






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__					(     snake_case					:   torch.Tensor ,				snake_case					:   torch.Tensor ,				snake_case					:   None							)->		Tuple:






     '''simple docstring'''



     ...


@overload
def   SCREAMING_SNAKE_CASE__					(     snake_case					:   torch.Tensor ,				snake_case					:   torch.Tensor ,				snake_case					:   torch.Tensor							)->		Any:






     '''simple docstring'''



     ...


def   SCREAMING_SNAKE_CASE__					(     snake_case					:   Tuple ,				snake_case					:   Any ,				snake_case					:   Any							)->		List[Any]:






     '''simple docstring'''



     UpperCAmelCase__	:						int       =       aatype == rc.restype_order['G']
     UpperCAmelCase__	:						Optional[int]       =       rc.atom_order['CA']
     UpperCAmelCase__	:						int       =       rc.atom_order['CB']
     UpperCAmelCase__	:						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:
          UpperCAmelCase__	:						List[Any]       =       torch.where(
              snake_case ,				all_atom_masks[..., ca_idx] ,				all_atom_masks[..., cb_idx] ,				)
          return pseudo_beta, pseudo_beta_mask
     else:
          return pseudo_beta


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






     '''simple docstring'''



     UpperCAmelCase__	:						int       =       batched_gather(
         snake_case ,				batch["residx_atom37_to_atom14"] ,				dim=-2 ,				no_batch_dims=len(atomaa.shape[:-2]							) ,				)

     UpperCAmelCase__	:						Tuple       =       atomaa_data * batch['atom37_atom_exists'][..., None]

     return atomaa_data


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






     '''simple docstring'''



     UpperCAmelCase__	:						Optional[Any]       =       template_feats['template_aatype']
     UpperCAmelCase__	:						Union[str, Any]       =       template_feats['template_torsion_angles_sin_cos']
     UpperCAmelCase__	:						int       =       template_feats['template_alt_torsion_angles_sin_cos']
     UpperCAmelCase__	:						List[str]       =       template_feats['template_torsion_angles_mask']
     UpperCAmelCase__	:						int       =       torch.cat(
         [
             nn.functional.one_hot(snake_case ,				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__					(     snake_case					:   Dict[str, torch.Tensor] ,				snake_case					:   torch.types.Number ,				snake_case					:   torch.types.Number ,				snake_case					:   int ,				snake_case					:   bool = False ,				snake_case					:   float = 1E-2_0 ,				snake_case					:   float = 1E8 ,				)->		str:






     '''simple docstring'''



     UpperCAmelCase__	:						Tuple       =       batch['template_pseudo_beta_mask']
     UpperCAmelCase__	:						List[str]       =       template_mask[..., None] * template_mask[..., None, :]

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

     UpperCAmelCase__	:						str       =       [dgram, template_mask_ad[..., None]]

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

     UpperCAmelCase__	:						List[Any]       =       batch['template_aatype'].shape[-1]
     to_concat.append(aatype_one_hot[..., None, :, :].expand(*aatype_one_hot.shape[:-2] ,				snake_case ,				-1 ,				-1							)							)
     to_concat.append(aatype_one_hot[..., None, :].expand(*aatype_one_hot.shape[:-2] ,				-1 ,				snake_case ,				-1							)							)

     UpperCAmelCase__	:						List[str]       =       [rc.atom_order[a] for a in ['N', 'CA', 'C']]
     UpperCAmelCase__	:						Dict       =       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=snake_case ,				)
     UpperCAmelCase__	:						int       =       rigids.get_trans()[..., None, :, :]
     UpperCAmelCase__	:						List[str]       =       rigids[..., None].invert_apply(snake_case							)

     UpperCAmelCase__	:						Optional[int]       =       torch.rsqrt(eps + torch.sum(rigid_vec**2 ,				dim=-1							)							)

     UpperCAmelCase__	:						Tuple       =       batch['template_all_atom_mask']
     UpperCAmelCase__	:						Union[str, Any]       =       t_aa_masks[..., n] * t_aa_masks[..., ca] * t_aa_masks[..., c]
     UpperCAmelCase__	:						Dict       =       template_mask[..., None] * template_mask[..., None, :]

