"""simple docstring"""

import inspect
import unittest
from math import floor

from transformers import CvtConfig
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 transformers import CvtForImageClassification, CvtModel
		from transformers.models.cvt.modeling_cvt import CVT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
		from PIL import Image

		from transformers import AutoImageProcessor







class 	UpperCAmelCase_			(		_lowercase):






							def  _UpperCamelCase     (						self					: Any    )			->			str:
												_UpperCamelCase 		=      self.config_class(**self.inputs_dict    )
												self.parent.assertTrue(hasattr(UpperCAmelCase_	,		'''embed_dim'''    )    )
												self.parent.assertTrue(hasattr(UpperCAmelCase_	,		'''num_heads'''    )    )







class 	UpperCAmelCase_			:






							def __init__(						self					: str	,		__UpperCamelCase					: str	,		__UpperCamelCase					: Optional[Any]=13	,		__UpperCamelCase					: Union[str, Any]=64	,		__UpperCamelCase					: List[str]=3	,		__UpperCamelCase					: str=[16, 48, 96]	,		__UpperCamelCase					: Optional[int]=[1, 3, 6]	,		__UpperCamelCase					: List[str]=[1, 2, 10]	,		__UpperCamelCase					: List[str]=[7, 3, 3]	,		__UpperCamelCase					: Any=[4, 2, 2]	,		__UpperCamelCase					: Union[str, Any]=[2, 1, 1]	,		__UpperCamelCase					: str=[2, 2, 2]	,		__UpperCamelCase					: str=[False, False, True]	,		__UpperCamelCase					: str=[0.0, 0.0, 0.0]	,		__UpperCamelCase					: Tuple=0.0_2	,		__UpperCamelCase					: int=1E-12	,		__UpperCamelCase					: Any=True	,		__UpperCamelCase					: Optional[Any]=True	,		__UpperCamelCase					: Dict=2	,		)			->			str:
												_UpperCamelCase 		=      parent
												_UpperCamelCase 		=      batch_size
												_UpperCamelCase 		=      image_size
												_UpperCamelCase 		=      patch_sizes
												_UpperCamelCase 		=      patch_stride
												_UpperCamelCase 		=      patch_padding
												_UpperCamelCase 		=      is_training
												_UpperCamelCase 		=      use_labels
												_UpperCamelCase 		=      num_labels
												_UpperCamelCase 		=      num_channels
												_UpperCamelCase 		=      embed_dim
												_UpperCamelCase 		=      num_heads
												_UpperCamelCase 		=      stride_kv
												_UpperCamelCase 		=      depth
												_UpperCamelCase 		=      cls_token
												_UpperCamelCase 		=      attention_drop_rate
												_UpperCamelCase 		=      initializer_range
												_UpperCamelCase 		=      layer_norm_eps






							def  _UpperCamelCase     (						self					: Dict    )			->			List[Any]:
												_UpperCamelCase 		=      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]    )

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

												_UpperCamelCase 		=      self.get_config()
												return config, pixel_values, labels






							def  _UpperCamelCase     (						self					: Dict    )			->			Any:
												return CvtConfig(
												    image_size=self.image_size	,		num_labels=self.num_labels	,		num_channels=self.num_channels	,		embed_dim=self.embed_dim	,		num_heads=self.num_heads	,		patch_sizes=self.patch_sizes	,		patch_padding=self.patch_padding	,		patch_stride=self.patch_stride	,		stride_kv=self.stride_kv	,		depth=self.depth	,		cls_token=self.cls_token	,		attention_drop_rate=self.attention_drop_rate	,		initializer_range=self.initializer_range	,		)






							def  _UpperCamelCase     (						self					: Optional[int]	,		__UpperCamelCase					: str	,		__UpperCamelCase					: Dict	,		__UpperCamelCase					: Union[str, Any]    )			->			List[str]:
												_UpperCamelCase 		=      CvtModel(config=UpperCAmelCase_    )
												model.to(UpperCAmelCase_    )
												model.eval()
												_UpperCamelCase 		=      model(UpperCAmelCase_    )
												_UpperCamelCase 		=      (self.image_size, self.image_size)
												_UpperCamelCase		,				_UpperCamelCase 		=      image_size[0], image_size[1]
												for i in range(len(self.depth    )    ):
																	_UpperCamelCase 		=      floor(((height + 2 * self.patch_padding[i] - self.patch_sizes[i]) / self.patch_stride[i]) + 1    )
																	_UpperCamelCase 		=      floor(((width + 2 * self.patch_padding[i] - self.patch_sizes[i]) / self.patch_stride[i]) + 1    )
												self.parent.assertEqual(result.last_hidden_state.shape	,		(self.batch_size, self.embed_dim[-1], height, width)    )






