


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	(						lowerCAmelCase_	,				lowerCAmelCase_	,				lowerCAmelCase_	)->	torch.Tensor:
				...



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



def      lowerCAmelCase	(						lowerCAmelCase_	,				lowerCAmelCase_	,				lowerCAmelCase_	)->	int:
				lowerCAmelCase_	: Any      			=			aatype == rc.restype_order['''G''']
				lowerCAmelCase_	: Any      			=			rc.atom_order['''CA''']
				lowerCAmelCase_	: Any      			=			rc.atom_order['''CB''']
				lowerCAmelCase_	: List[str]      			=			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:
								lowerCAmelCase_	: List[Any]      			=			torch.where(
								    lowerCAmelCase_	,				all_atom_masks[..., ca_idx]	,				all_atom_masks[..., cb_idx]	,				)
								return pseudo_beta, pseudo_beta_mask
				else:
								return pseudo_beta



def      lowerCAmelCase	(						lowerCAmelCase_	,				lowerCAmelCase_	)->	torch.Tensor:
				lowerCAmelCase_	: Tuple      			=			batched_gather(
				    lowerCAmelCase_	,				batch['''residx_atom37_to_atom14''']	,				dim=-2	,				no_batch_dims=len(atomaa.shape[:-2]	)	,				)

				lowerCAmelCase_	: Any      			=			atomaa_data * batch['''atom37_atom_exists'''][..., None]

				return atomaa_data



def      lowerCAmelCase	(						lowerCAmelCase_	)->	torch.Tensor:
				lowerCAmelCase_	: str      			=			template_feats['''template_aatype''']
				lowerCAmelCase_	: Optional[Any]      			=			template_feats['''template_torsion_angles_sin_cos''']
				lowerCAmelCase_	: Union[str, Any]      			=			template_feats['''template_alt_torsion_angles_sin_cos''']
				lowerCAmelCase_	: str      			=			template_feats['''template_torsion_angles_mask''']
				lowerCAmelCase_	: Optional[int]      			=			torch.cat(
				    [
				        nn.functional.one_hot(lowerCAmelCase_	,				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	(						lowerCAmelCase_	,				lowerCAmelCase_	,				lowerCAmelCase_	,				lowerCAmelCase_	,				lowerCAmelCase_ = False	,				lowerCAmelCase_ = 1e-2_0	,				lowerCAmelCase_ = 1e8	,				)->	torch.Tensor:
				lowerCAmelCase_	: int      			=			batch['''template_pseudo_beta_mask''']
				lowerCAmelCase_	: str      			=			template_mask[..., None] * template_mask[..., None, :]

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

				lowerCAmelCase_	: int      			=			[dgram, template_mask_ad[..., None]]

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

				lowerCAmelCase_	: str      			=			batch['''template_aatype'''].shape[-1]
				to_concat.append(aatype_one_hot[..., None, :, :].expand(*aatype_one_hot.shape[:-2]	,				lowerCAmelCase_	,				-1	,				-1	)	)
				to_concat.append(aatype_one_hot[..., None, :].expand(*aatype_one_hot.shape[:-2]	,				-1	,				lowerCAmelCase_	,				-1	)	)

				lowerCAmelCase_			,					lowerCAmelCase_			,					lowerCAmelCase_	: Any      			=			[rc.atom_order[a] for a in ['''N''', '''CA''', '''C''']]
				lowerCAmelCase_	: int      			=			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=lowerCAmelCase_	,				)
				lowerCAmelCase_	: int      			=			rigids.get_trans()[..., None, :, :]
				lowerCAmelCase_	: Any      			=			rigids[..., None].invert_apply(lowerCAmelCase_	)

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

				lowerCAmelCase_	: Optional[int]      			=			batch['''template_all_atom_mask''']
				lowerCAmelCase_	: List[Any]      			=			t_aa_masks[..., n] * t_aa_masks[..., ca] * t_aa_masks[..., c]
				lowerCAmelCase_	: Tuple      			=			template_mask[..., None] * template_mask[..., None, :]

				lowerCAmelCase_	: Optional[Any]      			=			inv_distance_scalar * template_mask_ad
				lowerCAmelCase_	: Dict      			=			rigid_vec * inv_distance_scalar[..., None]

				if not use_unit_vector:
								lowerCAmelCase_	: List[Any]      			=			unit_vector * 0.0