     UpperCAmelCase__	:						Union[str, Any]       =       inv_distance_scalar * template_mask_ad
     UpperCAmelCase__	:						int       =       rigid_vec * inv_distance_scalar[..., None]

     if not use_unit_vector:
          UpperCAmelCase__	:						Union[str, Any]       =       unit_vector * 0.0

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

     UpperCAmelCase__	:						Optional[int]       =       torch.cat(snake_case ,				dim=-1							)
     UpperCAmelCase__	:						Tuple       =       act * template_mask_ad[..., None]

     return act


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






     '''simple docstring'''



     UpperCAmelCase__	:						torch.LongTensor       =       nn.functional.one_hot(batch["extra_msa"] ,				23							)
     UpperCAmelCase__	:						str       =       [
         msa_ahot,
         batch['extra_has_deletion'].unsqueeze(-1							),
         batch['extra_deletion_value'].unsqueeze(-1							),
     ]
     return torch.cat(snake_case ,				dim=-1							)


def   SCREAMING_SNAKE_CASE__					(     snake_case					:   Rigid ,				snake_case					:   torch.Tensor ,				snake_case					:   torch.Tensor ,				snake_case					:   torch.Tensor ,				)->		List[Any]:






     '''simple docstring'''



     UpperCAmelCase__	:						Optional[int]       =       rrgdf[aatype, ...]

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

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

     # [*, N, 8, 2]
     UpperCAmelCase__	:						List[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.

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

     UpperCAmelCase__	:						Optional[Any]       =       default_r.compose(Rigid(Rotation(rot_mats=snake_case							) ,				snake_case							)							)

     UpperCAmelCase__	:						Optional[Any]       =       all_frames[..., 5]
     UpperCAmelCase__	:						Union[str, Any]       =       all_frames[..., 6]
     UpperCAmelCase__	:						Any       =       all_frames[..., 7]

     UpperCAmelCase__	:						Optional[Any]       =       all_frames[..., 4]
     UpperCAmelCase__	:						Optional[int]       =       chia_frame_to_bb.compose(snake_case							)
     UpperCAmelCase__	:						Tuple       =       chia_frame_to_bb.compose(snake_case							)
     UpperCAmelCase__	:						Union[str, Any]       =       chia_frame_to_bb.compose(snake_case							)

     UpperCAmelCase__	:						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 ,				)

     UpperCAmelCase__	:						Union[str, Any]       =       r[..., None].compose(snake_case							)

     return all_frames_to_global



def   SCREAMING_SNAKE_CASE__					(     snake_case					:   Rigid ,				snake_case					:   torch.Tensor ,				snake_case					:   torch.Tensor ,				snake_case					:   torch.Tensor ,				snake_case					:   torch.Tensor ,				snake_case					:   torch.Tensor ,				)->		List[str]:






     '''simple docstring'''



     UpperCAmelCase__	:						Optional[Any]       =       group_idx[aatype, ...]

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

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

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

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

     # [*, N, 14, 3]
     UpperCAmelCase__	:						Dict       =       lit_positions[aatype, ...]
     UpperCAmelCase__	:						Optional[Any]       =       t_atoms_to_global.apply(snake_case							)
     UpperCAmelCase__	:						Union[str, Any]       =       pred_positions * atom_mask

     return pred_positions










"""simple docstring"""



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
_lowerCAmelCase  :  List[str]   =			"""src/diffusers"""
_lowerCAmelCase  :  Union[str, Any]   =			"""docs/source/en"""
_lowerCAmelCase  :  List[str]   =			"""."""


def   SCREAMING_SNAKE_CASE__					(     snake_case					:   List[Any] ,				snake_case					:   Optional[Any] ,				snake_case					:   Optional[Any]							)->		str:






     '''simple docstring'''



     with open(snake_case ,				"r" ,				encoding="utf-8" ,				newline="\n"							) as f:
          UpperCAmelCase__	:						Any       =       f.readlines()
     # Find the start prompt.
     UpperCAmelCase__	:						str       =       0
     while not lines[start_index].startswith(snake_case							):
          start_index += 1
     start_index += 1

