




"""simple docstring"""






from typing import Optional

import torch
import torch.utils.checkpoint
from torch import Tensor, nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss

from ...activations import ACTaFN
from ...modeling_outputs import (
    BackboneOutput,
    BaseModelOutputWithNoAttention,
    BaseModelOutputWithPoolingAndNoAttention,
    ImageClassifierOutputWithNoAttention,
)
from ...modeling_utils import PreTrainedModel
from ...utils import (
    add_code_sample_docstrings,
    add_start_docstrings,
    add_start_docstrings_to_model_forward,
    logging,
    replace_return_docstrings,
)
from ...utils.backbone_utils import BackboneMixin
from .configuration_resnet import ResNetConfig


lowercase__			=		logging.get_logger(__name__)

# General docstring
lowercase__			=		"""ResNetConfig"""

# Base docstring
lowercase__			=		"""microsoft/resnet-50"""
lowercase__			=		[1, 2048, 7, 7]

# Image classification docstring
lowercase__			=		"""microsoft/resnet-50"""
lowercase__			=		"""tiger cat"""

lowercase__			=		[
    """microsoft/resnet-50""",
    # See all resnet models at https://huggingface.co/models?filter=resnet
]






class lowerCAmelCase__      (	nn.Module		):






			'''simple docstring'''



			def __init__(    self    , lowercase    , lowercase    , lowercase = 3    , lowercase = 1    , lowercase = "relu"    ):
							super().__init__()
							_lowerCamelCase	:     str       =       nn.Convad(
							    lowercase    , lowercase    , kernel_size=lowercase    , stride=lowercase    , padding=kernel_size // 2    , bias=lowercase    )
							_lowerCamelCase	:     Optional[int]       =       nn.BatchNormad(lowercase    )
							_lowerCamelCase	:     List[Any]       =       ACTaFN[activation] if activation is not None else nn.Identity()






			def    A_       (    self    , lowercase    ):
							_lowerCamelCase	:     List[str]       =       self.convolution(lowercase    )
							_lowerCamelCase	:     List[Any]       =       self.normalization(lowercase    )
							_lowerCamelCase	:     List[str]       =       self.activation(lowercase    )
							return hidden_state






class lowerCAmelCase__      (	nn.Module		):






			'''simple docstring'''



			def __init__(    self    , lowercase    ):
							super().__init__()
							_lowerCamelCase	:     Optional[Any]       =       ResNetConvLayer(
							    config.num_channels    , config.embedding_size    , kernel_size=7    , stride=2    , activation=config.hidden_act    )
							_lowerCamelCase	:     List[str]       =       nn.MaxPoolad(kernel_size=3    , stride=2    , padding=1    )
							_lowerCamelCase	:     List[str]       =       config.num_channels






			def    A_       (    self    , lowercase    ):
							_lowerCamelCase	:     Optional[int]       =       pixel_values.shape[1]
							if num_channels != self.num_channels:
											raise ValueError(
											    'Make sure that the channel dimension of the pixel values match with the one set in the configuration.'    )
							_lowerCamelCase	:     Tuple       =       self.embedder(lowercase    )
							_lowerCamelCase	:     List[Any]       =       self.pooler(lowercase    )
							return embedding






class lowerCAmelCase__      (	nn.Module		):






			'''simple docstring'''



			def __init__(    self    , lowercase    , lowercase    , lowercase = 2    ):
							super().__init__()
							_lowerCamelCase	:     Any       =       nn.Convad(lowercase    , lowercase    , kernel_size=1    , stride=lowercase    , bias=lowercase    )
							_lowerCamelCase	:     Any       =       nn.BatchNormad(lowercase    )






			def    A_       (    self    , lowercase    ):
							_lowerCamelCase	:     Tuple       =       self.convolution(lowercase    )
							_lowerCamelCase	:     Tuple       =       self.normalization(lowercase    )
							return hidden_state






class lowerCAmelCase__      (	nn.Module		):






			'''simple docstring'''



			def __init__(    self    , lowercase    , lowercase    , lowercase = 1    , lowercase = "relu"    ):
							super().__init__()
							_lowerCamelCase	:     Any       =       in_channels != out_channels or stride != 1
							_lowerCamelCase	:     Optional[Any]       =       (
							    ResNetShortCut(lowercase    , lowercase    , stride=lowercase    ) if should_apply_shortcut else nn.Identity()
							)
							_lowerCamelCase	:     Optional[int]       =       nn.Sequential(
							    ResNetConvLayer(lowercase    , lowercase    , stride=lowercase    )    , ResNetConvLayer(lowercase    , lowercase    , activation=lowercase    )    , )
							_lowerCamelCase	:     Dict       =       ACTaFN[activation]






			def    A_       (    self    , lowercase    ):
							_lowerCamelCase	:     Optional[Any]       =       hidden_state
							_lowerCamelCase	:     str       =       self.layer(lowercase    )
							_lowerCamelCase	:     List[Any]       =       self.shortcut(lowercase    )
							hidden_state += residual
							_lowerCamelCase	:     int       =       self.activation(lowercase    )
							return hidden_state






class lowerCAmelCase__      (	nn.Module		):






