'''simple docstring'''
import inspect
import unittest

from transformers import DataaVecVisionConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_multi_gpu, 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, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
  import torch
  from torch import nn

  from transformers import (
      MODEL_MAPPING,
      DataaVecVisionForImageClassification,
      DataaVecVisionForSemanticSegmentation,
      DataaVecVisionModel,
  )
  from transformers.models.dataavec.modeling_dataavec_vision import DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
  from PIL import Image

  from transformers import BeitImageProcessor






class       __lowercase		:







     """simple docstring"""






     def __init__(		self  ,      _lowerCamelCase  ,      _lowerCamelCase=1_0_0  ,      _lowerCamelCase=1_3  ,      _lowerCamelCase=3_0  ,      _lowerCamelCase=2  ,      _lowerCamelCase=3  ,      _lowerCamelCase=True  ,      _lowerCamelCase=True  ,      _lowerCamelCase=3_2  ,      _lowerCamelCase=4  ,      _lowerCamelCase=4  ,      _lowerCamelCase=3_7  ,      _lowerCamelCase="gelu"  ,      _lowerCamelCase=0.1  ,      _lowerCamelCase=0.1  ,      _lowerCamelCase=1_0  ,      _lowerCamelCase=0.0_2  ,      _lowerCamelCase=3  ,      _lowerCamelCase=None  ,      _lowerCamelCase=[0, 1, 2, 3]  ,      ):
         __UpperCamelCase						:						Optional[Any]		     =	parent
         __UpperCamelCase						:						List[str]		     =	1_0_0
         __UpperCamelCase						:						int		     =	batch_size
         __UpperCamelCase						:						Any		     =	image_size
         __UpperCamelCase						:						Union[str, Any]		     =	patch_size
         __UpperCamelCase						:						List[Any]		     =	num_channels
         __UpperCamelCase						:						str		     =	is_training
         __UpperCamelCase						:						Any		     =	use_labels
         __UpperCamelCase						:						int		     =	hidden_size
         __UpperCamelCase						:						Union[str, Any]		     =	num_hidden_layers
         __UpperCamelCase						:						Dict		     =	num_attention_heads
         __UpperCamelCase						:						Any		     =	intermediate_size
         __UpperCamelCase						:						str		     =	hidden_act
         __UpperCamelCase						:						Tuple		     =	hidden_dropout_prob
         __UpperCamelCase						:						Dict		     =	attention_probs_dropout_prob
         __UpperCamelCase						:						str		     =	type_sequence_label_size
         __UpperCamelCase						:						Dict		     =	initializer_range
         __UpperCamelCase						:						List[Any]		     =	scope
         __UpperCamelCase						:						str		     =	out_indices
         __UpperCamelCase						:						Union[str, Any]		     =	num_labels

         # in BeiT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
         __UpperCamelCase						:						Optional[int]		     =	(image_size // patch_size) ** 2
         __UpperCamelCase						:						Tuple		     =	num_patches + 1







     def lowerCAmelCase  (		self     ):
         __UpperCamelCase						:						List[str]		     =	floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]     )

         __UpperCamelCase						:						Optional[Any]		     =	None
         __UpperCamelCase						:						List[Any]		     =	None
         if self.use_labels:
             __UpperCamelCase						:						str		     =	ids_tensor([self.batch_size]  ,      self.type_sequence_label_size     )
             __UpperCamelCase						:						List[Any]		     =	ids_tensor([self.batch_size, self.image_size, self.image_size]  ,      self.num_labels     )

         __UpperCamelCase						:						str		     =	self.get_config()

         return config, pixel_values, labels, pixel_labels







     def lowerCAmelCase  (		self     ):
         return DataaVecVisionConfig(
             vocab_size=self.vocab_size  ,      image_size=self.image_size  ,      patch_size=self.patch_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  ,      is_decoder=_A  ,      initializer_range=self.initializer_range  ,      out_indices=self.out_indices  ,      )







     def lowerCAmelCase  (		self  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase     ):
         __UpperCamelCase						:						Tuple		     =	DataaVecVisionModel(config=_A     )
         model.to(_A     )
         model.eval()
         __UpperCamelCase						:						str		     =	model(_A     )
         # expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
         __UpperCamelCase						:						Optional[int]		     =	(self.image_size // self.patch_size) ** 2
         self.parent.assertEqual(result.last_hidden_state.shape  ,      (self.batch_size, num_patches + 1, self.hidden_size)     )







     def lowerCAmelCase  (		self  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase     ):
         __UpperCamelCase						:						Any		     =	self.type_sequence_label_size
         __UpperCamelCase						:						Optional[int]		     =	DataaVecVisionForImageClassification(_A     )
         model.to(_A     )
         model.eval()
         __UpperCamelCase						:						Union[str, Any]		     =	model(_A  ,      labels=_A     )
         self.parent.assertEqual(result.logits.shape  ,      (self.batch_size, self.type_sequence_label_size)     )