     UpperCAmelCase__	:						Union[str, Any]       =       start_index
     while not lines[end_index].startswith(snake_case							):
          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 |
_lowerCAmelCase  :  str   =			"""Model|Encoder|Decoder|ForConditionalGeneration"""
# Regexes that match TF/Flax/PT model names.
_lowerCAmelCase  :  List[Any]   =			re.compile(r"""TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")
_lowerCAmelCase  :  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.
_lowerCAmelCase  :  str   =			re.compile(r"""(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")


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


def   SCREAMING_SNAKE_CASE__					(     snake_case					:   Optional[int]							)->		List[Any]:






     '''simple docstring'''



     UpperCAmelCase__	:						List[str]       =       re.finditer(".+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)" ,				snake_case							)
     return [m.group(0							) for m in matches]


def   SCREAMING_SNAKE_CASE__					(     snake_case					:   int ,				snake_case					:   Tuple							)->		Union[str, Any]:






     '''simple docstring'''



     UpperCAmelCase__	:						Optional[int]       =       2 if text == "✅" or text == "❌" else len(snake_case							)
     UpperCAmelCase__	:						int       =       (width - text_length) // 2
     UpperCAmelCase__	:						Any       =       width - text_length - left_indent
     return " " * left_indent + text + " " * right_indent


def   SCREAMING_SNAKE_CASE__					(     )->		Union[str, Any]:






     '''simple docstring'''



     UpperCAmelCase__	:						Tuple       =       diffusers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
     UpperCAmelCase__	:						Any       =       {
         name: config_mapping_names[code]
         for code, name in diffusers_module.MODEL_NAMES_MAPPING.items()
         if code in config_mapping_names
     }
     UpperCAmelCase__	:						Optional[int]       =       {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.
     UpperCAmelCase__	:						Optional[Any]       =       collections.defaultdict(snake_case							)
     UpperCAmelCase__	:						Dict       =       collections.defaultdict(snake_case							)
     UpperCAmelCase__	:						List[str]       =       collections.defaultdict(snake_case							)
     UpperCAmelCase__	:						Tuple       =       collections.defaultdict(snake_case							)
     UpperCAmelCase__	:						Any       =       collections.defaultdict(snake_case							)

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

          if lookup_dict is not None:
               while len(snake_case							) > 0:
                    if attr_name in model_name_to_prefix.values():
                         UpperCAmelCase__	:						int       =       True
                         break
                    # Try again after removing the last word in the name
                    UpperCAmelCase__	:						List[str]       =       "".join(camel_case_split(snake_case							)[:-1]							)

    # Let's build that table!
     UpperCAmelCase__	:						int       =       list(model_name_to_config.keys()							)
     model_names.sort(key=str.lower							)
     UpperCAmelCase__	:						Dict       =       ["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).
     UpperCAmelCase__	:						List[str]       =       [len(snake_case							) + 2 for c in columns]
     UpperCAmelCase__	:						Tuple       =       max([len(snake_case							) for name in model_names]							) + 2

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

     UpperCAmelCase__	:						str       =       {True: "✅", False: "❌"}
     for name in model_names:
          UpperCAmelCase__	:						str       =       model_name_to_prefix[name]
          UpperCAmelCase__	:						Dict       =       [
              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(snake_case ,				snake_case							) for l, w in zip(snake_case ,				snake_case							)]							) + "|\n"
     return table


def   SCREAMING_SNAKE_CASE__					(     snake_case					:   Union[str, Any]=False							)->		List[str]:






     '''simple docstring'''



     UpperCAmelCase__       ,							UpperCAmelCase__       ,							UpperCAmelCase__       ,							UpperCAmelCase__	:						str       =       _find_text_in_file(
         filename=os.path.join(snake_case ,				"index.mdx"							) ,				start_prompt="<!--This table is updated automatically from the auto modules" ,				end_prompt="<!-- End table-->" ,				)
     UpperCAmelCase__	:						Union[str, Any]       =       get_model_table_from_auto_modules()

     if current_table != new_table:
          if overwrite:
               with open(os.path.join(snake_case ,				"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__":
 _lowerCAmelCase  :  Union[str, Any]   =			argparse.ArgumentParser()
 parser.add_argument("""--fix_and_overwrite""", action="""store_true""", help="""Whether to fix inconsistencies.""")
 _lowerCAmelCase  :  Dict   =			parser.parse_args()

 check_model_table(args.fix_and_overwrite)


