





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 					A_					:







       """simple docstring"""





       def __init__(     self						:						str    ,__A						:						Any    ,__A						:						Optional[int]=3    ,__A						:						List[str]=32    ,__A						:						Optional[int]=3    ,__A						:						Optional[Any]=10    ,__A						:						Any=[8, 16, 32, 64]    ,__A						:						Optional[int]=[1, 1, 2, 1]    ,__A						:						int=True    ,__A						:						Dict=True    ,__A						:						List[str]="relu"    ,__A						:						List[Any]=3    ,__A						:						Optional[Any]=None    ,__A						:						Any=["stage2", "stage3", "stage4"]    ,__A						:						str=[2, 3, 4]    ,__A						:						Optional[int]=1    ,)    ->       List[Any]:
         _lowercase						    =			parent
         _lowercase						    =			batch_size
         _lowercase						    =			image_size
         _lowercase						    =			num_channels
         _lowercase						    =			embeddings_size
         _lowercase						    =			hidden_sizes
         _lowercase						    =			depths
         _lowercase						    =			is_training
         _lowercase						    =			use_labels
         _lowercase						    =			hidden_act
         _lowercase						    =			num_labels
         _lowercase						    =			scope
         _lowercase						    =			len(__A	)
         _lowercase						    =			out_features
         _lowercase						    =			out_indices
         _lowercase						    =			num_groups







       def  __UpperCAmelCase			(     self						:						Any	)    ->       Optional[int]:
         _lowercase						    =			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]	)

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

         _lowercase						    =			self.get_config()

         return config, pixel_values, labels







       def  __UpperCAmelCase			(     self						:						Tuple	)    ->       List[str]:
         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[str]    ,__A						:						int    ,__A						:						Union[str, Any]    ,__A						:						int	)    ->       List[Any]:
         _lowercase						    =			BitModel(config=__A	)
         model.to(__A	)
         model.eval()
         _lowercase						    =			model(__A	)
         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						:						List[str]    ,__A						:						List[Any]    ,__A						:						Optional[Any]    ,__A						:						str	)    ->       Union[str, Any]:
         _lowercase						    =			self.num_labels
         _lowercase						    =			BitForImageClassification(__A	)
         model.to(__A	)
         model.eval()
         _lowercase						    =			model(__A    ,labels=__A	)
         self.parent.assertEqual(result.logits.shape    ,(self.batch_size, self.num_labels)	)







       def  __UpperCAmelCase			(     self						:						str    ,__A						:						List[str]    ,__A						:						Optional[Any]    ,__A						:						int	)    ->       List[Any]:
         _lowercase						    =			BitBackbone(config=__A	)
         model.to(__A	)
         model.eval()
         _lowercase						    =			model(__A	)

         # 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
         _lowercase						    =			None
         _lowercase						    =			BitBackbone(config=__A	)
         model.to(__A	)
         model.eval()
         _lowercase						    =			model(__A	)

         # 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						:						Tuple	)    ->       List[Any]:
         _lowercase						    =			self.prepare_config_and_inputs()
         _lowercase    ,		_lowercase    ,		_lowercase						    =			config_and_inputs
         _lowercase						    =			{'pixel_values': pixel_values}
         return config, inputs_dict





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







       """simple docstring"""





       SCREAMING_SNAKE_CASE_   :			int  			=	(BitModel, BitForImageClassification, BitBackbone) if is_torch_available() else ()
       SCREAMING_SNAKE_CASE_   :			Dict  			=	(
           {'''feature-extraction''': BitModel, '''image-classification''': BitForImageClassification}
           if is_torch_available()
           else {}
       )

       SCREAMING_SNAKE_CASE_   :			Union[str, Any]  			=	False
       SCREAMING_SNAKE_CASE_   :			List[Any]  			=	False
       SCREAMING_SNAKE_CASE_   :			Dict  			=	False
       SCREAMING_SNAKE_CASE_   :			Optional[Any]  			=	False
       SCREAMING_SNAKE_CASE_   :			Any  			=	False







       def  __UpperCAmelCase			(     self						:						Optional[int]	)    ->       Dict:
         _lowercase						    =			BitModelTester(self	)
         _lowercase						    =			ConfigTester(self    ,config_class=__A    ,has_text_modality=__A	)







       def  __UpperCAmelCase			(     self						:						Union[str, Any]	)    ->       Tuple:
         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	)    ->       Tuple:
         return







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







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







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







       def  __UpperCAmelCase			(     self						:						Tuple	)    ->       List[Any]:
         _lowercase    ,		_lowercase						    =			self.model_tester.prepare_config_and_inputs_for_common()

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

           _lowercase						    =			['pixel_values']
           self.assertListEqual(arg_names[:1]    ,__A	)







       def  __UpperCAmelCase			(     self						:						Optional[Any]	)    ->       Tuple:
         _lowercase						    =			self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*__A	)







       def  __UpperCAmelCase			(     self						:						int	)    ->       List[str]:
         _lowercase						    =			self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_backbone(*__A	)







