


import inspect
import unittest

from transformers import ResNetConfig
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 ResNetBackbone, ResNetForImageClassification, ResNetModel
						from transformers.models.resnet.modeling_resnet import RESNET_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
						from PIL import Image

						from transformers import AutoImageProcessor







class     lowerCamelCase__		:






			"""simple docstring"""






			def __init__(							self ,  snake_case ,  snake_case=3 ,  snake_case=32 ,  snake_case=3 ,  snake_case=10 ,  snake_case=[10, 20, 30, 40] ,  snake_case=[1, 1, 2, 1] ,  snake_case=True ,  snake_case=True ,  snake_case="relu" ,  snake_case=3 ,  snake_case=None ,  snake_case=["stage2", "stage3", "stage4"] ,  snake_case=[2, 3, 4] ,  ):

						'''simple docstring'''


						UpperCamelCase__   =    parent
						UpperCamelCase__   =    batch_size
						UpperCamelCase__   =    image_size
						UpperCamelCase__   =    num_channels
						UpperCamelCase__   =    embeddings_size
						UpperCamelCase__   =    hidden_sizes
						UpperCamelCase__   =    depths
						UpperCamelCase__   =    is_training
						UpperCamelCase__   =    use_labels
						UpperCamelCase__   =    hidden_act
						UpperCamelCase__   =    num_labels
						UpperCamelCase__   =    scope
						UpperCamelCase__   =    len(snake_case       )
						UpperCamelCase__   =    out_features
						UpperCamelCase__   =    out_indices




			def 			snake_case__			(							self       ):

						'''simple docstring'''


						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 			snake_case__			(							self       ):

						'''simple docstring'''


						return ResNetConfig(
						    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 ,  )




			def 			snake_case__			(							self ,  snake_case ,  snake_case ,  snake_case       ):

						'''simple docstring'''


						UpperCamelCase__   =    ResNetModel(config=snake_case       )
						model.to(snake_case       )
						model.eval()
						UpperCamelCase__   =    model(snake_case       )
						# 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 			snake_case__			(							self ,  snake_case ,  snake_case ,  snake_case       ):

						'''simple docstring'''


						UpperCamelCase__   =    self.num_labels
						UpperCamelCase__   =    ResNetForImageClassification(snake_case       )
						model.to(snake_case       )
						model.eval()
						UpperCamelCase__   =    model(snake_case ,  labels=snake_case       )
						self.parent.assertEqual(result.logits.shape ,  (self.batch_size, self.num_labels)       )




			def 			snake_case__			(							self ,  snake_case ,  snake_case ,  snake_case       ):

						'''simple docstring'''


						UpperCamelCase__   =    ResNetBackbone(config=snake_case       )
						model.to(snake_case       )
						model.eval()
						UpperCamelCase__   =    model(snake_case       )

						# 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
						UpperCamelCase__   =    None
						UpperCamelCase__   =    ResNetBackbone(config=snake_case       )
						model.to(snake_case       )
						model.eval()
						UpperCamelCase__   =    model(snake_case       )

						# 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 			snake_case__			(							self       ):

						'''simple docstring'''


						UpperCamelCase__   =    self.prepare_config_and_inputs()
						UpperCamelCase__, UpperCamelCase__, UpperCamelCase__   =    config_and_inputs
						UpperCamelCase__   =    {"pixel_values": pixel_values}
						return config, inputs_dict







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






			"""simple docstring"""






			_UpperCamelCase						:   List[Any]					=						(
			    (
			        ResNetModel,
			        ResNetForImageClassification,
			        ResNetBackbone,
			    )
			    if is_torch_available()
			    else ()
			)
			_UpperCamelCase						:   Optional[int]					=						(
			    {'feature-extraction': ResNetModel, 'image-classification': ResNetForImageClassification}
			    if is_torch_available()
			    else {}
			)

			_UpperCamelCase						:   Optional[Any]					=						True
			_UpperCamelCase						:   Dict					=						False
			_UpperCamelCase						:   Any					=						False
			_UpperCamelCase						:   Union[str, Any]					=						False
			_UpperCamelCase						:   Tuple					=						False




			def 			snake_case__			(							self       ):

						'''simple docstring'''


						UpperCamelCase__   =    ResNetModelTester(self       )
						UpperCamelCase__   =    ConfigTester(self ,  config_class=snake_case ,  has_text_modality=snake_case       )




			def 			snake_case__			(							self       ):

						'''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 			snake_case__			(							self       ):

						'''simple docstring'''


						return




			@unittest.skip(reason="ResNet does not use inputs_embeds"       )
			def 			snake_case__			(							self       ):

						'''simple docstring'''


						pass




			@unittest.skip(reason="ResNet does not support input and output embeddings"       )
			def 			snake_case__			(							self       ):

						'''simple docstring'''


						pass




			def 			snake_case__			(							self       ):

