


'''simple docstring'''




import inspect
import unittest

from transformers import BitConfig
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_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
       import torch
       from torch import nn

       from transformers import BitBackbone, BitForImageClassification, BitImageProcessor, BitModel
       from transformers.models.bit.modeling_bit import BIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
       from PIL import Image

class 		UpperCAmelCase_      :





     def __init__( self       :      Dict    ,      UpperCAmelCase__       :      Dict    ,      UpperCAmelCase__       :      Union[str, Any]=3    ,      UpperCAmelCase__       :      Tuple=3_2    ,      UpperCAmelCase__       :      Union[str, Any]=3    ,      UpperCAmelCase__       :      List[str]=1_0    ,      UpperCAmelCase__       :      Any=[8, 1_6, 3_2, 6_4]    ,      UpperCAmelCase__       :      Optional[int]=[1, 1, 2, 1]    ,      UpperCAmelCase__       :      Optional[Any]=True    ,      UpperCAmelCase__       :      Dict=True    ,      UpperCAmelCase__       :      List[str]="relu"    ,      UpperCAmelCase__       :      int=3    ,      UpperCAmelCase__       :      Tuple=None    ,      UpperCAmelCase__       :      Dict=["stage2", "stage3", "stage4"]    ,      UpperCAmelCase__       :      Optional[Any]=[2, 3, 4]    ,      UpperCAmelCase__       :      List[str]=1    ,      )				->  Union[str, Any]:
        lowerCAmelCase     =			parent
        lowerCAmelCase     =			batch_size
        lowerCAmelCase     =			image_size
        lowerCAmelCase     =			num_channels
        lowerCAmelCase     =			embeddings_size
        lowerCAmelCase     =			hidden_sizes
        lowerCAmelCase     =			depths
        lowerCAmelCase     =			is_training
        lowerCAmelCase     =			use_labels
        lowerCAmelCase     =			hidden_act
        lowerCAmelCase     =			num_labels
        lowerCAmelCase     =			scope
        lowerCAmelCase     =			len(UpperCAmelCase__    )
        lowerCAmelCase     =			out_features
        lowerCAmelCase     =			out_indices
        lowerCAmelCase     =			num_groups





     def 							__UpperCAmelCase      ( self       :      str    )				->  str:
        lowerCAmelCase     =			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]    )

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

        lowerCAmelCase     =			self.get_config()

        return config, pixel_values, labels





     def 							__UpperCAmelCase      ( self       :      Union[str, Any]    )				->  int:
        return BitConfig(
            num_channels=self.num_channels    ,      embeddings_size=self.embeddings_size    ,      hidden_sizes=self.hidden_sizes    ,      depths=self.depths    ,      hidden_act=self.hidden_act    ,      num_labels=self.num_labels    ,      out_features=self.out_features    ,      out_indices=self.out_indices    ,      num_groups=self.num_groups    ,      )





     def 							__UpperCAmelCase      ( self       :      List[Any]    ,      UpperCAmelCase__       :      Optional[Any]    ,      UpperCAmelCase__       :      Optional[Any]    ,      UpperCAmelCase__       :      Tuple    )				->  str:
        lowerCAmelCase     =			BitModel(config=UpperCAmelCase__    )
        model.to(UpperCAmelCase__    )
        model.eval()
        lowerCAmelCase     =			model(UpperCAmelCase__    )
        self.parent.assertEqual(
            result.last_hidden_state.shape    ,      (self.batch_size, self.hidden_sizes[-1], self.image_size // 3_2, self.image_size // 3_2)    ,      )





     def 							__UpperCAmelCase      ( self       :      Optional[Any]    ,      UpperCAmelCase__       :      Dict    ,      UpperCAmelCase__       :      Dict    ,      UpperCAmelCase__       :      List[Any]    )				->  Optional[int]:
        lowerCAmelCase     =			self.num_labels
        lowerCAmelCase     =			BitForImageClassification(UpperCAmelCase__    )
        model.to(UpperCAmelCase__    )
        model.eval()
        lowerCAmelCase     =			model(UpperCAmelCase__    ,      labels=UpperCAmelCase__    )
        self.parent.assertEqual(result.logits.shape    ,      (self.batch_size, self.num_labels)    )





     def 							__UpperCAmelCase      ( self       :      Any    ,      UpperCAmelCase__       :      Optional[Any]    ,      UpperCAmelCase__       :      Dict    ,      UpperCAmelCase__       :      str    )				->  str:
        lowerCAmelCase     =			BitBackbone(config=UpperCAmelCase__    )
        model.to(UpperCAmelCase__    )
        model.eval()
        lowerCAmelCase     =			model(UpperCAmelCase__    )

