


import copy
import inspect
import unittest

from transformers import AutoBackbone
from transformers.configuration_utils import PretrainedConfig
from transformers.testing_utils import require_timm, require_torch, torch_device
from transformers.utils.import_utils import is_torch_available

from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor


if is_torch_available():
 import torch

 from transformers import TimmBackbone, TimmBackboneConfig

from ...test_pipeline_mixin import PipelineTesterMixin






class _lowerCamelCase:
       def __init__(   self, lowerCamelCase, lowerCamelCase=None, lowerCamelCase=None, lowerCamelCase=None, lowerCamelCase="resnet50", lowerCamelCase=3, lowerCamelCase=32, lowerCamelCase=3, lowerCamelCase=True, lowerCamelCase=True, )			->  Tuple:




           """simple docstring"""

           _lowercase			:				Optional[Any] 						=						parent
           _lowercase			:				List[Any] 						=						out_indices if out_indices is not None else [4]
           _lowercase			:				Optional[int] 						=						stage_names
           _lowercase			:				Any 						=						out_features
           _lowercase			:				int 						=						backbone
           _lowercase			:				Optional[int] 						=						batch_size
           _lowercase			:				Dict 						=						image_size
           _lowercase			:				List[Any] 						=						num_channels
           _lowercase			:				Union[str, Any] 						=						use_pretrained_backbone
           _lowercase			:				Optional[int] 						=						is_training
       def      UpperCamelCase	(   self)			->  Dict:




           """simple docstring"""

           _lowercase			:				Any 						=						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
           _lowercase			:				List[str] 						=						self.get_config()

           return config, pixel_values
       def      UpperCamelCase	(   self)			->  Union[str, Any]:




           """simple docstring"""

           return TimmBackboneConfig(
               image_size=self.image_size, num_channels=self.num_channels, out_features=self.out_features, out_indices=self.out_indices, stage_names=self.stage_names, use_pretrained_backbone=self.use_pretrained_backbone, backbone=self.backbone, )
       def      UpperCamelCase	(   self, lowerCamelCase, lowerCamelCase)			->  List[Any]:




           """simple docstring"""

           _lowercase			:				Union[str, Any] 						=						TimmBackbone(config=lowerCamelCase)
           model.to(lowerCamelCase)
           model.eval()
           with torch.no_grad():
               _lowercase			:				Optional[int] 						=						model(lowerCamelCase)
           self.parent.assertEqual(
               result.feature_map[-1].shape, (self.batch_size, model.channels[-1], 14, 14), )




       def      UpperCamelCase	(   self)			->  int:




           """simple docstring"""

           _lowercase			:				List[str] 						=						self.prepare_config_and_inputs()
           _lowercase						,							_lowercase			:				List[Any] 						=						config_and_inputs
           _lowercase			:				Optional[Any] 						=						{'pixel_values': pixel_values}
           return config, inputs_dict






@require_torch
@require_timm
class _lowerCamelCase(		_a,					_a,					_a,					unittest.TestCase  ):
       lowercase_					:      Dict       					=     (TimmBackbone,) if is_torch_available() else ()
       lowercase_					:      Optional[Any]       					=     {"""feature-extraction""": TimmBackbone} if is_torch_available() else {}
       lowercase_					:      Union[str, Any]       					=     False
       lowercase_					:      Optional[Any]       					=     False
       lowercase_					:      Optional[Any]       					=     False
       lowercase_					:      Any       					=     False
       def      UpperCamelCase	(   self)			->  Tuple:




           """simple docstring"""

           _lowercase			:				Union[str, Any] 						=						TimmBackboneModelTester(self)
           _lowercase			:				str 						=						ConfigTester(self, config_class=lowerCamelCase, has_text_modality=lowerCamelCase)
       def      UpperCamelCase	(   self)			->  int:




           """simple docstring"""

           self.config_tester.create_and_test_config_to_json_string()
           self.config_tester.create_and_test_config_to_json_file()
           self.config_tester.create_and_test_config_from_and_save_pretrained()
           self.config_tester.create_and_test_config_with_num_labels()
           self.config_tester.check_config_can_be_init_without_params()
           self.config_tester.check_config_arguments_init()
       def      UpperCamelCase	(   self)			->  List[str]:




           """simple docstring"""

           _lowercase			:				int 						=						'resnet18'
           _lowercase			:				Any 						=						'microsoft/resnet-18'