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

				lowerCAmelCase_	: int      			=			torch.cat(lowerCAmelCase_	,				dim=-1	)
				lowerCAmelCase_	: Optional[Any]      			=			act * template_mask_ad[..., None]

				return act



def      lowerCAmelCase	(						lowerCAmelCase_	)->	torch.Tensor:
				lowerCAmelCase_	: torch.LongTensor      			=			nn.functional.one_hot(batch['''extra_msa''']	,				23	)
				lowerCAmelCase_	: Dict      			=			[
				    msa_ahot,
				    batch['''extra_has_deletion'''].unsqueeze(-1	),
				    batch['''extra_deletion_value'''].unsqueeze(-1	),
				]
				return torch.cat(lowerCAmelCase_	,				dim=-1	)



def      lowerCAmelCase	(						lowerCAmelCase_	,				lowerCAmelCase_	,				lowerCAmelCase_	,				lowerCAmelCase_	,				)->	Rigid:
				# [*, N, 8, 4, 4]
				lowerCAmelCase_	: int      			=			rrgdf[aatype, ...]

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

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

				# [*, N, 8, 2]
				lowerCAmelCase_	: Union[str, Any]      			=			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.

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

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

				lowerCAmelCase_	: str      			=			all_frames[..., 5]
				lowerCAmelCase_	: List[str]      			=			all_frames[..., 6]
				lowerCAmelCase_	: Tuple      			=			all_frames[..., 7]

				lowerCAmelCase_	: Tuple      			=			all_frames[..., 4]
				lowerCAmelCase_	: str      			=			chia_frame_to_bb.compose(lowerCAmelCase_	)
				lowerCAmelCase_	: List[str]      			=			chia_frame_to_bb.compose(lowerCAmelCase_	)
				lowerCAmelCase_	: Dict      			=			chia_frame_to_bb.compose(lowerCAmelCase_	)

				lowerCAmelCase_	: Any      			=			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	,				)

				lowerCAmelCase_	: Tuple      			=			r[..., None].compose(lowerCAmelCase_	)

				return all_frames_to_global




def      lowerCAmelCase	(						lowerCAmelCase_	,				lowerCAmelCase_	,				lowerCAmelCase_	,				lowerCAmelCase_	,				lowerCAmelCase_	,				lowerCAmelCase_	,				)->	torch.Tensor:
				# [*, N, 14]
				lowerCAmelCase_	: Dict      			=			group_idx[aatype, ...]

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

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

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

				# [*, N, 14, 1]
				lowerCAmelCase_	: List[str]      			=			atom_mask[aatype, ...].unsqueeze(-1	)

				# [*, N, 14, 3]
				lowerCAmelCase_	: str      			=			lit_positions[aatype, ...]
				lowerCAmelCase_	: Optional[int]      			=			t_atoms_to_global.apply(lowerCAmelCase_	)
				lowerCAmelCase_	: int      			=			pred_positions * atom_mask

				return pred_positions



import argparse
import collections
import importlib.util
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_table.py
_UpperCAmelCase     :					List[Any]       ="""src/diffusers"""
_UpperCAmelCase     :					Dict       ="""docs/source/en"""
_UpperCAmelCase     :					List[str]       ="""."""



def      lowerCAmelCase	(						lowerCAmelCase_	,				lowerCAmelCase_	,				lowerCAmelCase_	)->	List[str]:
				with open(lowerCAmelCase_	,				'''r'''	,				encoding='''utf-8'''	,				newline='''\n'''	) as f:
								lowerCAmelCase_	: Any      			=			f.readlines()
				# Find the start prompt.
				lowerCAmelCase_	: Optional[Any]      			=			0
				while not lines[start_index].startswith(lowerCAmelCase_	):
								start_index += 1
				start_index += 1

				lowerCAmelCase_	: List[Any]      			=			start_index
				while not lines[end_index].startswith(lowerCAmelCase_	):
								end_index += 1
				end_index -= 1

				while len(lines[start_index]	) <= 1:
								start_index += 1
				while len(lines[end_index]	) <= 1:
								end_index -= 1
				end_index += 1
				return "".join(lines[start_index:end_index]	), start_index, end_index, lines


