


'''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   (A						,				A						,				A ) -> torch.Tensor:






       """simple docstring"""







       ...

@overload
def _SCREAMING_SNAKE_CASE   (A						,				A						,				A ) -> Tuple[torch.Tensor, torch.Tensor]:






       """simple docstring"""







       ...

def _SCREAMING_SNAKE_CASE   (A						,				A						,				A ) -> Any:






       """simple docstring"""







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

def _SCREAMING_SNAKE_CASE   (A						,				A ) -> torch.Tensor:






       """simple docstring"""







       lowercase__       =				batched_gather(
           A						,				batch['''residx_atom37_to_atom14''']						,				dim=-2						,				no_batch_dims=len(atomaa.shape[:-2] )						,				)

       lowercase__       =				atomaa_data * batch['''atom37_atom_exists'''][..., None]

       return atomaa_data

def _SCREAMING_SNAKE_CASE   (A ) -> torch.Tensor:






       """simple docstring"""







       lowercase__       =				template_feats['''template_aatype''']
       lowercase__       =				template_feats['''template_torsion_angles_sin_cos''']
       lowercase__       =				template_feats['''template_alt_torsion_angles_sin_cos''']
       lowercase__       =				template_feats['''template_torsion_angles_mask''']
       lowercase__       =				torch.cat(
           [
               nn.functional.one_hot(A						,				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   (A						,				A						,				A						,				A						,				A = False						,				A = 1E-20						,				A = 1E8						,				) -> torch.Tensor:






       """simple docstring"""







       lowercase__       =				batch['''template_pseudo_beta_mask''']
       lowercase__       =				template_mask[..., None] * template_mask[..., None, :]

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

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

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

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

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

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

       lowercase__       =				batch['''template_all_atom_mask''']
       lowercase__       =				t_aa_masks[..., n] * t_aa_masks[..., ca] * t_aa_masks[..., c]
       lowercase__       =				template_mask[..., None] * template_mask[..., None, :]

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

       if not use_unit_vector:
              lowercase__       =				unit_vector * 0.0

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

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

       return act

def _SCREAMING_SNAKE_CASE   (A ) -> torch.Tensor:






       """simple docstring"""







       lowercase__       =				nn.functional.one_hot(batch['''extra_msa''']						,				23 )
       lowercase__       =				[
           msa_ahot,
           batch['''extra_has_deletion'''].unsqueeze(-1 ),
           batch['''extra_deletion_value'''].unsqueeze(-1 ),
       ]
       return torch.cat(A						,				dim=-1 )

def _SCREAMING_SNAKE_CASE   (A						,				A						,				A						,				A						,				) -> Rigid:






       """simple docstring"""







       lowercase__       =				rrgdf[aatype, ...]

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

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

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

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

       lowercase__       =				default_r.compose(Rigid(Rotation(rot_mats=A )						,				A ) )

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

       lowercase__       =				all_frames[..., 4]
       lowercase__       =				chia_frame_to_bb.compose(A )
       lowercase__       =				chia_frame_to_bb.compose(A )
       lowercase__       =				chia_frame_to_bb.compose(A )

       lowercase__       =				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						,				)

       lowercase__       =				r[..., None].compose(A )

       return all_frames_to_global

def _SCREAMING_SNAKE_CASE   (A						,				A						,				A						,				A						,				A						,				A						,				) -> torch.Tensor:






       """simple docstring"""







       lowercase__       =				group_idx[aatype, ...]

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

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

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

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

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

       return pred_positions








'''simple docstring'''

import copy
import inspect
import unittest

import numpy as np
from huggingface_hub import hf_hub_download

from transformers import VivitConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor


if is_torch_available():
 import torch
 from torch import nn

 from transformers import MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING, VivitForVideoClassification, VivitModel
 from transformers.models.vivit.modeling_vivit import VIVIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
 from transformers import VivitImageProcessor
class 						__lowerCAmelCase		:





      '''simple docstring'''






      def __init__(self					:					int     ,		UpperCamelCase					:					str     ,		UpperCamelCase					:					str=2     ,		UpperCamelCase					:					Union[str, Any]=True     ,		UpperCamelCase					:					Dict=True     ,		UpperCamelCase					:					List[Any]=10     ,		UpperCamelCase					:					Optional[Any]=10     ,		UpperCamelCase					:					Optional[int]=8     ,		UpperCamelCase					:					int=[2, 4, 4]     ,		UpperCamelCase					:					Dict=3     ,		UpperCamelCase					:					Union[str, Any]=768     ,		UpperCamelCase					:					Any=5     ,		UpperCamelCase					:					List[str]=4     ,		UpperCamelCase					:					Optional[int]=37     ,		UpperCamelCase					:					List[Any]="gelu_fast"     ,		UpperCamelCase					:					List[Any]=0.0     ,		UpperCamelCase					:					str=0.0     ,		UpperCamelCase					:					Tuple=0.02     ,		UpperCamelCase					:					Any=1E-06     ,		UpperCamelCase					:					str=True     ,		UpperCamelCase					:					List[str]=None     ,		):