     def lowerCAmelCase  (		self  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase     ):
         __UpperCamelCase						:						Dict		     =	self.num_labels
         __UpperCamelCase						:						Tuple		     =	DataaVecVisionForSemanticSegmentation(_A     )
         model.to(_A     )
         model.eval()
         __UpperCamelCase						:						Dict		     =	model(_A     )
         self.parent.assertEqual(
             result.logits.shape  ,      (self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2)     )
         __UpperCamelCase						:						Optional[int]		     =	model(_A  ,      labels=_A     )
         self.parent.assertEqual(
             result.logits.shape  ,      (self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2)     )







     def lowerCAmelCase  (		self     ):
         __UpperCamelCase						:						int		     =	self.prepare_config_and_inputs()
         __UpperCamelCase     ,       __UpperCamelCase     ,       __UpperCamelCase     ,       __UpperCamelCase						:						Any		     =	config_and_inputs
         __UpperCamelCase						:						Tuple		     =	{'pixel_values': pixel_values}
         return config, inputs_dict






@require_torch
class       __lowercase		(    A_      ,				A_      ,				unittest.TestCase    ):







     """simple docstring"""






     SCREAMING_SNAKE_CASE__		 =    (
         (DataaVecVisionModel, DataaVecVisionForImageClassification, DataaVecVisionForSemanticSegmentation)
         if is_torch_available()
         else ()
     )
     SCREAMING_SNAKE_CASE__		 =    (
         {
             "feature-extraction": DataaVecVisionModel,
             "image-classification": DataaVecVisionForImageClassification,
             "image-segmentation": DataaVecVisionForSemanticSegmentation,
         }
         if is_torch_available()
         else {}
     )

     SCREAMING_SNAKE_CASE__		 =    False
     SCREAMING_SNAKE_CASE__		 =    False
     SCREAMING_SNAKE_CASE__		 =    False







     def lowerCAmelCase  (		self     ):
         __UpperCamelCase						:						Dict		     =	DataaVecVisionModelTester(self     )
         __UpperCamelCase						:						Tuple		     =	ConfigTester(
             self  ,      config_class=_A  ,      has_text_modality=_A  ,      hidden_size=3_7     )







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







     def lowerCAmelCase  (		self     ):
         # Data2VecVision does not use inputs_embeds
         pass







     @require_torch_multi_gpu
     @unittest.skip(
         reason='Data2VecVision has some layers using `add_module` which doesn\'t work well with `nn.DataParallel`'     )
     def lowerCAmelCase  (		self     ):
         pass







     def lowerCAmelCase  (		self     ):
         __UpperCamelCase     ,       __UpperCamelCase						:						List[Any]		     =	self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
             __UpperCamelCase						:						Union[str, Any]		     =	model_class(_A     )
             self.assertIsInstance(model.get_input_embeddings()  ,      (nn.Module)     )
             __UpperCamelCase						:						str		     =	model.get_output_embeddings()
             self.assertTrue(x is None or isinstance(_A  ,      nn.Linear     )     )







     def lowerCAmelCase  (		self     ):
         __UpperCamelCase     ,       __UpperCamelCase						:						List[str]		     =	self.model_tester.prepare_config_and_inputs_for_common()

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

             __UpperCamelCase						:						Union[str, Any]		     =	['pixel_values']
             self.assertListEqual(arg_names[:1]  ,      _A     )







     def lowerCAmelCase  (		self     ):
         __UpperCamelCase						:						int		     =	self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*_A     )







     def lowerCAmelCase  (		self     ):
         __UpperCamelCase						:						Optional[int]		     =	self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_image_segmentation(*_A     )







     def lowerCAmelCase  (		self     ):
         if not self.model_tester.is_training:
             return

         __UpperCamelCase     ,       __UpperCamelCase						:						str		     =	self.model_tester.prepare_config_and_inputs_for_common()
         __UpperCamelCase						:						Dict		     =	True

         for model_class in self.all_model_classes:
             if model_class in [*get_values(_A     )]:
                 continue

             __UpperCamelCase						:						Optional[Any]		     =	model_class(_A     )
             model.to(_A     )
             model.train()
             __UpperCamelCase						:						int		     =	self._prepare_for_class(_A  ,      _A  ,      return_labels=_A     )
             __UpperCamelCase						:						str		     =	model(**_A     ).loss
             loss.backward()







     def lowerCAmelCase  (		self     ):
         __UpperCamelCase     ,       __UpperCamelCase						:						Any		     =	self.model_tester.prepare_config_and_inputs_for_common()
         if not self.model_tester.is_training:
             return

