


'''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 lowerCamelCase__			(						__lowerCamelCase       :							Optional[int]       ,    __lowerCamelCase       :							Optional[Any]       ,    __lowerCamelCase       :							Optional[int]							):







   '''simple docstring'''




   ...





@overload
def lowerCamelCase__			(						__lowerCamelCase       :							Optional[int]       ,    __lowerCamelCase       :							Dict       ,    __lowerCamelCase       :							Union[str, Any]							):







   '''simple docstring'''




   ...





def lowerCamelCase__			(						__lowerCamelCase       :							str       ,    __lowerCamelCase       :							Union[str, Any]       ,    __lowerCamelCase       :							str							):







   '''simple docstring'''




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





def lowerCamelCase__			(						__lowerCamelCase       :							str       ,    __lowerCamelCase       :							Dict							):







   '''simple docstring'''




   _UpperCAmelCase   :   str							    =batched_gather(
       __UpperCamelCase       ,    batch['residx_atom37_to_atom14']       ,    dim=-2       ,    no_batch_dims=len(atomaa.shape[:-2]							)       ,    )

   _UpperCAmelCase   :   int							    =atomaa_data * batch['atom37_atom_exists'][..., None]

   return atomaa_data





def lowerCamelCase__			(						__lowerCamelCase       :							Dict							):







   '''simple docstring'''




   _UpperCAmelCase   :   Union[str, 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   :   List[str]							    =torch.cat(
       [
           nn.functional.one_hot(__UpperCamelCase       ,    2_2							),
           torsion_angles_sin_cos.reshape(*torsion_angles_sin_cos.shape[:-2]       ,    1_4							),
           alt_torsion_angles_sin_cos.reshape(*alt_torsion_angles_sin_cos.shape[:-2]       ,    1_4							),
           torsion_angles_mask,
       ]       ,    dim=-1       ,    )

   return template_angle_feat





def lowerCamelCase__			(						__lowerCamelCase       :							Any       ,    __lowerCamelCase       :							Tuple       ,    __lowerCamelCase       :							Union[str, Any]       ,    __lowerCamelCase       :							Any       ,    __lowerCamelCase       :							List[str] = False       ,    __lowerCamelCase       :							Any = 1e-20       ,    __lowerCamelCase       :							int = 1e8       ,    ):







   '''simple docstring'''




   _UpperCAmelCase   :   Any							    =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   :   Dict							    =batch['template_pseudo_beta']
   _UpperCAmelCase   :   List[Any]							    =torch.sum((tpb[..., None, :] - tpb[..., None, :, :]) ** 2       ,    dim=-1       ,    keepdim=__UpperCamelCase							)
   _UpperCAmelCase   :   Dict							    =torch.linspace(__UpperCamelCase       ,    __UpperCamelCase       ,    __UpperCamelCase       ,    device=tpb.device							) ** 2
   _UpperCAmelCase   :   Tuple							    =torch.cat([lower[1:], lower.new_tensor([inf]							)]       ,    dim=-1							)
   _UpperCAmelCase   :   Optional[int]							    =((dgram > lower) * (dgram < upper)).type(dgram.dtype							)

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

   _UpperCAmelCase   :   Tuple							    =nn.functional.one_hot(
       batch['template_aatype']       ,    rc.restype_num + 2       ,    )

   _UpperCAmelCase   :   int							    =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   :   Any							    =[rc.atom_order[a] for a in ['N', 'CA', 'C']]
   _UpperCAmelCase   :   Optional[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=__UpperCamelCase       ,    )
   _UpperCAmelCase   :   List[Any]							    =rigids.get_trans()[..., None, :, :]
   _UpperCAmelCase   :   str							    =rigids[..., None].invert_apply(__UpperCamelCase							)

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

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

   _UpperCAmelCase   :   Optional[Any]							    =inv_distance_scalar * template_mask_ad
   _UpperCAmelCase   :   Union[str, Any]							    =rigid_vec * inv_distance_scalar[..., None]

   if not use_unit_vector:
      _UpperCAmelCase   :   Any							    =unit_vector * 0.0

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

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

   return act





def lowerCamelCase__			(						__lowerCamelCase       :							str							):







   '''simple docstring'''




   _UpperCAmelCase   :   Any							    =nn.functional.one_hot(batch['extra_msa']       ,    2_3							)
   _UpperCAmelCase   :   Any							    =[
       msa_ahot,
       batch['extra_has_deletion'].unsqueeze(-1							),
       batch['extra_deletion_value'].unsqueeze(-1							),
   ]
   return torch.cat(__UpperCamelCase       ,    dim=-1							)





def lowerCamelCase__			(						__lowerCamelCase       :							Optional[Any]       ,    __lowerCamelCase       :							List[str]       ,    __lowerCamelCase       :							Optional[int]       ,    __lowerCamelCase       :							Any       ,    ):







   '''simple docstring'''




   _UpperCAmelCase   :   Union[str, Any]							    =rrgdf[aatype, ...]

