


import inspect
import unittest

from transformers import RegNetConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device

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 RegNetForImageClassification, RegNetModel
 from transformers.models.regnet.modeling_regnet import REGNET_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
 from PIL import Image

 from transformers import AutoImageProcessor






class _lowerCamelCase:
       def __init__(   self, lowerCamelCase, lowerCamelCase=3, lowerCamelCase=32, lowerCamelCase=3, lowerCamelCase=10, lowerCamelCase=[10, 20, 30, 40], lowerCamelCase=[1, 1, 2, 1], lowerCamelCase=True, lowerCamelCase=True, lowerCamelCase="relu", lowerCamelCase=3, lowerCamelCase=None, )			->  Optional[int]:




           """simple docstring"""

           _lowercase			:				Union[str, Any] 						=						parent
           _lowercase			:				Any 						=						batch_size
           _lowercase			:				Tuple 						=						image_size
           _lowercase			:				Union[str, Any] 						=						num_channels
           _lowercase			:				int 						=						embeddings_size
           _lowercase			:				Any 						=						hidden_sizes
           _lowercase			:				str 						=						depths
           _lowercase			:				List[Any] 						=						is_training
           _lowercase			:				Dict 						=						use_labels
           _lowercase			:				List[str] 						=						hidden_act
           _lowercase			:				List[Any] 						=						num_labels
           _lowercase			:				Optional[Any] 						=						scope
           _lowercase			:				Union[str, Any] 						=						len(lowerCamelCase)
       def      UpperCamelCase	(   self)			->  int:




           """simple docstring"""

           _lowercase			:				Optional[int] 						=						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

           _lowercase			:				List[str] 						=						None
           if self.use_labels:
               _lowercase			:				str 						=						ids_tensor([self.batch_size], self.num_labels)

           _lowercase			:				Tuple 						=						self.get_config()

           return config, pixel_values, labels
       def      UpperCamelCase	(   self)			->  Any:




           """simple docstring"""

           return RegNetConfig(
               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, )
       def      UpperCamelCase	(   self, lowerCamelCase, lowerCamelCase, lowerCamelCase)			->  Optional[Any]:




           """simple docstring"""

           _lowercase			:				Union[str, Any] 						=						RegNetModel(config=lowerCamelCase)
           model.to(lowerCamelCase)
           model.eval()
           _lowercase			:				List[str] 						=						model(lowerCamelCase)
           # expected last hidden states: B, C, H // 32, W // 32
           self.parent.assertEqual(
               result.last_hidden_state.shape, (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32), )
       def      UpperCamelCase	(   self, lowerCamelCase, lowerCamelCase, lowerCamelCase)			->  Union[str, Any]:




           """simple docstring"""

           _lowercase			:				Union[str, Any] 						=						self.num_labels
           _lowercase			:				Optional[Any] 						=						RegNetForImageClassification(lowerCamelCase)
           model.to(lowerCamelCase)
           model.eval()
           _lowercase			:				Optional[Any] 						=						model(lowerCamelCase, labels=lowerCamelCase)
           self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels))




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




           """simple docstring"""

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






@require_torch
class _lowerCamelCase(		_a,					_a,					unittest.TestCase  ):
       lowercase_					:      Optional[Any]       					=     (RegNetModel, RegNetForImageClassification) if is_torch_available() else ()
       lowercase_					:      Optional[int]       					=     (
           {"""feature-extraction""": RegNetModel, """image-classification""": RegNetForImageClassification}
           if is_torch_available()
           else {}
       )

       lowercase_					:      Union[str, Any]       					=     False
       lowercase_					:      List[str]       					=     False
       lowercase_					:      Optional[Any]       					=     False
       lowercase_					:      List[Any]       					=     False
       def      UpperCamelCase	(   self)			->  Union[str, Any]:




           """simple docstring"""

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




           """simple docstring"""

           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)			->  Tuple:




           """simple docstring"""

           return
       @unittest.skip(reason='RegNet does not use inputs_embeds')
       def      UpperCamelCase	(   self)			->  Optional[int]:




           """simple docstring"""

           pass
       @unittest.skip(reason='RegNet does not support input and output embeddings')
       def      UpperCamelCase	(   self)			->  Optional[Any]:




           """simple docstring"""

           pass
       def      UpperCamelCase	(   self)			->  Dict:




           """simple docstring"""

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

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

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




           """simple docstring"""

           _lowercase			:				Tuple 						=						self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*lowerCamelCase)
       def      UpperCamelCase	(   self)			->  List[str]:




           """simple docstring"""

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

           for model_class in self.all_model_classes:
               _lowercase			:				Optional[Any] 						=						model_class(config=lowerCamelCase)
               for name, module in model.named_modules():
                   if isinstance(lowerCamelCase, (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)			->  Any:




           """simple docstring"""

           def check_hidden_states_output(lowerCamelCase, lowerCamelCase, lowerCamelCase):
               _lowercase			:				str 						=						model_class(lowerCamelCase)
               model.to(lowerCamelCase)
               model.eval()

               with torch.no_grad():
                   _lowercase			:				Any 						=						model(**self._prepare_for_class(lowerCamelCase, lowerCamelCase))

               _lowercase			:				List[str] 						=						outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

               _lowercase			:				int 						=						self.model_tester.num_stages
               self.assertEqual(len(lowerCamelCase), expected_num_stages + 1)

               # RegNet'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 // 2, self.model_tester.image_size // 2], )

           _lowercase						,							_lowercase			:				Any 						=						self.model_tester.prepare_config_and_inputs_for_common()
           _lowercase			:				Any 						=						['basic', 'bottleneck']
           for model_class in self.all_model_classes:
               for layer_type in layers_type:
                   _lowercase			:				Dict 						=						layer_type
                   _lowercase			:				Any 						=						True
                   check_hidden_states_output(lowerCamelCase, lowerCamelCase, lowerCamelCase)

                   # check that output_hidden_states also work using config
                   del inputs_dict["output_hidden_states"]
                   _lowercase			:				str 						=						True

                   check_hidden_states_output(lowerCamelCase, lowerCamelCase, lowerCamelCase)
       def      UpperCamelCase	(   self)			->  Optional[Any]:




           """simple docstring"""

           _lowercase			:				Union[str, Any] 						=						self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_image_classification(*lowerCamelCase)




       @slow
       def      UpperCamelCase	(   self)			->  str:




           """simple docstring"""

           for model_name in REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               _lowercase			:				Optional[Any] 						=						RegNetModel.from_pretrained(lowerCamelCase)
               self.assertIsNotNone(lowerCamelCase)



def 							UpperCamelCase_( )       ->		List[str]:
    _lowercase			:				int 						=						Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'					)
    return image






@require_torch
@require_vision
class _lowerCamelCase(		unittest.TestCase  ):
       @cached_property
       def      UpperCamelCase	(   self)			->  Any:




           """simple docstring"""

           return (
               AutoImageProcessor.from_pretrained(REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0])
               if is_vision_available()
               else None
           )




       @slow
       def      UpperCamelCase	(   self)			->  int:




           """simple docstring"""

           _lowercase			:				Dict 						=						RegNetForImageClassification.from_pretrained(REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]).to(lowerCamelCase)

           _lowercase			:				Dict 						=						self.default_image_processor
           _lowercase			:				Optional[Any] 						=						prepare_img()
           _lowercase			:				List[str] 						=						image_processor(images=lowerCamelCase, return_tensors='pt').to(lowerCamelCase)

           # forward pass
           with torch.no_grad():
               _lowercase			:				Optional[int] 						=						model(**lowerCamelCase)

           # verify the logits
           _lowercase			:				List[str] 						=						torch.Size((1, 10_00))
           self.assertEqual(outputs.logits.shape, lowerCamelCase)

           _lowercase			:				Optional[int] 						=						torch.tensor([-0.4_1_8_0, -1.5_0_5_1, -3.4_8_3_6]).to(lowerCamelCase)

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






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()


