
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    _a (						lowerCamelCase,     lowerCamelCase,     lowerCamelCase							):
 ...





@overload
def    _a (						lowerCamelCase,     lowerCamelCase,     lowerCamelCase							):
 ...





def    _a (						lowerCamelCase,     lowerCamelCase,     lowerCamelCase							):
 lowerCamelCase		:     Dict          =				aatype == rc.restype_order["""G"""]
 lowerCamelCase		:     Any          =				rc.atom_order["""CA"""]
 lowerCamelCase		:     int          =				rc.atom_order["""CB"""]
 lowerCamelCase		:     Tuple          =				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		:     Tuple          =				torch.where(
      lowerCamelCase,     all_atom_masks[..., ca_idx],     all_atom_masks[..., cb_idx],     )
  return pseudo_beta, pseudo_beta_mask
 else:
  return pseudo_beta





def    _a (						lowerCamelCase,     lowerCamelCase							):
 lowerCamelCase		:     List[str]          =				batched_gather(
     lowerCamelCase,     batch["""residx_atom37_to_atom14"""],     dim=-2,     no_batch_dims=len(atomaa.shape[:-2]							),     )

 lowerCamelCase		:     Dict          =				atomaa_data * batch["""atom37_atom_exists"""][..., None]

 return atomaa_data





def    _a (						lowerCamelCase							):
 lowerCamelCase		:     Any          =				template_feats["""template_aatype"""]
 lowerCamelCase		:     Optional[int]          =				template_feats["""template_torsion_angles_sin_cos"""]
 lowerCamelCase		:     List[Any]          =				template_feats["""template_alt_torsion_angles_sin_cos"""]
 lowerCamelCase		:     Any          =				template_feats["""template_torsion_angles_mask"""]
 lowerCamelCase		:     Any          =				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    _a (						lowerCamelCase,     lowerCamelCase,     lowerCamelCase,     lowerCamelCase,     lowerCamelCase = False,     lowerCamelCase = 1e-20,     lowerCamelCase = 1e8,     ):
 lowerCamelCase		:     Optional[int]          =				batch["""template_pseudo_beta_mask"""]
 lowerCamelCase		:     int          =				template_mask[..., None] * template_mask[..., None, :]

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

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

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

 lowerCamelCase		:     Union[str, Any]          =				batch["""template_all_atom_mask"""]
 lowerCamelCase		:     Dict          =				t_aa_masks[..., n] * t_aa_masks[..., ca] * t_aa_masks[..., c]
 lowerCamelCase		:     int          =				template_mask[..., None] * template_mask[..., None, :]

 lowerCamelCase		:     List[Any]          =				inv_distance_scalar * template_mask_ad
 lowerCamelCase		:     Any          =				rigid_vec * inv_distance_scalar[..., None]

 if not use_unit_vector:
  lowerCamelCase		:     Tuple          =				unit_vector * 0.0

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

 lowerCamelCase		:     Dict          =				torch.cat(lowerCamelCase,     dim=-1							)
 lowerCamelCase		:     str          =				act * template_mask_ad[..., None]

 return act





def    _a (						lowerCamelCase							):
 lowerCamelCase		:     torch.LongTensor          =				nn.functional.one_hot(batch["""extra_msa"""],     23							)
 lowerCamelCase		:     Tuple          =				[
     msa_ahot,
     batch["""extra_has_deletion"""].unsqueeze(-1							),
     batch["""extra_deletion_value"""].unsqueeze(-1							),
 ]
 return torch.cat(lowerCamelCase,     dim=-1							)





def    _a (						lowerCamelCase,     lowerCamelCase,     lowerCamelCase,     lowerCamelCase,     ):
 # [*, 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		:     int          =				r.from_tensor_axa(lowerCamelCase							)

 lowerCamelCase		:     Tuple          =				alpha.new_zeros((*((1,) * len(alpha.shape[:-1]							)), 2)							)
 lowerCamelCase		:     Union[str, Any]          =				1

