional[int]		 =						[rc.atom_order[a] for a in ["""N""", """CA""", """C"""]]
			a__     :		Union[str, Any]		 =						Rigid.make_transform_from_reference(
			    n_xyz=batch["""template_all_atom_positions"""][..., n, :]		,     ca_xyz=batch["""template_all_atom_positions"""][..., ca, :]		,     c_xyz=batch["""template_all_atom_positions"""][..., c, :]		,     eps=_lowercase		,     )
			a__     :		int		 =						rigids.get_trans()[..., None, :, :]
			a__     :		Tuple		 =						rigids[..., None].invert_apply(_lowercase)

			a__     :		Union[str, Any]		 =						torch.rsqrt(eps + torch.sum(rigid_vec**2		,     dim=-1))

			a__     :		Tuple		 =						batch["""template_all_atom_mask"""]
			a__     :		Tuple		 =						t_aa_masks[..., n] * t_aa_masks[..., ca] * t_aa_masks[..., c]
			a__     :		Optional[int]		 =						template_mask[..., None] * template_mask[..., None, :]

			a__     :		int		 =						inv_distance_scalar * template_mask_ad
			a__     :		Dict		 =						rigid_vec * inv_distance_scalar[..., None]

			if not use_unit_vector:
						a__     :		Dict		 =						unit_vector * 0.0

			to_concat.extend(torch.unbind(unit_vector[..., None, :]		,     dim=-1))
			to_concat.append(template_mask_ad[..., None])

			a__     :		int		 =						torch.cat(_lowercase		,     dim=-1)
			a__     :		Dict		 =						act * template_mask_ad[..., None]

			return act

def 			lowerCAmelCase_  (						_lowercase       :     Dict[str, torch.Tensor])							-> torch.Tensor:







			"""simple docstring"""
			a__     :		torch.LongTensor		 =						nn.functional.one_hot(batch["""extra_msa"""]		,     23)
			a__     :		Dict		 =						[
			    msa_ahot,
			    batch["""extra_has_deletion"""].unsqueeze(-1),
			    batch["""extra_deletion_value"""].unsqueeze(-1),
			]
			return torch.cat(_lowercase		,     dim=-1)

def 			lowerCAmelCase_  (						_lowercase       :     Rigid		,     _lowercase       :     torch.Tensor		,     _lowercase       :     torch.Tensor		,     _lowercase       :     torch.Tensor		,     )							-> Rigid:







			"""simple docstring"""
			# [*, N, 8, 4, 4]
			a__     :		Any		 =						rrgdf[aatype, ...]

			# [*, N, 8] transformations, i.e.
			#   One [*, N, 8, 3, 3] rotation matrix and
			#   One [*, N, 8, 3]    translation matrix
			a__     :		Any		 =						r.from_tensor_axa(_lowercase)

			a__     :		List[Any]		 =						alpha.new_zeros((*((1,) * len(alpha.shape[:-1])), 2))
			a__     :		str		 =						1

			# [*, N, 8, 2]
			a__     :		int		 =						torch.cat([bb_rot.expand(*alpha.shape[:-2]		,     -1		,     -1), alpha]		,     dim=-2)

			# [*, N, 8, 3, 3]
			# Produces rotation matrices of the form:
			# [
			#   [1, 0  , 0  ],
			#   [0, a_2,-a_1],
			#   [0, a_1, a_2]
			# ]
			# This follows the original code rather than the supplement, which uses
			# different indices.

			a__     :		Dict		 =						alpha.new_zeros(default_r.get_rots().get_rot_mats().shape)
			a__     :		Union[str, Any]		 =						1
			a__     :		Union[str, Any]		 =						alpha[..., 1]
			a__     :		int		 =						-alpha[..., 0]
			a__     :		Union[str, Any]		 =						alpha

			a__     :		str		 =						default_r.compose(Rigid(Rotation(rot_mats=_lowercase)		,     _lowercase))

			a__     :		Tuple		 =						all_frames[..., 5]
			a__     :		Any		 =						all_frames[..., 6]
			a__     :		Optional[int]		 =						all_frames[..., 7]

			a__     :		List[Any]		 =						all_frames[..., 4]
			a__     :		str		 =						chia_frame_to_bb.compose(_lowercase)
			a__     :		List[Any]		 =						chia_frame_to_bb.compose(_lowercase)
			a__     :		List[Any]		 =						chia_frame_to_bb.compose(_lowercase)

			a__     :		Tuple		 =						Rigid.cat(
			    [
			        all_frames[..., :5],
			        chia_frame_to_bb.unsqueeze(-1),
			        chia_frame_to_bb.unsqueeze(-1),
			        chia_frame_to_bb.unsqueeze(-1),
			    ]		,     dim=-1		,     )

			a__     :		str		 =						r[..., None].compose(_lowercase)

			return all_frames_to_global





def 			lowerCAmelCase_  (						_lowercase       :     Rigid		,     _lowercase       :     torch.Tensor		,     _lowercase       :     torch.Tensor		,     _lowercase       :     torch.Tensor		,     _lowercase       :     torch.Tensor		,     _lowercase       :     torch.Tensor		,     )							-> torch.Tensor:







			"""simple docstring"""
			# [*, N, 14]
			a__     :		List[str]		 =						group_idx[aatype, ...]

			# [*, N, 14, 8]
			a__     :		torch.LongTensor		 =						nn.functional.one_hot(
			    _lowercase		,     num_classes=default_frames.shape[-3]		,     )

			# [*, N, 14, 8]
			a__     :		int		 =						r[..., None, :] * group_mask_one_hot

			# [*, N, 14]
			a__     :		Tuple		 =						t_atoms_to_global.map_tensor_fn(lambda _lowercase: torch.sum(_lowercase		,     dim=-1))

			# [*, N, 14, 1]
			a__     :		Dict		 =						atom_mask[aatype, ...].unsqueeze(-1)

			# [*, N, 14, 3]
			a__     :		int		 =						lit_positions[aatype, ...]
			a__     :		List[str]		 =						t_atoms_to_global.apply(_lowercase)
			a__     :		Dict		 =						pred_positions * atom_mask

			return pred_positions