# Add here suffixes that are used to identify models, separated by |
_UpperCAmelCase     :					int       ="""Model|Encoder|Decoder|ForConditionalGeneration"""
# Regexes that match TF/Flax/PT model names.
_UpperCAmelCase     :					Any       =re.compile(R"""TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")
_UpperCAmelCase     :					Optional[int]       =re.compile(R"""Flax(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")
# Will match any TF or Flax model too so need to be in an else branch afterthe two previous regexes.
_UpperCAmelCase     :					Union[str, Any]       =re.compile(R"""(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")


# This is to make sure the diffusers module imported is the one in the repo.
_UpperCAmelCase     :					Dict       =importlib.util.spec_from_file_location(
    """diffusers""",
    os.path.join(TRANSFORMERS_PATH, """__init__.py"""),
    submodule_search_locations=[TRANSFORMERS_PATH],
)
_UpperCAmelCase     :					Tuple       =spec.loader.load_module()



def      lowerCAmelCase	(						lowerCAmelCase_	)->	Dict:
				lowerCAmelCase_	: Any      			=			re.finditer('''.+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)'''	,				lowerCAmelCase_	)
				return [m.group(0	) for m in matches]



def      lowerCAmelCase	(						lowerCAmelCase_	,				lowerCAmelCase_	)->	Any:
				lowerCAmelCase_	: Any      			=			2 if text == '''✅''' or text == '''❌''' else len(lowerCAmelCase_	)
				lowerCAmelCase_	: List[str]      			=			(width - text_length) // 2
				lowerCAmelCase_	: str      			=			width - text_length - left_indent
				return " " * left_indent + text + " " * right_indent



def      lowerCAmelCase	(						)->	Dict:
				lowerCAmelCase_	: Optional[int]      			=			diffusers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
				lowerCAmelCase_	: Tuple      			=			{
				    name: config_mapping_names[code]
				    for code, name in diffusers_module.MODEL_NAMES_MAPPING.items()
				    if code in config_mapping_names
				}
				lowerCAmelCase_	: str      			=			{name: config.replace('''ConfigMixin'''	,				''''''	) for name, config in model_name_to_config.items()}

				# Dictionaries flagging if each model prefix has a slow/fast tokenizer, backend in PT/TF/Flax.
				lowerCAmelCase_	: Union[str, Any]      			=			collections.defaultdict(lowerCAmelCase_	)
				lowerCAmelCase_	: Dict      			=			collections.defaultdict(lowerCAmelCase_	)
				lowerCAmelCase_	: Tuple      			=			collections.defaultdict(lowerCAmelCase_	)
				lowerCAmelCase_	: Optional[int]      			=			collections.defaultdict(lowerCAmelCase_	)
				lowerCAmelCase_	: Any      			=			collections.defaultdict(lowerCAmelCase_	)

				# Let's lookup through all diffusers object (once).
				for attr_name in dir(lowerCAmelCase_	):
								lowerCAmelCase_	: Tuple      			=			None
								if attr_name.endswith('''Tokenizer'''	):
												lowerCAmelCase_	: int      			=			slow_tokenizers
												lowerCAmelCase_	: Optional[Any]      			=			attr_name[:-9]
								elif attr_name.endswith('''TokenizerFast'''	):
												lowerCAmelCase_	: List[Any]      			=			fast_tokenizers
												lowerCAmelCase_	: Dict      			=			attr_name[:-13]
								elif _re_tf_models.match(lowerCAmelCase_	) is not None:
												lowerCAmelCase_	: Union[str, Any]      			=			tf_models
												lowerCAmelCase_	: Optional[int]      			=			_re_tf_models.match(lowerCAmelCase_	).groups()[0]
								elif _re_flax_models.match(lowerCAmelCase_	) is not None:
												lowerCAmelCase_	: Optional[Any]      			=			flax_models
												lowerCAmelCase_	: Optional[int]      			=			_re_flax_models.match(lowerCAmelCase_	).groups()[0]
								elif _re_pt_models.match(lowerCAmelCase_	) is not None:
												lowerCAmelCase_	: Tuple      			=			pt_models
												lowerCAmelCase_	: Tuple      			=			_re_pt_models.match(lowerCAmelCase_	).groups()[0]