           _lowercase			:				List[str] 						=						AutoBackbone.from_pretrained(lowerCamelCase, use_timm_backbone=lowerCamelCase)
           _lowercase			:				int 						=						AutoBackbone.from_pretrained(lowerCamelCase)

           self.assertEqual(len(timm_model.out_features), len(transformers_model.out_features))
           self.assertEqual(len(timm_model.stage_names), len(transformers_model.stage_names))
           self.assertEqual(timm_model.channels, transformers_model.channels)
           # Out indices are set to the last layer by default. For timm models, we don't know
           # the number of layers in advance, so we set it to (-1,), whereas for transformers
           # models, we set it to [len(stage_names) - 1] (kept for backward compatibility).
           self.assertEqual(timm_model.out_indices, (-1,))
           self.assertEqual(transformers_model.out_indices, [len(timm_model.stage_names) - 1])

           _lowercase			:				List[Any] 						=						AutoBackbone.from_pretrained(lowerCamelCase, use_timm_backbone=lowerCamelCase, out_indices=[1, 2, 3])
           _lowercase			:				int 						=						AutoBackbone.from_pretrained(lowerCamelCase, out_indices=[1, 2, 3])

           self.assertEqual(timm_model.out_indices, transformers_model.out_indices)
           self.assertEqual(len(timm_model.out_features), len(transformers_model.out_features))
           self.assertEqual(timm_model.channels, transformers_model.channels)
       @unittest.skip('TimmBackbone doesn\'t support feed forward chunking')
       def      UpperCamelCase	(   self)			->  List[Any]:




           """simple docstring"""

           pass
       @unittest.skip('TimmBackbone doesn\'t have num_hidden_layers attribute')
       def      UpperCamelCase	(   self)			->  List[Any]:




           """simple docstring"""

           pass
       @unittest.skip('TimmBackbone initialization is managed on the timm side')
       def      UpperCamelCase	(   self)			->  int:




           """simple docstring"""

           pass
       @unittest.skip('TimmBackbone models doesn\'t have inputs_embeds')
       def      UpperCamelCase	(   self)			->  Optional[Any]:




           """simple docstring"""

           pass
       @unittest.skip('TimmBackbone models doesn\'t have inputs_embeds')
       def      UpperCamelCase	(   self)			->  int:




           """simple docstring"""

           pass
       @unittest.skip('TimmBackbone model cannot be created without specifying a backbone checkpoint')
       def      UpperCamelCase	(   self)			->  str:




           """simple docstring"""

           pass
       @unittest.skip('Only checkpoints on timm can be loaded into TimmBackbone')
       def      UpperCamelCase	(   self)			->  Union[str, Any]:




           """simple docstring"""

           pass
       @unittest.skip('model weights aren\'t tied in TimmBackbone.')
       def      UpperCamelCase	(   self)			->  Union[str, Any]:




           """simple docstring"""

           pass
       @unittest.skip('model weights aren\'t tied in TimmBackbone.')
       def      UpperCamelCase	(   self)			->  str:




           """simple docstring"""

           pass
       @unittest.skip('Only checkpoints on timm can be loaded into TimmBackbone')
       def      UpperCamelCase	(   self)			->  Union[str, Any]:




           """simple docstring"""

           pass
       @unittest.skip('Only checkpoints on timm can be loaded into TimmBackbone')
       def      UpperCamelCase	(   self)			->  Optional[Any]:




           """simple docstring"""

           pass
       @unittest.skip('TimmBackbone doesn\'t have hidden size info in its configuration.')
       def      UpperCamelCase	(   self)			->  Optional[int]:




           """simple docstring"""

           pass
       @unittest.skip('TimmBackbone doesn\'t support output_attentions.')
       def      UpperCamelCase	(   self)			->  Optional[int]:




           """simple docstring"""

           pass
       @unittest.skip('Safetensors is not supported by timm.')
       def      UpperCamelCase	(   self)			->  List[Any]:




           """simple docstring"""

           pass
       @unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.')
       def      UpperCamelCase	(   self)			->  List[Any]:




           """simple docstring"""

           pass
       def      UpperCamelCase	(   self)			->  Optional[int]:




           """simple docstring"""

           _lowercase						,							_lowercase			:				Optional[Any] 						=						self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
               _lowercase			:				Union[str, Any] 						=						model_class(lowerCamelCase)
               _lowercase			:				Dict 						=						inspect.signature(model.forward)
               # signature.parameters is an OrderedDict => so arg_names order is deterministic
               _lowercase			:				List[Any] 						=						[*signature.parameters.keys()]