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

         for model_class in self.all_model_classes:
           _lowercase						    =			model_class(config=__A	)
           for name, module in model.named_modules():
             if isinstance(__A    ,(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	)    ->       Optional[int]:
         def check_hidden_states_output(__A						:						str    ,__A						:						List[str]    ,__A						:						int	):
           _lowercase						    =			model_class(__A	)
           model.to(__A	)
           model.eval()

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

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

           _lowercase						    =			self.model_tester.num_stages
           self.assertEqual(len(__A	)    ,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]    ,)

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

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

             check_hidden_states_output(__A    ,__A    ,__A	)







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







       def  __UpperCAmelCase			(     self						:						Any	)    ->       List[Any]:
         _lowercase						    =			self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_image_classification(*__A	)







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

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





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







       """simple docstring"""





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







       @slow
       def  __UpperCAmelCase			(     self						:						str	)    ->       Any:
         _lowercase						    =			BitForImageClassification.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]	).to(__A	)

         _lowercase						    =			self.default_image_processor
         _lowercase						    =			prepare_img()
         _lowercase						    =			image_processor(images=__A    ,return_tensors='pt'	).to(__A	)

         # forward pass
         with torch.no_grad():
           _lowercase						    =			model(**__A	)

         # verify the logits
         _lowercase						    =			torch.Size((1, 1000)	)
         self.assertEqual(outputs.logits.shape    ,__A	)

         _lowercase						    =			torch.tensor([[-0.6526, -0.5263, -1.4398]]	).to(__A	)

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





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







       """simple docstring"""





       SCREAMING_SNAKE_CASE_   :			Optional[int]  			=	(BitBackbone,) if is_torch_available() else ()
       SCREAMING_SNAKE_CASE_   :			Optional[Any]  			=	BitConfig

       SCREAMING_SNAKE_CASE_   :			Optional[int]  			=	False







       def  __UpperCAmelCase			(     self						:						List[Any]	)    ->       Tuple:
         _lowercase						    =			BitModelTester(self	)

'''simple docstring'''
import copy
import os

import cva
import numpy as np
from matplotlib import pyplot as plt


class 			SCREAMING_SNAKE_CASE__      :







				def __init__(       self     )->		Dict:







											'''simple docstring'''






											UpperCamelCase   			=     ''
											UpperCamelCase   			=     ''
											UpperCamelCase   			=     []
											UpperCamelCase   			=     0
											UpperCamelCase   			=     256
											UpperCamelCase   			=     0
											UpperCamelCase   			=     0
											UpperCamelCase   			=     0
											UpperCamelCase   			=     0







				def 						UpperCAmelCase_    (       self						,				A_     )->		str:







											'''simple docstring'''






											UpperCamelCase   			=     cva.imread(A_						,				0     )
											UpperCamelCase   			=     copy.deepcopy(self.img     )
											UpperCamelCase			,	UpperCamelCase			,	UpperCamelCase   			=     plt.hist(self.img.ravel()						,				256						,				[0, 256]						,				label='x'     )
											UpperCamelCase   			=     np.sum(A_     )
											for i in range(len(A_     )     ):
																		UpperCamelCase   			=     x[i] / self.k
																		self.sk += prk
																		UpperCamelCase   			=     (self.L - 1) * self.sk
																		if self.rem != 0:
																									UpperCamelCase   			=     int(last % last     )
																		UpperCamelCase   			=     int(last + 1 if self.rem >= 0.5 else last     )
																		self.last_list.append(A_     )
																		UpperCamelCase   			=     int(np.ma.count(self.img     ) / self.img[1].size     )
																		UpperCamelCase   			=     self.img[1].size
											for i in range(self.number_of_cols     ):
																		for j in range(self.number_of_rows     ):
																									UpperCamelCase   			=     self.img[j][i]
																									if num != self.last_list[num]:
																																UpperCamelCase   			=     self.last_list[num]
											cva.imwrite('output_data/output.jpg'						,				self.img     )







				def 						UpperCAmelCase_    (       self     )->		Any:







											'''simple docstring'''






											plt.hist(self.img.ravel()						,				256						,				[0, 256]     )







				def 						UpperCAmelCase_    (       self     )->		Optional[Any]:







											'''simple docstring'''






											cva.imshow('Output-Image'						,				self.img     )
											cva.imshow('Input-Image'						,				self.original_image     )
											cva.waitKey(5000     )
											cva.destroyAllWindows()


if __name__ == "__main__":
						lowerCAmelCase					:    Union[str, Any]           = os.path.join(os.path.basename(__file__), 'image_data/input.jpg')
						lowerCAmelCase					:    str           = ConstantStretch()
						stretcher.stretch(file_path)
						stretcher.plot_histogram()
						stretcher.show_image()