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

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

   # [*, N, 8, 2]
   _UpperCAmelCase   :   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   :   Optional[Any]							    =alpha.new_zeros(default_r.get_rots().get_rot_mats().shape							)
   _UpperCAmelCase   :   Dict							    =1
   _UpperCAmelCase   :   List[Any]							    =alpha[..., 1]
   _UpperCAmelCase   :   Union[str, Any]							    =-alpha[..., 0]
   _UpperCAmelCase   :   Any							    =alpha

   _UpperCAmelCase   :   Optional[int]							    =default_r.compose(Rigid(Rotation(rot_mats=__UpperCamelCase							)       ,    __UpperCamelCase							)							)

   _UpperCAmelCase   :   List[str]							    =all_frames[..., 5]
   _UpperCAmelCase   :   Dict							    =all_frames[..., 6]
   _UpperCAmelCase   :   Union[str, Any]							    =all_frames[..., 7]

   _UpperCAmelCase   :   Optional[Any]							    =all_frames[..., 4]
   _UpperCAmelCase   :   Tuple							    =chia_frame_to_bb.compose(__UpperCamelCase							)
   _UpperCAmelCase   :   Dict							    =chia_frame_to_bb.compose(__UpperCamelCase							)
   _UpperCAmelCase   :   Optional[int]							    =chia_frame_to_bb.compose(__UpperCamelCase							)

   _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   :   Any							    =r[..., None].compose(__UpperCamelCase							)

   return all_frames_to_global





def lowerCamelCase__			(						__lowerCamelCase       :							str       ,    __lowerCamelCase       :							Optional[int]       ,    __lowerCamelCase       :							Tuple       ,    __lowerCamelCase       :							List[str]       ,    __lowerCamelCase       :							int       ,    __lowerCamelCase       :							List[Any]       ,    ):







   '''simple docstring'''




   _UpperCAmelCase   :   str							    =group_idx[aatype, ...]

   # [*, N, 14, 8]
   _UpperCAmelCase   :   Dict							    =nn.functional.one_hot(
       __UpperCamelCase       ,    num_classes=default_frames.shape[-3]       ,    )

   # [*, N, 14, 8]
   _UpperCAmelCase   :   Union[str, Any]							    =r[..., None, :] * group_mask_one_hot

   # [*, N, 14]
   _UpperCAmelCase   :   Union[str, Any]							    =t_atoms_to_global.map_tensor_fn(lambda __lowerCamelCase							: torch.sum(__UpperCamelCase       ,    dim=-1							)							)

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

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

   return pred_positions



"""simple docstring"""


import importlib
import inspect
import json
import os
from collections import OrderedDict

# Build the list of all feature extractors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_trust_remote_code
from ...feature_extraction_utils import FeatureExtractionMixin
from ...image_processing_utils import ImageProcessingMixin
from ...tokenization_utils import TOKENIZER_CONFIG_FILE
from ...utils import FEATURE_EXTRACTOR_NAME, get_file_from_repo, logging
from .auto_factory import _LazyAutoMapping
from .configuration_auto import (
    CONFIG_MAPPING_NAMES,
    AutoConfig,
    model_type_to_module_name,
    replace_list_option_in_docstrings,
)
from .feature_extraction_auto import AutoFeatureExtractor
from .image_processing_auto import AutoImageProcessor
from .tokenization_auto import AutoTokenizer