        # verify feature maps
        self.parent.assertEqual(len(result.feature_maps    )    ,      len(config.out_features    )    )
        self.parent.assertListEqual(list(result.feature_maps[0].shape    )    ,      [self.batch_size, self.hidden_sizes[1], 4, 4]    )

        # verify channels
        self.parent.assertEqual(len(model.channels    )    ,      len(config.out_features    )    )
        self.parent.assertListEqual(model.channels    ,      config.hidden_sizes[1:]    )

        # verify backbone works with out_features=None
        lowerCAmelCase     =			None
        lowerCAmelCase     =			BitBackbone(config=UpperCAmelCase__    )
        model.to(UpperCAmelCase__    )
        model.eval()
        lowerCAmelCase     =			model(UpperCAmelCase__    )

        # verify feature maps
        self.parent.assertEqual(len(result.feature_maps    )    ,      1    )
        self.parent.assertListEqual(list(result.feature_maps[0].shape    )    ,      [self.batch_size, self.hidden_sizes[-1], 1, 1]    )

        # verify channels
        self.parent.assertEqual(len(model.channels    )    ,      1    )
        self.parent.assertListEqual(model.channels    ,      [config.hidden_sizes[-1]]    )





     def 							__UpperCAmelCase      ( self       :      Optional[Any]    )				->  List[Any]:
        lowerCAmelCase     =			self.prepare_config_and_inputs()
        lowerCAmelCase     ,  lowerCAmelCase     ,  lowerCAmelCase     =			config_and_inputs
        lowerCAmelCase     =			{'pixel_values': pixel_values}
        return config, inputs_dict




@require_torch
class 		UpperCAmelCase_      (  __lowercase       ,	__lowercase       ,	unittest.TestCase		):
     lowerCamelCase				:	List[str]		=     (BitModel, BitForImageClassification, BitBackbone) if is_torch_available() else ()
     lowerCamelCase				:	Union[str, Any]		=     (
         {'''feature-extraction''': BitModel, '''image-classification''': BitForImageClassification}
         if is_torch_available()
         else {}
     )

     lowerCamelCase				:	str		=     False
     lowerCamelCase				:	List[Any]		=     False
     lowerCamelCase				:	Dict		=     False
     lowerCamelCase				:	List[str]		=     False
     lowerCamelCase				:	List[Any]		=     False





     def 							__UpperCAmelCase      ( self       :      Optional[Any]    )				->  Tuple:
        lowerCAmelCase     =			BitModelTester(self    )
        lowerCAmelCase     =			ConfigTester(self    ,      config_class=UpperCAmelCase__    ,      has_text_modality=UpperCAmelCase__    )





     def 							__UpperCAmelCase      ( self       :      Dict    )				->  Optional[Any]:
        self.create_and_test_config_common_properties()
        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       :      Optional[Any]    )				->  Tuple:
        return





     @unittest.skip(reason='Bit does not output attentions'    )
     def 							__UpperCAmelCase      ( self       :      Any    )				->  Tuple:
        pass





     @unittest.skip(reason='Bit does not use inputs_embeds'    )
     def 							__UpperCAmelCase      ( self       :      Optional[Any]    )				->  Optional[int]:
        pass





     @unittest.skip(reason='Bit does not support input and output embeddings'    )
     def 							__UpperCAmelCase      ( self       :      Any    )				->  str:
        pass





     def 							__UpperCAmelCase      ( self       :      Union[str, Any]    )				->  Union[str, Any]:
        lowerCAmelCase     ,  lowerCAmelCase     =			self.model_tester.prepare_config_and_inputs_for_common()

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

           lowerCAmelCase     =			['pixel_values']
           self.assertListEqual(arg_names[:1]    ,      UpperCAmelCase__    )





     def 							__UpperCAmelCase      ( self       :      str    )				->  Union[str, Any]:
        lowerCAmelCase     =			self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*UpperCAmelCase__    )





     def 							__UpperCAmelCase      ( self       :      List[Any]    )				->  str:
        lowerCAmelCase     =			self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_backbone(*UpperCAmelCase__    )





     def 							__UpperCAmelCase      ( self       :      Tuple    )				->  Dict:
        lowerCAmelCase     ,  lowerCAmelCase     =			self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
           lowerCAmelCase     =			model_class(config=UpperCAmelCase__    )
           for name, module in model.named_modules():
              if isinstance(UpperCAmelCase__    ,      (nn.BatchNormad, nn.GroupNorm)    ):
                 self.assertTrue(
                     torch.all(module.weight == 1    )    ,      msg=F'''Parameter {name} of model {model_class} seems not properly initialized'''    ,      )
                 self.assertTrue(
                     torch.all(module.bias == 0    )    ,      msg=F'''Parameter {name} of model {model_class} seems not properly initialized'''    ,      )





     def 							__UpperCAmelCase      ( self       :      int    )				->  Any:
        def check_hidden_states_output(UpperCAmelCase__       :      Union[str, Any]    ,      UpperCAmelCase__       :      Optional[Any]    ,      UpperCAmelCase__       :      Tuple    ):
           lowerCAmelCase     =			model_class(UpperCAmelCase__    )
           model.to(UpperCAmelCase__    )
           model.eval()

           with torch.no_grad():
              lowerCAmelCase     =			model(**self._prepare_for_class(UpperCAmelCase__    ,      UpperCAmelCase__    )    )

           lowerCAmelCase     =			outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

           lowerCAmelCase     =			self.model_tester.num_stages
           self.assertEqual(len(UpperCAmelCase__    )    ,      expected_num_stages + 1    )