             '''simple docstring'''







             lowercase__       =				parent
             lowercase__       =				batch_size
             lowercase__       =				is_training
             lowercase__       =				use_labels
             lowercase__       =				num_labels
             lowercase__       =				image_size
             lowercase__       =				num_frames
             lowercase__       =				tubelet_size
             lowercase__       =				num_channels
             lowercase__       =				hidden_size
             lowercase__       =				num_hidden_layers
             lowercase__       =				num_attention_heads
             lowercase__       =				intermediate_size
             lowercase__       =				hidden_act
             lowercase__       =				hidden_dropout_prob
             lowercase__       =				attention_probs_dropout_prob
             lowercase__       =				initializer_range
             lowercase__       =				layer_norm_eps
             lowercase__       =				qkv_bias
             lowercase__       =				scope

             lowercase__       =				(
                 (self.image_size // self.tubelet_size[2])
                 * (self.image_size // self.tubelet_size[1])
                 * (self.num_frames // self.tubelet_size[0])
             ) + 1  # CLS token



      def    UpperCamelCase__	(self					:					int				):

             '''simple docstring'''







             lowercase__       =				floats_tensor(
                 [self.batch_size, self.num_frames, self.num_channels, self.image_size, self.image_size]				)

             lowercase__       =				None
             if self.use_labels:
                    lowercase__       =				ids_tensor([self.batch_size]     ,		self.num_labels				)

             lowercase__       =				self.get_config()

             return config, pixel_values, labels



      def    UpperCamelCase__	(self					:					List[str]				):

             '''simple docstring'''







             lowercase__       =				VivitConfig(
                 num_frames=self.num_frames     ,		image_size=self.image_size     ,		tubelet_size=self.tubelet_size     ,		num_channels=self.num_channels     ,		hidden_size=self.hidden_size     ,		num_hidden_layers=self.num_hidden_layers     ,		num_attention_heads=self.num_attention_heads     ,		intermediate_size=self.intermediate_size     ,		hidden_act=self.hidden_act     ,		hidden_dropout_prob=self.hidden_dropout_prob     ,		attention_probs_dropout_prob=self.attention_probs_dropout_prob     ,		initializer_range=self.initializer_range     ,		layer_norm_eps=self.layer_norm_eps     ,		qkv_bias=self.qkv_bias     ,		)
             lowercase__       =				self.num_labels
             return config



      def    UpperCamelCase__	(self					:					Optional[int]     ,		UpperCamelCase					:					Optional[Any]     ,		UpperCamelCase					:					List[str]     ,		UpperCamelCase					:					Dict				):

             '''simple docstring'''







             lowercase__       =				VivitModel(config=UpperCamelCase				)
             model.to(UpperCamelCase				)
             model.eval()
             lowercase__       =				model(UpperCamelCase				)
             self.parent.assertEqual(result.last_hidden_state.shape     ,		(self.batch_size, self.seq_length, self.hidden_size)				)



      def    UpperCamelCase__	(self					:					int     ,		UpperCamelCase					:					Union[str, Any]     ,		UpperCamelCase					:					Any     ,		UpperCamelCase					:					Optional[Any]				):

             '''simple docstring'''







             lowercase__       =				VivitForVideoClassification(UpperCamelCase				)
             model.to(UpperCamelCase				)
             model.eval()

             lowercase__       =				model(UpperCamelCase				)

             # verify the logits shape
             lowercase__       =				torch.Size((self.batch_size, self.num_labels)				)
             self.parent.assertEqual(result.logits.shape     ,		UpperCamelCase				)






      def    UpperCamelCase__	(self					:					Dict				):

             '''simple docstring'''







             lowercase__       =				self.prepare_config_and_inputs()
             lowercase__    ,lowercase__    ,lowercase__       =				config_and_inputs
             lowercase__       =				{'''pixel_values''': pixel_values}
             return config, inputs_dict






@require_torch
class 						__lowerCAmelCase		(lowercase_						,  unittest.TestCase  ):