__A       						=       logging.get_logger(__name__)

__A       						=       OrderedDict(
    [
        ('align', 'AlignProcessor'),
        ('altclip', 'AltCLIPProcessor'),
        ('bark', 'BarkProcessor'),
        ('blip', 'BlipProcessor'),
        ('blip-2', 'Blip2Processor'),
        ('bridgetower', 'BridgeTowerProcessor'),
        ('chinese_clip', 'ChineseCLIPProcessor'),
        ('clap', 'ClapProcessor'),
        ('clip', 'CLIPProcessor'),
        ('clipseg', 'CLIPSegProcessor'),
        ('flava', 'FlavaProcessor'),
        ('git', 'GitProcessor'),
        ('groupvit', 'CLIPProcessor'),
        ('hubert', 'Wav2Vec2Processor'),
        ('instructblip', 'InstructBlipProcessor'),
        ('layoutlmv2', 'LayoutLMv2Processor'),
        ('layoutlmv3', 'LayoutLMv3Processor'),
        ('markuplm', 'MarkupLMProcessor'),
        ('mctct', 'MCTCTProcessor'),
        ('mgp-str', 'MgpstrProcessor'),
        ('oneformer', 'OneFormerProcessor'),
        ('owlvit', 'OwlViTProcessor'),
        ('pix2struct', 'Pix2StructProcessor'),
        ('sam', 'SamProcessor'),
        ('sew', 'Wav2Vec2Processor'),
        ('sew-d', 'Wav2Vec2Processor'),
        ('speech_to_text', 'Speech2TextProcessor'),
        ('speech_to_text_2', 'Speech2Text2Processor'),
        ('speecht5', 'SpeechT5Processor'),
        ('trocr', 'TrOCRProcessor'),
        ('tvlt', 'TvltProcessor'),
        ('unispeech', 'Wav2Vec2Processor'),
        ('unispeech-sat', 'Wav2Vec2Processor'),
        ('vilt', 'ViltProcessor'),
        ('vision-text-dual-encoder', 'VisionTextDualEncoderProcessor'),
        ('wav2vec2', 'Wav2Vec2Processor'),
        ('wav2vec2-conformer', 'Wav2Vec2Processor'),
        ('wavlm', 'Wav2Vec2Processor'),
        ('whisper', 'WhisperProcessor'),
        ('xclip', 'XCLIPProcessor'),
    ]
)

__A       						=       _LazyAutoMapping(CONFIG_MAPPING_NAMES, PROCESSOR_MAPPING_NAMES)



def     _lowerCamelCase(__UpperCamelCase       )				-> Optional[int]:
	for module_name, processors in PROCESSOR_MAPPING_NAMES.items():
		if class_name in processors:
			_lowerCAmelCase								=model_type_to_module_name(__UpperCamelCase       )

			_lowerCAmelCase								=importlib.import_module(F'''.{module_name}'''    , """transformers.models"""       )
			try:
				return getattr(__UpperCamelCase    , __UpperCamelCase       )
			except AttributeError:
				continue

	for processor in PROCESSOR_MAPPING._extra_content.values():
		if getattr(__UpperCamelCase    , """__name__"""    , __UpperCamelCase       ) == class_name:
			return processor

    # We did not fine the class, but maybe it's because a dep is missing. In that case, the class will be in the main
    # init and we return the proper dummy to get an appropriate error message.
	_lowerCAmelCase								=importlib.import_module("""transformers"""       )
	if hasattr(__UpperCamelCase    , __UpperCamelCase       ):
		return getattr(__UpperCamelCase    , __UpperCamelCase       )

	return None







class 		lowerCamelCase__					:





			'''simple docstring'''



			def __init__(							self  ) ->     str:
				raise EnvironmentError(
				    """AutoProcessor is designed to be instantiated """
				    """using the `AutoProcessor.from_pretrained(pretrained_model_name_or_path)` method."""  )



			@classmethod
			@replace_list_option_in_docstrings(__UpperCAmelCase  )
			def 					_lowerCAmelCase		(							cls     ,     __UpperCAmelCase     ,     **__UpperCAmelCase  ) ->     Tuple:
				_lowerCAmelCase								=kwargs.pop("""config"""     ,     __UpperCAmelCase  )
				_lowerCAmelCase								=kwargs.pop("""trust_remote_code"""     ,     __UpperCAmelCase  )
				_lowerCAmelCase								=True

				_lowerCAmelCase								=None
				_lowerCAmelCase								=None

				# First, let's see if we have a preprocessor config.
				# Filter the kwargs for `get_file_from_repo`.
				_lowerCAmelCase								={
				    key: kwargs[key] for key in inspect.signature(__UpperCAmelCase  ).parameters.keys() if key in kwargs
				}
				# Let's start by checking whether the processor class is saved in an image processor
				_lowerCAmelCase								=get_file_from_repo(
				    __UpperCAmelCase     ,     __UpperCAmelCase     ,     **__UpperCAmelCase  )
				if preprocessor_config_file is not None:
					_lowerCAmelCase						,							_lowerCAmelCase								=ImageProcessingMixin.get_image_processor_dict(__UpperCAmelCase     ,     **__UpperCAmelCase  )
					_lowerCAmelCase								=config_dict.get("""processor_class"""     ,     __UpperCAmelCase  )
					if "AutoProcessor" in config_dict.get("""auto_map"""     ,     {}  ):
						_lowerCAmelCase								=config_dict["""auto_map"""]["""AutoProcessor"""]