           # Bit's feature maps are of shape (batch_size, num_channels, height, width)
           self.assertListEqual(
               list(hidden_states[0].shape[-2:]    )    ,      [self.model_tester.image_size // 4, self.model_tester.image_size // 4]    ,      )

        lowerCAmelCase     ,  lowerCAmelCase     =			self.model_tester.prepare_config_and_inputs_for_common()
        lowerCAmelCase     =			['preactivation', 'bottleneck']
        for model_class in self.all_model_classes:
           for layer_type in layers_type:
              lowerCAmelCase     =			layer_type
              lowerCAmelCase     =			True
              check_hidden_states_output(UpperCAmelCase__    ,      UpperCAmelCase__    ,      UpperCAmelCase__    )

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

              check_hidden_states_output(UpperCAmelCase__    ,      UpperCAmelCase__    ,      UpperCAmelCase__    )





     @unittest.skip(reason='Bit does not use feedforward chunking'    )
     def 							__UpperCAmelCase      ( self       :      Tuple    )				->  Any:
        pass





     def 							__UpperCAmelCase      ( self       :      str    )				->  Any:
        lowerCAmelCase     =			self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_classification(*UpperCAmelCase__    )





     @slow
     def 							__UpperCAmelCase      ( self       :      Optional[Any]    )				->  Dict:
        for model_name in BIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           lowerCAmelCase     =			BitModel.from_pretrained(UpperCAmelCase__    )
           self.assertIsNotNone(UpperCAmelCase__    )





def 	a_					( ):
   lowerCAmelCase     =			Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'				)
   return image

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





     @cached_property
     def 							__UpperCAmelCase      ( self       :      Optional[int]    )				->  Dict:
        return (
            BitImageProcessor.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]    ) if is_vision_available() else None
        )





     @slow
     def 							__UpperCAmelCase      ( self       :      Optional[Any]    )				->  Any:
        lowerCAmelCase     =			BitForImageClassification.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]    ).to(UpperCAmelCase__    )

        lowerCAmelCase     =			self.default_image_processor
        lowerCAmelCase     =			prepare_img()
        lowerCAmelCase     =			image_processor(images=UpperCAmelCase__    ,      return_tensors='pt'    ).to(UpperCAmelCase__    )

        # forward pass
        with torch.no_grad():
           lowerCAmelCase     =			model(**UpperCAmelCase__    )

        # verify the logits
        lowerCAmelCase     =			torch.Size((1, 1_0_0_0)    )
        self.assertEqual(outputs.logits.shape    ,      UpperCAmelCase__    )

        lowerCAmelCase     =			torch.tensor([[-0.6_526, -0.5_263, -1.4_398]]    ).to(UpperCAmelCase__    )

        self.assertTrue(torch.allclose(outputs.logits[0, :3]    ,      UpperCAmelCase__    ,      atol=1E-4    )    )




@require_torch
class 		UpperCAmelCase_      (  __lowercase       ,	unittest.TestCase		):
     lowerCamelCase				:	Optional[Any]		=     (BitBackbone,) if is_torch_available() else ()
     lowerCamelCase				:	Optional[int]		=     BitConfig

     lowerCamelCase				:	Optional[Any]		=     False





     def 							__UpperCAmelCase      ( self       :      Dict    )				->  int:
        lowerCAmelCase     =			BitModelTester(self    )









'''simple docstring'''




from __future__ import annotations

from typing import Any

class 		UpperCAmelCase_      :





     def __init__( self       :      Optional[int]    ,      UpperCAmelCase__       :      int    ,      UpperCAmelCase__       :      int    ,      UpperCAmelCase__       :      float = 0    )				->  None:
        lowerCAmelCase     ,  lowerCAmelCase     =			row, column
        lowerCAmelCase     =			[[default_value for c in range(UpperCAmelCase__    )] for r in range(UpperCAmelCase__    )]





     def __str__( self       :      List[str]    )				->  str:
        lowerCAmelCase     =			F'''Matrix consist of {self.row} rows and {self.column} columns\n'''