						'''simple docstring'''


						UpperCamelCase__, UpperCamelCase__   =    self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
									UpperCamelCase__   =    model_class(snake_case       )
									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] ,  snake_case       )




			def 			snake_case__			(							self       ):

						'''simple docstring'''


						UpperCamelCase__   =    self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*snake_case       )




			def 			snake_case__			(							self       ):

						'''simple docstring'''


						UpperCamelCase__   =    self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_backbone(*snake_case       )




			def 			snake_case__			(							self       ):

						'''simple docstring'''


						UpperCamelCase__, UpperCamelCase__   =    self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
									UpperCamelCase__   =    model_class(config=snake_case       )
									for name, module in model.named_modules():
												if isinstance(snake_case ,  (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 			snake_case__			(							self       ):

						'''simple docstring'''


						def check_hidden_states_output(snake_case ,  snake_case ,  snake_case       ):
									UpperCamelCase__   =    model_class(snake_case       )
									model.to(snake_case       )
									model.eval()

									with torch.no_grad():
												UpperCamelCase__   =    model(**self._prepare_for_class(snake_case ,  snake_case       )       )

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

									UpperCamelCase__   =    self.model_tester.num_stages
									self.assertEqual(len(snake_case       ) ,  expected_num_stages + 1       )

									# ResNet'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] ,  )

						UpperCamelCase__, UpperCamelCase__   =    self.model_tester.prepare_config_and_inputs_for_common()
						UpperCamelCase__   =    ["basic", "bottleneck"]
						for model_class in self.all_model_classes:
									for layer_type in layers_type:
												UpperCamelCase__   =    layer_type
												UpperCamelCase__   =    True
												check_hidden_states_output(snake_case ,  snake_case ,  snake_case       )

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

												check_hidden_states_output(snake_case ,  snake_case ,  snake_case       )




			@unittest.skip(reason="ResNet does not use feedforward chunking"       )
			def 			snake_case__			(							self       ):

						'''simple docstring'''


						pass




			def 			snake_case__			(							self       ):

						'''simple docstring'''


						UpperCamelCase__   =    self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_image_classification(*snake_case       )







			@slow
			def 			snake_case__			(							self       ):

						'''simple docstring'''


						for model_name in RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									UpperCamelCase__   =    ResNetModel.from_pretrained(snake_case       )
									self.assertIsNotNone(snake_case       )



def     UpperCamelCase_(			)->						List[Any]:
			UpperCamelCase__   =    Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"					)
			return image







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






			"""simple docstring"""






			@cached_property
			def 			snake_case__			(							self       ):

						'''simple docstring'''


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







			@slow
			def 			snake_case__			(							self       ):

						'''simple docstring'''


						UpperCamelCase__   =    ResNetForImageClassification.from_pretrained(RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]       ).to(snake_case       )

						UpperCamelCase__   =    self.default_image_processor
						UpperCamelCase__   =    prepare_img()
						UpperCamelCase__   =    image_processor(images=snake_case ,  return_tensors="pt"       ).to(snake_case       )

						# forward pass
						with torch.no_grad():
									UpperCamelCase__   =    model(**snake_case       )

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

						UpperCamelCase__   =    torch.tensor([-11.1069, -9.7877, -8.3777]       ).to(snake_case       )

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







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






			"""simple docstring"""






			_UpperCamelCase						:   Any					=						(ResNetBackbone,) if is_torch_available() else ()
			_UpperCamelCase						:   Optional[int]					=						False
			_UpperCamelCase						:   int					=						ResNetConfig




			def 			snake_case__			(							self       ):

						'''simple docstring'''


						UpperCamelCase__   =    ResNetModelTester(self       )






import numpy as np
def     UpperCamelCase_(			_A				:Optional[int]			,       _A				:Dict="utf-8"			,       _A				:Optional[Any]="surrogatepass"					)->						int:
			UpperCamelCase__   =    bin(int.from_bytes(text.encode(_A			,       _A					)			,       "big"					)					)[2:]
			return bits.zfill(8 * ((len(_A					) + 7) // 8)					)
def     UpperCamelCase_(			_A				:Optional[int]			,       _A				:int="utf-8"			,       _A				:List[str]="surrogatepass"					)->						Optional[Any]:
			UpperCamelCase__   =    int(_A			,       2					)
			return n.to_bytes((n.bit_length() + 7) // 8			,       "big"					).decode(_A			,       _A					) or "\0"
def     UpperCamelCase_(			_A				:Union[str, Any]			,       _A				:Tuple					)->						Optional[int]:
			if size_par + len(_A					) <= 2**size_par - (len(_A					) - 1):
						raise ValueError("size of parity don't match with size of data"					)

			UpperCamelCase__   =    []
			UpperCamelCase__   =    []
			UpperCamelCase__   =    [bin(_A					)[2:] for x in range(1			,       size_par + len(_A					) + 1					)]