								if lookup_dict is not None:
												while len(lowerCAmelCase_	) > 0:
																if attr_name in model_name_to_prefix.values():
																				lowerCAmelCase_	: Dict      			=			True
																				break
																# Try again after removing the last word in the name
																lowerCAmelCase_	: Tuple      			=			''''''.join(camel_case_split(lowerCAmelCase_	)[:-1]	)

    # Let's build that table!
				lowerCAmelCase_	: Union[str, Any]      			=			list(model_name_to_config.keys()	)
				model_names.sort(key=str.lower	)
				lowerCAmelCase_	: Tuple      			=			['''Model''', '''Tokenizer slow''', '''Tokenizer fast''', '''PyTorch support''', '''TensorFlow support''', '''Flax Support''']
				# We'll need widths to properly display everything in the center (+2 is to leave one extra space on each side).
				lowerCAmelCase_	: Optional[Any]      			=			[len(lowerCAmelCase_	) + 2 for c in columns]
				lowerCAmelCase_	: List[Any]      			=			max([len(lowerCAmelCase_	) for name in model_names]	) + 2

				# Build the table per se
				lowerCAmelCase_	: List[str]      			=			'''|''' + '''|'''.join([_center_text(lowerCAmelCase_	,				lowerCAmelCase_	) for c, w in zip(lowerCAmelCase_	,				lowerCAmelCase_	)]	) + '''|\n'''
				# Use ":-----:" format to center-aligned table cell texts
				table += "|" + "|".join([''':''' + '''-''' * (w - 2) + ''':''' for w in widths]	) + "|\n"

				lowerCAmelCase_	: Tuple      			=			{True: '''✅''', False: '''❌'''}
				for name in model_names:
								lowerCAmelCase_	: Optional[int]      			=			model_name_to_prefix[name]
								lowerCAmelCase_	: int      			=			[
								    name,
								    check[slow_tokenizers[prefix]],
								    check[fast_tokenizers[prefix]],
								    check[pt_models[prefix]],
								    check[tf_models[prefix]],
								    check[flax_models[prefix]],
								]
								table += "|" + "|".join([_center_text(lowerCAmelCase_	,				lowerCAmelCase_	) for l, w in zip(lowerCAmelCase_	,				lowerCAmelCase_	)]	) + "|\n"
				return table



def      lowerCAmelCase	(						lowerCAmelCase_=False	)->	str:
				lowerCAmelCase_			,					lowerCAmelCase_			,					lowerCAmelCase_			,					lowerCAmelCase_	: Optional[int]      			=			_find_text_in_file(
				    filename=os.path.join(lowerCAmelCase_	,				'''index.mdx'''	)	,				start_prompt='''<!--This table is updated automatically from the auto modules'''	,				end_prompt='''<!-- End table-->'''	,				)
				lowerCAmelCase_	: int      			=			get_model_table_from_auto_modules()

				if current_table != new_table:
								if overwrite:
												with open(os.path.join(lowerCAmelCase_	,				'''index.mdx'''	)	,				'''w'''	,				encoding='''utf-8'''	,				newline='''\n'''	) as f:
																f.writelines(lines[:start_index] + [new_table] + lines[end_index:]	)
								else:
												raise ValueError(
												    '''The model table in the `index.mdx` has not been updated. Run `make fix-copies` to fix this.'''	)


if __name__ == "__main__":
						_UpperCAmelCase     :					Optional[Any]       =argparse.ArgumentParser()
						parser.add_argument("""--fix_and_overwrite""", action="""store_true""", help="""Whether to fix inconsistencies.""")
						_UpperCAmelCase     :					int       =parser.parse_args()

						check_model_table(args.fix_and_overwrite)