        # Make string identifier
        lowerCAmelCase     =			0
        for row_vector in self.array:
           for obj in row_vector:
              lowerCAmelCase     =			max(UpperCAmelCase__    ,      len(str(UpperCAmelCase__    )    )    )
        lowerCAmelCase     =			F'''%{max_element_length}s'''

        # Make string and return
        def single_line(UpperCAmelCase__       :      list[float]    ) -> str:
           nonlocal string_format_identifier
           lowerCAmelCase     =			'['
           line += ", ".join(string_format_identifier % (obj,) for obj in row_vector    )
           line += "]"
           return line

        s += "\n".join(single_line(UpperCAmelCase__    ) for row_vector in self.array    )
        return s





     def __repr__( self       :      List[str]    )				->  str:
        return str(self    )





     def 							__UpperCAmelCase      ( self       :      Optional[int]    ,      UpperCAmelCase__       :      tuple[int, int]    )				->  bool:
        if not (isinstance(UpperCAmelCase__    ,      (list, tuple)    ) and len(UpperCAmelCase__    ) == 2):
           return False
        elif not (0 <= loc[0] < self.row and 0 <= loc[1] < self.column):
           return False
        else:
           return True





     def __getitem__( self       :      Any    ,      UpperCAmelCase__       :      tuple[int, int]    )				->  Any:
        assert self.validate_indicies(UpperCAmelCase__    )
        return self.array[loc[0]][loc[1]]





     def __setitem__( self       :      Dict    ,      UpperCAmelCase__       :      tuple[int, int]    ,      UpperCAmelCase__       :      float    )				->  None:
        assert self.validate_indicies(UpperCAmelCase__    )
        lowerCAmelCase     =			value





     def __add__( self       :      Any    ,      UpperCAmelCase__       :      Matrix    )				->  Matrix:
        assert isinstance(UpperCAmelCase__    ,      UpperCAmelCase__    )
        assert self.row == another.row and self.column == another.column

        # Add
        lowerCAmelCase     =			Matrix(self.row    ,      self.column    )
        for r in range(self.row    ):
           for c in range(self.column    ):
              lowerCAmelCase     =			self[r, c] + another[r, c]
        return result





     def __neg__( self       :      int    )				->  Matrix:
        lowerCAmelCase     =			Matrix(self.row    ,      self.column    )
        for r in range(self.row    ):
           for c in range(self.column    ):
              lowerCAmelCase     =			-self[r, c]
        return result





     def __sub__( self       :      str    ,      UpperCAmelCase__       :      Matrix    )				->  Matrix:
        return self + (-another)





     def __mul__( self       :      str    ,      UpperCAmelCase__       :      int | float | Matrix    )				->  Matrix:

        if isinstance(UpperCAmelCase__    ,      (int, float)    ):  # Scalar multiplication
           lowerCAmelCase     =			Matrix(self.row    ,      self.column    )
           for r in range(self.row    ):
              for c in range(self.column    ):
                 lowerCAmelCase     =			self[r, c] * another
           return result
        elif isinstance(UpperCAmelCase__    ,      UpperCAmelCase__    ):  # Matrix multiplication
           assert self.column == another.row
           lowerCAmelCase     =			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:
           lowerCAmelCase     =			F'''Unsupported type given for another ({type(UpperCAmelCase__    )})'''
           raise TypeError(UpperCAmelCase__    )





     def 							__UpperCAmelCase      ( self       :      Optional[Any]    )				->  Matrix:
        lowerCAmelCase     =			Matrix(self.column    ,      self.row    )
        for r in range(self.row    ):
           for c in range(self.column    ):
              lowerCAmelCase     =			self[r, c]
        return result





     def 							__UpperCAmelCase      ( self       :      List[str]    ,      UpperCAmelCase__       :      Matrix    ,      UpperCAmelCase__       :      Matrix    )				->  Any:
        assert isinstance(UpperCAmelCase__    ,      UpperCAmelCase__    ) and isinstance(UpperCAmelCase__    ,      UpperCAmelCase__    )
        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
        lowerCAmelCase     =			v.transpose()
        lowerCAmelCase     =			(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 	a_					( ):
          # a^(-1)
          lowerCAmelCase     =			Matrix(3	, 3	, 0				)
          for i in range(3				):
             lowerCAmelCase     =			1
          print(f'''a^(-1) is {ainv}'''				)
          # u, v
          lowerCAmelCase     =			Matrix(3	, 1	, 0				)
          lowerCAmelCase     ,  lowerCAmelCase     ,  lowerCAmelCase     =			1, 2, -3
          lowerCAmelCase     =			Matrix(3	, 1	, 0				)
          lowerCAmelCase     ,  lowerCAmelCase     ,  lowerCAmelCase     =			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 	a_					( ):
          import doctest

          doctest.testmod()




       testa()