			'''simple docstring'''



			def __init__(    self    , lowercase    , lowercase    , lowercase = 1    , lowercase = "relu"    , lowercase = 4    ):
							super().__init__()
							_lowerCamelCase	:     str       =       in_channels != out_channels or stride != 1
							_lowerCamelCase	:     Tuple       =       out_channels // reduction
							_lowerCamelCase	:     List[str]       =       (
							    ResNetShortCut(lowercase    , lowercase    , stride=lowercase    ) if should_apply_shortcut else nn.Identity()
							)
							_lowerCamelCase	:     Optional[Any]       =       nn.Sequential(
							    ResNetConvLayer(lowercase    , lowercase    , kernel_size=1    )    , ResNetConvLayer(lowercase    , lowercase    , stride=lowercase    )    , ResNetConvLayer(lowercase    , lowercase    , kernel_size=1    , activation=lowercase    )    , )
							_lowerCamelCase	:     Union[str, Any]       =       ACTaFN[activation]






			def    A_       (    self    , lowercase    ):
							_lowerCamelCase	:     int       =       hidden_state
							_lowerCamelCase	:     str       =       self.layer(lowercase    )
							_lowerCamelCase	:     Dict       =       self.shortcut(lowercase    )
							hidden_state += residual
							_lowerCamelCase	:     Union[str, Any]       =       self.activation(lowercase    )
							return hidden_state






class lowerCAmelCase__      (	nn.Module		):






			'''simple docstring'''



			def __init__(    self    , lowercase    , lowercase    , lowercase    , lowercase = 2    , lowercase = 2    , ):
							super().__init__()

							_lowerCamelCase	:     Union[str, Any]       =       ResNetBottleNeckLayer if config.layer_type == 'bottleneck' else ResNetBasicLayer

							_lowerCamelCase	:     str       =       nn.Sequential(
							    # downsampling is done in the first layer with stride of 2
							    layer(lowercase    , lowercase    , stride=lowercase    , activation=config.hidden_act    )    , *[layer(lowercase    , lowercase    , activation=config.hidden_act    ) for _ in range(depth - 1    )]    , )






			def    A_       (    self    , lowercase    ):
							_lowerCamelCase	:     List[Any]       =       input
							for layer in self.layers:
											_lowerCamelCase	:     Dict       =       layer(lowercase    )
							return hidden_state






class lowerCAmelCase__      (	nn.Module		):






			'''simple docstring'''



			def __init__(    self    , lowercase    ):
							super().__init__()
							_lowerCamelCase	:     Optional[int]       =       nn.ModuleList([]    )
							# based on `downsample_in_first_stage` the first layer of the first stage may or may not downsample the input
							self.stages.append(
							    ResNetStage(
							        lowercase    , config.embedding_size    , config.hidden_sizes[0]    , stride=2 if config.downsample_in_first_stage else 1    , depth=config.depths[0]    , )    )
							_lowerCamelCase	:     List[Any]       =       zip(config.hidden_sizes    , config.hidden_sizes[1:]    )
							for (in_channels, out_channels), depth in zip(lowercase    , config.depths[1:]    ):
											self.stages.append(ResNetStage(lowercase    , lowercase    , lowercase    , depth=lowercase    )    )






			def    A_       (    self    , lowercase    , lowercase = False    , lowercase = True    ):
							_lowerCamelCase	:     Any       =       () if output_hidden_states else None

							for stage_module in self.stages:
											if output_hidden_states:
															_lowerCamelCase	:     List[str]       =       hidden_states + (hidden_state,)

											_lowerCamelCase	:     Optional[int]       =       stage_module(lowercase    )

							if output_hidden_states:
											_lowerCamelCase	:     Optional[int]       =       hidden_states + (hidden_state,)

							if not return_dict:
											return tuple(v for v in [hidden_state, hidden_states] if v is not None    )

							return BaseModelOutputWithNoAttention(
							    last_hidden_state=lowercase    , hidden_states=lowercase    , )






class lowerCAmelCase__      (	lowercase		):






			'''simple docstring'''



			lowerCamelCase__ =     ResNetConfig
			lowerCamelCase__ =     """resnet"""
			lowerCamelCase__ =     """pixel_values"""
			lowerCamelCase__ =     True
			def    A_       (    self    , lowercase    ):
							if isinstance(lowercase    , nn.Convad    ):
											nn.init.kaiming_normal_(module.weight    , mode='fan_out'    , nonlinearity='relu'    )
							elif isinstance(lowercase    , (nn.BatchNormad, nn.GroupNorm)    ):
											nn.init.constant_(module.weight    , 1    )
											nn.init.constant_(module.bias    , 0    )






			def    A_       (    self    , lowercase    , lowercase=False    ):
							if isinstance(lowercase    , lowercase    ):
											_lowerCamelCase	:     Optional[Any]       =       value

lowercase__			=		R"""
    This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it
    as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
    behavior.

