





from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_A					:    Optional[Any]   =  logging.get_logger(__name__)

_A					:    Optional[Any]   =  {
    'hustvl/yolos-small': 'https://huggingface.co/hustvl/yolos-small/resolve/main/config.json',
    # See all YOLOS models at https://huggingface.co/models?filter=yolos
}


class    __SCREAMING_SNAKE_CASE			( __snake_case					):
       _UpperCAmelCase      :  List[Any]     =    """yolos"""







       def __init__(      self     :	Any		,					A     :	List[Any]=7_6_8		,					A     :	Any=1_2		,					A     :	int=1_2		,					A     :	Optional[int]=3_0_7_2		,					A     :	Dict="gelu"		,					A     :	List[Any]=0.0		,					A     :	Dict=0.0		,					A     :	Tuple=0.02		,					A     :	Dict=1e-12		,					A     :	Union[str, Any]=[5_1_2, 8_6_4]		,					A     :	Optional[int]=1_6		,					A     :	Tuple=3		,					A     :	List[Any]=True		,					A     :	Union[str, Any]=1_0_0		,					A     :	Optional[Any]=True		,					A     :	List[Any]=False		,					A     :	Any=1		,					A     :	int=5		,					A     :	str=2		,					A     :	List[Any]=5		,					A     :	Dict=2		,					A     :	Optional[Any]=0.1		,					**A     :	Union[str, Any]		,					)   ->Any:
          super().__init__(**A			)

          lowerCamelCase__   :						Union[str, Any]					   =							hidden_size
          lowerCamelCase__   :						Optional[int]					   =							num_hidden_layers
          lowerCamelCase__   :						List[Any]					   =							num_attention_heads
          lowerCamelCase__   :						Tuple					   =							intermediate_size
          lowerCamelCase__   :						str					   =							hidden_act
          lowerCamelCase__   :						Tuple					   =							hidden_dropout_prob
          lowerCamelCase__   :						Any					   =							attention_probs_dropout_prob
          lowerCamelCase__   :						Optional[Any]					   =							initializer_range
          lowerCamelCase__   :						str					   =							layer_norm_eps
          lowerCamelCase__   :						Optional[int]					   =							image_size
          lowerCamelCase__   :						int					   =							patch_size
          lowerCamelCase__   :						Union[str, Any]					   =							num_channels
          lowerCamelCase__   :						str					   =							qkv_bias
          lowerCamelCase__   :						List[str]					   =							num_detection_tokens
          lowerCamelCase__   :						Dict					   =							use_mid_position_embeddings
          lowerCamelCase__   :						List[str]					   =							auxiliary_loss
          # Hungarian matcher
          lowerCamelCase__   :						Tuple					   =							class_cost
          lowerCamelCase__   :						int					   =							bbox_cost
          lowerCamelCase__   :						str					   =							giou_cost
          # Loss coefficients
          lowerCamelCase__   :						Union[str, Any]					   =							bbox_loss_coefficient
          lowerCamelCase__   :						List[str]					   =							giou_loss_coefficient
          lowerCamelCase__   :						Dict					   =							eos_coefficient


class    __SCREAMING_SNAKE_CASE			( __snake_case					):
       _UpperCAmelCase      :  Optional[int]     =    version.parse("1.11"					)







       @property
       def 						__lowerCamelCase    (      self     :	List[Any]			)   ->Union[str, Any]:
          return OrderedDict(
              [
                  ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
              ]			)







       @property
       def 						__lowerCamelCase    (      self     :	int			)   ->Union[str, Any]:
          return 1e-4