               _lowercase			:				Dict 						=						['pixel_values']
               self.assertListEqual(arg_names[:1], lowerCamelCase)
       def      UpperCamelCase	(   self)			->  List[Any]:




           """simple docstring"""

           _lowercase						,							_lowercase			:				List[str] 						=						self.model_tester.prepare_config_and_inputs_for_common()
           _lowercase			:				str 						=						True
           _lowercase			:				List[Any] 						=						self.has_attentions

           # no need to test all models as different heads yield the same functionality
           _lowercase			:				str 						=						self.all_model_classes[0]
           _lowercase			:				Optional[Any] 						=						model_class(lowerCamelCase)
           model.to(lowerCamelCase)

           _lowercase			:				Union[str, Any] 						=						self._prepare_for_class(lowerCamelCase, lowerCamelCase)
           _lowercase			:				List[str] 						=						model(**lowerCamelCase)
           _lowercase			:				Optional[Any] 						=						outputs[0][-1]

           # Encoder-/Decoder-only models
           _lowercase			:				str 						=						outputs.hidden_states[0]
           hidden_states.retain_grad()

           if self.has_attentions:
               _lowercase			:				List[Any] 						=						outputs.attentions[0]
               attentions.retain_grad()

           output.flatten()[0].backward(retain_graph=lowerCamelCase)

           self.assertIsNotNone(hidden_states.grad)

           if self.has_attentions:
               self.assertIsNotNone(attentions.grad)




       def      UpperCamelCase	(   self)			->  List[str]:




           """simple docstring"""

           _lowercase						,							_lowercase			:				Tuple 						=						self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
               _lowercase			:				Union[str, Any] 						=						model_class(lowerCamelCase)
               model.to(lowerCamelCase)
               model.eval()
               _lowercase			:				int 						=						model(**lowerCamelCase)

               self.assertEqual(len(result.feature_maps), len(config.out_indices))
               self.assertEqual(len(model.channels), len(config.out_indices))

               # Check output of last stage is taken if out_features=None, out_indices=None
               _lowercase			:				Dict 						=						copy.deepcopy(lowerCamelCase)
               _lowercase			:				Optional[Any] 						=						None
               _lowercase			:				List[str] 						=						model_class(lowerCamelCase)
               model.to(lowerCamelCase)
               model.eval()
               _lowercase			:				List[str] 						=						model(**lowerCamelCase)

               self.assertEqual(len(result.feature_maps), 1)
               self.assertEqual(len(model.channels), 1)

               # Check backbone can be initialized with fresh weights
               _lowercase			:				Dict 						=						copy.deepcopy(lowerCamelCase)
               _lowercase			:				List[str] 						=						False
               _lowercase			:				List[Any] 						=						model_class(lowerCamelCase)
               model.to(lowerCamelCase)
               model.eval()
               _lowercase			:				Dict 						=						model(**lowerCamelCase)






from __future__ import annotations

from typing import Any






class _lowerCamelCase:
       def __init__(   self, lowerCamelCase, lowerCamelCase, lowerCamelCase = 0)			->  None:




           """simple docstring"""

           _lowercase						,							_lowercase			:				str 						=						row, column
           _lowercase			:				Any 						=						[[default_value for c in range(lowerCamelCase)] for r in range(lowerCamelCase)]
       def __str__(   self)			->  str:




           """simple docstring"""

           _lowercase			:				Tuple 						=						F'''Matrix consist of {self.row} rows and {self.column} columns\n'''

           # Make string identifier
           _lowercase			:				str 						=						0
           for row_vector in self.array:
               for obj in row_vector:
                   _lowercase			:				Optional[int] 						=						max(lowerCamelCase, len(str(lowerCamelCase)))
           _lowercase			:				List[str] 						=						F'''%{max_element_length}s'''

           # Make string and return
           def single_line(lowerCamelCase) -> str:
               nonlocal string_format_identifier
               _lowercase			:				Union[str, Any] 						=						'['
               line += ", ".join(string_format_identifier % (obj,) for obj in row_vector)
               line += "]"
               return line

           s += "\n".join(single_line(lowerCamelCase) for row_vector in self.array)
           return s
       def __repr__(   self)			->  str:




           """simple docstring"""

           return str(self)
       def      UpperCamelCase	(   self, lowerCamelCase)			->  bool:




           """simple docstring"""

           if not (isinstance(lowerCamelCase, (list, tuple)) and len(lowerCamelCase) == 2):
               return False
           elif not (0 <= loc[0] < self.row and 0 <= loc[1] < self.column):
               return False
           else:
               return True
       def __getitem__(   self, lowerCamelCase)			->  Any:




           """simple docstring"""

           assert self.validate_indicies(lowerCamelCase)
           return self.array[loc[0]][loc[1]]
       def __setitem__(   self, lowerCamelCase, lowerCamelCase)			->  None:




           """simple docstring"""

           assert self.validate_indicies(lowerCamelCase)
           _lowercase			:				Optional[Any] 						=						value
       def __add__(   self, lowerCamelCase)			->  Matrix:




           """simple docstring"""

           assert isinstance(lowerCamelCase, lowerCamelCase)
           assert self.row == another.row and self.column == another.column

           # Add
           _lowercase			:				Any 						=						Matrix(self.row, self.column)
           for r in range(self.row):
               for c in range(self.column):
                   _lowercase			:				int 						=						self[r, c] + another[r, c]
           return result
       def __neg__(   self)			->  Matrix:




           """simple docstring"""

           _lowercase			:				List[Any] 						=						Matrix(self.row, self.column)
           for r in range(self.row):
               for c in range(self.column):
                   _lowercase			:				List[str] 						=						-self[r, c]
           return result
       def __sub__(   self, lowerCamelCase)			->  Matrix:




           """simple docstring"""

           return self + (-another)
       def __mul__(   self, lowerCamelCase)			->  Matrix:




           """simple docstring"""

           if isinstance(lowerCamelCase, (int, float)):  # Scalar multiplication
               _lowercase			:				Dict 						=						Matrix(self.row, self.column)
               for r in range(self.row):
                   for c in range(self.column):
                       _lowercase			:				Union[str, Any] 						=						self[r, c] * another
               return result
           elif isinstance(lowerCamelCase, lowerCamelCase):  # Matrix multiplication
               assert self.column == another.row
               _lowercase			:				str 						=						Matrix(self.row, another.column)
               for r in range(self.row):
                   for c in range(another.column):
                       for i in range(self.column):
                           result[r, c] += self[r, i] * another[i, c]
               return result
           else:
               _lowercase			:				Tuple 						=						F'''Unsupported type given for another ({type(lowerCamelCase)})'''
               raise TypeError(lowerCamelCase)
       def      UpperCamelCase	(   self)			->  Matrix:




           """simple docstring"""

           _lowercase			:				List[Any] 						=						Matrix(self.column, self.row)
           for r in range(self.row):
               for c in range(self.column):
                   _lowercase			:				Union[str, Any] 						=						self[r, c]
           return result




       def      UpperCamelCase	(   self, lowerCamelCase, lowerCamelCase)			->  Any:




           """simple docstring"""

           assert isinstance(lowerCamelCase, lowerCamelCase) and isinstance(lowerCamelCase, lowerCamelCase)
           assert self.row == self.column == u.row == v.row  # u, v should be column vector
           assert u.column == v.column == 1  # u, v should be column vector

           # Calculate
           _lowercase			:				Dict 						=						v.transpose()
           _lowercase			:				Any 						=						(v_t * self * u)[0, 0] + 1
           if numerator_factor == 0:
               return None  # It's not invertable
           return self - ((self * u) * (v_t * self) * (1.0 / numerator_factor))


# Testing
if __name__ == "__main__":



 def 							UpperCamelCase_( )       ->		None:
     # a^(-1)
     _lowercase			:				Optional[int] 						=						Matrix(3				,					3				,					0					)
     for i in range(3					):
         _lowercase			:				int 						=						1
     print(F'''a^(-1) is {ainv}'''					)
     # u, v
     _lowercase			:				Dict 						=						Matrix(3				,					1				,					0					)
     _lowercase						,							_lowercase						,							_lowercase			:				Dict 						=						1, 2, -3
     _lowercase			:				List[Any] 						=						Matrix(3				,					1				,					0					)
     _lowercase						,							_lowercase						,							_lowercase			:				int 						=						4, -2, 5
     print(F'''u is {u}'''					)
     print(F'''v is {v}'''					)
     print(F'''uv^T is {u * v.transpose()}'''					)
     # Sherman Morrison
     print(F'''(a + uv^T)^(-1) is {ainv.sherman_morrison(lowerCamelCase_				,					lowerCamelCase_					)}'''					)



 def 							UpperCamelCase_( )       ->		None:
     import doctest

     doctest.testmod()

 testa()