         __UpperCamelCase						:						Dict		     =	False
         __UpperCamelCase						:						Dict		     =	True

         for model_class in self.all_model_classes:
             if model_class in [*get_values(_A     )] or not model_class.supports_gradient_checkpointing:
                 continue
             # TODO: remove the following 3 lines once we have a MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING
             # this can then be incorporated into _prepare_for_class in test_modeling_common.py
             elif model_class.__name__ == "Data2VecVisionForSemanticSegmentation":
                 __UpperCamelCase     ,       __UpperCamelCase     ,       __UpperCamelCase     ,       __UpperCamelCase						:						Optional[int]		     =	inputs_dict['pixel_values'].shape
                 __UpperCamelCase						:						List[Any]		     =	torch.zeros(
                     [self.model_tester.batch_size, height, width]  ,      device=_A     ).long()
             __UpperCamelCase						:						List[str]		     =	model_class(_A     )
             model.gradient_checkpointing_enable()
             model.to(_A     )
             model.train()
             __UpperCamelCase						:						Any		     =	self._prepare_for_class(_A  ,      _A  ,      return_labels=_A     )
             __UpperCamelCase						:						str		     =	model(**_A     ).loss
             loss.backward()







     def lowerCAmelCase  (		self     ):
         __UpperCamelCase     ,       __UpperCamelCase						:						Optional[Any]		     =	self.model_tester.prepare_config_and_inputs_for_common()

         __UpperCamelCase						:						Optional[int]		     =	_config_zero_init(_A     )
         for model_class in self.all_model_classes:
             __UpperCamelCase						:						List[Any]		     =	model_class(config=_A     )
             for name, param in model.named_parameters():
                 # we skip lambda parameters as these require special initial values
                 # determined by config.layer_scale_init_value
                 if "lambda" in name:
                     continue
                 if param.requires_grad:
                     self.assertIn(
                         ((param.data.mean() * 1E9).round() / 1E9).item()  ,      [0.0, 1.0]  ,      msg=f"""Parameter {name} of model {model_class} seems not properly initialized"""  ,      )







     def lowerCAmelCase  (		self  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase=2E-4  ,      _lowerCamelCase="outputs"  ,      _lowerCamelCase=None     ):
         # We override with a slightly higher tol value, as semseg models tend to diverge a bit more
         super().check_pt_tf_outputs(_A  ,      _A  ,      _A  ,      _A  ,      _A  ,      _A     )







     def lowerCAmelCase  (		self     ):
         __UpperCamelCase						:						str		     =	self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_image_classification(*_A     )







     @slow
     def lowerCAmelCase  (		self     ):
         for model_name in DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             __UpperCamelCase						:						Any		     =	DataaVecVisionModel.from_pretrained(_A     )
             self.assertIsNotNone(_A     )







def 			_UpperCamelCase			(  ):




    """simple docstring"""



    __UpperCamelCase						:						Optional[Any]		     =	Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'      )
    return image






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







     """simple docstring"""






     @cached_property
     def lowerCAmelCase  (		self     ):
         return (
             BeitImageProcessor.from_pretrained('facebook/data2vec-vision-base-ft1k'     ) if is_vision_available() else None
         )







     @slow
     def lowerCAmelCase  (		self     ):
         __UpperCamelCase						:						List[str]		     =	DataaVecVisionForImageClassification.from_pretrained('facebook/data2vec-vision-base-ft1k'     ).to(
             _A     )

         __UpperCamelCase						:						str		     =	self.default_image_processor
         __UpperCamelCase						:						Tuple		     =	prepare_img()
         __UpperCamelCase						:						Any		     =	image_processor(images=_A  ,      return_tensors='pt'     ).to(_A     )

         # forward pass
         with torch.no_grad():
             __UpperCamelCase						:						List[Any]		     =	model(**_A     )
         __UpperCamelCase						:						Optional[Any]		     =	outputs.logits

         # verify the logits
         __UpperCamelCase						:						Tuple		     =	torch.Size((1, 1_0_0_0)     )
         self.assertEqual(logits.shape  ,      _A     )

         __UpperCamelCase						:						Dict		     =	torch.tensor([0.3_2_7_7, -0.1_3_9_5, 0.0_9_1_1]     ).to(_A     )

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

         __UpperCamelCase						:						int		     =	[model.config.labelaid[i] for i in ['remote control, remote', 'tabby, tabby cat']]
         self.assertEqual(logits[0].topk(2     ).indices.cpu().tolist()  ,      _A     )





import logging

# standard libraries
import sys

import numpy as np
import pytest  # type: ignore

# Custom/local libraries
from matrix import matrix_operation as matop

_A       =					[[12, 10], [3, 9]]
_A       =					[[3, 4], [7, 4]]
_A       =					[[3, 0, 2], [2, 0, -2], [0, 1, 1]]
_A       =					[[3, 0, -2], [2, 0, 2], [0, 1, 1]]
_A       =					[[3, 0, 2], [2, 0, -2], [0, 1, 1], [2, 0, -2]]
_A       =					[1]
_A       =					[2]