    Parameters:
        config ([`ResNetConfig`]): Model configuration class with all the parameters of the model.
            Initializing with a config file does not load the weights associated with the model, only the
            configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""

lowercase__			=		R"""
    Args:
        pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
            Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See
            [`ConvNextImageProcessor.__call__`] for details.

        output_hidden_states (`bool`, *optional*):
            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
            more detail.
        return_dict (`bool`, *optional*):
            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""






@add_start_docstrings(
    """The bare ResNet model outputting raw features without any specific head on top.""",      lowercase,      )
class lowerCAmelCase__      (	lowercase		):






			'''simple docstring'''



			def __init__(    self    , lowercase    ):
							super().__init__(lowercase    )
							_lowerCamelCase	:     Union[str, Any]       =       config
							_lowerCamelCase	:     int       =       ResNetEmbeddings(lowercase    )
							_lowerCamelCase	:     Dict       =       ResNetEncoder(lowercase    )
							_lowerCamelCase	:     List[str]       =       nn.AdaptiveAvgPoolad((1, 1)    )
							# Initialize weights and apply final processing
							self.post_init()






			@add_start_docstrings_to_model_forward(lowercase    )
			@add_code_sample_docstrings(
			    checkpoint=_CHECKPOINT_FOR_DOC    , output_type=lowercase    , config_class=_CONFIG_FOR_DOC    , modality='vision'    , expected_output=_EXPECTED_OUTPUT_SHAPE    , )
			def    A_       (    self    , lowercase    , lowercase = None    , lowercase = None    ):
							_lowerCamelCase	:     Optional[int]       =       (
							    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
							)
							_lowerCamelCase	:     List[Any]       =       return_dict if return_dict is not None else self.config.use_return_dict

							_lowerCamelCase	:     str       =       self.embedder(lowercase    )

							_lowerCamelCase	:     str       =       self.encoder(
							    lowercase    , output_hidden_states=lowercase    , return_dict=lowercase    )

							_lowerCamelCase	:     Tuple       =       encoder_outputs[0]

							_lowerCamelCase	:     int       =       self.pooler(lowercase    )

							if not return_dict:
											return (last_hidden_state, pooled_output) + encoder_outputs[1:]

							return BaseModelOutputWithPoolingAndNoAttention(
							    last_hidden_state=lowercase    , pooler_output=lowercase    , hidden_states=encoder_outputs.hidden_states    , )






@add_start_docstrings(
    """
    ResNet Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for
    ImageNet.
    """,      lowercase,      )
class lowerCAmelCase__      (	lowercase		):






			'''simple docstring'''



			def __init__(    self    , lowercase    ):
							super().__init__(lowercase    )
							_lowerCamelCase	:     int       =       config.num_labels
							_lowerCamelCase	:     Union[str, Any]       =       ResNetModel(lowercase    )
							# classification head
							_lowerCamelCase	:     int       =       nn.Sequential(
							    nn.Flatten()    , nn.Linear(config.hidden_sizes[-1]    , config.num_labels    ) if config.num_labels > 0 else nn.Identity()    , )
							# initialize weights and apply final processing
							self.post_init()






			@add_start_docstrings_to_model_forward(lowercase    )
			@add_code_sample_docstrings(
			    checkpoint=_IMAGE_CLASS_CHECKPOINT    , output_type=lowercase    , config_class=_CONFIG_FOR_DOC    , expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT    , )
			def    A_       (    self    , lowercase = None    , lowercase = None    , lowercase = None    , lowercase = None    , ):
							_lowerCamelCase	:     str       =       return_dict if return_dict is not None else self.config.use_return_dict

							_lowerCamelCase	:     str       =       self.resnet(lowercase    , output_hidden_states=lowercase    , return_dict=lowercase    )

							_lowerCamelCase	:     Tuple       =       outputs.pooler_output if return_dict else outputs[1]

							_lowerCamelCase	:     str       =       self.classifier(lowercase    )