        # If not found, let's check whether the processor class is saved in a feature extractor config
				if preprocessor_config_file is not None and processor_class is None:
					_lowerCAmelCase						,							_lowerCAmelCase								=FeatureExtractionMixin.get_feature_extractor_dict(__UpperCAmelCase     ,     **__UpperCAmelCase  )
					_lowerCAmelCase								=config_dict.get("""processor_class"""     ,     __UpperCAmelCase  )
					if "AutoProcessor" in config_dict.get("""auto_map"""     ,     {}  ):
						_lowerCAmelCase								=config_dict["""auto_map"""]["""AutoProcessor"""]

				if processor_class is None:
					# Next, let's check whether the processor class is saved in a tokenizer
					_lowerCAmelCase								=get_file_from_repo(
					    __UpperCAmelCase     ,     __UpperCAmelCase     ,     **__UpperCAmelCase  )
					if tokenizer_config_file is not None:
						with open(__UpperCAmelCase     ,     encoding="""utf-8"""  ) as reader:
							_lowerCAmelCase								=json.load(__UpperCAmelCase  )

						_lowerCAmelCase								=config_dict.get("""processor_class"""     ,     __UpperCAmelCase  )
						if "AutoProcessor" in config_dict.get("""auto_map"""     ,     {}  ):
							_lowerCAmelCase								=config_dict["""auto_map"""]["""AutoProcessor"""]

				if processor_class is None:
					# Otherwise, load config, if it can be loaded.
					if not isinstance(__UpperCAmelCase     ,     __UpperCAmelCase  ):
						_lowerCAmelCase								=AutoConfig.from_pretrained(
						    __UpperCAmelCase     ,     trust_remote_code=__UpperCAmelCase     ,     **__UpperCAmelCase  )

					# And check if the config contains the processor class.
					_lowerCAmelCase								=getattr(__UpperCAmelCase     ,     """processor_class"""     ,     __UpperCAmelCase  )
					if hasattr(__UpperCAmelCase     ,     """auto_map"""  ) and "AutoProcessor" in config.auto_map:
						_lowerCAmelCase								=config.auto_map["""AutoProcessor"""]

				if processor_class is not None:
					_lowerCAmelCase								=processor_class_from_name(__UpperCAmelCase  )

				_lowerCAmelCase								=processor_auto_map is not None
				_lowerCAmelCase								=processor_class is not None or type(__UpperCAmelCase  ) in PROCESSOR_MAPPING
				_lowerCAmelCase								=resolve_trust_remote_code(
				    __UpperCAmelCase     ,     __UpperCAmelCase     ,     __UpperCAmelCase     ,     __UpperCAmelCase  )

				if has_remote_code and trust_remote_code:
					_lowerCAmelCase								=get_class_from_dynamic_module(
					    __UpperCAmelCase     ,     __UpperCAmelCase     ,     **__UpperCAmelCase  )
					_lowerCAmelCase								=kwargs.pop("""code_revision"""     ,     __UpperCAmelCase  )
					if os.path.isdir(__UpperCAmelCase  ):
						processor_class.register_for_auto_class()
					return processor_class.from_pretrained(
					    __UpperCAmelCase     ,     trust_remote_code=__UpperCAmelCase     ,     **__UpperCAmelCase  )
				elif processor_class is not None:
					return processor_class.from_pretrained(
					    __UpperCAmelCase     ,     trust_remote_code=__UpperCAmelCase     ,     **__UpperCAmelCase  )
				# Last try: we use the PROCESSOR_MAPPING.
				elif type(__UpperCAmelCase  ) in PROCESSOR_MAPPING:
					return PROCESSOR_MAPPING[type(__UpperCAmelCase  )].from_pretrained(__UpperCAmelCase     ,     **__UpperCAmelCase  )

				# At this stage, there doesn't seem to be a `Processor` class available for this model, so let's try a
				# tokenizer.
				try:
					return AutoTokenizer.from_pretrained(
					    __UpperCAmelCase     ,     trust_remote_code=__UpperCAmelCase     ,     **__UpperCAmelCase  )
				except Exception:
					try:
						return AutoImageProcessor.from_pretrained(
						    __UpperCAmelCase     ,     trust_remote_code=__UpperCAmelCase     ,     **__UpperCAmelCase  )
					except Exception:
						pass

					try:
						return AutoFeatureExtractor.from_pretrained(
						    __UpperCAmelCase     ,     trust_remote_code=__UpperCAmelCase     ,     **__UpperCAmelCase  )
					except Exception:
						pass

				raise ValueError(
				    f'''Unrecognized processing class in {pretrained_model_name_or_path}. Can\'t instantiate a processor, a '''
				    """tokenizer, an image processor or a feature extractor for this model. Make sure the repository contains"""
				    """the files of at least one of those processing classes."""  )



			@staticmethod
			def 					_lowerCAmelCase		(							__UpperCAmelCase     ,     __UpperCAmelCase  ) ->     Optional[int]:
				PROCESSOR_MAPPING.register(__UpperCAmelCase     ,     __UpperCAmelCase  )



