

import argparse
import os
import re


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_dummies.py
lowerCAmelCase__             =		"""src/transformers"""

# Matches is_xxx_available()
lowerCAmelCase__             =		re.compile(R"""is\_([a-z_]*)_available()""")
# Matches from xxx import bla
lowerCAmelCase__             =		re.compile(R"""\s+from\s+\S*\s+import\s+([^\(\s].*)\n""")
lowerCAmelCase__             =		re.compile(R"""^\s+if\s+not\s+\(?is\_[a-z_]*\_available\(\)""")


lowerCAmelCase__             =		"""
{0} = None
"""

lowerCAmelCase__             =		"""
class {0}(metaclass=DummyObject):
    _backends = {1}

    def __init__(self, *args, **kwargs):
        requires_backends(self, {1})
"""


lowerCAmelCase__             =		"""
def {0}(*args, **kwargs):
    requires_backends({0}, {1})
"""






def        lowerCamelCase_  (				UpperCAmelCase_				:  List[str]						)    ->					List[str]:




	'''simple docstring'''




	if _re_test_backend.search(UpperCAmelCase_						) is None:
		return None
	_UpperCamelCase :		List[Any]								=    [b[0] for b in _re_backend.findall(UpperCAmelCase_						)]
	backends.sort()
	return "_and_".join(UpperCAmelCase_						)






def        lowerCamelCase_  (				)    ->					Dict:




	'''simple docstring'''




	with open(os.path.join(UpperCAmelCase_				,							'__init__.py'						)				,							'r'				,							encoding='utf-8'				,							newline='\n'						) as f:
		_UpperCamelCase :		Union[str, Any]								=    f.readlines()

	# Get to the point we do the actual imports for type checking
	_UpperCamelCase :		Optional[Any]								=    0
	while not lines[line_index].startswith('if TYPE_CHECKING'						):
		line_index += 1

	_UpperCamelCase :		Optional[Any]								=    {}
	# Go through the end of the file
	while line_index < len(UpperCAmelCase_						):
		# If the line is an if is_backend_available, we grab all objects associated.
		_UpperCamelCase :		str								=    find_backend(lines[line_index]						)
		if backend is not None:
			while not lines[line_index].startswith('    else:'						):
				line_index += 1
			line_index += 1

			_UpperCamelCase :		Optional[Any]								=    []
			# Until we unindent, add backend objects to the list
			while len(lines[line_index]						) <= 1 or lines[line_index].startswith(' ' * 8						):
				_UpperCamelCase :		Union[str, Any]								=    lines[line_index]
				_UpperCamelCase :		int								=    _re_single_line_import.search(UpperCAmelCase_						)
				if single_line_import_search is not None:
					objects.extend(single_line_import_search.groups()[0].split(', '						)						)
				elif line.startswith(' ' * 1_2						):
					objects.append(line[1_2:-2]						)
				line_index += 1

			_UpperCamelCase :		List[str]								=    objects
		else:
			line_index += 1

	return backend_specific_objects






def        lowerCamelCase_  (				UpperCAmelCase_				:  str				,							UpperCAmelCase_				:  List[Any]						)    ->					Any:




	'''simple docstring'''




	if name.isupper():
		return DUMMY_CONSTANT.format(UpperCAmelCase_						)
	elif name.islower():
		return DUMMY_FUNCTION.format(UpperCAmelCase_				,							UpperCAmelCase_						)
	else:
		return DUMMY_CLASS.format(UpperCAmelCase_				,							UpperCAmelCase_						)






def        lowerCamelCase_  (				UpperCAmelCase_				:  int=None						)    ->					List[str]:




	'''simple docstring'''




	if backend_specific_objects is None:
		_UpperCamelCase :		List[str]								=    read_init()
	# For special correspondence backend to module name as used in the function requires_modulename
	_UpperCamelCase :		List[str]								=    {}

	for backend, objects in backend_specific_objects.items():
		_UpperCamelCase :		List[Any]								=    '[' + ', '.join(F'''"{b}"''' for b in backend.split('_and_'						)						) + ']'
		_UpperCamelCase :		int								=    '# This file is autogenerated by the command `make fix-copies`, do not edit.\n'
		dummy_file += "from ..utils import DummyObject, requires_backends\n\n"
		dummy_file += "\n".join([create_dummy_object(UpperCAmelCase_				,							UpperCAmelCase_						) for o in objects]						)
		_UpperCamelCase :		int								=    dummy_file

	return dummy_files






def        lowerCamelCase_  (				UpperCAmelCase_				:  List[Any]=False						)    ->					int:




	'''simple docstring'''




	_UpperCamelCase :		List[str]								=    create_dummy_files()
	# For special correspondence backend to shortcut as used in utils/dummy_xxx_objects.py
	_UpperCamelCase :		Union[str, Any]								=    {'torch': 'pt'}

	# Locate actual dummy modules and read their content.
	_UpperCamelCase :		int								=    os.path.join(UpperCAmelCase_				,							'utils'						)
	_UpperCamelCase :		Union[str, Any]								=    {
	    backend: os.path.join(UpperCAmelCase_				,							F'''dummy_{short_names.get(UpperCAmelCase_				,							UpperCAmelCase_						)}_objects.py'''						)
	    for backend in dummy_files.keys()
	}