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

 lowerCamelCase		:     Tuple          =				default_r.compose(Rigid(Rotation(rot_mats=lowerCamelCase							),     lowerCamelCase							)							)

 lowerCamelCase		:     Optional[int]          =				all_frames[..., 5]
 lowerCamelCase		:     List[str]          =				all_frames[..., 6]
 lowerCamelCase		:     Tuple          =				all_frames[..., 7]

 lowerCamelCase		:     Union[str, Any]          =				all_frames[..., 4]
 lowerCamelCase		:     Tuple          =				chia_frame_to_bb.compose(lowerCamelCase							)
 lowerCamelCase		:     List[str]          =				chia_frame_to_bb.compose(lowerCamelCase							)
 lowerCamelCase		:     Tuple          =				chia_frame_to_bb.compose(lowerCamelCase							)

 lowerCamelCase		:     Optional[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		:     Optional[Any]          =				r[..., None].compose(lowerCamelCase							)

 return all_frames_to_global





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

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

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

 return pred_positions






import unittest

import pytest

from transformers import (
    MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    MBartaaTokenizerFast,
    MBartConfig,
    MBartForConditionalGeneration,
    TranslationPipeline,
    pipeline,
)
from transformers.testing_utils import is_pipeline_test, require_tf, require_torch, slow

from .test_pipelines_common import ANY





@is_pipeline_test
class 							A__		(     unittest.TestCase):
      _UpperCAmelCase    :   Tuple   	=      MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
      _UpperCAmelCase    :   Optional[Any]   	=      TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
      def 						UpperCamelCase__							(				self   ,  __magic_name__   ,  __magic_name__   ,  __magic_name__				):
       if isinstance(model.config   ,  __magic_name__				):
        lowerCamelCase      ,   lowerCamelCase		:     Union[str, Any]          =				list(tokenizer.lang_code_to_id.keys()				)[:2]
        lowerCamelCase		:     Tuple          =				TranslationPipeline(model=__magic_name__   ,  tokenizer=__magic_name__   ,  src_lang=__magic_name__   ,  tgt_lang=__magic_name__				)
       else:
        lowerCamelCase		:     Tuple          =				TranslationPipeline(model=__magic_name__   ,  tokenizer=__magic_name__				)
       return translator, ["Some string", "Some other text"]
      def 						UpperCamelCase__							(				self   ,  __magic_name__   ,  __magic_name__				):
       lowerCamelCase		:     Optional[Any]          =				translator("""Some string"""				)
       self.assertEqual(__magic_name__   ,  [{"""translation_text""": ANY(__magic_name__				)}]				)

       lowerCamelCase		:     Dict          =				translator(["""Some string"""]				)
       self.assertEqual(__magic_name__   ,  [{"""translation_text""": ANY(__magic_name__				)}]				)

       lowerCamelCase		:     Any          =				translator(["""Some string""", """other string"""]				)
       self.assertEqual(__magic_name__   ,  [{"""translation_text""": ANY(__magic_name__				)}, {"""translation_text""": ANY(__magic_name__				)}]				)
      @require_torch
      def 						UpperCamelCase__							(				self				):
       lowerCamelCase		:     Any          =				pipeline("""translation_en_to_ro"""   ,  model="""patrickvonplaten/t5-tiny-random"""   ,  framework="""pt"""				)
       lowerCamelCase		:     Union[str, Any]          =				translator("""This is a test string"""   ,  max_length=2_0				)
       self.assertEqual(
           __magic_name__   ,  [
               {
                   """translation_text""": (
                       """Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide"""
                       """ Beide Beide"""
                   )
               }
           ]   ,  )
      @require_tf
      def 						UpperCamelCase__							(				self				):
       lowerCamelCase		:     str          =				pipeline("""translation_en_to_ro"""   ,  model="""patrickvonplaten/t5-tiny-random"""   ,  framework="""tf"""				)
       lowerCamelCase		:     Tuple          =				translator("""This is a test string"""   ,  max_length=2_0				)
       self.assertEqual(
           __magic_name__   ,  [
               {
                   """translation_text""": (
                       """Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide"""
                       """ Beide Beide"""
                   )
               }
           ]   ,  )
      @require_torch
      def 						UpperCamelCase__							(				self				):
       lowerCamelCase		:     int          =				pipeline("""translation_en_to_de"""   ,  model="""patrickvonplaten/t5-tiny-random"""   ,  framework="""pt"""				)
       lowerCamelCase		:     List[Any]          =				translator("""This is a test string"""   ,  max_length=2_0				)
       self.assertEqual(
           __magic_name__   ,  [
               {
                   """translation_text""": (
                       """monoton monoton monoton monoton monoton monoton monoton monoton monoton monoton urine urine"""
                       """ urine urine urine urine urine urine urine"""
                   )
               }
           ]   ,  )