       @property
       def 						__lowerCamelCase    (      self     :	Tuple			)   ->Dict:
          return 1_2






import unittest

from transformers import EsmConfig, is_torch_available
from transformers.testing_utils import TestCasePlus, require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
       import torch

       from transformers.models.esm.modeling_esmfold import EsmForProteinFolding


class       SCREAMING_SNAKE_CASE__					:





    def __init__(      self						,   a						,   a=13						,   a=7						,   a=False						,   a=True						,   a=False						,   a=False						,   a=19						,   a=32						,   a=5						,   a=4						,   a=37						,   a="gelu"						,   a=0.1						,   a=0.1						,   a=512						,   a=16						,   a=2						,   a=0.02						,   a=3						,   a=4						,   a=None						,   ):
         lowercase__    :   Optional[Any]      =       parent
         lowercase__    :   Dict      =       batch_size
         lowercase__    :   Union[str, Any]      =       seq_length
         lowercase__    :   Optional[Any]      =       is_training
         lowercase__    :   Tuple      =       use_input_mask
         lowercase__    :   List[str]      =       use_token_type_ids
         lowercase__    :   Optional[Any]      =       use_labels
         lowercase__    :   List[str]      =       vocab_size
         lowercase__    :   Optional[int]      =       hidden_size
         lowercase__    :   List[str]      =       num_hidden_layers
         lowercase__    :   Any      =       num_attention_heads
         lowercase__    :   int      =       intermediate_size
         lowercase__    :   Any      =       hidden_act
         lowercase__    :   Any      =       hidden_dropout_prob
         lowercase__    :   str      =       attention_probs_dropout_prob
         lowercase__    :   List[Any]      =       max_position_embeddings
         lowercase__    :   int      =       type_vocab_size
         lowercase__    :   List[Any]      =       type_sequence_label_size
         lowercase__    :   str      =       initializer_range
         lowercase__    :   List[str]      =       num_labels
         lowercase__    :   Union[str, Any]      =       num_choices
         lowercase__    :   Optional[int]      =       scope





    def       snake_case_				(      self):
         lowercase__    :   List[Any]      =       ids_tensor([self.batch_size, self.seq_length]						,   self.vocab_size)

         lowercase__    :   List[Any]      =       None
         if self.use_input_mask:
              lowercase__    :   Union[str, Any]      =       random_attention_mask([self.batch_size, self.seq_length])

         lowercase__    :   int      =       None
         lowercase__    :   Optional[int]      =       None
         lowercase__    :   Optional[int]      =       None
         if self.use_labels:
              lowercase__    :   Optional[int]      =       ids_tensor([self.batch_size]						,   self.type_sequence_label_size)
              lowercase__    :   str      =       ids_tensor([self.batch_size, self.seq_length]						,   self.num_labels)
              lowercase__    :   str      =       ids_tensor([self.batch_size]						,   self.num_choices)

         lowercase__    :   int      =       self.get_config()

         return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels





    def       snake_case_				(      self):
         lowercase__    :   str      =       EsmConfig(
             vocab_size=33						,   hidden_size=self.hidden_size						,   pad_token_id=1						,   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						,   max_position_embeddings=self.max_position_embeddings						,   type_vocab_size=self.type_vocab_size						,   initializer_range=self.initializer_range						,   is_folding_model=a						,   esmfold_config={'trunk': {'num_blocks': 2}, 'fp16_esm': False}						,   )
         return config





    def       snake_case_				(      self						,   a						,   a						,   a						,   a						,   a						,   a):
         lowercase__    :   Dict      =       EsmForProteinFolding(config=a).float()
         model.to(a)
         model.eval()
         lowercase__    :   Union[str, Any]      =       model(a						,   attention_mask=a)
         lowercase__    :   Dict      =       model(a)
         lowercase__    :   int      =       model(a)

         self.parent.assertEqual(result.positions.shape						,   (8, self.batch_size, self.seq_length, 14, 3))
         self.parent.assertEqual(result.angles.shape						,   (8, self.batch_size, self.seq_length, 7, 2))





    def       snake_case_				(      self):
         lowercase__    :   List[str]      =       self.prepare_config_and_inputs()
         (
             (
             lowercase__
         )      , (
             lowercase__
         )      , (
             lowercase__
         )      , (
             lowercase__
         )      , (
             lowercase__
         )      , (
             lowercase__
         )      , 
         )    :   int      =       config_and_inputs
         lowercase__    :   Optional[int]      =       {'input_ids': input_ids, 'attention_mask': input_mask}
         return config, inputs_dict