							def  _UpperCamelCase     (						self					: List[str]	,		__UpperCamelCase					: Union[str, Any]	,		__UpperCamelCase					: Optional[int]	,		__UpperCamelCase					: List[Any]    )			->			int:
												_UpperCamelCase 		=      self.num_labels
												_UpperCamelCase 		=      CvtForImageClassification(UpperCAmelCase_    )
												model.to(UpperCAmelCase_    )
												model.eval()
												_UpperCamelCase 		=      model(UpperCAmelCase_	,		labels=UpperCAmelCase_    )
												self.parent.assertEqual(result.logits.shape	,		(self.batch_size, self.num_labels)    )






							def  _UpperCamelCase     (						self					: int    )			->			Dict:
												_UpperCamelCase 		=      self.prepare_config_and_inputs()
												_UpperCamelCase		,				_UpperCamelCase		,				_UpperCamelCase 		=      config_and_inputs
												_UpperCamelCase 		=      {'''pixel_values''': pixel_values}
												return config, inputs_dict







@require_torch
class 	UpperCAmelCase_			(		_lowercase     ,				_lowercase     ,				unittest.TestCase):
							snake_case__								= (CvtModel, CvtForImageClassification) if is_torch_available() else ()
							snake_case__								= (
							    {"feature-extraction": CvtModel, "image-classification": CvtForImageClassification}
							    if is_torch_available()
							    else {}
							)

							snake_case__								= False
							snake_case__								= False
							snake_case__								= False
							snake_case__								= False
							snake_case__								= False






							def  _UpperCamelCase     (						self					: List[str]    )			->			int:
												_UpperCamelCase 		=      CvtModelTester(self    )
												_UpperCamelCase 		=      ConfigTester(self	,		config_class=UpperCAmelCase_	,		has_text_modality=UpperCAmelCase_	,		hidden_size=37    )






							def  _UpperCamelCase     (						self					: str    )			->			Dict:
												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[int]    )			->			Tuple:
												return






							@unittest.skip(reason='''Cvt does not output attentions'''    )
							def  _UpperCamelCase     (						self					: Any    )			->			int:
												pass






							@unittest.skip(reason='''Cvt does not use inputs_embeds'''    )
							def  _UpperCamelCase     (						self					: Union[str, Any]    )			->			int:
												pass






							@unittest.skip(reason='''Cvt does not support input and output embeddings'''    )
							def  _UpperCamelCase     (						self					: Union[str, Any]    )			->			Optional[int]:
												pass






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

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

																	_UpperCamelCase 		=      ['''pixel_values''']
																	self.assertListEqual(arg_names[:1]	,		UpperCAmelCase_    )






							def  _UpperCamelCase     (						self					: Optional[Any]    )			->			Tuple:
												_UpperCamelCase 		=      self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_model(*UpperCAmelCase_    )






							def  _UpperCamelCase     (						self					: Union[str, Any]    )			->			List[str]:
												def check_hidden_states_output(__UpperCamelCase					: str	,		__UpperCamelCase					: Union[str, Any]	,		__UpperCamelCase					: Dict    ):
																	_UpperCamelCase 		=      model_class(UpperCAmelCase_    )
																	model.to(UpperCAmelCase_    )
																	model.eval()

																	with torch.no_grad():
																						_UpperCamelCase 		=      model(**self._prepare_for_class(UpperCAmelCase_	,		UpperCAmelCase_    )    )

																	_UpperCamelCase 		=      outputs.hidden_states

																	_UpperCamelCase 		=      len(self.model_tester.depth    )
																	self.assertEqual(len(UpperCAmelCase_    )	,		UpperCAmelCase_    )

																	# verify the first hidden states (first block)
																	self.assertListEqual(
																	    list(hidden_states[0].shape[-3:]    )	,		[
																	        self.model_tester.embed_dim[0],
																	        self.model_tester.image_size // 4,
																	        self.model_tester.image_size // 4,
																	    ]	,		)

												_UpperCamelCase		,				_UpperCamelCase 		=      self.model_tester.prepare_config_and_inputs_for_common()

												for model_class in self.all_model_classes:
																	_UpperCamelCase 		=      True
																	check_hidden_states_output(UpperCAmelCase_	,		UpperCAmelCase_	,		UpperCAmelCase_    )

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

																	check_hidden_states_output(UpperCAmelCase_	,		UpperCAmelCase_	,		UpperCAmelCase_    )






							def  _UpperCamelCase     (						self					: Union[str, Any]    )			->			str:
												_UpperCamelCase 		=      self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_image_classification(*UpperCAmelCase_    )






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






							@slow
							def  _UpperCamelCase     (						self					: List[str]    )			->			Union[str, Any]:
												for model_name in CVT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																	_UpperCamelCase 		=      CvtModel.from_pretrained(UpperCAmelCase_    )
																	self.assertIsNotNone(UpperCAmelCase_    )






def 	lowercase						(		)		->  Tuple:
					_UpperCamelCase 		=      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[Any]    )			->			Union[str, Any]:
												return AutoImageProcessor.from_pretrained(CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0]    )