	_UpperCamelCase :		Any								=    {}
	for backend, file_path in dummy_file_paths.items():
		if os.path.isfile(UpperCAmelCase_						):
			with open(UpperCAmelCase_				,							'r'				,							encoding='utf-8'				,							newline='\n'						) as f:
				_UpperCamelCase :		Union[str, Any]								=    f.read()
		else:
			_UpperCamelCase :		Optional[int]								=    ''

	for backend in dummy_files.keys():
		if dummy_files[backend] != actual_dummies[backend]:
			if overwrite:
				print(
				    F'''Updating transformers.utils.dummy_{short_names.get(UpperCAmelCase_				,							UpperCAmelCase_						)}_objects.py as the main '''
				    '__init__ has new objects.'						)
				with open(dummy_file_paths[backend]				,							'w'				,							encoding='utf-8'				,							newline='\n'						) as f:
					f.write(dummy_files[backend]						)
			else:
				raise ValueError(
				    'The main __init__ has objects that are not present in '
				    F'''transformers.utils.dummy_{short_names.get(UpperCAmelCase_				,							UpperCAmelCase_						)}_objects.py. Run `make fix-copies` '''
				    'to fix this.'						)


if __name__ == "__main__":
							lowerCAmelCase__             =		argparse.ArgumentParser()
							parser.add_argument("""--fix_and_overwrite""", action="""store_true""", help="""Whether to fix inconsistencies.""")
							lowerCAmelCase__             =		parser.parse_args()

							check_dummies(args.fix_and_overwrite)








'''simple docstring'''

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 							SCREAMING_SNAKE_CASE__(		_UpperCamelCase   :					torch.Tensor     ,		_UpperCamelCase   :					torch.Tensor     ,		_UpperCamelCase   :					None )		->       torch.Tensor:





   '''simple docstring'''







   ...



@overload
def 							SCREAMING_SNAKE_CASE__(		_UpperCamelCase   :					torch.Tensor     ,		_UpperCamelCase   :					torch.Tensor     ,		_UpperCamelCase   :					torch.Tensor )		->       Tuple[torch.Tensor, torch.Tensor]:





   '''simple docstring'''







   ...



def 							SCREAMING_SNAKE_CASE__(		_UpperCamelCase   :					int     ,		_UpperCamelCase   :					Dict     ,		_UpperCamelCase   :					str )		->       int:





   '''simple docstring'''







   UpperCamelCase__			    =    aatype == rc.restype_order["G"]
   UpperCamelCase__			    =    rc.atom_order["CA"]
   UpperCamelCase__			    =    rc.atom_order["CB"]
   UpperCamelCase__			    =    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:
      UpperCamelCase__			    =    torch.where(
          _UpperCamelCase     ,		all_atom_masks[..., ca_idx]     ,		all_atom_masks[..., cb_idx]     ,		)
      return pseudo_beta, pseudo_beta_mask
   else:
      return pseudo_beta



def 							SCREAMING_SNAKE_CASE__(		_UpperCamelCase   :					torch.Tensor     ,		_UpperCamelCase   :					Dict[str, torch.Tensor] )		->       torch.Tensor:





   '''simple docstring'''







   UpperCamelCase__			    =    batched_gather(
       _UpperCamelCase     ,		batch["residx_atom37_to_atom14"]     ,		dim=-2     ,		no_batch_dims=len(atomaa.shape[:-2] )     ,		)

   UpperCamelCase__			    =    atomaa_data * batch["atom37_atom_exists"][..., None]

   return atomaa_data



def 							SCREAMING_SNAKE_CASE__(		_UpperCamelCase   :					Dict[str, torch.Tensor] )		->       torch.Tensor:





   '''simple docstring'''







   UpperCamelCase__			    =    template_feats["template_aatype"]
   UpperCamelCase__			    =    template_feats["template_torsion_angles_sin_cos"]
   UpperCamelCase__			    =    template_feats["template_alt_torsion_angles_sin_cos"]
   UpperCamelCase__			    =    template_feats["template_torsion_angles_mask"]
   UpperCamelCase__			    =    torch.cat(
       [
           nn.functional.one_hot(_UpperCamelCase     ,		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 							SCREAMING_SNAKE_CASE__(		_UpperCamelCase   :					Dict[str, torch.Tensor]     ,		_UpperCamelCase   :					torch.types.Number     ,		_UpperCamelCase   :					torch.types.Number     ,		_UpperCamelCase   :					int     ,		_UpperCamelCase   :					bool = False     ,		_UpperCamelCase   :					float = 1e-20     ,		_UpperCamelCase   :					float = 1e8     ,		)		->       torch.Tensor:





   '''simple docstring'''







   UpperCamelCase__			    =    batch["template_pseudo_beta_mask"]
   UpperCamelCase__			    =    template_mask[..., None] * template_mask[..., None, :]

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

   UpperCamelCase__			    =    [dgram, template_mask_ad[..., None]]