@require_torch
class       SCREAMING_SNAKE_CASE__					(__snake_case		,       __snake_case		,       unittest.TestCase     ):
    __lowerCamelCase						:       Dict														=     False

    __lowerCamelCase						:       Dict														=     (EsmForProteinFolding,) if is_torch_available() else ()
    __lowerCamelCase						:       Union[str, Any]														=     ()
    __lowerCamelCase						:       List[Any]														=     {} if is_torch_available() else {}
    __lowerCamelCase						:       Optional[Any]														=     False





    def       snake_case_				(      self):
         lowercase__    :   Tuple      =       EsmFoldModelTester(self)
         lowercase__    :   List[Any]      =       ConfigTester(self						,   config_class=a						,   hidden_size=37)





    def       snake_case_				(      self):
         self.config_tester.run_common_tests()





    def       snake_case_				(      self):
         lowercase__    :   Optional[int]      =       self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*a)





    @unittest.skip('Does not support attention outputs')
    def       snake_case_				(      self):
         pass





    @unittest.skip
    def       snake_case_				(      self):
         pass





    @unittest.skip('Esm does not support embedding resizing')
    def       snake_case_				(      self):
         pass





    @unittest.skip('Esm does not support embedding resizing')
    def       snake_case_				(      self):
         pass





    @unittest.skip('ESMFold does not support passing input embeds!')
    def       snake_case_				(      self):
         pass





    @unittest.skip('ESMFold does not support head pruning.')
    def       snake_case_				(      self):
         pass





    @unittest.skip('ESMFold does not support head pruning.')
    def       snake_case_				(      self):
         pass





    @unittest.skip('ESMFold does not support head pruning.')
    def       snake_case_				(      self):
         pass





    @unittest.skip('ESMFold does not support head pruning.')
    def       snake_case_				(      self):
         pass





    @unittest.skip('ESMFold does not support head pruning.')
    def       snake_case_				(      self):
         pass





    @unittest.skip('ESMFold does not output hidden states in the normal way.')
    def       snake_case_				(      self):
         pass





    @unittest.skip('ESMfold does not output hidden states in the normal way.')
    def       snake_case_				(      self):
         pass





    @unittest.skip('ESMFold only has one output format.')
    def       snake_case_				(      self):
         pass





    @unittest.skip('This test doesn\'t work for ESMFold and doesn\'t test core functionality')
    def       snake_case_				(      self):
         pass





    @unittest.skip('ESMFold does not support input chunking.')
    def       snake_case_				(      self):
         pass





    @unittest.skip('ESMFold doesn\'t respect you and it certainly doesn\'t respect your initialization arguments.')
    def       snake_case_				(      self):
         pass





    @unittest.skip('ESMFold doesn\'t support torchscript compilation.')
    def       snake_case_				(      self):
         pass





    @unittest.skip('ESMFold doesn\'t support torchscript compilation.')
    def       snake_case_				(      self):
         pass





    @unittest.skip('ESMFold doesn\'t support torchscript compilation.')
    def       snake_case_				(      self):
         pass





    @unittest.skip('ESMFold doesn\'t support data parallel.')
    def       snake_case_				(      self):
         pass





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



@require_torch
class       SCREAMING_SNAKE_CASE__					(__snake_case     ):





    @slow
    def       snake_case_				(      self):
         lowercase__    :   Dict      =       EsmForProteinFolding.from_pretrained('facebook/esmfold_v1').float()
         model.eval()
         lowercase__    :   Optional[Any]      =       torch.tensor([[0, 6, 4, 13, 5, 4, 16, 12, 11, 7, 2]])
         lowercase__    :   Optional[int]      =       model(a)['positions']
         lowercase__    :   Dict      =       torch.tensor([2.5_828, 0.7_993, -10.9_334]						,   dtype=torch.floataa)
         self.assertTrue(torch.allclose(position_outputs[0, 0, 0, 0]						,   a						,   atol=1e-4))