      @require_tf
      def 						UpperCamelCase__							(				self				):
       lowerCamelCase		:     Union[str, Any]          =				pipeline("""translation_en_to_de"""   ,  model="""patrickvonplaten/t5-tiny-random"""   ,  framework="""tf"""				)
       lowerCamelCase		:     Union[str, Any]          =				translator("""This is a test string"""   ,  max_length=2_0				)
       self.assertEqual(
           __magic_name__   ,  [
               {
                   """translation_text""": (
                       """monoton monoton monoton monoton monoton monoton monoton monoton monoton monoton urine urine"""
                       """ urine urine urine urine urine urine urine"""
                   )
               }
           ]   ,  )





class 							A__		(     unittest.TestCase):
      @require_torch
      @slow
      def 						UpperCamelCase__							(				self				):
       # We don't provide a default for this pair
       with self.assertRaises(__magic_name__				):
        pipeline(task="""translation_cn_to_ar"""				)

       # but we do for this one
       lowerCamelCase		:     Optional[Any]          =				pipeline(task="""translation_en_to_de"""				)
       self.assertEqual(translator._preprocess_params["""src_lang"""]   ,  """en"""				)
       self.assertEqual(translator._preprocess_params["""tgt_lang"""]   ,  """de"""				)
      @require_torch
      @slow
      def 						UpperCamelCase__							(				self				):
       lowerCamelCase		:     Optional[int]          =				MBartForConditionalGeneration.from_pretrained("""facebook/mbart-large-50-many-to-many-mmt"""				)
       lowerCamelCase		:     List[Any]          =				MBartaaTokenizerFast.from_pretrained("""facebook/mbart-large-50-many-to-many-mmt"""				)

       lowerCamelCase		:     Any          =				pipeline(task="""translation"""   ,  model=__magic_name__   ,  tokenizer=__magic_name__				)
       # Missing src_lang, tgt_lang
       with self.assertRaises(__magic_name__				):
        translator("""This is a test"""				)

       lowerCamelCase		:     Optional[int]          =				translator("""This is a test"""   ,  src_lang="""en_XX"""   ,  tgt_lang="""ar_AR"""				)
       self.assertEqual(__magic_name__   ,  [{"""translation_text""": """هذا إختبار"""}]				)

       lowerCamelCase		:     Optional[Any]          =				translator("""This is a test"""   ,  src_lang="""en_XX"""   ,  tgt_lang="""hi_IN"""				)
       self.assertEqual(__magic_name__   ,  [{"""translation_text""": """यह एक परीक्षण है"""}]				)

       # src_lang, tgt_lang can be defined at pipeline call time
       lowerCamelCase		:     str          =				pipeline(task="""translation"""   ,  model=__magic_name__   ,  tokenizer=__magic_name__   ,  src_lang="""en_XX"""   ,  tgt_lang="""ar_AR"""				)
       lowerCamelCase		:     Union[str, Any]          =				translator("""This is a test"""				)
       self.assertEqual(__magic_name__   ,  [{"""translation_text""": """هذا إختبار"""}]				)
      @require_torch
      def 						UpperCamelCase__							(				self				):
       lowerCamelCase		:     List[str]          =				"""patrickvonplaten/t5-tiny-random"""
       lowerCamelCase		:     Tuple          =				pipeline(task="""translation_cn_to_ar"""   ,  model=__magic_name__				)
       self.assertEqual(translator._preprocess_params["""src_lang"""]   ,  """cn"""				)
       self.assertEqual(translator._preprocess_params["""tgt_lang"""]   ,  """ar"""				)
      @require_torch
      def 						UpperCamelCase__							(				self				):
       lowerCamelCase		:     Dict          =				"""patrickvonplaten/t5-tiny-random"""
       with pytest.warns(__magic_name__   ,  match=r""".*translation_en_to_de.*"""				):
        lowerCamelCase		:     Optional[int]          =				pipeline(task="""translation"""   ,  model=__magic_name__				)
       self.assertEqual(translator.task   ,  """translation_en_to_de"""				)
       self.assertEqual(translator._preprocess_params["""src_lang"""]   ,  """en"""				)
       self.assertEqual(translator._preprocess_params["""tgt_lang"""]   ,  """de"""				)






      @require_torch
      def 						UpperCamelCase__							(				self				):
       with self.assertRaises(__magic_name__				):
        pipeline(task="""translation"""				)