   UpperCamelCase__			    =    nn.functional.one_hot(
       batch["template_aatype"]     ,		rc.restype_num + 2     ,		)

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

   UpperCamelCase__    ,		UpperCamelCase__    ,		UpperCamelCase__			    =    [rc.atom_order[a] for a in ["N", "CA", "C"]]
   UpperCamelCase__			    =    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=_UpperCamelCase     ,		)
   UpperCamelCase__			    =    rigids.get_trans()[..., None, :, :]
   UpperCamelCase__			    =    rigids[..., None].invert_apply(_UpperCamelCase )

   UpperCamelCase__			    =    torch.rsqrt(eps + torch.sum(rigid_vec**2     ,		dim=-1 ) )

   UpperCamelCase__			    =    batch["template_all_atom_mask"]
   UpperCamelCase__			    =    t_aa_masks[..., n] * t_aa_masks[..., ca] * t_aa_masks[..., c]
   UpperCamelCase__			    =    template_mask[..., None] * template_mask[..., None, :]

   UpperCamelCase__			    =    inv_distance_scalar * template_mask_ad
   UpperCamelCase__			    =    rigid_vec * inv_distance_scalar[..., None]

   if not use_unit_vector:
      UpperCamelCase__			    =    unit_vector * 0.0

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

   UpperCamelCase__			    =    torch.cat(_UpperCamelCase     ,		dim=-1 )
   UpperCamelCase__			    =    act * template_mask_ad[..., None]

   return act



def 							SCREAMING_SNAKE_CASE__(		_UpperCamelCase   :					Dict[str, torch.Tensor] )		->       torch.Tensor:





   '''simple docstring'''







   UpperCamelCase__			    =    nn.functional.one_hot(batch["extra_msa"]     ,		23 )
   UpperCamelCase__			    =    [
       msa_ahot,
       batch["extra_has_deletion"].unsqueeze(-1 ),
       batch["extra_deletion_value"].unsqueeze(-1 ),
   ]
   return torch.cat(_UpperCamelCase     ,		dim=-1 )



def 							SCREAMING_SNAKE_CASE__(		_UpperCamelCase   :					Rigid     ,		_UpperCamelCase   :					torch.Tensor     ,		_UpperCamelCase   :					torch.Tensor     ,		_UpperCamelCase   :					torch.Tensor     ,		)		->       Rigid:





   '''simple docstring'''







   UpperCamelCase__			    =    rrgdf[aatype, ...]

   # [*, N, 8] transformations, i.e.
   #   One [*, N, 8, 3, 3] rotation matrix and
   #   One [*, N, 8, 3]    translation matrix
   UpperCamelCase__			    =    r.from_tensor_axa(_UpperCamelCase )

   UpperCamelCase__			    =    alpha.new_zeros((*((1,) * len(alpha.shape[:-1] )), 2) )
   UpperCamelCase__			    =    1

   # [*, N, 8, 2]
   UpperCamelCase__			    =    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.

   UpperCamelCase__			    =    alpha.new_zeros(default_r.get_rots().get_rot_mats().shape )
   UpperCamelCase__			    =    1
   UpperCamelCase__			    =    alpha[..., 1]
   UpperCamelCase__			    =    -alpha[..., 0]
   UpperCamelCase__			    =    alpha

   UpperCamelCase__			    =    default_r.compose(Rigid(Rotation(rot_mats=_UpperCamelCase )     ,		_UpperCamelCase ) )

   UpperCamelCase__			    =    all_frames[..., 5]
   UpperCamelCase__			    =    all_frames[..., 6]
   UpperCamelCase__			    =    all_frames[..., 7]

   UpperCamelCase__			    =    all_frames[..., 4]
   UpperCamelCase__			    =    chia_frame_to_bb.compose(_UpperCamelCase )
   UpperCamelCase__			    =    chia_frame_to_bb.compose(_UpperCamelCase )
   UpperCamelCase__			    =    chia_frame_to_bb.compose(_UpperCamelCase )

   UpperCamelCase__			    =    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     ,		)

   UpperCamelCase__			    =    r[..., None].compose(_UpperCamelCase )

   return all_frames_to_global






def 							SCREAMING_SNAKE_CASE__(		_UpperCamelCase   :					Rigid     ,		_UpperCamelCase   :					torch.Tensor     ,		_UpperCamelCase   :					torch.Tensor     ,		_UpperCamelCase   :					torch.Tensor     ,		_UpperCamelCase   :					torch.Tensor     ,		_UpperCamelCase   :					torch.Tensor     ,		)		->       torch.Tensor:





   '''simple docstring'''







   UpperCamelCase__			    =    group_idx[aatype, ...]

   # [*, N, 14, 8]
   UpperCamelCase__			    =    nn.functional.one_hot(
       _UpperCamelCase     ,		num_classes=default_frames.shape[-3]     ,		)

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

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

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

   # [*, N, 14, 3]
   UpperCamelCase__			    =    lit_positions[aatype, ...]
   UpperCamelCase__			    =    t_atoms_to_global.apply(_UpperCamelCase )
   UpperCamelCase__			    =    pred_positions * atom_mask

   return pred_positions