_A       =					logging.getLogger()
_A       =					logging.DEBUG
_A       =					logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)



@pytest.mark.mat_ops
@pytest.mark.parametrize(
    ("mat1", "mat2")					,						[(mat_a, mat_b), (mat_c, mat_d), (mat_d, mat_e), (mat_f, mat_h)]						)
def 		lowercase_     (  A__					,						A__						)				->				Dict:
    """simple docstring"""

    if (np.array(A__						)).shape < (2, 2) or (np.array(A__						)).shape < (2, 2):
        with pytest.raises(A__						):
            logger.info(F'\n\t{test_addition.__name__} returned integer'						)
            matop.add(A__					,						A__						)
    elif (np.array(A__						)).shape == (np.array(A__						)).shape:
        logger.info(F'\n\t{test_addition.__name__} with same matrix dims'						)
        snake_case						=   (np.array(A__						) + np.array(A__						)).tolist()
        snake_case						=   matop.add(A__					,						A__						)
        assert theo == act
    else:
        with pytest.raises(A__						):
            logger.info(F'\n\t{test_addition.__name__} with different matrix dims'						)
            matop.add(A__					,						A__						)



@pytest.mark.mat_ops
@pytest.mark.parametrize(
    ("mat1", "mat2")					,						[(mat_a, mat_b), (mat_c, mat_d), (mat_d, mat_e), (mat_f, mat_h)]						)
def 		lowercase_     (  A__					,						A__						)				->				List[Any]:
    """simple docstring"""

    if (np.array(A__						)).shape < (2, 2) or (np.array(A__						)).shape < (2, 2):
        with pytest.raises(A__						):
            logger.info(F'\n\t{test_subtraction.__name__} returned integer'						)
            matop.subtract(A__					,						A__						)
    elif (np.array(A__						)).shape == (np.array(A__						)).shape:
        logger.info(F'\n\t{test_subtraction.__name__} with same matrix dims'						)
        snake_case						=   (np.array(A__						) - np.array(A__						)).tolist()
        snake_case						=   matop.subtract(A__					,						A__						)
        assert theo == act
    else:
        with pytest.raises(A__						):
            logger.info(F'\n\t{test_subtraction.__name__} with different matrix dims'						)
            assert matop.subtract(A__					,						A__						)



@pytest.mark.mat_ops
@pytest.mark.parametrize(
    ("mat1", "mat2")					,						[(mat_a, mat_b), (mat_c, mat_d), (mat_d, mat_e), (mat_f, mat_h)]						)
def 		lowercase_     (  A__					,						A__						)				->				Union[str, Any]:
    """simple docstring"""

    if (np.array(A__						)).shape < (2, 2) or (np.array(A__						)).shape < (2, 2):
        logger.info(F'\n\t{test_multiplication.__name__} returned integer'						)
        with pytest.raises(A__						):
            matop.add(A__					,						A__						)
    elif (np.array(A__						)).shape == (np.array(A__						)).shape:
        logger.info(F'\n\t{test_multiplication.__name__} meets dim requirements'						)
        snake_case						=   (np.matmul(A__					,						A__						)).tolist()
        snake_case						=   matop.multiply(A__					,						A__						)
        assert theo == act
    else:
        with pytest.raises(A__						):
            logger.info(
                F'\n\t{test_multiplication.__name__} does not meet dim requirements'						)
            assert matop.subtract(A__					,						A__						)



@pytest.mark.mat_ops
def 		lowercase_     (  )				->				int:
    """simple docstring"""

    snake_case						=   (3.5 * np.array(A__						)).tolist()
    snake_case						=   matop.scalar_multiply(A__					,						3.5						)
    assert theo == act



@pytest.mark.mat_ops
def 		lowercase_     (  )				->				Any:
    """simple docstring"""

    snake_case						=   (np.identity(5						)).tolist()
    snake_case						=   matop.identity(5						)
    assert theo == act





@pytest.mark.mat_ops
@pytest.mark.parametrize("mat"					,						[mat_a, mat_b, mat_c, mat_d, mat_e, mat_f]						)
def 		lowercase_     (  A__						)				->				List[str]:
    """simple docstring"""

    if (np.array(A__						)).shape < (2, 2):
        with pytest.raises(A__						):
            logger.info(F'\n\t{test_transpose.__name__} returned integer'						)
            matop.transpose(A__						)
    else:
        snake_case						=   (np.transpose(A__						)).tolist()
        snake_case						=   matop.transpose(A__					,						return_map=A__						)
        assert theo == act