							_lowerCamelCase	:     int       =       None

							if labels is not None:
											if self.config.problem_type is None:
															if self.num_labels == 1:
																			_lowerCamelCase	:     List[str]       =       'regression'
															elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
																			_lowerCamelCase	:     Optional[Any]       =       'single_label_classification'
															else:
																			_lowerCamelCase	:     Optional[int]       =       'multi_label_classification'
											if self.config.problem_type == "regression":
															_lowerCamelCase	:     Union[str, Any]       =       MSELoss()
															if self.num_labels == 1:
																			_lowerCamelCase	:     int       =       loss_fct(logits.squeeze()    , labels.squeeze()    )
															else:
																			_lowerCamelCase	:     List[str]       =       loss_fct(lowercase    , lowercase    )
											elif self.config.problem_type == "single_label_classification":
															_lowerCamelCase	:     Optional[int]       =       CrossEntropyLoss()
															_lowerCamelCase	:     Optional[int]       =       loss_fct(logits.view(-1    , self.num_labels    )    , labels.view(-1    )    )
											elif self.config.problem_type == "multi_label_classification":
															_lowerCamelCase	:     Union[str, Any]       =       BCEWithLogitsLoss()
															_lowerCamelCase	:     int       =       loss_fct(lowercase    , lowercase    )

							if not return_dict:
											_lowerCamelCase	:     Union[str, Any]       =       (logits,) + outputs[2:]
											return (loss,) + output if loss is not None else output

							return ImageClassifierOutputWithNoAttention(loss=lowercase    , logits=lowercase    , hidden_states=outputs.hidden_states    )






@add_start_docstrings(
    """
    ResNet backbone, to be used with frameworks like DETR and MaskFormer.
    """,      lowercase,      )
class lowerCAmelCase__      (	lowercase,      lowercase		):






			'''simple docstring'''



			def __init__(    self    , lowercase    ):
							super().__init__(lowercase    )
							super()._init_backbone(lowercase    )

							_lowerCamelCase	:     Tuple       =       [config.embedding_size] + config.hidden_sizes
							_lowerCamelCase	:     Optional[Any]       =       ResNetEmbeddings(lowercase    )
							_lowerCamelCase	:     Union[str, Any]       =       ResNetEncoder(lowercase    )

							# initialize weights and apply final processing
							self.post_init()






			@add_start_docstrings_to_model_forward(lowercase    )
			@replace_return_docstrings(output_type=lowercase    , config_class=_CONFIG_FOR_DOC    )
			def    A_       (    self    , lowercase    , lowercase = None    , lowercase = None    ):
							_lowerCamelCase	:     Optional[int]       =       return_dict if return_dict is not None else self.config.use_return_dict
							_lowerCamelCase	:     Any       =       (
							    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
							)

							_lowerCamelCase	:     str       =       self.embedder(lowercase    )

							_lowerCamelCase	:     Dict       =       self.encoder(lowercase    , output_hidden_states=lowercase    , return_dict=lowercase    )

							_lowerCamelCase	:     Optional[int]       =       outputs.hidden_states

							_lowerCamelCase	:     Optional[int]       =       ()
							for idx, stage in enumerate(self.stage_names    ):
											if stage in self.out_features:
															feature_maps += (hidden_states[idx],)

							if not return_dict:
											_lowerCamelCase	:     str       =       (feature_maps,)
											if output_hidden_states:
															output += (outputs.hidden_states,)
											return output

							return BackboneOutput(
							    feature_maps=lowercase    , hidden_states=outputs.hidden_states if output_hidden_states else None    , attentions=lowercase    , )





"""simple docstring"""






import qiskit







def 	_snake_case     (							lowercase__ = 2						):
				_lowerCamelCase	:     Optional[Any]       =       qubits

				# Using Aer's simulator
				_lowerCamelCase	:     Dict       =       qiskit.Aer.get_backend('aer_simulator'						)

				# Creating a Quantum Circuit acting on the q register
				_lowerCamelCase	:     Dict       =       qiskit.QuantumCircuit(lowercase__				,       lowercase__						)

				# Adding a H gate on qubit 0 (now q0 in superposition)
				circuit.h(0						)

				for i in range(1				,       lowercase__						):
								# Adding CX (CNOT) gate
								circuit.cx(i - 1				,       lowercase__						)

				# Mapping the quantum measurement to the classical bits
				circuit.measure(list(range(lowercase__						)						)				,       list(range(lowercase__						)						)						)

				# Now measuring any one qubit would affect other qubits to collapse
				# their super position and have same state as the measured one.

				# Executing the circuit on the simulator
				_lowerCamelCase	:     Optional[Any]       =       qiskit.execute(lowercase__				,       lowercase__				,       shots=1000						)

				return job.result().get_counts(lowercase__						)


if __name__ == "__main__":
			print(F"Total count for various states are: {quantum_entanglement(3)}")