							@slow
							def  _UpperCamelCase     (						self					: str    )			->			int:
												_UpperCamelCase 		=      CvtForImageClassification.from_pretrained(CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0]    ).to(UpperCAmelCase_    )

												_UpperCamelCase 		=      self.default_image_processor
												_UpperCamelCase 		=      prepare_img()
												_UpperCamelCase 		=      image_processor(images=UpperCAmelCase_	,		return_tensors='''pt'''    ).to(UpperCAmelCase_    )

												# forward pass
												with torch.no_grad():
																	_UpperCamelCase 		=      model(**UpperCAmelCase_    )

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

												_UpperCamelCase 		=      torch.tensor([0.9_2_8_5, 0.9_0_1_5, -0.3_1_5_0]    ).to(UpperCAmelCase_    )

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





'''simple docstring'''





from __future__ import annotations

import time

import numpy as np

SCREAMING_SNAKE_CASE__          =  [8, 5, 9, 7]
SCREAMING_SNAKE_CASE__          =  [
    [2, 0, 1, 1],
    [0, 1, 2, 1],
    [4, 0, 0, 3],
    [0, 2, 1, 0],
    [1, 0, 3, 0],
]
SCREAMING_SNAKE_CASE__          =  [
    [3, 2, 1, 4],
    [0, 2, 5, 2],
    [5, 1, 0, 5],
    [1, 5, 3, 0],
    [3, 0, 3, 3],
]


class 					snake_case			:



       def __init__(					self  ,UpperCAmelCase_  ,UpperCAmelCase_  ,UpperCAmelCase_  ,)	->				None:
           lowercase__									=    claim_vector
           lowercase__									=    allocated_resources_table
           lowercase__									=    maximum_claim_table



       def 				_a   (					self   )	->				list[int]:
           return [
               sum(p_item[i] for p_item in self.__allocated_resources_table   )
               for i in range(len(self.__allocated_resources_table[0]   )   )
           ]



       def 				_a   (					self   )	->				list[int]:
           return np.array(self.__claim_vector   ) - np.array(
               self.__processes_resource_summation()   )



       def 				_a   (					self   )	->				list[list[int]]:
           return [
               list(np.array(self.__maximum_claim_table[i]   ) - np.array(UpperCAmelCase_   )   )
               for i, allocated_resource in enumerate(self.__allocated_resources_table   )
           ]



       def 				_a   (					self   )	->				dict[int, list[int]]:
           return {self.__need().index(UpperCAmelCase_   ): i for i in self.__need()}



       def 				_a   (					self  ,**UpperCAmelCase_   )	->				None:
           lowercase__									=    self.__need()
           lowercase__									=    self.__allocated_resources_table
           lowercase__									=    self.__available_resources()
           lowercase__									=    self.__need_index_manager()
           for kw, val in kwargs.items():
               if kw and val is True:
                   self.__pretty_data()
           print("_" * 50 + "\n"   )
           while need_list:
               lowercase__									=    False
               for each_need in need_list:
                   lowercase__									=    True
                   for index, need in enumerate(UpperCAmelCase_   ):
                       if need > available_resources[index]:
                           lowercase__									=    False
                           break
                   if execution:
                       lowercase__									=    True
                       # get the original index of the process from ind_ctrl db
                       for original_need_index, need_clone in need_index_manager.items():
                           if each_need == need_clone:
                               lowercase__									=    original_need_index
                       print(F'''Process {process_number + 1} is executing.'''   )
                       # remove the process run from stack
                       need_list.remove(UpperCAmelCase_   )
                       # update available/freed resources stack
                       lowercase__									=    np.array(UpperCAmelCase_   ) + np.array(
                           alloc_resources_table[process_number]   )
                       print(
                           "Updated available resource stack for processes: "
                           + " ".join([str(UpperCAmelCase_   ) for x in available_resources]   )   )
                       break
               if safe:
                   print("The process is in a safe state.\n"   )
               else:
                   print("System in unsafe state. Aborting...\n"   )
                   break







       def 				_a   (					self   )	->				List[Any]:
           print(" " * 9 + "Allocated Resource Table"   )
           for item in self.__allocated_resources_table:
               print(
                   F'''P{self.__allocated_resources_table.index(UpperCAmelCase_   ) + 1}'''
                   + " ".join(F'''{it:>8}''' for it in item   )
                   + "\n"   )
           print(" " * 9 + "System Resource Table"   )
           for item in self.__maximum_claim_table:
               print(
                   F'''P{self.__maximum_claim_table.index(UpperCAmelCase_   ) + 1}'''
                   + " ".join(F'''{it:>8}''' for it in item   )
                   + "\n"   )
           print(
               "Current Usage by Active Processes: "
               + " ".join(str(UpperCAmelCase_   ) for x in self.__claim_vector   )   )
           print(
               "Initial Available Resources:       "
               + " ".join(str(UpperCAmelCase_   ) for x in self.__available_resources()   )   )
           time.sleep(1   )


if __name__ == "__main__":
 import doctest

 doctest.testmod()