      '''simple docstring'''






      lowerCAmelCase__      :  List[str]						      =							(VivitModel, VivitForVideoClassification) if is_torch_available() else ()

      lowerCAmelCase__      :  Tuple						      =							False
      lowerCAmelCase__      :  Optional[Any]						      =							False
      lowerCAmelCase__      :  str						      =							False
      lowerCAmelCase__      :  Any						      =							False



      def    UpperCamelCase__	(self					:					Dict				):

             '''simple docstring'''







             lowercase__       =				VivitModelTester(self				)
             lowercase__       =				ConfigTester(self     ,		config_class=UpperCamelCase     ,		has_text_modality=UpperCamelCase     ,		hidden_size=37				)



      def    UpperCamelCase__	(self					:					List[str]     ,		UpperCamelCase					:					List[Any]     ,		UpperCamelCase					:					List[str]     ,		UpperCamelCase					:					Tuple=False				):

             '''simple docstring'''







             lowercase__       =				copy.deepcopy(UpperCamelCase				)

             if return_labels:
                    if model_class in get_values(UpperCamelCase				):
                           lowercase__       =				torch.zeros(
                               self.model_tester.batch_size     ,		dtype=torch.long     ,		device=UpperCamelCase				)

             return inputs_dict



      def    UpperCamelCase__	(self					:					int				):

             '''simple docstring'''







             self.config_tester.run_common_tests()



      @unittest.skip(reason='''Vivit does not use inputs_embeds'''				)
      def    UpperCamelCase__	(self					:					Optional[Any]				):

             '''simple docstring'''







             pass



      def    UpperCamelCase__	(self					:					Optional[int]				):

             '''simple docstring'''







             lowercase__    ,lowercase__       =				self.model_tester.prepare_config_and_inputs_for_common()

             for model_class in self.all_model_classes:
                    lowercase__       =				model_class(UpperCamelCase				)
                    self.assertIsInstance(model.get_input_embeddings()     ,		(nn.Module)				)
                    lowercase__       =				model.get_output_embeddings()
                    self.assertTrue(x is None or isinstance(UpperCamelCase     ,		nn.Linear				)				)



      def    UpperCamelCase__	(self					:					Optional[int]				):

             '''simple docstring'''







             lowercase__    ,lowercase__       =				self.model_tester.prepare_config_and_inputs_for_common()

             for model_class in self.all_model_classes:
                    lowercase__       =				model_class(UpperCamelCase				)
                    lowercase__       =				inspect.signature(model.forward				)
                    # signature.parameters is an OrderedDict => so arg_names order is deterministic
                    lowercase__       =				[*signature.parameters.keys()]

                    lowercase__       =				['''pixel_values''', '''head_mask''']
                    self.assertListEqual(arg_names[:2]     ,		UpperCamelCase				)



      def    UpperCamelCase__	(self					:					str				):

             '''simple docstring'''







             lowercase__       =				self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_model(*UpperCamelCase				)



      def    UpperCamelCase__	(self					:					List[Any]				):

             '''simple docstring'''







             lowercase__       =				self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_video_classification(*UpperCamelCase				)



      @slow
      def    UpperCamelCase__	(self					:					Union[str, Any]				):

             '''simple docstring'''







             for model_name in VIVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                    lowercase__       =				VivitModel.from_pretrained(UpperCamelCase				)
                    self.assertIsNotNone(UpperCamelCase				)



      def    UpperCamelCase__	(self					:					Tuple				):

             '''simple docstring'''







             lowercase__    ,lowercase__       =				self.model_tester.prepare_config_and_inputs_for_common()
             lowercase__       =				True

             for model_class in self.all_model_classes:
                    lowercase__       =				self.model_tester.seq_length

                    lowercase__       =				True
                    lowercase__       =				False
                    lowercase__       =				True
                    lowercase__       =				model_class(UpperCamelCase				)
                    model.to(UpperCamelCase				)
                    model.eval()
                    with torch.no_grad():
                           lowercase__       =				model(**self._prepare_for_class(UpperCamelCase     ,		UpperCamelCase				)				)
                    lowercase__       =				outputs.attentions
                    self.assertEqual(len(UpperCamelCase				)     ,		self.model_tester.num_hidden_layers				)