			# sorted information data for the size of the output data
			UpperCamelCase__   =    []
			# data position template + parity
			UpperCamelCase__   =    []
			# parity bit counter
			UpperCamelCase__   =    0
			# counter position of data bits
			UpperCamelCase__   =    0

			for x in range(1			,       size_par + len(_A					) + 1					):
						# Performs a template of bit positions - who should be given,
						# and who should be parity
						if qtd_bp < size_par:
									if (np.log(_A					) / np.log(2					)).is_integer():
												data_out_gab.append("P"					)
												UpperCamelCase__   =    qtd_bp + 1
									else:
												data_out_gab.append("D"					)
						else:
									data_out_gab.append("D"					)

						# Sorts the data to the new output size
						if data_out_gab[-1] == "D":
									data_ord.append(data[cont_data]					)
									cont_data += 1
						else:
									data_ord.append(_A					)

    # Calculates parity
			UpperCamelCase__   =    0  # parity bit counter
			for bp in range(1			,       size_par + 1					):
						# Bit counter one for a given parity
						UpperCamelCase__   =    0
						# counter to control the loop reading
						UpperCamelCase__   =    0
						for x in data_ord:
									if x is not None:
												try:
															UpperCamelCase__   =    (bin_pos[cont_loop])[-1 * (bp)]
												except IndexError:
															UpperCamelCase__   =    "0"
												if aux == "1" and x == "1":
															cont_bo += 1
									cont_loop += 1
						parity.append(cont_bo % 2					)

						qtd_bp += 1

			# Mount the message
			UpperCamelCase__   =    0  # parity bit counter
			for x in range(0			,       size_par + len(_A					)					):
						if data_ord[x] is None:
									data_out.append(str(parity[cont_bp]					)					)
									cont_bp += 1
						else:
									data_out.append(data_ord[x]					)

			return data_out
def     UpperCamelCase_(			_A				:int			,       _A				:str					)->						Dict:
			UpperCamelCase__   =    []
			# Parity bit counter
			UpperCamelCase__   =    0
			# Counter p data bit reading
			UpperCamelCase__   =    0
			# list of parity received
			UpperCamelCase__   =    []
			UpperCamelCase__   =    []

			for x in range(1			,       len(_A					) + 1					):
						# Performs a template of bit positions - who should be given,
						#  and who should be parity
						if qtd_bp < size_par and (np.log(_A					) / np.log(2					)).is_integer():
									data_out_gab.append("P"					)
									UpperCamelCase__   =    qtd_bp + 1
						else:
									data_out_gab.append("D"					)

						# Sorts the data to the new output size
						if data_out_gab[-1] == "D":
									data_output.append(data[cont_data]					)
						else:
									parity_received.append(data[cont_data]					)
						cont_data += 1

			# -----------calculates the parity with the data
			UpperCamelCase__   =    []
			UpperCamelCase__   =    []
			UpperCamelCase__   =    [bin(_A					)[2:] for x in range(1			,       size_par + len(_A					) + 1					)]

			#  sorted information data for the size of the output data
			UpperCamelCase__   =    []
			# Data position feedback + parity
			UpperCamelCase__   =    []
			# Parity bit counter
			UpperCamelCase__   =    0
			# Counter p data bit reading
			UpperCamelCase__   =    0

			for x in range(1			,       size_par + len(_A					) + 1					):
						# Performs a template position of bits - who should be given,
						# and who should be parity
						if qtd_bp < size_par and (np.log(_A					) / np.log(2					)).is_integer():
									data_out_gab.append("P"					)
									UpperCamelCase__   =    qtd_bp + 1
						else:
									data_out_gab.append("D"					)

						# Sorts the data to the new output size
						if data_out_gab[-1] == "D":
									data_ord.append(data_output[cont_data]					)
									cont_data += 1
						else:
									data_ord.append(_A					)

    # Calculates parity
			UpperCamelCase__   =    0  # parity bit counter
			for bp in range(1			,       size_par + 1					):
						# Bit counter one for a certain parity
						UpperCamelCase__   =    0
						# Counter to control loop reading
						UpperCamelCase__   =    0
						for x in data_ord:
									if x is not None:
												try:
															UpperCamelCase__   =    (bin_pos[cont_loop])[-1 * (bp)]
												except IndexError:
															UpperCamelCase__   =    "0"
												if aux == "1" and x == "1":
															cont_bo += 1
									cont_loop += 1
						parity.append(str(cont_bo % 2					)					)

						qtd_bp += 1

			# Mount the message
			UpperCamelCase__   =    0  # Parity bit counter
			for x in range(0			,       size_par + len(_A					)					):
						if data_ord[x] is None:
									data_out.append(str(parity[cont_bp]					)					)
									cont_bp += 1
						else:
									data_out.append(data_ord[x]					)

			UpperCamelCase__   =    parity_received == parity
			return data_output, ack