                    # check that output_attentions also work using config
                    del inputs_dict["output_attentions"]
                    lowercase__       =				True
                    lowercase__       =				model_class(UpperCamelCase				)
                    model.to(UpperCamelCase				)
                    model.eval()
                    with torch.no_grad():
                           lowercase__       =				model(**self._prepare_for_class(UpperCamelCase     ,		UpperCamelCase				)				)
                    lowercase__       =				outputs.attentions
                    self.assertEqual(len(UpperCamelCase				)     ,		self.model_tester.num_hidden_layers				)

                    self.assertListEqual(
                        list(attentions[0].shape[-3:]				)     ,		[self.model_tester.num_attention_heads, seq_len, seq_len]     ,		)
                    lowercase__       =				len(UpperCamelCase				)

                    # Check attention is always last and order is fine
                    lowercase__       =				True
                    lowercase__       =				True
                    lowercase__       =				model_class(UpperCamelCase				)
                    model.to(UpperCamelCase				)
                    model.eval()
                    with torch.no_grad():
                           lowercase__       =				model(**self._prepare_for_class(UpperCamelCase     ,		UpperCamelCase				)				)

                    self.assertEqual(out_len + 1     ,		len(UpperCamelCase				)				)

                    lowercase__       =				outputs.attentions

                    self.assertEqual(len(UpperCamelCase				)     ,		self.model_tester.num_hidden_layers				)

                    self.assertListEqual(
                        list(self_attentions[0].shape[-3:]				)     ,		[self.model_tester.num_attention_heads, seq_len, seq_len]     ,		)



      def    UpperCamelCase__	(self					:					List[Any]				):

             '''simple docstring'''







             def check_hidden_states_output(UpperCamelCase					:					Dict     ,		UpperCamelCase					:					Optional[int]     ,		UpperCamelCase					:					Any				):
                    lowercase__       =				model_class(UpperCamelCase				)
                    model.to(UpperCamelCase				)
                    model.eval()

                    with torch.no_grad():
                           lowercase__       =				model(**self._prepare_for_class(UpperCamelCase     ,		UpperCamelCase				)				)

                    lowercase__       =				outputs.hidden_states
                    lowercase__       =				self.model_tester.num_hidden_layers + 1
                    self.assertEqual(len(UpperCamelCase				)     ,		UpperCamelCase				)

                    lowercase__       =				self.model_tester.seq_length

                    self.assertListEqual(
                        list(hidden_states[0].shape[-2:]				)     ,		[seq_length, self.model_tester.hidden_size]     ,		)

             lowercase__    ,lowercase__       =				self.model_tester.prepare_config_and_inputs_for_common()

             for model_class in self.all_model_classes:
                    lowercase__       =				True
                    check_hidden_states_output(UpperCamelCase     ,		UpperCamelCase     ,		UpperCamelCase				)

                    # check that output_hidden_states also work using config
                    del inputs_dict["output_hidden_states"]
                    lowercase__       =				True

                    check_hidden_states_output(UpperCamelCase     ,		UpperCamelCase     ,		UpperCamelCase				)






      @unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.'''				)
      def    UpperCamelCase__	(self					:					str				):

             '''simple docstring'''







             pass






def _SCREAMING_SNAKE_CASE   () -> List[Any]:






       """simple docstring"""







       lowercase__       =				hf_hub_download(
           repo_id='''hf-internal-testing/spaghetti-video'''						,				filename='''eating_spaghetti_32_frames.npy'''						,				repo_type='''dataset''' )
       lowercase__       =				np.load(A )
       return list(A )

@require_torch
@require_vision
class 						__lowerCAmelCase		(unittest.TestCase  ):





      '''simple docstring'''






      @cached_property
      def    UpperCamelCase__	(self					:					Optional[int]				):

             '''simple docstring'''







             return VivitImageProcessor() if is_vision_available() else None






      @slow
      def    UpperCamelCase__	(self					:					int				):

             '''simple docstring'''







             lowercase__       =				VivitForVideoClassification.from_pretrained('''google/vivit-b-16x2-kinetics400'''				).to(UpperCamelCase				)

             lowercase__       =				self.default_image_processor
             lowercase__       =				prepare_video()
             lowercase__       =				image_processor(UpperCamelCase     ,		return_tensors='''pt'''				).to(UpperCamelCase				)

             # forward pass
             with torch.no_grad():
                    lowercase__       =				model(**UpperCamelCase				)

             # verify the logits
             lowercase__       =				torch.Size((1, 400)				)
             self.assertEqual(outputs.logits.shape     ,		UpperCamelCase				)

             # taken from original model
             lowercase__       =				torch.tensor([-1.05_43, 2.07_64, -0.21_04, 0.44_39, -0.96_58]				).to(UpperCamelCase				)

             self.assertTrue(torch.allclose(outputs.logits[0, :5]     ,		UpperCamelCase     ,		atol=1E-4				)				)




